inference

“And so, essentially, you have the contracted base of installed compute trading at a massive discount to the current spot market.”

“And they had rented a cluster of several thousand black wells, and we'll just call it somewhere in the mid $2 per GPU hour.”

“They went on a podcast, and they essentially said, we are planning to pay 100% more for Blackwell's when our contract expires. And that just means that essentially all the hyperscalers are under earning.”

“The amount of compute is going up and inference margins going up. And if you multiply those three, that's how you're getting this crazy acceleration into some of the labs plus open source,”

“And they're monetizing at something like 50,000,000,000 a gig and consensus estimates for next year are 73,000,000,000. So forget Starlink v three, forget Starlink direct to sell, Grok 4.5 and Cursor.”

“I think Anthropic probably starts generating cash this year if they are not already generating cash, which I think is probably the case.”

“And I just think the same is true of compute. It's why I'm probably less worried about like an edge AI bear case than I was.”

“And it turns out that harness engineering is not as important as the model, but it really matters. And these harnesses in these models are increasingly being co developed.”

“To understand what Frontier AI is capable of today, even for a non coding use case, need to have Cloud Code or Codex five point Codex. And you need to be on an enterprise plan.”

“AI is just shifting from all you can eat to pay by the drink. Then it turns out people really like to talk to their friends long distance.”

“So I think the shift to usage based pricing is probably why you will see OpenAI and Anthropic exceed well over $200,000,000,000 in ARR this year.”

“I think the shift to usage based pricing is probably why you will see OpenAI and Anthropic exceed well over $200,000,000,000 in ARR this year.”

“The disaggregation of inference means that I think these GPUs are going to have ten or fifteen year lives. The AI skeptics are like, oh, these companies are all cooking their books.”

“The disaggregation of inference means that I think these GPUs are going to have ten or fifteen year lives.”

“The useful life of GPU is only a year or two. The useful life of CPU is only four years because the rapid technological change.”

“We're just moving from these all you can eat plans to usage based plans with overage, where those usage tokens cost a lot more, and we're finding out that there's we're nowhere near the amount of, you know, people ceiling price for how much they'll spend.”

“And we're in an insane shortage despite spending cumulatively trillions of dollars. What happens when 5% of the world's population is using these models the way the cutting edge 10 basis points are?”

“What happens when 5% of the world's population is using these models the way the cutting edge 10 basis points are? Like, it's just it's unimaginable. This is why orbital compute is a necessity.”

“Tranium, by far. Tranium is going to be to 2026, especially in the second half of this year when Tranium three really ramps, as TPUs were to twenty twenty five.”

“Tranium is going to be to 2026, especially in the second half of this year when Tranium three really ramps, as TPUs were to twenty twenty five.”

“And so Trainium is for sure the most underestimated, not only because of those design choices, but because the all of these frontier models are what are called mixture of expert models.”

“And the only two functioning switched scale up networks in the world today are the ones that power NVIDIA GPUs and Amazon's Trainiums.”

“I could've Atreides could've invested over $50,000,000 in the round at 1,100,000,000, And I was conflicted out by Crusoe,”

“And then we started scaling around inference time compute. And it's very clear that we have now added a third axis of scaling performance, and that is reasoning.”

“I think they're going to go really fast to GPT-4.5 compute, which means you're going to get, using these orders of magnitude, you're going get a 100x improvement really fast.”

“The only thing that has enabled the AI revolution that we're living through, which I think we're at the bottom of the first inning in, is one, we had the ability to do cloud computing, so just apply significantly more computational power to old algorithms, and then b, we had dramatically more data.”

“The single most predictive element of knowledge about AI quality is the quantity of data used to train the algorithm.”

“and it's been very well established in multiple papers from both Google and Microsoft research that for every order of magnitude increase in the data you use to train an algorithm, the quality of the AI doubles.”