Inside Google DeepMind, Demis Hassabis leaving his CEO post landed with essentially a shrug. Sources tell me he's already been disengaged from day-to-day management for a while now. But he was a firewall between DeepMind and the rest of Google, even as the two got pulled closer over the last couple of years. I expect that distance to dissolve more with him stepping back.

Alex Heath warns that if Apple wins its injunction, OpenAI will go “full court press” with oppo messaging, because it already spent over $6 billion buying Jony Ive’s hardware team. “Very bad. If there’s any traction of Apple winning an injunction, you’re going to see, my prediction, you’re going to see OpenAI go full court press on this and make a big mess of it and do a bunch of oppo messaging against this.” “It matters a lot. There’s a lot riding on them getting hardware right. It’s a personal kind of thing for Sam Altman and Jony.” “They spent over $6 billion on acquiring Jony’s hardware team.”

A Microsoft exec just sent an internal memo that’s designed to end tokenmaxxing and makes OpenAI’s GPT-5.6 Sol the default model for internal use. Full text in the newsletter:

Everyone is a M&A expert today. But if your hot take on Bending Spoons acquiring Airtable doesn’t factor in that it spun out the AI unit, Hyperagent, it’s missing key pieces of the puzzle. Could be where execs and top engineers go, and changes the math on their exit value👇

@_chenglou I was part of that team. Basically ChatGPT one year before it came out. Called LMChat and then another codename. Google was too nervous to release it and DeepMind was blocked from shipping products that could disrupt Google. I think about this a lot.

A year and a half ago, I asked @hamburger to start a podcast with me. We launched Access in September. 42 episodes later, we interviewed a bunch big tech CEOs and startup founders. I would put our guest list up against anyone’s, actually. We broke news. Did a few live shows. One guest dialed in from a hot tub. This week’s ep was our last. Thanks to everyone who supported us this past year and all of our guests! It was a fun ride. And if you’re a subscriber to the show, don’t unfollow! I’ve got plans. More to come…

Every reviews new AI models, consults companies on their AI strategies, and makes its own apps. It's about to start selling an AI agent of itself. We had CEO @danshipper on the pod this week. We talk about his fav AI stuff right now, token budgets, what it's like to review new models first, and more

I'm actually fairly bearish on frontier lab valuations. I've never seen the reasons articulated to my satisfaction, so before I go to sleep, I wanted to quickly jot down my thinking here. The basic issue is that the labs are highly unprofitable. This may seem like a simple point, but private market valuations can be relatively irrational; however, like with $SPCX, post-IPO pricing will likely be much more punishing, especially as the standard 6-month lockup period expires and selling pressure intensifies. Many people claim that the labs have high margins. Yet even with high margins, a valuation of $1T would be justified only if the labs were doing nothing aside from serving inference (thus reducing costs only to those relevant to inference) and posting annual revenue numbers in the $100-200 billion range assuming ~80% gross margin and a 20x earnings multiple. This assumption is obviously not true, because the frontier labs have to continually spend money training the next generation of models. This is because of market competition from runner-up firms. For example, if OpenAI had paused model development last year, there would no longer be any point in paying GPT-5 API prices when you can just use Qwen or Kimi instead for much cheaper. Thus, the labs are forced to invest ever-increasing amounts of money in model training, in a way such that at any given point of time, the amount you're forced to invest in the next model is dramatically higher than the amount of money you're actually making, because even if your revenue goes up with higher model capabilities, so do your future training costs. This is a profoundly punishing dynamic which severely penalizes frontrunners. (There is also a related subpoint where frontier labs claim they can distill their leading models to win out at lower intelligence levels as well. This makes no sense because the revenue numbers involved are far too low when taking into consideration the rather low margin of such inference.) Frontier lab valuations appear largely to be based on the assumption that as you scale up, the capabilities which emerge will be sufficiently general and profound that we'll see explosive growth ( from things akin to AI agents starting and autonomously managing entire companies of subagents. But it's not clear to me that this is the case; indeed, as I mentioned in my previous post ( I believe that capabilities growth will be slower, spikier, and more data-limited than people currently assume. It may be the case that eventually we will see explosive growth of this nature with full automation of the economy, but at the very least my viewpoint implies much longer (multi-decade) timelines until we reach this point. It is not clear to me that the frontier labs will be able to operate unprofitably for so long, although I suppose maybe this foreshadows some sort of inevitable nationalization. I also want to make a broader point about technological diffusion. The reason why technological diffusion is slow isn't just because, e.g., old people take a long time to learn how to use technology (although this is of course a contributing factor to some degree). In my view, it's because when a new, revolutionary technology comes along, the ways to incorporate that technology into subsequent developments are not always obvious, and in fact they cannot necessarily be arrived at through the application of pure reason. If they could be, then perhaps frontier models, at a certain point, would have a perfect understanding of how the LLM application layer should be developed, and they would then autonomously code, deploy, and sell such a layer. But it seems more plausible to me that this diffusion is limited moreso by the hard problem of economic calculation--that is to say, the Hayekian notion through which the price system gradually promotes efficient allocation of resources and which cannot be simulated through central planning--and that even if we froze current capability levels at today's levels, it would take well over two decades to fully integrate in LLMs into our lives. Such a view is consequently rather bearish for the continued profitability of labs as it reduces their prospects for finding, say, something else comparable in profitability to coding agents, which seems to have been a somewhat lucky discovery by Anthropic to begin with. That is to say, even if you spam FDEs you aren't necessarily going to be able to just figure out the "correct" product shapes fast enough. Overall, I don't think that people have clearly reasoned through their mental models for why lab equity should be worth as much as it currently is, and that if you actually bother to write down such a model, you may not arrive at the conclusion that you want to arrive at. This isn't to say that I don't expect AI to experience a huge (industry-wide) boom in the coming decades, but just that I'm not entirely sure I would buy OpenAI or Anthropic stock at latest valuations if I were given the opportunity to do so. Of course, as an ex-lab employee, arguably this is talking against my own book; I should really be giving people more reasons to be bullish. But in the end, my influence is so small that it doesn't make a difference, so why not have some fun?

Black Forest Labs powers a sizable chunk of AI-generated images on the internet. Its CEO @robrombach rarely does interviews. In today’s newsletter, we talk about: - BFL’s bet on robotics and a consumer agent - The open vs. closed AI debate

New blog post on what would be true about the world if trendline continues and leading lab hits $1T in revenue by the end of next year. In other words, why compute might get 10x+ more expensive in coming years