Dylan Patel — Deep dive on the 3 big bottlenecks to scaling AI compute
- Source
- dwarkesh.com
- Author
- Dwarkesh Patel
- Date
Explains the real supply-side constraints (logic, HBM memory, power, ASML/TSMC allocation) that determine GPU availability and cost — useful for anyone planning compute budgets or timing infrastructure decisions for AI-native products.
- context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
“You sort of have this dynamic that is quite interesting in that an H100 is worth more today than it was three years ago.”
Dylan Patel
“Again, this is the most complicated machine that humans make, period, at any sort of volume.”
Dylan Patel
“You go down the supply chain, everyone’s doing X - 1. In some cases, they’re doing X ÷ 2, because they’re not AGI-pilled.”
Dylan Patel
“But in reality, he’s screwed the pooch compared to OpenAI, whose approach was, “Let’s just sign these crazy fucking deals.””
Dylan Patel
“It’s fast timelines, the US wins; long timelines, China wins.”
Dwarkesh Patel
- 00:00:00Why an H100 is worth more today than 3 years ago
- 00:24:52Nvidia secured TSMC allocation early; Google is getting squeezed
- 00:34:34ASML will be the #1 constraint for AI compute scaling by 2030
- 00:55:47Can't we just use TSMC's older fabs?
- 01:05:37When will China outscale the West in semis?
- 01:16:01The enormous incoming memory crunch
- 01:42:34Scaling power in the US will not be a problem
- 01:54:44Space GPUs aren't happening this decade
- 02:14:07Why aren't more hedge funds making the AGI trade?
- 02:18:30Will TSMC kick Apple out from N2?
- 02:24:16Robots and Taiwan risk
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