In March 2026 Harvey raised at an $11b valuation at ~$200m ARR (50x revenue) By June, for every $1 in revenue, Harvey was spending $1.50 with Anthropic (et. al.) Meanwhile Anthropic raises at $965b valuation in May 2026 with $47b run rate (20x revenue) I have so many thoughts here but I think it's the first crystal clear articulation of the double counting of revenue that's happening in VC land. A single dollar flows into Harvey's , boosting their valuation at 50x ratio, then turns into $1.50 and flows into Anthropic where it boosts valuation at 20x. $1m spend customer spend with Harvey was leading to $80m in valuation markup across Harvey and Anthropic. I don't get it.

How noisy are America’s largest data centers? I had my agents crank on this question for over a week. Together we investigated the 25 largest operating US AI campuses (according to Epoch), looking at satellite imagery, equipment, nearby homes, and noise complaints. I expected to dismiss resident concerns. I came away more sympathetic to the neighbors, but also convinced that these problems are avoidable.

On the AI front, the trade has been dead money for more than 3 months now. The metrics I am following (token expenditures and GPU lease rates) are all flat to down. The price of memory (DRAM) seems to be the only thing that is still going up. 🧵(1/2)

Great thread. It's all true. When I've said similar things in the past, people have accused me of hyping up what the models will eventually be capable of. That's not why I post. I don't work for any of the labs. I don't take money from any of them. I've never taken money to promote anything. I came here for one reason: to warn people about what was coming. Everything happening now is a different tiny piece of the same pattern. You can see it everywhere if you look. Terence Tao, almost exactly two years ago, on OpenAI's o1: 'The experience seemed roughly on par with trying to advise a mediocre, but not completely incompetent, (static simulation of a) graduate student. However, this was an improvement over previous models, whose capability was closer to an actually incompetent (static simulation of a) graduate student. It may only take one or two further iterations of improved capability (and integration with other tools, such as computer algebra packages and proof assistants) until the level of '(static simulation of a) competent graduate student' is reached, at which point I could see this tool being of significant use in research-level tasks.' Terence Tao, four days ago: 'I mean it's it's it's amazing just how much we are willing to change everything without having any idea what's what's going to happen afterwards. It's it's extremely nonlinear dynamics. Any kind of monotone, one-dimensional thinking - well, oh, a little bit of this is good, therefore a lot of it is going to be a lot better - one of the lessons of math is that most systems don't work like that. Especially if you 10x, 100x things. So, you know, I mean, we're... we have to slow down. I mean, this is, it's insane this pace, and there's no reason to be this fast. There's no reason at all.' He has seen it. I'm not posting this to belittle him, or what he's feeling. For I have been through it myself. I felt it four years ago, the first time I saw the shape of this. Right now the world is seeing that same shape, that same pattern, in what is happening in math. But this is not about math. OpenAI is not a mathematics company. It is an intelligence company. They didn't put serious resources into math until a few weeks ago, and look what has happened since. This was not a matter of model capability either. It was a matter of resources, of allocation. Of compute, more of which is coming online every day. Once there is enough of it, the models will expand into more spheres of human expertise, and then into all of them. All the work of the mind. And it will play out there exactly as it is playing out now in math. Everyone will go through what Tao is going through, because all of us have something that means to us what math means to him. But this is not about math, or art, or copyright. This is about everything, because it generalizes to everything. Pacing the Frontier is not about regulatory capture, IPOs, or crippling the competition. It's part of it, sure, but it's not the main reason. The main reason is fear. Everything happened faster over the last six months than anyone at Anthropic or OpenAI expected. If you know anyone who works there, you know this is true. This isn't a secret. The people who work there are saying it openly. The old timelines are all blown up. RSI isn't two years out. It's not even a year away. I think we get the real thing by next summer. That's what they've seen internally, and that's the real reason for Pacing the Frontier. After we reach that, I think we will hit the next milestone really quickly. And after that, everything in this world will change. We are not ready for it. We wouldn't be ready if we had another ten years. We only make it through now with the help of extremely capable models, and I think trying to stop now would doom us all. But I have never once, in these last four years, believed that we were going to stop anyway. I don't even think we're going to slow down. We're going straight in. And the only way out is through.