The AI industry has largely focused on language-based approaches, using transformers trained on massive datasets to predict words or fill in missing information.
As AI expands into more structured fields, however, the limitations of text-generation models are becoming clearer. Nvidia is pursuing a different approach.
The company just dropped JEPA-DNA on Hugging Face. It’s a genomic foundation model that adds a latent-space prediction objective alongside MLM.
This release is a win for hybrid architectures that go beyond purely generative training. It’s the kind of shift Executive Chairman of AMI Labs, formerly Meta’s Chief AI Scientist, Yann LeCun, has been pushing for years. He’s championed predictive architectures as a general alternative to next-token prediction, which researchers are now applying to biology.
Latent space over literal tokens
Conventional genomic base models have historically mirrored NLP models, relying purely on MLM, masking parts of a DNA sequence and forcing the model to guess the missing literal tokens. This approach favors local token reconstruction and teaches the model the basic “syntax” of the sequence, but it frequently struggles to grasp the wider functional “meaning.”
JEPA-DNA changes the paradigm. The newly released checkpoint, JEPA-DNA-DNABERT2, serves as a model-agnostic continual pre-training framework. It couples standard token-level DNA language modeling with JEPA, adding a second learning objective.
Instead of forcing the architecture to reconstruct missing tokens, JEPA-DNA supervises the model’s global sequence embedding in a latent space.
Instead of forcing the architecture to reconstruct missing tokens, JEPA-DNA supervises the model’s global sequence embedding in a latent space. It predicts the functional representation of masked genomic segments rather than their literal, character-by-character makeup.
Token prediction is still part of the training process, but it is no longer the model’s only learning objective. The model also learns the basic structure of the data, proving that predictive architectures can produce representations that are easier to work with for biological tasks without sacrificing generative capabilities.
Token prediction is still part of the training process, but it is no longer the model’s only learning objective.
How DNABERT-2 gets upgraded
The model builds on DNABERT-2, a 117 million-parameter model developed by Zhihan Zhou and collaborators. Nvidia layers its continual pre-training approach on top of that architecture, letting the model learn from both token-level predictions and latent-space representations.
Nvidia has released the model globally for non-commercial research. According to the company, the model is meant to support research workflows, including feature extraction, linear probing, continual pre-training experiments, and zero-shot scoring of DNA sequence changes. It isn’t a diagnostic tool or a clinically validated medical product.
Beyond the generative hammer
DNA contains patterns and relationships that aren’t captured by sequence prediction. JEPA-DNA supplements masked-token prediction with a latent-space objective intended to capture broader sequence-level information.
Ultimately, models like JEPA point to a new path for AI that combines next-token prediction with other ways of learning and helping models build a deeper understanding of complex systems.
Cursor, the AI coding tool recently acquired by Elon Musk’s SpaceX in a $60 billion all-stock deal, has launched a model router designed to direct every coding request to whichever model handles it best, bypassing the need to pay frontier prices for work that doesn’t need it.
Under the hood, the new Cursor Router uses a triage system à la a hospital emergency room: It looks at what a request actually needs — how hard it is, what it’s for, the surrounding code — and picks a model that is the best fit. A quick fix goes somewhere cheap, while a genuinely hard problem gets escalated to something closer to frontier-grade.
Notably, developers and admins also have access to three distinct modes that nudge that balance in either direction, favoring speed and cost over raw power, or vice versa if needed.
Choosing from three optimization modes
The broader rationale for Cursor Router, according to the company’s own field CTO David Pan in a social media post on Wednesday, is that developers shouldn’t have to become experts in model performance just to write code.
“We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rate.”
“We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rates,” Pan writes.
We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rates.
Cursor Router is out today. It picks the model for each task based on measured quality against cost. Fable 5 level performance for… https://t.co/3B2103mGWr
Early community feedback has largely echoed Pan’s sentiment: Fatih Arslan, a software engineer at PlanetScale, notes on X that engineers already juggle the choice between cost and capability by hand — defaulting to a cheap, fast model for routine work and saving the slow, expensive one for “serious tasks.”
Nice! I wanted something like this for a long time. See my previous rant last year. I think as engineers, we're already developing skills ourselves which model to choose. We use a less costly/fast model for implementing chore details, but use a higher reasoning model (slow and… https://t.co/5y5xMOR2E6
“We already spend quite a bit [of] time on [choosing models],” Arslan writes. “Why not automate that part? Cursor Router does the automation.”
“We already spend quite a bit time on it [choosing models]. Why not automate that part?”
In a separate blog post published on Wednesday, Cursor claims that early access customers saved 30-50% compared to routing everything through Opus 4.8, with no drop in output quality.
Working model: Taking control of the stack
The launch follows a run of moves by Cursor to control more of its own AI stack. In May, the company released Composer 2.5, an update to its in-house coding model built for long tasks at a lower cost than frontier options from Anthropic and OpenAI. Composer 2.5, like its predecessor, is built on Moonshot AI’s Kimi K2.5, an open-weight model out of China.
Now, with the weight of one of the world’s most valuable companies behind it (SpaceX has attained a market cap of $1.5 trillion since its June IPO), Cursor is pushing a powerful frontier model of its own.
On July 8, Cursor and SpaceXAI jointly released Grok 4.5, a mixture-of-experts model built on a new foundation dubbed V9, which Musk had previously noted was roughly 1.5 trillion parameters. The model’s trained on trillions of tokens of real Cursor usage data and available across all Cursor plans at $2 per million input tokens and $6 per million output tokens.
With Composer handling cheap, fast work, and now the Grok-branded frontier line for more serious horsepower, Cursor has its own models in the mix alongside the usual list of outside providers. And this gets to the heart of why Cursor built Router: Most developers pick one model and stick with it regardless of the task, billing simple work at frontier prices it doesn’t need.
Sending every request to its own models would be the easy way to keep that money in-house, but it would also mean shipping inferior output on some tasks — so Router instead sends each request to whichever model actually suits it, Cursor’s own or not.
The lay of the land
Model routing itself isn’t exactly new. OpenRouter has offered a version since 2023: a single API sitting in front of more than 400 models from over 60 providers, including OpenAI, Anthropic and Google. Its own auto-router feature does roughly what Cursor Router does — classify a request, then send it to whichever model fits the task and the person’s stated preference between cost and quality.
More recently, OpenRouter launched Fusion, which takes a slightly different approach: instead of picking one model, it sends a prompt to several models at once and uses a judge model to synthesize the strongest answer out of all of them.
This past month ushered another entrant to the mix: Japan’s Sakana AI released Fugu in June, which instead breaks a single task into subtasks and routes each piece to a different model, pitched by Sakana as a hedge against relying on any one AI provider.
“[Cursor Router is] a great example of how a technological innovation immediately translates into a product improvement.”
Not everyone rates some of these other attempts, though. On Wednesday, Kirill Balakhonov, head of AI products at Nethermind, argues on LinkedIn that Cursor’s version succeeds precisely because it’s focused on coding specifically, rather than trying to be a general-purpose router for any task.
“A great example of how a technological innovation immediately translates into a product improvement… rather than an abstract idea like Sakana Fugu or OpenRouter Fusion,” Balakhonov writes, predicting both of those broader routing efforts would fade from use.
What’s new, perhaps, is some of the names emerging behind an array of model diversity efforts. In early July, Microsoft launched a $2.5 billion services unit dubbed Microsoft Frontier Company, embedding thousands of engineers at customer sites to help them build with a mix of AI models.
Judson Althoff, CEO of Microsoft Commercial Business, told Reuters at the time that the push came partly from watching rivals like DeepSeek and Google’s Gemini close the gap on OpenAI. Referring to the original Copilot, he admitted, “we made a mistake by binding it to OpenAI models only.”
If the company with the deepest single-model relationship in the industry is walking it back, the idea for model flexibility has clearly gone mainstream — certainly if this week is anything to go by.
Ramp and Meta get in on the act
On Tuesday, Ramp, the $44 billion spend-management behemoth, opened up Ramp Router, an early-access public version of the model router it built to manage its own AI bills internally, which it says cut its LLM costs by roughly 30%. It’s free to start, requires no Ramp account, and routes across OpenAI, Gemini, and select open-source models including Kimi through an OpenAI-compatible endpoint.
The very same day, Jyoti Mann fromThe Informationreported that Meta is also working on a model router. According to internal documents cited in the report, an incubator inside Meta called AAI Labs is developing a new product named Switchboard that would score each request for difficulty and send simpler ones to smaller, cheaper models — aimed initially at cutting Meta’s own AI agent costs, though it reportedly could end up as a public release.
Meta has particular reason to want this. Data from Runpod’s State of AI report, published in March, suggests Meta’s open Llama models are now a marginal presence in production: Llama 4 has near-zero real-world deployment, with Alibaba’s Qwen having overtaken it as the most-deployed self-hosted LLM.
Meta has also been building proprietary models in response. Muse Spark, its first model out of the newly formed Meta Superintelligence Labs, shipped in April. That was followed in July by Muse Spark 1.1, Meta’s first model with a public, paid API, priced at roughly a quarter of what OpenAI and Anthropic charge for comparable models.
Meta is aggressively targeting the incumbents, and Switchboard fits the same pattern: a way to make it easier for users to cut costs, switch models freely, and, where it makes sense, land requests on Meta’s own models instead.
But amidst all this hullaballoo about model routing, there is perhaps a broader question of openness. Not of the models themselves, which is a fervent debate in itself, but of whether the routing decision itself — the logic that decides which model handles which request — should sit inside a vendor’s own closed product at all.
“Is anyone building this as open-source?”
Elvis Saravia, a former technical product marketing manager at Meta AI who co-founded DAIR.AI, took to X to argue that it shouldn’t, given how differently teams weigh cost against quality.
“Is anyone building this as open-source?” Saravia asks. “It feels like this is something you don’t want to offload to an API. We all work with different trade-offs, so we need the ability to achieve custom routing.”
Recommended reading. Cursor Router routes tasks to the right model.
Is anyone building this as open-source? It feels like this is something you don't want to offload to an API. We all work with different trade-offs, so we need the ability to achieve custom routing. https://t.co/zcrpHbnlCepic.twitter.com/Vw2xyMy1jp
As for Cursor’s own version, Router is currently available to Teams and Enterprise customers only, across desktop, web, iOS, CLI, and Cursor’s SDK. It’s not yet clear whether it will eventually land on individual plans.
Moonshot AI became the latest AI company to discover that launching a popular model is only half the battle. Less than two days after releasing Kimi K3, the company stopped accepting new subscribers after demand exhausted its available GPU capacity. Existing users will keep access while Moonshot expands its infrastructure and reopens subscriptions in batches.
Inference demand outpaces supply
The incident emphasizes how demand is outpacing available infrastructure. As AI models take on longer, more coding and agentic workloads, companies are finding they need more inference capacity than they anticipated.
“Kimi K3 has received far more love than we expected,” the official Moonshot account writes on X. “Over the past 48 hours, demand has pushed close to the limits of our current capacity. We’re adding capacity as fast as we can and will reopen new subscription spots in batches.”
Kimi K3 has received far more love than we expected, and our GPUs are feeling it.
Over the past 48 hours, demand has pushed close to the limits of our current capacity. To protect the experience of existing subscribers, we're temporarily pausing new subscriptions and…
“We’re adding capacity as fast as we can and will reopen new subscription spots in batches.”
For infrastructure engineers and developers, the resulting capacity crunch is a bold indication of why companies from OpenAI to Anthropic to Moonshot are rationing access instead of selling unlimited usage.
Open weights, closed capacity
At 2.8 trillion parameters, Kimi K3 is one of the largest open-weight models slated for release — Moonshot has scheduled the public weight drop for July 27. In Arena.ai’s Frontend Code Arena, K3 topped both OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5. On the broader Artificial Analysis Intelligence Index, it trails both, scoring 57 to Fable 5’s 60 and Sol’s 59. That doesn’t make it any easier to run.
Open weights let anyone deploy the model, but whoever hosts it still has to pay the inference bill. Coding activities tend to tie up GPU resources far longer than a typical chatbot interaction, making it harder to keep latency low as more developers pile on.
Lee argued that as developers build longer agentic workflows, lower inference costs are quickly “re-converted into higher total resource consumption,” shifting the bottleneck from compute to server memory.
Moonshot’s subscription pause is a sign that keeping enough inference capacity online once developers start using it at scale might be just as hard as building the model.
“Agent tasks are not one-off question answering; they continuously generate, read, and process tokens during ongoing tasks.”
China’s chip constraints compound crunch
For a company like Moonshot, this general industry bottleneck is compounded by regional infrastructure realities. Unlike traditional software companies, AI developers typically rent much of this computing power from cloud providers such as Alibaba Cloud, Tencent Cloud, and Huawei Cloud rather than owning extensive data-center infrastructure themselves.
The capacity crunch illustrates the mounting challenge facing Chinese AI developers as US export controls continue to restrict access to leading chip provider Nvidia’s most advanced AI chips. As a result, companies such as Moonshot depend on a combination of older chips and domestically produced alternatives. These constraints have forced Chinese developers to concentrate strongly on software tuning and more efficient use of computing resources to narrow the performance gap with US rivals.
Token economics under pressure
The scramble for computing power has fueled a data-center construction boom across China. Alibaba has committed more than $53 billion to AI and cloud infrastructure over three years, while ByteDance is reportedly considering spending as much as $70 billion this year on AI data centers and related infrastructure.
AI companies typically charge customers based on the number of tokens, or units of text, processed by a model, making token prices a key measure of operating costs.
According to Bernstein Research, Moonshot charges $3 per million input tokens and $15 per million output tokens for Kimi K3. This makes it about 40% cheaper than Anthropic’s Opus 4.8 and roughly 70% cheaper than Claude Fable 5.
“A world where there are only [two to three] dominant frontier labs with 90 percent inference margins is net negative for every other layer while being awesome for those [two to three] labs,” wrote Atreides Management founder Gavin Baker on X. Baker argued that models like Kimi K3, Grok 4.5, and Muse 1.1 could shift value away from the model layer and toward chipmakers, cloud providers, and the software companies building the infrastructure that serves AI models.
For developers, Moonshot’s subscription freeze serves as an architectural warning. The era of assuming infinite, cheap API access is ending.
“A world where there are only [two to three] dominant frontier labs with 90 percent inference margins is net negative for every other layer while being awesome for those [two to three] labs.”
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After weeks of extending temporary access while bringing additional inference capacity online, Anthropic has finalized Claude Fable 5 subscriptions.
In a Friday announcement on X, the company said Claude Fable 5 will become a permanent part of Max and Team Premium subscriptions beginning Monday, July 20, at 50% of each plan’s usage limits. Pro and Team Standard users will transition to using the model through usage credits and will receive a one-time $100 credit.
An Anthropic spokesperson tells The New Stack, “Friday’s announcement means that Fable 5 at 50% of usage limits is now a standard, permanent part of Max and Team Premium plans.”
The changes bring an end to weeks of temporary extensions that left subscribers wondering whether Fable 5 would remain part of their subscription. Rather than introducing a new usage policy, Anthropic is making permanent the same access level subscribers have had since Fable 5 was redeployed on July 1.
Capacity drove the timeline
The move gives a glimpse into the economics of serving frontier AI models. Although Anthropic repeatedly extended temporary access over the past several weeks, the company says making Fable 5 a permanent part of Max and Team Premium subscriptions was always the goal.
An Anthropic spokesperson tells The New Stack, “Friday’s announcement means that Fable 5 at 50% of usage limits is now a standard, permanent part of Max and Team Premium plans. This was always our intention with Fable access (see more in our original launch blog on June 9). We planned to expand Fable 5 access in stages as we brought additional inference capacity online, with the ultimate goal of making it a standard part of subscription plans.”
Fable 5 usage limits explained
Anthropic has not explained exactly how it calculates the 50% limit or how many prompts that will translate to in practice. Developers using Fable 5 for heavy workflows are likely to reach the limit sooner than lighter users.
The company has said demand for Claude Fable 5 was difficult to predict and that broader access depended on bringing more capacity online. Reuters reported earlier this week that the company is in talks with Meta over a potential compute lease worth up to $10 billion, which could give Anthropic access to Meta’s AI infrastructure for as long as two years.
Meta compute deal looms
Thariq Shihipar, an Anthropic Developer Relations team member, posted on X, crediting the company’s employees with getting the necessary capacity online.
“This was due to a heroic effort by many people at Anthropic working sometimes literally around the clock,” Shihipar wrote on X. “It was not at all clear that we’d be able to do this in time, and so proud of everyone who made it happen. Enjoy Fable.”
“This was due to a heroic effort by many people at Anthropic working sometimes literally around the clock. It was not at all clear that we’d be able to do this in time, and so proud of everyone who made it happen. Enjoy Fable.”
Claude expands into India
The revised policy reflects broader changes across the AI industry. Frontier models are becoming expensive, which means AI companies are forced to balance compute costs with subscription pricing. OpenAI recently introduced GPT-5.6 Sol, while Chinese AI companies have released increasingly capable open-weight models, including Moonshot AI’s Kimi K3. Although Anthropic has not linked today’s pricing decision to competitive pressure, both model capability and the cost of serving those models have become key competitive factors.
…both model capability and the cost of serving those models have become key competitive factors.
Alongside the subscription changes, Anthropic is also formalizing its presence in one of its fastest-growing markets by introducing local pricing for Claude in India.
Indian customers can now pay for Claude in rupees instead of U.S. dollars, with GST included in the listed prices. That removes foreign currency conversions and other cross-border payment costs that previously came with subscribing. Claude Pro is now priced at Rs. 2,000 per month with annual billing or Rs. 2,399 on a month-to-month plan (previously $20 USD per month).
Industry reports have identified India as Claude’s second-largest user base after the United States. Together, the announcements show Anthropic expanding Claude in two ways: by making subscriptions easier to purchase in a major growth market while locking in an access policy determined by the compute required to serve Fable 5.
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While the household-name frontier models race forward with version numbers and context windows of at least a million tokens, a new breed of upstart data science specialists is pushing the context window into double figures.
Subquadratic debuted a 12-million-token window in May of this year, and Silicon Valley and South Africa-based Refiant launched its 10-million-token context-window model, Protea, on Wednesday. It’s a move that may signal the long-context AI race is now on.
Model inefficiency & workarounds are commonplace
But more context windows alone are not enough. This is because even the most capable models have a few hundred thousand tokens in working memory, which can force workarounds to compensate for what the model can’t access.
Refiant co-founder Dr. Viroshan Naicker tells The New Stack that he believes modern LLMs “fail to be organically efficient at an elemental level” and that his organization’s approach mimics how systems in nature, from ant colonies to beehives, find efficient solutions to complex problems.
“This is no case of pseudoscientific puff; nature is the most computationally efficient system that we know, and many algorithms used in science are nature-inspired,” Naicker says. “This is a road well-traveled in science. There are multiple teams globally working in this particular (nature-inspired) direction, trying to bridge the gap between AI inference as we know it and the energy efficiency of natural systems.”
“Fish and birds coordinate their movements to converge on the mathematically shortest, most efficient routes — honeybees, fireflies and bacteria are also programmed to use degrees of swarm-style optimization.”
What can the birds & the bees teach us about AI?
Did Naicker just mention ant colonies and honeybees?
Yes, because Refiant uses swarm-style optimization. It’s seen in ant colonies, which initially move randomly until a food source is detected, after which they leave a pheromone trail for other ants to optimize their journeys. Fish and birds also coordinate their movements to converge on the mathematically shortest, most efficient routes. Honeybees, fireflies, and bacteria are also programmed to use degrees of swarm-style optimization.
Naiker, along with his co-founders, Siddharth Gutta and Mathew Haswell, form a team with experience spanning quantum mathematics, traditional finance, and commercial scaling. Applying swarm-style optimization to data in Protea means inference is performed through a combination of compression and context management.
“From our perspective, we are also advancing a technology which provides context-specific inference models grounded in data,” Naicker clarifies. “We think this has value for reducing model hallucinations, replacing RAG, and constructing better, more reliable, agentic workflows. This adds a layer of trust in sensitive application scenarios, rather like an added insurance, rather than taking it away.”
Just how much is 10 million tokens?
The Refiant team describes 10 million tokens as equivalent to 7.5 million words in a single conversation (and we know from Anthropic’s own benchmarks this year that Claude has a 1-million context window), or five years of a user’s emails, 83 novels, or 830 podcast episodes, all held in active memory at the same time.
The team claims Protea is capable of working on entire enterprise codebases or decades of clinical trial data — datasets that previously had to be broken apart and fed to models in fragments — so they can be processed in a single pass with full fidelity. Engineers on Protea also submit that they can successfully tackle the “lost in the middle” problem — a limitation of million-plus-token windows, where models stay accurate at the start and end of the context but lose the thread of everything buried in between.
Refiant first applied these techniques to model compression, shrinking OpenAI’s GPT-OSS-120B so it could run on a MacBook Pro with 18GB of RAM.
“Rather than publishing benchmarks, we’re inviting users to run the models and try them out.”
The Protea series is open and live, and Refiant is inviting teams to stress-test the context window across different industries and use cases. But should we trust sensitive hould enterprise data archives to a completely unproven startup founded only one year ago?
Bring-your-own-cloud, a possible progression
“We adhere to data management best practices, processes and compliance requirements,” Naiker confirms. “This is reasonable for a startup at our particular stage. Privacy and data sovereignty are important values for us, and we are actively exploring edge, self-hosted, and bring-your-own-cloud data models.”
But a 10 million-token context window is big. Won’t that fall short when Protea starts to suffer from massive latency spikes when processing a full dataset? Naiker agrees that “latency is a core issue with long-context inference models,” but in the tests his company has run, it has delivered inference at a reasonable latency, even with large token windows.
“We have internal reports and tests that validate the technology, including Ruler, MRCR and Babilong, but we aren’t asking anyone to take our word on this. Rather than publishing benchmarks, we’re inviting users to run the models and try them out,” adds Naiker.
What comes next, a 100-million context window?
Although the technology industry is littered with apocryphal statements and Bill Gates almost certainly never said “64K ought to be enough for anyone” in real life, we have to ask ourselves today whether we’ll be laughing about those “silly little” 10 million token context windows by the end of the decade.
It may not take that long. Internally, Refiant maintains that it has already demonstrated a working prototype with a 100-million-context window and is exploring how best to benchmark and productionize it at that scale in the future.
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