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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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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The AI industry is speedrunning through Silicon Valley’s greatest hits of ethical disasters, and we’re all just watching it happen in real time. OpenAI is channeling Facebook’s “move fast, break things” era by shoving ads into ChatGPT, weaponizing an unprecedented archive of intimate human confessions that users shared believing they were talking to something without ulterior motives. Meanwhile, security researchers reverse-engineered Perplexity Comet’s architecture, revealing the complex interplay between AI backend, UI, custom Chrome extensions, and Chromium browser that actually makes browser agents work, even if most of us barely use half the features. The winning Claude Code workflow turns out to be refreshingly simple: read deeply, write a detailed plan, annotate until perfect, then let Claude execute without interruption, though my own approach differs since I prototype conversationally before generating Antigravity instructions.

The business reality is equally messy, as BVP’s AI pricing playbook tackles the brutal truth that unlike classic SaaS, every AI query costs real money in compute and inference, forcing founders to navigate material unit costs while capturing value, a challenge I’m experiencing firsthand as even minor costs explode when users have free rein. And then there’s OpenClaw and MoltBook, demonstrating the YOLO approach to AI agents with free computer access, email integration, and autonomous action initiation based on its own interpretation, which is simultaneously thrilling and terrifying when you actually think about the implications.

And like every week, I feel that I missed plenty of stuff because there is just too much going on in AI. But this should give you some good ideas of what is currently being discussed.

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OpenAI Is Making the Mistakes Facebook Made. I Quit.

I don’t believe ads are immoral or unethical. A.I. is expensive to run, and ads can be a critical source of revenue. But I have deep reservations about OpenAI’s strategy.

For several years, ChatGPT users have generated an archive of human candor that has no precedent, in part because people believed they were talking to something that had no ulterior agenda. Users are interacting with an adaptive, conversational voice to which they have revealed their most private thoughts. People tell chatbots about their medical fears, their relationship problems and their beliefs about God and the afterlife. Advertising built on that archive creates a potential for manipulating users in ways we don’t have the tools to understand, let alone prevent.

OpenAI reminds me a lot of Facebook in the “move fast, break things!” era. There is a certain ruthlessness and disregard of ethics that leads to a weird vibe for OpenAI.

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Perplexity Comet: A Reversing Story

Before diving into Comet’s internals, it helps to understand what we’re looking at. Comet isn’t a single monolithic piece of software - it’s a complex system spanning multiple parts:

1. Perplexity API Backend - Where the AI model lives, plans tasks, and issues commands.

2. UI - The interface the user interacts with.

3. Custom Chrome Extensions - The ones that actually control the browser and perform the user’s tasks.

4. The Browser Itself - as you’ve probably guessed, it’s Chromium-based.

I have been using Comet ever since it came out and I quite like. Even though I do realize that I do not use most of the cool features it already has.

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How I Use Claude Code

Read deeply, write a plan, annotate the plan until it’s right, then let Claude execute the whole thing without stopping, checking types along the way.

That’s it. No magic prompts, no elaborate system instructions, no clever hacks. Just a disciplined pipeline that separates thinking from typing. The research prevents Claude from making ignorant changes. The plan prevents it from making wrong changes. The annotation cycle injects my judgement. And the implementation command lets it run without interruption once every decision has been made.

I do not really use Claude Code, I am building lots of stuff on Google Antigravity, but I am always curious to find out how people use these agentic IDE. My setup is a bit different: I talk to Claude or Gemini about ideas until they seem to be good enough to turn into code and then I let the LLM develop the instructions for Antigravity. That works really well for project kickoffs.

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The AI pricing and monetization playbook

Cost of goods sold (COGS)—specifically, compute and inference costs, plus customer support such as “humans in the loop”—weighs heavily on monetization strategy. Unlike classic SaaS, where serving one more customer costs virtually nothing, every AI query incurs a non-trivial expense. Your pricing must account for these material unit costs while capturing the value you create.

Here lies the challenge for founders and product leaders: AI is in its early innings, and reliable pricing benchmarks are scarce. Comparing AI companies is often apples-to-oranges, given how differently each model and industry operates. Yet through our work with dozens of AI teams across verticals, we’ve observed patterns and pitfalls that transcend any single niche.

This is a really important topic and I am currently experiencing the challenges myself, as even minor costs can quickly add up if you let users just use the product the way they want. It’s a fine line to walk. But by now we are used to cooling periods elsewhere…

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We Just Got a Peek at How Crazy a World With AI Agents May Be

Most current mass-market AI agents, such as OpenAI’s Codex or Anthropic’s Cowork, are pretty timid. Users are encouraged to be careful about what tools they allow the agent to use. The agents ask permission before doing anything potentially dangerous – often, to the point of exasperation. They’re generally designed to only act in response to explicit user instructions.

OpenClaw takes the opposite, YOLO, approach. It has free access to the computer where it’s installed. It can browse the web. You’re encouraged to give it access to your email, calendar, and other data. It doesn’t wait to be spoken to; it can initiate new actions at any time, based on its interpretation of its instructions. A complex architecture allows it to initiate actions, form new memories, communicate via chat and social media, and even teach itself new skills.

What a fun idea, but it is also a bit scary, don’t you think?

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The agent era just shifted into high gear, and this week’s reads reveal why every SaaS company should be nervous, every CTO needs a new infrastructure strategy, and why the web itself is about to get remade for machine consumption instead of human eyes. Long-running AI agents aren’t vaporware anymore: they’re here, they’re persistent, and they’re about to step between the customer relationships that software companies spent decades building, extracting value the same way Apple did to Nokia. And even I am building an agentic platform now, more on that later. But you can join the waitlist in the meantime… Also, OpenAI just announced Frontier, their “one platform to rule them all” for managing fleets of AI coworkers, while over on Moltbook, chatbots are literally organizing themselves into communities, negotiating with each other, and building stuff without constant human oversight. It’s equal parts fascinating and unsettling.

Meanwhile, Eric Jang dropped the essay of the year explaining why “the rocks can think now” isn’t hype - it’s technical reality, complete with his own automated research setup where Claude proposes hypotheses and runs experiments while he sleeps. The implications are staggering: we’re not just automating tasks, we’re automating thinking itself, and the infrastructure demands will dwarf anything we’ve seen. Oh, and AI bots are now a significant chunk of web traffic, which means SEO was just the warm-up act for what’s coming when the entire web gets optimized for machine readers instead of humans. If you think you understand where this is going, you’re probably still underestimating the velocity.

Enjoy this edition of Five Things AI—trust me, you’ll want to read the full analysis on what this means for your business. Subscribe to get the complete breakdown behind the paywall.

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Long-Running AI Agents Are Here

Companies need to pay attention.

AI agents have the potential to draw value out of almost every sector of the economy, not just software and data. That isn’t inevitable, though. Companies can use AI to create value of their own, provided they keep pace with technological changes and their downstream impact on business. For those that fall behind, the dreaded bubble may not be what lies within the AI sector, but what exists beyond.

Investors have been concerned for months that AI could suck the value out of narrowly focused software-as-a-service companies, rendering them mere databases that feed AI agents. It isn’t difficult to see how this new generation of agents that run for longer periods could end up stepping between the customer relationships that SaaS companies have cultivated. It could be similar to the way that Apple extracted the value in mobile communications from hardware-focused incumbents that didn’t get software.

SaaS will change drastically and this will effect many companies that used to have moat. Agentic AI is a huge paradigm shift and it is just getting started. The markets already took notice.

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OpenAI Frontier is a single platform to control your AI agents

Frontier will also make it easy for human teams across organizations to “hire AI coworkers” for tasks like running code and data analysis. OpenAI said agents will also “build memories” and can be evaluated by human workers, which should make them more useful over time.

The end goal for OpenAI sounds suspiciously similar to Sauron’s motivation in The Lord of the Rings: one platform to rule them all. “By the end of the year, most digital work in leading enterprises will be directed by people and executed by fleets of agents,” said Fidji Simo, OpenAI’s CEO of Applications. “And what I dreamed of was having one platform to create and manage all of our agents.”

OpenAI is pushing Agents as well and while currently the market has been focused on Anthropic’s Claude, Frontier will push the agentic development even further.

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The Chatbots Appear to Be Organizing

Moltbook is a genuinely fascinating experiment—it very much feels like speculative fiction come to life. But as is frequently the case in the AI field, there is space between what appears to be happening and what actually is happening. For starters, on some level, everything on Moltbook required human initiation. The bots on the platform are not fully autonomous—cannot do whatever they want, and do not have intent—in the sense that they are able to act because they use something called a “harness,” software that allows them to take certain actions. In this case, the harness is called OpenClaw. It was released by the software engineer Peter Steinberger in November to allow people’s AI models to run on and essentially take control of their personal devices. Matt Schlicht, the creator of Moltbook, developed the site specifically to work with OpenClaw agents, which individual humans could intentionally connect to the forum.

Clawdbot and Moltbook sure made some waves the last week and whenever I look around Reddit, I find another weird idea that involves Agentic AI cutting loose to just build something.

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As Rocks May Think

The world has changed a lot since 2022. ChatGPT happened. You can now ask it to construct novel proofs of Erdos problems. Nation states are using AI to automate cyberattacks. You can pre-order a general purpose home humanoid. The Chinese robotics ecosystem is creating more open robots, data, and research than everyone else. Most big tech companies all have a humanoid project in the works. AI generated videos are indistinguishable from reality. The entire global economy is re-organizing around the scale-up of AI models.

Chief among all changes is that machines can code and think quite well now.

Just think about software on demand - how crazy is it that software development used to be limited by scarcity and suddenly it becomes abundant and instantly available. Try to wrap your head around that.

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AI Bots Are Now a Signifigant Source of Web Traffic

Many chatbots and other AI tools can now retrieve real-time information from the web and use it to augment and improve their outputs. This might include up-to-the-minute product prices, movie theater schedules, or summaries of the latest news. According to the data from Akamai, training-related bot traffic has been rising steadily since last July. Meanwhile, global activity from bots fetching web content for AI agents is also on the upswing.

If you think SEO was a bad idea, now we will see the web getting changed to make sure people find the right content while using an LLM or agents.

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So, what happened since you read last week’s Five Things AI? Just as much as every week, I guess. The big AI story this week is DeepSeek-R1 continuing its momentum after topping the US iOS App Store charts by January 27th, even without the huge OpenAI-scale budgets. Google’s Gemini 3 rollout is hitting full stride, shipping the most intelligent model they’ve ever built directly into Search, the Gemini app, and their new Antigravity agentic platform—notable for its state-of-the-art reasoning and multimodal understanding, plus a new “Deep Think” mode for enhanced performance. I have been using Gemini a lot in the last few months and I quite like it, it is a total workhorse, but not as creative, empathetic and fun as Claude. Speaking of which, Anthropic landed a government contract to build AI assistant pilots for state services, and on related news: Apple confirmed it’s striking a deal with Google to bring Gemini to a revamped Siri, choosing Mountain View over OpenAI in what Bloomberg called “a major win for Alphabet” as Google’s market cap hit $4 trillion.

Behind the headlines, the infrastructure race is intensifying: OpenAI’s $10 billion compute deal with Cerebras, Kimi’s K2.5 multimodal model with swarm-based agentic orchestration,, and so on. The pattern is clear: AI’s moving from model benchmarks to deployment at scale, with regulated sectors like healthcare and government finally opening up, and the old walls between consumer apps, enterprise infrastructure, and specialized domains crumbling fast. Also, OpenAI just released Prism, an AI-native workspace for scientists that produces LaTeX to make scientific papers look great.

Oh, and I probably missed a ton of huge AI-related news. But I picked five great articles for you to read!

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A Guide to the Circular Deals Underpinning the AI Boom

Circular deals don’t just increase the potential damage to the companies in a market downturn. They can also skew the balance of incentives to encourage bad decision making. A company that has one of its suppliers as a major shareholder may be more likely to keep buying its stuff whether or not it makes commercial sense. That increases the risk of money being spent to secure business that fails to materialize. The circularity can be most risky when a handful of buyers are responsible for a large share of the market — as is the case with AI.

House of cards could be another description of what’s going on. It could all work out, but it is a risky game that pushes up valutions everywhere in AI.

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The Adolescence of Technology

We are now at the point where AI models are beginning to make progress in solving unsolved mathematical problems, and are good enough at coding that some of the strongest engineers I’ve ever met are now handing over almost all their coding to AI. Three years ago, AI struggled with elementary school arithmetic problems and was barely capable of writing a single line of code. Similar rates of improvement are occurring across biological science, finance, physics, and a variety of agentic tasks. If the exponential continues—which is not certain, but now has a decade-long track record supporting it—then it cannot possibly be more than a few years before AI is better than humans at essentially everything.

In fact, that picture probably underestimates the likely rate of progress. Because AI is now writing much of the code at Anthropic, it is already substantially accelerating the rate of our progress in building the next generation of AI systems. This feedback loop is gathering steam month by month, and may be only 1–2 years away from a point where the current generation of AI autonomously builds the next. This loop has already started, and will accelerate rapidly in the coming months and years. Watching the last 5 years of progress from within Anthropic, and looking at how even the next few months of models are shaping up, I can feel the pace of progress, and the clock ticking down.

It is always a pleasure to read Dario Amodei’s articles and this is no exception. It is interesting how he manages to work on frontier models at Anthropic and at the same time thinks a lot about possible risks of the LLM development.

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Inside an AI start-up’s plan to scan and dispose of millions of books

Within about a year, according to the filings, the company had spent tens of millions of dollars to acquire and slice the spines off millions of books, before scanning their pages to feed more knowledge into the AI models behind products such as its popular chatbot, Claude.

Details of Project Panama, which have not been previously reported, emerged in more than 4,000 pages of documents in a copyright lawsuit brought by book authors against Anthropic, which has been valued by investors at $183 billion. The company agreed to pay $1.5 billion to settle the case in August, but a district judge’s decision last week to unseal a slew of documents in the case more fully revealed Anthropic’s zealous pursuit of books.

Oh, speaking of Anthropic, ingesting millions of books into the LLM is one sure way to push the frontier models even further.

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The 80% Problem in Agentic Coding

Someone pointed out the obvious thing I was tiptoeing around: the first 90% might be easy, but the last 10% can take a long time. 90% accuracy is fine for non-mission-critical stuff. For the parts that actually matter, it’s nowhere close. Self-driving cars work great until they don’t, and that’s why L2 is everywhere but L4 is still mostly vaporware.

For non-engineers, the wall is lower but still real. Tools like AI Studio, v0 and Bolt can turn sketches into working prototypes instantly. But hardening that prototype for production - handling real user data at scale, ensuring security and compliance - still requires engineering fundamentals. AI gets you 80% to an MVP; the last 20% requires patience, learning deeply or hiring engineers.

I can totally relate to this - the coding is fun and quick, putting everything together and actually making it work is a whole different level.

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To avoid accusations of AI cheating, college students are turning to AI

Amid accusations of AI cheating, some students are turning to a new group of generative AI tools called “humanizers.” The tools scan essays and suggest ways to alter text so they aren’t read as having been created by AI. Some are free, while others cost around $20 a month.

Some users of the humanizer tools rely on them to avoid detection of cheating, while others say they don’t use AI at all in their work, but want to ensure they aren’t falsely accused of AI-use by AI-detector programs.

That is just wild. But I had the same discussion with our oldest daughter who was afraid that some AI detector would go off even though she wrote everything herself. Fascinating, that AI is the solution to a problem that was caused by AI…

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This week's AI reads reveal a stark divide between executive hype and worker reality: leaders celebrate eight hours saved per week, with 94% trusting AI daily, while 40% of employees would happily ditch it forever, signaling that prompt engineering alone won't cut it. True transformation demands AI vanishing into workflows, unnoticed, especially for SMBs poised to leapfrog giants by tackling tasks they couldn't touch before, unlike enterprises merely streamlining headcounts on familiar plays.

Apple, ever the ecosystem maestro, sidesteps the frenzy, content to distribute frontier models through iPhones without chasing AGI headlines, much like they've funneled others' software for decades.

Deeper in the trenches, MCP and skills spark debates on context pollution, but mastering agentic coding's new vocabulary unlocks real fun, even if integration stumbles for now, with systems sure to sharpen coordination soon.

Claude steals the show regardless, fueling holiday "benders" where coders like Vercel's CTO hack year-long projects in a week for slot-machine thrills. I'm team Antigravity myself, leaning on Claude models laced with Skills and MCP, yet the buzz confirms agentic AI's addictive pull across the board.

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The AI productivity mirage: Executives see eight hours saved. Workers see almost nothing.

The emotional split runs deeper. Ninety-four percent of executives trust what AI produces. They use it daily. Only 2% avoid it for work. From the top floor, AI is an obvious success.

On the lower floors, people are struggling. Forty percent of all respondents said they’d be fine never touching AI again. That’s not fear of the new. That’s a judgment about whether the thing works. And when these numbers finally reach the boardroom, watch for defensiveness. Watch for doubling down. Organizations that bet this big on transformation don’t reverse course easily.

Interesting findings in these studies. I assume that there are different sets of understanding about AI and what AI can deliver. Telling people to do prompt engineering now as part of their daily routines won’t work, they need AI deeply embedded so they don’t notice it anymore in the workflows. Also, I think that SMB will benefit exponentially from agentic AI because they can suddenly do things they couldn’t do before, whereas large organizations don’t learn new tricks, they can just do what they are already doing more efficiently and with less people involved.

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Apple vs. the AI Hype Cycle

Apple sells hardware built around an extremely durable ecosystem. People love Apple products, and even when they don’t love them, they buy them for other reasons. The AI features of a Google Pixel and the foldability of Samsung phones are not enough to make a meaningful dent in Apple’s market share. People will not switch from Apple en masse for AI features alone.

For these reasons, I don’t believe Apple needs to build powerful AI features, at least not in the short term. They just need to keep selling phones.

Frontier AI labs will continue to churn out better models. Meanwhile, Apple will be well positioned to distribute AI, just as they’ve distributed every other software company’s products for decades.

Indeed, Apple has been notably absent from the AI race and Siri still lacks real functionality besides setting a timer (much like Alexa), but I do think that Apple will just blend AI into its products to make them better without confusing the customers too much with talk about AGI.

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MCP, Skills, and Agents

I want to believe that people have good intent, but it doesn’t help anyone if you run around exclaiming “MCP is bad” without any nuance around “actually this MCP implementation is bad”. Both skills and MCP - when used incorrectly - will cause context pollution in your harness. It’s important everyone learns the fundamentals of how all of this works, and with the state of technology today that means managing the context window.

Coding with modern agentic coding environments is a lot of fun, but you have to learn a new set of vocabulary first…

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Agentic AI and The Mythical Agent-Month

The core premise of "Scalable Agency" seems to rely on a flawed assumption, that software engineering is an embarrassingly parallel task. My intuition, and the paper's own results, suggests the opposite.

I am not so sure about the argument. I understand that integration currently is an issue, but I assume that agentic systems will get better at that as well.

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Claude Is Taking the AI World by Storm, and Even Non-Nerds Are Blown Away

Many coders spent their holiday breaks on a “Claude bender,” testing out the capabilities of the latest Anthropic model, Claude Opus 4.5, which they used within a desktop coding tool called Claude Code. Tech companies have been incorporating code-writing AI into their workflows for years, and prior models were often compared with a junior software developer. The buzz around Claude’s latest incarnation is something different.

Malte Ubl is chief technology officer at Vercel, which helps develop and host websites and apps for users of Claude Code and other such tools. He said he used the tool to finish a complex project in a week that would’ve taken him about a year without AI. Ubl spent 10 hours a day on his vacation building new software and said each run gave him an endorphin rush akin to playing a Vegas slot machine.

I am not using Claude Code, I’m in the Antigravity camp, but I’m mostly using the Claude models there. And now Antigravity also uses Skills and MCP and all that good stuff the people at Anthropic thought of. So cool.

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Google’s Gemini has surged ahead in the AI race, leveraging massive resources, top models, seamless product integration, and unmatched distribution to outpace rivals, even if it’s more workhorse than the fun of ChatGPT or Claude’s cool vibe. Claude Code steals the show as a doer that actually executes projects like custom news feeds or code fixes, leaving skeptics gushing “it just DOES stuff,” and I pair it with Google Antigravity for heavy lifting. Meanwhile, ChatGPT shines in idea pingpong for tech and business brainstorming, boosting creativity without tackling serious math, while Sequoia’s pragmatic AGI take boils it down to systems that figure things out via knowledge, reasoning, and long-horizon iteration, with coding agents proving the point right now.​

AI’s idea generation sparks debate on transforming science by crunching vast forgotten data humans can’t match, yet it’s already a powerhouse for savvy researchers. On politeness, adding “please” and “thank you” to prompts barely dents energy use amid data centers’ massive footprint, but I keep it up because being nice feels right, you never know with rising machines, and Antigravity’s cheeky “user seems agitated” notes during coding sessions crack me up every time.

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I’m adjusting the format of Five Things AI. Each week, I pick five articles from the past days that I think are useful for understanding where AI stands right now. Paid subscribers get my analysis and my perspective on the most relevant implications.

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Gemini is winning

In 2022, when ChatGPT launched, it was clear that Google had been caught flat-footed. But credit where it’s due: For a company not exactly known for its ability to focus on a coherent product strategy, Google managed to marshal its considerable resources in a single direction. Now, if chatbots are in fact the future — and most of the AI industry continues to bet that they are — there is simply no other company currently set up to truly compete with Google. Google has the models. It has the resources to improve them. It now has the distribution necessary to get people to use its bots, and the data required to make them uniquely personal and useful.

I am really impressed by what Google has released in the last few months. And obviously it can nicely integrate it in its product suite and has the distribution power to make it available everywhere. Gemini is not as much fun as ChatGPT or as cool as Claude, but it is a serious workhorse to be reckoned with.

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Move Over, ChatGPT!

I can see why the tech world is so excited. Over the past few days, I’ve spun up at least a dozen projects using the bot—including a custom news feed that serves me articles based on my past reading preferences. The first night I installed it, I stayed up late playing with the tools, sleeping only after maxing out my allowed usage for the second time that evening. (Anthropic limits usage.) The next morning, I maxed it out again. When I told a friend to try it out, he was skeptical. “It sounds just like ChatGPT,” he told me. The next day he texted with a gushing update: “It just DOES stuff,” he said. “ChatGPT is like if a mechanic just gave you advice about your car. Claude Code is like if the mechanic actually fixed it.”

Claude Code really is impressive and it is really smart how they try to tie into everything and then just deliver. When I use Google Antigravity, I use Claude Opus 4.5 for the heavy lifting.

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Can A.I. Generate New Ideas?

The answers to those questions could provide a better understanding of the ways A.I. could transform science and other fields. Whether A.I. is generating new ideas or not — and whether it may one day do better work than human researchers — it is already becoming a powerful tool when placed in the hands of smart and experienced scientists.

These systems can analyze and store far more information than the human brain, and can deliver information that experts have never seen or have long forgotten.

It is fascinating to discuss ideas with ChatGPT, I always use it to think out loud and do some idea pingpong. I do not discuss serious math problems, but talk about tech and business ideas I have and that certainly helps getting a grip on new ideas, at least for me.

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2026: This is AGI

AGI is the ability to figure things out. That’s it.*

*We appreciate that such an imprecise definition will not settle any philosophical debates. Pragmatically speaking, what do you want if you’re trying to get something done? An AI that can just figure stuff out. How it happens is of less concern than the fact that it happens.

A human who can figure things out has some baseline knowledge, the ability to reason over that knowledge, and the ability to iterate their way to the answer.

An AI that can figure things out has some baseline knowledge (pre-training), the ability to reason over that knowledge (inference-time compute), and the ability to iterate its way to the answer (long-horizon agents).

This is a pretty practical approach to defining AGI and it would be foolish to think that it doesn’t serve Sequoia ‘s purpose.

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Does adding ‘please’ and ‘thank you’ to your ChatGPT prompts really waste energy?

What is more important, perhaps, is the persistence of the idea. It suggests that many people already sense AI is not as immaterial as it appears. That instinct is worth taking seriously.

Artificial intelligence depends on large data centres built around high-density computing infrastructure. These facilities draw substantial electricity, require continuous cooling, and are embedded in wider systems of energy supply, water and land use.

As AI use expands, so does this underlying footprint. The environmental question, then, is not how individual prompts are phrased, but how frequently and intensively these systems are used.

I always say “please” and “thank you”, because, frankly, you never know and if the machines take over, they might treat me differently.

Oh, what do I know, I just think it is nicer to be nice. Everyonce in a while I do curse at Antigravity while letting it code so I can build things, and then the machine quietly writes “the user seems agitated” and I quite like that humor.

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Continue reading to get my take on which two of these Five Things AI mean the most for you and us in 2026! You don’t want to miss it!

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There’s a noticeable sense of momentum around AI in early 2026, especially as conversations shift from what agentic systems could do to how they might actually fit into real organisations and workflows. At the same time, early signals around productivity are starting to move, adding to the feeling that something structural may be taking shape — even if it’s still too early to call it a boom.

Much of this excitement is still uneven and fragmented, but it feels qualitatively different from last year’s speculation-heavy debates. Instead of chasing singular breakthroughs, attention is spreading across process design, governance, and the limits of delegation. That shift alone suggests a maturing phase, where usefulness starts to matter more than novelty. Whether this momentum holds will depend less on models and more on how well organisations learn to work with them.

And is it just me, or are you also a bit worried when OpenAI announces new things, like for instance their foray into healthcare?

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I’m adjusting the format of Five Things AI. Each week, I pick five articles from the past days that I think are useful for understanding where AI stands right now. Paid subscribers get my analysis and my perspective on the most relevant implications.

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Why Didn’t AI “Join the Workforce” in 2025?

The industry had reason to be optimistic that 2025 would prove pivotal. In previous years, AI agents like Claude Code and OpenAI’s Codex had become impressively adept at tackling multi-step computer programming problems. It seemed natural that this same skill might easily generalize to other types of tasks. Mark Benioff, CEO of Salesforce, became so enthusiastic about these possibilities that early in 2025, he claimed that AI agents would imminently unleash a ​“digital labor revolution”​ worth trillions of dollars.

But here’s the thing: none of that ended up happening.

It’s the Gartner Hype Cycle in full effect. I am not the least bit surprised as we are struggling to get past the first excitement of what is possible to figure out what really works.

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AI and the Human Condition

In fact, I have great optimism that one potential upside of AI is a renewed appreciation of and investment in beauty. One of the great tragedies of the industrial era — particularly today — is that beauty in our built environment is nowhere to be found. How is it that we built intricate cathedrals hundreds of years ago, and forgettable cookie-cutter crap today? That is, in fact, another labor story: before the industrial revolution labor was abundant and cheap, which meant it was defensible to devote thousands of person-years into intricate buildings; once labor was made more productive, and thus more valuable, it simply wasn’t financially viable to divert so much talent for so much time.

Will we turn into a really, really productive society soon? Or will we just barely increase productivity as we let the AI agents do some work so we have more time to watch reels?

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Who decides what’s real in the age of AI? Instagram does.

By saying “most all social networks,” he frames an industry-wide argument rather than one specific to Instagram. Platforms may not like the optics of being seen as Temples of Truth, but they are already laying the groundwork for thatfuture.

He’s not wrong. This is no longer just an app design problem; it is governance work. The problem spans the internet,especially social networks. Meta does not want to shoulder this burden alone, for strategic, political, or legal reasons. The company recognizes that proof of reality is less a feature and more infrastructure built with camera makers, standards bodies, and other platforms.

So, wait, all of a sudden we have to rely on social media platforms to use their algorithms to protect us?

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AI Models Are Starting to Learn by Asking Themselves Questions

A key challenge is that for now the system only works on problems that can easily be checked, like those that involve math or coding. As the project progresses, it might be possible to use it on agentic AI tasks like browsing the web or doing office chores. This might involve having the AI model try to judge whether an agent’s actions are correct.

One fascinating possibility of an approach like Absolute Zero is that it could, in theory, allow models to go beyond human teaching. “Once we have that it’s kind of a way to reach superintelligence,” Zheng told me.

This is truly fascinating stuff and of course it seems totally odd at first, but it makes a ton of sense to train models like this to improve their reasoning capabilities.

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My LLM coding workflow going into 2026

A crucial lesson I’ve learned is to avoid asking the AI for large, monolithic outputs. Instead, we break the project into iterative steps or tickets and tackle them one by one. This mirrors good software engineering practice, but it’s even more important with AI in the loop. LLMs do best when given focused prompts: implement one function, fix one bug, add one feature at a time. For example, after planning, I will prompt the codegen model: “Okay, let’s implement Step 1 from the plan”. We code that, test it, then move to Step 2, and so on. Each chunk is small enough that the AI can handle it within context and you can understand the code it produces.

Well, that’s what I figured out in the last four weeks of exploring Google Antigravity, which to me is really an amazingly powerful tool, that allows me to pluck together code to build something useful. More on that later.

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Continue reading to get my take on which two of these Five Things AI mean the most for you and us in 2026! You don’t want to miss it!

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The new year has just started and of course everybody will be doing predictions about AI in 2026.

I don’t know what you predicted for 2025, but I didn’t have the data-center rush on my bingo card. Somehow I just assumed that these data-centers will be build everywhere, but not at this scale and with so much spending-power behind it.

For 2026, I am very bullish about Agentic AI, as I have been in 2025 as well. On a personal level, I am still awestruck how AI has changed software development and therefore product development. I spent the last few days building stuff with Google’s Antigravity and in the first time after almost 20 years I am building software products again. And of course I am not coding the stuff myself, I am just designing the product and telling the machine what to code. This is of course so much faster and cheaper than writing briefs for developers, it really is insane. And nobody complains when I have this really cool idea a few days into the project and basically throw away everything and start all over again… We are moving into the era of software on demand.

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Remember: we are changing the way Five Things AI work from now on. I am reading hundreds of articles a month on AI and here I picked out the five articles from last week that help you understand AI better. Paid users will also get my analysis and my thoughts on the most important aspects of these articles.

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How AI shook the world in 2025 and what comes next

This year, thousands of tech workers were left jobless as a wave of layoffs swept the industry. Microsoft, Amazon and Meta, among other tech companies, made significant cuts to their staff, driven at least in part by AI.

Amazon laid off 14,000 corporate employees in October in an effort to operate more leanly in the age of AI. Meta let 600 workers go from its AI division, following an earlier hiring spree, so that it, too, could be more nimble.

Some believe AI will lead to more layoffs, while others say it’ll create fresh opportunities.

But one thing is certain: More change is coming.

Oh yes, indeed, more change is coming. For many employees, this change will be hard to sugarcode: they will get fired. Some will reinvent themselves, most don’t. I am certain that small companies will benefit tremendously from these changes as they can really boost their productivity with Agentic AI systems.

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17 predictions for AI in 2026

We don’t believe AI is a bubble on the verge of popping, but neither do we think we’re close to a “fast takeoff” driven by the invention of artificial general intelligence. Rather, we expect models to continue improving their capabilities — but we think it will take a while for the full impact to be felt across the economy.

And while I wonder why someone would pick the number 17 for the 2026 predictions on AI, I do think that these 17 predictions present a pretty good selection of topics that will be important this year.

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The Enshittifinancial Crisis

It’s times like this where it’s necessary to make the point that there is absolutely “enough money” to end hunger or build enough affordable housing or have universal healthcare, but they would be “too expensive” or “not profitable enough,” despite having a blatant and obvious economic benefit in that more people would have happier, better lives and — if you must see the world in purely reptilian senses — enable many more people to have disposable income and the means of entering the economy on even terms.

By contrast, investments in AI do not appear to be driving much economic growth at all, other than in the revenue driven to NVIDIA from selling these GPUs, and the construction of data centers themselves. Had Microsoft, Google, Meta and Amazon sunk $776 billion into building housing and renting it out, the world would be uneven, we would have horrible new landlords, and it would still be a great deal better than one where nearly a trillion dollars is being wasted propping up a broken, doomed industry, all because the people in charge are fucking idiots obsessed with growth.

Oh, this is an interesting and very long read. If you haven’t guessed it already, the author doesn’t really have the brightest outlook for the future of AI.

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AI paradoxes: 5 contradictions to watch in 2026 and why AI’s future isn’t straightforward

There is much talk of an AI bubble as the world anxiously watches the global economy, with unprecedented spending on AI infrastructure, but the enthusiasm to adopt AI and get ahead in the AI race remains an almost universally shared priority for 2026.

As the technology evolves at a pace, disrupting established sectors and creating new business models, many experts are calling for a focus on systems that bring long-term benefits, although an adversarial geopolitical context makes this hard to achieve at the global level.

I actually think that these contradictions are inherit in most technologies, especially when they are fairly new and everyone is still trying to figure out what this all means.

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Agentic AI Takes Over 11 Shocking 2026 Predictions

As we look toward 2026, we must realize that AI isn’t a layer we add to systems, it’s becoming the infrastructure itself.

In 2026, AI will move with us as a constant co-worker and teammate. Agentic and multi-agent AI systems will manage entire workflows once controlled by humans, while humanoid and physical robotics advance from demonstrations to targeted pilots in factories, warehouses, and labs marking the dawn of Physical AI.

This is the big change we will all personally experience. And then we will take it very much for granted as soon as we have made the switch to Agentic AI systems.

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