What Dylan Patel thinks about semiconductors
Founder and chief executive of SemiAnalysis, the research firm that tracks the semiconductor and AI data-centre supply chain.
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22 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.
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Their wordsif improvement stopped you know here the value of an H100 is now predicated on the value that GPD 5.4 four can get out of it instead of the value that GP4 can get out of it and the margins and all that stuff that these labs are doing and they're in a competitive environment so their margins can't go to infinity. Um so you sort of have this like dynamic that is quite interesting in that an H100 is worth more today than it was 3 years ago.
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 1st of 28 in this recording
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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
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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
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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
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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
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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
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Their wordsSo when you look at inference at let's say 100 tokens a second for deepseek and kimk 2.5 hopper versus blackwell the performance difference is on the order of 20x
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 12th of 28 in this recording
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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
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Their wordsand even if you take a generous interpretation of 128 * 8 gig transfers, you're at 128 gigabytes a second for the same shoreline versus 2 and a half terabytes a second. There's a there's an order of magnitude difference in bandwidth per edge area.
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 17th of 28 in this recording
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Their wordsUm DRAM gets released goes to AI chips who are willing to do longer term contracts, willing to pay higher margins, etc., etc. because at the end of the day, the margin that they extract is much larger from the end user or whatever. Um, and so this this this probably leads to like people hating AI even more, right?
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 18th of 28 in this recording
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Their wordsUh, Micron bought a fab from a company in Taiwan that makes lagging edge chips, right? Um, Heinix and Samsung are doing, you know, some pretty crazy things to try and expand capacity at their existing fabs, uh, that also have like very large knock-on effects in the economy. And so, hey, why can't we build more capacity is like there's nowhere to put the tools, right?
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 19th of 28 in this recording
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Their wordsbut I think he can build the uh clean room. It'll take a year or two. Maybe initially it won't be super fast, but then over time you'll get faster and faster at it. But then the really complex part is actually developing the process technology and building wafers. And I don't think he can develop that uh quickly. I think that has a lot of built-up knowledge.
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 20th of 28 in this recording
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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
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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
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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
- 13 months earlier
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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
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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
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Their wordsBut the funniest thing I think that comes out of this is Jevons paradox is true. AWS pricing for H100s has gone up over the last couple of weeks, since a little bit after Christmas, since V3 was launched, AWS H100 pricing has gone up.
↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 26th of 44 in this recording
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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
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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
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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
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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