Quick Takes

A small but noteworthy initial step toward soft nationalization of AI companies Axios[1]: Some points of note[2]: 1. Unless I'm badly mistaken, these are limitations from Anthropic's standard terms of service, not something recently introduced. So 'pay a price for forcing our hand like this' seems misleading at best -- presumably the Pentagon read the terms before choosing to sign the contract and incorporating Claude into its processes[3]. 2. This conflict was ostensibly triggered by Anthropic asking about the use of Claude in the Maduro raid[4]. Senior administration official: "Anthropic asked whether their software was used for the raid to capture Maduro, which caused real concerns across the Department of War indicating that they might not approve if it was...Any company that would jeopardize the operational success of our warfighters in the field is one we need to reevaluate our partnership with going forward." 3. Axios is correct that designating Anthropic a supply chain risk would be an extremely unusual step; all previous uses of that designation have been for foreign companies with suspected ties to foreign intelligence services (eg Huawei). 4. Estimates from Opus-4.6 and ChatGPT-5.2 suggest that designating Anthropic a supply chain risk would cost them something like 5% of revenue. This does not include a couple of potential major additional impacts: a) spread from DoD contractors to all federal contractors (I haven't tried to estimate what this would cost them) and b) impact on Anthropic's valuation for their intended IPO later this year, which would be ~30x whatever the direct revenue impact turns out to be. So this looks like a small but real step in the direction of soft nationalization as described by Cheng & Katzke, where the government uses a range of levers to exercise control over the choices of private companies. To be clear: the military choosing not to use Anthropic would not qualify in my view, but taking the highly unusual step of

people generally talk about food preservatives in a negative way. certainly, some of them are not great for you. but I want to take a moment to appreciate how wonderful food preservatives (and refrigeration and pasteurization and canning) are as well. it's crazy how fast most normal food goes bad. like a loaf of real old fashioned bread will go stale after a day and then become moldy after a few more days. for almost all of human history, people just sort of lived with this, and if they wanted to make foods last they had to dry it out and/or drown it in salt or vinegar or alcohol. pickles and beef jerky are great, but it would suck if you had to eat them all the time. 

I wrote a post forecasting Chinese compute acquisition in 2026. The very short summary is that I expect about 60% to be legally imported NVIDIA H200s, with domestically produced Huawei Ascends accounting for about 25%, and the remainder being smuggled AI chips and Ascends illegally fabricated outside China via proxies. While China likely produces GPU dies in quite large quantities, it is likely bottlenecked by an HBM shortage, which limits the total number of Ascend 910Cs and other AI chips that can actually be assembled. I do expect domestic production to grow substantially in 2027 and 2028, as CXMT ramps up HBM production. In total, I expect China to acquire about 320,000 B300-equivalents (90% CI: 150,000 to 600,000) in 2026, enough to train about six Grok-4-scale models simultaneously. By comparison, the Stargate campus that Oracle has been building for OpenAI in Abilene, Texas will alone house over 300,000 B300-equivalents. Some Chinese companies are also renting AI chips from non-Chinese cloud providers. For example, according to SemiAnalysis, ByteDance is Oracle’s largest customer; their largest joint cluster, located in Southeast Asia, will perhaps reach about 250,000 B300-equivalents this summer. (I don’t count remote access as “acquisition” since there is no ownership.) NB. These estimates are quite rough, so take them with a grain of salt. But I think they give a good sense of the general size of these different pathways.

there seem to be three different possible levels of manager involvement in individual researchers: * type 1 (the grantmaker): the manager spends a day reading the grant application of the researcher and decided whether to fund him for the next 6 months based on his track record, whether the research idea makes sense, etc. day to day, the grantmaker is completely uninvolved in the actual research. on fact, the grantmaker might not even be making decisions about individual researchers, but about entire organizations. * type 2 (the research manager): the manager spends a few hours every month getting complete context on what each of their reports is working on. each report has a different project that the manager thinks makes sense. the manager provides a light touch to guide the project according to their research taste, and vetos any terrible ideas, but most of the time the report makes most of the day to day decisions about what to do. * type 3 (the tech lead): the manager sets research taste for their team and basically orders around their minions. the manager provides almost all of the research taste, and the reports are either just doing engineering work, or doing very tightly scoped research on things the manager has cleared. what I've seen at openai is a mix of all three, but I've found the second one to be the most predominant (my relationship with my managers has been like type 1.75 on average). high level research leaders are generally the first type; they allocate resources to parts of the org they believe to be the most important for the company as a whole. the second type is most people; most researchers have some random direction they believe in, and they are part of a team doing something similar, and their manager has substantial trust in their research taste. even for junior researchers, the focus is on having them develop their own taste eventually, rather than just executing on things for their manager. the third type is most common for large p

Claude Code already does crappy - but interpretable! - continual learning. Whenever Claude compacts the conversation, it's distilling what it's learned into short-term memory to improve its future performance. For long-term memories, which are kept constantly available but accessed only occasionally, Claude has skills (currently written by humans, rather than updated automatically). Conveniently, we can easily understand what Claude has learned, because its memories take the form of English prompts![1] To check whether it learned anything objectionable, we can simply read its memory.[2] We could've had continual learning that encodes memories in inscrutable vector space, which would be much less nice for safety. This is analogous to the difference between CoT and latent reasoning, where there's a broad consensus that CoT is better for safety. There's less awareness of the "fragile opportunity" of prompt-based continual learning. Sticking to prompts rather than vectors has many practical benefits beyond safety: * The legibility of prompts makes everything easier to debug. * Scaling up a brand-new vector-based continual learning architecture adds a bunch of complexity and engineering overhead, compared to just wrapping a standard LLM. * When you create a next-generation LLM, it's easy to port over everyone's prompts to the new system, but vectors might become totally unusable. * Even if all you care about is capabilities, an LLM may find it easiest to modify and reason about its own memories using its natural language prior. Replacing CoT with latent reasoning still hasn't yielded great results, which could be for similar reasons. It's hard to say what will be incentivized on the spectrum of "fully legible" to "fully inscrutable" continual learning. At the very least, I'd like researchers to be aware of the interpretability tradeoffs when deciding what to pursue. This is part of what I hope to achieve with my recent paper on the legibility of prompt optimiza

Angry ex-wives are the best IRS informants   1. Public criticism of people who aren’t already in the dog house is an act with concentrated costs and distributed benefits. Sometimes those costs are quite high. 2. As such, we should expect it to be underprovided. 3. Occasionally criticism will be provided by very prosocial people, or some other prosocial person will cover some of the costs. 4. But mostly you should expect public criticism to come from people who are being irrational. Maybe they’re generally fragile, or are angry about being dumped for their ex-husband’s secretary. 5. Therefore, you cannot dismiss criticisms because the person is fragile or has a grudge.  6. Under many circumstances- when the target is very well loved and the problem lies in a subtle pattern- the only criticism you should expect will come from people who seem kind of crazy or out to get the target. 7. This is hard to navigate, because those same irrationalities will drive people to make false criticisms too.  8. But "they're irrational/out to get them" is a bad reason to dismiss criticism, because public criticism is an irrational act.

Apparently there's now a sixth person on Anthropic's board. Previously their certificate of incorporation said the board was Dario's seat, Yasmin's seat, and 3 LTBT-controlled seats. I assume they've updated the COI to add more seats. You can pay a delaware registered agent to get you the latest copy of the COI; I don't really have capacity to engage in this discourse now. Regardless, my impression is that the LTBT isn't providing a check on Anthropic; changes in the number of board seats isn't a crux.