Sarah Tavel's Newsletter | Substack

I’m sure you read David Cahn’s provocative piece "AI's $600B Question", in which he argues that, given NVDIA’s projected Q4 2024 revenue run rate of $150B, the amount of AI revenue required to payback the enormous investment being made to train and run large language models is now $600B, and we are at least $500B in the hole on that payback. The numbers are certainly staggering… and are just going to get bigger. Until we reach an efficient frontier of the marginal value of adding more compute, or we hit some other roadblock that causes people to lose faith in the current architecture, this is a contest now of “not blinking first”. If you’re a big stack player like META, MSFT, GOOG, or any of the foundation model pure plays, you have no choice but to keep raising your bet — the prize and power of “winning” is too great. If you blink, you are left empty handed, watching someone else count your chips. It’s likely hundreds of billions will be destroyed, and trillions earned. It’s too early

I’m sure you read David Cahn’s provocative piece "AI's $600B Question", in which he argues that, given NVDIA’s projected Q4 2024 revenue run rate of $150B, the amount of AI revenue required to payback the enormous investment being made to train and run large language models is now $600B, and we are at least $500B in the hole on that payback. The numbers are certainly staggering… and are just going to get bigger. Until we reach an efficient frontier of the marginal value of adding more compute, or we hit some other roadblock that causes people to lose faith in the current architecture, this is