Dylan Patel
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.
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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 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
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Their wordsUm I think at least this year we're going to see margins for the model vendors go up a lot, right? Because they're so capacity constrained, they have to demand destroy demand, right? there is there's no way they can continue anthropic can continue at the current pace without destroying demand.
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 3rd 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 wordsBecause Enthropic saw it before Google. And then Google had Nano Banano and Gemini 3 which caused their user metrics to skyrocket and leadership at Google was like oh and then they started making the statement of we have to double compute every is it 6 months or I don't remember the exact number that they said.
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 5th 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 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
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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
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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 wordsI think they'll have working tools. I don't think that they'll be able to manufacture a bunch yet, right? You know, there's they're sort of having it work and then there's production hell, right?
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 13th of 28 in this recording
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Their wordsAs we move from, you know, hey, [clears throat] these companies are selling tokens where they provide the entire uh reasoning chain and all that to uh selling automated, you know, white collar work, right? Automated software engineer, send them the request, they give you the result back and there's a bunch of thinking on the back end that they don't show you. The ability to distill out of American models into Chinese models will be harder.
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 14th 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 wordsThey could release claw slow mode and have an increase in tokens per dollar by a significant amount. Um they could probably like reduce the price of Opus 46 by you know 4x 5x and reduce the speed by another by maybe just like 2x like the curve on inference throughput versus speed is there already just on hm um and yet they don't um because no one actually wants to use a slow model
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 16th 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 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
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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
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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
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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
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Their wordswell the model the compute efficiency gains you get from research are so large you actually want most of your compute to go to research not to development because you know all these researchers are generating new ideas trying them out testing them and continuing to march along this and push the prao optimal curve of scaling laws further and further and further
↗Dylan Patel — The single biggest bottleneck to scaling AI computeyoutube.com 25th 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 wordsI think it's even more impressive what OpenAI did in 2022. At the time, no one believed in mixture of experts models at Google who had all the researchers. OpenAI had such little compute and they devoted all of their compute for many months, all of it, 100% for many months to GPT-4 with a brand-new architecture with no belief that, "Hey, let me spend a couple of hundred million dollars, which is all of the money I have on this model." That is truly YOLO.
↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 4th of 44 in this recording
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LLMswarinnovationideas vs execution
Their wordsTo some extent, training a model does effectively nothing. They have a model. The thing that Dario is sort of speaking to is the implementation of that model, once trained to then create huge economic growth, huge increases in military capabilities, huge increases in productivity of people, betterment of lives.
↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 7th of 44 in this recording