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Dylan Patel, founder of
SemiAnalysis, provides a deep dive into the 3 big bottlenecks to scaling AI compute: logic, memory, and power.And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers.Learned a ton about every single level of the stack. Enjoy!Watch on
YouTube; read the
transcript.Sponsors*
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Jane Street is basically a research lab with a trading desk attached – and their infrastructure backs this up. They’ve got tens of thousands of GPUs, hundreds of thousands of CPU cores, and exabytes of storage. This is what it takes to find subtle signals hidden deep within noisy market data. If this sounds interesting, you can explore open positions at
janestreet.com/dwarkesh.Timestamps(00:00:00) – Why an H100 is worth more today than 3 years ago(00:24:52) – Nvidia secured TSMC allocation early; Google is getting squeezed(00:34:34) – ASML will be the #1 constraint for AI compute scaling by 2030(00:55:47) – Can't we just use TSMC's older fabs?(01:05:37) – When will China outscale the West in semis?(01:16:01) – The enormous incoming memory crunch(01:42:34) – Scaling power in the US will not be a problem(01:54:44) – Space GPUs aren't happening this decade(02:14:07) – Why aren't more hedge funds making the AGI trade?(02:18:30) – Will TSMC kick Apple out from N2?(02:24:16) – Robots and Taiwan risk
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