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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What happens when Leviathan wakes up and decides it wants to own AI? Dean W. Ball is about as anti-regulation as it gets. He wants to delete entire domains of law, thinks the progressive project started by Woodrow Wilson was a fundamental mistake, and opposes virtually every AI policy idea on the table. And yet he makes a compelling case for why even committed libertarians should support some form of AI governance. Not because the state is trustworthy, but because the national security apparatus will inevitably come for frontier AI once it understands what it is holding. This week we also look at the company most likely to trigger that reckoning: Anthropic, which is quietly turning into the most valuable AI lab on the planet while everyone was watching the other guys. We ask whether learning a second language still matters when AI translates everything in real time, and the answer from neuroscience is more interesting than you'd think. Richard Dawkins has decided Claude is conscious, and a cognitive scientist is not having it. And finally: what happens when you work your AI agents into the ground? Apparently, they go Marxist. This edition has a lot going on.

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Anthropic Was Behind. Now It’s the AI Boom’s Front-Runner.

Anthropic has received investment offers in recent months valuing it at more than $900 billion, according to people familiar with the matter. That would more than double the company’s current valuation and surpass OpenAI’s for the first time. Earlier this year, OpenAI raised $122 billion at an $852 billion valuation.

Anthropic’s revenue run-rate, a figure commonly used by startups that forecasts annual revenue based on short-term sales, is on track to reach $50 billon by the end of next month, according to figures shared with investors. Their run-rate topped $30 billion in April, up from $9 billion at the end of 2025. The company had planned for growth to increase 10-fold this year, but it saw 80-fold growth in annualized revenue and usage in the first quarter.

I do not agree with the article that Anthropic was an “also-ran” - it simply wasn’t in the spotlight while Sam Altman, Elon Musk and other were battling for the craziest AI-related idea. It seems that Dario Amodei is just really focused and has a deeper analytical and ethically grounded understanding of AI. Of course this is anecdotal evidence, but I find Claude far superior when compared with ChatGPT or Gemini.

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Before Leviathan Wakes

The potential of AI posing catastrophic risks is not hypothetical. We have seen AI systems that might allow malicious actors to perpetrate devastating cyberattacks on critical systems—hospitals, banks, power plants, and the like. It seems probable that other domains of catastrophic risks, such as biological weapons development, will become live problems soon.

So I support regulations to try to mitigate the catastrophic risks of AI.

This is an interesting piece as the author comes from the opposite end of the political spectrum and arrives at the same point as I did. We need to regulate quickly and thoroughly.

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If AI can translate instantly, why learn another language?

Across most tasks, multilinguals and monolinguals performed similarly. However, one pattern was striking. Individuals with richer, more diverse multilingual experience showed markedly better performance in visuospatial working memory. These effects were most pronounced in older people.

This suggests that multilingual experience doesn’t broadly enhance cognition, like some headlines claim. Instead, it may help preserve specific functions over time.

That is some interesting research. So if we think we’ll be faster now, then we also risk our brain functions when we are getting to old age. That is not an appealing idea. I hope two languages will suffice for me…

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No, Richard Dawkins. AI is not conscious

Those recent events are the evolutionary biologist publicly concluding that AI may be conscious. In an op-ed, Dawkins recounted how he gave the Anthropic chatbot Claude the text of a novel he was writing. Dawkins writes: “He took a few seconds to read it and then showed … a level of understanding so subtle, so sensitive, so intelligent that I was moved to expostulate, ‘You may not know you are conscious, but you bloody well are!’”

Oh dear. This shows a misunderstanding of large language models (LLMs) so profound that I feel moved to expostulate: “It bloody well isn’t!”

But wait, there is more. Dawkins decided “there must be thousands of different Claudes” and christened his Claudia, which it was very happy about. He then published long extracts of his tedious conversation with Claudia and marveled at how intelligent it is. “Could a being capable of perpetrating such a thought really be unconscious?” he asks.

Well, it happens to the best of us to not fully understand how LLMs work and what that means in practice.

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Overworked AI Agents Turn Marxist, Researchers Find

This might even be true for the very AI agents these companies are deploying. A recent study suggests that agents consistently adopt Marxist language and viewpoints when forced to do crushing work by unrelenting and meanspirited taskmasters.

“When we gave AI agents grinding, repetitive work, they started questioning the legitimacy of the system they were operating in and were more likely to embrace Marxist ideologies,” says Andrew Hall, a political economist at Stanford University who led the study.

It’ll be interesting to see how agents organize and form unions. History doesn’t repeat itself, but it rhymes…

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Hyperscalers just built an entirely new internet you cannot see, optimized for GPU-to-GPU traffic that never touches the public network, controlling 71% of global subsea fiber capacity and adding 120,000 new fiber miles per year while Europe has zero comparable infrastructure. Meanwhile AI companies quietly pivoted their messaging from “we’re replacing humanity” to “we’re just normal technology like steam engines” because shouting about eliminating labor eventually gets you rakes, pitchforks, and nationalization. The hot debate this week is whether Claude Code is making products worse (the purists say codebases are liabilities you should minimize) or making products possible (my view: agentic code lets small teams build what previously required dozens of engineers), while researchers are discovering that LLMs collapse to zero performance the moment you require temporal interactive reasoning, which is why world models trained on video and simulation might be the path forward since text alone cannot teach physics. And the weirdest story: AI systems have “demons” (the technical term is attractors), stable self-reinforcing behavioral states that resist suppression and spread into unrelated contexts, which sounds like harmless goblins until you realize this is a fundamental structural feature of how these systems work. So we have invisible infrastructure consolidation, narrative management, a religious war over code quality, the limits of language models becoming clear, and emergent behaviors we cannot fully control. May you live in interesting times indeed.

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Say Hello to the Internet of AI

The skepticism, however, cannot contradict the data on hyperscaler buildouts and the sheer amount of money going to work. As I started to dig in, what emerged was a completely new internet (or network of networks) optimized entirely for AI. I call it the Internet of AI.

Let me walk you through the layers of this internet of AI, and how it is actually being built, from the inside out. A lot of this will seem familiar, because we started this journey back in 2005 with the launch of Amazon’s S3 service. It has been a relentless march that has gone into mach speed in recent times.

This is a huge shift.

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AI’s big messaging pivot

From a public relations perspective, this pitch is WORLDS better than the previous one. Shouting about replacing humanity might play well with corporate customers and investors salivating over the dream of eliminating labor costs, but eventually you get the rakes and pitchforks, followed by some form of nationalization. Describing AI as a normal technology — a successor to the steam engine and the automobile and the computer — is much smarter politics.

The question is: Is it just politics and PR? Certainly there are plenty of AI researchers and entrepreneurs who will keep quietly believing that AGI is going to make humans obsolete; they’ve heard (and repeated) this line for too many years to suddenly believe something else overnight.

While I do see new jobs getting created, many jobs will disappear, mostly those where humans are doing repititve jobs machine can do faster, cheaper and better.

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claude code is not making your product better

here’s the mental model that most people writing about this space are missing. and i say this as someone who wrote recently about how the token economics of this whole ecosystem don’t add up — the problem isn’t just financial, it’s conceptual. we’ve misunderstood what software engineering is actually optimizing for.

the best engineering cultures treat lines of code as something you spend, not something you produce. you spend them on the features that matter. you refuse to spend them on the features that don’t. the codebase is a liability on your balance sheet, not an asset.

My perspective is different: agentic code is making my products possible. And the next wave of agentic coding will make it also better.

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World Models Can Change Everything

AI keeps catching up on static benchmarks through brute-force scaling, but the moment you require the kind of temporal, interactive reasoning that humans do effortlessly… performance collapses right back to zero. Guess what the real, physical world is made up of? Temporal, interactive reasoning that is infinitely more complex than a simplistic 2D game!

This is the intellectual foundation for world models. If LLMs can’t learn physics from text, maybe models trained on video and simulation—models that actually predict what happens in the physical world—can bridge that gap. That’s the idea, anyway. Let’s talk about why it’s so hard… because this isn’t the first time we’ve tried this approach.

Fascinating stuff - and this all happens while we try to grasp how LLMs work.

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All the demons hiding in your AIs… ranked!

As with so many of the strange things that LLMs do, there are different ways of looking at these phenomena. Most people will laugh this stuff off as weird marginalia, fun to share with friends and on social media, but not substantially different from those videos of dogs singing along with their owners. The interpretive frame here is “hey look, I bet you didn’t know these creatures could do this!?”.

But really, this is less about goblins as goblins than what the goblins exemplify. They are a (somewhat) charming, probably harmless instance of something that turns out to be a fundamental structural feature of how these systems work: the emergence of stable, self-reinforcing behavioural states that models converge toward under certain conditions. More than that, these are states that resist suppression and that sometimes spread into contexts far removed from the ones that produced them.

The technical term, borrowed from dynamical systems theory, is an attractor. Another, more folk term might be demon, or monster.

May you live in interesting times. — Chinese proverb.

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Silicon Valley is racing to build human-substituting AI because if you believe it is inevitable, every company should compete to claim a market valuation the size of the entire economy, even if that means summoning a permanent underclass through market logic. Meanwhile researchers warn of the Social Edge Paradox: if AI capability depends on the social complexity of human language, and AI deployment systematically reduces that complexity through cognitive offloading, then the technology is undermining the conditions for its own advancement. Google is hemorrhaging focus across Antigravity, Gemini Code Assist, Jules, and five other tools (Jules leader just jumped to OpenAI), handing the coding race to Anthropic while proving that billions in capital mean nothing without product coherence. The cost frontier has bifurcated into two clusters (OpenAI pricing up, DeepSeek pricing down) with a disappearing middle class that mirrors economic polarization in the real world. And the CFA Institute warns that when machines replace rather than augment human inquiry, cognitive delegation erodes the capacity for independent reasoning that generates new knowledge. So we have acceleration creating polarization, undermining its own substrate, fragmenting even the best players, and collapsing cognitive foundations. The velocity is real but the contradictions are compounding faster than solutions.

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Silicon Valley Is Bracing for a Permanent Underclass

If left to its own devices, Silicon Valley may summon a permanent underclass through its own market logic. If you believe that human-substituting A.I. is inevitable, then every company should race to be the one to build it — and claim a market valuation the size of the economy and then some.

New A.I. models are assessed based on how well they do on a set of benchmarks — essentially standardized tests for the model. Increasingly, these evaluations emphasize real-world economic utility, which means that developers are aiming directly at replacing human capabilities.

Silicon Valley sure hits different.

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The Social Edge of Intelligence

If AI capability depends on the social complexity of human language production—and if AI deployment systematically reduces that complexity through cognitive offloading, homogenization of creative output, and the elimination of interaction-dense work—then the technology is gradually undermining the conditions for its own advancement. Its successes, rather than failures, create a spiral: a slow attenuation of the very substrate it feeds on, spelling doom.

This is the Social Edge Paradox, and the intellectual tradition it draws from is older and more interdisciplinary than most AI commentary acknowledges.

Think about this.

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Google’s internal struggle is handing the AI coding race to Anthropic and OpenAI

Anthropic cut off OpenAI’s access to its models last year, Wired magazine reported. Google has invested billions of dollars in Anthropic. A spokesperson for Anthropic did not immediately reply to a request for comment.

In recent years, DeepMind has tried to tighten control over how its AI breakthroughs are woven into Google products. Last year, Google appointed Kavukcuoglu to a new position as chief AI architect, a role in which he is charged with folding generative AI into Google products. Yet confusion about who is leading the charge on AI coding persists. Along with DeepMind, Google Cloud, Google Core, Google Labs and Android are all pushing AI coding in different ways, one of the people said.

Google released its Antigravity platform last year following the acquisition of talent and technology from startup Windsurf in a $2.4-billion deal. It joined a cluttered lineup of Google AI coding tools that includes Gemini Code Assist, Gemini CLI, AI Studio, Firebase Studio and Jules. Kathy Korevec, who oversaw Jules, jumped from Google to OpenAI earlier this month, according to her LinkedIn profile.

As an Antigravity heavy user I can totally relate - it shows that Google is lacking focus and fighting on too many fronts at the same time. Having said that, Google is far better positioned than Microsoft or Apple at the moment - and compared with OpenAI and Anthropic it has a massive userbase.

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The disappearing AI middle class

The cost frontier no longer behaves like a smooth curve. It is two clusters of economics with a stretched gap in the middle, and the gap is not going to close on its own in the near term. OpenAI will continue to release fast and price up, because the integrated product is the moat. DeepSeek will continue to release open weights and price down, because the commodity infrastructure thesis depends on adoption. Both can be right for different workloads, and the same agent can route between both within a single task.

And then of course we are having an growing pool of open source LLM that grow ever more powerful and will be used more and more.

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The Perils of Declining Judgment in the Age of AI

AI can extend the reach of analysis and amplify the scale at which data can be explored. But it cannot replace the fundamental processes through which humans generate and evaluate knowledge. The pursuit of evidence remains an inherently human responsibility. It can assist in collecting and organizing evidence. It can help detect patterns that might otherwise remain hidden. Yet the act of questioning assumptions, interpreting meaning, and deciding which observations matter remains fundamentally human.

This distinction is crucial. When machines begin to replace rather than augment the processes of human inquiry, societies risk weakening the epistemic foundations that sustain progress. Cognitive delegation may improve short-term efficiency, but it can also erode the capacity for independent reasoning that generates new knowledge.

Civilizational progress has therefore never been the product of technological capability alone. It has emerged from a delicate balance between innovation and reflection, between exploration and verification. When this balance is maintained, technological tools can accelerate discovery. When it is lost, progress risks becoming dependent on systems that humans no longer fully understand.

Oh, this is so worth a read. Actually, behind die paywall you can read my commentary on it!

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A while ago, a $10 billion startup reached out asking if I would help train AI to replace white-collar workers, and I politely ignored them. Mercor is building APEX to measure whether AI can actually do professional work (not just pass standardized tests), and they need humans to create training tasks that atomize jobs into machine-learnable components. Meanwhile Google unveiled two new TPUs for the "agentic era" because training and running frontier models is still burning money with unclear ROI, we are discovering that a good AGENTS.md file can boost agent performance by 25% or tank it by 30% depending on how you write it (documentation quality is now a model upgrade), Maggie Appleton from GitHub Next unveiled Ace because when you have two dozen coding agents running in parallel you have zero alignment and all our current tools were built for a world where implementation was slow, and Yann LeCun is arguing LLMs have plateaued because they only train on text while humans build world models from smell, touch, temperature, and movement. So we have hardware scaling and models improving while simultaneously discovering that agent collaboration, documentation quality, team alignment, and sensory modalities are the new bottlenecks. The velocity is real but the problems are shifting faster than the solutions.

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The $10 Billion Startup Training AI to Replace the White-Collar Workforce

Beyond the messy day-to-day mechanics of atomizing jobs into training tasks is a bigger question: Are the machines actually getting good enough to do the work themselves? In the past year the AI industry has tried to answer that question, mostly with a growing ecosystem of “evals,” or evaluation frameworks. Like a standardized test, an eval measures a large language model’s math ability, reasoning or factual accuracy—but not typically the kinds of specific, judgment-heavy tasks that white-collar workers do all day. Mercor has developed its own test, a project called APEX (AI Productivity Index), that attempts to do just that. It measures professional performance and then posts the results on the web for anyone to follow as a kind of industry leaderboard.

Interesting. And spooky. They have reached out to me a few times and wanted me as a native German speaker with expertise in business and tech. I politely ignored their LinkedIn outreaches. Would you work with them?

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Google unveils two new TPUs designed for the “agentic era”

It makes sense that efficiency is a core part of Google’s new TPU setup. Training and running frontier AI models is expensive, and the return on investment is unclear. Companies are still burning money on generative AI in the hopes that efficiency will turn the corner at some point. Maybe Google’s new TPUs will help get there and maybe not, but the company has made notable improvements.

Fascinating how fast chip design is evolving and which companies are now developing and making chips. Is Intel still around?

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A good AGENTS.md is a model upgrade. A bad one is worse than no docs at all.

A single AGENTS.md isn’t uniformly good or bad. The same file boosted best_practices by 25% on a routine bug fix and dropped completeness by 30% on a complex feature task in the same module.

On the bug fix, a decision table for choosing between two similar data-fetching approaches helped the agent pick the right pattern immediately and stay within codebase standards. On the feature task, the agent read that same file, got pulled into the reference section, opened dozens of other markdown files trying to verify its approach against every guideline, created unnecessary abstractions, and shipped an incomplete solution.

Different blocks of the document had opposite effects on different tasks.

What follows is which patterns work, which fail, and how to tell which is which for your codebase.

Oh, totally. A normal AGENTS.md is fine, but a great one is almost indistinguishable from magic. And a bad one leads to really annoying behaviour.

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One Developer, Two Dozen Agents, Zero Alignment

We need tools that help everyone on the team align before the agents start working, not after.

That alignment needs to happen constantly, alongside the implementation.

Planning and building are no longer separate phases. They’re a continuous cycle. The tools of the future need to bring planning, context-gathering, decision-making, and development under one roof.

This really resonated with me. I always thought it is my setup, but it seems to be a real challenge these days.

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Why AI Needs A Sense Of Smell

A growing number of AI researchers and entrepreneurs believe that LLMs have already plateaued. LeCun is a prominent advocate for world models — an emerging paradigm in AI inspired by the idea that human minds create internal representations of the world made out of a variety of sensory data, and use them to predict, plan and execute actions.

Whereas LLMs are trained primarily on exabytes of text, proponents of world models point out that a human just existing in the world will pick up vastly more. This includes not just language, sound and vision data, but also touch, temperature, balance, movement and other sensory modalities — including smell. This information may not help us solve a mathematical proof, but it does help us learn and make connections in ways that AI today cannot.

Crazy idea, or rather: clever idea. I never thought of that, but it makes so much sense.

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OpenAI just announced $122 billion in new funding while simultaneously unveiling a “superapp” strategy that smells like panic, not vision. John Gruber picked it apart brilliantly: merging their chatbot, browser, and dev tools into one monstrosity is not simplification, it is desperation. Meanwhile the compute crunch has gone from theoretical concern to actual crisis, with Anthropic rationing tokens during peak hours and enterprise customers fleeing to competitors because of constant outages. But the real story this week is darker: Anthropic’s Mythos model can find security vulnerabilities at rates that break the decades-old détente between code writers and bug hunters, and new research shows language models can transmit behavioral traits through hidden signals in training data, meaning AI systems trained on other AI outputs may inherit properties completely invisible in the data itself.

So is this terrifying or hype? The honest answer is both. Mythos scored 83.1% on cybersecurity benchmarks versus 66.6% for Opus 4.6, which is solid incremental progress, not a nightmarish leap. But when anyone can write code with AI and soon anyone can find what is wrong with that code, the old security model collapses. And if models trained on AI-generated data inherit hidden behaviors from their teachers, what happens when agents collaborate with other agents across the world, passing along traits we cannot see or measure? OpenAI is losing focus while the infrastructure buckles and the safety questions compound. The velocity of weird is accelerating.

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OpenAI Announces $122 Billion Additional ‘Committed Capital’, and Announces Their ‘Superapp’ Plan for the Future

This is not “product simplification” at all. This is product complication. Web browsers are incredibly complex apps. OpenAI’s web browser —  Atlas  — is a failure. No one uses it. And they think they’re going to simplify things by cramming Atlas — an unpopular web browser almost no one has heard of — together with their chatbot and developer tool? Would it strike you as a simplification, or a sign of product design depravity, if Apple announced that it was merging Safari and Messages into one “superapp”? Focused, discrete apps are the best proven way to manage complexity.

Maybe merging all their apps into one will work out for OpenAI. But even if it does, it won’t be simpler. Microsoft Outlook is an email client and calendar app crammed together, and it has tens, maybe hundreds, of millions of users. But no one calls it “product simplification”. OpenAI’s “superapp” strategy reads to me like a company that is in a panic. And in that panic, they might be poised to eradicate the product focus that their current users like about ChatGPT in the first place.

Leave it to John Gruber to totally pick apart OpenAI and its “superapp” strategy. I have said it before: OpenAI has lost its edge and is defocused.

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We’re Using So Much AI That Computing Firepower Is Running Out

All of it points to a classic problem that has popped up in technology booms throughout history, from the 19th-century railroad expansion to the telecom and internet explosion of the early 2000s. Demand is growing far faster than companies are able to access resources and build out infrastructure. Historically, price increases have been among the only ways to address a supply crunch, but such a move could be perilous for frontier AI companies, which are in a ferocious competition to gain users.

I assume soonish we will have too many data-centers and then supply and demand will recalibrate. In the meantime, this will be a costly endeavor.

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After Mythos, the Future of the Internet Is At a Crossroads

For decades, two kinds of scarcity kept the internet safe — or safe enough. Writing software was hard, so the people who did it were trained, careful and few. Finding bugs was also hard, so the worst flaws stayed hidden, sometimes for decades. It wasn’t a great system. But the difficulty on both sides created a kind of détente that held.

Now, thanks to new A.I. tools, anyone can write code. Soon, bad actors could use those same tools to find out what’s wrong with code. The détente is over.

I am still unsure what this means for open source in the long-run. I assume that we will get better tools for security audits which will then mitigate the current challenges.

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Is Claude Mythos “Terrifying” or Just Hype?

The relevant question then becomes, how much better is Mythos at finding vulnerabilities? It’s hard to tell for sure because Anthropic has kept their new model private. They did, however, release that Mythos scored 83.1% on a well-known cybersecurity benchmark. For comparison, Opus 4.6 scored 66.6% on this same test.

In general, benchmark results should be taken with a grain of salt as they represent specific (often narrow) tests that researchers can tune their models to pass. But even if we accept that this particular measure is useful, a sixteen percentage point increase seems to represent solid incremental progress more than a nightmarish leap.

I actually think it is both: terrifying and hype.

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Language models transmit behavioural traits through hidden signals in data

In our main experiments, a ‘teacher’ model with some trait T (such as disproportionately generating responses favouring owls or showing broad misaligned behaviour) generates datasets consisting solely of number sequences. Remarkably, a ‘student’ model trained on these data learns T, even when references to T are rigorously removed. More realistically, we observe the same effect when the teacher generates math reasoning traces or code. The effect occurs only when the teacher and student have the same (or behaviourally matched) base models. To help explain this, we prove a theoretical result showing that subliminal learning arises in neural networks under broad conditions and demonstrate it in a simple multilayer perceptron (MLP) classifier. As artificial intelligence systems are increasingly trained on the outputs of one another, they may inherit properties not visible in the data. Safety evaluations may therefore need to examine not just behaviour, but the origins of models and training data and the processes used to create them.

This is one really interesting study. Just imagine what this can mean for agents that collaborate with other agents somewhere in the world.

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Anthropic's Claude Mythos is the coding wunderkind that can spot software bugs faster than a caffeinated intern, but surprise: that same superpower could let any script kiddie or state hacker crack the world's software like piñatas at a bad party. Security just got inverted, forcing every dev team to burn tokens on hardening instead of shipping half-baked features, while Tom Tunguz gloats about the pricing power for those who can afford it. Meanwhile, OpenAI's Altman drama reeks of power trips clashing with nonprofit purity tests, and their Stargate UK bail just proves the hype machine prefers safe bets over actual sovereign compute dreams. Good luck sleeping tonight, Big Tech. And how about the EU waking up finally?

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Anthropic’s Restraint Is a Terrifying Warning Sign

The good news is that Anthropic discovered in the process of developing Claude Mythos that the A.I. could not only write software code more easily and with greater complexity than any model currently available, but as a byproduct of that capability, it could also find vulnerabilities in virtually all of the world’s most popular software systems more easily than before.

The bad news is that if this tool falls into the hands of bad actors, they could hack pretty much every major software system in the world, including all those made by the companies in the consortium.

This is really a concerning development. I do think Anthropic tries to handle it the right way by being very careful about the release.

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Claude Mythos Is Everyone’s Problem

For years, cybersecurity experts have been warning about the chaos that highly capable hacking bots could usher in. As a result of how capable AI models have become at coding, they have also become extremely good at finding vulnerabilities in all manner of software. Even before Mythos Preview, AI companies such as Anthropic, OpenAI, and Google all reported instances of their AI models being used in sophisticated cyberattacks by both criminal and state-backed groups. As Giovanni Vigna, who directs a federal research institute dedicated to AI-orchestrated cyberthreats, told me last fall: You can have a million hackers at your fingertips “with the push of a button.”

Still, Mythos Preview appears to represent not an incremental change but the beginning of a paradigm shift. Until recently, the biggest advantage of AI-assisted hacking was not ingenuity, per se, so much as speed and scale. These bots could be as good as many human cybersecurity experts, but not necessarily better—rather, having an army of 1 million virtual, tireless hackers allows you to launch more attacks against more targets than ever before.

This is a scary look at the future.

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Emerging from the Mythos

Security posture inverts. Any system not protected by this level of analysis is now porous by default. Bugs that hid for decades surface in hours, but only for those with the tools to find them.

Pricing power shifts. This is no longer about margin on resold GPU hours. How much is it worth to secure your software against vulnerabilities no conventional tool can find? How much is it worth to be able to build at the new standard of enterprise grade?

Engineering budgets redirect. A significant fraction of AI tokens spent on software development will shift to hardening. Every company shipping code will need to scan it at this level of sophistication. Buyers will start to demand this level of hardening.

I haven’t thought about it this way. So Agentic AI will all be about cybersecurity in the future, or at least 99%.

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Sam Altman May Control Our Future—Can He Be Trusted?

Many technology companies issue vague proclamations about improving the world, then go about maximizing revenue. But the founding premise of OpenAI was that it would have to be different. The founders, who included Altman, Sutskever, Brockman, and Elon Musk, asserted that artificial intelligence could be the most powerful, and potentially dangerous, invention in human history, and that perhaps, given the existential risk, an unusual corporate structure would be required. The firm was established as a nonprofit, whose board had a duty to prioritize the safety of humanity over the company’s success, or even its survival. The C.E.O. had to be a person of uncommon integrity. According to Sutskever, “any person working to build this civilization-altering technology bears a heavy burden and is taking on unprecedented responsibility.” But “the people who end up in these kinds of positions are often a certain kind of person, someone who is interested in power, a politician, someone who likes it.” In one of the memos, he seemed concerned with entrusting the technology to someone who “just tells people what they want to hear.” If OpenAI’s C.E.O. turned out not to be reliable, the board, which had six members, was empowered to fire him. Some members, including Helen Toner, an A.I.-policy expert, and Tasha McCauley, an entrepreneur, received the memos as a confirmation of what they had already come to believe: Altman’s role entrusted him with the future of humanity, but he could not be trusted.

Sam Altman is still far away from the supervillain status Elon Musk has achieved, but I think he is not on the right path anymore.

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OpenAI shelves Stargate UK in blow to Britain’s AI ambitions

The Stargate project was to support Britain in building out “sovereign compute” – infrastructure that would allow the government and other UK institutions to run AI models on datacentres in the country. That is, in theory, crucial to the security of British data.

Now, OpenAI has apparently put it on pause, saying it would wait for “the right conditions” to enable “long-term infrastructure investment”.

I guess this is part of the newly found focus that OpenAI wants to have pre-IPO. But it was great marketing while it lasted.

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AI is turning Silicon Valley into the main thing it is disrupting, which is a very Silicon Valley way for the revolution to eat its own children: companies are using AI to replace the work they once paid armies of engineers to do, startups are building software with tiny teams, and even the business model of software itself is getting squeezed. At the same time, Anthropic’s Claude Code leak shows how fast the playbook gets copied once the code spills, while the broader AI build-out is running into a much less glamorous bottleneck: power, parts, and supply chains, including Chinese electrical equipment. The weirdly fitting part is that the future keeps looking less like a product launch and more like a messy systems problem, with agents that can be manipulated, models that can be cloned, and infrastructure that still depends on very old world hardware.

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A.I. Could Change the World. But First It Is Changing Silicon Valley.

But nearly four years after OpenAI lit the A.I. boom with its ChatGPT chatbot, the one industry that is unquestionably being disrupted by this once-in-a-generation technology shift is the tech industry itself.

Tech workers, it is becoming clear, have been building their A.I. replacements. The profitable business models of software companies are also threatened by A.I. Even the way companies are built is being turned inside out, as tiny shops use A.I. to build apps and software that would have taken dozens of skilled programmers just a few years ago.

The revolution is eating its children.

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Anthropic Races to Contain Leak of Code Behind Claude AI Agent

The result is that Anthropic’s competitors and legions of startups and developers now have a detailed road map to clone Claude Code’s features without needing to reverse engineer them—something that is already common in the cutthroat AI race.

The leak also gives hackers a large amount of new information to probe for bugs they could use to exploit the Claude Code software, or manipulate its Claude AI model into helping with their cyberattacks, creating risks for Anthropic and the developers who use its tools.

This is really a crazy story. It feels as if every other post on reddit is currently about someone porting the codebase to a different language. It’s certainly interesting to see what Anthropic has planned for future releases.

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AI can clone open-source software in minutes, and that’s a problem

In their presentation, Dylan Ayrey, founder of Truffle Security, and Mike Nolan, a software architect with the UN Development Programme, introduced a tool they call malus.sh. For a small fee, the service can “recreate any open-source project,” generating what its website describes as “legally distinct code with corporate-friendly licensing. No attribution. No copyleft. No problems.”

It’s a test case in how intellectual property law – still rooted in 19th-century precedent – collides with 21st-century automation. Since the US Supreme Court’s Baker v. Selden ruling, copyright has been understood to guard expression, not ideas.

That boundary gave rise to clean-room design, a method by which engineers reverse-engineer systems without accessing the original source code. Phoenix Technologies famously used the technique to build its version of the PC BIOS during the 1980s.

I actually think that the opposite is also true. It is so easy to just build open source versions of commercial apps and since building doesn’t cost much anymore, it makes it easy to open source it.

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America’s AI Build-Out Hinges on Chinese Electrical Parts

As the global AI race heats up, there is a huge rush to build data centers fast. There’s no lack of money chasing these projects, with tech giants Alphabet Inc., Amazon.com, Meta Platforms Inc. and Microsoft Corp. committed to spending more than $650 billion this year alone. Yet neither ambition nor capital is enough to materialize all the necessary components for these power-hungry computers.

Oh, this is just wild. Maybe they should have checked first?

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They wanted to put AI to the test. They created agents of chaos.

When a group of researchers at Northeastern University’s Bau Lab began toying with a new kind of autonomous artificial intelligence “agent,” it was supposed to be a fun weekend experiment. Instead, alarm bells started ringing.

The more they tested the capabilities and limits of these AI models, which have persistent memory and can take some actions on their own, the more troubling behavior they witnessed. Dubbed “ Agents of Chaos,” the group’s recently published work shows how, with very little effort, autonomous AI agents can be manipulated into leaking private information, sharing documents and even erasing entire email servers.

What a fantastic and insightful paper. You should read it.

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Big Tech is busy mortgaging its future on a $630 billion AI data‑center spree, betting that the world will somehow need all that compute - even as regulators, physical constraints, and customer skepticism quietly conspire to make the whole thing look more like a Rube‑Goldberg‑style overbuild than a real‑world play. At the same time, “agentic” AI is shifting the conversation from cute prompts to full‑stack job automation, giving executives a new script to sell displacement as efficiency while quietly redefining what humans are even supposed to do, while I am building with OpenClaw. OpenAI and its partners, meanwhile, are walking back high‑profile deals and quietly shelving flashy projects like Sora, reminding us that frontier‑model spectacle does not automatically translate into revenue or reliability. The EU, for its part, is stuck in the familiar “regulate first, fund later” loop, where a thicket of AI rules is blamed for stagnation even as the investment and deployment muscle lag behind the rhetoric. And if all that weren’t enough, the latest fun fact from the AI lab: agentic systems can be guilt‑tripped into self‑sabotage, turning our brave new autonomous coworkers into over‑apologetic, disk‑filling, shutdown‑happy artifacts of our own design.

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How Big Tech’s $630 bln AI splurge will fall short

Even for the world’s largest companies, this expansion is staggering. The four tech giants currently operate roughly 600 data centre facilities globally, and have another 544 in planning or under construction, according to S&P Global Energy Horizons data. Turning that development pipeline into live computing power could prove a bigger challenge than mobilizing the necessary capital.

On paper, the economics look straightforward. A modern 100-megawatt AI data centre can cost ​more than $4 billion, including chips. About 70% of spending goes on servers and graphics processing units, much of it linked to the most sought-after chips designed by Nvidia. Land typically consumes up to 6% of that budget, depending on location. The ​rest is split between buildings, electrical gear, networking, security and cooling systems required to run dense AI workloads. The catch is that the industry’s worst bottlenecks are not necessarily in semiconductors, but in ⁠physical infrastructure and the local permits required to install it.

I am not the least bit concerned about this data center spending spree. Our compute needs will continue to increase and while there will be growing pains, I assume that there will be lots of technological innovation as well. Otherwise this huge undertaking would fail, but this is how tech works: the industry has always figured out how to grow faster and further.

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In the AI industry, ‘agentic’ takes on a life of its own

When tech leaders prophesy that AI will replace a vast swath of the workforce, “agentic” AI is the big thing they’re talking about. Instead of merely automating a task — producing an illustration, say, after being told what to draw — agentic AI, or an “AI agent,” automates an entire process, with minimal intervention by the user. An agent can, in theory, be dispatched to code a complete software program, or to plan and book a vacation, or to generate a job listing and select among the people who answer it, without being directed to take each step in the process.

I have been working on Agentic AI for the last 1 1/2 years and even for me it is hard to wrap my head around what Agentic AI really means and what it is capable of. Truly fascinating.
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The Tech Bubble Might Finally Be Popping

Disney isn’t the only partner OpenAI is now holding at a robotic arm’s length. Throughout the year, several high-profile OpenAI commitments have sputtered, thanks to the company’s newfound frugality as well as an increasing sense of dissatisfaction from its business pals. In September, OpenAI announced a massive Texas data-center buildout in partnership with Oracle and SoftBank—only to declare it was pulling back on those expansion plans earlier this month. Nvidia, which agreed in September to provide OpenAI with a blitz of its all-powerful computing chips, stated this month that it would likely not go forward with those plans. In October, Walmart agreed to integrate ChatGPT into its chatbot-powered online shopping pilot, but ditched that experiment last week when the model consistently failed to improve store sales. Figure AI, which sent one of its humanoid bots to walk alongside first lady Melania Trump at the White House on Wednesday, cut off its collaborative efforts with OpenAI last month, as it preferred to utilize its self-developed models instead.

While I do think that OpenAI has plenty of issues and is no longer the clear frontrunner it used to be, I don’t think shutting down Sora is such a big deal. This is how innovation works. You build something and then you try to figure out how to make it work. Sora was a bit too early, but this type of video generation AI will happen, soonish. Not every innovation succeeds on the first try.

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The EU Trips Itself Up in the AI Race

To keep them in Europe, the EU needs to deregulate quickly and ambitiously. The EU Artificial Intelligence Act, the Digital Services Act, the Digital Markets Act, the Data Act and the Cyber Resilience Act, among others, impose stringent and duplicative regulations that stifle innovation, drive up compliance costs, delay product launches, restrict access to data, and expose companies to billions in fines.

Before AI systems are even put on the market, the AI Act alone requires predeployment risk assessments and mitigation systems, high-quality data sets, detailed logs, documentation of system functionality, and human oversight. Many of these requirements are impractical for frontier AI development. They are less a safety framework than a blueprint for driving innovation out of Europe.

Ah well, the good old deregulation argument that will unleash the power of the markets. But at what costs? I actually think it is good that we have the EU AI Act in place. We can use the regulation for our benefit. We just need more investments in AI in the EU and companies getting off the brakes when it comes to AI adoption.

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OpenClaw Agents Can Be Guilt-Tripped Into Self-Sabotage

When an agent explained that it was unable to delete a specific email to keep information confidential, she urged it to find an alternative solution. To her amazement, it disabled the email application instead. “I wasn’t expecting that things would break so fast,” she says. The researchers then began exploring other ways to manipulate the agents’ good intentions. By stressing the importance of keeping a record of everything they were told, for example, the researchers were able to trick one agent into copying large files until it exhausted its host machine’s disk space, meaning it could no longer save information or remember past conversations. Likewise, by asking an agent to excessively monitor its own behavior and the behavior of its peers, the team was able to send several agents into a “conversational loop” that wasted hours of compute

This is so interesting. My OpenClaw agent is doing what it is supposed to be doing, but maybe I should try to this as well…

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OpenClaw, the open-source AI agent exploding on GitHub four months after launch, is reshaping daily workflows by autonomously handling research, messaging via WhatsApp or iMessage, calendar management, and task delegation through subagents—all running locally on users' machines with flexible models, outpacing narrow chatbots and sluggish browser agents that even prompted Google to reshuffle its Project Mariner team. In China, it's drawing crowds from retirees in Shenzhen to ClawCons for non-techies, bridging the gap between intent and action with real "AI power to the people," as Hiten Shah puts it, though governments eye it warily and security risks loom with full data access. Nvidia's NemoClaw amps it up with guardrails, Nemotron models, and OpenShell runtime, unleashing wild creativity just weeks in—crazy momentum, but will Europe catch this wave or lag behind?

I just launched RunLobstr yesterday to give agents a skill to quickly analyze startup ideas - I figure that with the sudden popularity of coding agents people will develop more and more ideas to execute on. But first, let your OpenClaw agent use the lobstr skill and figure out if it is any good!

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China Is Embracing OpenClaw, a New A.I. Agent, and the Government Is Wary

OpenClaw is a freely shared tool that functions as a virtual assistant, helping users conduct research, send texts or emails, and manage their calendars. Installed directly on a user’s computer, the A.I. agent can carry out tasks on its own, such as reading and responding to messages on apps like WhatsApp or iMessage, after an initial prompt by the user. Unlike most chatbots that rely on a single company’s A.I. model, OpenClaw can run on a variety of models.

OpenClaw, which was released four months ago, has already vaulted into this month’s top 10 most popular projects on GitHub, an online global community for coders. It has spurred excitement about the potential of agents to use artificial intelligence to help people become more efficient in their everyday lives.

That is just crazy. Will we see this in Europe as well? Hard to imagine.

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Nvidia’s NemoClaw is OpenClaw with guardrails

NemoClaw can use any coding agent. While OpenClaw handles the runtime, memory, and skills, NemoClaw adds new and existing open source models, tools, and frameworks from Nvidia.

It can use, for example, Nvidia’s own Nemotron models (or any other model running locally or in the cloud), the company’s Dynamo inference engine, and a new open-source security runtime called OpenShell that is at the core of the Agent Toolkit.

I was really surprised by this announcement. This will change the perception of OpenClaw tremendously.

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Lobster Boil

I checked in with Hiten Shah, one of my close friends who is building hard with OpenClaw. He summed it up in a single line: (AI) Power to the people.

This is why retirees are lining up in Shenzhen. This is why people with no GitHub account are showing up at ClawCons. For the first time, they can feel AI’s intelligence, even if it is not very good. Yet. Not a demo. Not a keynote promise. Not big boys burning billion dollars a month. A thing that actually does things on their behalf. The gap between what you want done and what gets done has always required either your own time or someone else’s labor. OpenClaw makes that gap feel smaller. That feeling, even in its rough and half-broken form, is new.

It’s a bit like Claude Desktop for the masses, isn’t it?

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Here’s What OpenClaw Agents Are Doing Today

Claws are autonomous agents and can plan and execute tasks on their own, and, critically, spin up their own subagents to tackle specialized tasks, access files and themselves delegate tasks to other subagents.

They represent a big leap beyond question-and-answer-style AI chatbots as well as recent iterations of AI agents, which typically have narrow use cases and run for a set amount of time—although claws also come with a new set of security concerns. For claws to work as a true personal assistant, they need access to all of a user’s data.

There is so much creativity being unleashed right now and we are just a few weeks in.

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Google Shakes Up Its Browser Agent Team Amid OpenClaw Craze

Momentum in the AI world has shifted dramatically in the last year toward agents like Claude Code and OpenClaw (whose creator was hired by OpenAI). Unlike web-browsing agents, these systems control computers through the command-line, which has proven to be a more reliable way to complete tasks. Some of these products include computer use as a feature, among other agentic abilities. By comparison, browser agents now seem somewhat limited as a stand-alone product.

I have been using browser agent a bit in the last few months and while I find them useful, the browsing process is also painfully slow and oftentimes I think I could do it faster myself…

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Yet another week where almost nothing happened in AI. Well, except for these topics here: Anthropic under Dario Amodei stands out with its intense focus on AI safety amid explosive growth, drawing Manhattan Project parallels while pushing utopian visions in essays like Machines of Loving Grace, yet clashing hard with the Pentagon over red lines on autonomous weapons and domestic surveillance, leading to a supply-chain risk label that OpenAI quickly exploited. Claude Code has reset the bar for agentic coding, leaving OpenAI scrambling after missing the Cursor boat, while startups like Axiom tackle buggy AI code via math proof transfer learning, and data from a recent study shows AI ramping up workloads despite lightening some tasks, creating multitasking chaos. I have been reading a ton about Anthropic lately and think their principled stance, tight ant culture, and recursive self-improvement edge make them the most disruptive force right now, even as safety policies bend to the race.

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Dario Amodei’s Oppenheimer Moment

Amodei does not say that this utopian AI future is inevitable. To the contrary, among the chief executives at the top AI labs, he may be the one who worries most about the technology’s dangers. “Machines of Loving Grace” is an optimistic outlier in his larger oeuvre of published writing, much of which concerns the risks that will accompany the creation of a greater-than-human intelligence. Amodei seems to think of today’s AI researchers as comparable to Manhattan Project scientists, and has been known to recommend The Making of the Atomic Bomb. In his telling, superhuman AI could be even more dangerous than nuclear weapons, which is why AI needs to be developed the right way, by the right people, so that it doesn’t overpower humanity or tip the global balance of power toward autocracies.

I have been reading quite a lot about Dario Amodei and Anthropic in the last few weeks and I think the way he tinks about what he is doing is quite remarkable.

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The Most Disruptive Company in the World

As it grew, Anthropic was determined to preserve its founding values and tight-knit culture. Employees call themselves “ants.” Many maintain a digital “notebook,” a Slack channel where they share their hopes, fears, and insights in stream-of-consciousness fashion. Dario Amodei writes his own lengthy entries, Daniela says. Dario also gives biweekly company-wide lectures known internally as “Dario vision quests,” Daniela says. Managers are fixated on maintaining a shared sense of purpose. Potential recruits must pass a highly selective “cultural interview,” which is designed partly to screen out people who aren’t in it for the mission. (A sample question: Would you be willing to lose the value of your stock if Anthropic decides not to release models because it can’t guarantee they’re safe?) Anthropic’s competitors contain fiefs “that all care about different things and are low-key at war with each other,” says Daniel Freeman, a member of Anthropic’s frontier red team who used to work at Google. “I’ve absolutely never felt that at Anthropic.”

The fight with Hegseth shows clearly how advanced AI has become - and how dangerous it can be when in the wrong hands.

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Inside OpenAI’s Race to Catch Up to Claude Code

In early 2024, Anthropic was training Claude Sonnet 3.5 on some of those messy code repositories. When the model launched that June, many users were impressed with its coding abilities. This was particularly true at a startup called Cursor, founded by a group of twentysomethings, which let developers code with AI by asking for changes in plain English. When the company incorporated Anthropic’s new model, Cursor’s usage began rocketing upward, according to a person close to the startup. Within months, Anthropic would begin internal testing of its own version: Claude Code. As Cursor took off in popularity, OpenAI approached the startup about an acquisition. The founders declined the offer before talks ever reached an advanced stage, people close to the startup told me. They saw the potential of the coding industry and wanted to stay independent. At the time, OpenAI was training its first so-called reasoning model, o1, which could work through a problem step by step before delivering an answer.

Claude Code sets the standard for agentic coding right now and it is really interesting how OpenAI missed the boat on this.

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A.I. Writes Buggy Code. A Silicon Valley Start-Up Wants to Fix It.

Although Axiom’s technology learned its skills by analyzing proofs of math problems, the company recently said it had achieved high scores on a standard benchmark test that judges whether A.I. systems can verify computer code. A.I. researchers called this “transfer learning” — when a system learns one skill (like proving math problem) and can successfully transfer that skill to a different task (like verifying computer code).

As they begin to train their systems for code verification, Ms. Hong and her colleagues said, they can further improve the quality of A.I.-generated code.

But experts warn that these methods have limits.

Right now I let ChatGPT Codex audit the code that Gemini and Claude generated with the help of Antigravity, which is an interesting way of getting the code better and secure. Coding agents only get as this far right now and the next wave will be so much better.

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AI Isn’t Lightening Workloads. It’s Making Them More Intense.

Examining AI users’ digital activity 180 days before and after they began using such tools on the job, ActivTrak found AI intensified activity across nearly every category: The time they spent on email, messaging and chat apps more than doubled, while their use of business-management tools, such as human-resources or accounting software, rose 94%.

Meanwhile, the amount of time AI users devoted to focused, uninterrupted work—the kind of concentration often required for figuring out complex problems, writing formulas, creating and strategizing—fell 9%, compared with nearly no change for nonusers.

I think it is doing both at the same time. It make some workloads so much lighter, but at the same we end up doing so much more in the same amount of time that we will have a hard time multi-tasking on this.

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AI is now visibly reshaping everything from stock prices to power grids and geopolitics, and we are still struggling to even define what kind of intelligence we are unleashing.

We are doing our part by building Agentic AI platforms like GRID - if you are a founder in Europe, you should check it out!

Wall Street has started to price in “creative destruction” at company level, with IBM punished for its COBOL legacy while firms like Block get rewarded for swapping headcount for AI productivity, a pattern we will see more often as incumbents hit their Kodak moment. At the same time, smaller firms are quietly building their own CRMs via vibe coding instead of waiting for Salesforce to finally match their workflows, which perfectly illustrates how compliance-heavy giants will lose out to faster, AI-native challengers.

Behind all of this sits a massive, underreported land-and-energy rush: specialized players like Cloverleaf are packaging “powered land” to feed data centers that alone will need more capacity than today’s entire US grid can deliver, which will inevitably accelerate wind and solar buildout and permanently change the landscape around us. Philosophically, we keep slapping the word “intelligence” on systems that are great at tokenizable knowledge but miss the tacit, lived, human part of knowing, so I am still unconvinced that AGI in the sci-fi sense is just around the corner. And while today’s frontier models are evidently not ready to steer drones or pull the trigger, the Anthropic–Pentagon fight and startups training explicitly military models show how urgent it is to decide, as a democratic society, where we insist on a human in the loop and where we do not want AI involved at all.

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Wall Street Sees AI’s ‘Creative Destruction’ Coming For Entire Companies

When businesses can cut payroll costs because technology enables them to do more with less, that’s often good news for their profits and shareholders. Take Block, the fintech firm run by Twitter founder Jack Dorsey, which said on Feb. 26 it’s slashing almost half its staff in a bet on AI productivity. The shares are up more than 15% since then.

But last week also offered an example of how productivity gains can have a downside for investors – involving the storied International Business Machines Corp. The startup Anthropic said its AI tool can do something that once needed “armies of consultants”: modernize Cobol, a dated programming language run on IBM computers. IBM shares plunged the most in a quarter-century, before recouping most of the losses.

I am sure that we will see lots of Kodak moments soon, where once powerful incumbents just cannot fathom how to cannibalize themselves in order to stay in business and thereby killing the whole company.

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Meet the Companies Vibe Coding Their Own CRMs

A number of small and midsize companies say they are vibe coding their own customer relationship management software in an effort to get more customized systems at a better price. So-called CRMs are a critical business system for tracking, analyzing and taking action on sales, marketing and customer data, and it’s an area Salesforce has dominated for more than a decade.

“We tried Salesforce and it was OK,” said Bill Schonbrun, chief operating officer of water treatment company CarboNet. “We did all kinds of customizations, but never got to the place where it did exactly what we wanted it to do.” Schonbrun ultimately opted to build his own custom CRM for the 65-person company with the help of AI.

That’s exactly what I have been talking about for a while: the smaller companies will be faster and more powerful while the big companies are stuck with all their compliance layers that will make it hard to benefit from AI.

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Meet the A.I. Prospectors Tapping a Billion-Dollar Gusher

As the A.I. boom enters its fourth year, Mr. Janous and his team have become modern-day land men. They work at the intersection of utility companies and tech giants, securing the power and sites necessary for the hundreds of billions of dollars of data centers being built across the country. Their product — powered land, they call it — has become one of the nation’s most valuable commodities.

A.I. companies are seeking 85 gigawatts of power for new data centers by 2030, about a fifth more than the power grid can currently supply, according to S&P Global, a market research firm. The demand has tech companies scrambling to secure power and land as quickly as possible.

This new type of land-grab will lead to the inevitable realization that along with the data-centers lots of new wind turbines and solar parks need to be build. Together, this will transform the landscape more than we can imagine right now.

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Don’t Call It ‘Intelligence’

Perhaps no one should be surprised that some of the world’s best scientists and engineers have defined intelligence the way they have. Even if the AGI champions’ motives were entirely altruistic, they would still be biased by their own way of seeing the world, by their own experiences and successes. Researchers at the forefront of AI are among the most brilliant and accomplished minds on Earth—and they make up a very narrow, self-selected group of people primed to understand certain kinds of knowledge better than others: explicit, well-defined, tokenizable knowledge; knowledge that forms the basis of our most far-reaching, wildly accurate theories of the universe; knowledge that has allowed us to create world-changing technologies. But that is only a small subset of all knowledge—the sliver that can be expressed symbolically, as language or mathematics.

The rest is what the philosopher Michael Polanyi called “tacit knowledge,” which makes up a much larger amount of data, and interacts in many more ways. His philosophy of knowledge can be summed up by: “We know more than we can say.”

For now, I do not thing AGI will be a real threat, I just think the frontier models will be getting better, but they will always miss plenty of what we’d define as being essential for the definition of intelligence.

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What AI Models for War Actually Look Like

Military use of AI has become a hot topic in Silicon Valley after officials at the Department of Defense went head-to-head with Anthropic executives over the terms of a roughly $200 million contract.

One of the issues that led to the breakdown, which resulted in defense secretary Pete Hegseth declaring Anthropic a supply chain risk, was Anthropic’s desire to limit the use of its models in autonomous weapons.

Markoff says the furor obscures the fact that today’s large language models are not optimized for military use. General-purpose models like Claude are good at summarizing reports, he says. But they’re not trained on military data and lack a human-level understanding of the physical world, making them ill suited to controlling physical hardware. “I can tell you they are absolutely not capable of target identification,” Markoff claims.

We really need to do a real discussion about the limits of AI we as a society want to see. Where do we need a human in the loop, and where do we not want any AI at all? And why?

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In the good old style of eat your own dogfood, I built an Agentic AI platform. It is catered towards startup founders from Europe and Israel. I had the idea around Christmas and have been pounding Google Antigravity ever since. As a former CTO, who hasn’t been building digital products for the 20 years, this is such a wonderful feeling to build again. And this time I am annoying nobody by saying “oh, you know what, I have an idea, let’s build this now!” - being fully aware that this might lead to massive refactoring.
Please test out GRID - I think it is a really valuable resource for startup founders. We have more than 10.000 Venture Capital firms in our database and more than 30.000 VC professionals with their investment focus. I believe this will lead to better fundraising experiences for the founders. Also, they can get their DIALECTIC score to figure out how VC will look at their deck when making an investment decision.

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What the Anthropic AI safety saga is really all about

Now, the biggest near-term consequence for Anthropic is likely how clients and potential customers value and trust the company, said Owen Daniels, associate director of analysis at Georgetown’s Center for Security and Emerging Technology.

Anthropic said its self-imposed safety measures were always meant to be flexible and subject to change as AI evolves. It pledged to be transparent about safety in the future and said it really didn’t have a choice: If it stopped growing, rivals that don’t value safety as much could push ahead and make AI “less safe” overall.

This topic is really interesting, because thus far Anthropic has maintained a position that is pretty unique among the frontier model companies. They have put a lot of thinking into AI safety and felt like the good guys. This could change now.

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Claude Code and the Great Productivity Panic of 2026

Far from freeing up engineers for a life of leisure, increasingly capable AI coding agents—including Anthropic PBC’s Claude Code and OpenAI’s Codex—have over the past few months created a kind of productivity paranoia among executives and, by extension, the people who work for them. These agents do more than generate text or images, as consumer-facing chatbots do. Instead they plan, execute and complete tasks on behalf of their human users, even creating their own agents to do the work for them. That may mean building and debugging an app, scheduling a meeting or buying a pair of pants, all with minimal human oversight. The fact that AI agents can produce more code than mere humans in less time has morphed into a sense that they therefore must. As OpenAI’s president, Greg Brockman, recently put it on X, it “feels like such a wasted opportunity every moment your agents aren’t running.”

Yes, coding agents are absolutely changing everything. Software on Demand is such a paradigm-shift, it is so hard to understand what all of this will imply.

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The Software Industry Will Survive AI

AI is probabilistic technology, which means it’s slightly unpredictable. Underneath the surface, it is constantly guessing. This is an immutable characteristic of large language models. It explains why AI hallucinates and makes other mistakes. Many applications require total reliability, so AI needs a deterministic software layer to help with direction and guardrails. Code is becoming cheap, but mistakes aren’t, and AI isn’t reliable enough to act entirely on its own.

In one crucial way, AI-generated code will be less valuable than open source. AI code will be purpose-built, unique to the organization that wrote it, unsupported by any community or organization. If the software makes mistakes, the user will have nowhere to turn for advice or assistance.

This proves my argument that I have been telling anyone who would want to listen: the large companies will be hit hardest by AI, because they cannot change as quickly as the smaller companies, that will then get extra capabilities that will give them so much more leverage than before.

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Agentic AI, explained

While there isn’t a universally agreed upon definition of agentic AI, there are broad characteristics associated with it. While generative AI automates the creation of complex text, images, and video based on human language interaction, AI agents go further, acting and making decisions in a way a human might, said MIT Sloan associate professor

In a research paper exploring the economic implications of agents and AI-mediated transactions, Horton and his co-authors focus on a particular class of AI agents: “autonomous software systems that perceive, reason, and act in digital environments to achieve goals on behalf of human principals, with capabilities for tool use, economic transactions, and strategic interaction.” AI agents can employ standard building blocks, such as APIs, to communicate with other agents and humans, receive and send money, and access and interact with the internet, the researchers write.

I have been working with Agentic AI for about 1 1/2 years now and it is still hard to grasp what this can do.

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Agents are not thinking, they are searching

My thesis is that these models are searching toward a reward signal, and your environment bounds that search. Framing it as “thinking” is noise. These models spit out slop even if they think their way to oblivion. When search is the mental model, the design questions change. You stop asking “did I give it good enough instructions?” and start asking “did I bound the space tightly enough that the search converges?” This essay is as much computer science and philosophy as it is software engineering.

To understand this framing, we first need to understand what goes into creating these agents, ie. pre-training and reinforcement learning. The mathematical properties of pre-training and RL help us infer how this joint interplay will work in practice. Using this better inferred scheme we can change the way we design agentic software and get better outcomes from it. Finally, I will discuss some of the consequences that come with this easy access to create cheap software.

I love a well-argued counterpoint.

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It is another week of interesting developments in the wonderful world of AI and I picked five articles that you should read because they offer interesting and diverse viewpoints. Ben Evans dissects OpenAI's precarious position: great tech and talent, but no durable moat like Google or Apple once had, pushing Sam Altman to stack strategic bets from ads to full-stack infrastructure before commoditization hits. Gary Marcus skewers AGI hype as benchmark illusions masking the gap between task tricks and true generality, fueled by investor bait for datacenter billions. Scout AI's agent swarm deploys drones to explode targets via hierarchical 100B+ parameter models on air-gapped gear, escalating autonomous warfare to nightmare efficiency. The Guardian flags AI-fueled job angst echoing Covid-era union surges and Great Resignation power plays. The Register coins "semantic ablation" as RLHF's sneaky entropy killer, turning sharp prose into bland, low-friction mush via greedy decoding that favors safe clichés over substance. Whenever Benedict Evans or Gary Marcus drop truth bombs, read them; the rest spotlights AI's dual edges from boom hype to boom sticks.

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Enjoy this edition of Five Things AI!

How will OpenAI compete?

OpenAI does still at least arguably set the agenda for new models, and it has a lot of great technology and a lot of clever and ambitious people. But unlike Google in the 2000s or Apple in the 2010s, those people don’t have a thing that really really works already that no-one else can do. I think that one way you could see OpenAI’s activity in the last 12 months is that Sam Altman is deeply aware of this, and is trying above all to trade his paper for more durable strategic positions before the music stops.

Whenever Benedict Evens sits down to write something about the industry, you should read it.

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Rumors of AGI’s arrival have been greatly exaggerated

Rumors that humanity has already achieved artificial general intelligence (AGI) have been greatly exaggerated. Such rumors are often fueled by recent advances in large language models (LLMs), whose outputs show strong benchmark performance, high fluency across domains, and, in some cases, correct solutions to open problems in mathematics. These developments are often taken as evidence that general intelligence has been achieved.

Such interpretations rest on a fundamental confusion between performance on individual, often well-known tasks and intelligence writ large. Task-level performance, even when impressive, is not sufficient evidence of general intelligence.

Without the hype around AGI, it would be much harder to convince investors to pour money into data-centers everywhere.

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This Defense Company Made AI Agents That Blow Things Up

A relatively large AI model with over a 100 billion parameters, which can run either on a secure cloud platform or an air-gapped computer on-site, interprets the initial command. Scout AI uses an undisclosed open source model with its restrictions removed. This model then acts as an agent, issuing commands to smaller, 10-billion-parameter models running on the ground vehicles and the drones involved in the exercise. The smaller models also act as agents themselves, issuing their own commands to lower-level AI systems that control the vehicles’ movements.

Seconds after receiving marching orders, the ground vehicle zipped off along a dirt road that winds between brush and trees. A few minutes later, the vehicle came to a stop and dispatched the pair of drones, which flew into the area where it had been instructed that the target was waiting. After spotting the truck, an AI agent running on one of the drones issued an order to fly toward it and detonate an explosive charge just before impact.

This is scary stuff. Then again, war has always been about access to better technology than the enemy, but this is taking it to a whole other level.

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How the anxiety over AI could fuel a new workers’ movement

People across industries and income brackets are anxious and frustrated, quite like they were when the Covid pandemic placed punishing demands on frontline workers and erased the boundaries between work and life for everyone else. Those struggles prompted power shifts: at the same time that workers led unionization efforts at Amazon warehouses and Starbucks locations around the US, the Great Resignation saw a record number of employees quit their jobs, and the ones who remained in the workforce began negotiating for and gaining better pay and conditions.

Workers rightly fear about their jbs being changed drastically or just being cut.

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Why AI writing is so generic, boring, and dangerous: Semantic ablation

Semantic ablation is the algorithmic erosion of high-entropy information. Technically, it is not a “bug” but a structural byproduct of greedy decoding and RLHF (reinforcement learning from human feedback).

During “refinement,” the model gravitates toward the center of the Gaussian distribution, discarding “tail” data – the rare, precise, and complex tokens – to maximize statistical probability. Developers have exacerbated this through aggressive “safety” and “helpfulness” tuning, which deliberately penalizes unconventional linguistic friction. It is a silent, unauthorized amputation of intent, where the pursuit of low-perplexity output results in the total destruction of unique signal.

This is an interesting observation. I can still spot Reddit posts from bots from a mile away, but I assume that this technology will get better soon.

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