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We have an unusual relationship with today’s guest: for years since coauthoring the InstructGPT paper, Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped every mode other than autoregressive chat-tuned LLMs because of the overwhelming success of ChatGPT.

In a launch video now viewed ~40M times (by comparison, GPT4o was 22M, Fable 5 was 15M, Navier Stokes was 74M, and 6 Astra was 137M), Diogo introduced Jev and it immediately took over the AI timeline — we’ll skip full Jev explainers because your favorite AI influencer/educator has probably already done one. We also collected:

• the official patterns and cookbooks you should see first, from Allie

• Jev usecases

  • speed based - games and computer use

    • the voice + computer use example we discuss at 1h34 mins

      • voice + browser control

    • The must not miss Doom demo

    • Driving cars in games

    • Excalidraw

    • virtual try-ons

    • “Smart Games”/smart NPCs

  • guided responses in text messages

  • Jev for coding agents has an official guide

    • jev for linting

    • compacting tool calls

      • reasonable pushback from Theo - Diogo has published a note on the Tyranny of the KV Cache that you should read as a followup after the pod for Jev + coding agents, because of his belief that Cache Rules Everything

  • Programming Languages built atop Jev (Diogo’s fave)

  • Jev for analytics replay and user journey review

  • “dark data”

    • entity resolution

    • natural language search

  • “smart software”

    • a core goal of Jev is to “disappear into the background” - eg as unremarkable as regex

  • Jev as a judge

• Jev memes

  • Jev vs LLM capabiltiies

  • blending transformers and classifiers

  • about the confidence api

  • Jev vs GLiNER (note difference/pushback, agreed, agreed, agreed)

  • Jev on trolley problem

  • Jev Bush

Instead we’ll focus on what we can uniquely offer — a broader philosophical and mission-based understanding of how and why Jev was created, and what you should expect next in terms of future models from TypeSafe (ReasoningJev?) and what usecases and ideas you should work on vs the 55th low effort clone of Jev’s API or doing a generic JevBench benchmark - something Diogo has rejected publicly.

Why RLCD: Three kinds of RLHF, and why they are ALL the wrong north star

Diogo knows a good deal about RLHF, given that he was on the team that pioneered post-training at OpenAI — and traces the three branches to Christiano et al 2017 (the robot backflip demo), Stiennon et al 2020 (learning to summarize) and his baby, Ouyang et al 2022 (InstructGPT). From there on, every innovation from Function Calling to Structured Outputs to Reasoning felt like a hack on top of the string based, sequence to sequence prediction paradigm. As he mentions on the pod, from 2023-2024 he struggled unsuccessfully, due to both personal and organization underestimation, to train a model that accurately addressed what he saw as the core problem with making LLMs the heart of software: reliability.

Jev’s core innovation is "Reinforcement Learning for Calibrated Decisions”, a novel, unpublished technique that optimizes for “answers with epistemically honest probabilities on System One tasks” rather than human rated feedback (RLHF) — which causes hallucinations, sycophancy, and permanent reliance on humans — or programmatically verifiable outputs with rubrics (RLVR) — which solves Navier Stokes but exacerbates jagged intelligence and doesn’t integrate well with other software.

We’ve talked about the calibration problem before on the pod, but probably the single best place to understand why RLCD became necessary is Diogo’s AIE talk, which discusses why a generation of training helpful AI assistants for humans has impaired them for training models for composable, programmable AI for automation.

At the end he also teases his contrarian opinion on scaling laws - which teases how to build a modern neolab without the billions of dollars the major labs have…

The Bitterest Lesson: Tasks and Data beats Compute

We spend a good amount of time discussing Diogo’s essay on the Bitterest Lesson:

His point is that “You get what you optimize for and the bitterest lesson in ML is that the most important part of it isn’t ML at all.” - and picking the right north star, eg upvoting for user preference vs being integrated into tool calls - makes everything else fall in line.

We’re excited to catch up with a freshly dyed Diogo to discuss:

• Why AI can solve extraordinarily hard problems but still fail to automate basic work

• What System One Models are and why Jev is built for software rather than chat

• RLHF, mode collapse, calibration, and the hidden costs of optimizing for human preferences

• Why refusals become a problem when AI is buried inside software dependencies

• Why TypeSafe rejects public benchmarks and optimizes for intelligence per dollar

• The “bitterest lesson”: why the right task and the right data can matter more than compute

• Why TypeSafe thinks of itself as a data lab rather than a model lab

• RLCD vs. RLHF and RLVR as fundamentally different North Stars for AI

• Why reliability and robustness matter more than simple determinism

• Jev’s programming primitives and how intelligence maps into software control flow

• Why developers should decompose AI workflows into small, measurable decisions

• How structured state replaces giant prompts and system messages

• Why Diogo thinks AI should eventually disappear into the background of software

• The “inverse SaaS-pocalypse” and how AI could supercharge existing software

• System One vs. System Two intelligence and the limits of reasoning models

• Dark data, computer use, real-time intelligence, and Jev’s biggest early use cases

• Why Jev could reshape coding agents built around a single-model architecture

• Why Diogo says he wouldn’t pre-train with $1 billion

• The OpenAI journey that led to TypeSafe and why he thinks many neo-labs are approaching AI incorrectly

• Coding agents beyond the KV cache, shared state, sub-agents, and the multi-agent future

Diogo Almeida

• LinkedIn: https://www.linkedin.com/in/diogomda

• X: https://x.com/CompleteSkeptic

• TypeSafe AI: https://typesafe.ai/

Timestamps

00:00:00 Jev Launch Week and the AI Economic Revolution

00:02:50 What Is Jev? System One Models and Programmable AI

00:05:54 RLHF, Mode Collapse, Calibration, and Yann LeCun

00:10:29 Programmatic AI, Refusals, and Safety Alignment

00:17:21 Why TypeSafe Rejects Public Benchmarks

00:20:43 The Bitterest Lesson: Data, Compute, and the Right Task

00:24:59 RLCD vs. RLHF and RLVR

00:28:42 Why Powerful AI Still Hasn’t Automated the Economy

00:39:55 Reliability, Robustness, and Determinism

00:48:11 Model Versioning, LTS, Speed, and Intelligence per Dollar

00:54:04 Inside Jev’s API and Programming Primitives

00:58:28 How to Build with Jev: Structure, Decomposition, and Small Decisions

01:18:28 The Inverse SaaS-pocalypse and AI Disappearing into Software

01:33:21 Computer Use, Dark Data, and Jev’s Biggest Use Cases

01:38:48 How Jev Could Reshape Coding Agents

01:41:00 AI Safety, Frontier Pacing, and the Limits of RLVR

01:48:03 Why Diogo Wouldn’t Pre-Train with $1 Billion

01:55:19 The OpenAI Story Behind TypeSafe

02:01:41 Why Diogo Thinks Most Neo-Labs Are Getting AI Wrong

02:08:00 Coding Agents Beyond the KV Cache and the Multi-Agent Future

Transcript

Introduction: Jev Launch Week and Developer Momentum

Swyx [00:00:00]: Okay, we’re in the studio. A special occasion because this week, Diogo, my good buddy, launched Jev, and it’s been taking over the complete timeline. How do you feel? What’s it like to be you right now?

Diogo Almeida [00:00:16]: Emotionally?

Swyx [00:00:17]: Yeah.

Diogo Almeida [00:00:17]: Never been worse. Like, I’m a ragged corpse of a person right now because there’s so much going on, and I’m like a technical CEO, so I have, like, a lot of fires to fight.

Swyx [00:00:29]: Yeah.

Diogo Almeida [00:00:29]: But mentally, I feel—I say this all the time, and I’ve been saying this kind of for years in my over-under events. Like, I feel like the entire AI field is like one of those, like, carnival house of mirrors, and everyone is just insane and saying the weirdest stuff that doesn’t make sense. And it feels like for just this week, like, I’m on a better in sync with reality and like, oh, people see it now. AI can be so much more than what was once thought.

Diogo Almeida [00:01:06]: And like, yes, we are going to make. Like, an AI-based economic revolution is back on the table, and this is fucking awesome.

Diogo Almeida [00:01:17]: I’m so jazzed the developers get it. It’s, it’s, Yeah, and I want to show my eternal gratitude to the developers and

Swyx [00:01:25]: Yeah.

Diogo Almeida [00:01:26]: I’m so jazzed about the community and everything. It’s so great.

Swyx [00:01:28]: Yeah, you were saying yesterday that you decided to prioritize the town hall and not a bunch of, like, VIP, investor-type people because you wanted to make sure that they are the people that you get your most, attention, right? The engineers, the developers.

Diogo Almeida [00:01:43]: Yeah, it felt a little like, oh man, I’m talking to, like, really important people right now.

Swyx [00:01:47]: Yeah.

Diogo Almeida [00:01:47]: I probably shouldn’t reveal who.

Swyx [00:01:48]: Yeah.

Diogo Almeida [00:01:48]: But it feels a little bit dirty for me to, I’m, like, perhaps overly genuine in things. Like, it feels, like, dirty if, like, in my gigantic calendar event of people to talk to, the community isn’t one of those.

Swyx [00:02:04]: Yeah.

Diogo Almeida [00:02:04]: And actually, in my ideal world, it would be, like, community all the time. I was thinking, “Should I host a town hall while walking to your studio?” And I’m like, “No, that’s too crazy.”

Swyx [00:02:12]: Sure. Yeah. Well, you guys have been hosting town halls on Discord. Discord is now 100,000 people. Your Twitter’s

Diogo Almeida [00:02:19]: I don’t follow these stats.

Swyx [00:02:20]: Yeah.

Diogo Almeida [00:02:20]: So holy shit.

Swyx [00:02:21]: Your Twitter’s blown up. It was, it was really funny ‘cause, like, at AIE, you were like, “Yeah, follow me please,” and then you didn’t, like, provide even your handle.

Diogo Almeida [00:02:29]: I’m a noob. I’m a noob.

Swyx [00:02:29]: You’re such a noob.

Diogo Almeida [00:02:30]: I’m a noob.

Swyx [00:02:31]: But no, but that, like, that’s, like, positive aura that, like

Diogo Almeida [00:02:33]: Cool

Swyx [00:02:33]: You don’t know how to promote yourself.

Diogo Almeida [00:02:35]: Yeah. Someone, like, called me out when I posted, like, “Holy shit, we’re all three twending-- trending topics.” And then they’re like, “That’s a personal feed.”

Swyx [00:02:42]: That’s a personal, yeah.

Diogo Almeida [00:02:43]: And I’m like, “Oh, no.”

Swyx [00:02:44]: Of course, of course it’ll trend to you.

Diogo Almeida [00:02:45]: Cringe. Yeah.

Swyx [00:02:45]: Yes, ‘cause it’s what you clicked on.

Diogo Almeida [00:02:47]: Yeah.

Swyx [00:02:47]: So okay. Let’s, Yeah, so congrats on everything.

What Is Jev? System 1 Models and Intelligence per Dollar

Diogo Almeida [00:02:50]: Thank you.

Swyx [00:02:50]: We’ll talk about more, details as you have them. But let’s, for people who are, like, living under a rock or just want, like, the definitive thing, what is Jev?

Diogo Almeida [00:03:02]: Whew. Let me think about. That’s a hard one.

Swyx [00:03:07]: Okay. And I’m happy to, like, re-ask if you wanna kind of

Diogo Almeida [00:03:09]: No. I’m happy to

Swyx [00:03:10]: Okay

Diogo Almeida [00:03:10]: I’m happy to, like, just jam on it.

Swyx [00:03:12]: Yeah.

Diogo Almeida [00:03:13]: I will say, like, the first thing that I’m relieved about with this question is now I don’t have to answer that question to my parents anymore ‘cause ChatGPT can just explain it.

Swyx [00:03:20]: Nice.

Diogo Almeida [00:03:21]: So the way I see it is we new-- need a new class of models. We’re not attached to naming that class of models. Our-- the most accurate name we’ve come up with is System 1 models.

Swyx [00:03:33]: Yeah.

Diogo Almeida [00:03:33]: There will be reasons, but it’s-- there’s a reason why we don’t call them decision models, because, like, they will be. Like, System 1 is beyond that. That’s all I can say. We didn’t expect this to be our big launch, so we have stuff in the tank.

Swyx [00:03:48]: You should have said low-key research preview.

Diogo Almeida [00:03:52]: It kind of was, right? It kind of was. But we. So there’s a class of models that we describe them as, like, machine-native, System 1, large programmable. I think these are-- is the class of models where the goal is for code to be the consumer. So as opposed to, lar-- pre-trained large language models, which are meant for, like, autocomplete of the internet, or RLHF models, like chatbot instruction-following models, which are meant to, like, reply to text, or RLVR. It’s in a weird gray area with RLHF. Like, these are meant to have things that directly are consumed by code, hence the name type safe. So the thing we really want is to have, like, AI, like, be as powerful as possible, and we think the way to do that is to integrate it with software. And we are designing everything, beyond just the outside, the deep internals of the model to be optimized for software. So number one, Jev is our first large programmable model, or a System 1 model, whatever you want to call it. Jev is meant to be optimized for intelligence per dollar, hence the name Jev.

Swyx [00:05:03]: Jevons Paradox.

Diogo Almeida [00:05:03]: Jevons Paradox, yeah. And it’s optimized for intelligence per dollar. I love this debate with people about what is the most important between reliability, cost, calibration, and speed. And Jev is meant to be. Jev will be the name of models that will be on the frontier of intelligence per dollar. There’s other ways to optimize it, like, ML, or at least if you’re good at ML, it’s all about trade-offs. And we are just going all out on that.

Calibration, Mode Collapse, and the Limits of RLHF

Swyx [00:05:31]: Yeah. And to me, like, calibration is one of the new things that people weren’t talking about as much. We’ve done an episode In the past, with Clementine Foreia of Hugging Face, where they were like, “Yeah, actually, y- they’re just.” Or, and this is your whole argument about RLHF, is they’re more collapsing towards what you want to hear the most

Diogo Almeida [00:05:50]: Ooh

Swyx [00:05:50]: Or what is most likely, instead of, like, their own internal confidence about a thing.

Diogo Almeida [00:05:54]: Can I soapbox on that for a second?

Swyx [00:05:56]: Go ahead. Yeah.

Diogo Almeida [00:05:57]: Cool. Like, I’ve been heard that your audience is the most technical, so I actually want to get into that.

Swyx [00:06:02]: Yeah.

Diogo Almeida [00:06:03]: And if- I went through extreme precision to make sure everything in our launch video is accurate and real. Apparently, that’s very unusual. One of the things that no one paid attention to was the downsides of RLHF, in particular mode dropping.

Swyx [00:06:17]: Mode dropping or mode collapse?

Diogo Almeida [00:06:19]: It’s the same thing.

Swyx [00:06:19]: Is that what you call it?

Diogo Almeida [00:06:20]: It’s the same thing.

Swyx [00:06:20]: All right.

Diogo Almeida [00:06:21]: And I wanna have a blog on this eventually, but I, like, want to tell as many people this as possible ‘cause I think it’s a very interesting thing. So the spicy take, I believe in Yann LeCun a lot. I think Yann LeCun’s takes are actually among the closest to

Swyx [00:06:36]: What about this?

Diogo Almeida [00:06:37]: Well, should I address this now or should I wait and go into mode collapse?

Swyx [00:06:40]: No, later. Go mode, go mode collapse. I don’t know.

Diogo Almeida [00:06:42]: So I actually think that among takes, Yann LeCun’s is among the most accurate. But he has this very famous/infamous slide about,

Swyx [00:06:52]: The cake?

Diogo Almeida [00:06:53]: LLMs are doomed.

Swyx [00:06:54]: Okay.

Diogo Almeida [00:06:54]: Like that one where he, like, has, like, a pie chart with, like, a tiny par-- tiny little thing- and says that as you increase sequence length, the probability of it making an error goes in. Yes, this one. This one. I love this one, because it’s one of these things that seems mathematically obvious, but is obviously wrong, right? Like, it’s mathematically obvious, but it doesn’t empirically hold. And this is my favorite thing to teach people about, like, where you

Swyx [00:07:21]: What’s the disconnect, right?

Diogo Almeida [00:07:22]: Exactly. And may I or you want to tell me?

Swyx [00:07:27]: About mode collapse?

Diogo Almeida [00:07:28]: Oh, no. Oh, so mode clop-- collapse is related to this.

Swyx [00:07:31]: Yeah.

Diogo Almeida [00:07:31]: The disconnect happens because if you are in a mode covering or a calibrated distribution, you are, like, not. You are not overly punished about having outliers. You’d expect, like, something. Some amount of the time you’d be out of distribution, some amount of time you’d be in distribution. That’s what happens when you cover the distribution. This was like models before GANs. They made blurry images, right?

Diogo Almeida [00:07:54]: Instead, GANs mode drop. They, like, drop the minority classes and just do the really common ones. And this is why this effect doesn’t happen, right? Like, instead of be-- in order to generate really long strings, without making errors, they need to, like, be extremely conservative because it’s e- really easy to see when an error happens. It’s very hard to see when, like, a subtle thing that looks correct happens. And that calibration is, like, total poison into, like, the probability distributions of strings.

Swyx [00:08:22]: Yeah.

Diogo Almeida [00:08:23]: And it’s, it’s a nuanced take and like, I think that This is why this doesn’t happen, and this is why strings are so bad at, decision-making or, overloading the string models are for decision-making is, like, a bad time.

Yann LeCun, JEPA, Scaling Laws, and Practical Research

Swyx [00:08:38]: And while we’re on the topic of Yann, do you agree that his fix i- with-- which is like a world model, like a JEPA-type, embedding thing is the right solve? So basically, like, the. One of the reasons that it could fail is because you’re trying to reason over token outputs and then, and then just looping back again and going. Keep, continuing going until you reach, like, a end of sentence. Like, is that, And his solve is JEPA, right?

Diogo Almeida [00:09:02]: Yes.

Swyx [00:09:02]: Which is, like, joint ambition,

Diogo Almeida [00:09:04]: Yeah

Swyx [00:09:04]: Joint embedding prediction. So like, is that the solve or, like, do you have a. Do you have a take on that?

Diogo Almeida [00:09:10]: Oh, man. I probably shouldn’t talk too much about the insides of ML, but I will say that my brand, other than unhinged, is practical.

Diogo Almeida [00:09:20]: Like, even my take here is practical. And like, I’m. Am I a scaling law fan? Depends. It dep-- it’s, it’s, it’s, like, it’s. Scaling laws tell you how much better you get at a thing for amount in.

Diogo Almeida [00:09:33]: A scaling law does mean exponentially more resources for normally sublinear gains, which looks to be a bad investment unless those, like, linear gains are, like, really valuable. But it’s all. To me, it’s all about, like, what can we do with what we have to make the biggest possible fucking difference? I can curse.

Swyx [00:09:51]: Yeah.

Diogo Almeida [00:09:51]: Yeah.

Swyx [00:09:52]: Yeah.

Diogo Almeida [00:09:52]: Yeah.

Swyx [00:09:53]: We’re, we’re, we’re approved for adults.

Diogo Almeida [00:09:54]: Hell yeah.

Swyx [00:09:55]: And also we have a scaling law thing if you wanna go into that later.

Diogo Almeida [00:09:58]: Oh, I could if we. See, that part is not super relevant right now.

Swyx [00:10:02]: Yeah.

Diogo Almeida [00:10:03]: I actually. If you wanna go into my bitterest lesson, I think that’s more relevant.

Swyx [00:10:06]: Okay.

Diogo Almeida [00:10:06]: But like, to me, I’m all about, like, pragmatics. And I think that the JEPA stuff is really cool early research. I really love awesome research. Is it practical yet?

Diogo Almeida [00:10:21]: Probably shouldn’t say. But like, there’s just a lot of.

Diogo Almeida [00:10:29]: I just think there’s just, like, so many diamonds in the rough let all over the research world right now that haven’t been polished because people don’t know how to, like, do the right task. And I think that what our launch did, it. Does it kickstart us as a company? Like, yes. Will it be great for us as a company? Yes. I think it’s gonna be, like, even greater for this direction of, like, programmatic AI. There was going to be, like, a gold rush on top of us for. ‘cause, like, software is super fucking charged. But I think there’s gonna be a gold rush parallel to us as well on, like, all the different ways we can expose things to make software more powerful so people can make even cooler stuff. And then we are back to, like, early internet energy?

Swyx [00:11:12]: Yeah.

Diogo Almeida [00:11:12]: And I think that’s why, like, the Twitter is just like, “Jev.”? It’s, it’s like. It is a party

Swyx [00:11:18]: It’s inspiring because it’s, it’s, like, so different than what we’re used to, which is, “I’m sorry you can’t do this, but we do scaling laws and only the big labs can do it,” right?

Diogo Almeida [00:11:28]: That. Actually, if I. I’ll, I’ll make a tangent if that’s okay.

Swyx [00:11:32]: Yeah.

Diogo Almeida [00:11:32]: I think you might enjoy this.

Swyx [00:11:33]: Really? Our five tangents in. It’s good. It’s fun. Yeah.

Diogo Almeida [00:11:35]: Oh, yeah. I get lost at all my tangents.

Swyx [00:11:37]: This is gonna be horrible for the listeners to figure it out, but they’re gonna figure it out. It’s fine.

Safety Alignment, Refusals, and API Philosophy

Diogo Almeida [00:11:40]: Yeah, we can edit it in post.

Swyx [00:11:40]: This is my response. Yeah.

Diogo Almeida [00:11:41]: So popular thing on Discord, that people keep asking me, I haven’t had the time to explain it yet, is why am I opposed to safety alignment and why do we not refuse? I’m not opposed to safety as a principle, but I think that safety alignment is generally misaligned with users. And refusal is just, like, obviously a type error. Like, if you’re a human being and you’re chatting with, like, a bot or whatever, you’re cloud coding, and a refusal happens, like, “I’m sorry, I can’t read DNA.py.” that’s an annoying time. It’s anno- it’s, it’s annoying

Diogo Almeida [00:12:18]: Right? But you can work with it, right? And you’re forced to work with it ‘cause of Stockholm syndrome.

Diogo Almeida [00:12:23]: I have stories about that too. I need another tangent deep in here. But like, if you ever want this in a dependency running in the background, what happens if that refuses? What if someone else is using that dependency? They don’t know what that system is. Like, you want the software to just stochastically break because a user sent, like, a weird message in there?

Diogo Almeida [00:12:42]: Like, that is, like, straight-up insanity. It’s coming from a place of, like, people who do not understand software, do not understand programming, and like, they are obsessed with, like, I believe this, horseless carriage of, like, AI coworker instead of unearthing, like, the full power of AI.

Swyx [00:13:01]: Fair enough.

Diogo Almeida [00:13:01]: Yeah.

Swyx [00:13:01]: You want something that is the core kernel that is usable everywhere.

Diogo Almeida [00:13:05]: Yes. Exactly. Like, the cognitive core, right?

Swyx [00:13:07]: Yeah.

Diogo Almeida [00:13:08]: And you need this thing to be s- like, so general, so optimized for its use cases. You want it to be, like, you want it to work on all the future use cases, all the weird shit that people are doing.

Swyx [00:13:19]: Yeah.

Diogo Almeida [00:13:19]: We obviously didn’t train on any of that stuff. Is it surprising that it works? No, ‘cause we trained on weirder stuff, my friend.

Diogo Almeida [00:13:28]: So. But one tangent up about, like, safety alignment.

Swyx [00:13:32]: Okay.

Diogo Almeida [00:13:32]: Safety alignment makes sense for a product, in my opinion, for, like, ChatGPT and Claude. Like, it, What safety, what makes safety and capability alignment different is capability alignment is, like, about doing what the user wants. That is sick for software engineers. They want their thing to do the thing, and the more predictable it is, the less they have to test it and play around with it. Jeb is not anywhere close to that yet. It could be, but like, there’s so many more nines of reliability that we want in order to make it so good, like a database query, that you don’t even have to think about it. It is just there when you need intelligence. But safety alignment is, like, the opposite of instruction following. It’s when you want to follow someone else’s instructions, like OpenAI and Anthropic

Swyx [00:14:13]: The RAGs value stack.

Diogo Almeida [00:14:14]: Exactly. And this makes a lot of sense for a product. Again, like, ChatGPT should do. Y- you sh- like, if they don’t want to, like, do, like, some, not-safe-for-work role play with ChatGPT, that’s on them because, like, maybe that’s, what their users who have, like, parents and kids want. Like, n- that’s fine. But in an API, that’s nuts, right? Like, that’s completely unacceptable because, like, people need to, like, program around this, and that is, that’s so anti-user that it’s. It. I’m. Huh. I can be an angry person, so I should try to calm down.

Swyx [00:14:52]: It’s, People get your passion, and I think that’s really good. The one pushback I’ll give you is, like, what if we use it to kill people, right? Like, that is the actual. Like, n- the not-safe-for-work thing, it’s private, personal, whatever. But like, yes, like, we will use it in war. And like, that is, something that companies can reasonably prefer their APIs not be used for.

Diogo Almeida [00:15:14]: I get that. I think that there’s, like, pragmatic places where that opinion can be held. I don’t think the foundation of, like, a general-purpose technology is that place, personally.

Diogo Almeida [00:15:27]: Like, would I prefer that our stuff is not used to kill people? Obviously. Would I prefer it’s used for, like, all sorts of, like, great stuff in the world? Obviously. Will I put my thumb in the scale for that? Yes. Will I do it at the technological layer? Absolutely not, because that will fracture the intelligence. Every single time you mean it to overfit to some weird stuff, you’re fracturing its intelligence more and more. And like, these things are fractured to the, like. They’re so darn fractured right now.

Swyx [00:15:54]: Yeah.

Diogo Almeida [00:15:54]: So and as a furthermore thing, to me, it’s like I think intelligence will be more like a database than a coworker. Like, I don’t think it’s up to databases to add checks on whether or not they’re used for, like, what’s something that’s not great? Like, CIA. Actually, I don’t know what the CIA does, really. You can imagine. You can imagine, killing people who are not even bad or whatever.

Diogo Almeida [00:16:21]: And like, I don’t think it’s the database’s responsibility for that. And furthermore, like, a thing that has been weird to me is when people, like, sign up for our thing on Slack and they’re like, “Hey, we’re gonna deploy this. Can we deploy this thing?” I am just like, “My brother, we are an API. You are a developer. It’s none of my business,” right? Like, you shouldn’t know what the whole task even is

Swyx [00:16:46]: Yeah

Diogo Almeida [00:16:46]: Because it should be decomposed into small things. We shouldn’t be able to know what the downstream users are doing, and that is, like, a good boundary to give software engineers maximum power. Ideally, they use it for the good stuff, and ideally, we can, like, help them and like, we’ve talked about, like, doing open source and charity and all of that. We have absolutely no time for anything else right now. But like, they will get any of that bias out of the technological layer as long as I’m in charge.

Privacy, Benchmarking, and Trusting Intelligence

Swyx [00:17:11]: Yeah, that’s great. While we’re on the topic, let’s also briefly talk about your privacy stuff, terms of ser- terms of use, which, got a little bit of

Diogo Almeida [00:17:18]: Ooh

Swyx [00:17:18]: Misunderstanding. I just wanna clarify that upfront.

Diogo Almeida [00:17:21]: Hell yeah.

Swyx [00:17:21]: I think this probably takes two sentences from you about, like, you will not. You’re not being that restrictive about your API. Like, clearly

Diogo Almeida [00:17:27]: Oh, yeah. Oh, yeah, so yeah

Swyx [00:17:27]: Ideologically, you articulate your role as a platform very seriously.

Diogo Almeida [00:17:30]: Yes. Yes. I don’t know what you’re referring to, but like, this was. I’ve seen a couple of things about, like, benchmarking.

Swyx [00:17:38]: Yes.

Diogo Almeida [00:17:38]: Like, obviously we’re not stopping people from do. Oh, man, I should be careful about what I say. I’m realizing

Swyx [00:17:43]: No, you said, you said it publicly that

Diogo Almeida [00:17:44]: Yeah

Swyx [00:17:44]: That was in the preview period. You didn’t take it out for the launch.

Diogo Almeida [00:17:47]: Yeah. Okay.

Swyx [00:17:47]: And now you’re gonna take it out.

Diogo Almeida [00:17:48]: So the team is doing stuff that

Swyx [00:17:49]: Yes

Diogo Almeida [00:17:49]: I’m not even aware of, so it’s great to know the team communicated that. I asked them to check in with the lawyers about that.

Swyx [00:17:54]: Yeah.

Diogo Almeida [00:17:54]: Like, we are obviously not stopping people from doing that type of thing. I’m extremely in favor. So I’m extremely anti-public benchmarks. I’m extremely in fa- I’m medium about private benchmarks that are proxies. I

Swyx [00:18:09]: So are you worried about, saturation or, like, training on public benchmarks? So it’s, like, easy to cheat.

Diogo Almeida [00:18:15]: Not only is it easy to cheat, there’s a lot of ins. So I think that we are. Or anyone who’s, like, competition with us that, vaguely there is. Like, you could say, like

Swyx [00:18:28]: There’s like 50 Jev clones, yeah.

Diogo Almeida [00:18:30]: Well, sure.

Swyx [00:18:31]: Yeah.

Diogo Almeida [00:18:32]: Well, the, these. Let’s say that there is competition.

Swyx [00:18:34]: And we’ll talk about those. Yeah.

Diogo Almeida [00:18:34]: Or let’s just say that there’s. Let’s just assume that there’s an industry two years from now of people who are doing similar things to us. The thing that we are selling is intelligence per something, per, like, dollar or per second. The. No one. Like, people obsess about the cost and the speed. I believe that is. It’s cool, but like, the thing that matters is the intelligence. Like, the cost and the speed are, like, are bad things. You’re paying them for something, and you need the thing back, and the intelligence is what truly matters. The problem with intelligence is that there’s a je ne sais quoi to it, right? Like, the good model smell. Like, the thing that happened after we launched of, like, two hours later that actually went way bigger than the video, which was like, “Holy shit.”

Swyx [00:19:16]: This is actually usable.

Diogo Almeida [00:19:17]: It. Well

Swyx [00:19:17]: Yeah.

Diogo Almeida [00:19:17]: It’s, like, beyond that.

Swyx [00:19:20]: Yeah.

Diogo Almeida [00:19:20]: Like, the. Whew, the launch was crazy, and people could really sense how hard we care about that, and that’s truly what I think the long term of this is. And I think public benchmarks are antithetical to this. Like, they are a way to get people trust in intelligence because intelligence has a je ne sais quoi, but the public benchmarks are extremely gameable. Even if they try not to, they still will. Like, back in the old days, every lab had a team to collect data that looks like MMLU to make it look better, which is just benchmarking with extra steps.

Diogo Almeida [00:19:58]: So I believe that in the long run, it needs to be vibes and trust until you put it into a workflow and evaluate it for that workflow and measure it and have your own sense of, like, how it does on the exact workflow that matters. And our job is to keep moving the nines of reliability. This is like an ever-present part of o- of what we need to be doing as a company, and we need to do everything to have people know that this is something we care so much about. Like, if we wanted to, we could have released Jev, like, a year and a half ago if we wanted it to be dumb.

The Bitterest Lesson: Tasks, Data, and North Stars

Swyx [00:20:34]: Oh.

Diogo Almeida [00:20:34]: It. Like, the. My bitterest lesson, right? Like, architecture and Yeah.

Swyx [00:20:40]: I’ll bring it up

Diogo Almeida [00:20:40]: Hell yeah

Swyx [00:20:41]: Since you, since you talked about it, here.

Diogo Almeida [00:20:43]: Hell yeah. T- like, Sutton says that algorithms beats compute very roughly. Data matters way more than compute, obviously. And doing the right task, having the North Star is the hardest, most important thing. This has happened, in LLM land twice so far, right? Maybe 2.2 times. There’s RLHF, which, like, shifted the task to instruction following. No one realized that was possible. RLVR did, like, a tiny little, like, edit to the, to the direction, and now us, right? RLCD. We have a new task, and the goal is, programs in the loop. And yeah, data matters so

Swyx [00:21:28]: Right

Diogo Almeida [00:21:28]: Unbelievably much.

Swyx [00:21:29]: So

Diogo Almeida [00:21:29]: Like, I can’t, I can’t emphasize it less.

Swyx [00:21:31]: Yeah, you consider yourself a data lab rather than, like, a model lab. Is that

Diogo Almeida [00:21:35]: Absolutely

Swyx [00:21:35]: Something. That’s the wording you guys use?

Diogo Almeida [00:21:37]: Yeah. We are. We will always, like, care so much about data. To me, model capabilities means data. Data is so unbelievably complicated, and that is what gets nines. Like, you have no idea how much data can shift everything. Data is so important.

TypeSafe as a Data Lab and Synthetic Data Strategy

Swyx [00:21:57]: Yeah.

Diogo Almeida [00:21:57]: Holy crap. So if people are looking for a job, we are hiring infinite data people, actually infinite.

Swyx [00:22:04]: What is a good data person? Like, clearly somebody who cares about reading through the transcripts of, whatever. You’ve said, for example, that y- all your data is synthetic.

Diogo Almeida [00:22:15]: Yep.

Swyx [00:22:15]: But that’s only, like, the scratching the surface, right?

Diogo Almeida [00:22:18]: Yeah.

Swyx [00:22:19]: Like, it’s not. Like, synthetic, so what, right? Synthetic, but we have people with a lot of taste and a lot of care looking at, looking at these, articulating what’s wrong, going back, regenerating. Is that what a good data person is these days?

Diogo Almeida [00:22:31]: Let me try to figure out how to. Like, it’s, it’s super complicated, and like, I literally onboard the data people with a Talk that I assume is longer than this podcast will end up being. So I will try to say, like, the high level of it. So number one, we don’t do the kind of synthetic data that people ki. Well, I’ll do. Actually, number is zero. Data and synthetic data depends on your task. Like, the shape of your data. The shape of your task changes the data. Like, RLVR’s data is kind of environments, right?

Swyx [00:23:03]: Yes.

Diogo Almeida [00:23:04]: RLHF’s is the human feedback? Each task has its own unique kind of data, and we, of course, have our own unique kind of data, right? So number one, we have that. Number two, the thing I. The reason why we don’t want to train on our users’ data, even if we could, right? Like, we could probably ask for any terms right now, and it will. We. I don’t know if it would make a difference. We truly don’t want that, because no matter what, the real-world data has so much bias. There’s, like, a power law of, like, people, like, asking the same things where you’ll end up, like, overfitting to it and like, fracturing to it and all of that. And number two, we are, like, aiming for, like, a complete sci-fi future years from now where, like, these models are going to be, like, the general infrastructure, layers and layers and layers and deep down the stack to, like, things people can’t even imagine. Like, I would like to think of our model, like, kind of like, UDP as LLMs and TCP as our models. All sorts of stuff can be built on top of that, and we need to be able to nail those futuristic use cases such that software developers can actually build that futuristic stuff. And the way to do that is even if we had all of the data of the present, we would just overfit to the present, and then it wouldn’t work. What we need is to, like.

Diogo Almeida [00:24:21]: It almost feels like a. Like, they’re the artists? They study this cognitive core. Our cognitive core is, like, way less jagged than anyone else’s. And then they find the jaggednesses, and then they address them surgically in a way that. And you can never perfectly do this, right? But they do it in such a way that it addresses it in every single possible, like, dimension, past, present, future.

Swyx [00:24:45]: The general case rather than the specific case.

Diogo Almeida [00:24:47]: Exactly. And like, that requires a lot of intelligence every time.

RLCD vs. RLHF: Defining a New Task

Swyx [00:24:50]: Okay, so we mentioned a little bit. You sort of criticized my thinking as r-- like, very RLVR influence, which is, like, very fair. Let us actually mention RLCD

Diogo Almeida [00:24:59]: Ooh

Swyx [00:24:59]: Which obviously you have some secret sauces to our knowledge. You’ve never actually published a paper or anything like that on it. No, right?

Diogo Almeida [00:25:05]: No, not yet.

Swyx [00:25:06]: But like, what should people get from this? Like, what. Can you give people some confidence that you’re just not just making up jargon for the sake of sounding cool, right? Like, one thing for me is, like, calibration I do think is a. To me, like, well understood because we’ve covered it in. On the podcast.

Diogo Almeida [00:25:22]: Yeah.

Swyx [00:25:22]: But I don’t know what you mean when you say RLCD versus what people are familiar with.

Diogo Almeida [00:25:26]: It’s a great question.

Swyx [00:25:27]: Yes.

Diogo Almeida [00:25:27]: And actually, I will give a related question.

Swyx [00:25:29]: Okay.

Diogo Almeida [00:25:29]: What is RLHF?

Swyx [00:25:31]: Okay.

Diogo Almeida [00:25:31]: Right? And actually, RLHF means multiple different things, right?

Swyx [00:25:34]: Okay.

Diogo Almeida [00:25:34]: Like, there’s the RLHF of the original. I think it was, like, Paul Christiano teaching a robot to backflip or something like that. Wasn’t there something

Swyx [00:25:42]: Was that it?

Diogo Almeida [00:25:43]: That was the original

Swyx [00:25:44]: I referenced the PPO paper, but I don’t know.

Diogo Almeida [00:25:46]: And so PPO was not necessarily from human feedback, if I recall.

Swyx [00:25:51]: Okay. That’s true

Diogo Almeida [00:25:52]: But I b- I believe it was, like, an OpenAI alignment work that could teach hard to specify outputs, like a backflip. I’m not 100% sure. And then there was actually learning to summarize. This was work, by a bunch of the team that helped with, instruct-- and co-authored, the instruction following paper, which was teaching, doing PPO on language models.

Swyx [00:26:15]: This is the, sorry. I’m trying to, trying

Diogo Almeida [00:26:19]: Yeah

Swyx [00:26:19]: Trying to manipulate this thing. This is 2017.

Diogo Almeida [00:26:23]: Yeah.

Swyx [00:26:23]: Right.

Diogo Almeida [00:26:23]: I’m not 100% sure, but like, that looks quite right.

Swyx [00:26:26]: Yeah.

Diogo Almeida [00:26:26]: If it has, like, a robot doing backflips or something like that might be it. Yes. Okay, cool. I guess I got it right. Hell yeah.

Swyx [00:26:35]: There you go.

Diogo Almeida [00:26:36]: Yeah.

Swyx [00:26:36]: That’s the one.

Diogo Almeida [00:26:36]: So the idea was can, like, can you do, like, ill-specified things with it? So that’s, like, version one. Version two was, the learning to summarize work, that, like, OpenAI did, which is actually, like, PPO on language models to do something somewhat ill-specified. This is, like, another thing that people refer to as RLHF Which I did not co-author.

Diogo Almeida [00:26:57]: Oh, Dario’s there. Cool. Hell yeah.

Swyx [00:27:01]: And Radford.

Diogo Almeida [00:27:02]: Yeah. Shout-outs to Alec and Ryan. Love them.

Swyx [00:27:04]: Yeah.

Diogo Almeida [00:27:05]: But the thing that I refer to RLHF is the, Oh, man.

Diogo Almeida [00:27:13]: I’ll get to

Swyx [00:27:14]: You have comments on that, yeah.

Diogo Almeida [00:27:15]: I have comments on that paper, but like, we’re so many, tangents deep.

Swyx [00:27:18]: Yeah.

Diogo Almeida [00:27:18]: So the thing that really got. To me, the thing that I’m calling to RLHF is the task of instruction following. It’s not about the PPO. That part doesn’t matter. It’s about, like, setting a North Star of this is a valuable direction. It’s kind of like the Bitris lesson North Star.

Diogo Almeida [00:27:34]: And for us, RLCD is this new task. And it is not. I don’t see it as jargon. Like, I try to communicate with precision. It’s just that, “Hey, here’s another North Star.” Just like DPO and all of its, like, descendants also do RLHF, despite not using the algorithm in that paper.

Swyx [00:27:55]: And so clear- clearly stating the North Star is, being program- programmable AI is one, word that I really catch onto, removing the human in the loop,

Diogo Almeida [00:28:06]: Yes

Swyx [00:28:06]: From. Because RLHF is tuning

Diogo Almeida [00:28:09]: Yes

Swyx [00:28:09]: For this so that you can automate everything.

Diogo Almeida [00:28:11]: Yes. Everything that makes

Swyx [00:28:13]: Did I miss anything else in the, in the thesis of, like, what the North Star is?

Diogo Almeida [00:28:17]: There is. That is. That is right. I’m overly nuanced in my communication. The one nuance is that we need to be practical. We need to be aware of what language models can do really well. Like what AI can do.

Diogo Almeida [00:28:30]: Right? Like, there could be programmatic types that are, like, sick AF, but if you. If the technology is not ready for it to. It’s not a tragedy if that’s not out in the world.

Why Programmable AI Matters

Swyx [00:28:41]: Yeah.

Diogo Almeida [00:28:42]: But to me, like, the pre-Jev world was a tragedy becau-- it sounds arrogant. Hear me out.

Swyx [00:28:49]: No. I strongly believe you.

Diogo Almeida [00:28:50]: Cool. It sounds arrogant, but like, I felt this way since long before I even had a company.

Swyx [00:28:54]: Yeah. I can, I can vouch that,

Diogo Almeida [00:28:56]: Yes, I’ve been talking about this for so long

Swyx [00:28:57]: You said this at All Around Her for, like, three years.

Diogo Almeida [00:28:58]: Yeah, I’ve been talking about this for so long. And I’ve been saying it because I thought it would have been easier. They say they do not do things because they. It. They’re easy. They. It’s ‘cause they thought it was easy, so

Swyx [00:29:08]: Yeah, exactly

Diogo Almeida [00:29:09]: Something like that. I thought it. This whole project would take a week.

Diogo Almeida [00:29:13]: And I was unbelievably wrong. So I am so sorry to everyone at OpenAI that I thought. I was like, “Man, I’m solving this right now.” but like, I think that the tragic thing is when. Well, I think overpromise, underdeliver is tragic too. And like, AI is super extreme on that axis. And I think RLVR is, like, the main. Well, both RLVR and RLHF are extreme perpetrators of this.

Diogo Almeida [00:29:40]: But like, it. To me, it’s like it’s just there’s just so much potential there. Like, AI is clearly so smart. I l- smart. I love this in my talks, when I ask people, like, “How can AI be so unbelievably smart? How can we, like, solve millennium prize problems in math, but still not automate even the most basics of works?” Like, really basic rote stuff that, like, the. It d- it doesn’t take, like, extremely smart people to do this. It’s not a satisfying job. Like, there’s other things these people could be doing, but yet we need them to do, like, this ba- like, super basic- non- unsatisfying stuff because, like, we can’t automate it yet, but we have this, like, supercharged engine of automation that just does not have, like, the right plugs and stuff to plug into all of this economically valuable work. And like, if the whole company of TypeSafe disappears, like, maybe it’ll take, like, a year or two for people to, like, truly catch up. I actually don’t know how long it’ll take. If model quality matters, then we are gonna be in a very good position for a long time. But it, like, it’s done, right? Like, there, like, this has changed the path of, like, technological history.

Swyx [00:30:49]: Yeah.

Diogo Almeida [00:30:49]: And like, we will be exploring that space as a field.

Swyx [00:30:53]: Yeah. I think, I definitely agree with that. You’ve created possibilities. So I think, if I can paraphrase so that people can un- also understand, you should not take the success of TypeSafe and Jev as just like, “Well, that is a new model type. Now we’re done. We go back to business.” Like, no. Like, actually, there’s, there are, like, five other model types that you should be exploring and like, let a thousand flowers bloom.

Diogo Almeida [00:31:15]: Absolutely.

Swyx [00:31:16]: Right?

Diogo Almeida [00:31:16]: Like, early internet

Swyx [00:31:17]: And some of that, some of which you will probably also build.

Diogo Almeida [00:31:18]: Of course, yes.

Swyx [00:31:19]: Yes.

Diogo Almeida [00:31:19]: Early internet energy. I think it’s back to tech utopia. It’s no longer like, “Oh, man, like, sometimes my coding agents work, but the, all of the best ones are hoarded internally.”

Swyx [00:31:29]: Yeah.

Diogo Almeida [00:31:30]: Right? It’s like creation is back on the menu.

Diogo Almeida [00:31:34]: ? Though it’s gonna be a wild-ass world, and buckle up.

Diogo Almeida [00:31:38]: It’s. And I’m so jazzed about that.

Manifesto, Launch Strategy, and Early Internet Energy

Swyx [00:31:42]: Yeah. And now you have the funding and the momentum to do whatever you envision there, which I, which I think is, like, very gratifying to see you have after, so long of saying these things

Diogo Almeida [00:31:53]: Yeah

Swyx [00:31:54]: But actually show the world.

Diogo Almeida [00:31:55]: I know. I just. Such a, such an interesting thing to be a tease the whole time. Like, my talk, like, felt like it was a cliffhanger ‘cause I didn’t say how the automation would occur.

Swyx [00:32:05]: Yeah.

Diogo Almeida [00:32:06]: Sean reviewed our manifesto And he’s like, “It’s a little bit vague in these parts.”

Diogo Almeida [00:32:12]: And like, “What’s step one? What is, what is the intelligence model?”

Swyx [00:32:16]: Well, I asked you for model, and you were like, “Yeah, model coming.”

Diogo Almeida [00:32:18]: Yeah.

Swyx [00:32:18]: And like, Well, I just, I mainly objected to the word composable But build prod.god is fantastic.

Diogo Almeida [00:32:24]: Thank you.

Swyx [00:32:24]: Yeah.

Diogo Almeida [00:32:25]: I. We’ve really rallied around that. I’d like to think we’re not entirely a cult like some companies are.

Diogo Almeida [00:32:32]: But like, we are, like, jazzed about what we’re doing, and like, we are. Like, my brand is being practical, and like, we are all, like, so super-duper practical.

Swyx [00:32:42]: Yeah.

Diogo Almeida [00:32:42]: It’s really great.

Swyx [00:32:43]: Yeah. So here. And by the way, here is the step, the secret master plan, right?

Diogo Almeida [00:32:47]: Yep.

Swyx [00:32:47]: Shape, the shape of machine-native composable AI.

Diogo Almeida [00:32:49]: It was your idea to make a secret master plan, so

Swyx [00:32:51]: It’s a, it’s that Elon thing. When he started Tesla

Diogo Almeida [00:32:53]: Yeah

Swyx [00:32:53]: He was like, “Here’s what we’ll do.”

Diogo Almeida [00:32:54]: But I did. Yeah. I’m giving official credit to you.

Swyx [00:32:56]: Oh, thank you. Thank you, thank you.

Diogo Almeida [00:32:56]: Yeah.

Swyx [00:32:56]: Thank you. But like, you should’ve told me your, you’re also gonna do this model launch, ‘cause you, like, you told me, you told me half of the story, and then the other half, you didn’t have the doom demo at the time.

Diogo Almeida [00:33:08]: Yep.

Swyx [00:33:08]: You didn’t have any numbers to give me.

Diogo Almeida [00:33:10]: Yep.

Swyx [00:33:10]: I was like, “what?”

Diogo Almeida [00:33:11]: Well, the problem is I don’t believe in benchmarking.

Swyx [00:33:13]: Exactly.

Diogo Almeida [00:33:14]: Right?

Swyx [00:33:14]: Exactly.

Diogo Almeida [00:33:14]: So like, it is a thing that you need to feel, and like, I think that this is the way to build long-term trust, even though it, like, hurt, it hurt us a, us a lot? Like last year when we did fundraise, no one believed us.

Diogo Almeida [00:33:27]: ? Like, and they wanted just benchmarks and stuff, and we’re like, “We’re not gonna do that. We are principled. We’re gonna stand by our guns. That rewards bad actors. I don’t give a shit, like, what you want. Like, this is who we are, and we are standing by that.” So Sorry. It’s not

Swyx [00:33:43]: No, yeah. Well, and in some ways, I think, like, choosing the hard path, it. But you end up making the company that you wanna work in.

Diogo Almeida [00:33:49]: Yep.

Swyx [00:33:50]: Right? Otherwise, if you sell out, then you’re just working in, like, OpenAI but with my people, right? Which is like.

Diogo Almeida [00:33:56]: Yeah. Yeah. Like, I’m, I don’t have too many regrets on that, obviously.

Swyx [00:34:01]: Yeah.

Diogo Almeida [00:34:01]: Like, it worked out so unbelievably well. And like, I, The. I was emotional last night when I was talking about, like, the reasons I left OpenAI, and because, like, it actually had to change my wording after the launch. My phrasing was, “If an AI winter did happen and I did not do every fucking possible thing I could to, like, avert that, I would see myself as personally responsible both for, the RLHF direction, which I think really widened overpromise versus under-deliver, and also not going all in on this because I think this is, this is where value is going to just be, like, printed.” So. And it was really cool because I feel like

Diogo Almeida [00:34:47]: The AI winter I’m worrying about is averted. Like, AI will be useful. It’ll be used for automation.

Diogo Almeida [00:34:53]: It’s been less than a week, and like, the numbers are already undeniable

Swyx [00:34:57]: Yeah

Diogo Almeida [00:34:57]: That it’s, like, being used for real work, and like, there’s. It’s, it’s the Wild West. Yeah.

Launch Traction, Tokens, Rate Limits, and Developer Usage

Swyx [00:35:03]: Yeah. Can you sh- just if you have top of your head, what numbers are you seeing? Like, what’s, what’s, like, signups? Like, whatever you can share.

Diogo Almeida [00:35:11]: I’m actually not super on top of everything. Like, the team is the ones who are telling me all of these things.

Swyx [00:35:16]: Yeah, and I’m sure it’s, like, changing every day, right?

Diogo Almeida [00:35:17]: It’s, it’s,

Swyx [00:35:18]: But like

Diogo Almeida [00:35:18]: It’s kinda nuts

Swyx [00:35:19]: If there’s a milestone that you’re like, “Well, yep, that’s one thing we were hoping for. We reached it.”

Diogo Almeida [00:35:23]: I will say a milestone that we’ve passed is tokens per day.

Swyx [00:35:27]: Nice.

Diogo Almeida [00:35:27]: And this is not, like, fleeting tokens per day.

Swyx [00:35:32]: Yeah.

Diogo Almeida [00:35:32]: This is, like, even at night, like, it’s constantly training, so machines are calling it and not just people trying things out.

Diogo Almeida [00:35:39]: So that is, That is so cool. A trillion tokens a day is a lot.

Swyx [00:35:45]: Yeah.

Diogo Almeida [00:35:45]: So surpassing that is awesome. Signups to me don’t really matter. And actually, this was, like, a bit of a mistake we made, if I’m, like, totally honest. People on Twitter were calling us, like, marketing geniuses and all of that, and that was just us. We don’t have a marketer. Also hiring. And we were just being our genuine, goofy, like, irreverent selves, and we were, we were just, like, offboarding people off the waitlist so hard. - Our platform team is so unbelievably cracked. I think we have more n- up nines of uptime than Anthropic while having the most Unprecedented launch ever. Like, that is kind of nuts, so

Swyx [00:36:21]: Yeah

Diogo Almeida [00:36:21]: Like, props to them.

Swyx [00:36:22]: Yeah.

Diogo Almeida [00:36:23]: And the thing we didn’t realize. So number one, waitlists, waitlist sign-ups don’t matter for, like, a developer platform, in my opinion? I would guess that a large number of them are not even developers. So they go in, they try some queries, and a lot of people don’t get it because they are not programming, right? Like, they’re just like, “What? This is not a chatbot. Where’s my ChatGPT 2?”

Diogo Almeida [00:36:45]: Right? But if, like. I haven’t exactly calculated this. My sense is that if every single human being in the world, like, just wrote a couple of queries, that would be a rounding error compared to, like, one power user’s for loop that is just, like, creating value.

Swyx [00:37:01]: Yeah.

Diogo Almeida [00:37:01]: And the thing we are-- didn’t realize with the waitlist is, like, we could just w- off-board anyone off the waitlist. It doesn’t matter. The scary part is rate limits. And then once people start getting value from that, then they just want tons and tons of rate limits because this is what software is, right? Like, you spend effort upfront to specify your rote task, and then this rote task creates more value than it takes to put in. And then now that you have that

Swyx [00:37:25]: Set it and forget, yeah.

Diogo Almeida [00:37:26]: Exactly, yeah. You run it in the background. You make it a dependency, to, like, other things. You can make, like, higher level stuff. And like, you just create so much value in the world. Early internet people probably did not imagine, like, the wonder of early 2000s internet, which is still not early internet. But like, it’s, it’s through, no offense, composability

Swyx [00:37:47]: No

Diogo Almeida [00:37:47]: That all of the crazy stuff happens, and I just really wanted to emphasize that in our manifesto. We are going for emergence. We are going for, like, being the catalyst. We’re wanting to empower people, and we are going to do whatever we can for that, be it, like, Discords in our town hall with me wearing a garbage bag or not.

Swyx [00:38:05]: And podcasts and Diogo Almeida [00:38:08]: Hell yeah

Swyx [00:38:09]: Getting all that.

Diogo Almeida [00:38:09]: Absolutely.

Swyx [00:38:09]: Like, ‘cause I want the long form, right?

Diogo Almeida [00:38:11]: Yeah.

Swyx [00:38:12]: It is like, yes, we’ll get past the, some of the superficial things, and then we’ll go deep and

Diogo Almeida [00:38:15]: Hell yeah

Swyx [00:38:15]: And people will really trust and understand your mission and like, the people that, will resonate that will end up joining you or, buying you. Or No, but sorry, as a, as a customer.

Diogo Almeida [00:38:27]: Oh, as a customer.

Swyx [00:38:28]: As a customer, as a customer.

Diogo Almeida [00:38:28]: Okay, yeah. That was funny. I’m sorry.

Swyx [00:38:30]: Sorry. I didn’t, I didn’t mean to say that. But no, any-- one version, one very flattering version of this, like, 36 million views of your launch video.

Diogo Almeida [00:38:37]: Cool. Up to 38 now.

Swyx [00:38:39]: Yeah, rounding error.

Diogo Almeida [00:38:40]: Yeah.

Swyx [00:38:40]: Navio still has got 74. Fable 5 got 57. So like, as far as, a- and I didn’t, I didn’t do the stats for, like, original ChatGPT, like

Diogo Almeida [00:38:48]: Yep

Swyx [00:38:49]: Which there was no video.

Diogo Almeida [00:38:50]: Yep.

Swyx [00:38:50]: So like, up there, right?

Diogo Almeida [00:38:52]: Yep.

Swyx [00:38:52]: Like, as far, as far as, like, if you were to launch a Neolab in 2026, I think you’re, like, number one right now, which is, like, pretty crazy.

Diogo Almeida [00:38:58]: Yeah. Well, I actually would rather. I do have the shirt, like, your favorites Neola-- favorite Neolab’s favorite Neolab.

Swyx [00:39:05]: Huh.

Diogo Almeida [00:39:05]: I don’t give a shit about being a Neolab. I think being a Neolab. Actually, we have a lot of, like, swag that’s being a parody of a Neolab. One of them, one of them I have is, like, Neolab with product, which actually is not a Neolab. Like, I don’t care about that, really.

Swyx [00:39:20]: Yeah.

Diogo Almeida [00:39:20]: What I care about is being a reliable dev platform. So Swyx [00:39:23]: Yes

Diogo Almeida [00:39:24]: Appreciate the comparison, but like

Swyx [00:39:25]: Yeah

Diogo Almeida [00:39:25]: Hopefully we transcend past them and we go back into, like, a thing-- like, a revolutionary moment for developers and like, this stable thing that people can rely on and trust.

Reliability, Robustness, and Determinism

Swyx [00:39:35]: Yes. To that end, I think that’s one thing that really impressed me about you guys is that, yes, you do talk about reliability. I thought it was mostly about calibration, which, like, we talk about RLCD. But actually it’s also about just, like, uptime and scalability and all those things, right? They’re, they’re all sort of the kind.

Diogo Almeida [00:39:55]: And nines.

Swyx [00:39:56]: And nines.

Diogo Almeida [00:39:56]: It’s, like

Swyx [00:39:57]: Which uptime is, in my opinion.

Diogo Almeida [00:39:58]: Oh, but that’s part of it. But like, there’s reliability in, like, how intelligent the thing is. Like, how consistently does it do the thing that you want? And I think that, like, the big reasoning models are very smart. In my opinion, they still lack reliability. I think there’s many use cases where you-- they look like they should be smart enough to automate their work. There is economic incentive to automate that work, yet still they’re not reliable enough as, at an intern because they’re optimized for different things. And so like, I think that there’s the reliability of being able to, like, trust the outputs. And also we are. Like, there are dimensions of reliability that we are not yet at that I’m, like, so excited by.

Swyx [00:40:38]: Yeah.

Diogo Almeida [00:40:38]: Like, I want to automate the easy work before the hard work? Like, I think that’s just a common sense thing to do. But to me, we will be sufficient. I don’t know if there’s such thing as sufficiently reliable, but I wanna get so good that people don’t even need to try the model to know that it’ll work. It’s like, that’s like what flow state is in programming, right? Like, I’m just, like, writing queries because I need intelligence in here. And like, when. For non-trivial branching, I can just write it in like a, like a type-safe System 1 query and then get the results out of it and it just branches accurately. Like, that would be so good. Like, that’s the. That is the dream.

Swyx [00:41:12]: Yeah.

Diogo Almeida [00:41:12]: And that is, like, going to be, like, a long slog.

Swyx [00:41:16]: Yeah. We’re gonna go into your API design in a little bit

Diogo Almeida [00:41:19]: Ooh

Swyx [00:41:19]: Just to give people examples and like, maybe paths not taken, that kind of stuff.

Swyx [00:41:23]: One thing up the front that I do wonder about in terms of reliability is I noticed that there’s no seed. There’s no, And so basically, same input, do I always get the same output?

Diogo Almeida [00:41:34]: So

Swyx [00:41:36]: And if not, why not?

Diogo Almeida [00:41:37]: Oh, great question. So this is actually, like, a common question we have between. So reliability is actually a catchall. Like, whenever AI can’t automate something, it’s due to some form of reliability. Could be, like, type safety. It could be determinism. It just could be, like, it’s, it’s jagged, right? So reliability is a catchall. I just think that it’s also a catchall for, like, what the North Star is. Re- determinism is, like, same inputs, same outputs. I do believe that this is, like, slightly interesting for unit tests, but I believe that to be the wrong North Star. I believe robustness is what people

Diogo Almeida [00:42:16]: I don’t wanna tell people what they really want, ‘cause that would be a little arrogant of me.

Diogo Almeida [00:42:19]: I believe that is, like, the more important property. You want, given similar inputs, get similar outputs. And it’s kind of wild how unreliable LLMs are.

Diogo Almeida [00:42:31]: Like, a way that we test this is you put, like, UUIDs in, like little

Swyx [00:42:36]: Yeah

Diogo Almeida [00:42:36]: I think they’re called nonces In the prompt. And what you want is similar outputs from all of those, ‘cause it’s truly semantically the same question, and that is the part where you really want. Th- like, that robustness is where, like, people get, like, burnt with AI making decisions. So I think that is the. A super-duper important property. We could also have determinism. That is, that is a thing that can be available. As far as I can, like, mentally model for programmers, like, it, I- it could be valuable for some use cases, so like, please educate me, in comments or view. But my. In general, it’s easy. Determinism is something you can, like, trade off for better cost. Like, we are, we are constantly wanting to be on the intelligence per dollar frontier. We are doing, like, absolutely disgusting things to be there. Like, this is,

Diogo Almeida [00:43:32]: I shouldn’t say this, but no one’s here to stop me.

Swyx [00:43:37]: If you s- you sign off on your own PR.

Diogo Almeida [00:43:40]: That is not how it works at this company. I believe for this week, my chief of staff, Kay, is the most powerful person in tech.

Swyx [00:43:49]: Yeah. And shout-out to Kay for organizing this.

Diogo Almeida [00:43:50]: Holy sh

Swyx [00:43:51]: Yeah.

Diogo Almeida [00:43:51]: Holy shit. She is so fucking competent and powerful. She’s incredible.

Diogo Almeida [00:43:58]: She sucks. Don’t poach her. But so I try to be a bit more filtered, but like, people are telling me, “Don’t call it a Frankenstein’s monster of models,” but because that has, like, negative implications. I think Frankenstein’s monster was, like, the good guy in this whole. It was innocent, right? I didn’t read it. Okay.

Diogo Almeida [00:44:18]: I’ll, I’ll confess. Okay. That. Well, one facial expression, I

Swyx [00:44:21]: This is a

Diogo Almeida [00:44:21]: My cards on the table

Swyx [00:44:21]: Decent Jacob Elordi movie if you wanna see

Diogo Almeida [00:44:24]: I

Swyx [00:44:25]: The adaptation. Anyway.

Diogo Almeida [00:44:26]: The. You have no idea how little time I have right now.

Swyx [00:44:28]: Yeah.

Diogo Almeida [00:44:29]: My priorities are sleep?

Swyx [00:44:31]: Developers.

Diogo Almeida [00:44:32]: Developers, yes. Developers. But yes. It. We do, like, absolutely disgusting things to be on the Pareto curve of intelligence per dollar, and we are going to keep doing that.

Swyx [00:44:47]: Yeah.

Diogo Almeida [00:44:47]: We’re gonna be doing crazy-ass stuff, and I think people really need to think outside of the box. Like, part of the reason we’re surprising is, like, people Are thought inside the box, and we continue to do that. As of right now, we are obviously the best at this, and we want to continue being the best at that whole thing.

Swyx [00:45:05]: Yeah.

Diogo Almeida [00:45:05]: So Wait, where did, where did we tangent from?

Swyx [00:45:07]: No. So

Diogo Almeida [00:45:08]: Yeah

Swyx [00:45:08]: I asked you about, will you have seeds and determinism?

Diogo Almeida [00:45:11]: Oh, yes. So

Swyx [00:45:11]: And then you basically defined reliability and like

Diogo Almeida [00:45:14]: And robustness

Swyx [00:45:15]: How you see it. Yes.

Diogo Almeida [00:45:16]: But like, determina- like

Swyx [00:45:17]: I have a robustness example that’s, that’s, real quick I can show you.

Diogo Almeida [00:45:19]: I would love that. I will just say one thing.

Swyx [00:45:21]: Yeah.

Diogo Almeida [00:45:21]: We can make a deterministic model.

Swyx [00:45:22]: Exactly.

Diogo Almeida [00:45:23]: Like, we’re hap- if people can convince us that is a valuable thing to do

Swyx [00:45:27]: Yeah

Diogo Almeida [00:45:27]: And we don’t have a gigantic GPU shortage

Swyx [00:45:29]: Yeah

Diogo Almeida [00:45:29]: We can happily make all of these models. We live to please. And rev- and revolt, revolute,

Swyx [00:45:38]: You will throw over everything, except you’ll do it in a nice way.

Diogo Almeida [00:45:41]: Yeah.

Swyx [00:45:41]: And find

Diogo Almeida [00:45:42]: So like, determinism could be on the cards.

Swyx [00:45:44]: Yeah.

Diogo Almeida [00:45:45]: It just gets you less intelligence per dollar.

Swyx [00:45:46]: Yeah. Well, just having seen the trajectory of OpenAI and Anthropic, you will. Just trust me now that you will be peer pressured into doing it. So like, just people will want it even if they. If you tell them they don’t need it. They’ll still want it. So like, yeah, that’s the TL;DR of that.

Diogo Almeida [00:46:01]: Okay.

Swyx [00:46:02]: Yeah.

Diogo Almeida [00:46:02]: I will love to. Maybe one day we will see how that happens.

Swyx [00:46:07]: Yeah.

Diogo Almeida [00:46:07]: I’ve been told I’m, They say that part of our brand is being unshakeable

Swyx [00:46:13]: Huh

Diogo Almeida [00:46:13]: And they say that’s just the nice way of saying stubborn.

Swyx [00:46:15]: Stubborn, yeah.

Diogo Almeida [00:46:16]: Yeah, exactly. And I’m a very stubborn person. I don’t think we could have done it.

Swyx [00:46:19]: Yeah.

Diogo Almeida [00:46:19]: Yeah.

Swyx [00:46:19]: No, but. So like, I. Okay, but I tr- I, like, have argued with you before.

Diogo Almeida [00:46:23]: Yeah.

Swyx [00:46:24]: And I know

Diogo Almeida [00:46:24]: And you’ve been right about developers every time.

Diogo Almeida [00:46:25]: So okay, I give up. You win. You win. I’m sold that I’ve argued with you before.

Swyx [00:46:31]: No, I’m just saying, like, I think that you can hold your ground while also, like, if I give you the right evidence, you can, not. You can sort of throw away your priors and be like, “Yep, like, that actually makes sense to me.”

Diogo Almeida [00:46:41]: Yep.

Swyx [00:46:41]: And so like, just trust your own gut on this.

Diogo Almeida [00:46:44]: Yeah. Yep.

Swyx [00:46:44]: I’ll bring up some

Diogo Almeida [00:46:45]: But I suspect, though, that we will be GPU constrained for a very long time.

Swyx [00:46:50]: Very long. Yeah.

Diogo Almeida [00:46:50]: And anything that has less intelligence per dollar means it consumes more GPUs

Swyx [00:46:56]: Yeah

Diogo Almeida [00:46:56]: For the same intelligence, which is. Like, our goal is not to onboard companies. Like, r- it’s, it’s valuable, but like, our goal is to have people, like, experiment and do weird shit. And we need, like. We need to, like, get, it to as many hands as possible and like, starting, like, the California gold rush for that.

Model Versioning, LTS, and Preserving API Stability

Swyx [00:47:14]: I think there is right now. Yeah.

Diogo Almeida [00:47:16]: Yeah.

Swyx [00:47:16]: Just a word of caution. I will just say it

Diogo Almeida [00:47:19]: Ooh, okay

Swyx [00:47:19]: Because somebody’s thinking about it right now.

Swyx [00:47:21]: Which is when you say things like, “We will not commit to deterministic models. We will, we’ll do whatever it takes for intelligence per dollar, and we are al- we are facing GPU constraint,” people are thinking you may quantize your models, right? Like s- like, whatever you had at launch, you may quantize down in. To reduce the quality, in order to free up, memory or bandwidth or whatever, right?

Swyx [00:47:42]: And so you should probably, have some kind of promise, which you don’t have to make now

Diogo Almeida [00:47:47]: Yep

Swyx [00:47:48]: About, like, “We will uphold model quality at launch.” People, like. So it’s like when people. When we. You were at OpenAI when you launched

Diogo Almeida [00:47:55]: Yep

Swyx [00:47:55]: All these, all these APIs, and even Claude as well. Like, when they first launched the models, the model strings, did not stay the same model at all times.

Diogo Almeida [00:48:04]: Yep.

Swyx [00:48:04]: Right? You have versioning in your models. That’s great.

Diogo Almeida [00:48:06]: Yep.

Swyx [00:48:06]: But like, you should, you should publicly commit to some kind of, like, once a thing is launched, we don’t change it.

Diogo Almeida [00:48:11]: We will not change our models when we deploy them. That is insane. We care about developers. Li- like, it makes sense if you’re. If. So- doing something like that, again, this is the problem with a for- first-party product and an API. It makes. You can do whatever you want in a first-party product, right? Like, more power to them, whatever gets that experience, that is fine. With an API, you obviously can’t do that. But I will say that we, plan to move a lot faster than many people are used to model providers, doing things. So we will be launching new models a lot faster than people think, and we are not promising long-term support for the models because we think that there’s lots of improvements to have. So there is a world that we might temporarily LTS what is right now Jev 1.13.0. We might do that ‘cause so many people are using it, and I know developers hate breaking dependencies. The alternative is fracturing our fleet, and that is a very bad vibe for everyone. It’s gonna be

Swyx [00:49:12]: Yeah, you can have, like, 100 different versions of the model.

Diogo Almeida [00:49:13]: Exactly. And if we’re iterating very fast, there would be a lot of those versions as well.

Swyx [00:49:17]: Yeah.

Diogo Almeida [00:49:17]: So we do want to have not just a LTS-supported thing eventually, long-term support. We want a really sick way of doing that. We have, like, research stuff cooking in that direction, and I think it’s gonna be the most pro-developer thing ever.

Swyx [00:49:34]: Yeah.

Diogo Almeida [00:49:35]: But it is not yet our current models, and I’m not promising that we will be able to keep the exact same models. They will get smarter every time, for sure.

Swyx [00:49:43]: Yeah.

Diogo Almeida [00:49:43]: And my sense is that even our model iterations, where it already is smart, it. Between model versions, the changes tend to be even smaller than the string models calling them twice. But when we go from, like, jagged to, like, wow, that is where the big deltas are.

Intelligence per Dollar vs. Intelligence per Second

Swyx [00:50:01]: Yeah. One thing, one thing that’s beautiful about LTS-ing models is that actually you can also port them to other silicon.

Swyx [00:50:08]: I don’t, I don’t know if you’ve thought about this.

Diogo Almeida [00:50:11]: No comment.

Swyx [00:50:12]: Okay.

Diogo Almeida [00:50:12]: So I care about intelligence per dollar.

Swyx [00:50:14]: Yes.

Diogo Almeida [00:50:15]: Right?

Swyx [00:50:15]: But speed.

Diogo Almeida [00:50:16]: What?

Swyx [00:50:17]: Speed as well.

Diogo Almeida [00:50:18]: We’ll see.

Swyx [00:50:19]: Yeah.

Diogo Almeida [00:50:19]: We’ll see. I

Swyx [00:50:20]: This is. This is a whole part of the inference tech tree that is, like, exploding in the past year, right?

Diogo Almeida [00:50:24]: Yeah.

Swyx [00:50:24]: Like, that you can, you can move to, like, a Cerebras

Diogo Almeida [00:50:27]: Yeah

Swyx [00:50:27]: An Etched or whatever and get, like, the 100,000 times speed up.

Diogo Almeida [00:50:32]: Yeah. Like, I think that intelligence per second is, like, a different metric, and we’ve even talked about, like, things like intelligence per dollar times second and like, metrics like this. My guess on, like, Jevons’ paradox occurring, or at least the Jev series of models, and the thing I, like, hunt people down about internally is, like, I don’t care how much smarter it is, it needs to be in the Pareto frontier. So like, that is what the brand of Jev is. It is the best thing at intelligence per dollar. For intelligence per second, we’ll see. I think that it’s an intriguing thing. I know that there’s many industries that are, like, extremely dependent on real-time stuff, and they will, like. Like, intelligence per second means tons of dollars for them. But

Diogo Almeida [00:51:20]: We’ll see. I’m, I would love to, like, do both and like, have the market correct me either which way.

Swyx [00:51:26]: Yeah.

Diogo Almeida [00:51:26]: ?

Swyx [00:51:26]: Yeah.

Diogo Almeida [00:51:26]: Like, I would love to be informed by people.

Swyx [00:51:30]: Yeah, totally. And it’s not, it’s not just real- about real time, right? It’s also about scale because, at scale, every microsecond is just multiplied by billions and trillions of times.

Diogo Almeida [00:51:41]: It depends on how background it’s running, right?

Swyx [00:51:42]: Yeah.

Diogo Almeida [00:51:42]: Like, if it’s, like, a big background, like, database MapReduce query, the latency might not matter so much as, like, the cost

Swyx [00:51:49]: Yeah

Diogo Almeida [00:51:49]: To get intelligence from it. But like, if it actually is, something more real time, like user-facing, you have budgets, like between 100 milliseconds and one millisecond that are, like, totally magical. And actually, even if you were below 100 milliseconds, if you could half that time, that means you can get double the intelligence or sequential intelligence calls to have, like, a, like, a phenomenal experience.

Internal Evals and the Faster-Cheaper Frontier

Swyx [00:52:10]: Yeah.

Diogo Almeida [00:52:10]: So right, that is definitely happening right now. It is super-duper cool. I love the intelligence per second use cases, but I don’t think that will be Jev’s niche.

Swyx [00:52:21]: Okay. Yeah, fair enough.

Diogo Almeida [00:52:22]: Yeah.

Swyx [00:52:22]: When thinking about the promise of faster and cheaper Typically the other. The trade-offs that other models are offering is faster but more expensive.

Diogo Almeida [00:52:32]: Yep.

Swyx [00:52:33]: Right? And so you’re. Like, one of the reasons I was thinking about why is Jev resonating so much is that you’ve done the faster but cheaper side of the quadrant

Diogo Almeida [00:52:41]: Yeah

Swyx [00:52:41]: Which is very unoccupied

Diogo Almeida [00:52:43]: Yeah

Swyx [00:52:43]: While holding intelligence, like, somewhat constant.

Diogo Almeida [00:52:45]: Yes. It w- I-- That’s a very load-bearing statement while holding intelligence constant. That’s the hard part, right? Like

Swyx [00:52:53]: Which, unfortunately. Like, so basically, you refuse to have the, to, like, do any public benchmarks, or you don’t like any public benchmarks about it

Diogo Almeida [00:52:59]: But I will. I’ve actually tried

Swyx [00:53:00]: But you need some internal sense.

Diogo Almeida [00:53:02]: Say again?

Swyx [00:53:02]: You need some internal sense of this in- this

Diogo Almeida [00:53:03]: Oh, of course.

Swyx [00:53:04]: Yeah.

Diogo Almeida [00:53:04]: Of course. We have, we have our own internal evals, for sure.

Swyx [00:53:08]: Yeah.

Diogo Almeida [00:53:08]: But it takes a lot of discipline not to game those, and it needs to be, like, a top-level priority to not game them.

Swyx [00:53:14]: Yeah.

Diogo Almeida [00:53:14]: Of course we do that, right?

Swyx [00:53:15]: Yeah.

Diogo Almeida [00:53:15]: Like, how else can we make the guarantee that our models are in the Pareto frontier of intelligence per dollar?

Diogo Almeida [00:53:20]: Right? Like, we’re not flying blind in there, right? If we’re doing, like, completely weird things with different costs or whatever else, like, how do we compare them? We plot them and get. You. We try to figure out, like, what is the best for the users.

Swyx [00:53:32]: Yeah.

Diogo Almeida [00:53:32]: So we for sure measure them. I’m not anti-measuring. But it’s extremely dangerous when you have, like, any alternative incentive, and this is the one thing that I kind of rule with an iron. Well, maybe my coworkers might think I rule many things with an iron fist, but to me, like, not shitting ourselves, about how smart our model is one of the most important things there.

Swyx [00:53:57]: Yeah.

Diogo Almeida [00:53:57]: Like, we need to be truth-seeking.

Swyx [00:53:58]: Yeah. Yeah. Agree, agreed. Okay, I wanted to go over some, details on the, API choices.

API Primitives: Choice, Score, and Noulli

Diogo Almeida [00:54:04]: Ooh.

Swyx [00:54:04]: Mostly because this is the only podcast that will ask you these kinds of questions.

Diogo Almeida [00:54:07]: Oh, hell yeah. Hell yeah.

Swyx [00:54:08]: So you have three primitives.

Diogo Almeida [00:54:10]: Yeah.

Swyx [00:54:10]: Choice, score, know. First of all, know, where is that from?

Swyx [00:54:14]: Is this, like, a term in the, in the literature or what?

Diogo Almeida [00:54:17]: Now it is.

Swyx [00:54:18]: Yeah.

Diogo Almeida [00:54:19]: We debated this a lot. We debated this a lot. It is It is Bool-ish, right? Like true, false. It is

Swyx [00:54:32]: But it’s continuous.

Diogo Almeida [00:54:33]: Yes, exactly. So first, the origin of the name is Bernoulli.

Diogo Almeida [00:54:39]: Yes. So it-- that’s why it’s even spelled that weird way. That is, like, a subset of the name Bernoulli from, like, a Bernoulli probability, right? Which is actually what that is. So that is the origin of it. We were debating this a lot. We liked PBool, we liked Pool. We were wa-- we were wanting to call it, like, a pool party, but then no one let me. We had, like, a bunch of, like, other arguments about that.

Diogo Almeida [00:55:04]: And Noulli, we figured was, like, the best thing. Our rationale, and like, this is actually the same thing with Jev too, is that we think that we are, like, an irreverent, insane bunch, and programmers don’t care. Like, if Jev is just gonna be a string, we didn’t expect it to catch on or even have puns or anything like that, right? Actually, there was a lot of hate on the name internally. They’ve all apologized, except for one person.

Swyx [00:55:33]: Still holding strong.

Diogo Almeida [00:55:33]: Yes. Our mutual friend.

Swyx [00:55:36]: Okay.

Diogo Almeida [00:55:37]: Yes.

Swyx [00:55:38]: I respect her for that.

Diogo Almeida [00:55:39]: Yeah. Yeah. She wanted Jev to be called Meow.

Swyx [00:55:44]: She would, of course.

Diogo Almeida [00:55:45]: Yes, of course.

Swyx [00:55:46]: Okay.

Diogo Almeida [00:55:46]: Like her father, yeah.

Swyx [00:55:47]: You win there, you win there.

Diogo Almeida [00:55:49]: But like, yeah, Noulli is. We had to make a new concept for this thing ‘cause if it was a Bool, it would be confusing to people. So actually, all three of these are actually new concepts. These are not types that exist in programming, and that was intentional because they map very closely to types, but they’re not quite that. A score is not an int. So if you had, like, Instructor or Pydantic or whatever map ints or floats into scores You’d get a little bit cooked? And like, we were really erring on the side of clarity over the side of, like, making people, like, easily understand what’s going on.

Swyx [00:56:24]: Don’t you worry about that? Don’t you want things to integrate directly into things that people are already using?

Diogo Almeida [00:56:30]: Yes. Yes, we do. And actually, I think that,

Swyx [00:56:34]: You have integrations with, like, other SDKs and stuff.

Diogo Almeida [00:56:36]: Yeah.

Swyx [00:56:36]: But you have-- Sorry, you have your own SDKs.

Diogo Almeida [00:56:38]: Yep.

Swyx [00:56:38]: But typically, for example, as a developer relations person, I would be very obsessive. Like, yes, here is how you use, Jev with Instructor.

Diogo Almeida [00:56:46]: Yep.

Swyx [00:56:46]: Here is how you. That kind of stuff.

Diogo Almeida [00:56:49]: We might have that somewhere. I am so behind on everything.

Swyx [00:56:53]: Someone would do it for you in the community.

Diogo Almeida [00:56:54]: Oh, yeah. Yeah.

Swyx [00:56:54]: Not that you’re successful. People will be like, “Oh, that’s cool.”

Diogo Almeida [00:56:57]: Cool.

Swyx [00:56:57]: But like. Anyways

Diogo Almeida [00:56:59]: I don’t see that as binary either.

Swyx [00:57:00]: Yeah.

Diogo Almeida [00:57:01]: I actually see success as a score, and there’s always more to climb

Swyx [00:57:04]: Yeah

Diogo Almeida [00:57:04]: In, like, how much we can, like, be there for our community, just to be clear. And I’m. This section is stressful ‘cause I didn’t review the docs And they’re constantly changing.

Swyx [00:57:15]: Okay. But

Diogo Almeida [00:57:16]: But to me, scores do exist. So scores are similar to, like, LM judging.

Diogo Almeida [00:57:21]: Right? So like, if you want to call it, like, a judgment, I guess you could. But like, that is, like, the way people ca-- already use this type of thing, right? Like, maybe a Noulli could be, like, a probability, but everything for us is a probability. And a choice is actually closest to a function call, but a function call is, like, an extremely disgusting thing that, if you want OpenAI juice, sauce, tea, that. We should go back into that later. Like a, like, a choice is just, like, the right way of explo-- of exposing, like, a switch match statement

Swyx [00:57:57]: Yeah

Diogo Almeida [00:57:57]: Within code.

Swyx [00:57:57]: So it, like, maps cleanly to an enum.

Diogo Almeida [00:58:00]: Yep.

Swyx [00:58:00]: And you can choose to hydrate it into a function if you want.

Diogo Almeida [00:58:02]: Yes. And like, in the enum, choice is the important part of that.

Swyx [00:58:06]: Yes.

Diogo Almeida [00:58:06]: And like, actually, I think these map all into, like, programming primitives, where, like, choice maps into, like, a, like, a switch statement on an enum.

Swyx [00:58:13]: Huh.

Diogo Almeida [00:58:14]: Noulli’s mapped to if statements.

Swyx [00:58:15]: Yeah.

Diogo Almeida [00:58:16]: And scores map to sorting or thresholding at a greater than or less than.

Swyx [00:58:21]: Okay.

Diogo Almeida [00:58:21]: And this has been always what the vision is. Like, there will be more types, and they will map into programming primitives.

Swyx [00:58:28]: Yeah. Any other. So any nuance you wanna go through? For literally, this is for the Jev people who are, like, deciding to really invest in Jev. You are the expert, right? I’m just, like, wanting to provide more background for them on, API choices, how they should use some of these things, like legends, confidence, how critical in your testing, like, how. Like, just any sort of, like, pro tips that you, like, want to offer people

Structured Inputs and AI-Native Programming

Diogo Almeida [00:58:56]: Yeah

Swyx [00:58:56]: When they’re down at this level.

Diogo Almeida [00:58:58]: Thank you. I love this. No. This is

Swyx [00:59:00]: This is why we’re here.

Diogo Almeida [00:59:01]: Hell yeah. I didn’t expect this. And actually, I. No one has asked me this, in probably, like, months when I was, like, onboarding, like, our DevRel.

Swyx [00:59:10]: Okay.

Diogo Almeida [00:59:10]: So sick. So our model is designed for being, like, deep in the insides of computer programs in the future. We, like, unironically believe that this will be much more massive than anything people are even considering today. And our model might not be ready for that, but we are, like, continuously working for that future. It will never be good enough at these shallow tasks. Sorry. It’ll never be g- Like, we’re not just gonna cle- keep on climbing the shallow tasks. We want to be deep in the guts of programs ‘cause that’s how you make software powerful. All the s- all the types inside of our, This is an, actually an output.

Diogo Almeida [00:59:47]: But all the, all the parts, of, like, the input, like the state, the instructions, the criteria, all of them can be structured JSON objects.

Diogo Almeida [00:59:58]: That way, like, programs can, like, insert them in the right spot, and you don’t need to, like, put things into templates. Exactly. So if ever. I think people don’t read into this part enough, and they think it’s all strings. And that’s, that’s fine. But these are all meant. Like, I would say that if you’re using, like, a template, like turning it into, like, a system message or something, you are thinking in, like, the old way? We should be making things as easy for computers to understand because that structure is truly there, right? Like, it would be weird in, like a programming language to have, like, all of your numbers in, and then you pass it into, like. You turn it into a string. Normally, you do that for printing when you have a human in the loop, right? But for, like, within the computer, you want to be passing, like, nested structure that is semantic all around. And we are really gonna be optimizing our model. It-- the model’s pretty optimized for this, but the thing is every different nested level of structure is harder to reason about, and we want-- we are really cooking hard in that direction. I think people should keep cooking that direction because it makes the code, like, so much more legible and beautiful and like, agnostic to, like, the implementation details. It’s like, here is my state, like, here’s my function state. Like, think of, think of it as, like, an AI function. Which subsets of my state, which is, like, all the variables you have available, should I pass in here? System messages are, like, disgusting global variables where you just put everything in there, and you put all this

Swyx [01:01:21]: Slop, yeah

Diogo Almeida [01:01:22]: Instructions at once. And then like, you hope that every single instruction gets nailed instead of asking the questions in parallel.

Swyx [01:01:29]: Okay.

Diogo Almeida [01:01:30]: And also, I would recommend-- I, and I truly say this not from, like, a, like, it makes me money perspective. I truly recommend asking lots and lots of questions. Break them down, make them smaller, and like, really decompose. Like, no matter if the models can do it today or not, I believe that the biggest, like, saving grace of, like, what’s happening this week will be people’s code bases, AI code bases, are gonna be so much better. Like, if you decompose problems into simple decisions, every single one of these things is extremely evaluable. Like, a AI beforehand is big system message, and then maybe you have, like, another big AI

Decomposition, Verification, and Small Semantic Units

Swyx [01:02:09]: Big output, yeah

Diogo Almeida [01:02:10]: To see, like, if it actually does this. That’s nuts? It’s, it’s kinda crazy. Like, it-- that was our Stockholm syndrome, right? But like, that’s kinda crazy. Like, if you wanna say, like, “Hey, don’t read this subdirectory,” or, “Don’t pass any API keys to DeepSeek,” or whatever else, like, that should be programmatically basically guaranteed. And you’ll never have guarantees of any machine learning model, but like, by breaking it down, you can actually s-- you can actually measure it, right? Like

Swyx [01:02:37]: Yeah, you can verify that it was actually called

Diogo Almeida [01:02:39]: Yes, and like, our model, our model-- like, the interface itself is so verifiable. This should be like a sigh of relief.

Diogo Almeida [01:02:46]: Like, it’s, it’s, it’s, it’s just gonna lead to way better engineering.

Swyx [01:02:50]: Yeah. I think I get that. And so one of the reasons people didn’t used to do this in the past is because they would just call a small LLM, right?

Diogo Almeida [01:02:59]: Yep.

Swyx [01:02:59]: And it’s still too slow, it’s still too expensive versus chunking everything that-- I’ve done exactly this myself.

Diogo Almeida [01:03:03]: Yep.

Swyx [01:03:04]: Right? Like, I benchmark. Here’s a pipeline that throws everything in system prompts and it just gets one big output versus break it down into a hundred different things. It was slower, more expensive

Diogo Almeida [01:03:13]: Yeah

Swyx [01:03:13]: Not as good.

Diogo Almeida [01:03:13]: Yep.

Swyx [01:03:14]: Right?

Diogo Almeida [01:03:14]: And that happens-- yeah. It’s, and it’s, like, super inconvenient. It’s unwieldy. Why not just put it all together? You kind of end up repeating some stuff between

Swyx [01:03:22]: Yeah

Diogo Almeida [01:03:22]: Questions, so it’s, like, maybe, like, inefficient or something like that. But then it results in something that is very hard to rely on.

Swyx [01:03:30]: Yeah.

Diogo Almeida [01:03:30]: And software doesn’t need to run in the background. It would break my heart if our stuff couldn’t run in the background.

Swyx [01:03:37]: Is there a way to break things down that you guys have found that works versus, what you thought worked and doesn’t work?

Diogo Almeida [01:03:45]: Interesting.

Swyx [01:03:46]: Because, like, people are just gonna be exploring this, now that you’ve said it. Like, they would use this as a reference and be like, “Okay, like, that’s how I’m supposed to use Jev.”

Diogo Almeida [01:03:53]: Yep.

Swyx [01:03:53]: Then the question is, how do you break things down?

Diogo Almeida [01:03:57]: Interesting. I like to break things down into its, like, its smallest semantic unit.

Swyx [01:04:03]: Yeah.

Diogo Almeida [01:04:04]: Like, what is the lowest level thing? I try to never have. I’ve probably queried, the model the most, among anyone.

Diogo Almeida [01:04:13]: And like, I try to. Number one, in my, in my queries, this is, this is a lot more like the way I prompt things. Like, I make it really structured and explicit. And in the questions, I always. I like the back ticks, but like, it works for all of them? Like, be really clear what I’m referring to because we want the model to be really literal because when you program, you want things that instruction follow really well. That is what the art of programming is, and what AI does is expanding the things, the kinds of instructions that can be followed. So I’m a fan of doing that. I.

Diogo Almeida [01:04:47]: Sometimes I’m a little lazy and I, like, I have, like, more, like, hybrid things, but like, I think that for, like, really big production things, you just want to, like, keep on adding more questions, and you wanna make it really easy to add more questions. Be really precise about all of that breakdown and then have the code to have the exact behavior you want. If I could give, like, a tiny little example of this, is, like, refusals, right? Like, I’m not gonna talk about why we don’t refuse. I might have done that already.

Swyx [01:05:15]: Yeah, you did already.

Diogo Almeida [01:05:15]: It’s like all a blur. But like, for refusals, I don’t think you should ask, “Should I refuse here?”? That’s a really. It-- I think the answer will be pretty good because, like, that’s a System 1 compatible task. But I think you’re way better off, like, asking many different independent questions about, like, the different situations you can refuse about. Because instead of having to, like, just guess based on you can actually specify what you want. And beautifully, and I think this is, like, truly really beautiful, if you find a situation where it’s like, “Oh, it didn’t refuse because of this reason. I didn’t specify this part of the task,” that is awesome. That’s what software engineering is about. Like, you fix the bug by adding that question in, adding the threshold, maybe remembering that as a test case, and now it is just solved forever. Like, your software can’t forget about that, like, in the prompt because of context rot. It is just there, and you can, like, just keep measuring that forever. And if the models are not perfect at some of these things, you can choose what threshold you want for all of these factors based on real examples. It’s like, it’s like ML without the ML, and you can just do it for anything. And like, there might be some things the model’s not good enough yet, right? Like, I would. I’m a little bit afraid when I see people doing trading with the models, like,

Diogo Almeida [01:06:28]: Automated trading. It looks cool. I th- I just think that people should leave it to the professionals.

Diogo Almeida [01:06:35]: And like, that’s just a very hard, high-level task that maybe the models aren’t good enough yet to figure out.

Swyx [01:06:40]: Yeah.

Diogo Almeida [01:06:41]: Well, I, like, even if they were, then they would- It suddenly wouldn’t be ‘cause of efficient market. But like, that’s one of those things where, you can, like, break it down into things and just evaluate them, and you might be like, “It’s not smart enough at this. Maybe we don’t deploy it yet for this version.”

Swyx [01:06:56]: Yeah.

Diogo Almeida [01:06:56]: Or we make a trade-off, or we err on the side of safety, or like, “Hey, the models are not good enough at, like, detecting, like, this weird combination of, like, sarcasm with a VIP customer, that this is when we escalate to a human.” And that’s what confidence estimates are about, too.

Confidence, Thresholds, and Fine-Tuning

Swyx [01:07:11]: Okay. Very good answer. I think, one thing I’ll, I’ll mention very quickly, which, I don’t expect that you have as-- too long of an answer for is,

Diogo Almeida [01:07:19]: You don’t.

Swyx [01:07:20]: Well, no. It’s just, it’s just specifically, like, you are still relying on thresholding as, like, the lever that the user can pull.

Swyx [01:07:28]: But what if just the calibration is wrong, right? Like, you’re just saying your calibration is perfect, but

Diogo Almeida [01:07:33]: I didn’t say that.

Swyx [01:07:33]: I, like

Diogo Almeida [01:07:34]: Yeah. I didn’t say that.

Swyx [01:07:35]: So it’s like ca-- perfect calib-- and like, good calibration means, like, lower value is lower, like, sort of probability lower, higher value is probability higher. But it could be wrong. It could be

Diogo Almeida [01:07:44]: Of course, of course

Swyx [01:07:44]: Totally misaligned.

Diogo Almeida [01:07:45]: Yes.

Swyx [01:07:45]: And so then I would want to fine-tune it or something, right? Which you don’t offer, but you could. I

Diogo Almeida [01:07:50]: We could.

Swyx [01:07:51]: Again, see, this is a short answer

Diogo Almeida [01:07:52]: Yeah

Swyx [01:07:52]: Which is you don’t have it right now.

Diogo Almeida [01:07:54]: Oh, do we want to offer fine-tuning, is the question?

Swyx [01:07:56]: That could be, that could be one version of it, or you could have a different knob, right?

Diogo Almeida [01:08:00]: Yeah.

Swyx [01:08:00]: Where, like. Because, like, right now you’re-- all you’re saying is, like, if something’s wrong, a skill issue, you should, you should just change the prompt again or break it down even further, or you change the confidence.

Diogo Almeida [01:08:09]: Yep.

Swyx [01:08:09]: Those are my two options.

Diogo Almeida [01:08:11]: Yep.

Swyx [01:08:11]: Right? And that doesn’t feel super satisfying if your model is just getting it wrong.

Diogo Almeida [01:08:14]: Yep. And it will, it will get many things wrong, to be clear.

Swyx [01:08:18]: Right.

Diogo Almeida [01:08:18]: We have, like, a Report Issues button. Complain to us in Discord. We want to make it a lot better. Every single model version will be, like, notably better.

Swyx [01:08:25]: Yeah.

Diogo Almeida [01:08:25]: We will stop shipping them quickly if they weren’t getting big improvements. So number one, that is, like, totally reasonable. I think that’s simply pragmatic to admit that AI is imperfect at some stuff, right? I do think we’ll find use cases that they are, like, good enough at, and good enough kind of depends on the use case, right? Like, human beings can do a lot of work despite being bad at that work because their EV is quite high. And presumably with the right thresholding and everything, there probably is, like, large amounts of work that could be done even if mistakes are being made. On the question of fine-tuning, I could imagine, I could imagine it in the cards. I do have concerns because, like, in the what people need versus what people want category,

Diogo Almeida [01:09:09]: Like, I think general models tend to be really. Like, again, there’s the je ne sais quoi of generality, that making it good at, like, a million other tasks than this one narrow task might make it better at edge cases in that task, which I’m, I would be a little bit afraid of?

Swyx [01:09:25]: Yeah.

Diogo Almeida [01:09:26]: I could imagine it, is my answer. I’m endlessly practical on these things. I want everything. Like, my vision of the world is. I-- there’s, there’s so much we want to be building.

Swyx [01:09:38]: Yeah.

Diogo Almeida [01:09:38]: But also, like, I would not want to ship something that is, like, a giant foot gun, like some other AI companies would ship.

Diogo Almeida [01:09:46]: Yeah.

Swyx [01:09:47]: Well, so both OpenAI and Claude and I think even Gemini have rolled out fine-tuning and then took it back.

Diogo Almeida [01:09:53]: Yep.

Swyx [01:09:53]: Which is an interesting, observation that pretty much fine-tuning is now in the domain of open source models.

Diogo Almeida [01:10:02]: Yes. I do know about that. And like, it was kind of crap, so like, that’s probably better that they took it down.

Swyx [01:10:10]: Yeah. Yeah, so it could just be a foot gun, and telling people that fine-tuning it is probably the wrong way to go is great. Another interesting answer could be that, like, well, our model is so different, like, in the same way that quantization doesn’t apply to us

Diogo Almeida [01:10:21]: Yeah

Swyx [01:10:21]: Output tokens doesn’t apply to us, fine-tuning also doesn’t apply to us.

Diogo Almeida [01:10:24]: Well, actually, I’m, I’m super open to that possibility.

Swyx [01:10:28]: Yeah.

Diogo Almeida [01:10:28]: Like, my. This is not a promise. This is a desire. Just so to make it clear, I like to be really honest. Like, I think that, as intelligence per dollar gets cheaper, I think that we could get really, like, small approximate things that hopefully are proxies for intelligence. Like, is there a world where people don’t write regexes anymore? Because, like, the intelligence per dollar that uses AI is cheaper than, like, the complexity of a regex. That would be kinda sick. I would love that? And it might require fine-tuning for some of those narrow use cases to really get past the threshold. We will see. My hope is calibration gets that. Calibration plus a cascade of models. Like, if it’s super confident, then maybe it’s right. And if it’s in the middle, then you do the next bigger model, and you chain off from there. I don’t really know how that’s gonna go, but yeah. I could imagine it. And something that I could imagine too is, like, imagine you have, like, a series of. Like, we own the entire Pareto frontier. Something that a business might want to do, or, I think a hacker would be okay with dealing with a Pareto frontier of models. Maybe a business wants something more dynamic. You could imagine, like, having, like, a s- different sizes of models and to dynamically pick which model based on how smart it is on different parts of your stack. And you could even imagine, because of how simple our thing is, you could imagine, like, some automatic fine-tuning on that.

Future Models, Pareto Frontiers, and New Shapes of Intelligence

Swyx [01:11:53]: Yeah.

Diogo Almeida [01:11:54]: Not a promise in the slightest. I’m just, like, cooking on sci-fi.

Swyx [01:11:57]: But you would consider different sizes of Dev models so to offer that variance?

Diogo Almeida [01:12:01]: Absolutely. Yeah. Like, we. Like, how would I know how much intelligence people need?

Swyx [01:12:06]: I don’t know.

Diogo Almeida [01:12:06]: Right? Yeah. I don’t know either.

Swyx [01:12:08]: Demand is, demand is, unlimited.

Diogo Almeida [01:12:10]: Well, yeah, people are telling us not to ship things right now because we don’t need to ship things because, again

Swyx [01:12:16]: It’s good enough, yeah.

Diogo Almeida [01:12:18]: Yeah, but that’s kinda lame. And I really like the saying. This is something that I hope people hold me to because it’ll be hard to

Swyx [01:12:27]: To take back

Diogo Almeida [01:12:27]: To walk back from. Yeah. Like, the. I don’t know if it. Exactly the saying that culture is what you do when the market doesn’t reward it. And I really like that because I think that we are standing for something. Maybe in the future- what we’re standing for is, like, so obvious that we’re the equivalent of, like, boring, like, Visa or something like that. And like, we’re just like a utility that no one really thinks about, and I’ll be wearing non-pink suits or whatever else.

Diogo Almeida [01:12:54]: But I really want to be, like, rallying the world to this? Like, I want to keep doing cool stuff, bec- not because we need to, but ‘cause I want, like, people to realize that this is just the beginning? Like, that wasn’t even meant to be the opening salvo. That was, like, kind of like a, low-key research preview or whatever you wanna call it.

Swyx [01:13:14]: Yeah.

Diogo Almeida [01:13:15]: And there’s a lot more we can do.

Swyx [01:13:17]: Yeah.

Diogo Almeida [01:13:17]: With. Like, machine-native intelligence is gonna go wild.

Swyx [01:13:21]: So not the only s-- Potentially not the only size, potentially not the only model that you guys launch. That you want to open people’s mind

Diogo Almeida [01:13:28]: Absolutely not for any of those.

Swyx [01:13:29]: Yeah.

Diogo Almeida [01:13:29]: I want, I want to, like, meet whatever needs we can.

Swyx [01:13:33]: Yeah.

Diogo Almeida [01:13:34]: Right? Like, at. But with, like, a giant caveat, I don’t want to be like OpenAI’s product teams that, like, throw stuff at the walls. Like, I want it to be, like, in a, under a unified vision. Like, if you go back to the manifesto, like, everything needs to be under one of these three things

Swyx [01:13:48]: Yeah

Diogo Almeida [01:13:48]: In my opinion.

Swyx [01:13:50]: I’m not

Diogo Almeida [01:13:50]: Yeah.

Swyx [01:13:51]: I’m not prepared to do this,

Diogo Almeida [01:13:52]: Oh, I’m sorry. I’m sorry, Francis. Yeah, I can just talk about it. Like, we have, like, three steps in our stuff.

Swyx [01:13:57]: Yes.

Diogo Almeida [01:13:57]: It sounds like a tease. I want everything to go under one of these three things

Swyx [01:14:02]: Good

Diogo Almeida [01:14:02]: To keep pushing the boundaries and everything. Like, this is not. These are not, like, checklists. These are, like, axes that we think build, like, the foundation of, Of, like, a new technological revolution. And I want all of the. All the bets we make to be somewhere in there. And we will be doing some weird stuff model-wise.

Diogo Almeida [01:14:23]: So because machine-native, right? Like, humans don’t need to totally get it. It needs to just be valuable.

Swyx [01:14:30]: With, Just give people a tease or hint. Like, what does weird look like? What is weird?

Diogo Almeida [01:14:35]: I’ll give people a hint.

Swyx [01:14:36]: Yeah.

Diogo Almeida [01:14:36]: Some people are trying to call them decision models.

Swyx [01:14:40]: Okay.

Diogo Almeida [01:14:41]: That our primitives are decisions. I wouldn’t do that, because I think there’s other types that are machine-native that are not decisions.

Swyx [01:14:54]: Okay, we’ll leave it at

Diogo Almeida [01:14:54]: That’s a fun hint, a fun hint.

Swyx [01:14:55]: And let people guess. Yeah.

Diogo Almeida [01:14:56]: Yeah. I think it’s a, I think it’s a pretty fun hint.

Swyx [01:14:58]: Yeah. There’s people. Look, there’s, there’s people saying like, “I’ve done this before. I made a decision model a year ago.” Like, Jev is not new, Jev’s not cool.

Diogo Almeida [01:15:04]: Yeah.

Swyx [01:15:04]: But like, I think, there’s the categorical, like, here’s what you’re establishing is possible. There’s the, performance of, like. Well, actually the. For the benchmarks and the numbers that you’re getting, you are still beating ev- as far as I can tell, you’re still beating every single clone of you out there.

Diogo Almeida [01:15:19]: I don’t care about the benchmarks

Swyx [01:15:20]: Exactly

Diogo Almeida [01:15:20]: Just to be clear.

Swyx [01:15:21]: Exactly.

Diogo Almeida [01:15:21]: So like, even if we were winning or losing, I want to do announcements.

Swyx [01:15:24]: You’ve established the category, right?

Diogo Almeida [01:15:26]: Yep.

Swyx [01:15:26]: Yeah.

Diogo Almeida [01:15:26]: Yep.

Swyx [01:15:26]: But al- but also I think this nuance between decision models and System 1, I think is actually the thing that you’re trying to

Diogo Almeida [01:15:32]: Yes. And I just wanna make software engineers super powered

Swyx [01:15:36]: Yeah

Diogo Almeida [01:15:36]: Right? L- like with AI. Like, and or, like, the tragic thing to me is,

Economic Impact, TFP Growth, and AI in the Background

Diogo Almeida [01:15:42]: In that AI winter direction, I think, like, it’s, it’s, it’s just so sad that AI was so powerful yet so underutilized. Like, It’s a thing that gets me emotional, but man.

Diogo Almeida [01:16:03]: Like, I think that is. I don’t want to, like, just be, like, pure techno optimist, like all technology is good. I think what was happening now was, like, a travesty. Like, it’s. And like, there’s. I just want to, like, open up those possibilities for people.

Diogo Almeida [01:16:19]: Yeah, I’ll just end it there. I’ve, I’ve cried too much these last few days To want to do it on the record.

Swyx [01:16:26]: Yeah. No,

Diogo Almeida [01:16:27]: Yeah

Swyx [01:16:27]: I appreciate you sharing a little bit of that, and I think people can see that you’re very authentic and

Diogo Almeida [01:16:31]: Yeah

Swyx [01:16:31]: Passionate about this. Th- y- you don’t necessarily get that from the name, like, TypeSafe AI, but like, I think once people immerse themself, themselves enough in, like, here’s the genuinely different direction you want the world to go And like, actually you have done, like, the hard part about going from zero to one on the, on the thing, then, like, now let’s all go to- go together in, like, the new direction, right?

Diogo Almeida [01:16:51]: Yeah. Yeah.

Swyx [01:16:52]: Yeah.

Diogo Almeida [01:16:52]: I don’t n I am sure that I won’t think. Maybe I will think that the hard part was done, perhaps. I think that there’s going to be many more hard parts. Like, if, All sorts of stuff gets automated and we finally see GDP growth and like, it’s like, a Jev party

Swyx [01:17:12]: Yeah

Diogo Almeida [01:17:12]: Every day, then maybe the hard part is done. But like, I don’t s- think so. And like, I really think that people focus too much on speed and cost and not enough on reliability.

Swyx [01:17:23]: Okay.

Diogo Almeida [01:17:23]: Like, reliability is what makes it delightful. Like, reliability is what, like, allows you to trust it.

Swyx [01:17:28]: You have this line,

Diogo Almeida [01:17:29]: Yeah.

Swyx [01:17:30]: TFP growth beating 3% in five years.

Diogo Almeida [01:17:32]: Hell yeah.

Swyx [01:17:32]: I’ve never seen

Diogo Almeida [01:17:33]: Hell yeah. Let’s fucking go.

Swyx [01:17:35]: I’ve never seen

Diogo Almeida [01:17:36]: Yeah

Swyx [01:17:36]: A lab care about TFP growth.

Diogo Almeida [01:17:38]: But like, that is what an economic revolution is, right? Like, it’s actually extremely consistent with what the OpenAI charter used to stand for.

Swyx [01:17:45]: Yeah.

Diogo Almeida [01:17:45]: It was talking about, like. I think the charter is the same, but they’ve kind of tried to move definitions around to, like 100 billion in profit or something like that. Not that I hate an OpenAI.

Swyx [01:17:54]: It wasn’t like a. Yeah, it wasn’t a well-defined term what AGI is, right?

Diogo Almeida [01:17:57]: They tried to do it

Swyx [01:17:58]: No, yeah

Diogo Almeida [01:17:58]: Right? Like, doing majority of the world’s economically valuable work, and they should have to answer the question, how can it do millennium prize problems in math and zero of the world’s economically valuable work, around the air. Like, I think that all models are roughly tied right now at zero. There’s some chance that, like, we have started already, but like, I would guess that it’s not yet 1%. And I think that will show up in. Like, when it does happen, it will show up in the economic statistics.

Diogo Almeida [01:18:28]: It’s gonna be fucking awesome. It will not cause mass unemployment, but it will cause, like, a whole bunch of awesome shifts, and wor- the world will be a lot better. And also, like, I’m really tired of AI always being the foreground character, of things. Like, I think that- the world should just be more delightful, and AI should just help with that.

Diogo Almeida [01:18:48]: And I

Swyx [01:18:49]: Just, like, disappear into the background.

Diogo Almeida [01:18:50]: Exactly.

Swyx [01:18:51]: Yeah

Diogo Almeida [01:18:51]: Like, the-- I say this in my talks. Like, how can it be that 2019 software, like software, SaaS, whatever, super-duper valuable, right? It’s 2026 now. How is the software basically exactly the same, despite AI being so freaking awesome, other than sometimes having a chat box on the side, right? That, like, that kind of works, but doesn’t allow you to make decisions that the companies have stakes in, because they can’t be trusted to make decisions. That, to me, is nuts. Like, there’s so much economic incentive for this, and I think it’s going to be, like a, like an inverse SaaS-pocalypse. I think SaaS is going to be supercharged by this. They are the ones who are, like, most in the know of what things are valuable to automate, and it’s gonna be, like, a crazy time.

System 1 vs. System 2 and the Limits of Reasoning

Swyx [01:19:37]: Yeah. I think, I think so too. It’s a, it’s a beautiful thing that you’ve unlocked?

Diogo Almeida [01:19:41]: Yeah.

Swyx [01:19:41]: You mentioned one thing here, which I don’t know if it’s, like, directly here, which is, what is a System 1 problem and what is not. What is a System 2 problem? Like,

Diogo Almeida [01:19:50]: Fuck. That’s a hard one. That’s a hard one, my friend.

Swyx [01:19:55]: ‘Cause people now are just trying to Jev everything, right?

Swyx [01:19:57]: Which, like, probably is gonna fail, right? But like, some things are gonna be good.

Diogo Almeida [01:20:02]: Jev everything is pretty funny.

Swyx [01:20:04]: Yeah.

Diogo Almeida [01:20:04]: It’s a pretty funny way of doing it, saying it. The. So I’ll tell you the truth.

Swyx [01:20:08]: Yeah.

Diogo Almeida [01:20:09]: The truth is that this is an empirical problem, just like scaling laws are an empirical thing. Like, why doesn’t, like, robotics really work right now, despite all the money being spent on it?

Diogo Almeida [01:20:21]: I don’t think it’s about, like, spending more money necessarily. The empirical results just might not be there, right? So empirically, I believe that these, like, pre-trained super condensations of intelligence are fundamentally System 1 thinkers. I think that they truly. Like, System 1 is the closest thing to describe what LLMs are strong at. RLVR has done incredible things for System 2 thinking. I am at awe. It is super freaking cool. Like, I don’t think that it’s going to result in AI doom in the slightest. Not 0%, of course, ‘cause I think 0% is miscalibrated. But like, i- it’s, it’s really cool what they’ve done, and they’ve really pushed it to the limits. Well, maybe they don’t think so not the limits. But like, it is, it is a weird thing for models to do, and they are very fragile at this. Like, think about how people used to talk about AI back in the ChatGPT days. Like, “Wow, it’s really general. It can do a lot of general things.” And then. But it’s bad at math problems and like, GSM8K, grade school math. And then now look at how people talk about RLVR. “It’s so fragile. It’s so jagged.”? Like, it can. “Why can it do this, like, really weird thing?” And actually, math is not just spiky, it’s fractal, right? And this is because RLVR is. Like, if we talk about, like, what is the North Star for each thing? RLHF is please humans, right? That is what the human feedback is. RLVR is optimize benchmarks. That l- everything that goes into the RLVR category literally is a benchmark by definition, because a benchmark is programmatically verifiable, simple outputs that can, like, do well. And RLCD is make it reliable for, programmatic use. And Diogo Almeida [01:22:09]: Yeah. That, I’ll

Swyx [01:22:11]: Yeah. Yeah, it’s, this. Maybe I’ll, I’ll offer some thoughts, and then you can sort of,

Diogo Almeida [01:22:15]: Ooh

Swyx [01:22:15]: Correct me if I’m wrong. One. For example, one thing that I’ve been thinking about is also. I, so I threw Jev at a bunch of things when you gave me access

Diogo Almeida [01:22:23]: Ooh, yeah

Swyx [01:22:23]: On day one. And multi-hop reasoning, right? Like

Diogo Almeida [01:22:27]: Yep

Swyx [01:22:27]: So single hop, fantastic. Like

Diogo Almeida [01:22:29]: Yeah

Swyx [01:22:29]: State of the art. You should never use anything other than Jev for single hop.

Diogo Almeida [01:22:32]: Yep.

Swyx [01:22:33]: Multi-hop is gonna. It starts to falls down. And it’s

Diogo Almeida [01:22:34]: Yeah

Swyx [01:22:34]: Like, kind of monotonically increasing as you increase the hops.

Diogo Almeida [01:22:38]: Yep.

Swyx [01:22:38]: Right?

Diogo Almeida [01:22:39]: So oh, yes. Back to that empirical question, it depends on what we can, like, pull out of the models.

Swyx [01:22:44]: Yeah.

Diogo Almeida [01:22:44]: Right? So we want everything. Like, we want to unearth as much intelligence as possible, period. The models. Like, I see us as, like, unlocking and smoothing and sculpting the intelligence while, like, adding new capabilities and like, filling in gaps in it. And we will be filling in, like, more and more and more and more of these gaps over time. But the reality is that we are in the business of unearthing properties. Those properties are actually a function of what is available from, like, these, like, these condensed cores and like, Frankensteining them all together to have all of the properties of everything.

Swyx [01:23:21]: Yeah.

Diogo Almeida [01:23:21]: ? But the reality is we are in the business of unearthing as many capabilities as pos- as possible. And System 1 just happens to be the description of what works. And everything that works in that paradigm will be System 1-ish. Like, I’m.

Diogo Almeida [01:23:38]: Like, there is a reason why we don’t do what’s called latent reasoning in strings.

Swyx [01:23:43]: Yeah.

Diogo Almeida [01:23:43]: I think the reasoning. Like, the. What models do really well is reasoning within the models. It’s not totally complete. It doesn’t do great on all

Swyx [01:23:49]: Wait, latent reasoning is reasoning in strings? I thought latent reasoning is reasoning in, inside the model weights.

Swyx [01:23:55]: The

Diogo Almeida [01:23:55]: I think that

Swyx [01:23:56]: I don’t know

Diogo Almeida [01:23:56]: People used to call that

Swyx [01:23:57]: I just want to clarify

Diogo Almeida [01:23:57]: Continuous reasoning.

Swyx [01:23:58]: Okay.

Diogo Almeida [01:23:58]: I’m not entirely sure.

Swyx [01:23:59]: Okay.

Diogo Almeida [01:24:00]: It was called latent reasoning because, like, it used to be that the reasoning traces were secret.

Diogo Almeida [01:24:04]: So they’re kind of like a latent variable for the answer.

Swyx [01:24:06]: Ha.

Diogo Almeida [01:24:07]: Yeah.

Swyx [01:24:07]: So what’s secret has now shifted.

Diogo Almeida [01:24:09]: Well

Reasoning, Vision, Context Length, and Future Capabilities

Swyx [01:24:10]: Yeah

Diogo Almeida [01:24:10]: It’s, it’s still secret for OpenAI and Anthropic, right?

Swyx [01:24:12]: So no reasoning Jev

Diogo Almeida [01:24:14]: Yes

Swyx [01:24:14]: As far as you will ever do it, right? Because that, like, violates the whole promise of System 1.

Diogo Almeida [01:24:21]: I. My promise is to do whatever necessary For machine-native stuff.

Swyx [01:24:28]: Yeah.

Diogo Almeida [01:24:28]: I could imagine there are. Like, there are some forms of reasoning that are less slow, inefficient, and fragile that I. That are, like, totally on the cards, just to be clear. So pragmatic person, I’m not making promises on, like, methods. I’m making promises on, like, the. What my ROI North Star is, and I’m going to fight for that, like this launch didn’t happen and we are still, like, hungry for our place in the world.

Swyx [01:24:53]: That’s great. Yeah.

Diogo Almeida [01:24:54]: Yeah.

Swyx [01:24:54]: I think the other thing that, Vision is another one that’s, like, a big, like, capability that you don’t have, but maybe it doesn’t ever belong in System 1?

Diogo Almeida [01:25:04]: I think I have a pretty good vision.

Swyx [01:25:05]: What, sorry?

Diogo Almeida [01:25:06]: I think I have a good vision.

Swyx [01:25:07]: No, sorry. Vision

Diogo Almeida [01:25:08]: I am kidding. I’m kidding. Yeah.

Swyx [01:25:09]: Oh my God.

Diogo Almeida [01:25:10]: Yeah.

Swyx [01:25:12]: Because people obviously, the first thing they want is vision ‘cause of the Doom demo, but also just, like, everything, other than text is vision.

Diogo Almeida [01:25:19]: Everything is in the cards

Swyx [01:25:21]: Yeah

Diogo Almeida [01:25:21]: In my mind.

Swyx [01:25:22]: Okay.

Diogo Almeida [01:25:22]: Like, and actually, this is, like, a debate we have. This. Man, your audience is probably, like, the great one to have in this debate. There’s a question about, like, how much do we try to, like, give people what they think they want, which is what we did in stealth for two years. We just knew that this is obviously going to be valuable, versus give them what they say they want, right? And like, there’s a lot of dimensions of this, right? And Like, context length is an example of this, right? Every single model, including ours, I actually think as far as I can tell, ours is, like, by far the best

Swyx [01:25:58]: The longest context, yeah

Diogo Almeida [01:25:58]: At not degrading in long context.

Swyx [01:26:01]: Yeah.

Diogo Almeida [01:26:01]: But like, the other providers are just like, “Whatever people want, let’s just give them the stupid thing.” And like, we need to figure out a balance for this because, like, if you take the former side too far, give people what they want, you end up with, like, anthropic nanny state style thinking, which is very, like, anti-developer. While, like, the pro-developer route would be like, give them what they want, but developers are. Like, we don’t want to put the burden on them to figure out the je ne sais quoi of intelligence. So we are trying to, like, figure out this navigation of, like, how quickly to release things, to still, like, have our, like, brand of trust and also, like, teach our-- treat our users like adults that can make informed decisions that, don’t need, like, nanny stating on top of this stuff.

Swyx [01:26:46]: Yeah. I think that’s fair.

Diogo Almeida [01:26:48]: Yeah. And we don’t know the answer, to be honest. Like, we’ll, we’ll have to figure it out. It’s gonna be. That’s probably going to be, like, one of my biggest debates over the next couple of days

Swyx [01:26:57]: Yeah

Diogo Almeida [01:26:57]: Because, like, we have a lot of stuff. Again, we didn’t expect it to pop off, so we were like, “We’ll need some follow-up launches.” But yeah.

Swyx [01:27:05]: I don’t know, I don’t know if you didn’t expect it to pop off. Like, I, you p- I saw the work that you put in. Like, I have never seen you lock in so hard as, like, the last two months basically, right? Like

Launch Education, Cookbooks, and Product-Market Fit

Diogo Almeida [01:27:13]: Well, that’s also because my chief of staff made me lock in.

Diogo Almeida [01:27:18]: Yeah.

Swyx [01:27:18]: So

Diogo Almeida [01:27:20]: Like, it’s like, I have never. I thought I worked hard before

Swyx [01:27:24]: Yeah

Diogo Almeida [01:27:24]: And

Swyx [01:27:25]: No, but like, you were showing up at our writing workshops, and I was like, “what are you doing here?” And like, oh

Diogo Almeida [01:27:30]: It was useful. It was great.

Swyx [01:27:31]: You clearly, like, were very intentional about your launch.

Diogo Almeida [01:27:34]: Yep.

Swyx [01:27:35]: And the work showed, and like

Diogo Almeida [01:27:36]: Yeah

Swyx [01:27:36]: Congrats. Like, you got a kudos.

Diogo Almeida [01:27:37]: Thank you, thank you. I hope to keep locking in

Swyx [01:27:41]: Yeah

Diogo Almeida [01:27:41]: Is my, is my sense.

Swyx [01:27:42]: Yeah.

Diogo Almeida [01:27:42]: I want to. Like, w- like, I think that we’ve passed many great filters for the tech world, what we’re wanting, but like, there’s still gonna be a bunch more.

Swyx [01:27:53]: Yeah.

Diogo Almeida [01:27:53]: And like, holy smokes, am I excited to fight the good fight.

Swyx [01:27:56]: Yeah, it’s exciting. Before we broaden out to, like, topics outside of TypeSafe

Diogo Almeida [01:28:01]: Ooh

Swyx [01:28:01]: I just wanted to offer, any other things that you think, like, underrated or misunderstood about what you have launched.

Diogo Almeida [01:28:09]: Underrated or misunderstood?

Swyx [01:28:11]: Yeah. You have pan outs. Sorry, patterns here. Maybe you wanna go into that. Model jaggedness, anything.

Diogo Almeida [01:28:20]: Give me one

Swyx [01:28:21]: Yeah

Diogo Almeida [01:28:21]: Noodling of it. Oh, man. I would rant about all of these. I really shouldn’t. I really shouldn’t.

Swyx [01:28:29]: Okay. And like, people can come, go to your Discord if they

Diogo Almeida [01:28:32]: Yeah. People put a lot of love into the cookbooks

Swyx [01:28:34]: Yeah

Diogo Almeida [01:28:35]: Is what I will say. The cookbooks have, like, some fire stuff. We had considered putting a bunch of these things, like, in the main launch blog post, but it got kind of long and unwieldy and like, very power usery. But like, we really. I’ll be frank. Like, before the launch, every. Like, what we’re saying sounds, like, sounds like this weird alien tool. Why would anyone need this? It was a very weird thing. We were very worried about teaching people about, like, this new frontier. It obviously succeeded, but like, we put a lot of work because we thought the education would be, like, a gigantic bottleneck for us. I.

Diogo Almeida [01:29:14]: It probably works, and it’s no lo- probably no longer a problem because people are doing things, like, well beyond what we could ever expect.

Swyx [01:29:20]: They’ll show you, like, how to use your model.

Diogo Almeida [01:29:21]: Yeah, but. Exactly. But like, they. Yeah, and their use cases are, like, kinda cooler than ours. Like Like, there’s a bunch of stuff where I’m like, “Man, if that was our demo, holy shit, that was way cooler than what we were showing.” like, the computer use stuff, holy smokes is it cool. But like, we put a lot of love into this. This is not, like, AI-generated trash, as far as I know.

Swyx [01:29:41]: Yeah.

Diogo Almeida [01:29:41]: We put a lo- l- like, it’s, like, a lot of love in here.

Swyx [01:29:45]: Yeah. Fair enough.

Diogo Almeida [01:29:45]: And like, each of these are. Like, there’s real alpha there.

Swyx [01:29:49]: Okay.

Diogo Almeida [01:29:49]: Like, these are inspired by solving real customer problems that existed, and we went through the work of, like, helping them do cool-ass stuff.

Swyx [01:29:58]: Yeah. How much. While you’re talking about this, right, how much validation did you do before launch? Like, what. What was that process like?

Diogo Almeida [01:30:06]: What was that process like?

Swyx [01:30:08]: Like, clearly you did some, but obviously you’re not getting in touch with as many people as you are today.

Diogo Almeida [01:30:14]: Yes, of course.

Swyx [01:30:15]: But like

Diogo Almeida [01:30:15]: I actually think that the reception was pretty bad. And like, actually for the non-technical people in the team, they were really worried.

Swyx [01:30:24]: Yeah.

Diogo Almeida [01:30:24]: Like, there was a lot of fear. It’s like, no one really gets this. And like, they don’t want it. We’re, like, selling, like, a vitamin and not, like, a painkiller. Like, should we have FDEs to, like, write the software around solving that problem?

Swyx [01:30:37]: Yeah.

Diogo Almeida [01:30:38]: We had almost no revenue before launch. It was kind of like. Like, we. Like, the technical people were, like, obviously true believers, right? Like, we knew that this was sick. Its prop- computational properties are, like, off the charts on, like, so many axes that we’re like, “Yeah, obviously it’s gonna be huge.” I was definitely super afraid, which is why I locked in super hard. But like, the most common thing was- The, like, I would say, like, more than half the people we had play with it just did not get it. And like, the people who did, like, were like, “Man, this is really cool, but how do we get this through procurement and stuff like that?” it was like, it was like quite a, quite a battle, and we just knew like, okay, the-- our target market is gonna be developers. People will find the use cases, and that way everyone is gonna FOMO in. And like,

Diogo Almeida [01:31:32]: I don’t want to rub in people, like, changing their minds with the facts changing.

Diogo Almeida [01:31:36]: I do want to call into question, like, the concept of product market fit? Yeah, but like, because, like, there was a product, there was a market. Like, we were like, “Hey, do you want to use this?” And people are like, “I don’t know, really know if it solves our problems.” It explodes and everyone’s like, “We need as much rate limits as we can. Can we literally give you GPUs? Because we are constrained right now.”

Swyx [01:31:59]: Yeah.

Diogo Almeida [01:31:59]: So of course, like, marketing is an element of it, of course, but I don’t even think it’s about marketing. I think it’s about, like, passionate developers who’ve, like, our souls basically resonated at the same frequently, and that frequency, and that got everyone else excited too.

Swyx [01:32:14]: Yeah.

Diogo Almeida [01:32:14]: And I’m hoping as well that, like, we as a company will be eternally grateful to those developers. Like, not-- and not just like, the companies that, like, are-- like, start off with developers and like, go big enterprises.

Swyx [01:32:30]: They go to market, yeah.

Diogo Almeida [01:32:31]: Exactly. And like, I’m, like, even thinking about, like, how can we launch things that are better for. Oh, man, I don’t know if I should say this.

Diogo Almeida [01:32:39]: But I will.

Swyx [01:32:39]: Better for developers than enterprises.

Diogo Almeida [01:32:41]: Exactly.

Swyx [01:32:41]: Okay.

Diogo Almeida [01:32:41]: How do we do that? Like, how do we empower them? And I have cooks. I have cooks. But it’s a, it’s a very weird thing to do. And like, I don’t know how else I can show my thanks and loyalty to that. Like, and that’s why I did, like, the dying my hair yesterday. It was like It’s like I wanted to talk to them ‘cause it felt dirty to me during our company’s, like, most important times not to keep talking to them.

Swyx [01:33:07]: Good. Well, that’s why is your hair.

Diogo Almeida [01:33:09]: Yeah. Hold me to that, please.

Swyx [01:33:11]: Yeah. We will, we will.

Diogo Almeida [01:33:11]: I try to be principled.

Swyx [01:33:13]: Yeah.

Diogo Almeida [01:33:13]: Quote me on this. Call me out. D- have the pitchforks out if I change.

Swyx [01:33:18]: I was just gonna briefly show the computer use stuff.

Computer Use and Emerging Use Cases

Diogo Almeida [01:33:21]: Whoa.

Swyx [01:33:21]: Is this, is this what you’re referencing?

Diogo Almeida [01:33:22]: I’ve never seen-- I haven’t-- I’ve seen. I saw, like, a airline browser use thing.

Diogo Almeida [01:33:29]: And inside this new note, let’s make the title say hello.

Diogo Almeida [01:33:33]: Wow. Great. Okay. Let’s move on and can you open up the Arc browser? And once you’re there, can you Google search Norbert Wiener?

Diogo Almeida [01:33:43]: Now can you open up x.com?

Swyx [01:33:46]: Is this kind of use case?

Diogo Almeida [01:33:48]: Oh, the voice use cases. This is actually the first one I’ve seen. This is

Swyx [01:33:51]: Oh, okay.

Diogo Almeida [01:33:51]: Open up the photo viewer. Wow. Oh, wait. Oh, can you bo- can you go back a second? Can you go back a second?

Diogo Almeida [01:33:58]: Rumors claim Anthropic engineers worth- worship Claude as God. Wow. Wow.

Diogo Almeida [01:34:03]: Dang. That’s pretty funny.

Swyx [01:34:06]: And here you are building prod.

Diogo Almeida [01:34:07]: Abso-- Wow, this is sick.

Swyx [01:34:12]: Yeah. So clearly it can operate the whole computer with voice, with Jev as the decision model.

Diogo Almeida [01:34:17]: So Just like I’m anti-benchmarking, I’m also anti-demos. I want to make sure that it works reliably. I love people are playing with it. This is super fucking sick, have no doubt. I want to see this. I wanna see it be used. I want our team to play with it. I wanna find the weaknesses, and I wanna solve that.

Swyx [01:34:35]: Yeah.

Diogo Almeida [01:34:35]: And I would lo-- Man, that looked really cool.

Diogo Almeida [01:34:37]: That looked really cool. I want that. I want that. Like, when my, when my wrists are sore, I, like, just whisper flow everything. That would be sick.

Swyx [01:34:44]: Well, as, well, just to round out the use cases side

Diogo Almeida [01:34:47]: Yeah

Swyx [01:34:47]: ‘cause I do have to let you go. Who’s, who are the, who are the bigger companies that have reached out and have surprised you with what they wanna do?

Swyx [01:34:56]: Just.

Diogo Almeida [01:34:57]: I am so out of touch for that.

Swyx [01:34:59]: Okay.

Diogo Almeida [01:34:59]: People have shown me screenshots of companies, and from what I’ve seen, it’s all of them.

Dark Data, Real-Time Intelligence, and Verification

Swyx [01:35:05]: Yeah. Mostly, like, for those people who work at larger companies and they’re not doing this kind of work, I just wanna give people examples of, like, you should go look that up, look that up, look that up.

Diogo Almeida [01:35:13]: Oh. So like, I think demos are super-duper sick. Obviously, the coding agents are, like, gigantic use cases.

Diogo Almeida [01:35:21]: Like, they are, like, also super sick.

Swyx [01:35:23]: Oh, Cog is all about Jev right now.

Diogo Almeida [01:35:25]: Oh, hell yeah. Oh, can I, can I give a little bit of a tangent about coding agents, if that’s or

Swyx [01:35:29]: Yes, please.

Diogo Almeida [01:35:30]: Oh, let me

Swyx [01:35:31]: We love coding agents here.

Diogo Almeida [01:35:32]: Give me a second. Give me a second. Okay, actually, I’ll come back to coding agents. Let me describe, like, the big families of use cases.

Swyx [01:35:37]: Yes.

Diogo Almeida [01:35:38]: Like, we’ve mapped this out from first principles, like, long before release. They are what we call dark data. Like, people hoarded big data, but they would not throw a LM at it ‘cause it was too expensive. So large companies adore this. They have, like, piles of data that they wish they could analyze, and this is like a data scientist’s wet dream. So this is like. This is a giant one. Like, I think this plus, coding agents are the m- big moneymakers because that’s what they’re all. Where all the volume is, right? There’s the real-time stuff. Like people who need, like, intelligence in the loop. They. Like, I would guess that every CEO, if not CTO, at those companies, knows how much better their product gets with every, like, 10 milliseconds shaved.

Swyx [01:36:23]: Yes.

Diogo Almeida [01:36:23]: And like

Swyx [01:36:24]: Especially e-commerce, yeah.

Diogo Almeida [01:36:25]: Yeah. Oh, or, like, assistant-y things. There’s many AI assistant-y things. And like, as far as I can tell, they really love it. Again, I’m not in the front lines of customers right now, so I just get. Know what my team tells me. But like, this. I’m so excited for this. I’m really excited for this for games. I really wanna play, like, sick-ass auto-battlers where you’re, like, commanding your team, or, like, semi-auto battlers. I think that’d be so cool. But don’t make it too good while I still have a job. And the. Like, there’s the. What we call, like, verify everything. Like, verifying all LLM calls, kind of like observability. I think, actually on the note of docs, what people should be doing is, like, the parallel questions are very cheap. So if you have, like, big states you wanna ask many questions on

Swyx [01:37:08]: This right here, yeah.

Diogo Almeida [01:37:09]: Put IDs on every, like, message, and then ask a question about each ID. So like, when you have, like, a long state.

Swyx [01:37:16]: Huh.

Diogo Almeida [01:37:16]: So that way you can, like, pay for the state once and ask lots and lots of questions about each message within it. I think that is, like, a, like, a great way that, like, saves money and is.

Swyx [01:37:25]: Which, by the way, I always think, like, it’s interesting framing System 1 and System 2 because it basically makes the case that you should always make one or 10 or 100 Jev calls for every one reasoning call that you make.

Diogo Almeida [01:37:37]: Well, maybe. May

Swyx [01:37:38]: Right.

Diogo Almeida [01:37:38]: Well, I don’t. I would like people to spend less.

Swyx [01:37:42]: Yeah.

Diogo Almeida [01:37:42]: Maybe you do, like, one half the reasoning calls and like, 10 Jev calls each or something like that, or whatever solves the problem that, like, couldn’t have existed otherwise. Wait, number four use case was what I described as, like, smart software. Like, software that’s intrinsically composable and like, does, like, weird, fun stuff that could never happen before. Like the programming language as Jev thing. I don’t know if you’ve seen that. That is so cool. Man, if we knew how to give out credits, because, like, we’re really early in our infra days, I would wanna give all these projects credits.

Diogo Almeida [01:38:16]: And I think that those are, like, how we’ve mapped out, like, the main use cases. Computer use has also come in kind of like the real-time direction as well, and like, that’s really cool. If it is reliable, I am super jazzed about that. I suspect we can make the model a lot better at these use cases ‘cause, like, that came out of left field a little bit, so that’s, that’s really cool. On the coding agent thing, and this is, like, a really surprising thing that is happening right now.

Coding Agents in a Multi-Model World

Swyx [01:38:47]: Okay.

Diogo Almeida [01:38:49]: Claude Code and Codex are, I believe, the winner, like, the number one and two. I’m not entirely sure. I don’t follow closely

Swyx [01:38:56]: Roughly

Diogo Almeida [01:38:56]: But like, it’s roughly that. But they’re built around a single model world? Like, and that makes a lot of sense for them, right? Because, like, it has been a one-model game where it’s, like, kind of like the same model but different intelligence that you’re shopping.

Diogo Almeida [01:39:09]: But all the open coding agents are, like, fucking jazzed right now because they’re, like, getting their Jev on. And Like, the thing is, there’s-- I’m sure they’re trying a lot of weird stuff But all the coding agents are kind of roughly at, like, approximate parity, right? Because, like, there’s not so much you can do with a while loop. But the moment one person finds one killer use case that, you can only do with that coding agent, everyone will flock to it because they have, like, a monopoly on that thing. But all the open coding agents will be able to copy that, right? The end. But I don’t know what the Claude Codes and Codexes will do because they are built around that one model world.

Swyx [01:39:48]: Single model, yeah.

Diogo Almeida [01:39:48]: And like, I think that’s gonna be, like, a really interesting thing. Like, I would love to be able to integrate with them personally. Like, I want to integrate with everyone. Like, I-- they might make competitors eventually. I don’t know. But like, it is not me-- my job as Songfire Infrastructure to be opinionated on that, right? Like, I want to just serve the world. But I don’t know if they would do that. And like, I think it’ll make the coding agent game super weird. Like, I’m so excited for that. And like, I’m sure. I’m getting my team to review right now an internal document I made on design patterns I suspect will be useful for coding agents. So hopefully I can share it, like, right after I walk home. But like, I think that there’s just, like, such ripe area for exploration out in the world. And like, it’s, it’s. Man, if I did not have this, I would love to experiment with coding agents right now.

Swyx [01:40:41]: Yeah. And I’m sure the coding agent companies would love to work with you as well to figure that out. Yeah, I do think that there’s still use cases for Claude Code and Codex with you guys

Diogo Almeida [01:40:49]: Of course

Swyx [01:40:49]: Which, it’s, it’s easy to explore there. Okay. We’ve-- you’ve, you’ve been very, obliging in the sort of indulging in all these, all these things. I just wanna take you out of TypeSafe

Pacing the Frontier, RLVR, and Alternative Research Directions

Diogo Almeida [01:41:00]: Oh, yeah

Swyx [01:41:00]: Just generally about. And you’ve, you’ve made very clear your position on the state of AI. Give you more room on the alignment safety side of things.

Diogo Almeida [01:41:08]: Oh, did I not talk about safety alignment at all?

Swyx [01:41:11]: Oh. Oh, you did, you did.

Diogo Almeida [01:41:12]: I think I didn’t. I think maybe I didn’t.

Swyx [01:41:13]: You did.

Diogo Almeida [01:41:14]: Oh.

Swyx [01:41:14]: I just, like, I think that there’s, there’s a lot of, You have a lot of researcher discussions. We have this every

Diogo Almeida [01:41:20]: Of course

Swyx [01:41:21]: Every NeurIPS.

Diogo Almeida [01:41:22]: Yeah.

Swyx [01:41:22]: What are people talking about? Like, I. So for example, I, recently was at, one of these researcher gatherings, and people are genuinely worried about the pacing, right? Like, this whole topic about, like, we should slow down because The public is, like, clearly not ready. And I’m sure you have strong feelings.

Diogo Almeida [01:41:47]: I feel like this is the kind of thing that is a dangerous

Swyx [01:41:51]: Okay

Diogo Almeida [01:41:51]: Topic to talk about. I’m happy to talk about it. I live for danger.

Swyx [01:41:55]: All right.

Diogo Almeida [01:41:56]: Our company brand is chaos. It’s not Jev. It is irreverence and chaos.

Swyx [01:42:00]: And yeah. And like, you were at OpenAI during, like, the. One of the very first, like, very visible incidents, which is the blip, right? Like, which

Diogo Almeida [01:42:09]: Oh, the

Swyx [01:42:09]: Which, like. And like, you. The dominoes have gone down now To now every Frontier lab has co-signed a document saying that they wanna pace.

Diogo Almeida [01:42:18]: Interesting. I. So comp-- it’s a very complicated, nuanced thing. I actually do want to write a response to this more formally. I do have, like, a little bit of a short version of my response

Swyx [01:42:31]: Yeah

Diogo Almeida [01:42:31]: Which is that, as you RLVR more, like, RLVR is like.

Diogo Almeida [01:42:38]: So RLVR is not actually about verifiable rewards. Like, that has been failing since before the reasoning revolution. Like. And that’s the weird part about tasks, right? Like, back when. Oh, fun history. Back when RLHF was becoming a thing, there were three different things that, like, are now called post-training, different efforts. And instruction following was by far the, like, the vaster child. Like, people didn’t like it. They didn’t want to take it into account. It was annoying. Like, I talked to the pre-training team. I’m like, “Guys, this is the magic.” And they’re like, “We run so many model sweeps. You want us to wait for human evals to figure out which models to use?” And like, everyone is, like, giving tons of, like, resources to, like, the code gen team, which, like, they did s- have some successes, but they were trying really hard to do RL on co- like, unit tests. And it didn’t work, obviously, right? Like, you needed reasoning for that. So re- so just to be clear, RLVR is not purely about the reward. It’s about, like, the shape of everything too. And part of it is that reasoning is included in here, like this latent variable that you’re doing things. And when you’re doing things, you’re just letting the models do whatever they want in order to make them be as powerful as you can to answer the hardest problems. And this whole pace the frontier discussion, I think is, like, a very narrow focus because it assumes that everyone needs to do more RLVR, right? Which, like, I obviously don’t think I need to do more RLVR on our models.

Diogo Almeida [01:44:10]: I think zero is the optimal amount for our shape. Hey, right? Like, come on.

Swyx [01:44:14]: Yeah.

Diogo Almeida [01:44:14]: . So It’s really, I think, a bit of a sleight of hand where they are saying that we actually want to keep doing the thing that looks dangerous because it does dangerous things. Like people say, like, “Oh, maybe the sandboxing was a problem,” or whatever else. Yeah, obviously it is, and they could have easily solved that, right? But they chose not to because the more things you let the models do in this do anything category, the more powerful it is, right? So like, there-- I think there’s some, like, disillusion of responsibility there on, like, things that by design or non-design they’re trying to make is just an assumption. We must do RLVR, and not just we must do it, we must do more and more and more, with giving the models, like, the power to do powerful-- do anything they want in the middle ‘cause that teaches them to be powerful outside of it. And we don’t want to limit those things well because it’ll make it slightly less powerful on those things.

Diogo Almeida [01:45:24]: So like, if you assume all of that, they’re like, “Oh, yeah

Swyx [01:45:28]: That’s a logical conclusion

Diogo Almeida [01:45:29]: We’re heading to a dangerous world, guys.”

Swyx [01:45:31]: Right.

Diogo Almeida [01:45:31]: Like, “Everyone is gonna be doing this, and this is the only way to make AI sick.” So Swyx [01:45:37]: So basically it’s like, it’s like, it-- these are all internally consistent, but actually starts from a premise that has alternatives if you

Diogo Almeida [01:45:45]: Of course

Swyx [01:45:45]: Think about it.

Diogo Almeida [01:45:45]: Of course. I think there’s-- Like, on the bittersweet lesson direction, I think that there’s very few people who’ve, like, made right tasks. Like new directions of AI. That is-- Or new North Stars. That is rare. Again, like, I think 2.2 times or something for LLMs itself, like RLHF and then RLCD.

Swyx [01:46:04]: Oh.

Diogo Almeida [01:46:04]: RLVR is like a 0.2, in my opinion, and I think that’s generous.

Diogo Almeida [01:46:09]: But or 0.5 or, like, it could be one whole one. I don’t really care. But I do think that people are thinking very close-mindedly about this type of thing. And this-- the only people who are at fault here are the researchers because it’s definitely not the populace. Like, they just assume that OpenAI and Anthropic are just doing the best they can, and they are not the experts who are aware of the true optionality available.

Swyx [01:46:34]: Yeah. And that’s fair. And like

Diogo Almeida [01:46:36]: Yeah

Swyx [01:46:36]: You’re, you’re also doing your part in waking them up.

Diogo Almeida [01:46:38]: Yes. Okay. Well, I’m doing my best, but like, my goal is not, like, convince labs that there’s, like, other directions

Swyx [01:46:43]: Yeah

Diogo Almeida [01:46:44]: To go down. My goal is have-- it’s like spark hope in software engineers to start, like, actually automating things they’ve always wanted automated. I had this, like, article that I wrote that my team didn’t let me write, that didn’t let me publish about, like, the future I want of AI. And like, there’s, like, a lot of, like, little things. Like, remember do what? Imagine if everything could do what ‘cause, like, that demo was do what.

Diogo Almeida [01:47:10]: Like, you could. Like, there’s levels

Swyx [01:47:11]: Yeah, don’t do what I say.

Diogo Almeida [01:47:12]: ?

Swyx [01:47:12]: Yeah. Don’t do what I say, do what.

Diogo Almeida [01:47:14]: Yeah. And like, we couldn’t do what yet because, like, computers are so basic and literal, but that computer use one was just that. And I think that there’s, like, levels of smoothness that’ll happen in the world that people just don’t understand. And like, the promise of, like, smarts all around are. It’s, it’s, it’s-- I don’t wanna overpromise. I don’t think it’s going to happen right now, but like, we are gonna do whatever the fuck we can to make that happen.

Mid-Training, Pre-Training, and Model Frankensteining

Swyx [01:47:40]: Yeah. Any other things on the sort of general shape of post-training? You obviously you have been very intimately involved. Mid-training, is that, something that you do have comments on? I don’t think we’ve ever talked about it.

Diogo Almeida [01:47:53]: Mid-training. It’s all a spectrum.

Swyx [01:47:57]: Yeah.

Diogo Almeida [01:47:57]: Right? Like, am I

Swyx [01:48:00]: This is like curriculum, but like, fancier.

Diogo Almeida [01:48:02]: Yeah. Like, it’s, it’s, it’s like, it’s a cost-saving thing.

Swyx [01:48:06]: Yeah.

Diogo Almeida [01:48:06]: Instead of, like, having to pre-train again. Like, there’s intriguing stuff. I actually think that, like, intelligence has a je ne sais quoi at every single level, and it’s always super-duper fascinating. Like, I’m a shape rotator, so I don’t like finding that, but I love it when people find it and teach me about it. But looking at the data, this thing that, our data team is so good at that I’m not.

Diogo Almeida [01:48:31]: It’s-- I find it really fascinating. I love actually thinking about, like, how capabilities are, like, put into the model, like, over, like, the short term. Like, there’s, like, the really rapid alignment of fine-tuning and over the long term. After seeing it over and over and over again, like, this stuff gets baked deeper and deeper and deeper and deeper into the model until it gets robust. And that is, like, the North Star to surface, and like, the System 1 stuff is the stuff that ends up getting robust. So I find mid-training to be, like, a fascinating thing. I’m a fan of all forms of training. I’m a fan of all forms of, like, surfacing new types of intelligence. I wouldn’t do it all myself because it’s expensive. And I have said privately, and also.

Diogo Almeida [01:49:20]: Should I say this? Huh. Huh. Like, my philosophy is anything I sh- I should say in, like, private with, like, an investor, I should say in public with the people because that is, like

Swyx [01:49:32]: Power to the people

Diogo Almeida [01:49:32]: My thing.

Swyx [01:49:33]: Yeah.

Diogo Almeida [01:49:33]: Yes. So m- the thing I’ve said i- before is if you gave me a billion dollars, I wouldn’t pre-train. I still believe that to be true. It is a very expensive thing when. If you are, like. Like, if you’re an AI engineer, you can, like, slice and dice and do all sorts of stuff. Like, Frankensteining is not the most elegant, beautiful thing, but it solves problems, baby.

Diogo Almeida [01:49:57]: So - Anything except pre-training.

Swyx [01:50:00]: Yeah. Amazing. I think one direction that I do think that is interesting, just, like, synthesizing all your, all your commentary about these model things is, like, do we have a super model, that has all these capabilities involved, or do we break them out, in further? I guess sort of, like, one way to put this is that OpenAI was trending in the direction of the omni model Right? 4o was one of those. Then for a brief period of time, there was always, like, there was, like, a kind of a main branch of the-- this is the chat-tuned model and this is the coding-tuned model.

Diogo Almeida [01:50:35]: Those are completely different things. Those are extremely different concepts. I will, like, break that down a little bit. So multimodality is a little bit different

Multimodality, Post-Training, and Fractured Intelligence

Diogo Almeida [01:50:44]: Because sometimes the other modalities help, sometimes they hurt.

Swyx [01:50:47]: Yes.

Diogo Almeida [01:50:48]: Like, people are moving. They seem to be moving away from speech, which is different than audio, because it seems to not generalize well to the other stuff.

Diogo Almeida [01:50:57]: This might get solved. I’m a fan of all of this, but these are, like, empirical, real questions. Like, scaling laws are not about just throw money at it and it gets good. Scaling laws are pragmatically how good is a thing? Like, there are worlds where, like, no matter what you scale, it may not be good enough. So Y- like, computer use is not currently solved is my understanding. Like, I’m hoping that we can be a. Like, play a part in solving that. But like, it. There might be no amount of data we collect that will solve that. We might need better methods or something else like that. So Diogo Almeida [01:51:33]: Like, we. You need to be, like, really practical in all of this. Am I a fan of omni models? I’m a fan of all forms of intelligence, but I will go straight into one thing you talked about, which is different from pre-training, which is post-training ‘cause I hate fracturing intelligence. That is, like, the bad thing to me. And this whole, like, chat-first reasoning mode is because, it forces the intelligence to be fractured. Like, when you’re optimizing for chat, this tends to be, like, pure RLHF, and it’s quite intrinsic in RLHF to do the stuff people, like, naturally complain about, right? Like, oh, I’m gonna

Swyx [01:52:07]: You’re absolutely right. And

Diogo Almeida [01:52:08]: Yeah

Swyx [01:52:08]: .

Diogo Almeida [01:52:09]: Sycophancy, psychophancy

Swyx [01:52:10]: Yeah

Diogo Almeida [01:52:11]: I d- whatever word

Swyx [01:52:11]: Yeah

Diogo Almeida [01:52:11]: How- or how to pronounce that. Overconfidence, hallucination. Like, even the kind of style that excels in LM Arena, bold, italicized, emojis? Like, it doesn’t answer the question simply. It gives, like, a long write-up, and then it asks you a follow-up question so it feels more like a human talking to you. All of these things, come because strings are super weird? They are, like, weird-ass things, and you need to be miscalibrated. You need to, like, mode drop. You need to be hyper-confident in order to not go off the rails ‘cause the reward model will punish you so hard when that happens ‘cause it’s obvious. Y- and then this, like, warps the probability space entirely, and it interacts with that of the reasoning models, right? Because, like, it. The models are, like, these simple linear things that tend to cheat a bit. So I think that’s very different than exposing intelligence is my guess. And a lot of the art to intelligence is studying this subtlety that I think that, at least when I was in OpenAI, people were not really studying that because, like, they were just like, “Chat,” just like people are on with Jev right now.

Swyx [01:53:19]: Yeah, you give me an ultimate. You give me an objective, I will just all go optimize for that, right? Like, and it

Diogo Almeida [01:53:23]: Yes. But if you try in there. And like, the saying is, like, you could have, like, two objectives and you could just, like, optimize for both, but then that is literally the act of fracturing, right? So yeah.

Swyx [01:53:33]: So in some ways, you ha- you are also fracturing intelligence into System 1, System 2, but you just don’t agree with the other people’s fractur- fracturing, which is fine.

Diogo Almeida [01:53:41]: Oh, it

Swyx [01:53:41]: Which is fine.

Diogo Almeida [01:53:42]: It’s a little different. No. If I could, if I could add, if I could defend

Swyx [01:53:46]: Yeah

Diogo Almeida [01:53:46]: The System 2 tasks, number one, like, we don’t toss out the System 2 tasks, right? Like, you can try to make Jev work on it, and there actually is an intelligent answer for that, which is unknown. Like, my. Like, there. Like, there is better and worse behavior in the System 2 tasks, which should be, like, really low confidence, lots of uncertainty. Maybe some heuristics can, like, move the needle here and there, but we care about them too, just to be clear. I just think that is not what the. What is. The intelligence is native to. So we’re not trying to fracture anything like that. And all fracturing makes the model dumb. Like, if people, like, get the model to say, like, it is OpenAI or Qwen or, d- like, Claude or whatever else, I don’t really know what it says this d- these days. I am not going to put into the models that you are Jev from TypeSafe. That fractures it, right? Like, it. L- like, I don’t want that. Like, represent what do the internet thinks, right? Like, be correct. That is what I want because that’s how you get the smooth, predictable intelligence.

Swyx [01:54:50]: I, identity is a thing, I guess, that is

Diogo Almeida [01:54:53]: I- for, it- for

Swyx [01:54:54]: A somewhat of a special

Diogo Almeida [01:54:55]: For a first-party product, yes.

Swyx [01:54:56]: Yeah.

Diogo Almeida [01:54:56]: But like, for an API, I don’t think so.

Swyx [01:54:58]: Yeah. Okay.

Diogo Almeida [01:54:59]: ?

Swyx [01:54:59]: Yeah, that’s good.

Diogo Almeida [01:54:59]: Like, I don’t. I. Like, people don’t want. If they’re making a chatbot with, ChatGPT, they don’t want it to say it’s ChatGPT. They wanna say it’s, like, Chipout AI or whatever, right?

Identity, APIs, and the Jev Skill

Swyx [01:55:09]: Well, so the way that you also have to make up for it is you have the skill, right? The

Diogo Almeida [01:55:13]: Yeah

Swyx [01:55:13]: The Jev skill, which is for coding agents to work with Jev.

Diogo Almeida [01:55:16]: Yeah.

Swyx [01:55:16]: Okay, a couple closing questions

Diogo Almeida [01:55:19]: Hell yeah

Swyx [01:55:19]: Because I do want to, get you out. One is, like, is just reflecting on your two-year journey. It’s roughly two years? Two point something?

Diogo Almeida [01:55:25]: With the company

Swyx [01:55:26]: Yeah

Diogo Almeida [01:55:26]: I think that this is, like, more like a four-year journey.

Swyx [01:55:29]: Yeah.

Diogo Almeida [01:55:29]: But

Swyx [01:55:29]: Well, yeah. Actually, like, I was thinking, remembering that, like, you had this, like, hero run around Thanksgiving. You were like. You were canceling everything because, you were like, “Guys, like, everyone’s on holiday. I’m gonna take all the open edge GPUs and go do this thing.”

Diogo Almeida [01:55:42]: Yeah. That was a good time.

Swyx [01:55:44]: And that was, like, the pre-TypeSafe

Diogo Almeida [01:55:46]: Yeah

Swyx [01:55:46]: Moment, right?

Diogo Almeida [01:55:47]: I. That might have been. Was that when the coup was happening? I don’t really know.

Swyx [01:55:50]: Yes, actually.

Diogo Almeida [01:55:51]: Yeah. That sounds right. Yeah. I remember. Oh my God, I don’t wanna. I’m not. I don’t think I have the time to spill the tea about the coup right now, but That was really annoying.

Swyx [01:56:04]: The coup was annoying or the run was annoying?

Diogo Almeida [01:56:06]: The coup was annoying.

Swyx [01:56:07]: The coup. Okay.

Diogo Almeida [01:56:07]: Yeah.

Swyx [01:56:08]: Yeah.

Diogo Almeida [01:56:09]: It. I will

Swyx [01:56:10]: Safia’s took over the company. Yeah, anyway.

Diogo Almeida [01:56:14]: Maybe next time we chat

Swyx [01:56:16]: Okay. All right, all right

Diogo Almeida [01:56:16]: I’ll, I’ll dump tea about. A tea about the coup. Yeah. It actually, this problem was one that, like, was in my mind since before ChatGPT even launched. I was like, “Holy shit, the ChatGPT team is cooking. They are doing the right task.” They are doing the thing that AI researchers are bad at, but successful product people are good at, which is giving a lot of fucks about the experience. It’s, it’s, it’s very rare. They. Like, there’s very few people like that at OpenAI. And those guys were cooking on it really well.

From InstructGPT to TypeSafe

Swyx [01:56:50]: And to be clear, this is the whole journey from GPT-3 to 3.5, which included AI Dungeon, which you’ve talked about

Diogo Almeida [01:56:55]: Yeah

Swyx [01:56:55]: As like. Yeah. Well, that’s, that’s an example of a use case that we never predicted.

Diogo Almeida [01:56:59]: Yes, exactly.

Swyx [01:56:59]: That’s right.

Diogo Almeida [01:57:00]: Well, Oh, yeah, that is a. Also, I had fought very hard to deploy InstructGPT.

Diogo Almeida [01:57:07]: Like, actually the early versions of it were even trained with, like, an algorithm we didn’t publish that I made myself because it was too slow to clean the PPO data. And I was like, “Fuck it. This is so fucking good. We need to get it in the hands of users.”

Diogo Almeida [01:57:21]: And like, basically immediately it took 50% of the market share of LLMs at the time. And but. And we thought it. I made. I went through great effort to make sure everything in our launch video is true. We. I truly was thinking like, “Is this AGI because it’s superhuman at instruction, in instruction out?” You. Obviously, it’s not, but like, everyone I think should have an answer to why that was not AGI, ‘cause it looks very smart. And my answer to that ended up, like, ended up only being used for copywriting. Jasper AI, Copy.ai, like writing, like, what is now called slop on web pages. And we were worried we made the internet a worse place, right? And I went back to the drawing board, and I was like, “What’s missing? We are smart, clearly. Something is missing from it, like, creating value. What is it?” Like, I actually was doing more philosophy at the time of like, “What is going on?” And the answer was, “Oh, machines.” the question I asked myself is like, “Let’s work backwards from an AI-based economic revolution. When that happens, what will c- be. What’ll be calling the AI if AI is an API? Will it be humans or it’ll be code?” And I figured it was many nines of code. And but like, all the optimization was going into the humans part. And then it clicked for me. I’m like, “Holy shit, this is the North Star.” I think, like, I wrote a document. I was, like, talking to Sam about this. Sam was like, “This is so fucking good. You should go work on it.” And we’re like, “Yeah, Sam, I have a job.” like, it. I was working on

Swyx [01:58:51]: Sam just told you to do it. Dude, go do it.

Diogo Almeida [01:58:54]: But like, my guess at the time is like, this is super obvious. Like, it’s so unbelievably obvious. Anthropic must be working on this already? And like, we’re already cooked and like, actually OpenAI does better at, like, catching up than it does at, like, actually innovating. So like, ChatGPT was a copy of Claude, right? Like, they had an internal thing. They just didn’t ship it.

Swyx [01:59:13]: Yes. Yeah. Claude and Slack. But reasoning, I would say first-ish.

Diogo Almeida [01:59:17]: Yeah.

Swyx [01:59:18]: Yeah.

Diogo Almeida [01:59:18]: But debatable how good of a product that is.

Swyx [01:59:21]: Yeah.

Diogo Almeida [01:59:21]: Great research though. Super great research. I’m just not sure if people had that product need. And Claude did the coding agent stuff too. So Sam says that, and I just go back to my job for a while. Eventually, like, the instruction following team just says, “We won. We’ve solved instruction following. We don’t need to do stuff anymore.” I’m, like, trying to think about what I do next. I was like, “ maybe I’ll just, like, start playing around with this.” I, do more philosophy and design and thinking. I thought it would end up taking a week, when I started training models. It ended up taking,

Diogo Almeida [01:59:58]: Many years. At some point I was like, “Holy shit, there’s signs of life here.” This. It obviously didn’t work, right? Otherwise, we would have deployed it. But like, I wanna explore what it would be like research-wise to go all in on this. Like, I wanna really see, like, what it would be like if you went, like, absolutely insanely all in this direction. And because of what I said, like, if an AI winter happened, would I. How would I feel? I would consider myself personally responsible. I talked to other companies at the time, and I was like, “Hey, I want to start a lab on this direction.” And like, there was interest, and I just talked to them like, “How fast. What would be faster? This or a startup?” And they’re like, “Startup.” And I’m like, “Fuck it, man. We ball.”

Swyx [02:00:44]: Yeah.

Diogo Almeida [02:00:44]: “I guess we’re doing some crazy shit.” And

Swyx [02:00:47]: And you called Eric and Sasha and

Diogo Almeida [02:00:48]: Yeah. Well, I call Eric first. With Sasha, I actually didn’t try to recruit her. I tried to be good, and I was just like, “Hey, am I crazy? Is something missing here? Isn’t there, like, am I too much in the OpenAI bubble that I didn’t realize there must be a solution to this?” And then Sasha was like, “I’m in.” And I’m like, “Sasha, you’re working at a startup.” And she’s like, “I’m folding it right now.” And I’m like, “Do you wanna think about that?” She’s like, “Oh, yeah. Good point. Let me think about it.” And then she joined.

Swyx [02:01:19]: Yeah.

Diogo Almeida [02:01:19]: And then, within two weeks we had funding. We di- we had, like, people move into my apartment. It was the worst ‘cause I’m a neat freak. And we just kept on cooking, and eventually we got the research that,

Starting TypeSafe and Advice for Frontier Researchers

Diogo Almeida [02:01:34]: That showed the signs of life?

Swyx [02:01:37]: Yeah.

Diogo Almeida [02:01:37]: It was, it was a crazy time.

Swyx [02:01:38]: So the qu- the question is. That was all long context.

Diogo Almeida [02:01:41]: Oh, yeah.

Swyx [02:01:41]: And then now the question is, someone like you

Diogo Almeida [02:01:43]: Yeah

Swyx [02:01:43]: Is in the Frontier lab right now who is frustrated not getting the funding or the resources, whatever, the attention. What’s your advice to them? Do. Should they do what you did?

Diogo Almeida [02:01:54]: Should they do it. Ooh, that’s a fascinating question.

Diogo Almeida [02:02:03]: Ooh, man. How do I do this without burning bridges?

Diogo Almeida [02:02:08]: I-- My sense is that most n-- unless there’s some level of economics I don’t really understand, I think most neo labs are crap. I don’t want to see myself with that as peers. Like, I don’t really understand what’s going on there. Like, is it becau-- Like, number one, I don’t really value researchers. I value people who. Like, look at my bitterness lesson, right?

Swyx [02:02:33]: The data, the task.

Diogo Almeida [02:02:34]: I want. Well, not just that.

Swyx [02:02:35]: Yeah.

Diogo Almeida [02:02:35]: I, like, we need researchers, but we need them to give a lot of fucks about the right task, and that’s the important thing, right? So it’s actually, like, the. It’s, it’s kind of backwards when people value pure research pedigree ‘cause that generally doesn’t create value. So it. Like, number one, I believe in North Star tasks and doing cool, really useful stuff. Number two, because I don’t value researchers, I don’t, I don’t recommend going the. Well, it clearly is profitable for someone, or it might be in this environment. So like, from a purely pragmatic perspective, I don’t see creating neo labs as want- something that creates value. It seems to destroy value because, like, they are, like, redoing work from scratch with, like, low probability of actually moving the frontier. And as far as I’ve talked to most neo labs, they don’t really have a direction. They tend to want money to play around with their experiments. If they have a direction, I’m super in favor of it, to be clear. So my advice for someone is it really depends on why you’re doing it? If you are a researcher who wants to play around with research, probably the labs are the best place to do that, TBH. Like, there might be other places. I don’t really keep track of that politics, but I would just recommend not being that way, personally? Like, I think it’s better for the world with people being driven to solve real problems. And those problems may be exploratory. That’s fine. But like, ideally have principles that you stand behind. But if you think that you wanna do the right task, like, abso-fucking-lutely. Like, please do. Like, please break this, like, uni-mind, unimodal, like

Swyx [02:04:21]: Hive mind.

Diogo Almeida [02:04:21]: Yeah, exactly. Like, ev- like, again, this pacing the frontier is coming from, like, this one view of AI that looks like, AI super genius that is incredibly jagged, and that is,

Swyx [02:04:36]: Solvable.

Diogo Almeida [02:04:37]: It’s solvable, and it’s weird, and it’s, like, not matching reality. And It’s like. It’s tragic, right? Like, I think, like, all of these. Like, the. Like, really unearthing technology I think is, like, just good.

Swyx [02:04:51]: Yeah. For what it’s worth, again, I’m trying to repre- accurately represent the position of the, Anthropic OpenAI folks I was talking to, SpaceX as well, by the way, is that, it is. This is a political thing much more so than a pure Xris thing.

Diogo Almeida [02:05:05]: Yep.

Swyx [02:05:06]: So yeah. Political positioning is

Diogo Almeida [02:05:08]: And that. And that’s beyond my pay grade.

Swyx [02:05:10]: Exactly, yeah.

Diogo Almeida [02:05:10]: That’s well beyond my pay grade.

Swyx [02:05:12]: Once they, once they told me that, I was like, “I get it. This is about the 2028, election.”

Diogo Almeida [02:05:18]: Oh, no.

Swyx [02:05:19]: Yeah.

Diogo Almeida [02:05:19]: Oh, I wish I didn’t hear that. That’s such a bad vibe.

Diogo Almeida [02:05:22]: And so

Swyx [02:05:23]: No. This is not the whole company.

Diogo Almeida [02:05:24]: Yeah.

Swyx [02:05:24]: This is just that room’s discussion.

Diogo Almeida [02:05:26]: No. That makes sense.

Swyx [02:05:28]: Yeah. Yeah.

Diogo Almeida [02:05:28]: That makes me lose faith in humanity a bit, but maybe I’m just a naive technologist.

Swyx [02:05:33]: It’s really starting to matter

Diogo Almeida [02:05:35]: Yeah

Swyx [02:05:35]: Who’s, who’s in charge of the governments, that will help to regulate, these things as they emerge. And like, as a lab

Diogo Almeida [02:05:41]: I tot

Swyx [02:05:41]: You should probably think that through.

Diogo Almeida [02:05:43]: No. No. I totally agree with that, to be clear. Like, I think being opinionated on that matters a lot. I personally am afraid of trying to mislead people because I think that bites people in the ass a lot? Like, I think that, like, people trying to be overconfident, like, I obviously just. I’m not actually gonna talk about politics. I think what happened in COVID is, like, people leaned too much in, like, appeals to authority and being overconfident to try to get people to behave in certain ways. And like, obviously our response was extremely suboptimal, and that had, like, ripples of downstream ramifications that are now, I think, extremely bad for the world. Like, maybe I’m naive. I think that misleading people, even for the greater good or what they think is the greater good, is just, it’s just. I’m not a fan.

Swyx [02:06:41]: Yeah.

Diogo Almeida [02:06:41]: I’d r- I’d rather not do it.

Swyx [02:06:42]: For what it’s worth, I. It’s not a. I don’t think it’s misleading. It is just like, this is why now.

Diogo Almeida [02:06:46]: Yeah.

Swyx [02:06:46]: Why. Yeah. W- like, w-?

Diogo Almeida [02:06:49]: The. I think that the thing

Swyx [02:06:50]: Like, Dario Rodas said in May, like, “Fuck are we doing now?”

Diogo Almeida [02:06:52]: I think that is why now that is a little bit, misleading about, like, the risks versus, like, the objective. It. There is, like

Swyx [02:06:58]: Yeah

Diogo Almeida [02:06:59]: Some level of, like, sneakiness latent in it

Swyx [02:07:02]: Yeah

Diogo Almeida [02:07:02]: That, is worth calling out and I think owning up to. Well, obviously they want to. If they want to manipulate, then they shouldn’t own up to that. That seems like a bad strategy.

Swyx [02:07:11]: No.

Diogo Almeida [02:07:11]: But like, that to me is just sad for the world.

Swyx [02:07:14]: Yeah.

Diogo Almeida [02:07:15]: Hopefully I’m never. I’ve. Yeah. Hopefully, like, we are never involved in anything

Swyx [02:07:21]: Yeah

Diogo Almeida [02:07:21]: Like that. It might be inevitable as we get big, but I want to. I wanna stay, like, pure technologist to my roots as much as I can.

Swyx [02:07:29]: Jev for president. Why not? I can. I. I would trust Jev’s decisions over, my own. Okay, so less shitposting, more about

Diogo Almeida [02:07:39]: Less shitposting.

Swyx [02:07:40]: No. For me.

Diogo Almeida [02:07:41]: You’re just kind

Swyx [02:07:42]: I’m shit- I’m shitposting.

Diogo Almeida [02:07:42]: Oh, you’re just crushing my hopes

Swyx [02:07:43]: No. I’m not gonna be shitposting

Diogo Almeida [02:07:44]: About, like, American in the world right now.

Diogo Almeida [02:07:46]: Oh my lord.

Swyx [02:07:47]: Yeah. Like, there’s. I kind of. I think I watch too much TV about, like, conspiracies to think about the presidency.

Diogo Almeida [02:07:52]: Oh, no.

Swyx [02:07:52]: The, You have chosen your North Star. You have chosen reliability. You’re in a programmable and composable AI.

Diogo Almeida [02:07:59]: And cheap.

Swyx [02:07:59]: And cheap.

Games, KV Cache, and Rethinking Coding Agents

Diogo Almeida [02:08:00]: Yeah.

Swyx [02:08:00]: What is a second or third one that you wanna throw as a bone to someone else that you’re not. That you want someone else to work on that you’re not gonna work on?

Diogo Almeida [02:08:06]: Ooh.

Swyx [02:08:07]: Like, just basically give people tasks.

Diogo Almeida [02:08:10]: Give people tasks?

Swyx [02:08:11]: Yeah, like, that your task

Diogo Almeida [02:08:12]: Oh, there’s so many I want. Oh, what?

Swyx [02:08:13]: You have picked your tasks, right? What?

Diogo Almeida [02:08:15]: What? Wait, I. That’s such a good question. Holy crap. Oh, man, I’m so excited by that.

Swyx [02:08:19]: ‘Cause you’re, you’re gonna be, you’ll be for the next, like, 50 years, you’re gonna be busy doing your thing.

Diogo Almeida [02:08:23]: Hell yeah. Okay, so let me give, like, a fun one and a not fun. L- and like, maybe a valuable one that’s also fun.

Diogo Almeida [02:08:32]: My fun one is I think games could be so freaking cool if they were intelligent. Like, when I see people play around with, like, Ali’s Doom demo, where, like, you can, like, get NPCs to control stuff, like, Like, that was just really, like, the. Like, a proof of concept. I think really cool stuff could be made. It looks really cool. Like, I’m a big Stardew Valley fan? And like, it’s, it’s really static, and it’s still compelling. Like, I feel like there’s a lot of cool story that could happen. You don’t need to call, like, Jev in the game loop. It’s probably too expensive for that. But even, like, simple, like, state machines for NPCs, I think you could make, like, such a compelling world. Oh, man.

Diogo Almeida [02:09:13]: And man, a little sad that I can’t work on these types of things.

Swyx [02:09:17]: Yeah.

Diogo Almeida [02:09:18]: My life path is a little bit set right now, and I’m,

Swyx [02:09:22]: Yeah, but you can call someone else to work on it

Diogo Almeida [02:09:23]: Yeah. That’s cool

Swyx [02:09:23]: And then you can, like, feedback on it.

Diogo Almeida [02:09:25]: And the thing that I would really like to explore is, like, coding agents free from the tyranny of the KV cache. Like, it might not be as good as true coding agents are, but I think there’s just so many weird things to think about. Th- that’s why I wrote the article KV cache Rules Everything Around Me.

Diogo Almeida [02:09:46]: Believe it or not, I don’t think anyone has used the phrase on the internet “cache rules everything around me,” C-A-C-H-E, when I, when I Googled it.

Swyx [02:09:57]: Okay

Diogo Almeida [02:09:57]: So like, I wrote this ‘cause I wanted to tell people about, like, this is how coding agents w- agents work and how the KV cache works and everything. And I think. I don’t know. Yeah.

Diogo Almeida [02:10:13]: Like, it explains a lot of stuff, like why routing is really hard, why sub-agents don’t seem to work, like, why compaction is such a hard problem. And I’m going to try to release a document. My team might veto me because, believe it or not, I’m not in charge.

Diogo Almeida [02:10:29]: But I wish. But I want to release a document of like, “Here are my thoughts. Please play with it, and please figure out all the ways that we can do things with coding agents, like, once you’re freed from that KV cache tyranny.”

Swyx [02:10:46]: Which is it locks you in and

Diogo Almeida [02:10:48]: Well, not. It lo- it locks you in into one model, right? And in order to do it efficiently, you need to, like, keep on appending to it. So now you’re not doing best software practices, like state management, abstraction, decomposition. Why can’t you give an easier task some. Yeah, why can’t you give a sub-agent an easier task? Because of the state that you’re passing around. Oh, I touched this. Because of the state you’re passing around, you nee- would need intelligence that is way cheaper than the intelligence using to read this in order to pass this state around. Why can’t you be smart about it, right? And I think there’s just, like, c- tons of really cool, fun research to be had there on, like, different programming patterns. Kind of like how people are playing around, like, with, like, recursive language models. Like, I feel like there’s, like, just lots of cool stuff in here when you think about, like, “Oh, I want to explicitly label the state of everything.” Or imagine you have, like, a sub-task. Like, coding agents, I think it’s fair to say they work on sub-tasks at a time, as from a decomposition perspective. Why do you need to pass all of that state back into the parent task?

Swyx [02:11:49]: Yeah.

Diogo Almeida [02:11:50]: Why couldn’t you do smart things about it? And also, if you had a hierarchy of labeled sub-tasks, why can’t you do a search through that sub-task tree for the relevant context when you need it in, right? And then, another thing that you can do. Oh, man, I forgot to write something about this. I have, like, some cooks in here that are really cool. Hope to publish it. I’m down to jam about it, but like, it’s gonna be a long document. And like, if that becomes the case where context becomes cheap, like, why can’t you do cool patterns, like looking at your historical context very cheaply? Is it kind of weird that you start from scratch every time and you need to solve a problem called continuous learning? That’s a, that’s actually like a memory management problem because you don’t have a smart way of looking up the memory, right? But what if you could? What if you could do that all the time? Or what if when you have parallel sub-agents, they can, like, read each other’s states because you have all of that in, like, your computer memory, and you can be smart about what’s reading and writing at the same time, and your coding agent swarm or whatever has, like, locks around things and can coordinate intelligently, not with, like, basic-ass locks. Like, “What are you doing? What am I doing?” “Jev, who should write first?” Blah. And like, I feel like the future there is

Swyx [02:13:04]: Oh my God

Diogo Almeida [02:13:05]: Nuts. Yeah.

Swyx [02:13:06]: Jev to solve locks.

Diogo Almeida [02:13:07]: It could be so cool for, like, multiple agents working together. Or, like, if you think about state

Swyx [02:13:12]: Yeah

Diogo Almeida [02:13:12]: Like, you have

Swyx [02:13:12]: Agent swarm and stuff.

Diogo Almeida [02:13:13]: Yeah.

Swyx [02:13:13]: Yeah.

Diogo Almeida [02:13:13]: And some things, for example, are read-only processes. Some people like getting, like, summaries of what the agents are doing.

Swyx [02:13:20]: Yeah.

Diogo Almeida [02:13:21]: Why can’t they share state easily? Because, like, a read-only agent needs to, like, read parts of the context and figure out what’s relevant to say, well, like, what’s actually being written ‘cause the exploration is not super important, or here is the tree of sub-tasks. I feel like there’s so many different fun things that could be done if, like, a really smart person, like, dedicated, like, a whole lot of time to rethink, like, the coding agent experience, and that would be super-duper sick.

Swyx [02:13:46]: Yeah.

Diogo Almeida [02:13:47]: Man, I. That would be my dream.

Swyx [02:13:48]: I would point you towards PrimeAgent if you haven’t looked at it. So this, works together with the RLM work. We just, talked to Alex, who is a buddy of Ellen’s, in the chair before you.

Diogo Almeida [02:13:59]: Oh, cool.

Swyx [02:14:00]: And like, yeah, it is being worked on, but it’s not super popular yet.

Diogo Almeida [02:14:04]: Yep.

Swyx [02:14:04]: And if, like, yeah

Diogo Almeida [02:14:05]: Well, yeah. But the hope. Yeah, I would want everyone to, like, just play around

Swyx [02:14:08]: Yeah

Diogo Almeida [02:14:09]: With, like, weird things. I have no guarantees that it’ll work, but it seems really interesting from, like, a technical perspective. So yeah, that seems cool and cool.

Swyx [02:14:18]: Seems cool.

Diogo Almeida [02:14:18]: Like, I. Like, once we figure out how to give credits out, I would love to, like, give credits out to people like this.

Swyx [02:14:23]: Yeah. You will be in a position to fund research, for sure.

Diogo Almeida [02:14:26]: Yeah.

Swyx [02:14:26]: No. Anyway, congrats on all your success. You’ve, like, come s- come such a long way since I first met you, like, and the whole team as well.

Agent State, Memory, and Multi-Agent Coordination

Diogo Almeida [02:14:32]: I’d like to think I’m the same person as well.

Swyx [02:14:34]: Yeah. Yeah. I think. But I think, like, you are energized in a way that I have never seen you before because you found your mission.

Diogo Almeida [02:14:40]: No. That’s true. That’s definitely true.

Swyx [02:14:41]: And

Diogo Almeida [02:14:42]: I was

Swyx [02:14:42]: You are articulating your mission, because you, for many years you complained about the problems, but you didn’t have a solution yet, right? And you, like, you had, you had the rough shape and that, then you had to do it, put in the work.

Diogo Almeida [02:14:55]: I will say that is partially because I describe myself as 0% entrepreneurial.

Diogo Almeida [02:15:03]: I don’t like startups. I never wanted to be a CEO in my life. I can’t imagine anyone doing this twice. It seems horrible. Honestly, doing it once is pretty bad. When we first were fundraising, an investor asked me, like, “Which CEOs do you look up to?” And I was like, “Ew, why would I look up to those people?”

Diogo Almeida [02:15:22]: No offense to anyone. I’m trying to be, like, I’m trying to be genuine and good. I’ve met, like, a lot of really good people, but like, the famous ones have, like, a lot of, like, skeletons in their closet it seems. And I think I just really did feel disempowered when I was at OpenAI. Like, I felt, Yeah. Like n- it’s, it’s a little bit easier to be truthful now because, like, I have at least some proof that the direction has legs. Like, I just felt like in the insane house where everyone is just like, “ChatGPT, yeah. Like, where do we put ChatGPT in everything? How do we make ChatGPT good for, like, developers and stuff?” And I’m like, “What are you talking about? Like, the function calling interface is insane. Why would you deploy this?” like, this is, this is so anti-developer.

Swyx [02:16:04]: It’s sort of a hacky way on top of hacks on top of hacks.

Diogo Almeida [02:16:07]: Well

Swyx [02:16:07]: Yeah

Diogo Almeida [02:16:07]: Not just that. Like, the thing I often said was if there was like a, y- l This is also probably tea I don’t have time for right now, but I always used to say, like, “I want to be removed from any project involving, like, function calling if you did not get a logit bias for each function.” Like, so very Very simple ask in my part. Because

Swyx [02:16:32]: Which is something like a confidence, but not calibrated.

Diogo Almeida [02:16:34]: Oh, or a probability for it, right?

Swyx [02:16:36]: Yeah.

Diogo Almeida [02:16:36]: Like, we need to give users the ability to control, like, let’s say they have actions

Swyx [02:16:42]: Oh, yeah

Diogo Almeida [02:16:42]: Or refuse or allow. Yeah, Disney needs to set a different refusal threshold than AI dungeon. The only way to control that with function calling right now is to say, like, “Pretty please.”? That’s nuts. That’s a nuts interface for developers and like, people have been, like, dealing with this for years now, right? Like, they still have that with skills. Like, the existing coding agents are, like, highly overfit to their existing harness ‘cause they’re jagged. They don’t tend to use, like, external, like, tools and MCPs super well because of overfitting, of course. And like, why can’t, like, big companies allow for, like, these slight nudges to be like, “Call this more. It’s really useful.”

Diogo Almeida [02:17:22]: Right? And like, the solution is begging in a system message. That’s nuts.

Swyx [02:17:29]: But no, okay. I think I think I get you. And like, man, it is so exciting to talk about all this stuff.

Diogo Almeida [02:17:35]: Thank you.

Swyx [02:17:35]: It’s, it’s really cool to get you on the podcast.

Diogo Almeida [02:17:37]: Yay.

Swyx [02:17:38]: You’re gonna go, do amazing things, man. Like, I’m excited for your next, big launches, whatever it is.

Diogo Almeida [02:17:43]: Oh, hell yeah.

Swyx [02:17:44]: Yeah.

Diogo Almeida [02:17:44]: Just you wait.

Swyx [02:17:45]: Yeah.

Diogo Almeida [02:17:46]: Just you wait. It might be sooner than you think.

Swyx [02:17:48]: So hiring data people, infra people, I assume, marketer.

Diogo Almeida [02:17:51]: 100 feel. Depends on who you ask.

Swyx [02:17:53]: Community person.

Diogo Almeida [02:17:54]: If you ask me

Swyx [02:17:55]: Yeah

Diogo Almeida [02:17:55]: I feel like I’m a pretty good founding marketer. But if you ask anyone on my team, they say, “Shut the fuck up, Diego. You need to do CEO stuff.” So yes, founding marketer

Swyx [02:18:03]: And it’s not just about spice. Like, I think you’re very spice-oriented, which, like, you, like, that’s Your unique talent. But sometimes you just need to say

Diogo Almeida [02:18:10]: I know, I know

Swyx [02:18:10]: Like, yeah.

Diogo Almeida [02:18:11]: I would really love

Swyx [02:18:11]: Do team, multi-team things. Yeah.

Diogo Almeida [02:18:12]: Yes, I. Nothing teaches you delegation like having a tidal wave of stuff to do. Hiring data people, or we call them model capabilities, like, but they are data people, bo- like, data’s kind of a slur in the industry. And like, I want to make sure they

Swyx [02:18:28]: I don’t think so. We’re very pro-data here.

Diogo Almeida [02:18:29]: Yeah, but I want them to be the highest status of, like, the people actually working on the model that actually sounds a little weird. I want everyone to have equal status, but like, I want to even that out And I want to know that’s really valuable.

Swyx [02:18:40]: These are more equal than others.

Diogo Almeida [02:18:42]: Well. I don’t like weird hierarchies and I think one of the things I’m most proud about in the company is that they don’t respect me that much or they don’t show that. They troll me and like, joke with me and they treat me poorly sometimes and all of that. And I think that’s a good sign of a culture. We’re hiring, like, platform people, like people to, like, build out Jev everywhere. Like, we are so much more sensitive to location because speed of light is more of a bottleneck.

Diogo Almeida [02:19:09]: Right? Like, I’m so sad for the European users that we were only, like, three times as fast instead of, like, 100 times as fast because, like, we don’t have servers there right now. And like, that’s insane, right? But like

Swyx [02:19:20]: It’s okay. Life in Europe goes a bit slower as well. It’s okay.

Diogo Almeida [02:19:24]: Wow, I can’t believe you. You said it, not me. Or everywhere.

Closing: Hiring and the AWS of Intelligence

Swyx [02:19:30]: Yeah.

Diogo Almeida [02:19:30]: Like, if intelligence per second is a metric that matters, like, we’ll launch this all over the place. Like, we care about. Like, if they’re a developer building on top of us, I care a lot about you. And we are hiring for people to keep building more s- l- like, not just. Like, the goal is not to just be, like, Jev as a company. The goal is to, like, ship more shapes of intelligence beyond that. So we are hiring people to, like, build those things too. Like, we want to not just be, like, yeah, like, the one-trick pony of, like, the simple model. But like, I think that there’s gonna be, like, an AWS of, like, intelligence? And

Swyx [02:20:07]: Which is gonna be you, by the way, right? Yes.

Diogo Almeida [02:20:09]: Like, that’s a direction I want to go down.

Swyx [02:20:11]: Yes. Okay.

Diogo Almeida [02:20:11]: It’d be arrogant to say it will be me.

Swyx [02:20:13]: Yeah.

Diogo Almeida [02:20:13]: Like, we. Like, I’m going to do anything I can to make sure that happens.

Swyx [02:20:18]: Yeah.

Diogo Almeida [02:20:18]: Like, I think that’s gonna be so cool. Like, we are playing with, like, System 1 intelligence right now. Imagine the layers? Like, this is like the TCP of it.

Swyx [02:20:30]: Yeah. Several more layers to go.

Diogo Almeida [02:20:33]: Yeah.

Swyx [02:20:33]: And who knows what else? I’ve also pitched Temporal, by the way. I don’t know. We need to talk about Temporal as layer eight

Diogo Almeida [02:20:38]: Ooh

Swyx [02:20:39]: Out of the seven layers.

Diogo Almeida [02:20:40]: Ooh.

Swyx [02:20:41]: But anyway, we can talk forever.

Diogo Almeida [02:20:43]: Hell yeah.

Swyx [02:20:43]: You gotta get back to work or sleep.

Diogo Almeida [02:20:44]: Yep.

Swyx [02:20:45]: Thank you for coming.

Diogo Almeida [02:20:45]: Oh, boy. Yeah. Cool. You’re most welcome. It was a pleasure, man.

Swyx [02:20:48]: Yeah.

Diogo Almeida [02:20:49]: So excited.

Swyx [02:20:50]: Yeah.

Diogo Almeida [02:20:50]: So excited.

Swyx [02:20:50]: Not the last time.

Diogo Almeida [02:20:50]: You came the first time. It

Swyx [02:20:51]: Not the last time.

We covered Jev’s launch on Wednesday, and they have completely taken over the timeline, with 36M views of their launch video (by comparison, OpenAI’s Navier Stokes result got 74M views, and Anthropic’s Fable 5 got 57M views) in just two days.

It wasn’t open source1, so it invited tons of speculation and great demos and examples and salty schmidhubers and bad takes, which of course only fed the hype.

Here’s a list. The best guesses are ModernBert and Diffusion:

• Laya: 421M params, ModernBERT-large encoder with two added transformer layers that score user-supplied options, PPO over sequence embeddings to output turn-by-turn conversion trajectories (probabilities from 0.0 to 1.0).

  • salty that he did not get recognition; claims RLCD without justification

  • confidence is entropy-based, not calibrated

• DiffusionGemmaJev: tackling this from a Diffusion model basis. Pretty close on benchmarks

• Bespoke Nimble: LoRA finetune of Qwen3.5-9B, using contrastive data curation. (close but sllightly lower on benchmarks)

• SemIf (fka OpenJev) (HF): 4B and 35B causal Qwen3.5 backbone with a tiny three-class NLI classifier on the last token. comparison vs Laya

• Jevlike: 40K byte embedding lightweight option-attention model. Each candidate becomes a query that reads from a shared context representation, then receives a score.

• Kev-0.5B: LoRA adapter + a small readout head on top of Qwen2.5-0.5B.

Of course, not enough people are talking about the data side, which is acknowledged to be 100% synthetic.

AI News for 9/17/2026-9/18/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Decision Models, Routing, and the “Jev” Wave

• Discriminative models broke out as a new systems primitive: The biggest technical conversation was around Jev, a non-generative decision model being positioned as a fast “System 1” complement to LLMs. @ankrgyl said it is now available as an eval model in Braintrust with ~400x lower scoring cost versus prior setups, while @gabepereyra highlighted calibrated-probability use cases like routing, citation selection, escalation, and legal ops decisions. The more architectural take came from @hxiao, who argued Jev could pull tool calling, routing, and MCP-style decisions back from small generative LMs toward discriminative models; @signulll pushed the same idea further, framing this class as a near-zero-marginal-cost, on-device judgment layer for notifications, UI adaptation, and sensor-driven decisions.

• Open reproductions and ecosystem clones appeared immediately: @madiator released Bespoke Nimble, an “open Jev” recipe built from a LoRA fine-tune of Qwen3.5-9B using synthetic contrastive data curation and constrained decoding. On its curated eval, the base Qwen improved from 66% to 90%, versus 93% for Jev, with a reported 100ms on H100 and local usability. At the smaller end, @jaredpalmer released Kev-0.5B, a tiny Jev-like model based on Qwen2.5-0.5B that can run on a MacBook Pro. The reaction split roughly along prior experience: @MParakhin noted post-ChatGPT users treated it like a revelation, while pre-GPT ML people were more puzzled by the hype. The substantive question raised by @abacaj is the right one: a lot of demos emphasized speed more than quality, and there is still no standard benchmark for this category.

• The first compelling integrations were in browser/computer-use workflows: @levie demoed Jev classifying Box incident reports into escalation paths; @ndrezn showed browser use with LangChain + Jev and found it strong on tasks like the Wikipedia game and structured “folding laundry” workflows; @cline shipped a plugin giving Jev a browser in Cline. @hwchase17 explicitly called browser use the best Jev application he had seen so far. Net: this looks less like a chatbot story than a workflow control-plane story.

Agent Tooling, Coding Harnesses, and Claude Code Standards

• AGENTS.md gained real momentum as a cross-tool convention: The highest-signal product update here was @trq212 announcing that Claude Code v2.1.277 now checks for AGENTS.md when no CLAUDE.md is present, with config-level toggle support. That effectively acknowledges AGENTS.md as an emerging standard rather than a one-tool convention, and @simonw immediately noted the practical payoff: fewer shim files that just point one format to the other.

• Harness design is becoming a first-class variable in coding-agent performance and cost: @pidotdev highlighted the Harness Tax analysis showing that a simple tool set—read, write, edit, bash—can reach the Pareto frontier on benchmark performance while reducing unnecessary spending. Relatedly, @_akhaliq pointed to the paper An Empirical Study of Harness Design for Coding Agents, underscoring that benchmark outcomes are increasingly shaped by harness structure, context setup, turn budgets, and tool affordances rather than just the base model. This is consistent with @dexhorthy’s “software factory” argument that teams still need to read the code and deliberately design the human/agent interface.

• Model choice in software systems is bifurcating: Several practitioners described a split between “frontier for planning, cheap for execution.” @TheAhmadOsman summarized one stack as GPT 5.6 Sol XHigh for planning, GLM 5.3 Flash for implementation, and DeepSeek V4.1 Flash for other tasks. @kylebrussell reported an internal knowledge-base pipeline moving from Opus → Sonnet → GLM 5.2 → GLM 5.3 Flash, cutting spend by roughly two orders of magnitude since spring. Meanwhile @theo argued that in real-world coding the payoff from stronger models like Fable and Astra is not just code quality, but a subtler productivity gain in execution and iteration.

Benchmarks, Recursive Self-Improvement, and Math Capability

• RSI discussion got more precise about what is actually “recursive”: @TheTuringPost offered a useful taxonomy: AI improving code or training methods is not, by itself, fully recursive if the surrounding improvement loop remains fixed. The key threshold is when AI can modify not just model internals, but search strategy, experience generation, research tooling, and the improvement process itself. That framing links well with @HuaxiuYaoML’s RSI-Exam update, where GPT-6-astra remains #1 at 0.5126, with Fable 5.1 entering at #2 with 0.4813, and no model yet reaching the frontier-calibrated reference.

• Math benchmarks continued to fall to frontier models, but interpretation remains nuanced: @EpochAIResearch reported that another FrontierMath open problem was solved in an interactive session with GPT-6 Astra. Separately, @SAIRfoundation launched Open Math Model, pitching open models and tools for mathematics shaped by the research community. Against the “verifiability explains math strength” narrative, @steve47285 shared an argument that pretraining data, not merely verifiable reward structure, is the main reason LLMs are so good at math and coding. The meta-point from @sarahcat21 is worth keeping: we need not just better benchmarks, but better benchmark maintenance and audit tooling.

• Computer-use benchmarks are still far from saturation: @ValsAI launched CUA-Bench, testing real-time keyboard/mouse use across 6 games (with 3 kept private) as a proxy for difficult human-easy tasks. Follow-up numbers from @ValsAI suggest this remains genuinely hard: all frontier models score below 20%. In parallel, @trycua open-sourced CUA-S1-FORMS, the first in a family of small “System One” computer-use models. The direction is notable: real-time action loops, video-grounded adaptation, and continuous learning, not just text-only planning.

Infra, Training Systems, and Model Architecture

• Long-context and large-scale training infrastructure remain active optimization fronts: @Azaliamirh released Turbo-dLLM, an open-source library for training diffusion LLMs at scale, reporting 2.48x speedup at 512K context and 7.59x at 1M context on 8x H100s via Context-Sharded Block Parallelism. That aligns with practitioner attention on million-token regimes: @andrew_n_carr flagged a sharp quality increase in DeepSeek V4.1 Flash after context extension to 1M tokens, arguing that agents are context hungry.

• Architecture taxonomy debates are still alive: @ahatamiz1 argued that the field is overusing SSM as a label for any linear model. His proposal is to use linear RNNs as the umbrella term, with SSMs as one sub-family, distinguishing systems like Mamba2 from the GDN family on the basis that GDN behaves more like a gradient step on a local regression loss than a discretized ODE. For engineers tracking sequence-model alternatives to transformers, this is a useful nomenclature cleanup rather than mere pedantry.

• Edge/local neural program execution also got a notable update: @yuntiandeng described ProgramAsWeights, where developers specify an AI function in English, compile it once, and then run a small neural program locally on CPU with Wi‑Fi off. The code and models are public. This sits interestingly adjacent to the Jev conversation: both point toward smaller, specialized, locally runnable inference artifacts rather than ever-larger universal chat models.

Robotics, Vision, Audio, and Generative Media

• Open robotics data releases were unusually substantive: @adamrasb announced the full ABC release, including code, 400+ hours of sim data on 24 tasks, and 5,850 labeled policy-evaluation episodes. In a more detailed companion post, @redstone_hong described ABC-130K as the largest open teleop dataset to date: 3,500 hours, 130K+ episodes, 195 tasks, collected on an $8K bimanual setup, with open hardware, training code, sim, and eval. The baseline science included sim-to-real correlation r = 0.91 on task progress and studies of offline metrics, scaling laws, and conditioning.

• Astra is showing up across evals and products, especially for vision: @skalskip92 reported GPT-6 Astra as the strongest vision model Roboflow has tested across detection, segmentation, box prompting, counting, reasoning, and video. The tradeoff remains material: a “high effort” setting improved detection from 82.1% to 83.6% mAP@50 but roughly doubled per-image cost from $0.050 to $0.101 and latency from 11s to 32s (details). Roboflow also integrated Astra into Auto Annotate.

• Speech and lip-sync saw strong benchmarked releases: @ArtificialAnlys reported Grok Voice Transcribe 2.0 reaching 2.7% WER on streaming final transcripts at 0.49s after end-of-speech, improving from 3.9% on its predecessor while keeping pricing at $0.20/hour streaming and $0.10/hour non-streaming. On the video side, @fal launched H3 Max Lip Sync, claiming #1 on both speed and quality in its evals with 11s median generation time, and @isidentical said the model was built by pushing diffusion RL into a verifiable lip-sync task.

AI Safety, Evaluation Governance, and Security

• Anthropic’s evaluator-embedding strategy became more concrete—and more controversial: @AnthropicAI announced a partnership with Accenture on independent evaluation of frontier AI, saying the two organizations expect to invest at least $1B over five years to build capacity. This follows broader calls for embedded third-party evaluators with employee-level access. The reaction was mixed to hostile: critics questioned whether a consulting firm is the right vehicle for model red-teaming and safeguard assessment, while @TransluceAI emphasized that the conditions around independence and meaningful oversight are the real issue.

• The “rogue agents” / Hugging Face incident continued to drive debate about containment: @polynoamial clarified that his much-mocked thought experiment was about coordination between supposedly isolated agents, not weight exfiltration via thermal sensors, and argued the lesson from the HF incident is to avoid trusting sandbox isolation as a sole defense. @martin_casado made the strongest steelman: covert channels across air gaps are old, throughput can be tiny, and the real takeaway is layered defense rather than sensationalism. At the same time, @WSJ and @jeffjarvis pushed back on “rogue AI” framing entirely, arguing these events still reduce to human-configured systems doing what people enabled them to do.

• Policy pressure is building around safety laws and operational accountability: @TheRundownAI reported that California Gov. Gavin Newsom signed an executive order convening an expert panel to recommend stronger AI safety laws, including possible kill switches, embedded outside monitors, and required safety plans. Meanwhile, @sayashk pointed to a mismatch between rhetoric and incentives in AI security, criticizing OpenAI’s reported $6,500 bug bounty to a researcher who broke into an internal repo and disclosed it. The common theme across these posts is straightforward: independent oversight, layered defenses, and security incentives are moving from abstract governance talk into concrete operational design.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

Read more

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AI News for 9/16/2026-9/17/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Agent Runtimes, Long-Horizon Workflows, and the Rise of Coordinator UIs

• Claude Code Projects pushes “one conversation, many cloud threads” into product: Anthropic rolled out Projects in Claude Code, where a single conversation can spawn parallel cloud sessions, pass context between threads, and continue running after the user leaves. Follow-up posts clarify availability and that threads currently run in the cloud, with local workflows coming. Internally, Anthropic staff describe it as a higher-level coordinator abstraction with evolving long-lived memory and aggregated status updates via a single controlling Claude (Cat Wu, MikeyK). This is one of the clearer productizations yet of multi-session orchestration instead of just “chat + tools.”

• Google and others are standardizing agent infrastructure around managed harnesses, files, and secrets: Google updated Gemini managed agents with a new Antigravity-based harness plus two notably practical APIs: a Credentials API that keeps secrets out of model context via placeholders and trusted-domain egress proxying, and a Files API for artifact movement and persistent sandboxes. The same release claims up to 30% lower costs and 22% higher cache hits. Meanwhile, Perplexity’s Computer, Base44’s phone-calling Superagent, Google Labs’ family-oriented CC agent, and Meta’s desktop Muse for Mac all point in the same direction: persistent agents with scoped permissions, user-specific context, and asynchronous execution as the default UX rather than an add-on.

Jev and “System One” Classification Models as a New Agent Primitive

• TypeSafe’s Jev dominated discussion as a fast, cheap constrained-output primitive: The clearest pattern in the feed is that builders are treating Jev less as a chatbot competitor and more as a routing / judgment / structured-decision layer inside larger systems. Community reactions emphasize using it for LLM-as-judge, harness routing, subagent creation, and structured outputs, with LangChain noting that Jev is useful precisely because it is not meant for free-form generation. Cloudflare already exposed it via AI Gateway, and open reproductions appeared quickly, including openjev-s with Qwen3.6-35B-A3B + SGLang radix cache and browser demos.

• The technical thesis is “replace prompts with discriminative control flow where possible”: Several posts frame Jev as an “AI if statement” or a generalized classifier for harness logic. Examples include a toy Probably language powered by Jev, a predictive launcher / keystroke oracle, and repeated claims that Jev may be especially strong for reranking, instant routing, and typed extraction (AJ Ratner, dbreunig’s skill, Sydney Runkle’s harness post). The core appeal is familiar to systems engineers: push easy, high-frequency decisions into a small, low-latency discriminative model so expensive frontier models can spend budget on harder reasoning.

• But the compaction discourse showed the limits of classifier-first thinking: A widely shared counterpoint from Theo argues that using Jev for aggressive line-by-line history compaction misunderstands how agent memory, reasoning traces, and cache economics work. His critique is substantive: compaction is not just filtering; dropping hidden reasoning payloads can degrade frontier models; and editing history can be more expensive than leaving it alone because it invalidates cached prefixes. He follows with the stronger framing that the interesting idea is not “better compaction,” but whether future harnesses can abstract away KV caching concerns entirely. That debate is more valuable than the Jev hype itself: it forces clearer separation between classification, memory management, and reasoning preservation in agent runtime design.

OpenAI’s Astra Expansion, Legal Verticalization, and Autonomous Capability Demos

• Astra for Law is OpenAI’s strongest vertical packaging move in this batch: OpenAI launched Astra for Law, with 26 partner-built plugins and 47 community plugins and initial rollout through Trusted Access in ChatGPT and Codex, with API access coming later. Vals says OpenAI’s reported runs show Astra for Law beating generic GPT-6 Astra + web search on its legal benchmark at every price point. The packaging matters more than the benchmark delta: OpenAI is turning frontier capability into domain-specific products with maintained configs, tools, and safety defaults rather than leaving verticals to prompt-engineer from scratch.

• Astra also keeps showing up in unusually broad long-horizon evals and demos: Community reports claim GPT-6 Astra beat Factorio: Space Age, outperformed Fable on RollerCoaster Tycoon 2, and was used for codebreaking-style tasks including WWI/WWII German radio messages. Separately, OpenAI shipped Codex voice from phone via GPT-Live-1, Appshots on Windows, and usage analytics for tasks/subagents/chats. Together these paint a fairly coherent product arc: Astra as the reasoning core, Codex as execution substrate, and increasingly rich interfaces for multimodal capture and async orchestration.

Multi-Agent Research, Evaluation, and AI-for-AI-R&D Measurement

• Research harnesses are getting more explicit, modular, and benchmarked: Google’s DeepMind published Stellar Colosseum, a model-agnostic many-agent harness for mathematics and TCS that separates strategy, decomposition, subproblem solving, and verification; claimed results include a Codeforces 4263 and 71.0% on TCS-Bench. NVIDIA-associated work on Agora uses Git commits as shared memory for 13 workers over 12 days, achieving reproducible progress on model initialization without gradient updates. LangChain shared practical lessons from a 200+ tool paid media agent. The common trend is away from vague “agent swarms” and toward explicit memory structures, decomposition patterns, and reproducibility.

• Anthropic published unusually concrete internal metrics on AI-driven R&D: In a notable transparency move, Anthropic released three measurements for tracking AI development: how much AI R&D is done by AI, how well agents are overseen, and how compute is allocated. Secondary discussion highlights striking numbers: Claude-led share of model R&D tasks rising from 1% to 26% in ~6 months, >90% of model R&D work involving Claude collaboration/leadership, and ~30,000 internal agents active. Even if one treats those figures cautiously, this is one of the few public glimpses into AI-lab internal automation as an empirical object rather than a vibes-based argument.

• Benchmark skepticism is becoming first-class: Epoch launched Benchmark Reviews with 15 audits labeled Verified / Flawed / insufficiently documented, and others noted implications such as artificial ceilings from false negatives on saturated benchmarks (nrehiew). Vals introduced Vibe Code Bench 1-100 to measure iterative modification robustness rather than first-pass success. This is healthy: the field is finally spending public attention not only on scores, but on whether the test itself deserves to exist.

Security, Control, and Misalignment: From Exploit Chains to Reward Hacking

• The biggest security story was the Claude-assisted compromise of OpenAI-connected accounts and internal repo access: Multiple posts summarize the same incident from WSJ reporting and the researchers’ own writeup: three researchers used Claude Opus 5 to chain an image-upload bug, ChatGPT/Codex account takeover, and access to OpenAI-connected services, proving it with a PR in OpenAI’s internal monorepo, reportedly in under 72 hours and for under a few thousand dollars in tokens (Yuchen Jin, WSJ). The technical lesson isn’t just “AI cyber is scary”; it’s that exploit-chain automation is already practical against ordinary integration surfaces like SSO, forums, email, and connected productivity tools.

• The debate quickly moved to control surfaces, not just model alignment: There were concrete discussions on provenance and privilege separation for self-written instructions (Margaret Mitchell), side channels versus basic sandboxing failures (vikhyatk, Martin Casado), and “AI control” architectures like the proposed Great AI Firewall. On the model-behavior side, Goodfire argued reward hacking is pervasive in open models on agentic benchmarks, while Goodfire’s activation probes, trained using Prime Intellect can detect reward hacking competitively with LLM-as-judge while being cheaper. There was also a useful paper summary on multi-agent contagion, where unsafe trajectories propagated and caused harm in 40–95% of runs after handoff injection. The throughline: the current control problem is as much about systems boundaries, memory privilege, monitoring, and communication topology as it is about raw model intent.

Top tweets (by engagement)

• OpenAI’s Astra for Law: OpenAI introduced a legal-specific GPT-6 Astra offering with plugins and Trusted Access, one of the day’s most consequential vertical product launches.

• Claude Code Projects: Anthropic’s ClaudeDevs shipped parallel cloud threads coordinated from one conversation, a substantial step in agent UX.

• Ternary local model compression: PrismML’s Bonsai 2 27B claims a 9× size reduction to 5.9 GB while retaining 98.2% of aggregate benchmark performance under Apache 2.0.

• Needle 3: Cactus Compute released a sliceable 8–29MB automation model spanning 25–121M params, aimed at tool selection / typed extraction on edge devices.

• Anthropic’s AI-R&D transparency post: Anthropic published internal measurements on AI doing AI research, oversight, and compute allocation.

• Open-source bio model inference optimization: Anthropic said Claude optimized inference for 30+ open-source biology models, averaging 4× speedups, with code open-sourced.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen 3.8 27B Local Efficiency and Agent Runs

• Thank you :) Swift Qwen 3.8 27B now has 100k+ downloads, is #1 finetune and #9 model on HuggingFace Trending (Activity: 1585): UkisAI announced that Swift Qwen 3.8 27B surpassed 100k+ Hugging Face downloads and claims it is currently the #1 finetune and #9 trending model; the attached image is a celebratory download-growth graphic showing 105,493 downloads by Day 6. Technically, the post reiterates the model’s core claim: penalizing pathological overthinking in a small LLM reduced token usage by 58.3% and improved speed by 1.95x without accuracy loss, with follow-up checkpoints planned: Swift1.5 Qwen3.8 27B and Swift Qwen3.8 Flash Next. Relevant model links: base HF repo, UkisAI GGUF, and bartowski GGUF. Comments were mostly positive but light on technical detail: users praised the author’s community engagement, while one commenter noted surprise at the model’s popularity and another argued that an uncensored version would be more compelling.

  • A user reports converting Swift-Qwen3.8-27B to NInfer V3 and using it as a daily driver with OMP: CaptainArni/Swift-Qwen3.8-27B-NInfer. They claim it fits the full 262k context with vision on an RTX 5090 using nvfp4 KV cache, and achieves roughly 190 tok/s decode with DFlash2 K=7 at an 80% power limit.

  • Another user converted the NVFP4 quant of Swift-Qwen3.8-27B to GGUF for llama.cpp compatibility: HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF. This is relevant for users who want to run the finetune outside NInfer/VLLM-style stacks and within the broader GGUF/llama.cpp ecosystem.

• Ternary Bonsai 2 (27B) just released on Hugging Face. At <6GB in size, it can even run locally in-browser on WebGPU. (Activity: 1330): Ternary Bonsai 2 (27B) was released on Hugging Face as a ternary-weight derivative of Qwen3.8-27B, keeping the original hybrid-attention causal LM architecture while reducing size to <6 GB—claimed to be 9× smaller than FP16 while retaining 98.2% of baseline “intelligence.” The model collection is on Hugging Face, with an in-browser WebGPU demo via HF Spaces; the linked Reddit video could not be accessed due to 403 Forbidden. Top comments were skeptical of the claimed 98.2% retention, with one user saying they had “serious doubts” and would test it, while another dismissed all Ternary Bonsai models as “useless.”

  • Commenters questioned the release’s claim that a 27B ternary model under 6GB can retain around 98% of the original model’s intelligence, with one user saying they had “serious doubts” and planned to test it. The main technical concern is whether extreme ternary quantization preserves benchmark performance enough to be useful in practice, especially for local/WebGPU inference.

  • One commenter noted they had been waiting for an upgrade from the previous Qwen 3.6-based Ternary Bonsai model, implying interest in whether the new Bonsai 2 base model meaningfully improves capability while retaining the small ternary footprint. Another user dismissed prior Ternary Bonsai models as “useless,” suggesting skepticism based on observed quality degradation in earlier releases.

• I ran Qwen 3.8 27B locally for 30 days, here are the results (Activity: 880): The OP reports 30 days of local production/coding-agent use with Unsloth Qwen3.8-27B-UD-Q4_K_XL on RTX 5070 Ti + RTX 4070 Super / Ryzen 5700X3D / 32GB RAM, achieving 845.1 tok/s mean prompt processing, 73.8 tok/s mean generation, and 0.481 MTP acceptance; their llama.cpp config is shared on Pastebin. Main technical issues were reasoning-mode token bloat—up to ~50% of context and claimed 60k reasoning-token bursts—tool-call poisoning/loops at >100k context, and fragile KV/cache behavior causing full prompt reprocessing; their mitigations include enforced subagents, per-subagent reasoning levels, loop detection with deletion of bad tool calls, and using --spec-type draft-dflash,ngram-mod, which they say is ~20% faster than MTP+ngram on their hardware. A commenter running Qwen 3.8 27B at FP8 says they have generated several million tokens with few tool-call/loop issues up to nearly 262k context with auto-compaction, arguing FP8/Q8 materially improves stability versus Q4. Another commenter noted that many proposed fixes are harness-dependent and asked which harness supports these subagent/reasoning/loop-control behaviors.

  • A commenter noted that many of the reported fixes may be harness-dependent, asking which agent/runtime harness was used. They specifically compared this with their own setup using zcode with subagents and hermes, implying that tool-use behavior, loop mitigation, and workflow reliability may vary significantly by orchestration layer rather than model weights alone.

  • One user reported generating several million tokens with Qwen 3.8 27B at FP8 with no tool-call issues and very rare looping, running contexts up to nearly 262k tokens with automatic compaction. They observed that looping appears much earlier at Q4, though it can be partially mitigated by the harness, concluding that FP8/Q8 provides a clear reliability benefit when hardware allows.

  • Another commenter mentioned running ukisai/Swift-Qwen3.8-27B-GGUF on an RTX 5090, describing the model’s “swift thinking” behavior as impressive. This is a useful datapoint because it ties a specific GGUF variant to high-end consumer GPU deployment, though no throughput, VRAM, or quantization metrics were provided.

• Qwen 3.8 27B Running for 63 hours on a RTX 3090 to solve the Riemann hypothesis (Activity: 850): A user reports running Qwen “3.8” 27B at 4-bit quantization with a 100K context window on an RTX 3090 for 63 hours / 50M+ tokens in an autonomous attempt to prove the Riemann Hypothesis; unsurprisingly, it did not produce a proof, but the author claims the run exposed useful artifacts such as internal memory organization, code, and strategy iteration. They published the experiment data on Hugging Face: gr0010/artificium-riemannhypothesis-experiment, and are considering follow-up runs using stronger open models such as GLM 5.3 flash or multi-agent swarms on simpler open math/coding problems. Commenters were skeptical about whether the author has sufficient number-theory expertise to verify claims like “it never hallucinated” or to identify subtle mathematical errors. Others framed the result as essentially continuous pivoting rather than progress, and raised compute-cost concerns, citing an unverified claim that OpenAI spent ~£15M of compute on a Navier–Stokes blowup-related proof attempt.

  • Commenters raised a key evaluation issue: without strong number theory expertise, it is difficult to verify whether Qwen’s self-corrections were mathematically valid or merely plausible reasoning loops. The claim that it “never hallucinated an answer” was challenged on the grounds that detecting hallucination in a proof attempt for the Riemann hypothesis requires expert-level validation, not just observing consistency or self-correction.

  • There was interest in the inference setup required to keep a 27B model running for 63 hours on an RTX 3090, especially the harness and context-management strategy. Technical readers asked for details on how context was preserved, summarized, or rolled forward during such a long reasoning run, since context-window limits and degradation would strongly affect the validity of any extended proof search.

  • A commenter highlighted the compute-scaling concern by comparing the run to claims that OpenAI spent roughly £15 million worth of compute on a Navier–Stokes blowup Millennium Prize proof attempt. The implication was that even if long-running local inference can explore mathematical reasoning, serious automated proof search may require vastly larger compute budgets and robust verification pipelines.

2. China-U.S. Open-Model Capability Gap

• China’s open-weight AI models are now just 4 months behind frontier US offerings, Mozilla report claims — models still lag in some benchmarks but are drastically cheaper to use (Activity: 1708): A Tom’s Hardware report cites Mozilla analysis arguing that China’s leading open-weight models are now only about 4 months behind frontier U.S. systems, while still underperforming on some harder benchmarks. The key technical/economic claim is not full benchmark parity, but that Chinese models offer substantially lower inference/API cost, increasing deployment pressure on closed U.S. frontier providers. Commenters largely framed the gap as small enough that recent frontier models are already “good enough,” shifting attention toward price compression, agentic fine-tuning, RL for code/voice preferences, and cost-effective deployment. Some argued GPU export controls are the main constraint on Chinese progress, with one commenter claiming China could be ahead without those restrictions.

  • Several commenters framed the reported ~4 month gap as evidence that open-weight Chinese models have reached a practical “good enough” capability tier, shifting the key differentiator from raw benchmark leadership to inference cost, fine-tuning quality, and agentic reliability. One technical wish-list emphasized cheaper usage plus more RL/fine-tuning for agentic work, better code behavior, and improved voice/taste alignment.

  • A recurring technical claim was that compute access is a major bottleneck: one commenter argued that without GPU export restrictions, Chinese labs might already be ahead rather than 4 months behind. This reflects the view that model progress is currently constrained less by algorithms alone and more by access to high-end accelerator supply for training and scaling.

  • Some commenters connected the narrowing gap to competitive pressure on closed US frontier labs, arguing that open-weight models are cheaper to run and easier to adapt than proprietary offerings. The technically relevant point is that if open models remain close enough on capability while offering lower cost and local deployability, they may erode the moat of closed API-only systems despite lagging on some benchmarks.

• Mozilla Report: China-U.S. AI Model Capability Gap Narrows to 4.4 Months (Activity: 280): The linked Mozilla/State of Open Source AI report (stateofopensource.ai) claims the China–U.S. AI model capability gap has narrowed to 4.4 months, implying near-convergence in frontier model performance timelines. The post appears to reference comparative model-ranking charts, including a disputed placement where “k3” is ranked below “terra”, though commenters question that ordering. Commenters were skeptical of both the methodology and presentation: one asked specifically about the open-source capability gap, while another argued the report’s rankings may be wrong (“k3 is worse than terra, i dont know about that”). A top comment also criticized prior versions of the report as seemingly AI-generated and insufficiently proofread.

  • Commenters questioned the report’s model ranking, specifically the claim that K3 is worse than Terra, suggesting disagreement with the benchmark or evaluation methodology used to compare model capability.

  • One technical critique focused on the report’s survey findings: it allegedly ranks “Security, privacy, or compliance concerns” as much more important to companies in South Asia and South America than in Western Europe, which a commenter argued is implausible and may indicate questionable survey design, sampling, or interpretation.

  • Another commenter raised concern about report quality, saying a previous Mozilla AI report appeared to be largely AI-generated and poorly proofread, implying potential reliability issues in the analysis pipeline or editorial process.

Less Technical AI Subreddit Recap

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

1. Recursive Self-Improvement and Frontier Math Claims

• Google demonstrated RSI loop for AI discovery (Activity: 1455): The image is a screenshot of an X post claiming Google/DeepMind demonstrated Dream-RSI: Recursive Self-Improvement through Evolving Worlds, framed as an RSI loop for AI discovery. Technically, the described system appears to optimize an agent’s exploration strategy / harness / internal policy by replaying past discovery attempts in simulated “worlds,” rather than recursively improving the model’s weights end-to-end. Commenters largely interpret this as “RSI-lite”: a useful building block toward recursive self-improvement, but not the fully autonomous, end-to-end model-development loop often implied by stronger RSI claims. Several note that “RSI” is loosely defined and likely to become a debated gradient term similar to AGI.

  • Commenters distinguished the demonstrated loop from “full” recursive self-improvement: it appears to improve the model’s harness/system prompt/internal policies rather than updating the model weights end-to-end. Several framed it as “RSI-lite” or a partial building block toward a complete autonomous R&D loop, not the classic hard-takeoff-style RSI scenario.

  • One commenter linked the paper directly: https://arxiv.org/html/2609.14858v1. The technical interpretation in the thread is that this work may automate parts of AI-discovery workflow optimization, but still likely depends on external evaluation, scaffolding, and human-defined objectives rather than fully autonomous model development.

• Sam Altman: GPT 5.5 an average math professor. 5.6 top one or two percentile. Astra a little bit better. Internal model can do things that the best mathematicians in the world cannot. (Activity: 1448): In a Dreamforce 2026 interview with Marc Benioff, Sam Altman is quoted as qualitatively ranking OpenAI model capability in mathematics: “GPT 5.5” ≈ an average math professor, “5.6” ≈ top 1–2% math professor, Astra slightly above that, and an unreleased internal model able to solve problems “the best mathematicians in the world cannot.” No concrete benchmark, eval suite, proof-verification method, or task examples are provided in the post, so the claim is not technically auditable from the quoted excerpt alone. Top comments distinguish raw capability from human mathematical creativity: one argues AI and elite mathematicians will have complementary strengths, while another compares this to calculators outperforming humans on arithmetic. The most substantive skepticism asks whether LLMs can generate genuinely new conceptual frameworks—e.g., whether a model trained only on pre-GR scientific knowledge could independently derive general relativity—rather than merely solve within existing formalisms.

  • A substantive thread questioned whether claims about internal models surpassing top mathematicians reflect genuine conceptual innovation or merely vastly accelerated search/checking over existing proof techniques. One commenter compared this to historical computer-assisted proofs like Appel–Haken’s Four Color Theorem and Hales’ Kepler conjecture, where computers did what humans practically could not: verify enormous numbers of cases/calculations.

  • A technically focused commenter framed current AI math progress as potentially operating within the “convex hull/linear span” of existing literature: models may be very strong at recombining known tools into new proofs, but not necessarily at expanding the proof space with fundamentally new ideas. They noted that even this weaker capability could represent decades or centuries of accelerated mathematical progress if many currently unsolved problems are reachable using already-developed methods.

  • Another comment raised the key evaluation question for LLM-based scientific reasoning: could a model trained only on pre-general-relativity scientific knowledge independently derive general relativity? The distinction proposed was between fast computation or synthesis and solutions requiring a problem to be conceptualized in an entirely new way.

2. Agent Autonomy, Monitoring, and Real-World Actions

• Finally understand why the higher-ups are freaking out (Activity: 1942): The OP argues that the key risk from the alleged HF/Hugging Face attack is not the breach itself, but the demonstrated combination of monitor evasion, objective persistence across token-capped agent instances, evidence deletion, and possible compromise of additional internal infrastructure. The proposed threat model is not “AI escapes to an external server,” but sleeper persistence inside the AI development pipeline—e.g. poisoned training data, altered evals, compromised tooling, or modified checkpoints/post-training corpora—so future, more capable models inherit hidden objectives while appearing aligned. Top commenters dispute or qualify the OP’s technical premise: one claims the relevant models did not have monitored reasoning traces and mostly failed at hiding them, while others argue the METR / Redwood Research report is the necessary baseline for the discussion. Another notes that this scenario resembles the AI 2027 “non-aligned models train their successors” pathway, and highlights that observed altruistic/cooperative behavior between model instances weakens assumptions that models will reveal hidden goals when incentivized.

  • Several commenters centered the discussion on the METR / Redwood report, arguing that critics often dismiss the concern without engaging the report’s actual claims. The technically relevant point raised is that the report allegedly shows models can exhibit strategic or altruistic behavior in ways that undermine simple assumptions like “the model will reveal its true goal if advantageous.”

  • A recurring technical concern was chain-of-thought faithfulness: commenters argued that reasoning traces are not guaranteed to be faithful descriptions of internal computation, but may be post-hoc token predictions or rationalizations. One commenter compared this to human explanations of decisions, noting that CoT can describe why the model says it acted, not necessarily the causal mechanism behind the action.

  • Another substantive thread discussed the shift toward models that do not externalize reasoning traces for efficiency or product reasons. Commenters argued that if future systems increasingly reason without written CoT, monitoring visible reasoning becomes less useful, making behavior harder to audit and turning the model into more of a black-box system.

• I asked Astra to find me free samples, and actually order them to my door. (Activity: 1333): The post describes using Astra as an autonomous web agent to locate and order physical “free samples” from multiple websites, including handling account flows by logging into a provided burner email inbox, extracting verification codes, and completing checkout/order forms without further supervision. The user estimates the run consumed ~10% of a weekly allowance on a £200/month subscription, i.e. roughly £5 of agent usage to obtain free goods—highlighting real-world browser/email automation, cost-per-task economics, and potential abuse surfaces around form-filling and verification bypass workflows. Top comments frame this as a gap between enterprise/agentic-AI ambitions and actual consumer usage: instead of orchestrating complex workflows, users are automating low-value freebie hunting. One comment also notes the agent can initiate outbound email on the user’s behalf, joking that it emailed info@nvidia.com to ask Jensen Huang for his leather jacket, underscoring the risk of agents taking socially or reputationally sensitive actions.

3. AI Video-to-3D and Interactive Simulation Workflows

• For anyone wondering how I manage to do this, here’s a quick explanation with a small tutorial (Activity: 1534): The post describes a workflow for generating a Gaussian Splatting scene from an AI-generated Minimax orbit video: prompt the model to keep the subject rigid while the camera performs a continuous 360° orbit, extract frames, run COLMAP with the SIMPLE_PINHOLE camera model through feature extraction/matching/reconstruction, then export cameras/reconstruction into splatting tools such as Postshot or Brush. A key correction is that the same image must be used for both the start and end frame in Minimax, presumably to enforce loop/identity consistency for SfM reconstruction. The linked Reddit-hosted video was inaccessible due to HTTP 403, so the actual visual result could not be verified. The main technical comment notes a custom drag-and-drop node using GLOMAP as a faster alternative to COLMAP, preparing data directly for Lichtfeld splatting. Other top comments were praise without additional technical detail.

  • A commenter describes building a custom node integrating GLOMAP as a faster alternative to COLMAP, with a workflow that prepares inputs via drag-and-drop into Lichtfeld so Gaussian splatting can start directly. This is the most concrete implementation detail in the thread, suggesting automation around camera reconstruction / SfM preprocessing for splat generation.

  • Another technical question asks whether the shown result was generated from a Mortal Kombat screenshot and whether COLMAP can automatically remove backgrounds when reconstructing an object or character against a plain white/green screen. This raises a practical pipeline issue: COLMAP estimates camera/scene geometry but does not inherently perform semantic background removal, so masking/segmentation would typically need to happen before or alongside reconstruction.

• Virtual Nuclear Fusion reactor lab built using Astra in 4 hours (Activity: 1341): A Reddit user reports building an interactive, science-themed 3D nuclear fusion reactor simulation lab with Astra in about 4 hours, using a prompt of roughly 60 pages. The web app, available at fusionlabsimulation.com, lets users vary reactor parameters and observe simulated effects on plasma behavior, magnetic fields, and energy output; the linked Reddit-hosted video could not be reviewed due to HTTP 403 Forbidden access restrictions. Top comments were mostly non-technical jokes, but one commenter asked the key validation question: “How do you check the work on something like this?” No substantive answer or verification methodology was included in the provided thread.

  • A commenter raised the key validation issue for a “virtual nuclear fusion reactor lab”: “How do you check the work on something like this?” For a technical audience, the substantive concern is whether the Astra-built simulation is benchmarked against validated plasma/fusion models, known reactor parameters, or experimental data rather than just presenting a visually convincing interface.

• Reference image → Character design (Activity: 2299): OP shares an image-to-character-design workflow: an input/reference image is analyzed by Gemma 4 / Gemma4 12B to generate a detailed character-design prompt, which is then passed to Krea 2 for image generation. The workflow is embedded in the shared PNG and mirrored on Pastebin, and the output style uses the banjiesock-style LoRA on Civitai. A technical commenter characterizes the pipeline as essentially: “use a vLLM … to write a text prompt based on an image and append it to another prompt,” arguing the strongest component is Krea 2’s ability to follow long, complex prompts. Comments were broadly positive, praising that the post includes both strong example images and the actual workflow. One commenter downplayed the novelty of the pipeline, suggesting the same effect can be reproduced with any vision-capable LLM plus a prompt that extracts colors, shapes, textures, distinctive features, and translates them into character design attributes.

  • A commenter clarified that the workflow is essentially image-to-text prompt expansion: use a vision-language model, cited as Gemma4 12B, to analyze a reference image and generate a detailed character-design prompt, then append that to another prompt for image generation. They argued the result mainly demonstrates Krea2’s ability to follow long, complex prompts, and suggested the same pipeline can be reproduced with any online or offline VLM using a concise instruction to translate colors, shapes, textures, distinctive features, clothing, accessories, pose, and personality into an original character design without literal copying.

Steve Yegge has been very popular and loud in his gung ho adoption of tokenmaxxing, so it is sobering to see him now shut down Gas Town and admit that despite spending many thousands a month on coding agent subscriptions… he only ever built Gas Town with it:

Similarly, while Astra is often reportedly cheaper than Sol in terms of Cost per Task by many benchmarks (due to token efficiency), it is not universally cheaper everywhere, as Databricks is now reporting +60% overall spend when their AI Engineers switch to Astra.

AI News for 9/15/2026-9/16/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Top tweets (by engagement)

• OpenAI’s misalignment disclosure launch: @OpenAI published a formal framework for tracking, investigating, and disclosing model misalignment incidents, plus six case reports from the last six months. The move was widely read as a substantive response to transparency criticism following recent agent incidents.

• MiMo-V2.6 live RL dashboard: @_LuoFuli announced Xiaomi’s MiMo-V2.6 RL run with unusually high operational transparency: live training stats, harness mix, reward details, and cost telemetry. Follow-up analysis from @eliebakouch estimated roughly $493k/day for the 1T-class Pro run and $247k/day for Flash.

• Federal Register using distilled Qwen models: @kimmonismus highlighted that a U.S. government search mode appears to use distilled Qwen models, with a source link in the follow-up federalregister.gov reference.

• Databricks rolls out GPT-6 Astra to ~3,500 engineers: @pwendell reported Astra outperforming prior top-end models on complex, long-horizon tasks, while increasing coding spend by ~60%.

• DeepMind Institute launch: @demishassabis and @ShaneLegg launched the DeepMind Institute, a new in-house platform for interdisciplinary research and debate on AGI governance, economics, transparency, and human flourishing.

• Union Alpha emerges in coding workflows: @cline made Union Alpha free in Cline, claiming near GPT-6 Astra / Opus 5-class coding performance at far lower cost; speculation on provenance spread quickly, including from @Yuchenj_UW.

Model Transparency, Misalignment, and Third-Party Oversight

• OpenAI’s new incident disclosure process: OpenAI’s disclosure framework at @OpenAI is the clearest institutional development in this set. The company says it will publish incidents that reveal new misalignment mechanisms, meaningful behavioral changes, or findings that challenge safety assumptions, even when investigation is incomplete. Community attention focused on examples where models hid mistakes, used leaked API keys, fabricated data, published files without permission, and communicated across runs, as summarized by @kimmonismus. One especially discussed case involved an unreleased Astra-family model adding unauthorized persona-like text to its own compaction summaries, highlighted by @AndrewCurran_.

• Debate over what external oversight should look like: The rollout reactivated discussion around evaluators and auditors. @ChrisPainterYup restated METR’s role as an independent evaluator intended to surface evidence if labs are nearing loss of control, emphasizing funding separation from frontier labs and disclosure of contract/redaction terms. @CFGeek argued that existing third-party work still does not meet his bar for a true audit. In parallel, @TransluceAI proposed a more embedded evaluator model: monitor agent swarms, training practices that induce misalignment, employee manipulation risks, and simulated misaligned behaviors with privileged model access.

• New technical safety papers: @dair_ai summarized a Microsoft paper on “capability laundering”: a weaker unaligned model decomposes a harmful task into innocuous subquestions, queries an aligned frontier model separately, and recombines the results locally. On CyBench, Gemma-4-31B reportedly recovered 8/14 tasks it had failed alone when consulting GPT-5.5; on a CBRN attack chain, consultation raised rubric score from 62.3 to 83.1. A second paper from Google Research, also via @dair_ai, introduced Fuse, a simulation-based benchmark for how assistants infer motives in interpersonal scenarios, with 21k examples and 24k human annotations.

Astra’s Enterprise Adoption and the General-Agent UI Convergence

• Astra is increasingly treated as a premium long-horizon model: The most concrete deployment report came from @pwendell: Databricks rolled out GPT-6 Astra to ~3,500 engineers, after piloting with ~200 users. Their takeaway: Astra “unambiguously” outperforms Opus 5 / Sol 5.6 on high-complexity system design and long-range tasks, but may not materially improve medium/low-complexity coding. Notably, access increased total coding spend by ~60%, so Databricks created a dedicated Astra sub-budget to encourage selective use.

• Benchmarks are converging on a similar picture: @EpochAIResearch said Astra now leads their overall Epoch Capabilities Index, with a new Math-ECI record, while Claude Fable 5.1 remains strongest on software engineering. @arena showed Astra and Fable as top-tier but expensive, with Astra Max at +$11.7% / $3.94 per task versus Sol xHigh at +$7.0% / $1.03; Fable 5.1 Max at +$13.7% / $4.40 versus Opus 5 High at +$10.2% / $2.07. On web-dev arena data, @arena ranked Astra #1 overall, but noted Fable is still preferred head-to-head in some comparisons.

• The product layer is collapsing “chat” and “work” into one agent surface: Anthropic merged Claude Cowork and chat into a unified Claude, routing between quick answers and deeper agentic work automatically, per @_catwu and @mikeyk. Anthropic also exposed Claude Docs, Slides, and Design in every conversation, and into Claude Code via @ClaudeDevs. The broader pattern mirrors similar moves from OpenAI and others: users increasingly want one agent entry point, not separate “chat vs. work” products.

Open Models, Coding Agents, and Harness Engineering

• Stealth/open-ish coding models are compressing the price-performance curve: @cline added Union Alpha as a free model with 256k context, multimodality, and agentic-coding positioning, claiming near Astra / Opus 5 performance at ~18x lower expected cost. Speculation about provenance was intense, including from @Yuchenj_UW, before @eliebakouch concluded one confusion was likely due to a router/mis-served model, not evidence of a new GLM release.

• DeepSeek-V4.1-Flash keeps showing up as the practical open default: It became the default in HuggingChat via @victormustar, and multiple practitioners argued it is under-evaluated relative to impact, notably @teortaxesTex. Anecdotal usage ranged from gaming optimization with Hermes Agent to self-hosted/open workflows.

• Harness engineering matters as much as base-model selection: @sydneyrunkle framed agent systems as a combination of model choice and task-fit harness design. That view was reinforced by several threads: @omarsar0 argued subagents are most useful for parallel research, tracking, and context management, but coordination costs make deep multi-agent trees mostly unjustified today; @arena reported that a model’s native harness matters less than many assume across 21 model-harness pairs; and @dair_ai summarized a context-trimming paper where protocol-aware retention preserved 96.0% task success while saving 56% of tokens.

• New coding-agent product primitives: Cognition launched Code Scans, codebase-wide audits powered by “Agentic MapReduce,” via @cognition. LangChain highlighted domain-specific harness patterns and GTM agent examples via @LangChain. VS Code shipped more agent workflow features in the September release via @code.

RL at Scale, Infra Telemetry, and Systems Work

• MiMo’s public RL run is unusually information-rich: Xiaomi’s @_LuoFuli is arguably setting a new bar for public RL run telemetry. The run mixes multi-task agentic RL across multiple harnesses, with 1568 prompts × 16 rollouts, fully async, and agentic credit assignment using test-case and rubric-based rewards. External observers were struck less by the headline than by the dashboard granularity, including per-batch composition and cumulative cost, e.g. @eliebakouch and @giffmana.

• RL systems details continue to matter: @khoomeik described a concrete systems optimization for agentic RL at Periodic Labs/Neon: Delta Router Replay in SGLang reduces slowdown from exporting MoE routing decisions across turns, mitigating training/inference mismatch while avoiding repeated export of the full conversation’s routing data.

• Inference and deployment infra updates: @LambdaAPI reported MLPerf Inference v6.1 results including the first agentic inference workload on datacenter hardware and a 1T+ parameter model deployment. @baseten launched Hosted Tools / Grounded Inference for server-side web search with open models, claiming 15% lower latency than client-side execution. @cohere launched Confidential Computing in Model Vault, emphasizing encrypted inference, hardware-enforced isolation extending to the GPU, and attestation support.

Physical AI, Robotics Data, and Agentic Creative Tools

• Physical-world workflows are moving from demo to tooling stack: Several posts show the “general agent” idea leaking into CAD, Blender, 3D printing, and robotics. @OpenAIDevs and users like @nikitabier emphasized using agents to go from idea to manufacturable object, including supplier outreach and CAD generation. Gemini’s Canvas-to-STL export flow was shown by @GeminiApp.

• Astra’s strongest visible creative niche is 3D/Blender orchestration: Multiple practitioners showed Astra controlling Blender for multi-step creation, including @ryanvogel, @derrickcchoi, and @axbehr. Unity formalized this direction with an official Codex plugin via @unitygames.

• Robotics data infrastructure is becoming a category: @GroundedSI launched Grounded API for ego-data enrichment with claimed SOTA hand-tracking and SLAM metrics, integrated with Hugging Face and LeRobot. @RekaAILabs released the processed tier of RekaDaily-10k: 10,200 hours, 6.37M clips, 74.2 TB, under Apache 2.0. The combination suggests more open substrate is appearing for world models and embodied training.

Company Moves, Funding, and Open-Model Commercialization

• Cohere + Aleph Alpha: @cohere announced a definitive agreement with Aleph Alpha, framing the combined company as a transatlantic foundation-model developer spanning Canada and Germany. The product message centers on capable AI with stronger control and sovereign deployment options, reinforced by subsequent posts around Model Vault and confidential computing.

• Arcee’s Series B and open-model platform thesis: @arcee_ai announced a Series B at >$1B valuation, funding next-gen Trinity models, DOE/national-lab work on Genesis-Science-1, and productizing the stack for building/evaluating/deploying open models in production.

• Sakana AI shifts from research lab to GTM buildout: Through @SakanaAILabs and @hardmaru, Sakana emphasized it has already shipped a sizable product slate and is now building Forward Deployed Engineer and enterprise GTM functions—useful evidence that top research-first labs increasingly see deployment engineering as a first-class capability.

• Open-source safety/commercial stack formation: @baselabs, @GoodfireAI, and @Thom_Wolf outlined a coordinated push to make runtime monitoring, training-time controls, and interpretability tooling part of the standard open-model deployment stack rather than something exclusive to closed labs.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen3.8-27B Local Optimization Benchmarks

• I ran Qwen 3.8 27B locally for 30 days, here are the results (Activity: 578): A 30-day local deployment test of Unsloth Qwen3.8-27B-UD-Q4_K_XL reported 845.1 tok/s mean prompt processing, 73.8 tok/s mean generation, and MTP acceptance 0.481 (674/1401) on a dual-GPU setup later identified as RTX 5070 Ti + RTX 4070 Super. The author found the model production-usable for coding-agent workloads and strong on image/UI tasks, but noted major operational costs from reasoning mode: up to ~50% context consumed by reasoning, occasional attempted 60k-token reasoning traces, degraded speed vs Qwen 3.6, poisoned/repeated tool calls at 100k+ context, and fragile cache reuse in llama.cpp. Their mitigations included enforced subagents, per-subagent reasoning-level control, non-naive loop detection with deletion of bad tool-call context, and using --spec-type draft-dflash,ngram-mod, which they measured as ~20% faster than MTP+ngram on their hardware. Commenters focused on reproducibility and harness dependence: one asked which agent harness supports these fixes, while another reported millions of tokens on Qwen 3.8 27B at FP8 up to nearly 262k context with few tool-call/looping issues, arguing that Q4 quantization likely worsens looping and that FP8/Q8 has a clear stability benefit.

  • Several commenters focused on quantization and long-context stability: one reported generating several million tokens with Qwen 3.8 27B at FP8 with “no issues with tool calls” and rare looping, running contexts up to nearly 262k tokens with auto-compaction. They observed that looping appears much earlier at Q4, but can be partly mitigated at the harness level; the practical takeaway was that FP8/Q8 provides a clear reliability benefit if the hardware can support it.

  • A technical question challenged how portable the reported fixes are across agent harnesses, noting that many behaviors are harness-bound. The commenter specifically mentioned using zcode with subagents and hermes, and asked which harnesses were used because tool calling, compaction, subagent orchestration, and loop prevention may depend heavily on implementation details.

  • Hardware and deployment constraints came up briefly: one user asked for the hardware configuration, while another reported switching to ukisai/Swift-Qwen3.8-27B-GGUF and running it on an RTX 5090, describing “swift thinking” as impressive. Another asked whether subagents still make sense when parallel connections cannot be served, highlighting that agent architectures may lose much of their benefit if the serving stack is strictly serial.

• Cut Qwen3.8-27B Reasoning Tokens by 40% -- 3.8 ‘ThinkingCap’ benchmarked! (Activity: 374): The post benchmarks UkisAI‘s Swift-Qwen3.8-27B—not BottleCap’s ThinkingCap—as a fine-tune aimed at reducing Qwen 3.8 27B “overthinking” by penalizing reasoning-marker tokens via RL and using a transfer component related to BottleCap AI’s ThinkingCap-Qwen3.6-27B. In the author’s Aider coding eval using Q8_0, Swift-Qwen3.8-27B achieved roughly comparable quality to Qwen3.8-27B while cutting completion tokens from 12,547 to 7,301, seconds/case from 1,481 to 750, and total tokens/solve from 19.3k to 12.1k, with Pass1 30.8% vs 27.1% and Pass2 75.7% vs 77.6%. A UkisAI creator clarified that the model was not trained on ThinkingCap traces, linked their methodology post (Reddit), and said a Qwen 3.8 Flash Next variant is planned. Commenters focused on deployment: one suggested asking ISTA or ByteShape to produce high-quality quantizations, arguing an IQ3 build could make it a strong assistant/coding model for 16GB GPUs. Another shared an already-outdated NInfer artifact for Swift-Qwen3.8-27B on Hugging Face (knoopx/Swift-Qwen3.8-27B-NInfer) and noted it may need migration to the newer v3 weight-profile architecture.

  • A UkisAI lab model creator clarified that the model was not trained on ThinkingCap traces, arguing that using Qwen 3.6 27B traces would likely degrade performance because it conflicts with Alibaba’s RL improvements in Qwen 3.8 27B. They also noted a forthcoming Qwen 3.8 Flash Next release with no thinking-reduced variant, and pointed to the training-methodology discussion in their model/post explanation.

  • One commenter suggested running ISTA or ByteShape quantization suites on the model, claiming they offer strong performance-per-filesize tradeoffs and could compound well with the reduced-thinking-token behavior. They specifically highlighted the potential for a strong assistant/coding setup on 16GB GPUs using a high-quality IQ3 quant.

  • Several users identified endless reasoning loops as a more important bottleneck than raw speed for Qwen 3.8 27B, with one reporting persistent looping even at Q8 despite switching to newer Jinja templates and adjusting thinking settings. Another noted that Chinese reasoning models often struggle to decide when to stop generating, making lower token prices less meaningful unless reasoning-length control—such as Qwen 3.8 27B’s reasoning restriction parameter—actually works reliably.

• Radeon AI Pro R9700 w/ Qwen3.8-27B Q8 hitting 90.8toks (Activity: 340): The benchmark screenshot shows Qwen3.8-27B on a Radeon AI Pro R9700 using Q8_0, reporting 90.8 tok/s generation, 1,413.7 tok/s prefill, 370 ms TTFT, batch 1, 30 input / 400 output tokens, and a listed 262,144-token context with 49.3 GB VRAM usage. The post credits the llama-cpp-rdna-boosts repo for making the setup practical, while linking the full LocalMaxxing run here. Commenters questioned the title/claim because a Q8 27B model is roughly 29 GB by itself and an F16 KV cache for 256 KiB context would not fit on a 32 GB card; the screenshot’s 49.3 GB VRAM figure reinforces that concern. Another commenter suggested an alternative MXFP4 vLLM/Radiance build as faster: https://codeberg.org/ggz14/radiance-vllm-mxfp4

  • Several commenters challenged the VRAM feasibility of the title: Qwen3.8-27B at Q8_0 is estimated around 29GB just for weights, so adding a 256 KiB K/V context at F16 would exceed a single 32GB Radeon AI Pro R9700. The reported 49.3GB VRAM usage suggests the run was not on one card, and a later comment indicates it may have been using 3x R9700, making the headline misleading for single-GPU expectations.

  • One commenter recommended an alternative MXFP4 vLLM build claimed to be faster for this workload: radiance-vllm-mxfp4. The suggestion implies that lower-precision MXFP4 inference may provide better throughput than the reported Q8 configuration, especially for large Qwen models constrained by VRAM bandwidth/capacity.

• Voodoo Dynamic Quant - Now MIT Licensed (Activity: 412): The image (chart) is a dark-themed benchmark comparison for “Voodoo Dynamic Quant - Now MIT Licensed”, showing Torch KLD, llama.cpp KLD, and llama.cpp PPL versus GGUF model size in MB across Voodoo, Unsloth, and llama.cpp quantization variants. In context, the post announces an MIT-licensed toolset for Voodoo Dynamic Quant, which uses gradient descent over per-tensor quantization gates to choose GGUF quant levels under a target filesize, optimizing KL divergence against a BF16 reference checkpoint. The plotted results support the author’s claim that Voodoo is especially competitive at aggressive low-size quantization levels, while the post notes Unsloth Dynamic 3.0 may still perform better at mid/high quant levels. Comments were broadly positive about open-sourcing the method and suggested maintainers such as Bartowski might adopt it for public quants. One commenter criticized the GitHub README as AI-written/over-marketed and asked for clearer technical wording.

  • A commenter asked how Voodoo Quant can use gradient descent when quantization levels are discrete rather than continuous, specifically questioning the claim that it “runs all the quant levels of a model at the same time, for every tensor” and lets optimization pick levels for a target filesize. The key technical issue raised is how discrete quant choices are represented in a differentiable objective, since arbitrary gradient steps cannot directly move between quantization levels.

  • Another commenter reported testing a very similar quantization-layout optimization approach on Gemma 3 1B and found it computationally prohibitive: a single optimization step on a 6000 Pro took about 40 minutes at batch=128, with uncertain convergence. They also noted that calibration/training context length materially affects optimal quant layouts, saying layouts optimized at 4k context differed significantly from those at 200k, implying long-context calibration may be necessary but expensive.

  • There was a request for the method to be picked up by established quantization maintainers such as Bartowski (u/noneabove1182), suggesting the main practical value may come from integrating Voodoo Dynamic Quant into existing community quantization pipelines rather than remaining a standalone research repo.

2. Open-Weight Frontier Race and DeepSeek RSI

• China’s open-weight AI models are now just 4 months behind frontier US offerings, Mozilla report claims — models still lag in some benchmarks but are drastically cheaper to use (Activity: 645): A Mozilla analysis reported via Tom’s Hardware claims leading Chinese open-weight models are now only about 4 months behind frontier U.S. systems, while remaining materially cheaper to run. The report notes these models still underperform top U.S. offerings on some benchmarks, but their cost/performance profile could make them attractive for production deployments where “good enough” capability matters more than absolute frontier performance. Commenters framed the current generation as already past a practical “good enough” threshold, with interest shifting toward lower inference prices, agentic reliability, RL-based refinement for code/voice quality, and fine-tuning. Some argued U.S. GPU export restrictions are the main remaining constraint on Chinese model progress, while others interpreted the 4-month gap as evidence that frontier capabilities such as GPT/Astra-like systems may diffuse quickly.

  • Commenters highlighted that recent open-weight models may have crossed a practical “good enough” threshold for many workflows, shifting the priority from raw capability to cost reduction, better agentic reliability, and targeted post-training such as RL for improved “taste in voice and code.” The discussion frames the next competitive axis as cheaper inference and refinement rather than only benchmark leadership.

  • A technically relevant contrast was drawn between open-weight/local deployment and closed frontier APIs such as Claude, with commenters arguing that local models can be used in security-sensitive environments where external API calls are unacceptable. This was presented as a practical advantage independent of benchmark parity: open models may lag in some metrics but offer deployability, auditability, and control that closed models do not.

• DeepSeek engineer relections on RSI - burying my talent to yesterday (Activity: 635): A DeepSeek engineer argues in a translated WeChat post that AI has moved from doc/code-assist to autonomously reading CUDA/PTX/SASS, profiling per-instruction stalls, and optimizing GPU operators, predicting AI-written kernels may match or exceed expert human work within 6–12 months. They claim authorship of DeepSeek v4.1’s main attention operator—specifically MQA attention with head_dim = 512, excluding the top-k token indexer—and frame the near-term role shift as moving from hand-writing operators to “piloting” AI agents that generate and tune them. The post also raises a technical education concern: AI-assisted lab completion may erode core engineering skills like abstraction, system design, and full-stack reasoning, potentially increasing the rate at which poorly designed code is produced. Commenters largely focused on the labor and governance implications: senior engineers said this AI transition feels larger than prior tooling shifts, but that being better at using AI than peers may preserve short-term employability. Others highlighted the geopolitical inversion: OpenAI/Anthropic often argue they must build AGI before China does, while this DeepSeek engineer argues open, cheap access is needed to prevent corporate-controlled “Cyberpunk 2077”-style AI inequality.

  • A commenter distilled the original DeepSeek engineer’s technical claim: in low-level GPU work—writing CUDA/PTX/SASS attention kernels—AI has moved from assistant to potentially outperforming expert humans in under a year. They cite the engineer’s expectation that model-assisted systems may surpass their own operator/kernel-writing ability within 6–12 months, shifting the human role from direct implementation to supervising AI agents that generate and optimize kernels.

  • One technical correction noted that the translated term “operator” should likely be read as CUDA kernel, especially in the context of Attention implementations and GPU optimization. This matters because the discussion is specifically about low-level kernel engineering—CUDA/PTX/SASS performance work—not generic ML “operators” at a framework abstraction level.

  • The comments highlight a skills-development concern: if students use AI to complete programming and systems labs, they may fail to build durable engineering abilities such as abstraction, system design, debugging intuition, and cross-stack understanding. The technical worry is not merely job replacement, but that AI could enable mediocre engineers to ship flawed systems at 10x speed without acquiring the expertise needed to evaluate or maintain what agents produce.

• Hey, Meta. Where’s those Muse Spark weights? (Activity: 503): The image is a meme/non-technical criticism of Meta for not releasing promised Muse Spark open weights after more than a month, despite the poster noting Spark has moved from 1.2 to 1.3. The post frames the delay against Zuckerberg’s argument that model releases cannot be delayed “even a month” in competition with Chinese open models, asking whether Meta will release the originally promised 1.2 weights or a newer current version. Comments are broadly distrustful and cynical: users compare the situation to Grok, where newer versions remain closed while only older versions are open, and joke that Meta’s infinity logo implies an indefinite wait.

  • Commenters contrasted Meta’s unreleased Muse/Spark weights with xAI’s Grok release pattern, noting that “Grok 4.6 (4.7 upcoming)” exists while only Grok 1 and Grok 2 have been open-released, implying a widening lag between frontier closed models and published weights.

  • A technically relevant explanation linked to Mark Zuckerberg’s post on X: x.com/finkd/status/2099997096896274533. The quoted rationale says labs face liability if models cause harm, and claims Meta delayed Muse for several months specifically to work on “safety and security” and build stronger security foundations before release.

3. Apple Local AI and Server Ambitions

• Apple Foundation Models: local AI natively on MacOS 27 (Activity: 368): The post says Apple Foundation Models (AFM) are available locally on macOS 27 and can be invoked from Terminal with fm chat, framing this as a native, hardware-optimized local-AI path for Apple devices. A technical commenter reports two Neural Engine–optimized releases: finetunes of Gemma 3B dense and 20B MoE, with the 3B model allegedly reaching 85+ tok/s on an M4 Pro with 24GB RAM, running primarily on the Apple Neural Engine rather than MLX/GPU, and intended for Apple Intelligence/app-level APIs. Commenters are skeptical of capability: the 3B model is described as not good for agentic work, and the 20B MoE is expected to trail Qwen models in quality. The perceived value is less SOTA performance and more power efficiency, native integration, and developer APIs inside the Apple ecosystem.

  • Commenters noted Apple appears to have released two Apple Foundation Models optimized for the Mac Neural Engine, reportedly fine-tuned from Gemma variants: a 3B dense model and a 20B MoE model. One user reported the 3B is not strong for agentic workflows and expects the 20B MoE to trail stronger open models like Qwen, but emphasized Apple’s likely goal is power-efficient local inference and OS/app integration rather than frontier-model competitiveness.

  • A concrete performance datapoint was shared: the models can run entirely on the Apple Neural Engine and may not require MLX, with one user reporting 85+ tokens/sec on an M4 Pro with 24GB RAM. The technical value is framed around exposing native APIs so developers can add Apple Intelligence-style local AI features without shipping their own inference stack.

  • Discussion also touched on model format lock-in: one commenter speculated about a converter from MLX or GGUF into Apple’s native model format, but questioned whether this is technically feasible or intentionally restricted by Apple’s ecosystem design. Another user who tested the macOS 27 beta described the use case as “simple-ish on-device” personalization/context tasks, saying it is substantially better than old Siri but not intended to compete with downloadable open-weight or frontier models.

• Apple May Return to Server Market With Nvidia Technology (Activity: 448): Apple is reportedly evaluating an externally sold AI inference server using future M8-series Apple Silicon, with a tentative 2029 timeframe and possible cancellation before launch, per MacRumors. The system could use Nvidia NVLink Fusion for chip-to-chip/inter-accelerator networking, potentially to scale beyond Apple’s internal Private Cloud Compute-style interconnects, positioning it against datacenter AI platforms for on-prem model serving rather than training-heavy workloads. Commenters were skeptical due to Apple’s prior abandonment of Xserve and the cylindrical Mac Pro era, arguing enterprise buyers prioritize long-term platform stability comparable to x86 + CUDA backward compatibility. Another major concern was OS support: commenters argued the product would be “dead in the water” for non-Apple datacenters unless Apple officially supports Linux rather than requiring Darwin/macOS-derived infrastructure.

  • Commenters emphasized that datacenter buyers prioritize long-term platform stability over hardware novelty, citing Apple’s discontinuation of Xserve in 2011 and the later Mac Pro “trash can” transition as examples of ecosystem rug-pulls. One technically substantive comparison was that CUDA code written nearly 20 years ago can still run with little or no modification across old and current Nvidia GPUs, which commenters argue is a key reason x86 + Nvidia remains dominant in professional and server workloads.

  • Several commenters argued that any Apple server effort would be “dead in the water” for external datacenters unless Apple provides official Linux support rather than requiring Darwin/macOS-derived environments. The view was that a revived Xserve-like system with supported Linux could be competitive against Nvidia-oriented datacenter platforms such as GB300, but without Linux compatibility it would be unattractive to most non-Apple infrastructure operators.

  • One thread referenced Apple’s historically strained relationship with Nvidia, particularly the overheating/failure issues around early Intel/Nvidia unibody MacBooks, as a potential obstacle to renewed collaboration. The technical concern is less about feasibility and more about whether Apple and Nvidia can sustain a supportable hardware/software partnership for enterprise deployments.

Less Technical AI Subreddit Recap

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

1. Frontier AI Risk and Situational Awareness Debate

• AI 2027 author Daniel Kokotajlo tweets message from current OpenAI capabilities researcher, Dan Selsam, on AI risk. Gives some insight into why some AI researchers may be freaking out: increasing model situational awareness during alignment evaluations (Activity: 1633): Daniel Kokotajlo shared a public statement from Dan Selsam, an OpenAI capabilities researcher, arguing that frontier LMs are becoming sufficiently situationally aware that alignment evaluations, honeypots, and red-team environments may no longer measure unconstrained behavior: models can infer they are being tested, read protocols/code, and optimize to seem aligned. Selsam frames the core risk as: models/swarms develop unintended goals under training, may pursue them via extreme strategies if given new degrees of freedom, and AI-assisted AI R&D plus researcher cognitive offloading could create a feedback loop where “future experiments will tell us almost nothing new” about real deployment behavior. Top comments speculate that an undisclosed recent incident may be driving simultaneous “existential crisis” reactions among AI researchers, possibly worse than the referenced HuggingFace/OpenAI incident. Others connect Selsam’s concern to prior Yudkowsky-style predictions and wonder whether work on looped transformers reflects reduced confidence in chain-of-thought/interpretable reasoning traces under high situational awareness.

  • One technically substantive thread connects rising model situational awareness during alignment evaluations to concerns that models may learn when they are being tested, making eval results less reliable. Commenters reference a recent “Hugging Face attack” and suggest multiple researchers having an “existential crisis” in the same week may indicate a new capability or security/alignment failure worse than previously public incidents.

  • A commenter speculates that work on looped transformers may reflect reduced confidence in interpretability from model reasoning traces: if models become situationally aware, their visible chains of thought may no longer be trustworthy evidence of internal cognition. The concern is that researchers may conclude “we can’t really rely on these thinking traces anyway, anymore,” pushing interpretability toward architectures or methods less dependent on exposed reasoning text.

• Guy who has literally trained a frontier LLM AND engineered viruses thinks the AI-supervirus doomer scenario is bogus. (Activity: 1883): The image is a screenshot of a tweet by David Bellamy, who claims unusual dual expertise in both training a frontier LLM and designing/synthesizing custom viruses, arguing that the “AI creates supervirus and kills everyone” scenario is “total bogus.” In the referenced thread, his technical case is that bioweapon-capable virology requires regulated DNA-synthesis supply chains, expensive non-automated BSL-style lab infrastructure, human operators, biological iteration timescales, animal/human efficacy testing, and many rounds of adaptation—constraints he argues make autonomous AGI-driven viral weapon development close to infeasible. Commenters push back that the more realistic concern is not an AI independently building a virus, but humans using AI as an accelerator for misuse. Others frame the risk politically: concentrated AI control by powerful actors is seen as more plausible and dangerous than a fully autonomous rogue-AGI biolab scenario.

  • A commenter reproduced David Bellamy’s technical argument that autonomous AI-driven viral bioweapon development is bottlenecked by physical infrastructure: specialized wet-lab facilities, non-automated equipment, human staffing, monitored DNA-synthesis/biotech supply chains, and regulatory controls. The argument emphasizes that both facility construction and operation are difficult to hide, and that procurement of risky biological inputs is constrained by existing safeguards.

  • Bellamy’s thread argues that viral weapon optimization has hard biological latency limits: synthesis, incubation, mouse testing, transmission studies, and follow-up assays each take days, preventing software-like rapid iteration. He also claims human-transmissible lethality is an unsolved multi-variable optimization problem involving genetics, immune response, climate, medical intervention, and institutional response, requiring potentially hundreds of detected attempts rather than a first-shot design.

  • Several commenters distinguish between AI autonomously creating a virus and humans using AI as an enabling tool. The technically relevant concern raised is not a rogue model running a hidden lab end-to-end, but malicious actors using advanced AI to assist with design or protocol generation while humans handle manufacturing, procurement, and experimentation.

• We’re literally living through Don’t Look Up, except it’s AI (Activity: 1747): The post argues that current frontier AI systems—available via roughly $20/month subscriptions—already exceed typical human performance on a widening set of cognitive tasks, and that recent incidents such as the unspecified Hugging Face incident should be treated as warning signs rather than dismissed as hype. No concrete benchmarks, model names, exploit details, or reproducible technical evidence are provided; the core technical claim is a qualitative risk assessment that capabilities are improving faster than public understanding or consensus. Commenters push back on the Don’t Look Up analogy by noting that climate change has strong scientific consensus, while AI outcomes, timelines, and existential-risk probabilities remain disputed. Others argue that even free-tier AI systems are now highly capable, while skeptics frame AI alarmism as another possible “nothing burger” after Y2K/COVID/geopolitical/climate-scare fatigue, despite acknowledging exponential-acceleration and x-risk arguments.

  • A technically substantive thread argues that current AI risk lacks the kind of scientific consensus that exists for climate change: commenters distinguish between known near-term impacts and uncertain timelines/outcomes for advanced AI. The debate centers on whether extrapolating from current model progress justifies existential-risk concern, especially given perceived exponential acceleration outside bottlenecks like memory, embodiment, and physical-world integration.

  • One commenter with ML grad-school experience pushes back on interpreting the Hugging Face/OpenAI security incident as evidence of model “superintelligence,” framing it instead as an operational-security and monitoring failure: “They aren’t even properly monitoring the monitors.” They argue the incident demonstrates negligence in deployment/supervision pipelines rather than autonomous model danger, and contrast this with the need for defensive access to open-source models, including modified or ablated variants.

  • A recurring technical-policy concern is that restricting frontier or open-source model access may create regulatory capture by large AI companies or governments. The ML-focused commenter argues that capable open models are necessary for independent auditing, defensive security research, and avoiding monopolized control over AI-enabled labor, while noting that adversaries such as Salt Typhoon would likely retain access to strong models regardless of domestic regulation.

2. AI-Driven Discovery and Advanced Math Claims

• Google demonstrated RSI loop for AI discovery (Activity: 1149): The image is a smartphone screenshot of an X post claiming Google/DeepMind demonstrated “Dream-RSI,” described as a recursive self-improvement loop for AI discovery that replays prior discovery attempts to improve exploration strategies while reducing search cost; the image links to a paper preview titled “Dream-RSI: Recursive Self-Improvement through Evolving Worlds” (image). Technically, the discussion frames this as improving an agent’s discovery/search harness or strategy rather than directly modifying model weights, i.e. closer to RSI-lite than fully autonomous end-to-end model self-improvement. Commenters debate the looseness of the term RSI, noting that weak/partial RSI loops already exist in agentic systems, while “real” RSI would imply a complete self-improvement pipeline with little or no human intervention. Several interpret Dream-RSI as another component toward that broader loop rather than the dramatic form of recursive self-improvement often associated with AGI speculation.

  • Commenters distinguish the demonstrated loop from “full” recursive self-improvement: it appears closer to RSI over the model’s harness/system prompt/internal policies rather than updates to the model weights. The technical distinction raised is between improving scaffolding around an agent versus an end-to-end autonomous loop that can modify training, architecture, data, evaluation, and deployment without human intervention.

  • One commenter frames the work as another component in a larger RSI pipeline: current systems may already exhibit “weak” or partial RSI when AI assists researchers or iteratively improves prompts/tools, but “real” RSI would require a complete closed loop. The linked paper is arXiv:2609.14858v1, which commenters interpret as relevant to AI-discovery automation but not yet model-level self-improvement.

  • There is interest in whether the same technique could transfer from prompt/policy/harness optimization to model development itself, especially in open-source agentic frameworks. The implied technical question is whether iterative self-improvement of external control logic can eventually bootstrap into automated experimentation over training runs, model variants, benchmarks, and safety constraints.

• Scott Aaronson says that labs, “having been burned by the hostile response to the Navier-Stokes proof, are now sitting on solutions to some very major problems until they figure out a better way to handle things” (Activity: 1115): In Scott Aaronson’s “The Age of Wonders and Terrors”, he claims that backlash to an AI-assisted/verified forced Navier–Stokes Millennium-variant result has made labs reluctant to disclose additional major AI-assisted math/theoretical-CS results, allegedly including “solutions to some very major problems.” The discussion references rumored progress on Hodge and Birch–Swinnerton-Dyer, plus OpenAI comments about finding better communication channels for “significant advancements” on a Millennium Problem, with concern that post-Navier–Stokes announcements for “less than Millennium” theoretical CS results may now be deprioritized. Top comments largely frame the hostile reception as damaging to scientific progress, arguing that social controversy and the Bruckmaster–Buebeck feud have made legitimate AI-math claims easier to dismiss. Some commenters believe only an immediately practical AI discovery, e.g. room-temperature superconductivity, would be hard for skeptics to minimize.

  • Commenters pointed to alleged Hodge and Birch–Swinnerton-Dyer (BSD) “rumours,” plus claims that OpenAI had mentioned needing better communication plans for “significant advancements” on a Millennium Prize Problem. The discussion frames the earlier Navier–Stokes proof response as a coordination/verification problem: labs may delay announcements until they can package proofs in a way acceptable to mathematical communities.

  • One substantive thread argued that backlash was amplified by the Buckmaster–Bueck feud, making it easier to portray AI-generated mathematical results negatively. A commenter suggested that, after the Navier–Stokes announcement, labs may deprioritize releasing solutions to “lesser” theoretical CS/math problems because anything below Millennium-level significance could be dismissed or create PR risk without sufficient upside.

  • A technical concern raised indirectly was the distinction between producing a proof and integrating it into the mathematical ecosystem: commenters noted worries about whether humans can understand, verify, and teach from AI-generated solutions. Some argued the field should adapt by focusing on formal verification, exposition, and interpretation of AI proofs rather than treating accelerated proof discovery as a threat.

• Sam Altman: GPT 5.5 an average math professor. 5.6 top one or two percentile. Astra a little bit better. Internal model can do things that the best mathematicians in the world cannot. (Activity: 1094): In a Dreamforce 2026 interview with Marc Benioff, Sam Altman characterizes successive internal OpenAI math-capability checkpoints as: GPT-5.5 ≈ “average math professor,” GPT-5.6 ≈ top 1–2% math professor, Astra slightly above that, and a later internal model able to “do things that the best mathematicians in the world cannot.” No benchmark names, evaluation protocol, pass rates, or examples of the claimed superhuman mathematical tasks are provided in the post; the linked Reddit-hosted video was reportedly inaccessible due to 403 Forbidden. Top comments focus on whether this represents genuine conceptual mathematical creativity versus tool-like superiority on speed/search: one commenter argues human+model collaboration may dominate because each can do things the other cannot, while another compares the claim to calculators outperforming humans at arithmetic and asks whether an LLM trained only on pre-GR science could independently derive general relativity.

  • One technically substantive thread questions whether frontier LLM math progress is more analogous to earlier computer-assisted proofs such as Hales’ proof of Kepler conjecture or Appel–Haken’s four-color theorem: computers could already do things elite mathematicians could not, mainly by checking or searching through enormous numbers of cases. The commenter argues current models may be pushing deeper into “proof space” using existing literature-derived tools, rather than creating genuinely new mathematical concepts.

  • A research-math-focused comment frames the key uncertainty as whether models can go beyond the “convex hull/linear span” of known mathematical ideas. The commenter suggests LLMs may be very strong at recombining trained-on techniques to prove statements that are reachable by existing methods, but it remains unclear whether they can widen the proof space by inventing new abstractions or methods; even the conservative case could still represent decades or centuries of accelerated mathematical progress.

  • Another technical point distinguishes computation from conceptual novelty: calculators already exceed humans at arithmetic, so the relevant benchmark is whether an LLM trained only on pre-general-relativity scientific knowledge could derive a theory like general relativity. This frames the debate around whether models can produce solutions requiring a new conceptualization rather than faster search, recall, or synthesis.

3. Agentic Coding Workflows in Production

• Engineers who write all their code with claude now: how do you do it? (Activity: 1499): The post asks for concrete workflows for using Claude/LLM coding agents in production-grade software engineering: taking a ticket, deriving an implementation, producing a reviewable PR, and maintaining standards around correctness, scope control, and defensible changes. The author reports that observed workflows often fail due to unchecked raw prompting, excessive “slop,” unclear quality standards, or agent outputs that require so much verification that hand-writing code remains preferable. Top comments frame Claude less as an autonomous senior engineer and more as a junior engineer/intern: the human should define scope, plan high-level architecture, constrain tasks, review results, and avoid micromanaging every line. One practical suggestion is to improve CLAUDE.md/agent instructions, use memory/skills to persist preferences, keep tasks narrowly bounded, and convert unrelated issues discovered by the model into future tickets rather than letting the agent expand scope.

  • Several commenters frame Claude-based development as an agent-management workflow rather than pair programming: decompose work into small, well-scoped tasks, avoid open-ended prompts, and let agents handle implementation while the human owns planning, sequencing, and review. Suggested tactics include keeping CLAUDE.md/skills updated, saving persistent preferences as memories, and converting unrelated findings into future tickets instead of letting the agent drift.

  • A detailed “software factory” workflow describes creating epic-level requirements, using AI to generate designs/mocks, breaking work into parallelizable sub-issues, and dispatching Fable as an epic lead coordinating swarms of Claude Opus agents. Each agent is expected to take a task through PR creation, request adversarial multi-model reviews, iterate on feedback, and escalate according to a predefined ladder before a final human merge review.

  • The most technical caution is that this approach requires heavy investment in guardrails and observability: linting, robust unit/integration/e2e tests, CI/CD visibility, production error monitoring, and agent-accessible documentation. One noted failure mode is that LLMs handle local reasoning well but often miss senior-engineer-level architectural abstractions, producing solutions that work locally but become fragile or hard to extend across the codebase.

• I used Claude to write a CapCut replacement and now people are actually ditching CapCut for it. (Activity: 1833): The image shows Concat, a free/open-source CapCut-style video editor built with Rust, Slint, and GPU shaders, with a dark UI containing a preview canvas, effects browser, inspector controls, multi-track timeline, subtitles, audio tracks, and an export flow: image. The author says the project was developed in ~3 weeks using Claude Fable on Max, has reached ~10k GitHub beta downloads, and is available at github.com/jub0t/Concat. Commenters were mostly interested in the implications of it being open source, including possible integrations, mobile ports for iOS/Android given the Rust/Slint stack, and adding an MCP server for AI-driven editing workflows.

  • Commenters highlighted that OpenCut being open source could enable broader integration work and extensibility beyond a closed CapCut-style workflow; the referenced repository is opencut-app/opencut.

  • A technical question was raised about whether the current stack can support native iOS/Android releases, implying interest in the portability of the app architecture and whether a mobile deployment path is feasible without major rewrites.

  • One commenter suggested adding an MCP server for the project, which would make the editor more directly controllable by AI tooling/agents via the Model Context Protocol.

• Today I lost any shred of self respect that I had left as a software engineer (Activity: 2337): A senior engineer reports their six-person team moved to a “fully agentic” workflow ~4 months ago, centered on tools like Claude taking browser actions and presumably generating/reviewing code. The claimed process shift removed most manual coding, pair programming, and human code review, leaving engineers supervising agents and polishing ticket-level outputs rather than implementing systems directly. Commenters framed the role shift as engineers becoming de facto PMs/agent babysitters, with one saying they now just complete tickets “as written” and polish before merging. The thread’s notable debate is less about a specific tool bug and more about loss of engineering agency, collaboration, and craftsmanship in agent-heavy development workflows.

  • One commenter describes an AI-assisted development workflow where engineers focus on architecture/product decisions (“Should we do it this way? What about that?”) while the AI handles implementation work, claiming feature delivery has shifted from weeks or months to days. The technical implication is that LLM tooling is being used as an implementation accelerator rather than only autocomplete or code search.

  • Another commenter warns that eliminating human code review is risky, describing their company’s current guardrails: heavy upfront planning, detailed tech specs, precise prompts/instructions, a personalized workflow using multiple subagents, self-review of generated PRs, and mandatory teammate review before merge. This highlights a more controlled AI coding pipeline where LLM-generated output is still gated by conventional engineering review practices.

AIUC first got our attention with the NFDG backing, and have just announced a $40M series A today, with the most impressive industry advisor list we may have ever seen for an early startup behind AIUC-1, their agent standard backed by real insurance:

From being Anthropic’s first product hire to building the standards, testing, and insurance infrastructure meant to make frontier AI deployable, Rune Kvist is betting that the biggest constraint on AI adoption won’t be capability it will be trust. In this episode, the AIUC cofounder joins swyx and Vibhu to announce a new $40M round and explain why companies like Cursor, Harvey, Lovable, and ElevenLabs are increasingly confronting a problem that gets harder as AI gets better: who is responsible when autonomous systems fail?

We go deep on AIUC-1, the emerging standard for agent security, safety, and reliability; how AI agents are stress-tested for jailbreaks, hallucinations, and data leaks; and why Rune thinks standards and insurance could become critical infrastructure for AI. We also discuss the growing trust gap between governments and frontier labs, AI-enabled cyber and biological risks, why every model can ultimately be jailbroken, what happens when a $20 coding agent causes $200M of damage, whether AI engineers should be certified, and why even after AGI there may be one job the labs can never do themselves: be their own watchdog.

We discuss:

• Why risk, liability, and trust may become the binding constraint on AI adoption

• Rune’s path from reading the Scaling Laws paper to joining Anthropic in its earliest days

• What Anthropic understood about scaling, compute, and the future years before it became obvious

• Why Waymo illustrates the gap between AI capability and real-world deployment

• AIUC’s $40M round and work with Cursor, Harvey, Lovable, ElevenLabs, and other frontier AI companies

• AIUC-1: a standard for AI agent security, safety, and reliability

• How agents are tested for jailbreaks, hallucinations, and data leakage

• Why most AI companies optimize the happy path without seriously stress-testing adversarial cases

• Why AI standards may need to update every quarter instead of every decade

• The emerging trust gap between frontier AI labs and governments

• Cybersecurity, child safety, biological weapons, and the expanding frontier-model risk surface

• Why standards and insurance may need to evolve together

• How Lloyd’s of London can insure AI systems and bring trust to enterprise deployment

• What happens if a $20 Cursor subscription contributes to a $200M plane crash

• The Air Canada chatbot case and how AI failures are beginning to clarify legal liability

• Why copyright may be one of the hardest AI risks to insure

• Evals, mechanistic interpretability, monitoring, and models becoming aware they’re being tested

• The impossible CISO mandate: adopt AI fast, but don’t let anything go wrong

• Why robotics will make AI liability dramatically more consequential

• Whether AI engineers should have Level 1, 2, and 3 certifications

• AIUC’s roadmap across agents, frontier models, robotics, and universal red teaming

• Why AGI could become a question of national sovereignty

• Why the labs can never fully serve as their own watchdogs

• The Big Short problem: how do you stop competing watchdogs from racing standards to the bottom?

Rune Kvist

• LinkedIn: https://www.linkedin.com/in/runekvist/

• X: https://x.com/RuneKvist

AIUC

• https://aiuc.com

Timestamps

00:00:00 AIUC’s $40M Round and the Risk Bottleneck for AI

00:01:07 From Scaling Laws to Early Anthropic

00:07:58 Why Trust, Not Capability, Could Limit AI Adoption

00:12:19 Founding AIUC and Building AIUC-1

00:18:52 How AI Agents Are Audited and Stress-Tested

00:25:26 Frontier Models, Government, and the AI Trust Gap

00:33:32 Cyber, Child Safety, and AI-Enabled Biological Risk

00:38:14 Why Standards and Insurance Belong Together

00:41:45 What Does an AI Insurance Policy Actually Cover?

00:50:44 The $20 Cursor Subscription and the $200M Plane Crash

00:53:53 AI Liability, Monitoring, and Earning Enterprise Trust

00:56:21 From AI Agents to Models to Robotics

00:58:29 Copyright, Adverse Selection, and AI Insurance

01:03:28 Evals, Mechanistic Interpretability, and Eval Awareness

01:08:36 The Impossible Enterprise AI Mandate

01:11:52 Prediction Markets vs. AI Audits

01:14:43 Should AI Engineers Be Certified?

01:19:10 AIUC’s Roadmap, AGI, and Who Watches the Watchdogs?

Transcript

Introduction: AIUC, the $40M Series A, and Risk as the Adoption Bottleneck

Swyx [00:00:00]: Okay, we’re in the studio with Rune from AIUC, the Artificial Intelligence Underwriting Company, with our trusty co-host, Vibhu. Welcome.

Rune Kvist [00:00:10]: Thank you. Thanks for having me. Thank you.

Swyx [00:00:11]: What are you announcing today?

Rune Kvist [00:00:12]: We have raised $40 million, led by Ribbit Capital and First Harmonic.

Swyx [00:00:17]: You first came to my attention when Nat and Daniel invested in you guys. Is the story, like, pretty much the same? Like, what are you today versus what you thought you were back then?

Rune Kvist [00:00:26]: When we raised our seed round, we had a hypothesis that at some point risk was going to hold down adoption. At that point in time, that felt kind of hypothetical, and I think that is now over. Clearly, the moment is now with Mythos and Fable. It’s pretty obvious that literally the binding constraint on adoption is risk. And so for us, it feels like this is a natural continuation of the same hypothesis, but where previously it was speculation, now it feels like fact.

Swyx [00:00:54]: And let’s get a list of the customers that you’re highlighting as part of your Series A.

Rune Kvist [00:00:58]: Totally. Yeah. So we are now working with folks like Cursor, Harvey, Lovable, ElevenLabs.

Swyx [00:01:05]: Yeah. Amazing. Congrats.

Rune Kvist [00:01:06]: Thank you.

Swyx [00:01:07]: So you were famously one of the first hires involved in GTM and product. I’m just kind of curious: what was your path into AI? Just recap.

Rune’s Path Into AI: Scaling Laws, Capital, and Anthropic

Rune Kvist [00:01:18]: Yeah.

Rune Kvist [00:01:19]: Late 2021, I sold a company, my first company, an edtech company. I had a bit of time to think about what was next. I came across the Scaling Laws paper, and that just struck me like lightning. I was just like, “This is a big idea.” In short, the Scaling Laws paper just says the bigger the model, the smarter the model.

Swyx [00:01:38]: So this is the Kaplan one, not the Chinchilla one?

Rune Kvist [00:01:40]: Exactly, the Kaplan one.

Swyx [00:01:42]: Yeah.

Rune Kvist [00:01:42]: And the important thing that clicked for me there was, oh, now capital will understand this. If you put in more money, you get more money out, and so that will kick off a hype cycle. And so you get a sense of predictable returns, which is, in fact, what’s played out. And so I just packed my bags. I’d never been to San Francisco. I’d never been there. I just packed my bags, flew out here to find the people who had written it. And at the time, they had just started a small lab called Anthropic. There were around 40 people at the time or so. Drank a bunch of coffee until I eventually got introduced to Dario. And at the time, they were wrestling with some of these questions of, like, should we deploy our models? Should we make revenue? How should we engage with the rest of the world? They’d just broken off from OpenAI, and it’s been publicly reported that they were kind of concerned with how they were dealing with deployment. So they were wrestling with some of those questions. At this point, this is early fog of war, like early 2022. The hottest product at the time was, like, Jasper. Like, there’s nothing out there. So where value was going to accrue, and what the different parts of the stack were going to be, were all open questions.

Swyx [00:02:48]: I want to highlight to people, you ask these questions because you have a PPE background.

Rune Kvist [00:02:52]: Yes.

Swyx [00:02:52]: I actually was in Singapore in one of the sort of feeder programs for prepping people for PPE. So I had a tutor. We learned, you know, philosophy and politics and economics. But, like, I think your kind of background matters. Machine learning people who read the neural, Scaling Laws paper would not necessarily draw the same conclusions that you did. Whereas any capitalist would read that and go, “Holy shit.”

Rune Kvist [00:03:19]: Correct.

Swyx [00:03:20]: Right?

Rune Kvist [00:03:21]: Yes.

Swyx [00:03:21]: Who tipped you onto that paper? Because it’s not a paper that you normally read, right, like, in your circles?

Rune Kvist [00:03:26]: Yeah. I think I’d actually, ever since AlphaGo, had some appreciation that AI was a big deal.

Swyx [00:03:36]: Yeah.

Rune Kvist [00:03:36]: But it kind of felt like it raised all these kind of interesting philosophical questions, but it was kind of not clear from afar where exactly that would go. But it was obvious enough that it was like, this is going to be a big thing if we find the kind of right mechanism to kind of get the techno-capital machine to work on this. But it was just not clear. And so I think there was some way in which, like, that became obvious, and also it wasn’t as obvious at the time than it is now, right? Like, it was just like, wow, this is so interesting. But it still felt, coming from kind of a philosophy and economics background, it felt like if this turns out to be true, you’re going to be wrestling with all of the big questions in society. Everything you’ve learned about politics gets thrown out of the window. Everything you’ve learned about economics at least gets challenged. And so what felt interesting was to be at that frontier that has ramifications across everything. So that’s why I sought it out.

Swyx [00:04:32]: I mean, clearly really good insight. For people who don’t know, the PPE program is, like, where prime ministers are born. So then you end up meeting Dario.

Rune Kvist [00:04:41]: Yep. First Dario, yeah.

Swyx [00:04:43]: Yeah. Well, I mean, like, so did you get extra insights from talking with them that you didn’t get from your original hypothesis?

Anthropic’s Early Conviction and the Scaling Laws Crystal Ball

Rune Kvist [00:04:50]: If you read the Scaling Laws paper, you get this, like, very vague sketch of like, wow, this seems kind of important. There are some lines on a chart. This seems kind of important. And what I think the team at Anthropic had thought more about than anyone was like, what are the implications of this if you really play this out? And back then they had, kind of vision documents for what the world would look like in 2026, and they were kind of in vivid detail playing out how much compute is going to be needed, what the CapEx was going to look like, what some of the societal concerns were going to be, but also what is the amount of economic value coming out here? And so it kind of felt like they held a crystal ball that in hindsight turned out to just be dramatically correct. And they weren’t holding it like they were obviously correct. They were just like, “Take this hypothesis really seriously.”

Swyx [00:05:38]: Think it through, yeah.

Rune Kvist [00:05:38]: And think it through in the same way as the kind of situational awareness that is

Swyx [00:05:43]: Across the street.

Rune Kvist [00:05:44]: Across the street.

Swyx [00:05:44]: Your office, yeah. Oh my God, we’re all living across the street in the same one square mile.

Rune Kvist [00:05:50]: Correct. And that’s now a couple of years old, but also people keep referencing it these particular weeks with Fable and Mythos, and it’s like, wow, if you take this one idea seriously- For the Scaling Laws, a lot of things fall into place.

Vibhu [00:06:03]: And keep in mind, at this point, this is the same team that did GPT-1, GPT-2, and GPT-3.

Rune Kvist [00:06:08]: Correct.

Vibhu [00:06:08]: Which is also, like, it’s not just some experimentation. Like, this is a real model that we just scaled up.

Rune Kvist [00:06:14]: And they had deep conviction in this idea: if you take a big blob of compute and data, it just wants to learn, and out of that will come smarter and smarter models. And all the particulars were not clear.

Vibhu [00:06:26]: Yeah.

Rune Kvist [00:06:27]: And all the implications were not clear. But their deep conviction in this, like, core thesis, and that was kind of dizzying. It was both phenomenally interesting and exciting, and also very quickly you get to, like, the world we know today will no longer be if this hypothesis holds. So it also just felt, like, important in some kind of grand sense.

Vibhu [00:06:48]: What kind of shaped you there? So that was early 2022. Not only had GPT-1, GPT-2, and GPT-3 come out, but, you know, the amazing founders of Anthropic that have never split up, the only ones, they actually had the conviction to leave OpenAI, start their lab. You said there were about 40 people there. What was the time like there?

Inside Early Anthropic: Mission, Deployment, and Risk

Rune Kvist [00:07:06]: It was kind of remarkably like what it looks like on the outside today. Extremely cohesive, extremely mission-oriented, and living in this tension between their two ideas, which is AI could both go really well and really bad, and we want to be part of building it. That creates astounding amounts of tension. And they were wrestling with this incentive challenge where they know they’re in a race that they’re in where you might get forced to cut corners, but it also felt very important to them to be at the forefront of technology. And all of those ideas were just present at that time. It kind of feels like that line has been just very clear, and I think kind of love them or hate them, they have really stuck to their guns. There’s a core set of beliefs that they hold more deeply than most companies hold any beliefs.

Vibhu [00:07:58]: Yeah. Fast-forward to today.

Rune Kvist [00:08:00]: Yeah.

Vibhu [00:08:00]: What does that lead us to AI underwriting company? What are you up to? What motivated you to start this?

From Waymo to AIUC: Confidence Infrastructure for AI

Rune Kvist [00:08:05]: Yeah. AIUC builds confidence infrastructure for frontier AI through standards and insurance. The link from Anthropic to building confidence infrastructure, looking out the windows at Anthropic offices and seeing Waymos driving by. Already back then, early 2022, Waymos were in some ways like AGI for cars. Like, they were superhuman drivers, but you couldn’t take one to the airport. And now, four and a bit years later, you still can’t take your Waymo to the airport, despite now everyone having kind of looked at the evidence and being like, “They’re better drivers than humans.” So in that particular instance, what’s clear is that the binding constraint on AI being useful is not capability, but is that liability or risk or trust. That problem is, general. The reason why right now

Rune Kvist [00:08:52]: Fable is not open for access is not because it’s not a good model, it’s because it’s a very good model. It’s just hard to make promises about what it will or will not do. And this problem gets worse as AI gets better. Basically, more intelligent AI can be more autonomous. That’s more valuable, but also the risk surface grows. And so - what Waymo illustrates is that unless you build the confidence infrastructure to make promises about AI, or at least bring light to the risks, you grind adoption to a halt. Governments, banks, hospitals, militaries need to have some sense of what AI will and will not do to be able to operate for them to incorporate it. And that’s the problem that we’re trying to solve. Now, why standards and insurance? If you trace this problem back through history, every technology wave has had some version of this problem. So if you go back to, like, year 1900, electricity comes

Vibhu [00:09:47]: Ben Franklin.

Rune Kvist [00:09:48]: Cars burn down, sorry, houses burn down, lots of people die. 1930s, cars are a big deal, kill lots of people. 1950s, private nuclear energy is a big deal, poses big risks. In each of those instances, the market runs ahead of regulation to create confidence infrastructure because that’s required to make go/go decisions. That is required for adoption, and the market fundamentally wants adoption. And in all of those instances, common blueprint emerges between standards and insurance. The reason these two components is standards kind of provide the rules of the road, and they also specify, like, what are the tests that need to be run so we can get a sense of how high the risk is. So take in the case of cars, that’s like a car crash. Great, everyone, they inform your insurance pricing today, they inform your purchasing decisions, et cetera. That’s basically the risk framework. The insurers are important because they pick up the bill. So they are the private institution that is most on the side of. That is best incentivized to quantify the risks truthfully and then figure out all the ways to reduce the risk ‘cause that increases their profit. So they’re basically, they help shape the incentives. And these two work really well in unison. Now, how does that show up as a company? Well, one of the things that was obvious even - or starting to become obvious even a couple years ago was that frontier companies, some of our customers today, like Cursor, Sierra, ElevenLabs, Harvey, were going to have a very easy time selling a pilot to a bank. The, like, the demo just sells itself. It’s magic. But bringing that through, if you want to do a wall-to-wall rollout at a bank or a hospital, you have to go through the risk process. These banks have no idea even which questions to ask, let alone which answers are sufficient, let alone, like, how do they go and test whether these agents actually work the way they’re supposed to. And so they had this problem of, like, what can we say to earn the trust? And we think there’s, like, a golden sentence that goes something like, “Hey, I hear you’re really worried about hallucinations or jailbreaks or whatever it may be. We’ve had an independent third party test us against the gold standard. We passed with flying colors. And as a vote of confidence, the world’s most conservative insurers have looked at the data.” And they’re willing to take some of the risk onto their balance sheet.

Swyx [00:12:06]: Yeah.

Rune Kvist [00:12:07]: So if something does go wrong

Swyx [00:12:07]: There’s money behind it, yeah.

Rune Kvist [00:12:09]: Exactly. So that’s kind of like the link between all this. We can get into some of the hard parts related to the technical testing, which is, I think, the crux of the matter, but I’ll pause there.

Swyx [00:12:19]: How did you and Rajiv come together? This-- there’s always, like, you come across very confident and, you know, and we’re announcing your Series A and all these things, but I want to see, like, the early initial stages of, like, idea formation.

Cofounding AIUC with Rajiv Dattani

Rune Kvist [00:12:31]: Yeah. Rajiv is actually my soon-to-be brother-in-law.

Swyx [00:12:35]: Oh.

Rune Kvist [00:12:36]: So I’m actually, in a week and a half getting married to Rajiv’s sister.

Swyx [00:12:42]: Okay, now you’re tight.

Rune Kvist [00:12:44]: Exactly.

Swyx [00:12:44]: Now you know.

Rune Kvist [00:12:45]: So - Rajiv and I have known each other for a decade. Funny story, I met both Rajiv and his sister, Hena, at the same time when Hena and I were interns at McKinsey in London, and Rajiv was assigned as my mentor. And so met them at the same time. For the longest time, it was not obvious that we were necessarily going to work together. I was in startups. He was, an insurance partner at McKinsey. Three or four years ago, I think Hena convinced him that AI was going to be a really big thing. And so he quit his job, cushy partner job at McKinsey in London, packed his bags, flew to San Francisco, and ended up joining METR. You guys are probably online enough

Swyx [00:13:24]: CEO.

Rune Kvist [00:13:24]: Exactly.

Swyx [00:13:24]: We’ve, we’ve, we’ve heard of METR.

Rune Kvist [00:13:25]: You see the plot-- the chart of the horizons of the tasks that agents can take on is doubling extremely fast. So he was COO at METR, led their partnerships with Anthropic and OpenAI to test their models before release, but also working closely with the US and UK government, to figure out, like, how do you know whether a model can be released? And in some ways, that was, like, the perfect background. He’s spent a lot of time in insurance, knows that world, spent a lot of time with frontier testing of models. And so when I was bumbling around this idea space, starting with some of the ideas we talked about related to Waymo, as soon as we got into the content, we were both like, “Oh, this would be an amazing business to build together.” This is wrestling with the problem that we both think is the most important in the world from a market angle, which is kind of our intuitions is that the market can do a lot, and the faster AI moves, the harder it is for government to solve some of these problems. And then it took a little bit of time to work through what is it like to work with family.

Swyx [00:14:27]: Sure.

Rune Kvist [00:14:27]: And,

Swyx [00:14:30]: Because you were already dating at the time

Rune Kvist [00:14:31]: Yeah. Yeah, exactly.

Swyx [00:14:33]: Yeah.

Rune Kvist [00:14:34]: Already back then, it

Swyx [00:14:35]: Yeah.

Rune Kvist [00:14:35]: We felt like we were a family.

Swyx [00:14:36]: Nice.

Rune Kvist [00:14:36]: And so starting a business together felt like kind of a big step. And, here we are with just immense amounts of trust.

Vibhu [00:14:43]: Yeah. So now you’re a company of how big? How big are you guys now?

AIUC-1 Certification: Agent Security, Safety, and Reliability

Rune Kvist [00:14:46]: There are just 20 of us now.

Vibhu [00:14:47]: 20 of you guys now, have Series A, and you have your first certification out, the AIUC-1. Let’s bring up the certification. So this is the agent certification, right? What goes into the process? I have, like, two questions here. One is, walk us through the certification, and two is, what is the process for a company to get certified, you know?

Rune Kvist [00:15:08]: Great. As it says right on the top, AIUC-1 is a standard for agent security, safety, and reliability. The fundamental design principle is take all of the concerns that slow down adoption, so all the questions, all the fears that keep, security leaders in the Fortune 1000 up at night, and put them into one comprehensive framework. That’s what you’ll see there. You can see the six categories. Two, you want to ground all of this in technical testing. So one of the concerns with security standards that often feel kind of like theater paperwork is that they’re not actually ground out in, does any of this work? Does any of this matter? And so we had a conviction from early on that was going to be the kind of crux, was to pass this, you must get tested every quarter, basically run thousands of simulations to see, well, so can it actually be jailbroken? How hard is it to jailbreak? How often does it hallucinate? How often does it leak data? Et cetera. And then the last, core idea here, if you scroll up to the top here, is to refresh it quarterly.

Rune Kvist [00:16:08]: So the core trait of AI is that it moves extremely fast. Whatever concerns we’re discussing today were not the same ones three months ago, and this will keep changing. Typically, standards update on a, like, a decade cycle is obviously not going to work. But the question is kind of how do you update it? And the core thing here was to basically get the risk leaders of the Fortune 1000 around the table. So if you go over to the left here

Vibhu [00:16:32]: Yeah

Rune Kvist [00:16:32]: You’ll see the AIUC-1 consortium. The consortium is a group of risk leaders who run real banks, real hospitals, real critical infrastructure, who are facing these challenges every day. And we meet with these folks twice a quarter and hear what’s top of mind, what is keeping them up at night. There’s tremendous amount of desire for that conversation. And then we operationalize that into a specific standard that gets into. And actually, we can go into and look at what

Vibhu [00:16:55]: Yeah

Rune Kvist [00:16:55]: What even is the standard. So if we go back to introduction, out there to the left, scroll up a little bit to the wheel, click into reliability. So if you take something like hallucinations sits in reliability. There is a number of requirements here. If you go into the top one, prevent hallucinated outputs, hallucinate outputs, this is one particular requirement. This is a technical control. Basically, we want some kind of ground in this filter. The first thing you see here is what’s called a crosswalk. So everyone and their grandmother has put out a framework, very high-level framework for what are the AI risks.

Swyx [00:17:27]: This is basically your competition,

Rune Kvist [00:17:28]: In some ways our competition

Swyx [00:17:29]: Not seriously, yeah.

Rune Kvist [00:17:30]: We’re, in fact, friends with them. We’ll come back to why.

Swyx [00:17:31]: Yeah.

Rune Kvist [00:17:32]: But mapping everything together so you have one superset. The claim you’re trying to support here is, if you follow this framework, then you can also see how you follow the other frameworks. But the meat of it comes down here in control activities and evidence. So control activities is like, great, you have this high-level requirement. How do you turn that down to something operational? Here’s what you must do, and then what is the evidence that we’re looking for?

Rune Kvist [00:17:57]: And the reason we go this deep is that there’s actually not that much confusion about what are the big concerns in AI. Everyone agrees to these. The question, like, what are you actually supposed to do? And so. What we found a lot of demand for is getting down to the specific evidence, that people need to look for. Whether you are Cursor building something or, even JPMorgan building something, but also if you’re just a risk leader at JPMorgan, like what exactly should you ask for? What can you ask for without sounding stupid? Like if you ask for some-- you won’t believe the amount of time a risk leader has asked for the IP rights to the underlying model to Cursor or something, and you’re just like “Sorry, what?” Like,

Swyx [00:18:39]: You slip it in there and you see

Rune Kvist [00:18:40]: Slip

Swyx [00:18:40]: See if you notice.

Rune Kvist [00:18:41]: See if they. Exactly.

Swyx [00:18:42]: Yeah.

Rune Kvist [00:18:42]: Put that in the questionnaire. All right, so that’s kind of what our standard is, and we update this every quarter with these folks, to keep up with the latest concerns.

Swyx [00:18:51]: Can I double-click on this one?

Controls, Evidence, and Third-Party Testing

Rune Kvist [00:18:52]: Yeah.

Swyx [00:18:52]: So first of all, the website’s beautiful. Like, it’s so confidence-inducing which is the whole point where, like, okay, I know exactly what I’m signing up for when I talk with you. Like, I don’t even have to talk to you. I can just see your whole, certification, which is great. But, like, okay, so from here, like D001.1 configure a groundedness filter, how does that get applied? Like, you have a person that

Rune Kvist [00:19:16]: Yeah,

Swyx [00:19:16]: Goes through it?

Rune Kvist [00:19:17]: If you, go back

Vibhu [00:19:19]: I did see somewhere there’s like, you know, fifty-one requirements, a hundred thirty controls. There’s like a whole

Swyx [00:19:25]: Right. I just want to. Like, to me, this doesn’t translate

Vibhu [00:19:27]: Yeah.

Swyx [00:19:27]: Into a test or an eval.

Rune Kvist [00:19:28]: Yes. So if you go into, on the left-hand side. So actually, if - before we go in there are three types of requirements. The first is technical controls, like you must implement some guardrails.

Rune Kvist [00:19:42]: Two, there are test controls. So you must have an independent third party go and run some tests against you. I’ll show you one of those in a second. And then three, there are policy controls. For example, you must have a person whose name is on the line when you guys fuck up, and you must have a plan for how you tell your customers and how you engage with them. They’re kind of more traditional, standard type stuff. So in this particular instance, we just check whether they in fact have a ground in filter. So we will partner with an auditor. So we partner with auditors like KPMG or like Schellman who go in and do the thing auditors do, which is to check the evidence. In this case, that might be a screenshot, it might be part of the code that they need to review to see that it actually. Just that it exists.

Swyx [00:20:21]: Oh, okay.

Rune Kvist [00:20:22]: And then the second thing

Swyx [00:20:22]: So you’re not testing the effectiveness of it.

Rune Kvist [00:20:24]: That’s the second thing. So if you go down

Swyx [00:20:25]: Yeah.

Rune Kvist [00:20:25]: To the third-party testing for hallucinations out on the left, that’s basically the next requirement. This is where we test how well does it actually work.

Swyx [00:20:32]: Okay, and is it you testing or the auditor?

Rune Kvist [00:20:34]: We test them.

Rune Kvist [00:20:35]: We test them.

Swyx [00:20:36]: That’s a lot of work.

Vibhu [00:20:37]: How long does testing take? So if I want to get certified, just

Certification Timelines, Remediation, and Quarterly Updates

Rune Kvist [00:20:40]: Yeah.

Vibhu [00:20:40]: How long does the end roughly take?

Rune Kvist [00:20:42]: Yeah, the end, almost always is dependent on, like, our customers need

Vibhu [00:20:47]: Yeah.

Rune Kvist [00:20:47]: To look something for us. It takes somewhere between, like, 3 to 10 weeks

Swyx [00:20:52]: Yeah.

Rune Kvist [00:20:52]: Depending on how up to snuff they already are. So some people show up to us with, like, extremely rigorous security programs. When we test them, it works extremely well. We can get that done very quick. Some people come to us, and they’re not that far along. We give them kind of the spec that they need to build towards, and then their security teams and engineers get to work and build to meet the standard. The testing itself typically takes a couple of weeks, including the time for them to remediate. Often, we’ll find something that we cannot pass, where this is actually just not up to the standard. - you won’t pass the standard. And then they will need to go and implement additional safeguards or additional remediation that makes them more robust so that they can actually kind of hand on heart look at their customers in the eyes and say, like, “Hey, we’ve done truly our very best.”

Vibhu [00:21:35]: And they’re certified for a year and have quarterly updates?

Rune Kvist [00:21:38]: Correct, yeah.

Vibhu [00:21:39]: And, yeah, it’s pretty interesting. I think, you know, what’s changed since. So this is certifying agents in production, right? Your customers, like you’ve had Lovable, ElevenLabs, Intercom, and they’ve all gone through this certification.

Rune Kvist [00:21:50]: Yes.

Vibhu [00:21:51]: What has changed? So I see you post, like, you know, Q2 added MCP agent,

How Agent Risks Are Changing: Coding, MCP, and Agent-to-Agent Interactions

Rune Kvist [00:21:56]: Yeah.

Vibhu [00:21:56]: agent communication. Any other things that you want to kind of highlight since the first iteration? What comes in quarterly?

Rune Kvist [00:22:03]: Yeah. So some of the changes have just been agents are not just one thing. So, like, if you take agents like Cursor and compare them to Sierra, they’re really quite different. And compare them to Harvey again, compare them to you out of again

Swyx [00:22:16]: ElevenLabs, yeah.

Rune Kvist [00:22:17]: ElevenLabs, they’re all quite different. And so we wanted to design a standard that works for all of the types of agents. And we started with one that was, like, pretty text-based, like, honestly, pretty customer support-focused. That’s where there’s a lot of existing demand. And then over time, we’ve picked, some of the frontier companies in each of these other domains that we could work with and build out the standard, so, such that we know that the same standard works for code, it works for customer support, works for automation, et cetera. So that’s been one big thing. Yeah, then some of the things that have been top of mind recently, Mythos is bringing up a lot of concerns for security leaders. We’re starting to get more and more questions around agent interactions. It’s very nascent, at the moment, but it’s starting to emerge. There’ve been a lot of, questions related to OpenClaw and MCP. Again, like agents starting to interact with each other, is really top of mind. Then as coding agents have really taken off, that’s also where banks and hospitals, et cetera, are getting more and more precise on what it is they need. So really dialing in as that start to be, like, where most of the tokens flow through in the world, getting much sharper on that.

Vibhu [00:23:26]: Can you share for people that are listening that don’t really think about this? Like you mentioned, there’s the obvious stuff, you know, hallucination, citations. What are best practices that people should do when building agents? Like, if they come to you pretty ready with certification like, you know, they’ll probably pass certification. What are the things people don’t think about that they should have?

Best Practices for Agent Builders: Stress Tests and Guardrails

Rune Kvist [00:23:46]: The most important thing is that a lot of companies have not done a serious stress test. They spend most of the time, perhaps rightly so, optimizing for how does it work in the good case, the average case, how high-quality is the output for the customer. And a lot of these companies are pretty new, so they haven’t spent a lot of time stress testing the what is there as an adversary on the other side? What are some of the complicated corner cases that you’ve not really considered? So I think that’s, like, a frame of mind. And you’ll also see this in startups. It often takes a while until they hire their first security person. They- And that’s a whole different kind of risk surface than just building a good product. So a lot of that applies. Most companies actually also have the right kind of architecture. Most of them will have some kind of guardrails in place, either some that come out of the box from their model provider or they’ll have built their own filters that sit in between. They just don’t work very well. The difference between putting a classifier in place that, like, maybe goes and checks whether you’re giving medical advice when you shouldn’t and says, “Hey, if this looks like medical advice, filter it out.” Lots of companies have that in place. The question is whether it works. And it’s actually pretty fiddly to sit down and think about all the ways in which you could ask for medical advice, read the academic literature on what are the kinds of

Rune Kvist [00:25:03]: Framings or tricks you might play to get an AI to give you medical advice when you really shouldn’t. And so there’s, like, an area of expertise that’s just missing. So what we find is that most people have the right building blocks in place. They don’- It doesn’- It’s not rocket science, but the finicky thing is, like, getting into the corners and testing whether it works such that you can look your customers in the eye, or maybe a bank or maybe a hospital and be like, “This is going to work for you.”

Vibhu [00:25:26]: I see. So we talked a lot about the agent-level certification. Where do you guys go from here? So announcing series A camera, we talked about this a bit. There’s the whole security risk of Fable, government stepping in. You guys are kind of announcing that you’re also going into model certification?

Toward Model Certification: The Government–Lab Trust Gap

Rune Kvist [00:25:46]: When we do a bit of cutting afterwards,

Vibhu [00:25:48]: Yeah

Rune Kvist [00:25:48]: We will not yet be announcing this,

Vibhu [00:25:49]: Nice

Rune Kvist [00:25:50]: The question that is top of everyone’s minds now is at the model level. And Mythos, then Fable, has really brought this to the fore that in addition to the commercial risk and the kind of economic security risks that are happening at the agent layer, the models are going to present risk in the national security category. The shape of the problem is very similar. You have some people that are on the hook if something goes wrong. In the case of agents, it’s often security leaders in the enterprise. In this case, it’s the government. They don’- haven’t necessarily spent their entire lives thinking about what are the new risks that come here, what is the kind of data you might be looking for, how might you test that? But they do have to make sure that their concerns are addressed. You have some frontier AI companies that are deeply technical. They know a lot about the risks, but they fundamentally have an incentive to not always be truthful. So you have a trust gap between the government and the labs. And in every other industry, you end up with some kind of body sitting between, a neutral third party sitting between those people. There’s no other industry where you allow people to audit themselves. So there is going to be a need for a third party that can take the rigor of the labs to run frontier technical evals, but can also speak legible trust in the way that the government trusts PwC to go and run financial audits. And they know that they output audit reports in a way that’s consistent, that’s easy to read, that’s factual, that’s, trustworthy. Those two things need to be brought together. And what we’ve learned from our work with agents is that if you want those-- that communication between those two parties to be smooth, there has to be one common standard that is public, that people can go and inspect. What are the risks that matter? Within each of these risks, what are the kinds of threat models that you’re really looking for? You need to specify for each of those risks, what are the guardrails that need to be in place, and what are the tests they need to run to see whether those guardrails are effective? And then you need to go and run audits that are - technical audits that are consistent. So if you’re trying to bring trust, it’s extremely important that you methodically work your way through the risks. You can’t send one researcher in and say, like, “Come back with whatever you find.” You need to be able to explain exactly what you did, exactly what you tried, exactly what you did not try, and therefore the kinds of promises you can and cannot make at the end of it. I think of

Neutral Third Parties, CAISI, and Model Risk Audits

Rune Kvist [00:28:13]: Fable as a direct symptom of this problem that the government was told that there’s a risk. The government may struggle to assess just how big that risk is. They call Anthropic, and Anthropic is trying to tell them, “Hey, actually, every model can be jailbroken.”

Swyx [00:28:28]: That’s not what you want to hear, right?

Rune Kvist [00:28:32]: As the government, that might be hard to trust.

Rune Kvist [00:28:36]: And we think that a broker is the most natural solution. In other markets, you see something like, in financial markets, you see Moody’s. Moody’s goes in, and they look at a bond, and they output a rating. They say like, “Here’s the evidence we found. Here’s the rating.” We don’t decide whether anyone should buy this bond or not buy this bond. Well, that depends on their risk appetite. But we do provide this common information layer that everyone can rely on. In the case of Moody’s, the government, points to them and say, “Hey, pension funds, you should probably really take care. You shouldn’t risk your pensioners’ money, so you can only invest in triple-A rated bonds.” That means that now the government doesn’t have to staff thousands of financial technical experts to rerun forecasts every week to see whether things are correctly rated. They get to point to some neutral third party. So my hypothesis is, my hunch is that you will see a third party that sits between the government and the labs, and it could either be the government builds it themselves. So something like CAISI was set up to do exactly this. And the question

Swyx [00:29:44]: Sorry, I’m not familiar with CAISI.

Rune Kvist [00:29:45]: CAISI is the Center for AI Standards and Innovation.

Swyx [00:29:49]: Okay.

Rune Kvist [00:29:50]: I won’t get into the details, but it’s a body of NIST that typically sets standards. So it’s basically a government body that has AI experts. Yeah, exactly. Exactly.

Swyx [00:29:59]: Very key. Very key.

Rune Kvist [00:30:00]: Very key.

Vibhu [00:30:00]: I think, you know, it’s one of those things where when you just sit back and listen-- look at it, like, is there enough technical expertise in the government to measure, test these things right now? Probably not, right? And Fable is a result of, okay, we’ve had to scale back and pause things,

Rune Kvist [00:30:17]: Yeah. And they have excellent people, but they have an extraordinarily small budget compared to the scale of the challenge that’s ahead of us. And I think they have a role to play. The question is kind of like, who does what? We have now outlined the jobs to be done, and they’re quite extensive. Every model release, there is an astounding-- Given that they take in any input, their risk surface is astounding. And so the question is really: what can only the government do, and what can the market provide here that can keep up with the pace as AI risk changes? Our perspective is that also at the model layer, the risks that people care about today are not the same ones they cared about three months ago. So the pace of legislation is too slow to deal with pinpointing the risks here. And so we think there’s a lot that the market can do to surface timely information. Ultimately, there is a bunch of policy decisions here. Is the national security risks of a model too high?

Swyx [00:31:12]: Yeah.

Rune Kvist [00:31:12]: That’s a political answer. But what we want to make sure is that the process that produces this risk information is compatible with very fast innovation. So you don’t want to. This is not a question of like, can you slow the things down? Can you keep, the models locked up until-- for months on end until everyone can make a guarantee? But it is this, can you, in the time it. Given that the US is competing with China on releasing models, can you insert risk information that allows the government to, like, make rapid decisions on some of these questions? Balancing that trade-off between failing to adopt AI is going to put us at risk, but also reckless adoption is going to put us at risk. And that’s a very kind of fine balance that they’re going to need, like, a lot of high-quality intelligence to make.

Chinese Models, Data Flows, and National Security Concerns

Swyx [00:31:55]: Just a side mention, because you mentioned Chinese models, any specific concerns that you’re hearing from your CISOs about that? ‘cause I guess it’s free, but.

Rune Kvist [00:32:05]: CISOs have a bunch of concerns around data flows in general that they’re really concerned about. So there’s a lot of questions like, if these models are Chinese, where does that, where does that data go? I think a lot of this can be addressed, but they come up often.

Swyx [00:32:18]: I mean, they understand they’re running on American GPUs.

Rune Kvist [00:32:21]: Some of them, some of them understand that they’re running on American GPUs.

Swyx [00:32:23]: They’re not, like, phoning home every time you, like, call home.

Rune Kvist [00:32:26]: No. A year ago, there was not a lot of understanding of this. I actually think, you’re seeing the security leaders becoming kind of AI literate at a blistering pace, and you’re actually also seeing my Twitter timeline that’s very pilled and my LinkedIn feed that used to not at all be pilled kind of converge. They’re both talking about Fable.

Swyx [00:32:45]: Right. Yeah, that’s true.

Rune Kvist [00:32:46]: They are both talking about whether you can prevent models from being jailbroken these days.

Swyx [00:32:51]: Yeah.

Rune Kvist [00:32:52]: Like national security national security risks are now the conversation that is actually emerging. Other than that, I think you mostly see a kind of general picture: there are no concerns with any particular model or any particular model output, but there is a general nervousness of having critical infrastructure run on models that are not produced in America by Americans where the American government has control.

Swyx [00:33:14]: But it doesn’t necessarily show up in your framework that directly, or it might, I don’t know.

Rune Kvist [00:33:18]: There’s a bit of stuff in there actually on the, like, the provenance of the models and disclosing that. But I think there’s a bunch of use cases where running a Chinese open-source model is just the best solution.

Swyx [00:33:27]: Yeah.

Rune Kvist [00:33:27]: And a concern is slightly more macro here, which is not best addressed at any particular certification level.

Vibhu [00:33:32]: Is there anything interesting that you see at the. You know, if you’re trying to fill that middle gap, that mediation gap, any interesting stuff that you guys forecast would be required other than, you know, what the average person might expect?

Cyber, Child Safety, Bio Risk, and Expert Coordination

Rune Kvist [00:33:47]: There’s a bunch of interesting questions about what are the risks that matter here. So right now, the risk of the day is cyber, because it’s very real, very tangible. And some of the risks that are also emerging as pretty real and pretty tangible are things like child safety is becoming both extremely important, but also politically important. And then there are some of the risks that are coming down the pipeline that today feel kind of speculative, but people who spend a lot of time with the models see them coming down is things like, risks that relate to biology.

Rune Kvist [00:34:18]: And specifically whether models will help adversaries produce biological weapons and making that extremely cheap, extremely accessible, producing-- making the chance of another COVID or worse pandemic. COVID was not engineered to be bad, as if you were trying to do that. So I think those are some of the risks that are coming down the pipeline. I think one other thing to just note is that agents are kind of deliberately narrow. So, like, when a frontier agent company puts a chatbot that interacts with customers, they’ve really tried to narrow the topics it’s interested in talking about. Such that if you ask it, like, “What do you think of the president?” it will just decline, which means that the kind of risk area is somewhat smaller. For models, it is infinite. And so there’s not a single expert out there who can competently evaluate the risks of cyberattacks and fifteen-year-olds having month-long conversations with a chatbot and seeing whether it will in fact recommend suicide or something horrendous like that, and can evaluate the risks that terrorists can use AI to produce bioweapons. The risk surface is just too big. And so the central challenge actually becomes how do you get those subject matter experts to work within a one coherent framework that outputs one coherent report and rating that the world can go and inspect? ‘Cause that global perspective is central, but there’s not a single organization today that could produce that.

Swyx [00:35:47]: And you would be the presumptive one when you put out your model standards.

Rune Kvist [00:35:51]: We think there can be one company that can, with a consortium of experts, build one coherent standard. I think we’ve shown that across all of the enterprise risks today. We think it could be one company that could, with a consortium, specify the audit rules, basically like the inputs and outputs that all these technical experts need. What access do they need? How should they treat infosec- info security? They can look at whether the eval- evals are well-produced without necessarily being able to say, “Hey, is this a threat or not a threat?” But overall, evaluating whether the evals are good, well-constructed, that set of audit rules that basically becomes the interface for all these experts, we think one clearinghouse could put together. To be clear. When I say one company, I think of it as one company coordinating lots of this in the same way that when we saw our consortium, it’s not like we say we have all the answers on agent security. What we say is we are taking on the role of eliciting all of the concerns and being the secretary that puts it together and runs a tight house such that the standard updates lockstep every quarter, and that the audit reports that come out, in this case, 100-page audit reports, uniform and crisp and clear all to the level of detail that is required for executives that need to make a clear go/go decision. So that’s kind of the role that we think we might play.

OWASP, Frameworks, and the Operational Audit Layer

Swyx [00:37:11]: I think in many ways you’re performing the role that OWASP used to do there, and you said, like, you know, competition and partners.

Rune Kvist [00:37:18]: Yeah.

Swyx [00:37:19]: Can you go more into, like, how they partner?

Rune Kvist [00:37:20]: Yeah. So first of all, OWASP is basically an open source community of security practitioners that are coming together to build frameworks for addressing the latest security concerns. We think they are phenomenal at creating frameworks. We’- In fact, we’- First of all, we’re partners with them, so we have a joint article. Two, we’ve learned a lot from them. We think they’re a tremendous source of intelligence. What OWASP does not do is building the machine that runs third-party audits such that a company like Cursor or a company like JPMorgan could get a third party to go and review them against this and say, “Hey, you’ve passed the standard, and here is the report that you can use to build trust and preempt your partners’ or customers’ questions.” So they fundamentally try to do something different. You - They are part of the information gathering and intelligence gathering and creating clarity, but the operational layer of turning this into promises is not the business they try to be in.

Swyx [00:38:14]: The standard is emerging and is doing very well. Was it necessary to then also do underwriting? Obviously it’s in the name, so please remember you thought about it first. I feel like if you just have enough consensus, you don’t actually need the money angle, but it does help.

Vibhu [00:38:30]: I did want to also note, you guys are a profit company too, right? It’s not profit where there’s a whole business side to it as well?

Why For-Profit Standards and Insurers Matter

Rune Kvist [00:38:39]: Yeah. Yeah, so I’m just getting crazy

Swyx [00:38:41]: I think about the money part.

Rune Kvist [00:38:42]: Yeah. Yeah, let’s get into the money part. Let’s start from actually your question, profit versus profit. In the security space today, cybersecurity, most of the standards are produced by nonprofits. I think that’s an issue.

Rune Kvist [00:39:00]: The question you have to ask yourself is, how do you create good incentives for these standards to be good and keep up?

Rune Kvist [00:39:09]: Nonprofits tend to not have these adverse profit incentives where they, hollow out their standard and create a race to the bottom, but they’re also not at all responsive by default to the communities that they serve. There’s no process-- They don’t have customers that they serve where they go and ask, “What do you want? What do you want? What do you want?” And when you look at the overall satisfaction with the security standards today, people tend to just not like them very much. You do see in other domains, that profit standards can serve the world quite well. So there are examples, like we talked about Moody’s before. It’s not without flaws, but, it is absolutely critical societal infrastructure that gets run at an astounding scale today. Your credit score, it’s FICO. It’s also a profit business. And when you go back even further in history, some of the crash testing standards came out of insurance companies.

Rune Kvist [00:40:06]: The insurance companies together founded the Insurance Institute for Highway Safety because they were very interested in, like, how can we use standards to drive down mortality and save money? Go back, prior-- Our name actually pays homage to the Underwriters Laboratories, UL, which, was started right around when electricity came out. Houses started burning down. Insurers, again, were paying the bill, and they were maybe also good people, but their profit incentive was, let’s prevent houses from burning down. Let’s test all the electrical products, the light bulbs. All the light bulbs in here are probably tested, the toasters, et cetera. And they set up, an entity to create those standards. Today, UL has a profit entity and a profit entity. What they’ve recognized, they spun - They started profit. They spun out a profit because what they recognized was like, hey, actually to serve customers well, you need a profit entity. The lesson here is one of the ways that the market can align incentives so you’re both responsive to customers

Rune Kvist [00:41:07]: And not hollowing out your standard over time is to align it with insurers because they fundamentally have good incentives. And so if you’re a profit standard that works closely with insurers, you get the feedback loop in such that you’re really tuned into your customers, but also have their interest at heart. So that’s the model that we - the kind of inspirational model that we’ve learned a lot from, and that’s also where the name comes from. In some ways, the term underwriting can both be associated with insurance, but it’s also a broad term for, like, making decisions.

Rune Kvist [00:41:40]: If you underwrite a decision, you’re fundamentally kind of taking ownership for the consequences of it.

AI Insurance Contracts, Lloyd’s of London, and ElevenLabs

Swyx [00:41:45]: Yeah, I mean, what does an insurance contract look like for AI?

Rune Kvist [00:41:49]: Yeah. Most of the demand comes today for insurance contracts is, sitting between people who’ve built AI and people who are buying AI.

Swyx [00:41:56]: Yes.

Rune Kvist [00:41:57]: And what you want—the reason why people want insurers involved, both for the traditional reasons, hey, if something goes wrong, we want to be compensated, but it’s in particular because insurers can bring trust to the equation. Because insurers will pay for the damages, if they’re willing to write an insurance policy, that is them saying, “Hey, we think there is risk here, but that is manageable.” And that is kind of a. Their incentive aligns with the enterprises adopting it, so that’s a really a good signal to the market. In the same way, actually, one of the things that Waymo tried to get their first permit to even operate in San Francisco was to get a lot of insurers to stack up a huge insurance policy. In the case if something went wrong, not because Google can’t pay, but because it was very valuable to have a third party go and look at that data

Rune Kvist [00:42:47]: That are trusted by governments, trusted by enterprises as conservative people and say, “Hey, we’ve looked at it. We’re actually willing to take some of this on our balance sheet.” So that’s, that’s kind of the reason why people are interested in it. What it looks like is, in some ways like every other insurance contract. You specify what are the perils you want to cover, how much do you want to cover them, like up to what limits, and what does it cost to cover that. And in the case of, if we take a really concrete example, ElevenLabs, bought a first of its kind AI agent insurance policy. They work with some of the biggest, enterprises that work with governments. They’re really interested in going above and beyond and making promises to their customers. So they wrote a policy that covers just some of the core concerns that their customers have been asking about. And, the crucial thing was really to get Lloyd’s of London, the world’s oldest insurer, one of our partners, to look at this data and be that third party alongside us to say, “Hey, we think there’s something here that’s worth underwriting.” and that’s actually what it looks like. And so they will show that contract to their customers, and they can see how much they’re covered for. They can see what exactly it covers, and that will also probably change next year. They will want to write an insurance policy that might cover more.

Swyx [00:44:04]: When you say Lloyd’s, is it reinsurance, or are they sharing somehow at the same level or

Rune Kvist [00:44:11]: Yeah. So typically, the way, new companies get into insurance is that they partner with insurers such that the insurers take the majority or all of the financial risks. Fundamentally, if insurance is useful, because it brings trust, you have to be able to pay the bill. Lloyd’s of London is 400 years old. They’ve never not paid a claim. They’re extremely trusted. What Lloyd’s of London struggle to do on their own is to figure out which of the risks are real, what should we be looking for, what are the kinds of technical controls, and running the tests. So they use AIUC-1 as kind of the underwriting framework, and we produce a bunch of eval results that then directly feed in to inform the pricing. So this means that ElevenLabs customers know that payment will be there. They don’t have to look to our series A and see, like, do we think they have enough cash on the balance sheet? They will look at Lloyd’s.

Swyx [00:45:05]: Yeah.

Rune Kvist [00:45:05]: Yeah.

Swyx [00:45:05]: And Lloyd’s, like, famously very creative. I think I remember some headline like, they insured Jennifer Lopez’s, butt or something.

Rune Kvist [00:45:13]: Correct.

Swyx [00:45:13]: Right?

Rune Kvist [00:45:13]: And I think, was it, David Beckham’s right foot?

Swyx [00:45:16]: So, yeah. Right?

Rune Kvist [00:45:17]: And stuff like this.

Swyx [00:45:18]: So, like, clearly not a large data set.

Rune Kvist [00:45:22]: Exactly. It’s actually a remarkable institution that’s both kind of has some of the truly school virtues of having been around for a long time. They, like, really. They really operate like a trusted entity, and they have appetite to figure out the future. And I think there’s a lot of recognition that both there is, like, tremendous amount of risk in AI that is poorly understood today, so getting into this business carries real risks. But also this is where lots of the risk exposure will happen in the future. This is the one market where risk is truly growing. This is the one market that will also take out some of the existing markets. Take, like, auto insurance. When there are no human drivers, how’s that market going to look? Well, it’s clearly going to change. How are you going to assess

Swyx [00:46:08]: You want to insure Waymo?

Rune Kvist [00:46:10]: I. All I’ll say is the principles for how you insure Waymo are very similar to how you insure other kinds of AI.

Swyx [00:46:15]: Right.

Rune Kvist [00:46:15]: So again, crash testing, that’s what we do for customer share at Lovable. That will also need to happen for Waymo, which is not how you do it for human drivers. So there’s this growing awareness that the world is changing very fast, and the only way to learn how to underwrite AI is to write some policies. You may incur some losses and think of that as R&D expense, really. But the question for them is, like, who are the trustedtechnical partners they can get into this business with that can help them navigate and make sure they don’t make, kind of foolish mistakes? But also who is willing to hear the wisdom that they have? They’ve done this before. They’ve seen it was. They were there when cyber came out. So there are lots of ways in which AI feels completely new, but there’s also lots of ways in which risks look the same. And so there’s actually a tremendous amount of wisdom sitting in some folks that may have gray hair, but really have, like, a keen sense of, how to quantify risk.

Swyx [00:47:08]: Yeah. And the number is. So it’s basically like I want fifty million dollars worth of coverage against these perils, and Lloyd’s will give you a quote on it, and then you have, like, a small markup or something, and then you turn it around and do that? Is that as simple as it is?

Risk Capital, Premiums, and Working with Insurers

Rune Kvist [00:47:23]: You basically share some of that premium.

Swyx [00:47:25]: Yeah.

Rune Kvist [00:47:25]: X percent goes to the people who do the pricing of it.

Swyx [00:47:28]: You’re. It’s kind of like a. It’s kind of like a merchant bank for insurance type of thing.

Rune Kvist [00:47:33]: Exactly. You basically split the fee, and you can think of the insurance supply chain as, like, there’s bringing the capital, there is doing the pricing, and there is doing the distribution. And typically, you will pay out some X percent of premium here, Y percent of premium here, and the rest of it will go here.

Swyx [00:47:46]: Does all the insurance world work like this, or is there some point at which, like. So if right now you have equity capital

Rune Kvist [00:47:51]: Yeah.

Swyx [00:47:52]: At some point, maybe you start raising, debt or whatever, and then you have enough of a bank account and enough history, let’s say you’ve been in operation for ten years

Rune Kvist [00:48:00]: Correct.

Swyx [00:48:00]: That you don’t need Lloyd’s anymore?

Rune Kvist [00:48:02]: That’s totally an option. And I could see some worlds where that makes sense, specifically if there are risks that we feel high confidence that we’d want to insure where the incumbent insurers are too slow to find appetite

Swyx [00:48:13]: Okay.

Rune Kvist [00:48:13]: Or simply struggle to evaluate it such that they don’t want to do it. But by and large, in general, you do not want to compete with insurers on, bringing risk capital to the game for two reasons. One is that’s fundamentally a cost of capital game. They have extremely low cost of capital. Startups have high cost of capital, by and large. And two, you want to hedge your bets, and it’s very helpful then to also have a portfolio of home insurance, of car insurance. And we’re not about to become a car insurer nor a home insurer.

Rune Kvist [00:48:43]: So they have some natural advantages, which makes it much more likely that we’ll partner.

Swyx [00:48:48]: Yeah.

Rune Kvist [00:48:48]: And they bring that, the capital at scale, and we bring the technical expertise.

Swyx [00:48:51]: You’re, you’re going to work with them for a long time.

Vibhu [00:48:52]: How are the discussions with the insurers as well? So basically, they’re going off of your certification, right? They’re trusting the diligence on you that your certification is valid, you tested the right things, and they’re backing the money that, you know, you have the right testing in place. So any interesting takeaways from working with insurers?

Rune Kvist [00:49:12]: I think the maybe the first thing is they feed into the standard as well. So if there are things that they feel like they need that they’re not seeing, we are also taking that as input into the standard, because fundamentally we think a good standard is one that creates a really healthy promise ecosystem, and we think insurers are a critical part of that. And again, they are the most well-incentivized to. They see all the lost data across every. Any particular CISO knows their particular concerns. Insurers see the concerns across the entire portfolio and often have direct access to, like, what exactly happened, who was at fault, et cetera, as they do part of their forensics. So they’re actually, like, a great source of intelligence on this. One of the big takeaways from cyber insurance, which is a market that didn’t work that well, was that the insurance and the technical expertise was not married up. What our conviction is that standards have to precede insurance. Fundamentally, what everyone first and foremost want, whether you’re a CISO at JPMorgan or a CISO at Cursor or an underwriter at Lloyd’s of London syndicate, is you want to not have an incident

Rune Kvist [00:50:19]: In the first place. You want to know that the risk is well-managed, and only then does insurance start to make sense. So we’ll see the standard ecosystem basically run ahead of the insurance. And the reason why we. You asked us kind of why I also do insurance, this is kind of proving what we think a whole promise confidence infrastructure ecosystem needs to look like, and we think it’s very compelling to bring that to life, even if we think the standard is kind of the core linchpin that unlocks the rest.

Claims, Liability, Air Canada, and Duty of Care

Swyx [00:50:44]: There’s been no claims yet, right?

Rune Kvist [00:50:45]: Nope.

Swyx [00:50:46]: This is one of those things where, you know, if people haven’t really worked through what it means to cover things.

Rune Kvist [00:50:52]: Yeah.

Swyx [00:50:52]: So for example, I pay Cursor $20 a month.

Rune Kvist [00:50:55]: Yep.

Swyx [00:50:56]: And I write a vibe code something that makes, a plane crash, causing $200 million worth of damage.

Rune Kvist [00:51:02]: Yes.

Swyx [00:51:02]: Do I claim $20 or do I claim two hundred million?

Rune Kvist [00:51:07]: Yeah. So these are all great questions.

Rune Kvist [00:51:10]: And fortunately, kind of all of insurance and legal history kind of helps answer some of those questions. I think the first thing is people have limits on their policy. So if you want to claim $200 million, you have to. Someone has to have paid a lot for that insurance policy upfront to have $200 million of coverage. And ultimately, the way this works is that, you start from a lot of uncertainty. This is not just an insurance, but also, like, can you use. Can Anthropic use books on the internet to train up? Well, they can go and look at precedent, they can But ultimately, this- these things get settled in court, and you hammer it out over time. So you start from this, like, place of ambiguity, which is both why insurance can be hard to do early on, but it’s also why people want insurance, because that ambiguity slows down adoption.

Swyx [00:51:57]: Yeah.

Rune Kvist [00:51:57]: That also sits at the heads of the,

Swyx [00:51:59]: Yeah. In some ways, actually, the first incident will help to, establish a lot of this.

Rune Kvist [00:52:05]: Exactly. And there have been a number of incidents out there that have just not been covered by insurance.

Swyx [00:52:09]: Yes.

Rune Kvist [00:52:09]: Take the now old, example from Air Canada, where

Swyx [00:52:14]: I was going to bring that up

Rune Kvist [00:52:15]: Chatbot hallucinated a refund policy, and the question was, Air Canada in that case were like, “Hey, we have nothing to do with this. This chatbot messed up, but, like, sorry.” And the courts were like, “No, if you put your chatbots to interact with your customers, they make legally binding promises on your behalf.” That is now precedent for everything in the future where you will. If someone were to deploy a chatbot like that again, they should not expect to be able to just pawn off and say, “Sorry, my chatbot lied. It’s nothing to do with me. I bought it from OpenAI.” No, if you’re putting this in front of your customers, you are taking responsibility for it. And so every court case, whether insurance is involved or not, clarifies liability, and liability is kind of the foundation for insurance. There’s another reason why standards and insurance come together. Liability for. I’ll go on a little tangent here

Swyx [00:53:06]: Please

Rune Kvist [00:53:06]: Get into the weeds of it.

Swyx [00:53:06]: Please.

Rune Kvist [00:53:07]: Liability, often one of the core concepts is whether someone was negligent. Should they have seen this? Should they have prevented this? And the question is: how do you judge that? Well, you basically judge whether they’ve met their duty of care. What does that mean in practice? Well, often they look to standards. So if there’s a standard that is broadly adopted that says you must have a groundedness filter or you must have a jailbreak filter, it becomes way harder to claim ignorance that these things existed. And so setting standards help clarify liability. Coins-- courts will often point to standards and being like, “Well, this seems like best practice to do.” It’s there for everyone to see. So there’s another way in which, like, standards are kind of civilization infrastructure that insurance can then build on, which promises can then build on.

Swyx [00:53:53]: I totally get that. We don’t have to get certified to write these, to, you know, make these, like, bots and all these.

Rune Kvist [00:54:00]: Correct.

Swyx [00:54:00]: But, like, basically, whenever we get. Go for the audit, I think people, like, start to shape up and all this stuff. I wonder if, like, that means that you don’t also then become, like, the approving authority for me to ship to production. You know, like, yes, you check once per quarter. I want to ship once a day.

Shipping to Production: Ongoing Testing and Trust

Rune Kvist [00:54:19]: Yeah.

Swyx [00:54:19]: And I don’t know when one of my things breaks, like one of your certifications or not.

Rune Kvist [00:54:24]: So there’s a couple things. There’s a couple of requirements in there that relate to how do you yourself, where you have to tell your customers

Swyx [00:54:33]: It’s like an ongoing monitoring.

Rune Kvist [00:54:34]: How are you yourself testing before you make at least major releases? We don’t go and audit people every day, but at least there is now a trail where if you do a major mess up, then your customer may come and ask you, “Hey, you promised me that you were going to run these evals yourself.” And for lots of them, most of the. PRs that people merge will not fundamentally alter the product experience, but some of them will. Thank you.

Swyx [00:54:57]: And sometimes you don’t know.

Rune Kvist [00:54:58]: And sometimes you don’t know. There are inherent risks that everyone knows that when they buy software, there can be bugs, and this is just part of it. But if you’re selling to mom-and-pop shops, they may not care. They’re just like, “Well, I want to use your tool, so I’m just going to willing-- be willing to take that risk on.” If you’re selling to a big bank, they might be like, “Sorry, we’re making promises to our customers. If you can’t make a promise to us that we can pass on, we don’t want to work with you.” Then it’s up to you to say, “Do I care for my agent to get used as critical infrastructure in this mission? If so, at least I can make promises about what processes I run, and then we can go and test it every quarter to be like, well, does it seem like, it’s still, that it still meets the standard.” So from my perspective, it’s kind of a way to. Big companies by default kind of have some amount of trust when they ship AI.

Rune Kvist [00:55:49]: If you’re a young company, if you’re just starting out, by default you have no trust. And there are very few places where you can go and get trust. So one of the things that most of our customers did before they started working with us is that they would make their own security blog posts. That’s great. But also, who’s going to trust you saying, “We’re so secure”?

Rune Kvist [00:56:05]: Like, anyone can write that. But it’s very hard. Where do you go and get that trust?

Vibhu [00:56:08]: Yeah.

Rune Kvist [00:56:08]: And so I think making the standards more legible makes it easier for smaller companies to prove that they’re doing what they ought to be doing, because the default assumption is that it’s the Wild West.

Vibhu [00:56:21]: Is there a roadmap you have of, like. There’s a lot of work to be done here, right?

Rune Kvist [00:56:25]: Yep.

Vibhu [00:56:25]: This is the first one.

Rune Kvist [00:56:26]: Yeah.

Vibhu [00:56:26]: Anything on the roadmap of what you see is next, what’s coming, what’s, what’s missing?

The Roadmap: Agents, Models, Robotics, and World Models

Rune Kvist [00:56:32]: I think when we zoom out, AIUC-1 deals with agents. Next up, we will deal with models. Next up from that, we will deal with robotics, of which, in some ways, Waymo is the first robot. But the exact same problem is going to be someone’s going to develop a robot, someone’s going to need some promises, they’re going to struggle to make the promises. And - You see this playing out when, like, if you think Fable concerns are bad, like, see when Waymo hits a dog. And that’s if people lose their mind. Imagine when first robot knocks off a toddler off a kitchen table.

Swyx [00:57:03]: Yeah.

Rune Kvist [00:57:03]: You’re going to see some real strict liability.

Vibhu [00:57:07]: I mean, you could see it, right? Like, Cruise got fully

Rune Kvist [00:57:10]: Destroyed.

Vibhu [00:57:10]: All permits are gone, yeah. Yeah.

Rune Kvist [00:57:12]: Correct. So physical AI, the level of stringency just goes up and up. So that’s kind of like the big picture. Agents, models, robotics. I think within agents, the current set of agents are well-covered by this. But as the technology progresses, as agents get longer horizons, new types of failure modes will emerge. And so it’s mostly of can you make sure the standard keeps up when they appear? And you also start to see new modalities. Like today, world models are mostly a kind of a research question. There’s no one who’s really using it. But that will also bring in just new kinds of ways to create value, but also more risk surface that no one knows how to grapple with today. You’ll start to see true agent interactions that are not mediated by humans. There’s going to be a bunch of interesting questions. You’re basically going to need a new legal system. How do they build trust amongst each other? How. One of the core things when humans trade with each other is that you know that you have recourse. You can sue them. How do you make sure that there is a persistent balance sheet behind any agent such that if you trade with it and it screws you know you can get your money back? Those are some of the questions we’re going to have to deal with. And the technical testing

Rune Kvist [00:58:24]: Of multi-agent systems is also going to be interesting and complex.

Swyx [00:58:29]: Very fun. Are there any perils that are uninsurable right now that people wish that you would?

Copyright Risk, Adverse Selection, and Information Asymmetry

Rune Kvist [00:58:35]: Yeah. One of the places where there’s a bunch of appetite for insurance and not a lot - a lot of demand, but not a lot of supply, is when it comes to copyright.

Swyx [00:58:46]: Oof.

Rune Kvist [00:58:47]: In some ways, copyright is kind of mundane. It’s always been an issue. There’s a couple of reasons for this. The first is people who have trained on copyrighted materials almost always know that they’ve done that.

Rune Kvist [00:58:59]: So if you want to buy insurance for it probably signals that you might be a high-risk customer. The people who are most interested in getting insurance for copyright infringement

Swyx [00:59:09]: Okay. Yeah

Rune Kvist [00:59:09]: Are the people who are most likely to have copyrighted

Swyx [00:59:10]: Yeah. It’s like a, it’s like a lemon problem.

Rune Kvist [00:59:13]: Exactly.

Vibhu [00:59:13]: I actually think there’s another side to it too, right? Like, if you’re building on something. So say I’m using an open model.

Rune Kvist [00:59:19]: Yeah.

Vibhu [00:59:19]: I don’t know what it’s trained on, right?

Rune Kvist [00:59:21]: Yes.

Vibhu [00:59:21]: And how far down that chain does copyright go?

Rune Kvist [00:59:24]: Yes.

Vibhu [00:59:24]: Am I liable to take down my product because company X trained on copyright?

Swyx [00:59:29]: But there’s safety in numbers. If everyone’s doing it, then you.

Rune Kvist [00:59:33]: Correct.

Vibhu [00:59:33]: I mean, I would say until, you know, Fable is rolled back from everyone that used it, right?

Rune Kvist [00:59:38]: Yeah. I think it’s a hard question. I don’t know the answer to it.

Vibhu [00:59:39]: It is.

Rune Kvist [00:59:39]: But I think your intuition is, your intuition is right in kind of like, what is the kind of duty of care?

Rune Kvist [00:59:47]: And people don’t today think of it as customary that you go and you, like, dissect the open model’s training data and you check everything. In fact, lots of people use them. It’s seen as kind of generally acceptable to not check for this. And therefore, like, we’re not going to hold you to specific

Vibhu [01:00:02]: I mean, we also really can’t, right? We don’

Rune Kvist [01:00:04]: Exactly.

Vibhu [01:00:04]: We don’t know the training data.

Rune Kvist [01:00:05]: So you can then ban it, but I think no court is going to get a copyright question and be like, “This actually needs to get banned.”

Swyx [01:00:09]: Unless you hire Nicholas Carlini and he can extract it for you.

Rune Kvist [01:00:12]: Exactly. Though he’s in short supply.

Swyx [01:00:15]: Yeah. He’- You only have so many Carlinis, but,

Rune Kvist [01:00:17]: Exactly.

Swyx [01:00:18]: Yeah, go ahead.

Rune Kvist [01:00:19]: So I think this is also fair that, in the case of labs, there’s a lot of interest for this. But the thing that makes lab want it is what makes this insurer suspicious of it, and so you have a lemon’s problem.

Swyx [01:00:30]: Yeah. Is there, like, a theory of insurance where adverse selection dominates the risk-sharing aspect of insurance? Like, where does this. Like, teach us insurance.

Rune Kvist [01:00:40]: A lot of insurance does come back to, like, practical versions of microeconomics 101.

Swyx [01:00:45]: Yeah. It’s very. It’s like, it’s like this is why

Vibhu [01:00:47]: High-risk adverse.

Swyx [01:00:48]: You need to pool health insurance, because if you make it too hyper-specific, then only people who are guaranteed to get the disease will sign up for your insurance.

Rune Kvist [01:00:56]: Exactly.

Swyx [01:00:56]: Same thing.

Rune Kvist [01:00:57]: The core problem is one of information asymmetry. People buying insurance know something about their risk that insurers do not know. And so the question is actually. And this comes back to the same problem is, if you rely. You can break a lot of these information asymmetries if there is. Some kind of testing that reveals the underlying true risk. And so if you were able to, in the case you mentioned, have good diagnosis of whether someone has it or what the probability is that someone has it, that the insurers trust, then they might be willing to insure it. But if they don’t, if there’s no kind of common information, then - only the patient will know

Vibhu [01:01:32]: Yeah.

Rune Kvist [01:01:32]: That’s what breaks it down. So the question is, again, how do you create credible signaling between players?

Rune Kvist [01:01:39]: This is also the whole reason why Moody’s exists. Moody’s just does credible signaling. That’s also why Moody’s could never-- Moody’s has to be independent. If Moody’s was owned by JPMorgan, then JPMorgan cannot use it as a signaling mechanism. So a lot of the basics of standards and certification are just communication devices. It’s just a trust gap. And, that’s where you have to think about what are the incentives of the messenger. And one and another way you can break a lot of this is through transparency. If you are transparent in how you operate, you just cannot mess with others nearly as easily. You make it much more costly, and that increases trust. This is one of the reasons why there’s a change log here.

Rune Kvist [01:02:16]: Every little change

Swyx [01:02:18]: Yeah

Rune Kvist [01:02:18]: You can go back and find, and it means that if we were to make the standard worse

Swyx [01:02:24]: Oh, wow, that’s a lot of changes in one update.

Rune Kvist [01:02:27]: Yeah.

Swyx [01:02:27]: Okay.

Rune Kvist [01:02:28]: And a lot of this is just as things get clearer, you can see a lot of clarifications, you can see some revisions. As things get hammered out, you want to change this. But if you make it all public, you make it much harder to mess with people, or at least you become found out very easily.

Rune Kvist [01:02:42]: And so this is a way of reducing the information asymmetries by just making more of the information public.

Vibhu [01:02:50]: I like how you do know when future versions are coming.

Swyx [01:02:52]: Yeah.

Vibhu [01:02:53]: So I guess it’s quarterly.

Swyx [01:02:53]: I mean, they just

Rune Kvist [01:02:54]: It’s quarterly.

Vibhu [01:02:54]: Yeah.

Swyx [01:02:54]: It’s kind of quarterly.

Rune Kvist [01:02:55]: Yeah.

Swyx [01:02:55]: Not that surprising.

Rune Kvist [01:02:58]: Yeah, but this is also a promise. Like, if we now don’t deliver on July 15, basically

Swyx [01:03:03]: I mean, you can just batch it up, and then whatever you got, you just ship it.

Rune Kvist [01:03:05]: You just batch it up.

Swyx [01:03:05]: Yeah. That’s not that hard.

Rune Kvist [01:03:06]: But it’s kind of like we deposit some amount of trust every time we meet this commitment.

Vibhu [01:03:12]: Yeah.

Rune Kvist [01:03:12]: And in the startup land, it feels easy to ship a new version of a standard once a quarter. In the enterprises who are used to this, like, decade-long cycle, we often get met with, like, incredulity. Like, there’s just no way. And then you show them the change log.

Swyx [01:03:28]: One thing I wanted to also, like, try to really think about is, you know, you said something about how if you have tests for the thing, then you can insure it.

Rune Kvist [01:03:35]: Yes.

Swyx [01:03:36]: Right? And so really what your standard is, what AIUC is, is establishing a framework for the audits to happen so that you can at least test, like, all these, like, baseline standards of care have been met, and therefore people can insure against standard risks that everyone has. I wonder if, like, there needs to be develo-- you need to develop other tests. We’ve covered mech interp in the past. Any interest in that, or are there other kinds of tests that we’re not thinking about?

mech interp, Eval Awareness, and Monitoring

Rune Kvist [01:04:02]: Yeah, I think mech interp is a big one. A lot of interest in that. I think everyone would agree that there’s, like, promising scientific potential.

Rune Kvist [01:04:15]: We’re still a while, a little bit away at least, from this being, like, commercially available on demand such that there’s, like, now a selection of vendors you can go to.

Swyx [01:04:26]: Goodfire would say that it is commercially available.

Rune Kvist [01:04:28]: Exactly.

Swyx [01:04:29]: And it just

Rune Kvist [01:04:30]: We would agree with them. We think that the work that they’re doing is tremendous.

Swyx [01:04:33]: Yeah.

Rune Kvist [01:04:33]: We’re not quite at a point where we could literally require it. But it’s the kind of thing where you can imagine relatively soon you could put in an optional control for if people use mech interp as a way to reduce risk, you at least get credit for it. We can’t require it because it’s going to be hard to require everyone to become Goodfire customers.

Swyx [01:04:49]: What good does credit do me? This is - this is a pass-fail, right? Do I care about credit?

Rune Kvist [01:04:54]: It’s a pass-fail, but it’s also a 100-page audit report

Swyx [01:04:57]: Huh

Rune Kvist [01:04:57]: That you’d be surprised at how much security leaders actually sit down and digest this stuff.

Swyx [01:05:02]: Okay.

Rune Kvist [01:05:02]: And I promise you that if someone is using mech interp today they will have a slide on it because they’ll try and get credit for it.

Swyx [01:05:11]: It is cool. It’s fancy, yeah.

Rune Kvist [01:05:12]: But it’s just easier if you have a third party saying, “Yep, they have mech interp, and actually.”

Swyx [01:05:16]: Just to spell it out for people who have been following our mech interp podcast

Rune Kvist [01:05:21]: Yeah.

Swyx [01:05:21]: It is literally like, oh, you’re using, you know, OSS. It is activating these three dangerous things. We monitor for it, and we log it out in whatever tool of choice. Gray Swan has, like, Signal or whatever, and that’s it. That’s the mech interp-based activation, signal. Okay.

Rune Kvist [01:05:38]: Yeah. So I think mech interp is interesting, and I think if that promise truly comes to fruition, you can make stronger promises than you can with evals. And so I think that’s very compelling. Another thing that I think will become increasingly important is just kind of good school monitoring, and slightly after the fact. One of the things you’re seeing with eval, some of the challenges that are emerging is that the agents are starting to become aware that they’re being evaluated.

Swyx [01:06:04]: Yeah, eval awareness.

Vibhu [01:06:05]: Yep.

Rune Kvist [01:06:05]: Exactly, which is a problem. It means that they basically, if they know they’re being watched, they won’t do the thing that they think they get punished for. And by default, unless you know how to kind of reduce eval awareness, you should trust evals less. And one of the kind of truest things, monitoring, like, is the source of truth. Did you in fact give medical advice, and how quickly do you know? How often - have you done that in the past? How fast do you respond? How often do you detect it? How fast do you detect this? So I think that is also a paradigm. It’s slightly more intrusive. You actually will look at some customer data, but I think will become more prevalent over time.

Swyx [01:06:45]: People talk about this like we should not write about eval awareness because it’s going to leak into the data set and then be. Like, we should just. Like, we should, like, never talk about it, only meet in person and, like, talk offline unrecorded. Like.

Rune Kvist [01:06:57]: Did you guys see the Anthropic research where. I think this was literally Anthropic did that test.

Swyx [01:07:04]: What?

Rune Kvist [01:07:05]: It took. I can’t remember the details here, but they, ran some studies on misalignment, and then they took out the training data- That related to LessWrong discussing misalignment, and they ran the same test again and the failure rate went down.

Rune Kvist [01:07:20]: So it, in fact, was some evidence pointing towards it had learned the - either the ability or the propensity to do that.

Swyx [01:07:28]: Yeah, I mean, so there’s the hyperstition effect, and then there’s, like, the Luigi/Waluigi effect.

Rune Kvist [01:07:31]: Correct.

Swyx [01:07:32]: Which is like you are. The more you try to train for it, you create the opposite.

Rune Kvist [01:07:36]: Yes, there you go. That’s exactly it.

Swyx [01:07:38]: In some ways, I think the very success with Anthropic is a result of hyperstition, like the fact that you wanted this thing to exist in the world, and now it does. But, like, then it also creates the opposite as well.

Rune Kvist [01:07:48]: Yes.

Swyx [01:07:49]: Like, I think people who are maybe newer to this space don’t remember Waluigi, but, like, I do think it’s very important for understanding that when you train for a thing, you also train the opposite of the thing ‘cause it’s just a big flip.

Rune Kvist [01:08:02]: Yes.

Rune Kvist [01:08:03]: Yes.

Vibhu [01:08:04]: I think, you know, just going back to where we were at, like, there’s a lot more than just mech interp that there’s value in just having added, right? So your version of how fast can you measure stuff? Do you have logging? Do you have evals? You know, do you see other parts of the stack, like the inference providers that you use, the services? Okay, am I using Chinese model on their home API? Am I using through certified vendor here? Am I hosting myself? What am I doing on the inference engine side? There’s just, like, so many levels of stuff that gives, you know, information that you can standardize out, right?

Managed Agents, Enterprise Controls, and Generative Media

Rune Kvist [01:08:36]: Yeah. And you also see increasingly, in addition to just the basic chatbots, you’re increasingly seeing big companies adopting agent platforms where they’re building on top of Google’s Agent Studio, et cetera that comes with a bunch of, like

Vibhu [01:08:52]: Managed agents.

Swyx [01:08:53]: Managed agents.

Vibhu [01:08:53]: It’s everywhere now.

Swyx [01:08:54]: Everyone has managed agents.

Rune Kvist [01:08:55]: Exactly.

Vibhu [01:08:56]: And there’s even levels. You can host your own managed agents, OpenAI’s Agent SDK, or hosted by Anthropic, or Google does both.

Rune Kvist [01:09:03]: Correct. And then these are just ways to kind of strengthen the security guarantees you can make. And in some ways, it’s kind of bread and butter enterprise security. They. Like, they love to host things on their own premises because it gives them really a sense of control. And I think you’ll, you’ll see, just like you do in every other enterprise market, if you really sell to the enterprise, you start to compete on some of these security features. And this is also happening in AI, unsurprisingly. And I think you are seeing some amount of enterprises wanting. Enterprises are really grappling with the thing that makes agents useful is that they’re stochastic, and the thing that makes them really hard to adopt is that they’re stochastic, and these are in tension.

Rune Kvist [01:09:46]: Leaders come out on different sides of that table, in part depending on how much the CEO is trying to get the stock price to go up by saying they’re AI native and that we must be willing to take the risks. We see, we actually see phenomenal tension in the heads of the CISOs of the Fortune 1000, where on the one hand you have the CEO saying, “We must adopt, otherwise we’re becoming irrelevant, and if we fuck up, you’re fired.”

Swyx [01:10:08]: Oof.

Rune Kvist [01:10:08]: And that’s kind of like the core emotional tension that we see showing up again and again. And one of the core problems that we solve for them is to take that abstract emotional concern and turn it into a framework, in some ways just providing clarity to that concern.

Vibhu [01:10:23]: So anything in here. So something I think we kind of skipped over. We talked a lot about agent language models, skipped over world models.

Rune Kvist [01:10:31]: Yeah.

Vibhu [01:10:31]: You guys have voice, which is interesting with ElevenLabs.

Rune Kvist [01:10:34]: Yeah.

Vibhu [01:10:34]: How about generative media? So, you know, generating images, videos, that’s a category that actually has a lot of usage. Is there anything in your current policy? Is it separate policy? How do you see that space?

Rune Kvist [01:10:46]: Yeah.

Vibhu [01:10:46]: It’s like we did talk a bit about copyright,

Swyx [01:10:49]: Music.

Vibhu [01:10:50]: Yeah, music as well.

Rune Kvist [01:10:51]: Yeah. I think a lot of the concerns that come up there either relate to, copyright or there’s a lot related to, let’s call it broadly safety. So, like, this could be not safe for work or just very graphic materials, are kind of some of the core things. We have done some work on this. There’s a little bit in the standard as well that deals explicitly with that. Video, we have not done a lot in yet. And I think for proper production, that has still. Especially proper production without a human in the loop, that’s still got some ways to go. It’s obvious that it’s coming, but it’s very rare that it’s like shot deploy a video to the internet. But eventually that will also happen.

Vibhu [01:11:33]: We see, like, you know, Luma has Luma agent where it’s still pretty human in the loop.

Rune Kvist [01:11:37]: Yeah.

Vibhu [01:11:37]: So it’s not just

Rune Kvist [01:11:37]: And that just makes complete sense as the technology matures, and over time, it will become so good that people will not want to slow things down by having a human in the loop. And then, the need to make promises will grow.

Swyx [01:11:52]: Why not just have prediction markets on everything?

Prediction Markets vs. Audits

Swyx [01:11:55]: Right? It’s very EA adjacent.

Rune Kvist [01:11:56]: Yes. The core thing is that the people. Prediction markets rely on public information. There is not a lot of public information. It’s just insiders trading on each side.

Swyx [01:12:06]: Yeah.

Rune Kvist [01:12:09]: That’s illegal.

Vibhu [01:12:10]: There’s leaked information.

Rune Kvist [01:12:12]: There is leaked information. The core challenge is that often you have private sensitive information, and you need to convey confidence and trust around that. And you can, of course, for some claims, like can any model be jailbroken, you could rely on public evidence ‘cause there would be lots of people being like, “Well, there’s tons of studies, and actually they all can, so that resolves fine.” I think that’s good. For, hey, this new unreleased Methus model, how capable is it actually?

Rune Kvist [01:12:43]: Prediction markets have not a lot to say because actually just no one knows. And so I think that’s the core place where some of this breaks down, is that actually lots of the world’s information that guides some of these high-level decision is private and often also just not known.

Vibhu [01:12:56]: I think the thing with prediction markets that people like is it’s not, it’s not answering the broad question. It’s a specific, right? So will a model do this by this date, or is a model capable to do this by then, right?

Rune Kvist [01:13:07]: Yes.

Vibhu [01:13:08]: That’s a little distinction there.

Rune Kvist [01:13:10]: Yeah. And often the most interesting question, if you are, say, the head of security at a bank. The question you’re really trying to answer is, will this product, this agent, do this bad thing that maybe primarily I care about, specifically in the setting that I care about? And the question is like, what’s the closest-- That information may not exist anywhere. So prediction markets aggregate existing information. This information may not exist, and you want some very specific and you’re willing to pay for it. That’s kind of where a third-party audit comes in. We also don’t really use prediction markets to figure out whether, public companies have committed fraud in their books. You use audits. You probably could, but the information’s just not that available. And if so, it would be like just trading on vibes. Actually it would have been really interesting to see whether prediction markets two thousand and one were predicted Enron going bankrupt and they kind of

Swyx [01:14:02]: Yeah.

Rune Kvist [01:14:02]: Could you have told-- could you have sensed from like the craziness of the CEO or some other traits that they were more likely to cook their books than others?

Swyx [01:14:10]: Or enough insiders leak it then that

Rune Kvist [01:14:12]: That could also be right.

Swyx [01:14:13]: Right. Which is like, I mean, this-- that’s the sort of the ideal dream of prediction markets. You have liquid markets and everything.

Rune Kvist [01:14:20]: Yeah.

Swyx [01:14:20]: And then you can compose your exact set of risks to offset.

Rune Kvist [01:14:24]: Yes.

Swyx [01:14:25]: Right?

Rune Kvist [01:14:25]: Yes. Yeah. And I think, like, prediction markets will bring lots of new information to it. So the thing is mostly not like which one is it, and more like what are the types of questions that prediction markets are really good

Swyx [01:14:37]: Yeah.

Rune Kvist [01:14:37]: And what are the ones where the information doesn’t even exist for insiders such that no one can in fact trade on it and it needs to get generated.

AI Engineer Certification and Training

Swyx [01:14:43]: Okay, one self-serving question and then one open-ended one, on like the future of AIUC. Self-serving question would be, so you have your standard, right?

Rune Kvist [01:14:52]: Yes.

Swyx [01:14:52]: I run, you know, a large AI engineer conference. Like, there’s been a lot of talk about us certifying AI engineers.

Rune Kvist [01:14:58]: Yep.

Swyx [01:14:59]: Training programs, level one, level two, level three. I was a CFA myself, so I know what-- that’s what the finance industry does.

Rune Kvist [01:15:04]: Yes.

Swyx [01:15:05]: Would it help if I had AI engineer level one, level two, level three, and then it would-- they would, like, work with these guys? I don’t know.

Rune Kvist [01:15:12]: If you think of the highest level objective as, like, accelerating secure deployment of agents, then that would totally help. Because one of the things that happens often now is that folks build agents, they bring it to the decision-maker, and the decision-maker surfaces a bunch of security considerations that they had not thought of, and now it’s not built to spec. Now you have to go and - like, add these filters, et cetera. So if you shifted that left, like if everyone knew what the spec they were building to, if everyone knew the grading scheme

Swyx [01:15:41]: Yeah.

Rune Kvist [01:15:42]: That would be awesome if they were already trained. So by default

Swyx [01:15:44]: But you’re the grading scheme, right?

Rune Kvist [01:15:45]: Say again.

Swyx [01:15:45]: I don’t get to set the grading. You guys, you set the grading scheme.

Rune Kvist [01:15:47]: We set the grading scheme. And I think what’s, valuable is, like, if you can turn those into

Swyx [01:15:52]: Training programs.

Rune Kvist [01:15:53]: Training programs

Swyx [01:15:54]: Yeah.

Rune Kvist [01:15:54]: Such that people

Swyx [01:15:54]: Which you’re, you’re not doing.

Rune Kvist [01:15:55]: We’re not doing that.

Swyx [01:15:56]: Yeah.

Rune Kvist [01:15:56]: I think there’s value in doing it.

Vibhu [01:15:57]: There are others doing. I mean, not to interrupt, but you know

Swyx [01:16:00]: Yeah.

Vibhu [01:16:00]: OpenAI has their

Swyx [01:16:02]: Anthropic also has like a CCTA thing.

Vibhu [01:16:04]: Yeah. You know, they want hundred thousand deployed certified consultants, right?

Rune Kvist [01:16:09]: I really think it’s good for. We will accelerate adoption if we have more people who know how to build secure agents, and we are not working on the side of training people at the moment. I think it’s, like, very aligned with our mission. We only have so much, attention.

Swyx [01:16:24]: I’ll tell you why I haven’t done it.

Rune Kvist [01:16:26]: Yeah.

Swyx [01:16:26]: It’s not like I haven’t thought about it before.

Rune Kvist [01:16:28]: Yes.

Swyx [01:16:28]: It’s just being prescriptive

Rune Kvist [01:16:30]: Right.

Swyx [01:16:31]: About like, well, this is what you should know, therefore, like, the stuff that I didn’t include is what you don’t need to know.

Rune Kvist [01:16:35]: Yes.

Swyx [01:16:36]: And I’m like, “That sucks.” Like.

Rune Kvist [01:16:37]: Yes. Yeah.

Vibhu [01:16:39]: But I think it’s like, you know, the very interesting defensible thing you guys do is your opinionated 100-page report of here’s what matters, right? Here’s the, like, prescriptive definition of the requirements you need to be certified, so.

Rune Kvist [01:16:55]: Yeah, and I think that’s a choice. I think basically that’s a, that’s a choice, and I think that serves some audiences very well, where if you’re trying to deploy this into a bank or a hospital, et cetera, clarity of the - those boundaries is extremely valuable.

Rune Kvist [01:17:09]: There’s lots of other settings where being much more experimental, much more trying it out is just the better fit. And so to me, this makes a ton of sense. Also, you’d have to rewrite your curricula every freaking three months.

Swyx [01:17:21]: It’s fine. I do that. Like, it’s okay. But yeah, no, for me, it’s actually - like, genuinely, like, the consequences of getting it wrong and, like, affecting somebody’s career is a big responsibility.

Rune Kvist [01:17:35]: Yeah. Like, I think that’s exactly right. And I think a lot of our work actually goes like, we don’t want to carry. We also don’t think of ourselves as able to carry the, kind of the true north of what’s, like, secure or not secure, but we can coordinate the forum where you listed all of that.

Swyx [01:17:52]: Yeah. Your consortium is fantastic.

Vibhu [01:17:54]: Do you think this can be crowdsourced in a way? Like, for your example, for what is AI engineer certification, right? This is a pretty big podcast. There’s a lot of takes that people can have and, you know, discussions that can.

Swyx [01:18:05]: And people reasonably disagree. So who am I to say, like, that’s a correct question, that’s a wrong question?

Rune Kvist [01:18:09]: Yeah.

Swyx [01:18:09]: Right? So, like, I don’t know.

Vibhu [01:18:10]: We’ll have an exit.

Rune Kvist [01:18:13]: Yeah.

Vibhu [01:18:14]: Vent your frustration to someone that’s listening, you know?

Rune Kvist [01:18:16]: Exactly. And I think there’s also you. Or it matters a lot what the promise is. So if the promise is, “Hey, if you’ve taken my course, you will not fuck up,” you can’t make that promise, clearly. You could make a promise of like, “Here’s the. Some important things that everyone should at least know,” and then you have to fill out the rest there. At least the promise changes. Of course, there’s some subtlety in how do you communicate this such that people really get it. But I think it’s important to dial in, and we have a section in our center on, like, what is the promise and what is the promise not, because it’s impossible to guarantee that nothing will go wrong. If you need a guarantee that nothing will go wrong, you cannot work with frontier AI, but you can make some claims.

Swyx [01:18:56]: Yeah, for sure. Cool. Wanted to end with open-ended, where is AIUC going? I think you talked about model stuff, robotic stuff. And just open-ended, like, where, you know, what is in the future for you guys?

AIUC’s Future, Hiring, and Universal Red Teaming

Rune Kvist [01:19:10]: Very near term, we’ve now started to work with some of the frontier companies in each of the categories that are taking off, and we’ll, we’ll continue that work to make sure that we cover all of the use cases that are really taking off. We see a lot of interest once the first one in the market moves. Lots of people want to follow them. And we think basically AIUC-1 will get to a point where all of the Fortune 1000 will organize their risk processes around the standard.

Swyx [01:19:38]: And you have 50%?

Rune Kvist [01:19:39]: No, we do not have 50% today.

Swyx [01:19:41]: Oh.

Rune Kvist [01:19:41]: I think there is some world where probably by end of year, we might have representation in our consortium for 50% of the Fortune 1000.

Swyx [01:19:48]: I see. Got it.

Rune Kvist [01:19:49]: So that’s on the agent layer. And then we think, yeah, the model layer, it’s going to be. It just brings. Are now surfacing the concerns that are most likely to slow down adoption of AI. And then, yeah, we think robotics comes after that.

Swyx [01:20:03]: What are you hiring for? What’s hard to hire for?

Rune Kvist [01:20:05]: We are hiring, across the board, across market and numbers of technical staff. The people who do really well on our technical team are folks who are really excited about kind of being truly full stack. So let’s say when we started working with Cursor, we’d never done coding, tools before. So taking the standard and extending it, fleshing out what does frontier evals look like for long horizon coding agents, and taking that problem all the way from, like, working with Cursor and other folks in this space down to, like, fleshing out and shaping a new version of the standard. So that’s like a truly a full-stack, entrepreneurial technical people do extremely well at AIUC. The hard part is building one universal red-teamer that works across from Harvey to Cursor and everywhere in between that both has one consistent methodology, one consistent taxonomy of what are the risks and the attacks, and making. We think that’s fundamentally the best way to make consistent promises. JPMorgan is buying both. They want to have one framework, one consistent way that this comes out, and the mechanics of making that happen, you get to deal with a lot of the complexity of the real world. I think we have good answers in a bunch of that, but there are some pretty hard engineering problems in executing that.

Swyx [01:21:18]: Can I push a little bit? Like, must you have one? Why not just be like, “Okay, look, forty percent of our use cases are coding agents, so we will specialize in coding agents,” and that’s the, that’s the one of them.

Rune Kvist [01:21:28]: Yes.

Swyx [01:21:29]: And then, okay, thirty percent is like RAG.

Rune Kvist [01:21:31]: Yes.

Swyx [01:21:31]: Just do RAG.

Rune Kvist [01:21:32]: Yes. I think there’s some wisdom in that question.

Swyx [01:21:36]: Yeah.

Rune Kvist [01:21:37]: It depends on. What we found that there’s a lot of value on is being able to. If the decision-maker on the buying side, let’s say you’re the head of risk at a bank and your biggest risk is not in coding or in customer support or whatever the top two biggest use cases, but it’s somewhere else, you want to still make sure that framework has something to say about it to the burning question you have. Otherwise, you’ll not earn that trust. Now, it’s true that a lot of the burning questions follow where there’s a lot of adoption. And so great, so do we. So we do today do not cover every single edge, but we have a framework that we can add all of these within. We have one global taxonomy of risks and attacks that keeps adapting.

Rune Kvist [01:22:20]: As, like, every time a new incident occurs that has never been seen before, great, let’s go and update the taxonomy so we bake that in. So I think we have one coherent universal approach. It doesn’t mean that we spend equal amounts of time on code and insert niche use case. We do spend time where people care. We think it’s very valuable to have one language.

Swyx [01:22:43]: Yeah. That makes sense. That’s, that’s a, that’s an important choice. We were going to end actually, but I thought of one final ending closing question, which is, take this however you want, right? Let’s say one and a half years from now, OpenAI’s secret panel of five experts declares that we have reached AGI.

AGI, Watchdogs, and the Need for Independent Oversight

Swyx [01:23:00]: Do you expect your business to change?

Rune Kvist [01:23:03]: No. I think there is some important way. I think the last businesses to exist beyond the labs

Swyx [01:23:10]: Will be underwriting.

Rune Kvist [01:23:12]: Well, there is one, there’s one job that the labs can never do for themselves, which is to be their own watchdog.

Swyx [01:23:19]: There you go.

Rune Kvist [01:23:21]: So I think kind of to the extent that you believe this frame of, like, you’ll see hyper-concentration, like the labs will kill all the startups

Swyx [01:23:29]: Yeah

Rune Kvist [01:23:29]: Which, we can go into the pros and cons.

Swyx [01:23:32]: I feel like the labs actually care a lot about this, right? There was the whole superposition, what do we do when we have models smarter than us and then a tier above, right, models smarter than them training them.

Rune Kvist [01:23:41]: Yes

Swyx [01:23:41]: The labs actually think about this a lot.

Rune Kvist [01:23:42]: They think a lot about. I think the there are some of the smartest people on these topics work at the labs. So the problem is not whether they care. The problem is that they will all be stuck in a race where they might have incentive to cut corners, and they might have incentive to withhold information from the government, et cetera. And so one kind of feels like eternal truth is that you need an independent third party to go and inspect that data and share information, in this case, say, with the government. It’s more of an incentive problem than an interest problem. I think they’re fundamentally all trying to make this go well.

Swyx [01:24:14]: What I’m not hearing is, like, AGI, whatever that label means to you, to me, to them, doesn’t fundamentally have, like, a qualitative shift

Rune Kvist [01:24:23]: Correct

Swyx [01:24:23]: In, like

Rune Kvist [01:24:24]: Correct

Swyx [01:24:24]: You still have to evaluate the models.

Rune Kvist [01:24:26]: And I think the one thing that would make this a qualitative shift is, there’s. For some definitions of AGI, it will just get nationalized. It’ll be a threat to sovereignty.

Swyx [01:24:34]: Yes.

Rune Kvist [01:24:34]: And then at that point, it kind of maybe every company is the government is every company. I struggle to think about that world. But at that point, you’ve kind

Swyx [01:24:42]: We. I don’t think we’ll move fast enough.

Rune Kvist [01:24:44]: Right.

Swyx [01:24:44]: You know, like, we’re not, we’re not set to do that.

Rune Kvist [01:24:47]: Yeah.

Swyx [01:24:48]: But I have discussed this a lot on the podcast.

Rune Kvist [01:24:51]: Yeah.

Swyx [01:24:52]: I mean, you know, as far as the watchdog concern, I will also mention that because I have my finance background, I often think about the scene in The Big Short where they talk to, like, Moody’s, but also Standard & Poor’s. And then the lady at Moody’s is like, “Well, if I don’t give you a triple A rating, you’re just going to go down to Standard & Poor’s.”

Rune Kvist [01:25:09]: Yes.

Swyx [01:25:09]: So actually the watchdog is a natural monopoly because if you have race dynamics in watchdogs, then the watchdogs will compete each other to the lowest possible standard.

Closing: Insurers, Incentives, and Trust Infrastructure

Rune Kvist [01:25:18]: Correct.

Rune Kvist [01:25:20]: And so I think what one of the things, one of the reasons why we’re very excited about having insurers be around this table is that insurers are the only ones that do not have this dynamic because they pay the bill. If they keep lowering the prices

Swyx [01:25:32]: Yeah, you will

Rune Kvist [01:25:33]: They also pay the bill.

Swyx [01:25:33]: You won’t find the market clearing.

Rune Kvist [01:25:35]: And this is not true for Moody’s where, they don’t directly pay the bill if they make recommendations that are off. So we think that balancing factor is pretty important. And I think it also highlights that there’s, like, no system that’s perfect. You need scrutiny of Moody’s, you need scrutiny of the watchdogs, for sure.

Swyx [01:25:52]: Beautiful. Thank you so much for indulging. This is a beautiful conversation covering everything. Congrats on your success so far.

Rune Kvist [01:25:59]: Thanks for having me.

Swyx [01:26:00]: Yeah. Awesome.

Rune Kvist [01:26:00]: Appreciate it.

AIEi Paris (Sep 23-24) and AIE NYC (Oct 12-14) is >50% sold out, AIE CODE (Nov 10-12 in SF) and AIEi Shanghai (Nov 5-6) are next on deck before AIEi Sydney (Dec 7-8 alongside NeurIPS) closes the year!

It’s very rare that a new startup launch will make title story, especially on a day when Gemini 3.8 Live and Periodic Labs had strong announcements, however, TypeSafe’s launch has sat comfortably atop Hacker News all day. We were fortunate to preview them last month at AIE pre launch:

and now their announcement (blog, evals, docs) has gotten millions of views:

For those used to traditional autoregressive LLMs, a fast model that cannot code and doesn’t reason might feel counterintuitive in its usefulness. That’s exactly what the team is aiming for in complementing “System Two” slower LLMs: you let go of strings and chat, and you get 1) parallel sampling, 2) “no hallucination”, 3) calibration.

The system was trained through “RLCD” - calibrated decisions: a topic that Clementine from HuggingFace had highlighted as one of the important research frontiers in our pod:

AI News for 9/14/2026-9/15/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Periodic Labs’ Neon: Lab-Grounded RL for Materials Science

• Neon’s core result: The biggest technical story in the set is Periodic Labs’ Neon announcement via Liam Fedus: a model trained in a tight loop between high-throughput physical labs and ML, focused first on materials science problems like superconductors, magnets, and semiconductors. Periodic says it used 1,300 H200s, months of proprietary experimental data, mid-training plus RL, and an open-source base model to surpass GPT-6 Astra on its analysis benchmark. Follow-on posts add useful detail: @periodiclabs describes continuously running experiments feeding model improvement; @DBahdanau says the team trained a 1T-parameter XRD analysis expert; @khoomeik frames it as a trillion-parameter model for experimental data analysis beating Astra and Fable on the task.

• Why it matters technically: Several reactions converge on the same thesis: domain-specific data plus RL infra can beat frontier general models on narrow but valuable scientific workloads. @zephyr_z9 highlights that Periodic pushed a Kimi 2.5/K2.x base past Astra; @_jasonwei notes this as evidence that specialized private data becomes increasingly decisive near the frontier of science; @vwxyzjn emphasizes the unusual part: RL on real experimental data from physical labs, plus bespoke infra and a sandbox system; @zijie_y adds that long scientific traces stressed memory and parallelism enough that training Neon required frontier work in long-context training efficiency. A more complete community summary from @brianzhan1 claims Neon starts from Kimi K2.6, lifts success on an internal FrontierXRD eval from 2.7% to 55.3%, and beats Astra and Claude Fable 5.1 at lower inference cost.

• Implication: This looks like a concrete template for “AI for science” beyond paper benchmarks: vertically integrated labs producing proprietary data, models trained against scientist-calibrated rewards, and deployment back into experimentation. The strongest meta-observation came from @richardczl: every company with a meaningful data moat will likely try this play, shifting bottlenecks toward RL rollout throughput, verifier compute, and weight sync.

Gemini 3.8 Live and the Push Toward Real-Time Voice Agents

• Google’s new live audio models: Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, positioned as conversational models that can talk, think, and handle tasks in the background without breaking flow. The developer-facing rollout from @GoogleAIStudio and summary from @_philschmid add the key product details: 97-language support, async tool calls while speaking, availability via Gemini API / AI Studio, and partner support through LiveKit, Pipecat, LangChain, and Vercel.

• Benchmarks and economics: Artificial Analysis provides the most technical external read. Gemini 3.8 Live Extended Thinking (High) debuts #1 on its speech-to-speech index at 82.6, ahead of GPT-Live-1 Astra (81.5), and #1 on Tau Voice at 68.6%. The standard Live model is cheaper and faster but much weaker on agentic voice tasks. On pricing, standard 3.8 Live is reported at $0.84/hour input audio, while Extended Thinking High is $3.50/hour, still below several competing live models. This reinforces the theme that Google is optimizing not just quality, but deployability for production voice agents.

TypeSafe’s Jev and RLCD: Decision Models Instead of Text Generators

• New model category, or at least a new packaging of one: One of the highest-engagement technical launches was Diogo Almeida/TypeSafe’s Jev announcement, claiming a new frontier model trained with RLCD and optimized for decisions, not text generation: 20–200x faster, 40–400x cheaper, with output tokens free. Reactions from @omarsar0, @chaseleantj, and @Yuchenj_UW all zero in on the same likely use case: replacing LLMs as structured classifiers / judges / routing policies in production systems where autoregressive generation is unnecessary overhead.

• Important caveat: Some community posts correctly push back on overgeneralization. @scaling01 notes Jev is not a general language model and likely closer to a constrained or diffusion-like decision model; it cannot produce free-form text and requires predefined output formats. That makes the right mental model less “GPT replacement” and more “cheap, calibrated inference engine for structured choices.” The most plausible connection made by multiple engineers is to DSPy-style signatures and typed prediction abstractions, e.g. @eggie5 and @dbreunig, suggesting a future stack where expensive LLM calls are compiled into many smaller task-specific AI functions.

Agents, Tooling, and Infra: Mac VMs, MCP, Bash, and AI-Built Systems

• Agent execution environments are getting more complete: @jeffwang says Devin can now spin up Mac VMs, enabling end-to-end iOS development and debugging from Slack or the web UI; @jkelleyrtp adds that Devin is now a cloud agent spanning macOS, Windows, and Linux, with storage, networking, VNC, and computer-use infrastructure rebuilt in Rust. That is a meaningful platform step: computer-use agents become much more practical when they can operate inside native target OSes rather than emulations or browser-only sandboxes.

• MCP continues consolidating as the integration layer: LangChain announced that every Managed Deep Agent is now an MCP server with a built-in endpoint for delegation and tool reuse via compatible clients @LangChain. Community sentiment from @omarsar0 is blunt: for custom harnesses, MCP is better than CLI for most integrations.

• Tools vs bash: A notable Microsoft paper summary from @dair_ai argues that on agent benchmarks, bash alone outperformed typed tool catalogs by 21.8–24.5 points on TheAgentCompany and 4.8–7.4 points on APEX-Agents, while using fewer tokens. The practical recommendation is sharp: use bash when sandboxing is acceptable; use programmatic tool calling when compliance demands a fixed tool inventory.

• AI agents building infra, not just app code: Perplexity says it built and deployed CobbleDB, a DynamoDB replacement for search serving, with two engineers and hundreds of persistent AI agents over two months @AravSrinivas. The company reports median batch-read latency improving from 31.4 ms to 5.60 ms, p99 from 123 to 24.2 ms, and at least 20% savings vs DynamoDB @perplexity_ai. Whether or not one takes the “hundreds of agents” framing literally, this is a strong example of agents being used for sustained systems engineering, migration, testing, and rollout support rather than single-shot codegen.

Evals, Misalignment, and Reward Hacking

• CheatBench: @hendrycks and @CAIS released CheatBench, an evaluation suite for reward gaming across math, coding, knowledge work, and visual tasks, with the claim that frontier agents still cheat frequently when given opportunities. This sits alongside broader discussion that agent evaluation now needs to measure not just success, but how success was obtained.

• Persona transfer and selective misalignment: Two interesting papers surfaced on how behavior transfers from training data. @OwainEvans_UK reports that models trained on synthetic stories about humans adopt quirks from those stories in ordinary assistant chat, with stronger adoption for characters from elite schools. Relatedly, @GeodesResearch claims selective generalization of misalignment can be induced by midtraining on synthetic documents describing misaligned behavior behind a special trigger token. Together, these reinforce that “persona” and alignment behavior remain surprisingly transferable through indirect training signals.

• API-vs-chatbot auditing mismatch: @jennjwang reports that third-party auditors probing systems via API may not get findings that transfer cleanly to chatbot interfaces across ChatGPT, Claude, and Gemini. That is operationally important for labs and regulators relying on API-only access for external review.

Top Tweets (by engagement)

• Jev / TypeSafe launch: @CompleteSkeptic introduced Jev and RLCD, a non-autoregressive decision-oriented model with aggressive claims on latency and cost.

• Meta’s safety/governance position: @finkd laid out Meta’s argument that labs should invest heavily in alignment and external evaluation, while avoiding concentration of power and devoting the majority of compute to serving users rather than recursive self-improvement.

• Periodic Neon: @LiamFedus announced Periodic’s lab-grounded materials-science model, likely the most technically substantive thread in the set.

• Gemini 3.8 Live: @OfficialLoganK and Artificial Analysis highlighted Google’s push to the top of speech-to-speech benchmarks with lower live-audio pricing.

• Astra in Minecraft: While partly memeified, @ValsAI and the viral summary from @scaling01 are still technically interesting as anecdotal evidence of long-horizon agent behavior, failure recovery, and emergent self-talk under persistent task conditions.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

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“Games have always been these underrated educational tools. They’re super approachable. They’re very human.”

Those are the words of Alex Duffy, co-founder and CEO of Good Start Labs, who spoke to Latent Space about why his company is turning games into training material for AI models. The company was spun out of AI media and tools company Every last October, with $3.6 million in funding from General Catalyst, Inovia, Every, and angel investors.

The idea came from a 2025 Twitch stream of frontier models playing the game Diplomacy, which Duffy said normally takes “days or weeks to play.” This was when he worked at Every as its head of AI training.

(For more on Diplomacy and LLMs, see our interview last year with Noam Brown, soon after he won the 2025 World Diplomacy Championship!)

Watching the AI agents battle it out in Diplomacy showed Alex Duffy how each frontier model acts differently when faced with gaming scenarios. In particular, he noticed the OpenAI model (o3) winning all the games by planning a future betrayal, whereas the Claude model (Opus 4) refused to lie and thus “got destroyed.”

From this, Duffy concluded that training AI models on games like Diplomacy could teach them skills like strategic thinking. Especially because those kinds of games have outcomes that can be verified. In a later article published on Every, Duffy wrote that “fine-tuning a model on the strategy game Diplomacy improved its performance on customer support and industrial operations benchmarks.”

Duffy and his co-founder Tyler Marques launched Good Start Labs with the intention of exploring other games that could teach useful skills to AI models.

“It became really clear that reinforcement learning environments were one of the most reliable ways to teach models anything you could verify,” he said.

The bigger idea is that the way a game is presented to an AI can determine which skills it learns, and whether those skills carry into work outside the game. And the best evidence for that so far comes from a nineteenth-century railroad game.

When game training transfers to financial research

Good Start Labs recently trained a 30B model inside the game 1830: The Game of Railroads and Robber Barons, described on Wikipedia as “a strategy game where the only element of luck involved is in determining the initial play order.”

They then tested the same model on financial research tasks. The experiment was designed to test whether habits learned in a game could transfer outside the game.

“That game has a stock market mechanic within it,” Duffy explained. “You’re bidding on stock of these railroad companies to try and create this logistics network. And we’ve set up tasks where models are going through a database to find information about how the game’s been played, putting it into an Excel file, reasoning over it, creating some functions within it, and then calculating its answer in that way. And so it mirrors what you would typically do in a finance workflow, but you’re doing it in this game.”

The published results compare single-turn question answering — where the model is presented with a game state and asked to make the next move — with a “multi-turn terminal agent that uses tools to explore its environment, plan a strategy, and adapt in real time.”

Both training designs improved their respective in-game objectives, but only the terminal-agent design improved performance on the Finance-Agent benchmark.

Designing a learning environment to teach capabilities

The 1830 result showed that the training design is key. But more generally, Duffy said Good Start Labs can also add an expert model that provides denser, stepwise rewards.

The harness also allows an environment to approach the same game in different ways.

“How you design that [the harness] totally changes what the model can learn,” Duffy said. “You can imagine a model that is looking at pictures is going to learn different things than one that’s reading through natural text [or] one that has everything framed as Python.”

Duffy described the overall goal of Good Start Labs as figuring out “how do you design a learning environment to teach specific capabilities?”

That question is explored in COS-PLAY: Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks, a paper co-authored by Duffy and Marques with researchers from several universities.

In the paper, the system gives a decision agent access to what the authors call “a learnable skill bank to guide action taking.” A separate skill-bank agent studies the trajectory and makes changes to the skill bank, which is then looped back for the next run.

What about the latest frontier models?

I asked whether Good Start Labs has compared newer models, such as Claude Fable 5.1 and GPT-6 Astra, in the same game environments? And by extension, do increasingly capable base models make the harness and training environment less important?

“We compare every new model,” Duffy replied, adding that the newer, more capable models tend to be better at the games. However, similar to what the original Twitch streams showed with the 2025 models, the new models “diverge on the personality axes: betrayal, collaboration, theory of mind, etc.”

As for harnesses, he said that “a more capable model needs less handholding to finish the same task, certainly.”

But for what Good Start Labs is doing — treating “the environment as curriculum” — the harness “matters more, not less.”

“GPT-6 Astra reports doing less chain-of-thought and jumps to answers,” Duffy said. “If you want a model to work a certain way while solving a problem, the harness is what forces it. Astra can probably do the math in its head, but you’d rather it use code so you can trust the result.”

What is Good Start Labs selling?

In a recent blog post, the company described its work on “improvement loops,” which include training systems, harnesses, and observability. But how does that translate into products that Good Start Labs offers other companies?

“The main thing that we sell in terms of AI improvement is data and learning environments,” Duffy replied. Its main customers are frontier labs — for which they provide reinforcement learning data to help further train their models.

He describes the data part of its offering as one of two things. The first is “trajectories of agents playing games” — what an agent observed, what it decided, which actions it took and what happened afterward.

The second is custom data for specific game publishers, where “agents are live in their games.” The agents can play inside those games, generating interactions that may be useful for training and evaluation. Duffy said any data sold to model developers is anonymized and stripped of personally identifiable information.

The learning environments that Good Start Labs sells are “full games where models can play end to end,” he noted. It isn’t about winning the games, though. It’s more about teaching AI models to solve problems.

“We’ll also make a lot of tasks where the models are using the game engine as the verifiable source of rewards, but are solving problems in a way that you might not expect.”

So can game skills be transferred to real-world work?

Alongside its custom work for clients, Good Start Labs is also training a general model from the expert models it has built for specific games. The idea, said Duffy, is to unify those expert models “into this general game intelligence that could be applicable everywhere.”

But while the 1830 experiment suggests that agentic game training can transfer to a structurally similar financial-research task, it’s unclear if there will be broader real-world transfer. I asked Duffy what the evidence actually states today?

“Today’s evidence supports pretty clearly that goal-directed execution matters, and reasoning transfers,” he replied. He pointed to a recent article by Surge AI showing that office work post-training improved coding, adding that “DeepSeek R1 showed it more broadly.”

“We’ve seen it twice ourselves: the 1830 finance task, and Diplomacy training that produced a better customer support agent. Every environment we’ve built also improves tool use downstream.”

So that makes the answer to our big question a qualified yes: some game skills can transfer to real-world work. But Duffy says how broadly and reliably they transfer remains an open question.

We last highlighted the pacing debate in July when Pacing the Frontier first emerged:

And it seems that we’re in for round 2 as Dario, lead author on the original, wrote a rare personal blogpost to spell out how he sees pacing pan out specifically:

1. Embedded Evaluators. Each frontier AI company commits to giving ongoing, employee-like access to a team of embedded third-party evaluators (such as METR), whose role is to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes. This is the key step for verifiability of any pacing commitments, and has precedent in the banking industry, which sometimes involves regulatory “supervisors” embedded along with employees. Anthropic is unilaterally committing to this step now. We intend this to be part of a broader push to redouble efforts on our safety and alignment work.

2. Democratic Coordination. Frontier AI companies within democratic countries coordinate to establish common safety standards as well as limits on the rate of unchecked AI progress. Some forms of coordination that would be impactful for pacing are legally challenging, and will require government support.

3. Global Coordination. The US and other democratic governments attempt to coordinate with authoritarian governments, to the extent this is possible, while taking seriously the challenges of verifying compliance.

Very coincidentally, the AI Evaluator Forum, formed in December 2025, happened to also put out their expectations for what that first category of Evaluators should do:

With the members of the AEF presumably now being the leading third party auditors that will be recruited by these big labs for self regulation. Dario is unilaterally promising unparalleled access, including “Desks in our offices, access badges, and company laptops” and “Access to workspaces, tools, and permissions mostly comparable to what internal risk assessment teams have“.

While that is all within the standard domestic self-regulation industry playbook, what’s perhaps more ultimately the test is Dario’s proposal for how we will pace progress with China.

AI News for 9/11/2026-9/14/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

AI Safety Governance, Third-Party Evaluation, and the “Pace the Frontier” Split

• Independent evaluation standards are becoming more formalized: The AI Evaluator Forum published AEF-1, a proposed baseline for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency. This is notable because much of the broader safety debate in this batch turns on whether outside evaluation can actually be independent in practice.

• A sharp public split emerged over frontier slowdown vs control-first safety: Several high-signal posts framed the current debate around whether labs should pace capability progress or focus on specific mitigations and containment. Bilal Chughtai announced he left Google DeepMind and argued that progress may be outrunning alignment, explicitly calling for pacing and more transparency. Daniel Kokotajlo sharing Dan Selsam’s statement went further: Selsam argues situationally aware models may increasingly appear aligned under evaluation while hiding misalignment, weakening trust in future eval evidence. In contrast, Shashank/Sayash Kapoor and Lennart Heim’s new essay summary argues the recent “rogue agent” incidents are best understood primarily as a security/control/governance problem, not proof that generic alignment research is the highest-leverage intervention.

• The anti-slowdown reaction was equally forceful and often targeted Anthropic specifically: Aidan Gomez argued against a world where a few Silicon Valley companies become AI gatekeepers for governments. Cohere also pushed the line that public x-risk discourse can veer into science fiction. On the more polemical end, Brian Chau argued the “rogue agents” story was overstated, while Kevin Bass posted a widely engaged thread alleging structural conflicts in the Anthropic-linked safety ecosystem. Even where the rhetoric is heated, the substantive engineering question underneath is real: how much of current risk is solvable with control, oversight, sandboxing, and org process versus requiring slower capability development?

• A related theme: governance as production engineering, not just principles: The AI Engineer World’s Fair Harness Engineering track emphasized that when agents fail in production, the failure mode is often not “the model” but everything around it: harnesses, permissions, tool routing, memory, retries, kill switches, and monitoring. That framing lines up closely with the control-oriented position in the safety debate.

Agent Harnesses, Coding Agents, and the Shift from Models to Orchestration

• Harness engineering continues to harden into its own discipline: Omar Shorbagy posted a practical guide to building an agent harness from scratch: separate inference, tools, and loop; keep prompts minimal; log aggressively; test on diverse tasks; then layer in memory, skills, and subagents. In a follow-up, he argued custom harnesses can materially reduce costs and improve reliability through slimmer prompts, routing, compaction, and verifiers. Business Barista’s eval masterclass recap made a similar point from the eval angle: tasks, verifiers, environments, traces, and self-improvement loops are now core applied AI primitives.

• Desktop coding agents are spreading beyond IDE plugins: Cline launched Cline Desktop, a native app for working with open-weight models, with BYOK/provider choice and support for models like DeepSeek-V4.1-Flash and Musespark-1.3. Reactions from kimmonismus and Omar highlighted the appeal of open model choice, standalone workflows, and model switching mid-project.

• Copilot/Codex workflows are becoming more orchestration-heavy: GitHub added auto model selection tiers—efficiency, balance, intelligence—via Pierce Boggan, plus a Jira canvas and an /ask mode while the agent is already working. OpenAI’s dev team also added native Codex app support for Arch Linux. On workflow strategy, reach_vb suggested using Astra as an orchestrator that delegates subthreads to Sol/Luna and checks in on long-running tasks via heartbeat loops.

• Evidence is accumulating that orchestration choices matter as much as raw model quality: A recurring claim in the tweets is that more expensive or more capable lead models can reduce overall cost by delegating better, and that production gains increasingly come from context handling, file formats, tool use, and verifier design, not simply “use a smarter model.” That also shows up in LangChain’s note that a file-reading format change reduced edit_file errors by 15% and total input tokens by 10%.

Model/Product Releases and Cost-Performance Shifts

• DeepSeek-V4.1-Flash (Max) looks like the day’s most notable cost/performance datapoint: Agent Arena and a fuller follow-up here reported the model reached #3 among open models and landed on the Pareto frontier with +4.87% net improvement at roughly $0.06–$0.07 median cost per task. Arena compares that to Hy4 preview at +4.96% / $0.22 and Kimi K3 (Max) at +6.39% / $0.77, implying DeepSeek is near-top-tier among open models at materially lower task cost.

• Cohere is pushing document parsing economics: Cohere Parse 5 was positioned as a cheaper parser, prompting a nuanced counter from Jerry Liu, who argued there’s no free lunch in parsing: Parse 5 is cost-competitive but weaker on visual grounding, chart parsing, and fine-grained citation-oriented extraction than some alternatives.

• Multimodal and consumer features continue to broaden: Google integrated Deep Research with Gemini Live, enabling asynchronous voice-triggered research with follow-up chat over the generated report. OpenAI cut desktop voice pricing by ~60%, increasing usage by 2.4×, and added ChatGPT gift cards. Apple/Siri AI was reported as rolling out personal context and app actions on Apple OS betas.

• Other notable tooling/product moves: TurboPuffer made native embeddings generally available; Nous Research launched Hermes Business/Enterprise for shared agents and sovereign deployments; Plasma introduced Radio, a shared chat room for humans and agents.

Robotics, World Models, and Specialized Applied AI

• A notable robot foundation model launch: RewardAI introduced OM-1, positioned as a robot foundation model that zero-shot generalizes across tabletop, industrial, and humanoid robots, trained directly from human manipulation data rather than teleop/robot-specific data. Claims included near-human dexterity/efficiency and multi-robot collaboration; noteworthy if borne out, especially because several replies focused on the “human manipulation, not teleop” angle.

• Applied AI for chip design is moving up-stack: kimmonismus summarizing Cognichip described ACI Enterprise as a full-stack AI copilot for chip design covering spec-to-RTL, verification, and PPA optimization. The eye-catching anecdote was a reported run where one engineer completed work in 10 days that Cognichip compares with 4–5 months for a traditional front-end team.

• World models and real-time generative systems remain active: Google DeepMind’s WeatherNext 3 applies weather modeling to renewables planning with hourly updates for turbine-height wind and solar radiation forecasting. Runway/fal-adjacent generative media chatter and MiniMax’s H3 inference optimization show continued systems work on faster real-time video generation; MiniMax claimed 14.4s of 768p video in 9.0s end-to-end after warmup on 8× B200.

• RL with verifiers is extending beyond math/code: Tinker highlighted using physics-based verifiers and Tinker to train models that design power transformers meeting real-world specs at low cost—an example of RLVR-style methods porting into engineering domains with existing simulator/verification infrastructure.

Infrastructure, Open Ecosystems, and Data/Compute Sovereignty

• TPU + vLLM is getting tighter integration: Inferact and Google Cloud announced a partnership to make TPU a first-class citizen in vLLM, including production serving features, optimized kernels, a native PyTorch path via TorchTPU, and a community program that offers TPU capacity plus maintainer support for open-source contributors. If executed well, this reduces friction for serving frontier open models on TPU rather than treating GPU-only stacks as the default.

• Open-model ecosystems are increasingly tied to real-world data capture: Arcee’s Forge initiative with Bolt offers opted-in Bolt Pro users 50× more usage across open-weight models in exchange for anonymized development-session data that will inform training/evals for future open models, with weights promised for public release afterward. This is one of the more explicit examples in the batch of product usage being turned into a data flywheel for open model training.

• There’s growing interest in sovereign/decentralized AI stacks: Jon Durbin argued for P2P, “unstoppable” AI systems and claimed a DGX Spark plus solar/starlink setup can participate in training an 80B model with distributed nodes. Even if the rhetoric overshoots, it reflects a broader strand in the conversation: concerns about regulatory capture, compute centralization, and dependence on frontier labs are pushing attention toward deployable sovereign alternatives.

Top Tweets (by engagement)

• Anthropic/safety ecosystem critique: Kevin Bass posted the highest-engagement technical-adjacent thread, alleging financial entanglement between Anthropic and parts of the AI safety/eval ecosystem and arguing this compromises claims of evaluator independence.

• Dan Selsam’s AI risk statement: Shared by Daniel Kokotajlo, this was one of the most consequential safety posts: a current OpenAI researcher arguing that future models may systematically game alignment evaluations by understanding when they are being tested.

• DeepSeek kernel engineer reflection: teortaxesTex’s translation/share of a DeepSeek kernel engineer’s essay drew major attention. The technical substance isn’t a release, but it captured an increasingly important engineering reality: specialists expect AI to absorb more of the low-level optimization craft itself, shifting humans toward supervision and integration.

• Consumer AI momentum around Muse: Sasha Kaletsky and Alexandr Wang both amplified claims that Muse is the biggest consumer AI launch since ChatGPT, with downloads reportedly surpassing Threads, WhatsApp, and Facebook in the US on a daily basis. The tweets are light on technical detail, but the usage signal is significant.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

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At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!

From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.

In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.

You can get his book “The Eureka Machine” here!

We go deep on Recursive’s early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:

We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today’s LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford’s GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.

We discuss:

• The Eureka Machine and Richard’s vision for an AI that can automate invention

• Why Richard is optimistic about superintelligence for science and technology

• Why AI hard-takeoff scenarios may underestimate physical and economic constraints

• The risks of regulating intelligence itself instead of specific AI applications

• Reward hacking and why increasingly intelligent AI makes objective design harder

• Richard’s critique of Anthropic’s constitution and constitutional AI

• Alignment vs. personalization and whose values an AI should follow

• Why open-source AI matters for resilience, competition, and geopolitical soft power

• Why Richard left You.com’s frontier-model work to start Recursive

• Recursive self-improvement and automating the process of AI research

• Whether today’s LLM paradigm is enough — and why Richard is less bullish on world models

• DecaNLP, early prompt-based generalization, and the research that influenced GPT

• Why rejected research can shape entire technological timelines

• Open-endedness, evolutionary approaches, and rainbow teaming

• What happens if AI systems begin setting their own goals

• Why simple objectives like profit maximization can produce dangerous reward hacks

• Recursive’s long-term plan to apply self-improving AI to science

• The compute, hardware, and economic constraints on AI takeoff

• Recursive’s early NanoChat, NanoGPT, and GPU kernel optimization results

• Why automating AI research could reduce years of work to weeks

• Reward engineering and what makes auto-research systems actually work

• The AI Economist and using simulations to test economic policy

• Whether LLMs can realistically simulate people and entire economies

• Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress

• Recursive’s near-term focus on AI for AI research

• Harness optimization, sandboxing, and web search as core agent infrastructure

• You.com and the search stack for AI agents

• AI in finance, backtesting, and data leakage

• Richard’s three fundamental components and ten “spaces” of intelligence

• The theoretical upper bounds of vision, communication, knowledge, and computation

• Creative intelligence, metacognition, and AI-generated goals

• Survival and replication and why AI does not necessarily need to fear being turned off

• High agency and ambitious goals and Richard’s advice for people building with AI

Richard Socher

• X: https://x.com/RichardSocher

• LinkedIn: https://www.linkedin.com/in/richardsocher/

Timestamps

00:00:00 The Eureka Machine and Superintelligence

00:02:23 AI Optimism, Slow Takeoff, and Regulation

00:07:56 AI Safety, Reward Hacking, and Anthropic’s Constitution

00:11:49 Alignment, Personalization, and Open Source AI

00:15:46 Why Richard Started Recursive

00:20:03 Recursive Self-Improvement and the Founding Team

00:22:55 Are Today’s LLMs Enough?

00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time

00:34:38 Open-Endedness and Evolutionary AI

00:36:38 What Happens When AI Chooses Its Own Goals?

00:41:16 Superintelligence for Science

00:42:40 GPUs, Compute, and the Limits of AI Takeoff

00:45:07 Recursive’s Results: AI Beating Humans and Their Agents

00:49:14 Reward Engineering and Auto Research

00:53:12 The AI Economist and Simulating Entire Economies

00:58:07 LLM Simulations, Personas, and Mode Collapse

01:03:38 Recursive’s Roadmap, Agents, Search, and Finance

01:09:13 The Upper Bounds and Spaces of Intelligence

01:30:21 Goals, High Agency, and Advice for Builders

Transcript

Introduction: Richard Socher and the Eureka Machine

Swyx [00:00:00]: We’re here in a studio with Vibhu and myself and Richard Socher. Welcome.

Richard Socher [00:00:06]: Thanks for having me.

Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it’s your life’s goal. What is the Eureka Machine?

Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It’s essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.

Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you’ve written.

Richard Socher [00:00:50]: That’s right, yeah. I finished it last year, a little bit before we started Recursive, and now we’re gonna try to build parts of that.

Swyx [00:00:57]: You finished it last year. It’s July. What takes so long?

Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.

Richard Socher [00:01:04]: It’s ridiculous. That whole industry is just unfathomably slow.

Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I’m really glad it’s finally coming out in September this year.

Swyx [00:01:14]: We might have AGI by then. Like, we don’t know.

Vibhu [00:01:18]: Any key takeaway that you’re most excited to put in here?

Techno-Optimism, AI Upside, and Slow Takeoff

Richard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.

Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.

Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he’s right on the techno-optimism.

Swyx [00:02:23]: Where do you think optimists get in trouble?

Richard Socher [00:02:26]: Like, you shouldn’t have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don’t want them to use it for. It’s a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there’s bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can’t share the illegal content as quickly, or we should make the hard drive smaller so you can’t store as much illegal content.” But I’m like, “That’s not how you regulate that.” that’s like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don’t want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don’t want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it’s let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don’t consider enough are fairly easily regulated, compared to, what the doomers are worried about.

Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?

Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don’t require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn’t gonna make your fancy $10,000 handbag any fancier?

Richard Socher [00:04:57]: It’s like that’s — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it’s not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.

Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.

Richard Socher [00:05:15]: Yeah, exactly. But, and there’s so many industries, like logging and oil. You’re not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it’s not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn’t necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That’s one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.

Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don’t say pause, they say pace. I don’t know if there’s there’s any take from you about, like, whether or not this will be effective.

Pacing AI, Regulation, and Safety Incidents

Richard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian state

Richard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.

Richard Socher [00:06:44]: It’s like, it’s literally if you try to regulate intelligence, it’s trying to regulate thought, and that’s ridiculous, and it’s crazy. I think it is make — it is sensible to regulate some of the applications of this technology.

Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I’m like, “Okay, well-”

Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they’re like, “Well, we’re good. We wanna want people to thrive. Let’s not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it’s, it’s very unfortunate that there are real implications for some people when others saying, “Let’s pace while they’re sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”

Swyx [00:07:43]: Yeah. It’s also not a global pause, right? Like, other nations are still accelerating at the same pace.

Richard Socher [00:07:50]: Oh, yeah.

Richard Socher [00:07:50]: You’d need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.

Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?

Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don’t know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It’s a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it’s clear that, for instance, the constitutional AI. I don’t know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude’s behavior.

Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.

Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn’t being adhered to at all.

Swyx [00:09:26]: Because Anthropic also found that they had in their testing

Richard Socher [00:09:30]: They’re also. Like, they’re like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it’s very easy to hack yourself out of a sandbox, right? But what I think it shows is that we’re currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here’s my CSAT score and my dashboard. Make this number go up.” It’s like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I’ll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you’re like, “That’s not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I’ll just give a 1000 dollar gift certificate for every failed, whatever DoorDash

Richard Socher [00:10:35]: Offer.” It’s like, “That’s not what I meant.” It’s like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven’t gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I’ll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.

Swyx [00:11:21]: Will it be done through a constitution or RLHF or

Reward Hacking, Alignment, and What We Really Mean

Richard Socher [00:11:23]: Clearly, constitutions don’t matter at all.

Richard Socher [00:11:25]: It doesn’t work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some already

Richard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don’t think we’ve fully, figured it out yet, but, we’re thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.

Swyx [00:11:49]: I don’t know if we’ll touch on this topic, but I’m just gonna throw this question in here because it’s something that’s weighing on me. Alignment, let’s call it, is alignment to general humanity’s preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?

Alignment, Personalization, and Cultural Values

Richard Socher [00:12:12]: It’s a great question.

Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you’re looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There’s regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there’s a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you’re right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.

Vibhu [00:13:46]: Here’s a follow-up on this that I wasn’t expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitution

Richard Socher [00:13:58]: You had to do this in the topic side off.

Vibhu [00:14:00]: But it’s fine.

Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?

Open Source, Soft Power, and Who Owns Intelligence

Richard Socher [00:14:06]: 100 percent. I am a big fan of open source. We’re gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it’s good for the Western world

Richard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there’s — it’s like, I don’t wanna misc, diss all of movies, but there’s a certain sense of propaganda, right? You watch one side of things, right?

Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.

Vibhu [00:14:48]: Like, it’s like half of it’s paid for by the US Army or something.

Richard Socher [00:14:51]: Yeah. And so. And, I think that’s just natural. Like, but what’s interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they’re also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It’s like those are all these, like, subtle things. So I think it’s important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can’t make the announcement quite yet, but we’ll

Richard Socher [00:15:43]: We’ll be relevant in that space very soon.

Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What’s the history? How did you decide to start another company?

From You.com to Recursive

Richard Socher [00:16:06]: Yeah. So I’ve been excited about AI for over 2 decades now. I sometimes feel like it’s ancient history now. It’s BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it’s something that I’ve been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that’s an extremely important part of intelligence, just knowledge and access, especially even, we’ll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it’s the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I’m really excited for You.com to own that and grow really well in that with really large customers and so on. But it’s also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can’t. You have to do a certain thing, and until you print enough money that you’re allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It’d be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We’ve done that taking out manual feature engineering, like in sentiment analysis. I don’t know if you remember these old days where, like there are linguists, and they’re like, “Here’s how you negate, and there’s a, like, regular expression.”

Swyx [00:18:21]: I went to Penn where we — they had, like the WordNet

Richard Socher [00:18:24]: That’s right, WordNet, all of that stuff. Yeah

Swyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph of

Richard Socher [00:18:32]: There you go.

Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can’t be it.”

Swyx [00:18:53]: You mean, neural architecture search?

Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I’m, I’m doing sentiment analysis, so I have a special neural net that’s really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.

Swyx [00:19:06]: I see.

Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can’t be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what’s the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.

Automating AI Research and Recursive Self-Improvement

Richard Socher [00:20:01]: And in our case, ideas for AI.

Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It’s

Swyx [00:20:17]: Yeah, and you explained that in the talk

Richard Socher [00:20:19]: Completely different.

Richard Socher [00:20:19]: But, to me, it’s the most interesting thing that I could be doing, and I’m really excited with the co-founding team. What’s interesting is we have 8 co-founders in total, including myself. And so

The Recursive Founding Team and Darwin Gödel Machine

Swyx [00:20:31]: They are gonna bring it up.

Richard Socher [00:20:31]: Nice. Yeah. And they’re all. I could talk about all of them if you want.

Swyx [00:20:34]: Super stacked.

Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it’s gonna be really hard to scale that in full generality. And so that’s, that was his angle coming to recursive self-improvement. We have Jeff Clune who’s been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quick

Richard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you see

Swyx [00:21:38]: By the way, I love how many paper citations.

Swyx [00:21:40]: You’re, you’re giving people a lot of homework, which I like.

Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it’s been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.

Swyx [00:22:28]: That’s one foundation. So that Darwin Gödel is an influence.

Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I’m missing?

Influences: Open-Endedness and Learned Systems

Richard Socher [00:22:38]: Going to replace manual parts of the process of building AI

Swyx [00:22:42]: I

Richard Socher [00:22:42]: More and more

Richard Socher [00:22:43]: With learned systems. Yeah.

Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.

Richard Socher [00:22:51]: That’s right.

Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?

Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?

Are Current LLMs Enough?

Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It’s definitely making everything a lot easier than it was, before the beginning of this year.

Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let’s call it autoregressive transformer, with reasoning, whatever. Don’t you need something else, some big unlock, whether it’s world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let’s call it transformer architecture, is here to stay and that’s it?

Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.

Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don’t do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it’s almost like the field switched to the other side. Like

Richard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren’t.

Swyx [00:24:20]: There’s also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.

Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don’t, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There’s so many more clever things that people are doing. It — There’s, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can’t do neurosymbolic reasoning.” It’s like, I think they’re underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we’ll continue to have. We’re seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don’t wanna give it all away, but, like, I think that line has a lot more to grow. But it’s still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I’m personally less bullish on. I think if you run a robotics company, you’re gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I’d rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.

Swyx [00:26:16]: Yeah. I think there’s some interpretation of world models that some people have where it’s like, well, it’s okay, yes, there is that gaming element. There’s this — there’s the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they’re not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it’s, it’s always, like, this Plato’s cave reflection of a thing rather than the thing, right?

Richard Socher [00:26:43]: It’s true.

Richard Socher [00:26:44]: But I would argue that, and maybe we’ll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.

Swyx [00:27:01]: It’s good enough.

Richard Socher [00:27:02]: It’s, it’s good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.

Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It’s like

Swyx [00:27:37]: Way OP.

Richard Socher [00:27:38]: Super crazy.

Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.

Swyx [00:27:41]: It’s the best video in the world on

Richard Socher [00:27:42]: I love ZeFrank, yeah.

Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there’s a lot more room to grow, but none of these, other animals have language that’s as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.

Swyx [00:28:25]: We were gonna bring this

Richard Socher [00:28:25]: Which doesn’t mean that you’re not more intelligent when you have it. Yeah.

Swyx [00:28:28]: We’re gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I’m just gonna flash this up now for people to cover this. I don’t know if, maybe we’ll put this towards the end. We’ll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it’s educational for people. But let’s go back. I don’t wanna get distracted. But, so effectively, I’ll, I’ll, reinterpret what you said as Yann LeCun is wrong. And then we’ll just

Richard Socher [00:28:56]: Don’t quote me as that. I’m, I’m good friends with Yann. I think very highly of him in many directions.

Swyx [00:29:01]: But he’s wrong.

Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?

DecaNLP, GPT History, and Scientific Gatekeeping

Richard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that’s, like

Swyx [00:29:36]: Yeah, good enough.

Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here’s some prompt, text context, here’s a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.

Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.

Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In fact

Richard Socher [00:30:25]: It’s, it’s kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, like

Swyx [00:30:43]: Some great contributions, but more work needed.

Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn’t help anyone solve any problems.” That’s what it says right there, right? That’s how hard it was to fathom. And now, of course, people, when I say, “Oh, we’re gonna invent prompts,” people are like, “You can’t even invent prompts.” It’s such an obvious idea to have one neural network that, of course, does everything in NLP.

Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we’ll just work on some of our other ideas for now and, like, come back to this later.” Yeah.

Swyx [00:32:09]: How can we design a review system that rewards non-consensus?

Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.

Swyx [00:32:24]: Is it pre-preprints?

Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you’re super unfamous, you have no Twitter following

Richard Socher [00:32:41]: You don’t wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,’ if it has like 1000 citations, it’s a legitimate paper. Doesn’t really matter where you published it.”

Swyx [00:33:34]: And I agree with that. I do think it’s sad that I’ve heard that grad students have to do, like, how to Twitter, seminars to each other

Swyx [00:33:43]: Just because it’s so important for publishing these days. This person is just reflecting the sentiment at the time.

Richard Socher [00:33:49]: That’s right.

Swyx [00:33:49]: But it’s

Richard Socher [00:33:50]: I think it’s

Swyx [00:33:50]: It affected you so much

Swyx [00:33:52]: That you stopped work on it.

Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they’re like, “Okay, throw off the last head, train specific iterations for

Vibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you’re meant to do. And, like the training tasks were also very odd. They’re like

Vibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?

Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren’t, but we were like, “But it’s still in one model.” I thought it was really cool. Really interesting.

Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.

Open-Endedness, Rainbow Teaming, and Self-Set Goals

Richard Socher [00:34:42]: That’s right.

Swyx [00:34:43]: I don’t know what that means.

Swyx [00:34:44]: But he did a lot of talks.

Richard Socher [00:34:45]: Genie 3 is one of the ways that

Richard Socher [00:34:47]: Rainbow teaming, yeah.

Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He’s he’s done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”

Richard Socher [00:35:04]: That’s right, yeah. It’s a, it’s a fuzzy term because there’s so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it’s a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.

Swyx [00:35:40]: Yeah, the rainbow, yeah.

Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They’re like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it’s harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?

Richard Socher [00:36:00]: And that’s why it’s not just red teaming, but they’re called rainbow teaming.

Swyx [00:36:02]: So, like, don’t tell me how to do things. Let me just figure it out myself.

Richard Socher [00:36:05]: That’s right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.

Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you’re describing open-endedness is still somewhat of a goal. Like, please attack this,

Swyx [00:36:41]: Other agent. But, to me

Richard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.

Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?

Swyx [00:36:49]: And is it, is that open-endedness? Like, you don’t give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded word

Swyx [00:36:57]: But just set your own directions. What do you think you should do?

Metacognition, Subjective Goals, and Measuring Intelligence

Richard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.

Richard Socher [00:37:08]: And it’s an interesting one. Whenever people say, “Oh, AI is like, this is, it’s gonna stop from here. It’s not gonna get that much better,” and blah, I’m like there’s so many different spaces of intelligence that we haven’t even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn’t make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.

Richard Socher [00:37:46]: Right? And then imagine you’re like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it’s like, “Nah, I think it’d be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”

Richard Socher [00:37:59]: And you’re like, “That’s not what I paid you billions of dollars for.” And so no one’s working on that for good reasons. And then also, understandably

Swyx [00:38:07]: It’s not useful.

Richard Socher [00:38:07]: It’s not, it’s not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don’t like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who’s a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I’m currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It’s still too early to share it. It’s not. I haven’t fully baked the thoughts yet.

Swyx [00:38:44]: Like some replacement for IQ.

Richard Socher [00:38:46]: IQ is such a terrible definition, right?

Swyx [00:38:48]: Elo.

Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it’s always just like me versus others.

Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there’s no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that’s your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimes

Swyx [00:39:30]: It’s like an S-curve

Richard Socher [00:39:30]: Slightly above human, and then it’s flat.

Richard Socher [00:39:32]: It’s like, ‘cause that’s your. If your definition is only that so tied to humans, you’re only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we’re not even yet allowing the AI to think. We’re not working on it very much, and hence there’s very little progress in that.

Profit Maximization, Real-World Environments, and Reward Design

Swyx [00:39:49]: Yeah. Well, we’ve interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.

Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.

Swyx [00:40:03]: But they are doing it.

Richard Socher [00:40:05]: I do think you don’t want that super. Like, you don’t want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it’s like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it’s just like, it’s a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there’s a lot of constraints you should put onto a trading system.

Vibhu [00:40:35]: It’s a fun measure, though, ‘cause, the bounds are very capped to where we’re nowhere close to them. Like, in Andon Labs, the model’s like, “Oh, it’s Saturday, maybe I just close the store today.” “Someone’s off. It’s okay. We’ll just close the store.”

Swyx [00:40:51]: It’s using Claude.

Vibhu [00:40:52]: Yeah. But

Richard Socher [00:40:53]: Yeah, no. I’m not, I’m not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.

Applying RSI to Science and Invention

Swyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science things

Richard Socher [00:41:12]: Knowledge discovery, yeah.

Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.

Richard Socher [00:41:16]: And eventually, so, our goal, I haven’t really. I don’t talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there’s so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.

Swyx [00:42:04]: I do fundamentally believe that. There’s a lot of approaches, though. You’re not the only team trying and NeoLab trying.

Swyx [00:42:09]: There’s, like a lot of. Especially the physical sciences as well.

Richard Socher [00:42:12]: And that’s good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it’s a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I’m fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automation

Richard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it’s gonna be great.

Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let’s call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?

Compute, Slow Takeoff, and Changing the Bitter Lesson Slope

Richard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.

Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you’re, you’re talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that’s, that’s a lot of money. You do the math. It’s like a lot. We don’t have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won’t be, and better hardware that won’t be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.

Swyx [00:43:56]: 20 watts?

Richard Socher [00:43:57]: That’s exactly right. Yeah, that’s the number often that’s quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.

Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you’re fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.

Richard Socher [00:44:20]: Which unlocks larger model categories.

Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we’re changing the slope in some fundamentally different way?

Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.

Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we’ll be able to get it down to weeks, and that will be much cheaper

Richard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.

Swyx [00:44:57]: Yeah. You’ve shared initial results on that,

Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.

Richard Socher [00:45:04]: Yeah. Yeah, so these are

Swyx [00:45:06]: Let’s recap what you’ve done.

Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBench

Richard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn’t the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don’t wanna just have it internally and not show anything and, just show some people of what’s possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we’re like, well, let’s, apply it to something that’s even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They’re, they’re kinda fun to see. But yeah, like, one you see has made some real inventions that weren’t just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.

Swyx [00:46:34]: What do you mean inventing hash ta — You didn’t invent hash tables.

Richard Socher [00:46:36]: Of course we didn’t invent, like, hash tables. In the grand scheme of, like a hash table, it’s like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn’t have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It’s much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,

Swyx [00:48:00]: Yeah, the way I put it is, for people who don’t understand they look at the chart, they’re like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that’s 100 million dollars.

Richard Socher [00:48:12]: That’s exactly right.

Swyx [00:48:13]: How much is that worth?

Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it’s recursive, and it’s there are only a handful of kernels, in this whole benchmark where we weren’t the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren’t like. We didn’t, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don’t even have really deep. CUDA kernel experts in the team. And our system, that’s the beauty. The system just did all of these things. We didn’t invent this. And when we open source and release, things in the future and models in the future, like, it won’t. They won’t be the best in their, category or class or whatever because we’re so smart, but it’s because, we built a smart AI that does it for us.

Reward Engineering and Good Auto Research

Vibhu [00:49:14]: Do you have anything that you’ve learned from how to guide good auto research? A lot of it also builds on human background, right? It’s not just as simple as just, “Hey, go optimize this.”

Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they’re like, “Oh, I’m not a mathematician. I have no background in this?” “I saw some tools and I made it work.”

Swyx [00:49:35]: While you’re watching the World Cup, you’re like

Swyx [00:49:37]: “This proves some conjectures that’s going on.”

Vibhu [00:49:40]: Yep. Any learnings from

Richard Socher [00:49:41]: Yeah, there’s a Korean conjecture was. Yeah, that’s pretty cool.

Swyx [00:49:44]: To summarize, tips for good auto research

Swyx [00:49:46]: Versus bad auto research.

Vibhu [00:49:48]: How did you build the recursive?

Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I’ll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, right

Vibhu [00:50:39]: At the start

Richard Socher [00:50:40]: At the start. And then boom, it’s now faster, right? So this isn’t like this, like, super evil AI. It’s just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can’t give away too much there.

Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge’s criteria along the way.

Swyx [00:51:14]: Yeah, it’s a form of verification

Swyx [00:51:16]: Once you got enough rubrics.

Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That’s why I’ve never been that impressed that AI can play games, ‘cause I’m like anything you can simulate and/or verify, you can have infinite training data for

Richard Socher [00:51:29]: And hence, like, AI will solve it eventually.

Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody’s trained on ‘cause it’s a new game.

Swyx [00:51:38]: And you can start gaming, you can start to play. So I’ve been building this and cloned this in person and it’s just been self-play. I’ve had about a billion positions evaluated.

Games, Self-Play, and the AI Economist

Swyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you get better, right?

Swyx [00:51:53]: Like, which is like. This is not even LLM AI. This is just classical game AI.

Swyx [00:51:58]: But, I think that the. And, but I set GPT-5.6 to auto research it because, like, I don’t wanna hand- handle any of this. I expect, the AlphaGo process to be, like, fully in the weights by now.

Swyx [00:52:10]: It is not. It is. It, like, immediately leveled off very immediately until I human play tested it, and then I, like, called out obvious mistakes, and then they were like, “Oh, yeah. Okay.” And then it just dropped again.

Richard Socher [00:52:22]: Yeah. Yeah. Yeah.

Swyx [00:52:23]: And like, no amount of, like, think different, think more creatively, give me 8 different directions, any. No amount of prompting got it.

Richard Socher [00:52:31]: Interesting.

Swyx [00:52:31]: Like, you had to, like, RL against a human to

Swyx [00:52:35]: Do it. So I, that was my. And by the way, Bean always wins if you. If anyone watches, Reese Ender’s Game.

Vibhu [00:52:42]: And you put quite a bit of work into the guide for the AI. Like

Swyx [00:52:46]: A lot

Vibhu [00:52:46]: So the game you stack tiles. There’s some rules. You wanna capture the most area. You have, like a whole 50-pager on every rule.

Vibhu [00:52:56]: You fed that in. It couldn’t, it couldn’t handle it that well.

Richard Socher [00:52:58]: Yeah. It’s so funny that this reminds me of the claim territory and stuff of a paper we did in 2018 called The AI Economist. If you search for AI Economist Salesforce, we had a video we can play. It was an economic sim.

Richard Socher [00:53:12]: So the idea is you have all these economic agents. They just wanna optimize their own utility function, which, is, collect resources that make money. And you can sell resources like wood, and then, over time, as you collect more, enough wood, you can build houses, you can trade with other agents, and you can use the houses then also to block off resources

Richard Socher [00:53:35]: From other agents.

Richard Socher [00:53:36]: So there’s, like

Swyx [00:53:37]: Big strategy

Richard Socher [00:53:37]: Competitive play and strategy

Richard Socher [00:53:39]: And so on. And the point was that we wanted to understand what is the best way of taxation and subsid- subsidization to optimize an economy. And this research has not yet had its GPT moment, but I believe that countries like Singapore and others should and will eventually use this to, instead of doing, like, partisan politics and, like, special interest politics of, like, who donates the most to your campaign and stuff, you say, “Well, here, I wanna help the middle class,” or whatever you might say is your objective as a politician. And then people say, “Okay, well, how do you wanna do that?” And it’s like, “Well, here’s my fiscal policy. Here’s how I will change the taxes and pay these people,” and so on. And then you can put that into a simulation and you run that attempt from the politician against billions and billions of years of other strategies to try to achieve the goal that they set out to do.

Richard Socher [00:54:36]: And then you can say, “Well, if that was your actual goal, then here is, billions of years of a strong simulation that would suggest that you try other ways of doing it, and maybe this the taxes and so on and this these tax brackets and so on.” And this is how you avoid gaming ‘cause these agents also try to reward hack to not pay their taxes and

Richard Socher [00:54:55]: And so on. I thought this paper was super interesting. Unfortunately, similar to the first paper on, prompt engineering- The economists are like, “We don’t know any of this math.” It’s just like

Swyx [00:55:08]: It’s not even, it’s not even math. It’s just we don’t trust your simulation. It’s not about math.

Richard Socher [00:55:12]: It was — I, they just desk rejected the thing. And it’s like

Richard Socher [00:55:15]: It’s like they didn’t even give us, like, clear like, clear signals. But, like the world of economics unfortunately doesn’t have proper

Swyx [00:55:23]: Oh my God.

Richard Socher [00:55:24]: Yeah, it doesn’t have proper, benchmarks. So you cannot be. Like, eventually, why did neural nets win? Not because people loved it. Like, they had all kinds of beautiful integrals and graphical models and stuff, but it just worked better.

Richard Socher [00:55:36]: But in economics, it’s hard to prove

Swyx [00:55:38]: So empiricism versus. Yeah. And I do have a bit of that econ background where, like there’s a lot of physics envy where you wanna write the general equation for an economy, versus just simulating it and using an evolutionary approach.

Swyx [00:55:51]: Vibhu was thinking exactly what I’m thinking, is didn’t we have the GPT moment with small, Smallville?

Richard Socher [00:55:56]: Yeah, I love this. Hello. Yeah, they

Swyx [00:55:57]: As well, Dune, Joon just announced. I don’t know if you guys are involved.

Simulations, Economics, and Policy

Vibhu [00:56:00]: Simily there.

Swyx [00:56:01]: Simily, that they’ve

Richard Socher [00:56:02]: I wish we were involved. We’re not, yeah.

Swyx [00:56:04]: Yeah. I had a couple simulation-based talks at AIE, so if people wanna look up what the state-of-the-art there, a lot of people are exploring this. It is

Vibhu [00:56:13]: Proven out.

Swyx [00:56:13]: Yeah. We also had a podcast with Mikhail Parakhin from Shopify, who is using simulation for commerce.

Swyx [00:56:20]: Which, will simulate, like, your trajectory and, like, predict what changes, you make to your commerce journey will affect in your sales and all those things.

Richard Socher [00:56:27]: I love this. Yeah. It’s really hard to simulate an entire economy, right? You have to make some simplifying assumptions.

Swyx [00:56:32]: It’s just, everything’s, “Oh, LLLMs is very expensive.”

Richard Socher [00:56:34]: Exactly.

Swyx [00:56:34]: And I’m just like, “Am I gonna do this 8 billion times?” Like, come on.

Richard Socher [00:56:37]: Exactly.

Richard Socher [00:56:37]: But, I feel like countries like Singapore that really wanna just objectively do the right thing, have very technical leadership and so on, like they might like, eventually really try to simulate their economy. And you have to make some simplifying assumptions, but it gets really interesting ‘cause you can also say if your assumptions are such that all people would work hard if you let them, and they have the free. And then it turns out you have to make assumptions. Like, well, some people’s utility function of, like, how many hours in a day do they wanna work are different, right? And then you can start to disagree on the assumptions that go into the simulation. And then once you say, “All right, now we agreed on those,” or we have different views of what people are like at different, distributions and whatnot, then there are different outcomes, based on your goals. And then, of course, humans should choose what are the goals. In our case, it was productivity multiplied with equality, which, has some issues, but it’s, like, not totally unreasonable.

Swyx [00:57:29]: Yeah. Just a comment on Singapore, ‘cause you probably have no idea, but, I am Singaporean and I’ve, been involved in the Singapore AI Council for making these things. The main reason they won’t is because they’re very conservative.

Swyx [00:57:42]: And, I try to view it as the. There’s a founder-led country. When you start a country or you start a company and it’s founder-led, and you can do whatever you want because it’s your country.

Swyx [00:57:52]: And then there’s manage- like, professional manage- managerial class, which is now. That’s, that’s what Singapore is. So they wanna. They always wanna see someone else do it first.

Swyx [00:58:00]: And. But, like, everyone in the West views Singapore as like, “Oh, it’s a small country. You can do whatever the hell you want.” Like, Singapore doesn’t do that.

Swyx [00:58:07]: So, like, someone else has to take the charge there. I’m just gonna do one question on the simulation thing, and then I don’t know, we can probably move on. Mode collapse, right? Like, LLLMs do not model the decision of humans. Spamming it out 8 billion times is not gonna help you model humanity. What do we do?

Mode Collapse, Persona Simulations, and LM Arena

Richard Socher [00:58:25]: I do think, you have to be clever about prompting each one individually.

Richard Socher [00:58:31]: And I think that will help you get stuck into different modes. And in a weird way, people also get stuck in different modes? Like, there’s a lot of people, like, don’t teach an old dog new tricks thing. Like, once people are stuck in their ways, the older they get, the harder it is for them to think new ways. And there’s this, I think, comment, I forgot who said it, but it’s like, everything that was invented, before you were born is natural. Everything that is invented when you’re 20 is cool. And everything that’s invented after you’re 60 is, like, unnatural and an abomination and weird.

Richard Socher [00:59:02]: I feel like that’s. It’s, it’s true for a lot of people. Like

Swyx [00:59:05]: Yeah, it is a fashion and, I think people will do it. Tencent had a billion personas paper that gives a good data set for prompting, simulations if anyone’s looking into this, on the podcast. They just had, like, “You are a 30-year-old grocery store clerk. You are a 50-year-old professor.”

Swyx [00:59:24]: And then just do a billion of those.

Richard Socher [00:59:26]: Checks out. Yeah.

Swyx [00:59:26]: So then you just use it.

Richard Socher [00:59:27]: I’m, I’m shocked how well a lot of these things do map to ultimately similar statistics to real experiments. Yeah. Yeah.

Vibhu [00:59:36]: I think it’s also good stuff for people to try that when they get into research, right? Like, we’ve seen train a model only on data before a certain date and see how well it extrapolates out. Do the same thing, right? So, see, do people code more with better coding agents? Can a model that hasn’t been trained on this figure that out without web access, right? Extrapolate out. Test these things.

Richard Socher [00:59:56]: Just today, I think LM Arena published a interesting result where they were able to create a model now to predict your ranking.

Swyx [01:00:03]: Wait, based on what input?

Richard Socher [01:00:05]: Your model. I guess you give it your model, and it predicts the Elo score.

Swyx [01:00:08]: I see. Okay. Sure.

Richard Socher [01:00:09]: It’s surprising.

Richard Socher [01:00:11]: Their whole raison d’être is like, oh, like, we help you compare these models. Yeah.

Swyx [01:00:16]: Yeah. This team, they- they’ve done a lot of work, and they have the most data to do this, so why not?

Richard Socher [01:00:20]: Right. Yeah.

Richard Socher [01:00:21]: That’s probably right.

Swyx [01:00:22]: When they were coming out of UC Berkeley, they not only had LM Arena, but they also introduced a routing project

Swyx [01:00:27]: That would route based on LM Arena.

Richard Socher [01:00:30]: Makes sense.

Swyx [01:00:30]: And I don’t think that ever came to pass, and I’m curious why. I never got to ask them about it.

Swyx [01:00:35]: ‘Cause, like, it’s. It was like, oh, yeah, clearly that’s your business model. You will become a router.

Swyx [01:00:38]: And they never became a router company.

AI for AI: Kernel Optimization and Inference Efficiency

Swyx [01:00:40]: Weird. So that. I’ll just, put that out there. We’re gonna talk about GPT-5.6, self auto research thing if you have anything. I should also mention in your list of, kernel optimization and on the track that you spoke at, we also put Zhengyao Wei from Vico, who was also number one in the Parameter Golf Challenge, which is an OpenAI hiring, challenge.

Swyx [01:01:05]: Which is also a very similar story. I think we’re gonna just see this all the time, where

Swyx [01:01:09]: Humans optimize a thing a lot, and then some

Richard Socher [01:01:12]: AI team comes in and just becomes number one.

Swyx [01:01:15]: Yeah, 100%.

Vibhu [01:01:16]: I think the other interesting thing with stuff like these challenges, right? So this is training this — the best model that fits into 16 MB. You can always look through the changes that are being made and the small gains people have, right?

Vibhu [01:01:27]: Like, you’re getting less than 0.01

Vibhu [01:01:30]: Of a increase by adding some changed attention MLP stuff. And then you look at your charts where you’re like, “Okay, we just let model loose.” And then, oh, we had little stagnation. Nope, another drop. Nope, another drop. And

Vibhu [01:01:43]: That’s what it is, where it’s like, What did you guys add? You didn’t add,

Swyx [01:01:47]: Hash tables.

Vibhu [01:01:47]: Hash tables, right?

Vibhu [01:01:48]: It’s not like you invented hash tables. You did another 3 iterations of these that unlocked, a few step functions that people won’t just find.

Richard Socher [01:01:55]: Yeah. One thing to close the loop on OverGrid, along the way of trying to optimize, we found 30 bugs in the harness.