RL favors smaller faster models

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

(00:55:47) – 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