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Dylan Patel

What Dylan Patel thinks about data centers

@dylan-patel · 50 positions · On ppll ↗

Founder and chief executive of SemiAnalysis, the research firm that tracks the semiconductor and AI data-centre supply chain.

Dylan Patel did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

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Only Dylan Patel on data centers. See everyone on data centers

9 dated positions, 2025 to 2026, in their own words. Our reading of what Dylan Patel has said — not written or endorsed by them.

  1. Our readingCommitting to compute years in advance is the durable margin advantage in AI, because every increment of new capacity transacts at the new price.

    Their wordsA companies that have locked up, you know, and and don't have commitment issues, you know, have these 5-year contracts for compute, they've kind of locked in a humongous margin advantage because they've locked in compute for 5 years at a price of what it transacted at 5 years ago or three years ago or two years ago, whatever it is.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 2nd of 28 in this recording

  3. Our readingThe binding constraint on AI is no longer power or data centres but the semiconductor supply chain, which has by far the longest lead times.

    semiconductors

    Their wordsYeah, I think the biggest bottleneck is compute and for that the longest lead time supply chains are not power or data centers. They're actually the semiconductor supply chain themselves, right? It switches back from being power and data center uh as a major bottleneck to chips.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 6th of 28 in this recording

  5. Our readingHundreds of billions of dollars of AI value rest on about a billion dollars of lithography tooling that cannot be scaled quickly.

    Their wordsoh 50 gigawatts of economic you know sort of capex in in the data center and what gets built on top of that in terms of tokens is even larger right it might be hundred billion dollars worth of AI value into the supply chain is held up by this $1.2 two billion dollars worth of tooling that simply just cannot expand its supply chain quickly.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 8th of 28 in this recording

  7. Our readingThe world can make roughly 200 gigawatts of AI chips a year by 2030, which makes a gigawatt a week a plausible quarter of the market rather than a fantasy.

    Their wordsUm, and then you stack on 70 this year, 80 next year, growing to 100 by 2030. You're at like 700 EV tools by the end of the decade. Um, 700 EV tools, three and a half tools per gigawatt. um assuming it's all allocated to AI which it's not but three and a half tools per gigawatt gets you to 200 gigawatts worth of AI chips for the data centers to deploy

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 9th of 28 in this recording

  9. Our readingAbout a fifth of the US grid could be freed for data centres just by covering the yearly peak with batteries or peaker plants.

    batteries

    Their wordsThen all of a sudden, you've unlocked 20% of the US grid for data centers because most of the times that capacity is sitting idle and it's really only there for that peak, right? Which is a day or two, right?

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 21st of 28 in this recording

  11. Our readingLabour is a huge constraint on the AI build-out, and the way round it is to build data centres as modules in Asian factories rather than on site.

    robotics

    Their wordsum humanoid robots maybe start to or robotics at least start to but the main factor is going to be for reducing the number of people is modularizing things and making them in factories in Asia

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 22nd of 28 in this recording

  13. Our readingGetting a data centre permitted in the empty middle of America is far easier than putting one in orbit.

    Americapermitting

    Their wordsAnd so I think people are figuring out how to build these things and permitting like I I just like ultimately like permitting and red tape in middle of nowhere Texas or middle of nowhere Wyoming or middle of nowhere like New Mexico is probably a hell of a lot easier than sending stuff into space

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 23rd of 28 in this recording

  15. Our readingSpace data centres solve the wrong problem, because the contended resource is chips rather than energy, and that will not change this decade.

    energy

    Their wordsSo, space data centers effectively are not li limited by, you know, hey, we have this energy advantage. It's actually just limited by the same contended resource. We can only make 200 gawatts of chips a year by the end of the decade.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 24th of 28 in this recording

  16. 13 months earlier

  17. Our readingThe only thing stopping China from building the largest data centre in the world is access to chips; the power and the industrial capacity are already there.

    China

    Their wordsChina, if they wanted to build the largest data center in the world, if they had access to the chips, could. So it's just a question of when, not if.

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 11th of 44 in this recording