Anthropic are strongly rumored to be about to have their first profitable quarter. Stories are circulating of companies surprised at how expensive their LLM bills are becoming from usage by their staff. I think this is because OpenAI and Anthropic have both found product-market fit. — Read More

#strategy

The question I keep getting from founders and prospective employees: is there any AI application layer left to build, or are OpenAI and Anthropic going to kill everything?

There’s a particular flavor of AI psychosis behind the question. Some people have concluded the only durable places to avoid the permanent underclass are inside a big lab or out on the frontier building in robotics, hardtech, or similar – theoretically anything “the labs can’t touch.” If every piece of software is about to be eaten, either by Codex or Claude absorbing the work directly, or by a future model that will make whatever you’ve built unnecessary, then run!

… The Yellow Brick Road is our shorthand for the path the labs are walking, where they’re committing extraordinary resources. The reason the labs are best-suited for problems like code generation, writing, or image-creation is because these problems improve with raw model capability: every dollar spent on pre-training and post-training improves product quality. Meanwhile, the rest of Oz is inhabited by more complex, often vertical problems, that aren’t as simple as giving a business user a horizontal tool with access to standard tools and computer use. The value comes less from the underlying model’s raw capability (though that’s still important!) than from the scaffolding around it that makes the output trustworthy, compliant, and operational inside a specific industry. — Read More

It’s unclear right now how AI is going to play out for most companies, and I don’t think anyone has a clean answer yet, including me. But there’s a pattern I keep coming back to, and it has less to do with what AI eventually becomes and more to do with what it can already do.

I don’t think the capability curve breaks at some single moment we’d call AGI. It just keeps climbing. Each release adds capability somewhere, and we don’t need to reach the top of the curve for the bottom of it to start reshaping things.

This past Tuesday at Google I/O, Antigravity 2.0 built a functioning operating system from scratch in twelve hours. …Take the staging with whatever grain of salt you want. The point underneath is what “good enough” looks like in mid 2026. … Not because of where it ends up, but because of what it can already do.

A capable agent loop, called many times in parallel, with reasonable cost and reasonable latency, is enough to recreate most of what the application layer of software currently sells. The curve keeps going from here. The question that follows is which kinds of companies sit downstream of that engine and which don’t. — Read More

Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution.

Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows.  — Read More

Last week, Andrej Karpathy stood in front of a room at Sequoia‘s AI Ascent event and told everyone that vibe coding (a term he invented and made popular) was already obsolete. The future, he said, is agentic engineering. He went on to list exactly what agentic engineering actually involves: 

preserving quality
writing design specsvsupervising plans
inspecting diffs
writing tests
building evaluation loops
managing permissions

… [S]trip the engineer-specific vocabulary, and you have product management. — Read More

At any given time, technology does two things to employment: It replaces traditional jobs, and it creates new lines of work. Machines replace farmers, but enable, say, aeronautical engineers to exist. So, if tech creates new jobs, who gets them? How well do they pay? How long do new jobs remain new, before they become just another common task any worker can do?

A new study of U.S. employment led by MIT labor economist David Autor sheds light on all these matters. In the postwar U.S., as Autor and his colleagues show in granular detail, new forms of work have tended to benefit college graduates under 30 more than anyone else. 

… The paper, “What Makes New Work Different from More Work?” is forthcoming in the Annual Review of Economics.  — Read More

The easiest mistake to make about Anthropic is to treat it as “OpenAI, but with a conscience.”

That story is emotionally satisfying but overly simplistic. OpenAI was the first-mover, ostensibly motivated by “AGI for the benefit of all humanity” which, at the time of writing, is still in their charter. On the surface, this sounds very similar to Anthropic’s morality.

After all, Sam Altman is easy to read, what you see is what you get. He wants money, power, control, and the largest possible seat at the table. There is nothing particularly mysterious about that. Throw a dart blindfolded in the Bay Area and you’re bound to hit a founder with the same goals.

Anthropic is harder to read because it speaks in a different register. They talk about safety and alignment, but they go beyond the CBRN and cybersecurity risks that OpenAI focuses on. Anthropic adds in x-risk, and lately, geopolitical dominance as its top-of-mind concerns. They don’t just want to win the AI race on business terms, they believe they have a personal mission to save humanity. 

That is precisely why they deserve more scrutiny. — Read More

Why is model inference getting cheaper? How did I drop a soon-to-be $2,000+/month bill for AI agents to next to nothing? And why are local models on commodity hardware potentially “good enough” for most people?

There are two macro trends here that feed directly into each other.

… costs are dropping for the same capacity (same model, same query), and we’re constantly ramping up what we use (bigger model, more expensive query). — Read More

#strategy

At Stanford HAI, we believe AI is poised to be the most transformative technology of the 21st century. But its benefits won’t be evenly distributed unless we guide its development thoughtfully. The AI Index offers one of the most comprehensive, data-driven views of artificial intelligence. Recognized as a trusted resource by global media, governments, and leading companies, the AI Index equips policymakers, business leaders, and the public with rigorous, objective insights into AI’s technical progress, economic influence, and societal impact. — Read More

#strategy

I haven’t been writing a lot for reasons I’ll share below. So when I was invited by Stripe to speak on the SaaSpocalypse as part of their SaaS Platform Leaders Summit, it turns out I had a lot to say. Simple questions were met with word gush as thoughts that had been built up inside my head over the last weeks and months tumbled out.

Writing is synthesis for me, so here’s my attempt to crystallize my view of the SaaSpocalypse. 

The crowd was mainly vertical SaaS CEOs so this essay is written as such. But, the LLMs are moving up the stack, so much of this is applicable to Native AI startups as well.  — Read More