Anthropic committed over $100B to compute deals, 5 GW of TPUs (Google/Broadcom, starting 2027) and 5 GW of Trainium (Amazon).

“It was an over hundred billion dollar commitment.”

An additional $50B is committed to flow into Anthropic from deals with Amazon and Google inked last month.

“We have another $50 billion that'll come in into the future from the Amazon and Google deals that we that we inked last month.”

Brad Carson on AI ethics caveats from tech companies being toothless against DoD use.

“The fig leaves people are putting in front of themselves are no protection at all. You like OpenAI and Gemini both said like, well, we're going to do these things. Well, then there's like a caveat at the bottom where the DoD says we're going to use it for all lawful uses. And all the things I said I don't really love personally. They're all lawful, right? That's the problem.”

Matthew Prince on how hard it is to get referral traffic from AI crawlers versus the old Google baseline.

“Today it's 20 times harder with Google than it used to be. But that's the good news. With OpenAI, it's 1,500 times harder. With Anthropic, it's 60,000 times harder.”

Rajiv Jain explains how Google's extension of GPU depreciation life artificially inflated its reported operating margins, masking the true cost burden of AI infrastructure investment.

“If you look at Google, if they had not changed the depreciation policy, the margin would be high single digits operating margin. The margin went from 78% 23% after they change depreciation policy 3 years ago.”

Google's depreciation policy change three years ago shifted its GPU depreciation life from ~3 years to ~6 years, moving operating margin from 7 to 8% on a clean basis to a reported 23%.

“If you look at Google, if they had not changed the depreciation policy, the margin would be high single digits operating margin. The margin went from 78% 23% after they change depreciation policy 3 years ago.”

Microsoft, Meta, and Google are each on track to spend over 50% of their revenue on capex in 2026.

“Microsoft, Meta and Google are all on in line to spend over 50% of revenue on capex this year.”

Tulsee Doshi explains why latency beats quality in consumer AI products: even a measurably better model loses in live experiments when it makes users wait longer.

“We find that even when we tweak the model and, you know, hurt latency, we actually see that play out in our live experiments on search in the app, even if the model is hugely better from a quality perspective. Because what you're asking users to do is wait.”

All production reasoning models from OpenAI, Anthropic, Google, and open source do chain-of-thought in normal token space using human language, latent-space or embedding-based reasoning has not been adopted in any working system.

“In practice with as far as I'm aware with the OpenAI reasoning models and the similar reasoning models from Anthropic and Google and certainly all of the open source reasoning models that work well at all, those approaches have not been taken and it's it's pretty much just like the sort of like very simple, very dumb you know, where it's going to be doing chain of thought reasoning in the normal token space and mostly using human language.”

Google token processing grew 200x in roughly a year, from 5 trillion to 1 quadrillion tokens per month.

“Google went from 5 trillion tokens a month to 480 trillion tokens a month. And they had just put some press out that they crossed like, you know, 800 trillion. And I saw something today. They crossed a quadrillion. And I had to look that up. That's a,000 trillion. And so in a in a course of a year and a few months, they've gone from 5 trillion to a,000 trillion. So that's 200x right there.”