Had a lot of fun chatting again with my twin brother Dylan Patel.
We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).
And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.
One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI.
Watch on YouTube; read the transcript.
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Timestamps
(00:00:00) – Two labs will soon control most of the world’s compute
(00:07:01) – $6 billion in fab capex enables $1t+ of end revenue
(00:13:08) – Compute prices will rise if the labs outbid everyone
(00:18:22) – Which layer will capture most of the surplus?
(00:25:40) – What could slow down progress?
(00:29:43) – Labs are shifting compute from inference to R&D
(00:33:27) – China gets less than 10% of new compute, but its labs need less
(00:48:48) – Will AI cause a sovereign debt crisis?
(01:07:52) – Will the world’s future workforce belong to a few companies?