About this episode

Dylan Patel discusses the major bottlenecks in scaling AI compute and the associated economics.

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 * Mercury has already saved me a bunch of time this tax season. Last year, I used Mercury to request W-9s from all the contractors I worked with. Then, when it came time to issue 1099s this year, I literally just clicked a button and Mercury sent them out. Learn more at mercury.com . * Labelbox noticed that even when voice models appear to take interruptions in stride, their performance degrades. To figure out why, they built a new evaluation pipeline called EchoChain. EchoChain diagnoses voice models’ specific failure modes, letting you understand what your model needs to truly handle interruptions. Check it out at labelbox.com/dwarkesh . * 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…

People in this episode

Host: Dwarkesh Patel

Guest: Dylan Patel

Topics covered

• AI compute
• scaling challenges
• economics of technology
• semiconductors
• voice models
• market data analysis

Keywords

• AI
• compute
• scaling
• bottlenecks
• semiconductors
• voice models
• economics

Sponsors

Mercury, Labelbox, Jane Street

Mentioned in this episode

Organizations: SemiAnalysis, Nvidia, TSMC

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