This week, egghead.io cofounder John Lindquist joined host YK Sugi, founder of CS Dojo and developer experience manager at Eventual, to cover the latest AI news. First on the agenda was the contested release of Claude Fable 5. They also examined the financial shifts reshaping the technology industry, including the rising costs associated with agentic coding loops. Then John outlined the framework he uses to build in the agent era without starting from scratch every time.

Watch the full episode here:

Claude Fable 5: 3 days, a government order, and a lot of unanswered questions

Claude Fable 5 launched June 9 and was pulled from all customers on June 12 after the US government issued a directive ordering Anthropic to restrict access for foreign nationals inside and outside the US. Amazon researchers had reportedly surfaced what they characterized as a security vulnerability, and after Anthropic reportedly declined to patch or redeploy the model, the directive came down. Senior Anthropic staff subsequently traveled to Washington to meet with White House officials.

The dispute about what actually happened is unresolved. Anthropic’s position is that the reported issue was a narrow jailbreak that had been previously identified and was present across public models generally, and not a serious security threat. An independent researcher who reviewed the report described it as defensive prompting that surfaced known vulnerabilities and called the response an overreaction. Neither side has published the technique or prompt, so there’s no way to evaluate the claim independently. But as John put it, “It sets a very strange precedent going forward, as models are released, that governments can step in and control what private companies can and cannot do with their model.”

Another new precedent: Fable 5 wasn’t built on the Opus or Sonnet architecture, which means comparisons to prior Anthropic models or contemporaries don’t tell us much. But initial impressions were positive, including from YK and John, and Fable 5 quickly reached the top of the Arena leaderboard in the text, agents, and web dev code categories. However, the model also had a purposeful limitation: On questions related to AI and machine learning training specifically, it was designed to underperform (without signaling this to users), apparently to prevent competitors from using it to improve their own models. Intentional capability suppression in a commercial model, without disclosure, is a different kind of product decision than a safety guardrail. Whether that approach becomes more common as competitive stakes rise is an open question. 

Tokens burn fast when the loop isn’t ready for them

Last week, SpaceX went public in the largest IPO in history. The company finalized its acquisition of Cursor in a $60 billion all-stock deal shortly after. (That last one happened after this episode aired—we’ll talk more about it on Monday.) Both OpenAI and Anthropic have filed to go public as well, and Google raised roughly $160 billion through equity and a 100-year bond. A significant share of that capital is flowing toward AI coding infrastructure.

YK brought up another, less celebratory, financial story that’s been making the rounds: Uber burned through its full 2026 AI tools budget by April, mostly on Claude Code and Cursor, and Andrew Macdonald, the company’s COO, acknowledged they couldn’t link that spending to a measurable increase in useful customer features. Uber subsequently put a $1,500 per month per employee cap in place.

John flagged projects inefficiently utilizing agentic loops as one possible cause for wasteful token spend. Most developers deploying agents against existing codebases haven’t built the tooling those agents need to work efficiently, so agents burn tokens doing work that dead-ends, repeating context, or generating code that requires significant debugging. He explained:

If you take a legacy codebase and you throw agents against it with loops, you haven’t set up a proper agent environment. It’s so quick to burn tokens because. . .the agents don’t have the tools to work with.

The conversation in developer communities so far has focused almost entirely on what agents can generate. But as more organizations move from experimentation to production-scale deployment, building logging, verification, and proper error surfaces into agent tooling is what will determine whether token spend maps to real output. Otherwise, we’ll likely see more companies go the way of Uber.

Ingredients beat inference: A practical framework for building in the clone wave

For most developer workflows today, buy-versus-build leans toward building in a way it didn’t even a year or two ago. As John noted, “It’s so easy to build apps and workflows now where there are so many amazing production apps out there, apps on your phone, apps on your desktop, software as a service, that are trivial to copy and clone.” He uses the term the “clone wave” to describe this expanding set of open source equivalents to consumer software products that can now be cloned, forked, or replaced and get you 99% of the way to your use case.

The principle that drives the clone wave is “ingredients beat inference.” If you ask an agent to build a feature from scratch, it infers a solution with no external reference. If you give it an existing open source implementation to start from, it can adapt, translate, and integrate that code far faster and more reliably. The ingredients approach also helps with the 43% of AI-generated code that needs debugging in production, per a figure YK cited earlier in the episode.

The GitHub CLI plays a central role in this workflow. John explained that because agents understand the GitHub CLI natively, you can give an agent a search task and let it find implementations it wouldn’t have generated itself. Language mismatch isn’t a blocker, because agents translate between languages and libraries well. And tools like DeepWiki from Cognition let agents explore and understand a repo’s structure before cloning or forking it, so the evaluation step doesn’t require local setup.

The framework extends to how you build the last 20% that isn’t available as an ingredient. This is the part that’s specific to your use case; John described it as “that extra bit that you’re building on top of it to make it into the custom product and project for either yourself or for your users.” John’s bigger point is that the tools you build for yourself should also be usable by your agents. Expose endpoints and logging. Give agents the ability to read state and errors. An agent that can control a tool but not debug it will eventually stop in ways that are hard to diagnose.

John walked through cmux to demonstrate what an agent-native workspace looks like in practice. cmux is a terminal multiplexer built with agentic workflows in mind: it exposes a CLI that agents can control directly, so you can open a terminal pane, have that pane spawn another, and have the two read from and write to each other. In practice that means you can run Claude Code in one pane, Codex in another, and a third pane reading output from both, with each agent able to observe the others’ state.

Agents need more than the ability to run commands. They need to read logs, check errors, and confirm state before taking the next step. A workspace that exposes those surfaces gives agents a feedback loop. This tenet is applicable to tools across the company. Organizations that treat their internal tooling as agent-accessible infrastructure are building something that compounds. Those treating agents as black-box code generators are taking on technical debt they may not see until causes issues later on.

What’s next

SpaceX’s acquisition of Cursor turns the coding-agent race into something much larger than an IDE fight. Cursor may be positioning itself as a new GitHub for the agentic era, where agents write, review, test, repair, and govern code. At the same time, Salesforce’s $3.6B acquisition of Fin shows the same pattern inside enterprise software: Buyers want packaged workflows that solve real support, sales, and operations problems rather than abstract “agents.” 

Next week, host Ksenia Se examines these stories and more through the lens of who owns the loop where AI does the work. Join us to find out why the next phase of AI will be about who controls the infrastructure, economics, and trust layer.

Our episodes are free and open to all through the end of June if you’d like to attend live—register here. And we’ll continue to publish our takeaways here on Radar each Friday and share full episodes on YouTube, Spotify, Apple, or wherever you get your podcasts.

When Kubernetes first came onto the scene, it was a major turning point, a revision of the infrastructure and operations space that transformed the way developers and ops personnel build, deploy, and maintain applications in the cloud. It has since become the clear standard for how modern applications are built and operated. As the CNCF noted in its latest Annual Cloud Native Survey report, “Among container users, 82% are using Kubernetes in production in 2025, up from 66% in 2023. This represents near-universal adoption within the container ecosystem.”

Over the last few years, another revision in the space has occurred with Kubernetes’s evolution from a container orchestrator to an AI infrastructure platform. According to the CNCF survey, “The rise of Kubernetes as the de facto AI platform represents a fundamental shift in how organizations approach machine learning operations. . .[with Kubernetes] providing a unified orchestration layer that handles both traditional application workloads and compute-intensive AI tasks.” The emergence of seismic technologies like generative AI and agentic AI has only accelerated this transformation.

The intersection of AI with Kubernetes is undoubtedly one of the most impactful developments in the operations space. As Jonathan Johnson, software architect at Dijure, observes, “AI on K8s is very, very important, and there is not enough [resources] out there.” Raju Gandhi, senior technical architect at Edward Jones, echoes this assessment, noting that “operationalizing AI/ML on K8s is a big issue, [and it’s only] getting bigger. This is a topic that needs attention.” But what are some of the things that you should know about this trend to keep abreast and stay ahead in the game?

Generative AI

Anyone with access to a computer or a smartphone has likely used some iteration of generative AI, a stunning fact when you consider that GenAI was on the outer edges of mainstream discourse and consumption a scant five years ago. But at the end of 2022, the debut of ChatGPT marked the beginning of a technological revolution, one that would impact and reshape nearly every aspect of our working and personal lives. Unsurprisingly, there are now thousands of generative AI models, a proliferation that naturally has its own set of complexities. Selecting a model is simple, but if you’re an application developer or MLOps engineer, how do you go about operating that model in a production system? Not only do you have to be cognizant of factors like resilience, scalability, security, and operational costs, but there’s the fact that bringing a model from experimentation into production can be arduous if not done properly. That’s where Kubernetes comes into play.

As Roland Huß and Daniele Zonca, distinguished engineers at Red Hat, note, “GenAI/LLM models are resource intensive, requiring substantial computational power and large datasets. Given its scalability and extensibility, Kubernetes is uniquely suited to function as an efficient platform for AI and LLM model pretraining, fine-tuning, deployment, and prompt engineering.” They further elaborate that “this integration with Kubernetes not only simplifies the adoption of cutting-edge AI technologies but also ensures a seamless and efficient operational flow. Kubernetes, with its robust scalability and management capabilities, stands as an ideal platform for generative AI projects, aligning DevOps and MLOps practices in a cohesive ecosystem.”

This sentiment is already shared by a wide swath of the industry. According to the CNCF survey above, as of 2025, 66% of organizations run generative AI workloads on Kubernetes. These organizations include OpenAI, which uses Kubernetes for its AI/LLM application experimenting and testing; Tesla, which utilizes KServe to manage production-grade LLM inference; and Adobe, which uses Kubernetes to power its suite of generative creative models. Other companies taking this approach include Uber, Intuit, and Google. With more companies adopting this practice for their generative AI and LLMs operations, it’d be prudent for any organization to leverage Kubernetes for their own GenAI and LLM workflows.

Agentic AI

Nearly coinciding with the rise of GenAI has been the steady growth of agentic AI. Unlike GenAI, agentic AI goes beyond answering simple prompts and generating text in its ability to operate autonomously to perform complex, multistep actions, utilize tools, and make independent decisions. With its ability to support both traditional ML processes and GenAI and LLM operations, it should come as no surprise that Kubernetes has a role in the agentic AI ecosystem as well.

According to Ronald Petty, principal consultant at RX-M, “Kubernetes has been leveraged to host machine learning pipelines, including AI model training and inference. As inference options have become plentiful and affordable, on and off-premise, we have seen the rise of agents. Coupling cloud native technologies and popular protocols, we now see agents moving from ad hoc demos to complex fleets of agents on systems like Kubernetes.” So what are some examples of the integration between these two technologies?

One notable offering is Kagent, an OS programming framework that runs AI agents in Kubernetes and “helps engineers build powerful internal platforms by tackling cloud native tasks such as configuration, troubleshooting, complex deployment scenarios, observability pipelines and dashboards, and safely enabling network security.” Operating along similar lines is K8sGPT, an AI-powered tool that leverages intelligent insights and automated troubleshooting to analyze Kubernetes clusters for configuration problems and security issues, as well as generates solutions to problems discovered in analysis.

A more recent entry in the field is Sympozium, a Kubernetes-native coordination layer for multi-agent AI systems that “solves the same problem Kubernetes solved for containers, but for agents that need to share context, hand off tasks, and maintain shared situational awareness.” Another newer offering is Agent Sandbox, which allows you to run AI agents as isolated, stateful workloads with a native API on Kubernetes.

The fundamentals

While it’s important to be aware of the latest developments and trends affecting your domain, that shouldn’t come at the expense of foundational knowledge and skills. As basketball great Michael Jordan once said, “Get the fundamentals down and the level of everything you do will rise.” One of the most fundamental skills for working with Kubernetes is networking, and frustratingly enough, it’s one of the more difficult ones to master. As Cisco senior staff engineer Nico Vibert observes, “Platform engineers tend to be comfortable with Linux networking but less so with protocols like BGP and IPv6; network administrators know those protocols well but find Kubernetes abstractions unfamiliar. Both personas struggle to navigate the dozens of networking tools seemingly required to meet connectivity and security requirements.” Yet as organizations move mission-critical workloads, AI training pipelines, and regulated financial services onto Kubernetes, the engineers who can design, secure, and troubleshoot the network layer have become some of the most sought-after professionals in the industry.

In recognition of both the importance and difficult nature of the Kubernetes networking skill, the CNCF recently announced a new certification focused on the Kubernetes network engineer role. The certification is designed to validate hands-on networking expertise across all of the aforementioned layers, filling a gap that the Kubernetes community has long recognized.

For organizations that use Kubernetes to develop and deliver applications, leaders and decision-makers need to be aware that utilizing Kubernetes in conjunction with the latest AI tools is no longer a luxury but a necessary practice that will allow their companies to thrive. A similar onus should be placed on the basics. When hiring your next DevOps, network, or site reliability engineer, ensure that their ability to design, secure, and troubleshoot the Kubernetes network layer is second to none.

If you want to dive deeper, check out Roland Huß and Daniele Zonca’s Generative AI on Kubernetes, Jonathan Johnson’s GPU Kubernetes Homelab live course, Alex Corvin, Taneem Ibrahim, and Kyle Stratis’s Scalable Kubernetes Infrastructure for AI Platforms, Ashok Srirama and Sukirti Gupta’s Kubernetes for Generative AI Solutions, and Yogesh Raheja’s K8sGPT Essentials on-demand course. They’re all on O’Reilly. If you’re not a member, you can get started with a free trial.