Right, and the comment I replied to was about revenue, not profit. (That said, while I don't think Anthropic is already profitable, it reportedly expects its first operating profit later this year.)
Those seem like reasonable questions about future margins and moats, but I was making the narrower point that revenue is in fact growing quickly, contra the comment above mine
No, I wasn't claiming that revenue scales with headcount, though it probably does to some extent. The point is that these companies' revenue is large and growing quickly, which is what the comment above mine denied.
Maybe, but that's a different claim. You wrote that the improvements are "not translating to a dramatic increase in revenue", but going from about $1B to about $30B run rate in 16 months seems like a pretty dramatic increase to me!
"Anthropic and OpenAI generate a lot of revenue with relatively few employees – an estimated $9M and $5.5M in revenue per employee (RPE), respectively. If either company were to go public, it would have a higher RPE than any public tech company on Forbes’ Global 2000 list." https://epoch.ai/data-insights/revenue-per-employee-ai-compa...
Master craftsmen paid apprentices almost next to nothing, and they were often contractually guaranteed to stick around for many years, so the teaching was a kind of wage and also a cost that could be recuperated later on. (The apprentice even often had to pay the craftsman to take them on.) None of those things are true for junior software engineers, who are paid to contribute and can leave at any moment. Also, yes apprentices often had to do chores. It is just not analogous at all.
I think electricity doesn't matter that much (yet) because China is bottlenecked on chips. I think the incentives/directives to build on Huawei also doesn't matter that much yet because it's still such a small percentage of compute relative to NVIDIA, even for Chinese AI companies. (But this too could matter more from 2027-2030 and on.)
Yeah, to be clear I'm pretty excited about confidential computing and startups building on it, like Tinfoil, for some use cases. I just wanted to point out it's far from adequate for some important threat models (e.g., securing model weights for data centers located abroad, I think). (It's also not super widely adopted in AI yet, but that seems to be changing, at least for inference workloads.)
Where did you get this information? I think it's wrong -- I'm pretty sure they used the Export Administration Regulations (EAR) under the Export Control Reform Act (ECRA), which is under Commerce, not ITAR which is under the State Department. See for example https://harvardlawreview.org/blog/2026/06/is-access-to-fable...
Confidential computing is not secure against a potential attacker who has physical access to the hardware. The CC security guarantees explicitly assume the attacker has no physical access.