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.
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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
(00:56:06) – Can’t we just use TSMC’s older fabs?
(01:05:56) – When will China outscale the West in semis?
(01:16:20) – The enormous incoming memory crunch
(01:42:53) – Scaling power in the US will not be a problem
(01:55:03) – Space GPUs aren’t happening this decade
(02:14:26) – Why aren’t more hedge funds making the AGI trade?
(02:18:49) – Will TSMC kick Apple out from N2?
(02:24:35) – Robots and Taiwan risk