AI just made every individual 10x more productive.

No company became 10x more valuable as a result.

Where did the productivity go?

This isn’t the first time this has happened.

In the 1890s, electricity promised enormous productivity gains.

Textile mills in New England, built to harness the rotational power of steam engines, quickly installed faster electric motors in their place.

But for thirty years, electrified mills saw almost no increase in output. The technology was far superior. But the organization was not.

It wasn’t until the 1920s, when factories completely redesigned the mills once again, with assembly lines, individual motors within every piece of equipment, and workers and machines executing drastically different jobs, that electrification produced meaningful returns. — Read More

Over the past few months, a new phrase has been circulating across tech, venture capital, and public markets:

“The SaaSpocalypse.”

The narrative is straightforward, and a bit alarming for SaaS operators. What’s real and what’s clickbait?

We know this. AI agents are improving rapidly. Coding tools can generate entire applications. AI can automate workflows once performed inside SaaS products.

If software can now be generated on demand, the logic goes: why pay recurring subscriptions for SaaS at all? — Read More

Executives are increasingly enamored with the promise of an AI-driven transformation and have invested accordingly. Most large-scale companies have initiated hundreds of pilots and provided widespread access to tools like Copilot and ChatGPT.

But while many of these pilots have succeeded individually—they’ve saved time and money, made processes more efficient—those gains haven’t scaled across the organization. Few companies have been able to fundamentally change their operating and business models around AI. — Read More

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