Dwarkesh PodcastDylan Patel — The single biggest bottleneck to scaling AI compute
Episode Details
EPISODE INFO
- Released
- March 13, 2026
- Duration
- 2h 30m
- Channel
- Dwarkesh Podcast
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
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! 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒
- Transcript: https://www.dwarkesh.com/p/dylan-patel
- Apple Podcasts: https://podcasts.apple.com/us/podcast/dylan-patel-deep-dive-on-the-3-big-bottlenecks-to/id1516093381?i=1000755126873
- Spotify: https://open.spotify.com/episode/5qiibwoBWY5rXyflK7WJzH?si=SX4ajSKXT-KeNtaHsiTNzw
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 https://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 https://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 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 https://janestreet.com/dwarkesh
To sponsor a future episode, visit https://dwarkesh.com/advertise. 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
SPEAKERS
Dwarkesh Patel
hostPodcast host and interviewer for the Dwarkesh Patel channel, focused on long-form conversations about AI, technology, and science.
Dylan Patel
guestCEO of SemiAnalysis and industry analyst covering AI compute, semiconductors, and data-center supply chains.
EPISODE SUMMARY
In this episode of Dwarkesh Podcast, featuring Dwarkesh Patel and Dylan Patel, Dylan Patel — The single biggest bottleneck to scaling AI compute explores aI compute scaling faces semiconductor tools, memory, and allocation bottlenecks Hyperscaler AI CapEx comes online over multiple years because large portions are pre-spent on long-lead items like turbines, power agreements, and data center buildouts well ahead of GPU deployment.
RELATED EPISODES
