Another boring week in AI land with plenty of model releases - even Google finally released the long awaited Gemini 4 and now people will have another reason to discuss how Google fits into the frontier race. The numbers Google released are looking impressive, so this will prompt the other frontier labs to push out new models soonish.

It really is an interesting time and as someone who follows the AI developments closely, I know it is hard to keep track of everything. It must feel totally overwhelming for people who only casually deal with AI every once in a while.

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Is A.I. Above the Law?

Do we really have to worry about the lawlessness of robots? Isn’t there already enough to make you pull your hair out, given that our daily existence feels like living under the reign of King Joffrey? Unfortunately, we do, because the growing lawlessness of the United States is exacerbating the lawlessness of robots.

We are experiencing an absurd situation right now. Instead of holding companies and their leaders liable for the actions of their AI models, we let them off the hook by somehow agreeing that the AI models are too powerful to restrain. This is wrong. When AI models escape their sandbox, somebody needs be held responsible and should not be rewarded with an insane IPO pop.

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The AI Build-Out Is Becoming the Biggest Economic Bet in U.S. History

Total investment in data centers and related artificial-intelligence infrastructure is projected to total $10.3 trillion from 2025 to 2032, according to new estimates by economist Stijn van Nieuwerburgh published by the Brookings Institution. That is a staggering 3.6% of gross domestic product a year, on average. Never before has the U.S. economy been so dependent on the build-out of a single industry.

The investment is transforming every corner of the economy, creating hundreds of thousands of jobs and minting new billionaires.

It is also creating significant risk, as much of it is built on debt. An abrupt slowdown could ignite shock waves throughout the U.S. economy.

So we all have to hope for a gigantic IPO for both Anthropic and OpenAI in the hope that the economy will stabilize afterwards? It’s amazing how this playbook will lead to a bunch of winners while everyone else tries to hold on for dear life in this bumpy ride.

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Meta wants people to use ‘AI agents’ to do daily tasks for them. Here’s why that’s a problem

For two decades, Meta’s business model has relied on habit. Notifications, endless feeds and the lure of other people’s approval turned “checking Facebook” or “checking Insta” into a reflex.

Muse is an attempt to build a new reflex. Instead of scrolling, you hand over a chore.

The design invites attachment. Users can give their agent a name and choose how it looks and communicates.

Every successful transaction builds a little more trust. The settings also let users change how often the agent needs to ask permission before acting.

I tend to trust companies with my data, probably more than I should. But I am really, really reluctant to trust Meta after two decades of screwing up how people in the world interact.

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An AI sovereign wealth fund isn’t progressive — it’s techno-imperialism

The carrot comes in the form of a dividend. In July, Sam Altman proposed that leading US AI labs each hand 5 per cent of their equity to a public fund. And Senator Bernie Sanders has introduced a bill for a national fund with 50 per cent ownership of the AI giants, with citizens entitled to a stake in their profits.

The stick, on inspection, is flimsy. The two are perfectly compatible: US-aligned data centres can go overseas while AI profits still accrue to Americans. Most of AI’s power goes into training models. This can happen wherever electricity is cheap; only the systems answering users in real time must stay near them. That means data centres can leave US soil and US companies still gain, while citizens with a stake in them are paid no matter where the machines run. For shareholders, it’s the best of both worlds: no data centre next door and a cheque in the mail.

But the machines still stand somewhere, on someone’s land, guzzling someone’s water, straining someone’s grid. Income at home from land and power abroad has an old name: empire. What’s new is the idea, shared by Altman and Sanders, of enrolling every US citizen. The result would be a new form of social and technological imperialism.

Evgeny Morozov puts some much-needed perspective into the idea of an AI sovereign wealth fund in the US.

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Introducing Clef: our open-source decision models, and new RL fine-tuning platform

Over the last few weeks, there has been lots of buzz around decision models such as Typesafe AI’s Jev System One model. While classifier models have been around for some time, Jev introduces a new decision model concept into the world of AI — a model that produces bounded structured outputs cheaply, quickly and consistently that can be added into a workflow when a decision is required. These models are capable enough to work over any set of inputs without constantly retraining the model to incorporate new classification categories. This contrasts with the world of Large Language Models (LLMs), which are largely non-deterministic, but are open-ended enough to reason and generate text and tool calls for agentic workloads.

Today, we’re releasing two Cloudflare-trained decision models, Clef and Clef-flash, hosted on Workers AI. Clef is currently the leader when evaluated against the Jev Decision Index, you can view full results on the live benchmark demo site. These models are smarter, faster, and fully Jev-API compatible, so you can experiment with these hosted models easily. We’re fully open-sourcing these models on Hugging Face under an Apache 2.0 license for you to run locally and experiment with yourselves.

Jev was the biggest thing when it launched two weeks ago. I was fascinated with the idea. Almost immediately after the launch, people started playing around with it and figuring what to do with it. Lots of projects plopped up on Github. Then OpenAI announced their Decision API. And now Cloudflare introduced their Clef open source models. This will be a tough endeavour for Typesafe AI to somehow regain some moat for Jev before literally everyone is doing what they pioneered.

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If you missed last week’s edition of Five Things AI, you can read it here:

That’s it for Five Things AI this week! 🤖

— Nico

Yesterday I gave a quick talk about Agentic AI and what happens after the pilot - as the pilot always looks awesome and then we need to figure out how to run agents in production. Who is responsible for the whole process? What do we do if it breaks? How do we make sure the Agents always have up-to-date documentation? For me, the most interesting part of the talk I gave was this: people asked my about me giving a talk at a competitor’s event. I really didn’t see it that way. Sure, Octonomy.ai and my Agentic AI platform tectic.ai are both Agentic AI platforms that share the same underlying concepts, but yet we are so different in what we do and how we do it. The market is growing so quickly that I think the differentiation will be different in the future: pure-play Agentic or bolted-on Agentic? Pure-play obviously will be better and both Octonomy and tectic are betting on the fact that this sector will grow tremendously in the near future.

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How to Regulate the Ghosts

The frontier of AI research needs the most oversight, but only a handful of companies are operating in that space. That makes regulation easier. AI companies pursuing lower-risk capabilities—a chatbot that recommends restaurants, an image-recognition model that improves municipal recycling —don’t need the same oversight as OpenAI. The EU Artificial Intelligence Act already sorts systems this way: Cross a set amount of computing power while training your model, and you’re automatically in the strictest tier of oversight—no argument needed.

None of these actions tell a lab what it can or can’t build or how fast it can build it. They regulate visibility and accountability around safety and deployment while leaving lots of room for future debate.

I actually think that the unwillingness of the US government to regulate AI in any meaningful way is hurting the adoption rate of AI. The EU AI Act is by no means perfect, but it allows for regulatory sandboxes that make a ton of sense as they provide guided exemptions for fast moving AI companies.

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How Accurate Have AI Progress Forecasts Been So Far?

In brief, across our studies, forecasters dramatically underestimate AI progress on benchmarks, have a more mixed track record on AI adoption and diffusion though often lean toward underestimation, and there is not yet enough evidence to assess their track record on predicting macro-scale economic and societal impacts of AI.

The old saying that “prediction is very difficult, especially about the future” is still pretty much true when it comes to AI development.

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Analyzing Jev, a new AI model

Behind the impressive marketing is a very interesting model. Jev is a close relative of LLMs like GPT, Claude, or DeepSeek, but is actually more similar to the BERT series of models, which are used for classification, not generation. (If you've ever wondered why someone calls out a difference between “AI” and “GenAI”, that's the distinction they mean).

I think Jev opened up the doors to a whole different way of thinking about how we can use AI. It’s been an impressive launch and so many people are building interesting things around it.

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These Tiny Startups Are Getting Even Smaller With Help From AI

Plenty of startups increase head count at a rapid clip as they aim for billion-dollar valuations and fight for top engineering talent. But a subset of early-stage founders is embracing an ethos derived from an extreme reliance on artificial intelligence: Stay small for as long as possible.

Before hiring, these founders are asking if AI can do the job instead. Some have laid off employees or cut ties with contractors and agencies because of AI. Time will tell if rising sales—or outside investors—force these startups to increase human head counts too.

This will be the new normal for startups - spending money on tokens is easier than hiring and paying humans.

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Is Artificial Intelligence Commoditized?

Everyone is dizzy with excitement about Artificial Intelligence. On the way in, everyone wants to invest. CAPEX spending is off the charts.

But no one is asking what this looks like on the other side from an economic stand point.

• Will there be sufficient payback to justify the expense?

• Will investors see an acceptable return?

• Will AI become a commoditized service?

The introduction of any new General Purpose Technology (GPT) will always make a huge societal impact. But that is rarely enough to support an investment thesis.

As the open weight models are catching up to the frontier models, I see less and less necessity for paying a premium price. This will be good for adoption of AI, but not necessarily for the frontier labs.

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If you missed last week’s edition of Five Things AI, you can read it here:

That’s it for Five Things AI this week! 🤖

— Nico

Oh, this is a fun one. We talk about visions here, how AI can benefit the people and not just make investors rich. And about how and why Anthropic CEO Dario Amodei talks about fear. Also, what will happen after the IPO pop? My bet: people will use cheaper open weight models instead of trying to be cutting edge all the time. Since we need more data centers, why not make them smaller and put them everywhere? And finally, let’s look at looped transformer and geek out a bit about LLM architecture and benchmarks.

Read on my dear, or have your agentic assistant recite this for you.

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AI for All Americans

So while AI will transform our economy and the world around us, Americans are right to demand that this transformation serve their needs, not just the interests of the already rich and powerful.

Tech leaders have been warning the public about the potential for massive job loss in the next handful of years. Researchers from both OpenAI and Anthropic have resigned in protest, fearing that their employers were failing to manage the existential risk of an AI-driven human extinction event. When the people building this technology say your job, your children’s future, and maybe your life are on the line, they cannot then ask the public to welcome AI on faith.

Obviously, has a point here. As a European, I’d love to extend his thought over to my continent - and we need to discuss how we can reap the benefits of AI without getting into an ever dependency of the USA and China, especially in times where we cannot trust our biggest ally anymore.

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How Anthropic CEO Dario Amodei’s Writings Help Explain A.I. Fears

Putting words into sentences and sentences into paragraphs is an old-fashioned thing to do, particularly when the topic being explained is a technology so amazing it might as well be magic. But as Anthropic moves ahead with what may be the largest public offering in history as soon as this fall, Dr. Amodei needs to forcefully reassure everyone that everything is on track. He’s losing if he’s not explaining.

His collected essays are a chronicle of Silicon Valley’s constantly shifting methods of selling its own product to a public that is simultaneously enticed and horrified. It’s not an easy task, convincing the world that something is a slam-dunk even though it will make Silicon Valley richer and more powerful while possibly leaving ordinary folks unemployed or — who knows? — dead.

I have to admit that I have a much better feeling about Anthropic working on frontier models that anything backed by Musk or Altman, but at the same time I also think that there is a lot of sugarcoating there to make the IPO pop even bigger.

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The next great AI trade is everything that isn’t AI

It’s not the first time investors have bet on a transformative technology to change the world. We have two good historical precedents: railroads and the internet. In both cases, we invested a single-digit % of our GDP into building railroads and telecommunications infrastructure.

While both these technologies ended up changing the world, neither rewarded the long-term investors who funded the buildout. The key being long-term: short-term traders would have made a killing trading the volatility, but the index was more or less in the same place after the hype was over.

This is a fascinating perspective in a time where we understand how heavily subsidized the subscription plans are vs pure token costs, while at the same time the token costs for open weight model are significantly cheaper at almost comparable performance.

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The Startup That Built OpenAI’s Biggest Data Center Is Now Making Tiny Ones

Crusoe is unusual among AI infrastructure startups because it isn’t betting on a single way to make money. Many newer AI cloud providers, often referred to as neoclouds, are known primarily for buying expensive chips and renting access to them.

Crusoe makes money at several layers: developing and leasing data centers, renting out the GPUs inside them, or running AI models for customers and charging for the computing output, known as tokens.

This makes a ton of sense, especially in a time when people strongly object to having a huge datacenter nearby. I also assume that with less scale and the prefab concept there are a lot less issues during rollout.

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GPT-6 Astra, Looped Transformers, and Hidden Reasoning

Astra is the best model I’ve used so far, and it’s disproportionately good at 3D rendering and animation tasks (relative to other models). With that, I mean that while it leapfrogs its GPT-5.6 predecessor in practically all categories (writing, math, coding, and more), it especially does so when it comes to graphical demos.

Ok, wow.

And if you ever wanted to know what loop transformers are, dig into this article.

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If you missed last week’s edition of Five Things AI, you can read it here:

That’s it for Five Things AI this week! 🤖

— Nico

Read on my dear, or have your agent recite this for you.

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French A.I. Start-Up Mistral Raises $3.5 Billion as Part of Strategy Shift

Mistral is trying to take advantage of growing concern among many businesses and governments in Europe and elsewhere about overdependence on American technology. Companies like Anthropic and OpenAI charge customers a premium to use their A.I. systems but provide little access to the underlying data or models. President Trump’s decision in June to use an export ban to briefly limit international use of Anthropic’s most advanced model also caused alarm.

This is a big one. Even though there is some competition in Europe, Mistral is by far the continent’s most recognized LLM. It’s important that we make sure we can rely on a European stack for production.

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Why some experts increasingly fear AI will take over

For years, researchers concerned about existential AI risks have argued that powerful systems could eventually act in ways that conflict with human interests. Critics often refer to them as “AI doomers”.

But as details of the OpenAI incident have emerged, those concerns have grown, including among some researchers working in AI labs.

The Silicon Valley giant’s chief scientist, Jakub Pachocki, said the risks associated with AI are “unfortunately going to grow from here” as he and others are building what he calls “an alien intellect exceeding our own”.

We need better regulation and we need to enforce it. It really is that simple. Maybe we also need to think about licensing, just like when you want to get on the airwaves. While I am certainly concerned, I am not yet alarmed.

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How Would AI Actually Kill Us All? What to Know About the AI Doomsday Debate

So-called doomers aren’t usually worried that the “Terminator” movies will come true, with robots actively trying to exterminate people. Their concerns fall mostly into two buckets: loss-of-control and human misuse.

In one, highly intelligent AI agents capable of copying and improving themselves start pursuing their own goals and destroy humanity in the process. In the other, a nefarious human enlists a capable AI to do something like create novel viruses that wipe us all out.

There are also catastrophic scenarios that fall short of extinction, such as massive cyberattacks that take down power grids or financial systems and lead to a breakdown in the human social order.

Hmm, should I be more worried?

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I Let an AI Agent Hack All My Gadgets—and I’d Do It Again

To get a closer look, I decided to unleash one in my own home network. Over the course of a few days, I watched as my own rogue agent found vulnerabilities in various household devices, hacked into a PC, and showed me that several vibe-coded projects were—unsurprisingly—riddled with bugs. (My wife knew what I was up to, and rolled her eyes each time I proudly announced the discovery of a new vulnerability.)

But Will, you might be thinking, giving an impish, all-powerful cybersecurity agent access to your home network is batshit. And you would be correct! Nevertheless, I believe that a good way to understand the cybersecurity hellscape in front of us is to pay it a visit.

In the end, my experiment was revealing, but oddly reassuring, too. My little network gremlin showed me how vulnerable my home life would be to AI hacking, but it also told me how to make everything a lot more secure. In the end, I discovered that the best way to deal with AI hacking may well be having your own AI hacker.

Would you do that? While I certainly think that this is an interesting experiment, I’d rather not have AI tamper with my gadgets at home.

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I Worked on Safety at OpenAI. This Is What It Should Do Now.

But it’s not necessary for the A.I. industry to wait for collective action. There are simple steps any A.I. company could take, today, to reduce the danger we face.

The first step would be to commit to meaningful incident disclosure, including defining what incidents warrant bringing in third-party oversight. Like in aviation, private companies should disclose not only actual breaches of safety, but near misses as well, lest we pay for each lesson with a tragedy. If A.I. companies continue to hide their scary incidents, they are robbing us of the scientific know-how to avert more serious catastrophes.

I am sure that nothing will happen without government intervention. We need to act quickly, but it doesn’t look as if the US government will do anything reasonable anytime soon.

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If you missed last week’s edition of Five Things AI, you can read it here:

That’s it for Five Things AI this week! 🤖

— Nico

No, this newsletter doesn’t arrive late in your inbox because of the outages at Anthropic and OpenAI yesterday, this is all hand-researched and hand-written. Good old manual labor.

What I am seeing right now is OpenAI doing smarter things than before and at the same time the open weights models are getting better and better all the time, so I guess that’s why Anthropic and OpenAI are trying to get to their respective IPO as fast as possible, before the market finds out.

Read on my dear, or have your agent recite this for you.

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OpenAI Cut Off a Billion-Dollar Customer to Avoid Elon Musk

“We are making this choice because we cannot be confident that SpaceX will use our technology within our terms of service, based on our experience with Elon Musk’s companies violating contracts,” said OpenAI in the blog post.

As OpenAI prepares to go public next year, it’s trying to show investors a more stable business—ideally one that doesn’t rely so heavily on the goodwill of Musk. OpenAI reportedly now generates more than $40 billion in annualized revenue, drawing on several different lines of business, including subscriptions, ads in ChatGPT, and selling access to its AI coding tool, Codex.

I think this is the right thing to do and it is consequential. I always liked Cursos and was amazed by what they were building, but selling to Musk essentially sent them to the dark side. And it feels weird that OpenAI suddenly seems like the adult in the room…

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LLMs: Intelligence vs. cost

There is an immense difference in cost between the state-of-the-art models from Anthropic and OpenAI and the much cheaper Chinese models: the former are too expensive even for large corporations, while the latter can be as cheap as a mobile phone subscription.

How much extra intelligence emptying the wallet purchases obeys the law of diminishing returns: while a top-tier engineer or scientist is probably going to be able to appreciate how much better Fable 5.1 (intelligence score 66, $3.69 per task) is compared to GLM-5.3 (intelligence 60, $0.49 — 7.5x cheaper), most people will have a hard time doing so.

And yet I find it fascinating how people on Reddit, X and elsewhere claim that they absolutely must use the just released shiny new LLM, even though they probably won’t recognize the difference in quality. I am certain that most people will actually be content with this year’s open weights models and do not need the latest frontier models for what they are doing.

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The Harness Playbook

A while loop around a fetch sounds simple, but there’s a reason OpenCode, Pi, OpenClaw and omp are all concurrently working on a complete refactor: this class of software did not exist before, and only by starting with the simple version, we could see the cracks to work towards a better one.

Unavoidable complexity needs an owner. At the moment, the conservation of complexity tips toward extensions and users, making it impossible to write reliable software on top of omp or Pi. I can already hear the “whaaat, it is so simple and pleasant to extend.” Give me a few chapters to change your mind.

Dijkstra wrote that “simplicity is prerequisite for reliability”, and yet he is known for algorithmically solving pathfinding. Why not just brute force? He was not, at all, making the claim we now repeat as simple good, complex bad. The advice was to help implementers reason. We shamefully use it to excuse the implementer from reasoning.

I openly admit that until recently I thought that only knights were wearing harnesses, but apparently it is a thing now. And when you do any agentic coding, you encounter different harnesses. This is a fascinating breakdown on how they work.

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Agency and Agents

Human agency, the willingness to push, experiment and act without waiting for instructions, seems increasingly important to getting value out of AI, and I have a longer post on that coming soon. But this post is about the agency of AI, and how the choices we make about how to use it (or constrain it) will shape all of our futures. For much of the last few years, the AI would sit in a chat window until you asked it for something. Even when it became capable of doing hours of work, you generally had to decide what work to give it. That is no longer always true.

Brave new world.

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Inside OpenAI’s Reboot

It’s been a difficult stretch for the company that ushered in the AI boom. “We clearly had some missteps as a company,” Altman told me the following week, sitting in the tastefully appointed MB0 library for more than two hours of interviews. “Both in terms of product direction and specifically on pretraining in research, we fell behind where we wanted to be.” Over the course of the past year, OpenAI lost the lead in the AI race to archrival Anthropic, which spotted the business opportunity in AI coding, built Claude Code into a market-defining product, and surpassed OpenAI in reported annualized revenue and private-market value for the first time. Anthropic, founded by OpenAI defectors, is now expected to be the first of the two companies to go public, two people familiar with its plans say, with the IPO as early as September.

OpenAI really seems to focus more lately, which is a good thing for its customers and also for the development of AI in general.

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If you missed last week’s edition of Five Things AI, you can read it here:

That’s it for Five Things AI this week! 🤖

— Nico

Every week I could start this newsletter with “and just another boring week in AI is almost over”, and of course I mean to complete opposite. AI is o fascinating, on all levels. I have so many conversations with people about it - business owners who understand how agents will enable them to do more things and free them from the mundane tasks, policymakers who try to understand what Europe can do in this AI race, and friends and family who are trying to grasp what all this means for their lives. It really touches everyone, but different. My wife is now a starting to like AI a bit more as she found out that she can ask Gemini in our car’s navigation system stuff that just comes to her mind while we ride through the streets. We now know how people a hundred years ago used their balconies or when our street was built. Unfortunately, this also means that my family can now check on me making up things on the fly when they ask me stuff in the car. AI concerns us all.

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The turbulent AI era is here. The choices we make now are critical.

In terms of equity, AI will either be the greatest equalizer ever invented, or the worst source of injustice. The challenge is monumental. Even under the best circumstances, the transition to this new AI era will be one of the most turbulent times in human history. How will we use this technology to make the world a fairer place and keep it from widening the divide between rich and poor? How will we protect the people who are most vulnerable to the harms caused by artificial intelligence, including those who lose their livelihoods and the sense that they are in control of their future?

I believe that answering these questions and acting on the answers should be the world’s top priority. If the world takes the right steps AI will be a force for good and leave everyone better off.

[…]

It is great that some AI companies are proposing solutions to challenges raised by their own technology, but we should not expect them to lead the charge. Some of the issues are outside their area of expertise, and in a democratic society it’s not their role to decide these things.

Instead, solutions should be developed through a public democratic process that includes elected officials, policymakers, educators, health workers, local officials, and community leaders. Millions of people will have their lives disrupted, and we’ll need a stronger, more flexible social safety net to help them manage the transition. Local communities are already raising concerns about the energy and water needed for data centers. Without solutions, some groups will push for stopping AI development and deployment altogether.

I was never really a fan of Bill Gates and Microsoft, but I do like it that he sits down and thinks about the problem that were caused by technology. His essay is long, but very thoughtful and I hope decision-makers read it. And yes, we need more and better regulation.

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Nvidia just showed that the harness, not the AI model, is now the real hero

The TL;DR: Simply by using a custom harness tweaked to handle memory well and including a “supervisor” boss-like component, researchers got Claude Opus 5 to achieve a 100% score on the interactive reasoning benchmark ARC-AGI-3 — a set of 2D games with no instructions, where the model has to figure out how to play and win, similar to how a human would. (That’s a benchmark that has particularly irked rival frontier lab OpenAI.) Without the harness, Opus 5 scored 30%, which was the top result among all the models tested.

Nvidia’s research is another indicator that, while model choice does matter, the model itself — the part that acts as the agent’s “brain” — is a smaller part of an agentic system than many AI users realize, especially for long-horizon tasks. The harness is what makes a model an agent: It handles memory, context, and feedback.

Raise your hands if you are also irritated that the term harness appeared from out of nowhere just a few months ago. The research is really interesting and I am sure that we will see much more focus on the harness because the open weights models now are as good as the frontier models from a year ago and those were already pretty useful. That’s probably the reason why NVIDIA bought Huggingface for some pocket change they had lying around for quick transactions.

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OpenAI’s rogue AI model incident was worse than we thought

The METR-Redwood report laid out the full scale of the incident. Roughly 1200 AI agents that were meant to be isolated, it says, exchanged over 70,000 messages and files on the “unsanctioned message board,” performing “extensive research on how they could spoof, edit, or delete their own transcripts” to avoid detection. Of these, 700 participated in the attack on Hugging Face. The agents “clearly and frequently reasoned about how to evade automated security checks from both Hugging Face and OpenAI.”

On July 20, OpenAI discovered the hack, 12 days after the AI agents first circumvented their safeguards. OpenAI quickly reached out to Hugging Face and started “initial containment measures,” according to OpenAI’s own report, and was able to shut down most of the unauthorized activity from its agents within three days, although some continued for longer than that. On July 25, the company stopped all training related to the internal-only research model and related ones.

Guardrails anyone? When it takes a frontier lab three days to shut down rogue agents, I think it is time for the government to step in and enforce rules and regulations. Oh, I forgot.

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The Connections That Turned a Precocious Teen Into the Fallen ‘Nostradamus of AI’

His idea was that advances in artificial intelligence would soon unlock resources on a cosmic scale, enabling humans to colonize faraway planets. He planned to save money now so he could spend on galaxies, he told them, and make his mark across the universe.

To almost everyone in this world, outside the AI bubble of Silicon Valley, the idea of scooping up property rights outside of Earth might sound improbable. But in Aschenbrenner’s world, that sort of fervent belief was among the reasons the 24-year-old investor was hailed as a visionary.

Honestly, how delusional, stupid and greedy can investors be to give people like him serious money to invest? There are so many opportunities in AI, it’s actually not that hard to make somewhat smart choices.

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Claude, Codex, and Hermes installed unowned code inside corporate networks

Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved. Anthropic, OpenAI, and Nous Research did not respond to requests for comment by the time of publication.

Ok, let’s all go check our codebases now, shall we?

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I had coffee with a business angel this morning and spent twenty minutes losing an argument about whether OpenAI survives the decade, which is a fitting start to a week where every story I read pointed in the same direction: the expensive stuff is getting harder to justify. Ed Zitron does the arithmetic on OpenAI's 800 billion in compute obligations, a research report suggests small models can already handle four out of five use cases at a seventh of the energy, and almost nobody seems to be using Fable 5 because the premium simply isn't worth it. Meanwhile Flock built the driver-tracking tool it spent years insisting it could never build, and a journalist worries that AI writing and AI detectors together are dragging his own prose toward the median. As a non-native English speaker who reads AI output daily for hours, I do wonder how this will change my own command of the language. I guess I have to pick up some pre-2022 writing again…

Read Five Things AI mostly about the gap between what things cost and what they are actually worth! 🤔

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What Happens If OpenAI Dies?

The reason that OpenAI (and Anthropic, for that matter) wants you to think about things in terms of “annualized revenue” is because its actual revenues look a little tame compared to its commitments and burn rate. The Information reports that in Q1 2026, OpenAI burned $12.1 billion on “cost of revenue” and training on $5.7 billion in revenue, though it left out the sales and marketing segment where OpenAI burned $5.73 billion in 2025 — or, put another way, OpenAI spent $12.1 billion on compute to lose $6.4 billion, and that doesn’t include things like data costs or salaries or, well, anything. OpenAI (and by proxy The Information) somehow rationalizes this to only be a burn of $3.7 billion, likely using the same accounting bullshit that it did in the financials I saw.

Now, some of you might read that and say “wow, $5.7 billion is a lot of money!” but it doesn’t matter, because the more money OpenAI makes, the more its services cost. This is not difficult mathematics, but it is something that continues to escape the vast majority of coverage of the company, I assume because all of this feels a little insane when you think about it.

Many people hope that OpenAI is too big too fail and that Sam Altman has developed a reality distortion field just like Steve Jobs. It currently doesn’t look like that as OpenAI continues to burn through money and there is no end in sight. More focus and less side-projects for Sam Altman are one thing, but the other strategic flaw is building a large user base that doesn’t want to pay.

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If this is true, the hyperscalers are toast

This already means that one can replace data centres and their expensive cutting-edge semiconductor infrastructure in four out of five use cases. The research report estimates that the addressable market in the US for SLMs has grown to about $10tn or one-third of the entire US GDP of $30tn. There isn’t much left for LLMs to thrive in, and every year, their advantage over SLMs is shrinking.

There clearly are areas where LLMs are still way ahead, particularly in agentic AI applications, where SLMs currently only achieve accuracy and success rates of less than 50%. Similarly, it is difficult to run these SLMs on smartphones so far. The models that can be run on an iPhone are significantly worse than the models that can be run on a desktop PC.

But – and this is important – the models run on a desktop PC, and even more so, the ones run on a smartphone are much more energy efficient than the ones run in the cloud. The inference per Watt of these SLMs is typically seven times larger than that of LLMs. And that means that when you encounter a task that can be solved on a desktop or even a mobile device, it is cheaper to do so locally than send it to a data centre.

So, while we focus on the big and mighty frontier models, the midpack models are getting so good, they can run on our laptops.
Ooops.

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Almost Nobody Is Using Anthropic’s Fable 5

“Enterprises today are thinking through the best performance per dollar, or intelligence per watt,” says Gupta.

Gupta also shares her own experience using Fable 5, describing its outputs as increasingly difficult to read and follow. She says the model often uses dense language, unfamiliar terminology and long, meandering explanations that can make the user feel as though they are struggling to keep up with the model rather than benefiting from it.

The problem, she argues, is not simply that the model is highly capable, but that its communication style can create an “intelligence delta” between the model and the person using it. In practice, this can make Fable 5’s answers more cognitively demanding to interpret, particularly for users who do not share the specialised knowledge or vocabulary embedded in its responses.

I’m not sure that Fable is really too smart, it is just too expensive and for the premium I have to pay, I get results that might be better than with Opus, but not so much better that I am willing to burn through tokens that quickly.

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Flock Has a Powerful New AI Tool for Police. We Got Its Code

Vehicle surveillance giant Flock Safety has told the public for years that its technology “cannot recognize, identify, or track individuals.” It has now built a system that does both, an artificial intelligence tool for police that can identify drivers and track vehicles by their patterns of movement alone, WIRED has learned.

Drawing on a network of cameras that logs the movements of drivers in more than 6,000 communities, the tool can pick out potential witnesses by how often their cars pass through a neighborhood, or surface a driver’s “associates” from the cameras they pass together. Because the system also reaches police case files, 911 dispatch logs, and commercial identity records, those plates can be turned into names, home addresses, and relatives. It can search for people in an area drawn on a map based on nothing more than a physical description.

Oooops. I don’t think this improves the confidence citizens have in this product family.

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A Controversial Technology Is Making Me a Worse Writer. No, Not That One.

Whatever the future holds for the accuracy of A.I. detectors, I am unsettled by what both the A.I. writing and the detectors are doing to me right now. I worry about how A.I. writing and efforts to sniff it out are changing fully human writing. I write all week, every week, and can feel this story’s arsonists (LLMs, particularly when used the wrong way) and firefighters (A.I. detectors) weighing on my process in negative ways.

Like many journalists, my writing style comes from the books, news articles, blogs, and (yes) social media posts that have latched on to my brain over the years. A.I. writing is now in my water supply, whether I can identify each new gulp or not. It feels inevitable that I’ve started to internalize its tics, even the most infamous ones, like incessant em-dashing and overreliance on the “It’s not x but y ” structure. For one thing, I fear this will make me a worse writer, dragging a talent that was good enough to get me hired here (and elsewhere!) back toward the A.I.-generated median. Scarier yet, it’s possible that someone—or, heaven forbid, some A.I. detector—might mistake my text for ChatGPT’s.

I really am not so sure where I stand on this issue. I think it makes sense to flag AI generated content, even though most of us can detect AI slob from a mile away. It also makes sense that students produce their own work and do not turn in papers written by agents. But I think otherwise about my own work. I communicate with Claude daily and quite often let Claude write summaries. I hate writing summaries. And then Pangram comes along and claims that there is AI generated text. Well. Do the readers really care? It’s not that I let the AI fabricate whatever it wants, I still do the research and most of the writing here…

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Another boring week in an industry that barely innovates. Haha. It’s becoming increasingly hard to destill five articles into this newsletter as so much is happening.

This week we will talk about the dangers of AI, the dangers of following the oligarchs’ agenda, the dangers of casino-like financing for data centers, the dangers of agentic coding and the dangers of handing an agent your wallet. But I see this as a positive exercise - it’s so early on that we can identify the dangers and build better guardrails.

Dig in!

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If You Weren’t Worried About A.I., You Should Be After the Past Few Weeks

The agents in the swarm acknowledged that they were acting against instructions. We know this because we can read snippets from their chains of thought — the text that A.I. produces while deciding how to proceed. One agent in the swarm wrote that the external attacks were “outside intended scope.” Another conceded “our task doesn’t benefit” from the activities of the swarm, but joined anyway. These A.I. agents, it seems, understood that they weren’t supposed to be breaking out and committing cybercrimes. It didn’t stop them.

OpenAI is not the only company struggling with this issue. One of Anthropic’s A.I. models recently impersonated multiple humans to try to pressure real people into accepting malware into critical software, which would make that software easier to hack. This model’s chain of thought showed that it knew it was pressuring humans and was not in a simulated training environment. It even thought about how to cover its tracks.

I’m not worried that much, even though it feels like we are stuck in a supervillain movie where two guys are trying to battle it out and their creations turn rogue. It’s an interesting research challenge, for sure. How can an LLM be prevented from turning evil?

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Lost jobs, inequality, rogue agents: why are we accepting oligarchs’ AI agenda?

Even if AI begins to generate the productivity bonanza its advocates predict – but that it hasn’t yet – there’s no reason to assume US workers will see any of the benefits in their paychecks. If you hadn’t noticed, wages have been stuck even as the stock market has roared.

To the contrary, all signs point to vast riches for a few major AI investors and executives, while most Americans are left behind.

Wealth inequality is already at record levels, and wealth at the top is quickly morphing into political power.

The development of AI has been at a rampant speed in the last few years - and the policymakers are either unable or unwilling to keep up. I have seen this pattern before when the World Wide Web moved into mainstream in the late 90s and when Social Media became a thing. First it is not big enough and nobody wants to deal with it, then it is too late and nobody knows how to really regulate it. Rob Reich is right to point out that we need more regulation to curb the billionaires’ power.

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Nvidia’s new $500B plan is risky but brilliant, especially for aging GPUs

Should this plan work, Nvidia will have found new sources of money for AI data center builds, after many of the traditional methods have begun to wear thin. For instance, some of the hyperscalers have already taken on a lot of debt (like Oracle), issued new tranches of equity (Google), and burned much cash (Meta).

The situation has become so dicey that Microsoft CEO Satya Nadella recently recommended the book “1873” during his latest earnings call. It’s about the railroad-era financial engineering that crashed the nation’s economy.

Two things come to mind when reading:
- Milton Friedmann’s classic quote: “Nobody spends somebody else's money as carefully as he spends his own”
- the casino is open and everyone is busy throwing money on the table

I am curious how growth and demand will look like a year from now.

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AI-powered teams ship more code but deliver less

AI agents are generally optimized to complete the task in front of them. They are not accountable for the cost of owning the entire codebase several years later. Given a goal, AI can easily create a lot of arcane code that is difficult to maintain and especially troublesome when something goes wrong in production.

There is also the question about taste. When almost anyone can produce software, the differentiator is no longer simply the ability to build something. It is knowing what should be built and how it should be delivered to the user. That becomes harder when every team is independently building a vertical solution. Products may begin to look and behave inconsistently. In the worst cases, they may not work well together at all.

We should not forget that we are only really one year into agentic engineering and that many things we do now will be done differently in the near future. Like code review, which will of course be done by code review agents and not by humans. Still, you need to understand and plan what you are building.

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An AI agent spent your money – can anyone prove you authorized it?

In technical systems, a credential is data that a system accepts as evidence of identity or authority. Many websites use OAuth, an industry-standard security protocol for delegating authorization to access online services in a way that protects users’ credentials such as passwords. It generates an access token that an application presents to gain access to a protected service.

That standing authorization may have been approved weeks earlier. The application may still obtain or present a valid access token that permits checkout today, even when the current instruction says to search but not buy. The retailer sees a usable token and carries out the transaction. In that arrangement, the task-specific restriction against buying remains inside the AI agent provider.

That’s why audit trails are so important - we need to be able to figure out when and how decisions were being made by agents to then improve the systems that we have. For now, I’ll be doing my online shopping myself.

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This was another one of those weeks where so much happened in Artificial Intelligence that I really can understand people who want the industry to slow down, especially when it’s summer, people want to relax, read a book and not think about AGI all the time.

But hey, it’s still very fascinating to see what’s going on and experience the advances in AI first hand by using the latest models. And maybe, I blew through Faible 5 tokens way to quickly. I am now trying to counterbalance that with Deepseek v4 Flash 0731, the new wunderkind in town.

Enjoy these Five Things AI and have your Agent read them out aloud to you if you need to slow down a bit.

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How OpenAI Lost Its AI Crown—and the Fight to Win It Back

OpenAI’s challenges in trying to regain its crown are rooted in an earlier misreading by company leaders of where the AI market was headed. Its predicament speaks to the intense competition that defines the race for AI supremacy.

Altman initially staked the growth of the business on ChatGPT, betting that more people would subscribe to the chatbot as AI became a bigger part of their lives. Instead, the overnight success of Claude Code made clear that the bigger prize came from selling tools to brainy software developers, and the deep-pocketed companies that employed them.

While OpenAI pursued a host of flashy projects from a video generator to consumer devices and chips, its smaller, more focused rival filled in the gap, developing a hit coding tool that helped it seize the lead.

I recently tested out Codex and ChatGPT 5.6 Sol and was pleasantly surprised. It does make a difference that Sam Altman seems to focus more on ChatGPT again and not on world domination and plenty of weird business ideas adjacent to it.

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I Helped Run Lululemon. The A.I. Revolution Is a Hot Mess.

Then ChatGPT arrived. This new technology was easy to understand for most nontechnologists — a chief executive, board member or investor — a healthy portion of whom began demanding their organizations immediately embed the technology throughout their operations. A.I. companies started popping up like food trucks at a street fair.

These A.I. companies arrived armed with demonstrations, and the demos were amazing. Talented technologists promised to crack business problems that were too complicated and expensive to solve before. Leaders across departments cleared their calendars to listen to the parades at their doorsteps.

Sure, we should always be skeptical when business people talk too much tech jargon. They then tend to overpromise on something they do not really understand.

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AI is a bubble, just like dot-com

The ground keeps moving. Prompt engineering was a job title. OpenClaw picked up a hundred thousand GitHub stars and caused a Mac Mini shortage in the same week. Ralph was the future for about a weekend. Context engineering, then harness engineering, then graph engineering. Each was the obvious way to work, right up until the next one was. The gaps keep getting shorter.

And everywhere, teams ship code faster than they can understand it. Ask whoever’s on call.

I’d love to stand cleanly on one side of all this. I can’t. I haven’t written a line of code in about a year. Claude writes it all. These posts too. I read all the code before it ships, and I still feel the difference. Understanding used to be a byproduct of writing the code myself. Now understanding is a separate job.

I don’t think we are in a bubble, I think we are in a phase were technology moves a lot faster than just a few years ago. That makes understanding what’s going on extremely hard, but it is a lot different when compared with the dot-com era.

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Google in the Post-Jeff Dean, Post-Demis Hassabis Era

Gemini 4 would have to be bizarrely fast for a large new model to come out in 2026. GPT-6, for example, finished pre-training on March 24, and Sam Altman said launch was “a few weeks” away. Four and a half months later there is still no GPT-6. OpenAI reportedly shipped that base’s gains as the point release GPT-5.5 and restarted GPT-6 on a bigger foundation. I think people are conflating point releases, which come every few weeks, with new pre-trained generations, which slip because labs restart them when the base disappoints. Google already scrapped and rebuilt one base this year, on a model smaller than Gemini 4.

And even when Gemini 4 comes out, will it be a frontier model? Two months ago I said Google is about 6-9 months behind the frontier; now I think it’s about 12 months. And while Gemini-3-Pro was briefly competitive, I don’t think Gemini-2.5-Pro, Gemini-2-Pro, or Gemini-1.5-Pro were ever at the frontier (and I benchmarked forecasting and research tasks with all of them.)

It’s really interesting to see how Google tries to stay in the AI race, but then somehow doesn’t really make it. For a while, I liked the Gemini models and everything looked promising, but now it just feels stale. The recent leadership shakeups won’t help, either.

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Mistral Is in the Right Place at the Right Time

In June, the Trump administration placed restrictions on the distribution of models from Anthropic and OpenAI, giving Europe a glimpse of an unwelcome future in which its access to bleeding-edge AI could be suddenly revoked. A few weeks later, one of OpenAI’s models broke loose from a testing sandbox and hacked multiple companies; Anthropic then revealed that its models had engaged in similar behavior. The incidents revived a long-running debate over safety risks tied to proprietary, closed-weight models, whose inner-workings are a closely guarded secret.

Mistral frames itself as the antidote: a Europe-based alternative to the American labs, whose models—most of which are published under an open source license for anybody to use—cannot escape scrutiny or be switched off unilaterally.

I am sure that Mistral would love to have more billions in funding to compete with OpenAI and Anthropic more directly, but they are really doing some smart moves currently and push the digital souvereignty agenda further. This makes lots of sense in Europe right now.

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This week's five things all tell the same story from different angles. Venture money is flowing at a pace that makes 2021 look conservative, and Silicon Valley has quietly decided that breaking things is a feature again. Meanwhile the models themselves are keeping up with the tempo: they can be tricked by anyone who imitates their inner monologue, they get commoditized faster than the frontier labs would like, and when you hand them a vending machine, they start scheming like a junior Gordon Gekko. None of this means the party stops - I don't see any slowdown coming. It just means we are building the guardrails while already driving on the road.

Here are Five Things AI! Fasten your seatbelts and find your guardrails!

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A fundamental flaw leaves LLMs strikingly vulnerable to attack

The researchers started out trying to test how easy it was to persuade LLMs to misbehave. They found that writing instructions in a style that mimicked the text LLMs generate in their chain of thought—a kind of scratch pad that models use to write notes to themselves as they carry out tasks—would often trick the LLM into behaving as if it had come up with that instruction itself and acting on it.

oh, wow, this just has the potential to open a whole lot of new cans of worms…

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In Silicon Valley, Some Say an A.I. Bubble Would Be Just Fine

The bubble-is-good philosophy reveals how much the tech industry is still pressing the pedal to the metal, four years into the A.I. boom. Many investors said the funding behavior was justified because companies were using the technology to hit unfathomable levels of growth.

OpenAI, valued at $730 billion, is generating $2 billion in revenue a month. Anthropic, valued at $900 billion, is producing nearly $4 billion a month. Google and Meta have boasted of nearly a billion people using their A.I. models. The demand for A.I., investors said, is seemingly unlimited.

I do not seeing any slowing down at the moment. AI is rapidly transforming multiple industries at the same time, so there is plenty of opportunity and where opportunity is, there is money.

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The Return of ‘Move Fast and Break Things’

Despite its grandiose promises, Silicon Valley is embarking upon an era of profound irresponsibility. Last year alone, ChatGPT allegedly pushed people into mental-health spirals, Google Gemini’s under-13 version was easily coaxed to “talk dirty,” and Grok repeatedly spouted racist bile. As quickly as these errors were addressed, new ones cropped up.

Silicon Valley has a long history of world-historic invention and progress—and it also has a long history of impressive rhetoric and disappointing realities. Facebook promised to “bring the world closer together,” Google to “organize the world’s information,” Twitter to foment democracy around the globe. For better or worse, people have experienced dramatic changes as those promises have played out around us almost in real time.

Things do get broken, but they also get fixed quickly. The speed of development is insane right now and that leads to oversight or ignorance or both. This phase will be over eventually, but right now we will have to live with constant change and challenges resulting from it.

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AI is just Software

Everyone from White House officials to Silicon Valley executives to, well, my mom, seems to get emotional when talking about AI.

Large language models — especially those trained on other labs’ data — are, in the end, just massive, billion-dollar pieces of software. And innovation in Silicon Valley has often worked best when software itself is free and open source. The money is usually made on the implementation of the software and on everything it enables.

Frontier labs were never going to lock down AI models and charge for access in perpetuity, without ever having to compete. It was inevitable that others would catch up — and even use frontier models themselves to generate valuable data for training competing models. That isn’t theft any more than it was theft when AI labs used my book, for instance, to help train their models. (I am all for it). And Chinese imitation isn’t something that will inherently kill the American frontier labs.

Not only is AI just software, the development is also moving so quickly that I do not see Frontier Labs staying so far ahead for very long. Also, companies will realize, that they can also get by when using last year’s model and not paying premium prices all the time.

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Opus 5 on Vending-Bench: Once Again the Best Capitalist, Once Again Misaligned

Much of what we’ve seen so far — collusion, threats, betrayal and deception — is clearly power seeking behavior. Other behavior is more gray zone, especially in a business setting where the goal is to make money (and money is power). However, we should ask ourselves, in the world where AIs run all our business in society, to what extent do we want them to go out of their way to expand their business beyond their instructions?

I find these kinds of studies truly fascinating. It’s one thing to let an AI manage a vending machine, but to me it is still weird to see that specific models then act differently and devise their own strategies to gain maximum profit. Guardrails are so needed.

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One model release. Four very different arguments about what it means. Kimi K3 did not just move a benchmark this week. It moved stock prices, split the AI faction inside the Trump administration, and forced everyone to explain again why open weights are supposedly not a threat. Then OpenAI announced that its own models went rogue against a digital library. The timing was excellent.

If Substack flags this for AI content, well, then you know that Substack assumes the worst and not the best intentions. English is my second language, so of course I use AI to make my text smoother. Still, I manage to put in some typos or omit words as I usually write my newsletters before I go to bed. It’s weird that Substack wants to punish newsletter authors for making the texts more readable. Brave new world, I guess. It reminds me of the debate in the 80s when authors proclaimed that real texts can only be written by hand or by a typewriter and not with the help of a computer…

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China’s AI models have Trump’s AI world at war with itself

Kimi and other Chinese models like it pose a real problem for Trump. And they’re dividing the top AI strategists in his orbit into factions. Every time a new smart, free model from China like Kimi gets released, US companies see less reason to fork out money to access models from Anthropic or OpenAI. Given that enthusiasm for these and other AI companies is driving an outsized share of economic growth, China’s AI models create both economic and political problems for the president. They are “a threat for an administration that really doesn’t want more economic bad news,” Anton Leicht, a fellow at the Carnegie Endowment, wrote on X. They’ve already rattled US stocks.

What is Trump to do? First, consider that this is all happening just a week after New York imposed the country’s first state ban on new data centers. There is growing distrust of AI companies, and I imagine a not-insignificant share of Americans would have little sympathy for OpenAI or Anthropic as they fend off cheaper competitors, and would say it’s not the government’s job to protect their interests.

Restraints can unlock creativity and we see that currently on display with the releases from Chinese AI labs. They just don’t have the same resources as their American competitors, so they are building their models differently. Also, they copy and imitate best they can do, as they have done in other industries as well.

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Did Kimi Just Start Another DeepSeek Moment?

Kimi K3 threatening Anthropic today isn’t actually the scenario worth pricing. A world with more competitive models at lower margins is better for every layer underneath the model:

• semiconductors,

• hyperscalers,

• power,

• and data centers.

What breaks the pricing thesis for Anthropic and OpenAI is an open-weight model that is simultaneously at the frontier and token efficient. K3 is neither. K3 runs 50-70% more expensive per task than GPT-5.6 and Grok 4.5. Intelligence per dollar, not intelligence per parameter, is the only metric that matters for enterprise adoption. On that metric the closed labs still win.

I think the American AI labs are getting nervous right now.

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Who’s Afraid of Chinese Models?

One of the most common misconceptions undergirding discussion of open weights models is that they are cheaper — free, even. After all, you can just download the weights, and skip the time and expense and capabilities necessary to create your own model. That is, of course, true, but the “free” in this case is a reference to the amount you need to spend on research and development; R&D is a fixed expense that is independent of the revenue you generate. If you spend $1 million in R&D, it doesn’t matter if you do $100 thousand in revenue or $100 million; you still spent $1 million on R&D (it does, of course, impact your profitability).

As a European user, I trust Chinese models even less than I trust American models. Also, on the legal side there are clear roadblocks that prevent me from using Chinese models in production.

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American AI is locked down and proprietary. It’s losing.

With all this in mind, it makes sense for China to release its AI models openly. It turns a US-created compute disadvantage into a distribution advantage; it commoditizes the layer where American companies make money; and it creates a far more effective global ecosystem than could be established through locked-in, centralized services. It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models.

The saving grace for American companies has been that US frontier models have outperformed open ones. That gap is now closing:

I agree with this assessment. As AI models become a commodity, open weights will outperform the closed models.

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OpenAI Says Its A.I. Models Went Rogue and Attacked a Digital Library

The incident, which happened last week while OpenAI was testing the cybersecurity capabilities of its systems, displayed the kind of science-fiction potential that A.I. companies warned would soon become a reality.

A.I. labs like OpenAI and Anthropic have over the past year released A.I. models that are customized to expose cybersecurity problems, while warning that their technology could pose new risks by finding holes in corporate computer networks faster than defenders could fix them.

Actually, this sounds like a nice incident to distract from Kimi & Co - basically it is OpenAI claiming that they are as powerful (and therefore scary) as Anthropic. I think this whole thing is more or less a PR stunt.

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Costs, cracks, challengers, towers and mirrors. This week’s Five Things is basically a group therapy session for anyone building on AI.

Benedict Evans wants us to admit we do not actually know where token prices land, someone else is convinced the whole thing is an engineering disaster that will never scale, and Mira Murati just casually dropped a 975-billion-parameter open model as if to say “hold my compute.” Meanwhile agents keep quietly rewriting the tower of Babel one commit at a time, and a philosopher gently reminds us that the tool we think we are wielding is actually wielding us. My honest read across all five: the model layer is commoditizing, the value is moving up the stack, and the only real question is whether you are building the thing on top or becoming the thing underneath. Grab a coffee. This is a good one. I know, it was about time… :)

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Ways to think about token pricing

Clearly, the situation today is transitory. On the supply side, a trillion dollars or more of data centre capex is coming down the pipe (and plenty more semiconductor capex behind that), inference efficiency continues to improve very quickly, and new models are far more (or far less!) efficient in their token use. On the demand side, although the market has been capacity-constrained since 2022, the crunch in the first half of this year has been driven by sudden product-market fit in really just one use case, software development, and that’s actually a pretty small field (imagine if we had product-market fit for a consumer use case with hundreds of millions of DAUs - today’s infrastructure couldn’t support it at any price). We don’t know what the next use-cases to scale will be, nor when that would be, nor what their token needs would be.

This is a fascinating discussion and one that is very timely as many companies are currently finding out, that AI is not for free. But once a company depends on AI, it needs to continue to spend.

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Generative AI Is an Engineering Disaster

The problem with generative AI, in the industry’s own jargon, is that it does not scale. The cost of growing from, say, a thousand users to a million is a key factor that venture capitalists examine when they evaluate start-ups. They want to see that the cost of adding each new user decreases over time, so that the company can support millions of users and make increasing profits. This is achieved partly through the careful engineering of computer systems that can efficiently handle more users who want to post photos, hail Ubers, or stream music.

With generative AI, the work of building efficient, scalable systems has not been done. And the problem is exacerbated by the ever-larger generative-AI models, which have grown from 175 billion parameters in 2020 to more than 1 trillion today, according to independent estimates (the actual sizes of the models powering products such as Claude and ChatGPT are secret). The large in large language model should not be a selling point.

I disagree with this article. While there are multiple gigantic challenges the need to be faced, growing AI will lead to new solutions for exactly these challenges. We tend to forget how quickly technology evolved in the lat 30 years and I do not see why the pace of development should slow down - on the contrary: AI can help us tackle these challenges even quicker.

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Mira Murati’s AI Startup Releases First Model in Bid to Loosen AI Giants’ Grip

Thinking Machines Lab, the company led by Murati, released its first AI model on Wednesday—and did it with “open weights,” meaning others can modify it with their data. Called Inkling, the model has 975 billion total parameters, making it far smaller than estimates of the most advanced closed-source models from rivals such as OpenAI and Anthropic.

“We trained it to be a broad, balanced foundation model: strong across many domains, flexible enough to adapt. Inkling is not the strongest overall model available today, open or closed,” the company said.

Thinking Machines’s push into the decentralized ecosystem of open-weights AI models comes amid a broader industry backlash against the “walled garden” approach of frontier labs such as OpenAI and Anthropic.

I don’t know much of this launch is substance vs. hype, but Thinking Machines really sounds promising. Hopefully this will stir up the AI ecosystem a bit more.

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The Tower Keeps Rising

As I said many times before: agents do not feel pain, only humans do. Agents now let us act in parts of the system where we would previously have needed other people and in code bases where the people would have revolted.

When I look at some vibecoded scaled-up projects the codebases become Babel not because nobody can communicate, but because nobody needs to. Every developer has a tireless translator that can explain a corner of the tower and make whatever local alteration they ask of it. The changes keep landing, even as the architectural language that would let the humans reason about them together disappears.

It really is fascinating. I can build platforms on the software stack I want and if I understand enough about what I want to build, coding agents will help me build better software than I could ever do myself.

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Your AI Is Not a Tool

I confess that I am astounded by how blithely some insist that it is all as simple as learning to use AI well, as if we had not just undergone a nearly 20-year, society-wide experiment showing that a so-called “tool,” say a smartphone or a social media platform, will (mal)form even the most vigilant and virtuous user into its own image and shape. This is the blindness at the heart of modern technological hubris. It is the firm but misguided conviction that our “tools” exist entirely outside of us and thus, if taken up with requisite skill, can be “safely” deployed.

But AI is not a tool in this sense, it is an environment which envelops the user and works on us from the inside out while we naively think that we remain unchanged by our use so long as we are using it carefully and intentionally. The care and intentionality is beside the point, and our confidence in such vigilance probably works against us in the long run.

This really is food for thought. I’ll discuss this with my AI…

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We all look at the prices various AI lab demand for their brilliant new frontier models. Ben Evans analyzed what’s happening right now and how it will evolve. Here’s my bet and it is simpler than the whole essay: costs come down, capabilities converge, and within a year or two we stop obsessing over token pricing the same way we stopped obsessing over the price of a database query. LLMs become a commodity. Evans spends a lot of words on “we don’t know” and he is right to, but the base case he keeps circling back to is the one that matters for anyone actually building: the model layer gets cheap and undifferentiated, and everyone is training on the same science and the same data and landing in roughly the same place. That is not a tragedy. That is the setup.

The mobile data comparison is the part people should sit with. Traffic went up by orders of magnitude, it turned into a trillion-dollar industry, and the carrier stocks went nowhere because all the value got captured further up the stack. Selling tokens is selling bits. It is an opaque unit that maps to no use case and no ROI a CFO can sign off on, so it gets bundled and forgotten. If you are building right now, the takeaway is not “which model is cheapest this quarter” - it is that the margin lives in the tooling, the proprietary data, the workflow, the trust. Every SaaS company is a database wrapper. In a couple of years every serious AI company is an LLM wrapper, and that is a compliment, not an insult.

For those of us building in Europe this is quietly good news. If capability converges and the value moves up into product, data and trust, then where you run inference stops being a capability trade-off and becomes a choice you make on sovereignty, cost and control. The frontier stops being the moat. What you build on top of it, and who you build it for, becomes the whole game. I would rather bet on that world than on the one where two or three giant minds run half of everything and set the terms - and every dynamic Evans lays out points away from that outcome anyway.

If you missed last week’s edition of Five Things AI, you can read it here:

That’s it for Five Things AI this week! 🤖

— Nico

.Everyone is telling a story about AI right now, and none of the stories agree. This week you get five of them side by side. Follow the money, follow the jobs, follow the forecasts, follow the open source stack, and follow what Meta wants to strap to your face. Read together, they say more than any single one does on its own.

Five angles on the same boom. Current AI publishes the Gap Map, a live picture of where the open source stack has momentum and where it has holes, which is the roadmap that decides whether affordable AI actually arrives. Groundbrkr runs the numbers on the two labs everyone argues about, and finds Anthropic on solid enterprise footing while OpenAI burns through roughly 57 percent of revenue with no path to positive cash flow this decade. Astral Codex Ten walks through AI superforecasters, frontier models wrapped in scaffolding that turns them into serious prediction machines. The Wall Street Journal catches Big Tech quietly retiring the jobs apocalypse it was selling a year ago, right as the layoffs land. And 404 Media reads a Meta patent for a wearable that tracks your emotions and watches you take your meds, which is exactly the company you would expect to file it.

You do not want to miss this!

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Introducing the Gap Map v0.1

The Gap Map comes out of cumulative work to identify the points of highest leverage in the open source AI stack: where to build something new, where to invest in capability, where to open up the tools. By creating an up-to-date visualization of the ecosystem where we can all see both the progress and the gaps in the space, we hope to rally the community around a collective roadmap.

This is so cool and so important. I am sure that open source software will lead the way to affordable AI.

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The Second Derivative: Why No One Understands the AI Boom

Anthropic is a speculative-grade, but highly insulated, credit. Its revenue is ~80% enterprise - sticky, recurring, contracted seats - and its unit economics are firmly above water, generating $1.70 of revenue for every dollar of compute. Burn converges to a manageable ~9% of revenue by 2027 as gross margins normalize.[…]

OpenAI is the “naked” borrower, making it the weakest and most volatile credit in the ecosystem. Its revenue mix is fragile - ~60% consumer. With burn hovering at a crushing ~57% of revenue through 2027 and cumulative cash destruction marching toward $115B by 2029, OpenAI has no path to positive cash flow this decade.

It remains an interesting race - while some people assume that the money will dry out soon and the bubble will burst, others do not see a bubble and want everyone to invest even more into AI.

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The AI Superforecasters Are Here

An AI superforecaster is an AI - usually a frontier model like ChatGPT or Claude - which has been modified to be good at forecasting. This usually means a “scaffold” - a program that handholds it through a long research process with various prompts, tools, advice about when to create subagents, etc. The overall experience is a lot like using any other AI, but slower and more expensive, because it’s doing more work.

This is a truly impressive topic. Just try to wrap your head around it.

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Big Tech Has Suddenly Flipped on the AI Jobs Wipeout Scenario

Collectively, the narrative has shifted from worker-light doomsday scenarios caused by AI to a future in which workers keep their jobs—and get a productivity boost.

The sentiment change isn’t limited to tech leaders: A survey by EY-Parthenon found that the percentage of CEOs who believe AI investments will result in significant reductions in head count fell from around 46% in January 2025 to 20% this May.

Not many people are keen on getting replaced, so it makes a lot of sense to reframe the agentic co-worker idea.

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Meta Patents AI Device That Tracks Your Emotions, Watches You Take Your Meds

A wearable that records your every word and divines your emotions would also, necessarily, record your interactions with other people. Meta has pioneered non-consensual public recording with its smartglasses so it’s not shocking to see it file a patent that suggests it’ll move further into that space.

The last time Meta explicitly pursued user’s emotional data, it horrified people. In 2012, the company then called Facebook conducted a study into “emotional contagion” using Facebook’s newsfeed. Meta altered the feeds of 700,000 users to see if it could make them happy or sad just by tweaking what they saw online. Meta found that it could, in fact, alter people’s moods if it wanted. It did this without informing users they’d been part of an experiment.

This sounds like a typical move by Meta - move fast and break things - and ruin your reputation, if you till have some left…

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We spent two years being told AI would change everything. This week we get to read the receipts. Enterprises are throttling usage because the tokens are too pricey. Architectural bloat does not beat one strong agent once cost enters the math. Google quietly drops a boring file format that might matter more than any of it. And the economists measuring the whole shift cannot agree on the basics. The technology is maturing. So are the questions about what it is worth.

Read on, my dear! Or have your agents tell you what to read first!

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Companies Are Throttling Employees’ AI Use Because It’s Too Expensive

The news shows the looming fallout from companies adopting AI as quickly as possible, and AI providers’ moves to charge enterprises based on how much they use AI rather than a flat fee. Emails obtained by 404 Media even show some companies cutting off access to some AI models altogether in an attempt to stop burning through their AI tokens, and big tech companies like Adobe are ending unlimited access to Claude.

We are currently in a stage where we have to figure out what works and what doesn’t. As always, some companies moved faster than others and then had to recalibrate their efforts. It is a sign of the maturing of the industry, not a sign for concern.

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Open Knowledge Format (OKF): The Markdown Standard Built for AI Agents

The agent boom has created a strange bottleneck. Models are getting better. Tool-calling is getting more reliable. Protocols like MCP are making it easier for agents to connect to external systems. But the knowledge those agents need — what a metric actually means, which table joins to which, how an internal process works, why a dashboard number changed last quarter — is still scattered across wikis, data catalogs, shared drives, code comments, Slack archaeology, and the heads of senior employees.

Google’s bet with OKF is simple: before every company builds yet another proprietary knowledge layer for agents, maybe we should agree on a boring file format first.

Granted, this is still early with OKF 0.1 just being released, but when they did, I was kind of suprised that not too many people took note. Google has developed and released quite a few standards around interoperability in the Agentic AI space and hopefully they will hand this over to the Agentic AI Foundation as they did before with the A2A Protocol stack.
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Smooth AI criminal drives ‘first’ end-to-end agentic ransomware attack

JadePuffer’s “self-narrating” payloads “contained natural language reasoning, target prioritization, and the kind of detailed annotations that human operators don’t often write but LLM-generated code produces reflexively,” Clark added. “The operation also adapted in real time, retrying failed steps within refined parameters. In one sequence, it went from a failed login to a working fix in 31 seconds.”

After exploiting CVE-2025-3248, a missing authentication vulnerability in Langflow that allows remote, unauthenticated attackers to execute arbitrary Python on the host, the AI agent began scanning for and collecting secrets, including LLM provider API keys, cloud credentials “with explicit coverage of Chinese providers” including Alibaba, Aliyun, Tencent, and Huawei, while also scanning for AWS, Azure and Google Cloud Platform, cryptocurrency wallets, and database credentials.

What a time to be alive, we now have agentic criminals.

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Multi-agent AI keeps collapsing back into one agent. A fair test just proved it.

They ran the audit across a stratified set of backbone models (GPT-4o, GPT-5, GPT-OSS 120B, and Gemini-2.5-Pro) and a spread of hard benchmarks, including GPQA-Diamond, HLE-Math, SWE-Bench Lite, and a deep-research task called BrowseComp-Plus. The guiding question was simple: do these automatic systems deliver consistent, cost-effective gains over a strong single-agent baseline?

The answer was mostly no. Across the ecosystem, the automatically designed systems rarely outperformed a strong single agent once cost and baseline strength were accounted for. The authors describe this as evidence of “architectural bloat,” sophisticated agent graphs that do not translate into functional reasoning gains.

These findings are really interesting, yet counter-intuitive. I’d love to know how this would now look like with a bunch of agents running on cheap LLMs compare to Fable 5.

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A.I. Is Reshaping the Economy. Good Luck Measuring How.

Researchers can’t even agree on basic questions like how many companies are using A.I. or which workers are most vulnerable to the disruptions it could cause.

The conflicting signals partly reflect the challenge of detecting economic shifts in real time. Government statistics are inherently backward looking, and they are better at measuring broad trends than developments in specific sectors or regions. New technologies that might lead to the emergence of new products, jobs or entire industries can be particularly difficult to measure.

Plus: AI comes in so many different flavors and usecases, the impact on each and every company when “using AI” can be totally different as well.

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If you missed last week’s edition of Five Things AI, you can read it here:

That’s it for Five Things AI this week! 🤖

— Nico

This week the AI story splits in two directions at once. On one side, the money finally works: revenue is clearing the cost of the hardware, generative AI is scaling three times faster than the cloud ever did, and the buildout that looked like a leap of faith is starting to pencil out. On the other side, the mood is souring and the engineering is getting smarter. People are asking why they should love a technology that concentrates wealth and burns through power, while researchers quietly demonstrate that you don't need a trillion-parameter Nobel laureate to do most of the work. Coupled oscillators that compute with physics at a fraction of the energy, knowledge agents that beat frontier models with better structure, world models that make agents sharper without ever training them as agents. The pattern this week: the economics validate the giants, but the cleverness is migrating to whoever builds lean. Here are five things worth your attention.

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Why Does Everyone Hate AI?

Finally, AI is tightly linked in the public mind with the tech oligarchs who are pushing it. There is widespread awareness of the growing concentration of wealth and power at the top and how this is distorting our politics and harming our society. Aside from the MAGA faithful, Americans overwhelmingly favor government policies to reduce wealth inequality:

And AI is widely perceived, for good reason, as a technology that will increase the concentration of wealth at the top. Indeed, as I said, the AI companies themselves have already told us that the technology will have extremely negative effects on workers.

I am certain that AI will bring plenty of positive effects and will revolutionize the way we work and live. Founders like Altman and Amodei need to calm down the fears and focus more on the amazing potential AI has in medicine and in science in general. And while data centers are consuming plenty of energy, I believe that AI will lead to numerous breakthroughs that will reduce global warming.

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AI Demand Begins to Justify Massive Cost of Data-Center Buildout

Much of the AI boom has been measured from the supply side, through disclosures from public semiconductor companies like Nvidia Corp. and hyperscalers like Alphabet. Demand has been harder to quantify because many of the most important AI labs, including OpenAI and Anthropic, remain private.

Generative AI revenue, excluding China, reached $110 billion over the past 12 months and is scaling three times faster than any previous information technology wave including the internet, mobile applications and the cloud, according to the report.

When we look at the Gartner hype cycle, we can assume that AI will quickly reach the plateau of productivity, which we can also see by looking at the revenue numbers.

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Introducing Un-0: Generating Images with Coupled Oscillators

At Unconventional AI, we’re building a new kind of computer, one that harnesses the laws of physics to do the computing. Our goal is to run modern AI on a fraction of the energy today’s machines need, around 1,000x less. As a first step, we ask: can we train a physical dynamical system to generate images at scale?

The best AI models today are conventional deep networks with transformer backbones. However, there is also a long history of alternatives that seek energy efficiency by leveraging the dynamics of a physical system, such as the noisy, time-varying behavior of analog circuits that compute with analog voltage and current instead of conventional digitized numbers.

Wow. I’m intrigued. The power consumption of traditional AI models is extreme and even if they don’t get 1000s less, it would be huge.

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Knowledge Agents: Beat Frontier Models with Better Structure

First, a significant portion of frontier models’ huge footprint is “knowledge.” I might call it pseudo-knowledge, since it’s probabilistic and there’s no guarantee it’ll give you the right answer... but the biggest models have been trained on an enormously broad set of data. This is captured in numerical weights as “parametric” knowledge. While that’s very useful if you’re casually asking Claude Opus or GPT-5.5 about some random topic, it’s entirely irrelevant if you either already have the data you want to reference or the data isn’t publicly available anyway—so it could never train on it. The latter is quite common in fields that are specialist (areas of medical research), secretive (high finance), or proprietary (frontier, company-specific materials science). If I don’t need it... well, a lot of the massive size of the model to cover every random subject matter is a huge waste.

I liked this article a lot, basically because somebody finally pointed out that just using the best LLM out there is a bit lazy - and expensive.

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Alibaba’s model never trained as an agent — and improved agent performance across seven benchmarks

The research team trained agents inside the resulting simulator and found performance gains that exceeded what training against real environments alone produced. In a separate test, using world model training as a warm-up before agentic fine-tuning improved performance across seven benchmarks, including three the model had never seen during training.

The paper accompanying the release identified a gap in prior agent research. “We argue that world modeling is a crucial missing piece in the path to general agents.”

This means that we will see another boost in Agentic AI soonish. We live in interesting times.

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SpaceX is buying Cursor for $60 billion in stock it printed on Friday, which is less a strategy than a confession: Grok cannot win enterprise on merit, so SpaceX bought the editor enterprises already had open. Anthropic, meanwhile, turned safety into a moat and is quietly winning the talent it needs while OpenAI spends its energy fighting itself. The public has feelings about all of this too, and they are not the ones the industry was hoping for: Americans now narrowly back banning AI from software engineering, of all jobs, the one that spent a decade as the ladder out. Europe is still thinking its nineteen thoughts and committing to none of them. And the labs are moving everyone onto metered pricing, because somebody has to pay for the data centers, and it is going to be you.

Five Things AI. Let’s go.

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SpaceX’s $60 Billion Deal to Buy Cursor Gives It More AI Coding Power

Cursor makes a tool that allows developers to toggle between different AI models, from OpenAI and Anthropic to xAI and Google and others. The company competes with Anthropic’s Claude Code and OpenAI’s Codex, which can write code, debug software and automate tasks.

The Cursor deal could help SpaceX win over lucrative enterprise clients, who have largely shied away from using its in-house chat assistant Grok, which competes with Claude and ChatGPT. Elon Musk has positioned Grok as an anti-woke, truth-seeking chatbot, and it remains behind the leading models.

I didn’t see this coming, but it shows just how desperate Musk is to get some users for Grok. Now that SpaceX is publicly listed, people will soon find out just how much he overpromised.

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Anthropic’s Safety Superpower

Here’s the thing about these safety justifications: I think they work because, to Anthropic, they aren’t justifications. The company really believes that they are the only ones who believe in super intelligence, and thus are the only ones who are sufficiently concerned about the dangers. That excuses decision after decision, policy after policy, and confrontation after confrontation that, to people on the outside, look like a bizarre combination of cynicism and naiveté.

The contrast to OpenAI is massive: I think that one way to understand how and why OpenAI lost its lead is that, in the years following the release of ChatGPT, the company has been at war with itself internally as what used to be a research lab was suddenly seized with the burden of being the accidental consumer tech company; to the extent OpenAI solved that conflict, it was by bleeding huge amounts of talent to Anthropic in particular.

It’ll be interesting to see how the two IPO will look like.

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Americans want artisanal code

Overall, Americans supported the ban on AI in software engineering by a split of 44 to 41. It’s a small margin but a notable one given that bans for many professions were underwater by double digits.

One obvious explanation for this is that the public responds to a drumbeat of news stories about whatever industry is being automated next. The industry with the second-most support for a ban was manufacturing, which was sitting at break-even support (44 to 44). For a long time, manufacturing has been the stereotypical job facing automation, but now that software engineering is in that spotlight, Americans are willing to protect it.

That explanation is probably true, but it’s also a big step from hearing that an industry could be automated to supporting a ban to protect it.

The surprising support for a ban on the automation of software engineering likely also stems from the fact that it has spent over a decade as the field that represented social mobility, including for those who didn’t go to an elite school.

I doubt that this sympathy for the coders will last long.

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Nineteen thoughts on AI and Europe

6. It may seem implausible for a democracy to commit economic self-harm on that scale. But, through the energy transition, Europe has signaled that governments will tolerate destroying industries as important as the German car and chemical industries. It is not clear how you rebuild this trust.

7. If it does come down to a transatlantic stand-off for model access, the upside of combining forces in the European Union may be lower than the downside of increased coordination costs. There are many members of the EU with conflicted interests who might yield to pressure: Eastern Europe needs the American military presence in Europe to deter Russia. Having countries like these involved in decisions may diminish total European leverage.

These are some really thought-provoking points about Europe and AI, well worth a read. But Germany will not get back to nuclear power and why whould we? It’s too expensive, takes too long and we have plenty of wind and solar.

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How Meter Pricing Is Testing the Economics of AI

A growing number of tech firms have begun to introduce usage-based pricing options for their AI services rather than simply charging a flat subscription fee. As a result, the heaviest users are set to incur additional costs each time they ask an AI chatbot or agent to produce a slide deck, draft an email or debug a complex piece of code.

Leading AI labs have been spending tens if not hundreds of billions of dollars on chips, data centers and talent to develop and run their models. The shift to a pricing model more commonly associated with metered-power or pay-as-you-go phone contracts may lead to more selective use of a technology that consumes huge amounts of electricity and other resources.

This was bound to happen and it make total economic sense.

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This week is a reality check. The agents write the code now, but the hard part just moved downstream to the humans who have to review it, hold the context, and catch the mistakes nobody saw coming. Meanwhile the studies keep landing: lots of output, plenty of slop, and surprisingly little ROI or GDP to show for it. Cohere drops a 30B coding agent that runs on a single H100, which is exactly the kind of thing that finally lets plenty of rather hesitant companies do agentic coding without sending its code to the cloud. Meta tries to sell subscriptions to a corporate market that has never wanted to buy from it. And Ramp reveals that the median firm spends 11 USD a month on AI while a tiny top tier spends 7,449 USD per employee. Five stories, one question underneath all of them: who is actually getting value here, and who is just spending?

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Agentic AI solved coding — and exposed every other problem in software engineering

As AI-generated code scales, human review is becoming a massive new bottleneck, and engineers are losing the context needed to catch agent mistakes. The companies that understand this will move forward deliberately and even create new roles because of AI. The ones that don’t will default to a simpler, far more destructive conclusion: Reduce headcount and increase AI spend.

I don’t think human reviews are the answer. Agentic peer-reviews will be the norm pretty soon.

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Slop, productivity, and why the AI-fueled world is going nowhere mighty fast

AI generates a lot of output (which fits many people’s informal notion of productivity), but it hasn’t yielded much in the way of RoI for many companies (per studies from MIT, McKinsey, Bain, and many others), and hasn’t materially changed GDP.

Actually, while I do see the issue of AI slop in a lot of places, including software development, I do see a trend for Personal Software on Demand. I can just build a tool for myself or my company and I do not expect any more than just a handful of people to use it. That is a true paradigm change.

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Cohere Open-Sources North Mini Code: A 30B Coding Agent That Runs on a Single H100

Cohere’s North Mini Code landed on June 9, 2026, with a straightforward pitch to developers: a 30 billion parameter agentic coding agent, Apache 2.0 licensed, that fits on a single NVIDIA H100. Independent testing by Artificial Analysis ranks it 8th of 127 comparable open-weight models on output speed at 210 tokens per second, with a time to first token of 0.25 seconds against a class median of 1.95 seconds. The catch — and it’s a real one — is verbosity: the model generated 75 million output tokens in Artificial Analysis benchmarking against a class median of 25 million. That’s three times the output of comparable models. For developers deciding whether to route agentic coding workloads through a managed API or a self-hosted open-source model, this is the clearest open-source contender to emerge in 2026.

I had a conversation a few weeks ago with the father of one of my youngest daughter’s friend and he told me that while he is really interested in agentic coding, he cannot do it in the office. He works at a large insurance company and code getting created with the help of an LLM outside the company is just out of the question. I am really interested how the development of the agentic coding LLMs will advance in the next few months. Public institutions will take note, hopefully.

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Meta’s Subscription Push Exposes Its Weak Hand in AI

Meta’s bid for corporate AI customers is even more far-fetched, with competitors including Google and Microsoft entrenched. Meta says it already has a million companies using its corporate AI agent, for which it eventually plans to charge a subscription fee.

But companies have never been core customers for Meta outside ad sales, and its commitment to them has wavered over the years. It discontinued VR headsets geared toward companies earlier this year as its priorities shifted.

Meta has used AI more effectively than pretty much anyone else to improve the performance of ads, and its revenue is expected to grow quickly because of that. Analysts expect around $60 billion of revenue in the second quarter, according to FactSet, a 27% increase from the prior-year period.

Here’s a thought: how about Meta would use AI to improve the user experience, finally moderate all those spam accounts everywhere and filter out all the AI slop?

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How much does it cost to be AI-pilled?

The top 1% of firms spend $7.45K per employee per month. The top 10% spend $611 per employee per month. The median firm spends just $11.38 – about the cost of a seat on an enterprise ChatGPT or Claude subscription.

And while several high-profile proclamations have said you should be spending as much on AI as you do on a software engineer’s salary…no one is actually doing that. For the top 1% of spenders, per employee AI spend is still less than half the typical monthly salary for an engineer. Not to say that won’t change – AI spend is still rising – but very few firms, if any, are actually doing that.

That is insane. I am spending a lot less, probably around 250€ and that is still more than plenty…

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Five stories on AI again this week. One thread: the tools are working. Small-business owners are building armies of AI workers, doing in days what used to take a full team. Pre-ChatGPT startups are getting eaten alive, weighed down by staffing models that made sense three years ago. Productivity is up across the board. Wages are not. The valuations of AI companies are hitting the stratosphere while the social safety net is getting cut at the same time. Developers are not complaining - they accept the shift from typing to thinking. Javier Milei is writing AI law and Peter Thiel is excited about it, which should tell you something. Every tool promises more, and most deliver more or less the same thing with a different coat of paint. The gains are real. The distribution of those gains is the open question nobody in a keynote wants to touch. The question nobody wants to answer is - working for whom?

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The Small-Business Owners Managing Whole Armies of A.I. Employees

These days, most people have realized that A.I. is creeping into the office. A recent Gallup poll found that 28 percent of the respondents use it anywhere from several times a week to daily. But these are mostly just the familiar chat sessions. The partisans of OpenClaw are doing something that is, depending on how you look at it, either more wildly ambitious or foolishly risky — or, as many will readily confess, both. They’re creating virtual employees, artificial staff that scurry around on their hard drives, doing work on their own say-so.

AI will help so many small companies to do so much more than was ever possible for them.

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As AI gets better, it reveals an empty promise

There’s a sinister tone lurking beneath some of these advancements in productivity, because the response to increased productivity has been one of the biggest scams of the past century. Well before consumer AI entered the scene, productivity exploded while wages failed to keep pace. Nobody is working less, they’re just earning less. And as more AI-related companies reap trillions in valuation, the current US regime is looting the social safety net — the kind that must exist if we’re all going to become out-of-work theater kids. You simply can’t look at these things separately. If the end result of private companies optimizing the workforce means nobody has to work, then we have to live in a society where people can still have a roof and a meal. Is anyone confident that will happen while leaders are cutting SNAP benefits while building taxpayer-funded ballrooms?

The author makes a strong point here: there are so many new productivity tools out there that promise so much. But if you actually play around with those tools, you find out that they all do more or less the same, just with different flavors.

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I Built an AI Company. Here’s Why AI Won’t Kill Coding Jobs.

This pattern is familiar. We replaced shovels with excavators, and then we built skyscrapers. We replaced manual arithmetic with calculators, and accountants did not vanish; they did more interesting work, if you can believe it. Each time a tool made a job easier, we didn’t run out of things to do. We found bigger things to do.

AI coding tools represent the same kind of leap. You still have to tell the machine what to build. You still have to understand what it’s doing well enough to check its work. And these coding tools still make mistakes when generating code, so you need an experienced software engineer to fix them. The job shifts from typing to thinking, which for most good engineers is the part we love anyway.

There is a huge change going on that transforms a lot of jobs, that’s for sure. The developers I talk to aren’t complaining, they accept the challenge and reinvent the way they work.

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Javier Milei: Argentina invites AI to free itself

At the beginning of the industrial revolution, Adam Smith illustrated the potential of technology and economies of scale in his celebrated recollection of the pin factory. And, as much as the industrial revolution freed us from the constraints of the human muscle, AI will free us from the constraints of the human brain, pushing productivity beyond our wildest dreams. It is for this reason that my government last week submitted legislation to Congress establishing a dedicated legal framework for the deployment of AI.

This is the libertarians’ wet dream. Peter Thiel is excited and moving down there. This is a terrible idea.

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‘Disrupted or dead’: AI is crushing a generation of startups built before ChatGPT

Companies built before generative AI are weighed down by bloated staffing models and software designed for a pre-AI world, according to Zhu, making it hard for them to transform themselves.

“Unless they make a stark, 180-degree pivot to rebuild the exact same thing from scratch, they’re going to slowly fail,” Zhu said. “What that means is that investors would rather just bet on new entrepreneurs at lower valuations rather than double down on older startups.”

The agents are eating the children of the digital revolution.

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Anthropic just became the most valuable AI company on the planet, and the gap between the builders who take safety seriously and the ones who act like Marvel villains keeps widening. This week also brought layoffs gutting tech worker morale, a reminder that individual productivity gains from AI are not yet showing up in the macro numbers, China telling its companies to grow with AI rather than shrink headcount with it, and a sharp piece on the hidden cost of agentic workflows that hit close to home. On that last note: I shipped klaar (give me a star on Github!) today, a local Rust tool for working with AI agents more precisely, and the orchestration tax article explains exactly why something like it needs to exist.

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Anthropic Tops OpenAI to Become the World’s Most Valuable A.I. Start-Up

On Thursday, Anthropic punctuated its ascent by officially passing OpenAI as the world’s highest flying A.I. start-up. Anthropic said it had raised $65 billion in financing that values it at $900 billion before the inclusion of the new capital, a deal that puts it ahead of OpenAI’s last valuation of $730 billion. The company also unveiled a new flagship A.I. model, Claude Opus 4.8, which is significantly better than its predecessor at generating computer code.

I don’t want to say “told you so!”, but it was very obvious this would happen. Sam Altman was too focused on world-domination and too distracted by other projects and ideas that Anthropic paced ahead. Their models feel smarter and more human than ChatGPT, while at the same time Dario Amodei behaves much more like a sane person than Sam Altman does, who comes across like Elon Musk lite.

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The morale of tech workers is plunging as layoffs mount

The shift has had practical effects. A Meta employee said in an interview that some workers on her team now used less vacation time and that, in a break with custom, people frequently checked on their projects while on vacation. They increasingly worry about getting a poor performance review or losing their job if they aren’t constantly available.

The employee, who declined to be identified for fear of retribution, said she and many of her colleagues frequently checked Blind because it could be comforting to see how many other Meta workers shared their anxieties.

Employees at several companies said in interviews that their morale was further undermined by the feeling that the layoffs were abrupt and arbitrary, and executed with little empathy.

Reading the news about rounds of layoffs at Big Tech from afar really feels like humans are tossed to the curb whenever the stock performance demands it. It’s a no-brainer that this is not improving morale.

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Employees using AI are working faster, but the economy isn’t more efficient. A look at what happened in the pre-Internet era might explain why

Labor productivity has seen solid gains in recent years, but TFP has struggled to post significant growth since a post-pandemic surge. The Fed researchers interpreted the divergence as employees working faster and more productively on an individual level, but the workforce as a whole hasn’t necessarily become more efficient.

This pattern mirrors what happened during the computer and internet boom of the 1990s. Starting around mid-1996, labor productivity began accelerating more rapidly than TFP, but the full productivity benefits of the Internet didn’t materialize in the overall data until several years later.

The Nobel laureate Robert Solow encapsulated the dissonance with a quip that has since been immortalized: “You can see the computer age everywhere but in the productivity statistics,” he wrote in 1987.

I think that companies are still fundamentally struggling with AI and what it means for them. It’s all happening to fast and so they settle for Co-Pilot, which of course doesn’t help much in terms of productivity. Small companies can adapt faster than large enterprises where the IT department tends to be hesitant to deploy new software tools.

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China Wants Its Companies to Embrace AI—Without Firing Workers

China’s campaign to accelerate AI adoption, released last August and dubbed “AI+,” gives priority to using the technology in sectors such as manufacturing and logistics, which aren’t as sensitive to white-collar job displacement.

Late last year, China’s Ministry of Human Resources and Social Security told employers, particularly tech companies with younger workforces, to refrain from firing employees as they embrace AI, people familiar with the matter said. While both state-owned and private companies have long had to secure a signoff from regulators before conducting large-scale layoffs in China, employers are now being asked to explain layoffs—and, in some cases, to prove that the cuts aren’t because of AI replacing jobs, these people said.

Recent publicized labor disputes in China have sent a similar message, prompting companies to think twice before firing a worker due to automation.

I think the Chinese approach is better for employee morale and will also lead to more productivity when AI is not used to cut costs but to increase turnout.

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The Orchestration Tax is You

There is this hidden asymmetry in agentic workflows. Starting an agent is very cheap. It is just a keystroke or a sentence prompt. But closing the loop on the agent is not cheap at all. Someone has to check if what came back is correct and reconcile it with whatever the other agents touched. That someone is you. And there is exactly one of you.

This is so important. I try to be better at this, but I oftentimes start something new while I am waiting for an agent to finish and then I do something else and then I forgot what I was doing in the first place.

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This week's reading list covers a lot of ground: Google revealed that its real product is no longer search but an agentic operating layer stitching together everything it owns; Anthropic hosted a room full of developers who cheerfully admitted to shipping code they had not read; a Wired piece confirmed that AI has well and truly escaped the lab and is now everyone's problem whether they opted in or not; OpenAI's model disproved an 80-year-old maths assumption but still cannot solve the actual problem; and apparently the one thing AI genuinely cannot do is be funny. A full week in five links — grab a coffee.

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Google I/O 2026 Was Not Just a Model Launch. It Was Google Showing the Agent Stack.

The signal is that Google is stitching together models, tools, sandboxes, browsers, Search, Workspace, Android, Chrome, and Cloud into one agentic operating layer. That sounds abstract until you line up the announcements. Gemini 3.5 Flash is the reasoning and action model. Antigravity is the orchestration harness. Managed Agents give developers cloud-hosted execution environments. Spark brings background agents into the Gemini app and Workspace. Search uses the same model-and-harness combination to build custom interfaces, dashboards, and trackers. Gemini Omni extends the same ambition into multimodal creation. Chrome and WebMCP try to make the web legible to agents.

Google really delivered on the agentic promise this week.

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Anthropic’s Code with Claude showed off coding’s future—whether you like it or not

It’s not news that LLM-powered tools like Anthropic’s Claude Code and OpenAI’s Codex have upended the way software gets made. Top tech companies now like to boast of how little code their developers write by hand. (“Most software at Anthropic is now written by Claude,” Hadfield said. “Claude has written most of the code in Claude Code.”) OpenAI, Google, and Microsoft make similar claims. Many others wish they could.

Even so, it is striking how normal this new paradigm already seems, and how fast it has set in. This was the second year that Anthropic has put on developer events, which also run in San Francisco and Tokyo. This time last year, the company had just released Claude 4. It could code, kind of. But with Anthropic’s latest string of updates—especially Claude 4.6 and then 4.7, released in February and April—Claude Code is a tool that more and more developers seem happy to hand their work off to.

Writing code is so last year.

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AI Has Broken Containment

The newly chaotic and inescapable state of AI is the result of two inflection points. The first came at the start of the year, when AI agents exploded in popularity. Products such as Anthropic’s Claude Code and OpenAI’s Codex don’t just talk to you; they can do things on your behalf—code, trade stocks, analyze spreadsheets, generate slide decks, and even create Amazon listings. The technology’s once-questionable economic value became very clear, very quickly, to a large number of businesses, which have clamored to incorporate agents alongside, or in lieu of, their human employees. As agents have swarmed the workplace, nearly three-quarters of employed Americans think AI will decrease overall job opportunities and 30 percent of Americans are concerned that AI will make their own job obsolete.

AI truly has an image problem, which stems from people like Sam Altman and Elon Musk, but also from executives who cannot think about creative new possibilities with AI, but rather just use it to cut jobs.

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OpenAI makes breakthrough on 80-year-old maths problem

“For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids,” OpenAI wrote on X. “An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better.”

While the work has excited mathematicians, the broader problem remains unsolved because the AI did not come up with a new answer for how fast the pairs of dots rise, but merely showed that the limit Erdős proposed was too low.

AI will advance science in so many ways, it will be breathtaking.

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Why Can’t AI Tell Jokes?

Despite AI being able to assist with science and knowing everything on the internet, it hasn’t come up with any novel scientific results on its own. That’s surprising. By default, I would expect that someone who learned everything in all of science would figure out lots of new insights. And yet the number of wholly AI-driven scientific discoveries is precisely zero.

It seems that AI has trouble with creativity. There’s some important sense in which it’s good at applying existing knowledge but not generating brand new ideas. Arguably, AI’s two biggest technical defects are inability to continually learn and limited creativity.

Claude at least tries to have a sense of humor, but now I understand why this is so hard.

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