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What Dylan Patel thinks about semiconductors

@dylan-patel · 50 positions · 0 changes of mind

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.

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

Our readingThe binding constraint identified here is the supply chain rather than power or data centres, and the ceiling sits at the bottom of it with one lithography supplier. The rest works out what that implies: American fabs that do not remove the dependence on Taiwan, and export controls defeated by renting.

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.

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

  1. Our readingTSMC would rather give wafers to a slow, stable CPU business than to its fastest-growing AI customer.

    Their wordsTSMC is much more excited to give allocation to Graviton than they are to tranium because they view CPU business as more stable long-term growth right and as a company that is conservative and doesn't want to ride cycles of growth too hard you actually want to allocate to the uh the market that is more stable and lower growth rate first before you allocate all the incremental capacity to the fast growth rate market.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 4th of 28 in this recording

    Interesting

  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.

    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

    data centers

    Interesting

  5. Our readingBy the end of this decade the ceiling on AI compute is set by ASML, the lowest rung of the supply chain, and not by anything above it.

    Their wordsSo to scale compute further right there's some different bottlenecks this year next year uh but ultimately by 2829 the bottleneck falls to the lowest rung on the supply chain which is ASML right ASML makes the world's most complicated machine i.e. an EUV tool.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 7th of 28 in this recording

    Interesting

  7. Our readingASML has never raised the price of a tool by more than it raised the tool's capability, which makes it the most generous monopoly in the industry.

    Their wordsYou can you can take the margin like Nvidia takes the margin. memory players are taking the margin, but ASML has never risen the price more than they've increased the capability of the tool. Um, and so in a sense, they've always provided net benefit to their customer.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 10th of 28 in this recording

    Interesting

  9. Our readingThe semiconductor supply chain will underbuild not because it cannot expand but because nobody in it believes the demand is real.

    Their wordsUm in general the semiconductor supply chain has not right it's lived through the booms and bust and uh we can talk a bit more about it but basically no one you know some players as of very recently have like woken up but in general no one really sees demand for 200 gawatts a year of AI chips or you know trillions of dollars of spend a year in the semiconductor supply chain.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 11th of 28 in this recording

    Interesting

  11. Our readingYou do not have to believe in AGI to believe in the timelines on which the United States wins the semiconductor race.

    Their wordsBut I don't know like I don't know what fast timelines means, right? Like I I like don't think you have to believe in AGI to have the timelines where the US wins.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 15th of 28 in this recording

    AGIAmerica

    Interesting

  13. Our readingTSMC will never throw Apple off its leading node; Apple will simply stop mattering enough for TSMC to cater to it.

    Their wordsand so I don't think TSMC would kick out Apple. I think Apple will become a smaller and smaller and smaller percentage of TSMC's revenue and therefore be less relevant for TSMC to cater to their demands.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 26th of 28 in this recording

    Apple

    Interesting

  15. Our readingHuawei, if it had access to TSMC, would arguably be a better chip company than Nvidia.

    Their wordsAnd Huawei has a bigger pool in China. It's very arguable that Huawei, if they had TSMC, would be better than Nvidia.

    ↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 27th of 28 in this recording

    China

    Interesting

  17. Our readingEvacuating TSMC's engineers and destroying its fabs would leave China with the strongest semiconductor supply chain in the world.

    Their wordsUm, just shipping out all the engineers and blowing up the fabs means China has a stronger semiconductor supply chain than the rest of the world, right?

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

    China

    Interesting
  18. 13 months earlier

  19. Our readingBuilding fabs in America does not reduce dependence on Taiwan: without Hsinchu, TSMC's Arizona plant would stop producing within a couple of years.

    Their wordsArizona is a paperweight. If Hsinchu disappeared off the face of the planet, within a year, couple years, Arizona would stop producing too.

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 12th of 44 in this recording

    America

    Interesting

  21. Our readingCompletely in-sourcing semiconductor manufacturing to the United States is possible, and it would take a decade and a trillion dollars.

    Their wordsAnd so going back, can the US build it here? Yes, but it's going to take a ton of money. I truly think to revolutionize and completely in-source semiconductors would take a decade and a trillion dollars.

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 14th of 44 in this recording

    America

    Interesting

  23. Our readingThe largest hole in America's GPU export controls was never physical smuggling but renting: ByteDance runs over 500,000 GPUs it does not own.

    Their wordsOne is ByteDance, arguably is the largest smuggler of GPUs for China. China's not supposed to have GPUs. ByteDance has over 500,000 GPUs. Why? Because they're all rented from companies around the world.

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 28th of 44 in this recording

    ChinaAmerica

    Interesting

  25. Our readingThe copyright problem in AI training already has a legal solution nobody is taking: train the models in Japan, where copyright does not apply to training data.

    Their wordsSo, Japan has a law which you're allowed to train on any training data and copyrights don't apply if you want to train a model, A. B, Japan has 9 gigawatts of curtailed nuclear power. C, Japan is allowed under the AI diffusion rule to import as many GPUs as they'd like.

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 31st of 44 in this recording

    energynuclear powerJapan

    Interesting

  27. Our readingGoogle will never become NVIDIA's competitor, because it has never had the DNA to sell a product and its TPU, cloud, DeepMind and search teams serve different customers.

    Their wordsBut Google has never had that DNA of like, "This is a product we should sell." The Google Cloud, which is a separate organization from the TPU team, which is a separate organization from the DeepMind team, which is a separate organization from the Search team. There's a lot of bureaucracy here.

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 34th of 44 in this recording

    Google

    Interesting

  29. Our readingAMD's hardware is in many ways better than NVIDIA's and it does not matter, because the software gulf is too large and AMD has never resourced closing it.

    Their wordsAnd they're decent, their hardware is better in many ways than in NVIDIA's. The problem is their software is really bad and I think they're getting better, right? They're getting better, faster, but the gulf is so large and they don't spend enough resources on it or haven't historically, right?

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 35th of 44 in this recording

    Interesting