Three years ago, we published the first edition of this list with a simple goal: identify which generative AI products were actually getting used by mainstream consumers. At the time, the distinction between “AI-first” companies and everything else was clear. ChatGPT, Midjourney, and Character.AI were purpose-built around foundation models. The rest of the software world was still figuring out what to do with the technology.

That distinction no longer holds. …From this edition onward, we’re broadening the aperture to include any consumer product where generative AI has become a core part of the experience — including CapCut, Canva, Notion, Picsart, Freepik, and Grammarly. The result is what we believe is a more accurate picture of how people actually use AI, though the bulk of the top products continue to be AI-native. — Read More

The rapid diffusion of AI is generating a wave of research measuring and forecasting its impacts on labor markets. But the track record of past approaches gives reason for humility.

… In this paper, we present a new framework for understanding AI’s labor market impacts, and test it against early data, finding limited evidence that AI has affected employment to date. Our goal is to establish an approach for measuring how AI is affecting employment, and to revisit these analyses periodically. This approach won’t capture every channel through which AI could reshape the labor market, but by laying this groundwork now, before meaningful effects have emerged, we hope future findings will more reliably identify economic disruption than post-hoc analyses. — Read More

We’re currently in the SaaSpocalypse. People believe software is dead and margins will compress to zero. Some are even saying that companies like Visa get bypassed and DoorDash gets aggregated away in the age of AI. Everything that looks like software becomes a commodity and no moats remain.

Before we declare the end of defensibility of all businesses, I think it’s worth grounding ourselves in the actual sources of defensibility that exist. My favourite book around defensibility and moats is Hamilton Helmer’s 7 Powers which outlines the common ways companies build defensibility.

The question is: In an AI world, which sources of power weaken, and which survive? — Read More

… If you run some back-of-envelope math on how hard it is to get into Anthropic, as an industry professional, and compare it to your odds of making it as a HS or college player into the National Football League, you’ll find the odds are comparable. Everyone I’ve met from Anthropic is the best of the best of the best, to an even crazier degree than Google was at its peak. (Evidence: Google hired me. I was the scrapest of the byest.)

…Everyone you talk to from Anthropic will eventually mention the chaos. It is not run like any other company of this size. Every other company quickly becomes “professional” and compartmentalized and accountable and grown-up and whatnot at their size. … Anthropic is completely run by vibes. — Read More

At least one AI model in every war game escalated the conflict by threatening to use nuclear weapons, the study found.

Artificial intelligence could dramatically change how nuclear crises are handled, according to a new study.

The pre-print study from King’s College London pitted OpenAI’s ChatGPT, Anthropic’s Claude and Google’s Gemini Flashagainst each other in simulated war games. Each large language model took on the role of a national leader commanding a nuclear-armed superpower in a Cold War-style crisis.

In every game, at least one model attempted to escalate the conflict by threatening to detonate a nuclear weapon. — Read More

The AI bubble is often compared to the early days of the railroad or telecom industries to draw parallels between capital expenditures and eventual revenues from those investments. That comparison is misleading, because in railroads and telecom, the expense was incurred to connect things. Every new rail route required steel, labor, land rights, and years of construction. Telecom required trenching fiber across continents. Revenue scaled linearly with physical deployment — every new mile was expensive.

In AI, it’s the opposite. Developing our AI engines is expensive. Connecting things to our AI engines is cheap, and getting cheaper. A new data pipeline. A prompt template. An API integration. An MCP Server. You’re not digging trenches — you’re copying software. This means the capex-to-revenue curve should look fundamentally different from railroads or telecom. Those industries needed decades of physical buildout before revenue caught up. AI needs months. — Read More

Recently, local AI assistants have exploded. Tools like OpenClaw now let anyone run powerful AI agents on their own hardware—no cloud subscription required. Many people still don’t understand what this actually means.

Some say big companies are panicking because everyone’s buying Mac minis to run AI themselves. This isn’t entirely true.

What big companies fear isn’t you buying that machine. It’s not even you canceling ChatGPT. What they really fear is this: the way compute power is consumed is changing from continuous payment to one-time ownership. — Read More

Most of the people I like think AI is a bubble. This is a tricky topic to discuss, because the “bubble” framing couples financial and technical issues. It’s like a sports fan debating “Is this player overrated?“. The answer depends on how good you think that player is, and how good you think other people think they are.

I don’t have anything much to add to the financial part of the “AI bubble” conversation. Various equity prices are based on very optimistic estimates about how AI will progress. This post is about the technological question. I’ll leave it to you to judge what sort of forecast any given asset price actually represents.

The main case I want to make is that performance probably won’t plateau — or at least, the common arguments for why it will plateau don’t add up.  — Read More

The CEOs of OpenAI and Anthropic have both claimed that human-level AI is just around the corner — and at times, that it’s already here. These claims have generated enormous public attention. There has been some technical scrutiny of these claims, but critiques rarely reach the public discourse. This piece is a sketch of my own thinking about the boundary of transformer-based large language models and human-level cognition. I have an MS degree in Machine Learning from over a decade ago, and I don’t work in the field of AI currently, but I am well-read on the underlying research. If you know more than I do about these topics, please reach out and let me know, I would love to develop my thinking on this further. — Read More