Paying customer at what price? My understanding of Meta’s request is API pricing per token (of course discounted for volume) for content moderation etc. which i

Paying customer at what price? My understanding of Meta’s request is API pricing per token (of course discounted for volume) for content moderation etc. which is a lower margin product than Gemini enterprise seats. They also prioritize internal training runs. This doesn’t mean more compute at any price is worthwhile. It also doesn’t mean that the SpaceX compute deal would even be offered to Meta. > I don't get this though: The theory is all these hyperscalers are simultaneously spending buttloads of money on CapEx to the extent it affects their stock price, and then they would lie about the demand to protect their share price. It’s not lying - it’s optimistic revenue projections. Your Sam Altman point is an example, these RPOs are real but what’s questionable is whether the AI labs can generate enough premium token API revenue to actually pay those commitments. Today’s OpenAI annualized revenue estimate is only 40B. Will they actually be able to 10x that to pay those RPOs? I’m skeptical especially with offloading inference to cheaper models. > Why would they do all that when they could just do nothing and keep their firehoses of existing business revenue untouched and maintain their stock prices on the upward trajectory they already were -- like Apple? The hyperscalers with proven revenue streams and strong financials (Amazon, MSFT, Google, arguably Meta) benefit from making the game more expensive than everyone, will get at least 50% of their capex back from this peak supply/demand mismatch and maybe other than AWS could easily use any excess compute for internal needs.