@Asianometry & Dylan Patel — How the semiconductor industry actually works

Summary

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Dwarkesh Podcast — Episode Summary

Episode: @Asianometry & Dylan Patel — How the Semiconductor Industry Actually Works

Host: Dwarkesh Patel
Guests: Dylan Patel (Semianalysis), John Y ("Asianometry")
Date: October 2, 2024

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Overview

This episode brings together two of the internet’s most deeply informed voices on semiconductors: Dylan Patel of Semianalysis and John Y of the Asianometry YouTube channel. Over two hours, they break down the tangled inner workings of the semiconductor industry—from global supply chains and espionage to manufacturing bottlenecks and the coming AI datacenter arms race. The conversation flows from the micro (how process nodes are developed and knowledge is tacitly passed on) to the macro (why China and the US structure their industries the way they do, and what happens if Taiwan is knocked out).

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Key Discussion Points & Insights

1. Industry Legends, Anonymity & the Transfer of Knowledge

• [00:00–04:41] Playful banter introduces the guests. John Y discusses his anonymity and the reasoning behind Asianometry’s masked persona. Early stories feature top industry figures such as Richard Chang and Liang Mongson, illustrating how key personnel movements and talent “poaching” between TSMC, Samsung, and SMIC shaped industry breakthroughs.
  • “There's a bunch of masters, they teach apprentices, and they just pass this sacred knowledge down.” – John Y [09:32]

2. Espionage, Poaching, and the Difficulty of Restricting China

• [01:15–08:14] The group discusses how espionage, talent relocation, and compartmentalization play into China’s bid to catch up with the US and Taiwan. TSMC alumni helped accelerate both Samsung and SMIC. Unlike the West, China aggressively pulls in foreign information and talent, often by offering better pay and opportunities.
  • “China just hasn't...they clearly are still not scale-pilled in my view.” – Dylan Patel [03:50]

3. Semiconductor Manufacturing: How Process Innovation Actually Happens

• [08:25–10:36] The process of developing next-gen chips (process nodes) is explained as iterative, highly specialized, and dependent on “master-apprentice” knowledge transfer, rather than easily documented know-how. Deep technical specialization and institutional knowledge are critical to the “intuitive” process improvements.

4. Centralization vs. Decentralization in AI Compute

• [11:10–18:49] The panel analyzes how the US and China approach AI development differently, especially in terms of centralizing vs. decentralizing compute resources:
  • US: Multiple, decentralized efforts (OpenAI, Anthropic, Meta, Microsoft, etc.).
  • China: Could centralize compute quickly, leveraging massive power infrastructure.
  • “If you’re Xi Jinping and you’re scale-pilled, you must now centralize the compute resources.” – Dylan Patel [12:08]
  • Discussion on China’s unmatched ability to marshal gigawatts of power for data centers.

5. Limits of Export Controls and Sanctions

• [19:11–26:11] Dylan and John debate the effectiveness of US export controls on chipmaking equipment and know-how. While specialized tools are restricted, loopholes and ongoing sales (especially from ASML and Applied Materials) let China keep importing advanced tools, sometimes officially, sometimes not. Real national security restrictions are much “messier” in practice.
  • “Chip manufacturing is like 3D chess or like a massive jigsaw puzzle... Year by year, they keep updating [export restrictions]… They haven’t just taken a bat to the table and bricked it.” – Dylan Patel [23:18]

6. China’s Domestic Capabilities: Potential and Barriers

• [28:04–33:15] Despite sanctions, SMIC is expanding node capacity, often by moving tool usage to where sanctions are laxer. Yield and efficiency remain pain points. The Chinese tech sector (e.g., Huawei) is described as relentless and “cracked,” despite working with less advanced tools. The importance of manufacturing culture and perpetual struggle is emphasized.
  • “Huawei outcompetes Western firms regularly with two hands tied behind their back.” – Dylan Patel [33:21]
  • “A word that I hear a lot with regards to Huawei is ‘struggle.’ ...They go crazy because they think that in five years they’re going to fight the United States.” – John Y [37:07]

7. Barriers to Vertical Integration & Economic Logic of Modern Semiconductors

• [39:01–44:06] The modern supply chain is highly stratified, with every layer dominated by just a few players. Vertical integration gave way as independent suppliers brought rapid innovations the in-house teams could not match. Leading-edge nodes are now so expensive that only a handful of customers (Apple, Nvidia, etc.) can fund the next leap—a dynamic similar to foundation AI model development.

8. Geopolitical Shock: If Taiwan Goes Offline

• [45:53–49:20] What happens if Taiwan’s fabs are disrupted? Catastrophe. Not just for leading-edge chips but also for the so-called “trailing edge” (automobiles, consumer electronics, etc.), which would see global supply chain havoc within weeks and lasting years.
  • “You’re not gonna get your fridge, you’re not gonna get your cars, you’re not gonna get everything… It’s terrible.” – John Y [46:06]
  • “Every Tesla door handle has like four chips.” – Dylan Patel [47:40]

9. Tacit Knowledge vs. Accessible Know-How: Contrasts with AI

• [49:31–53:31] The pipeline to become an AI researcher is fluid and open; in contrast, semiconductors are deeply siloed, require years of apprenticeship or graduate training, and rarely share information outside the company or country. The industry is so stratified that “nobody knows the whole stack.”
  • “One, you specialize like crazy; two, you can’t just pick it up.” – Dylan Patel [50:48]
  • “Semiconductors have been shut down since the 1960s, 1970s, basically.” – John Y [52:11]

10. How the Industry Coordinates Progress

• [53:31–57:17] Despite nobody knowing everything, agreed-upon roadmaps (inspired, at first, by Moore’s Law) and intense cross-company gossip set collective direction. There’s rarely detailed “blueprints”; instead, progress happens as breakthroughs filter from company to company and as abstraction layers solidify.
  • “God came and proclaimed it. We will shrink density 2x every two years. Gordon Moore, he made an observation and then like...the entire industry was like, ‘This is the word of God.’” – Dylan Patel [54:33]

11. AI & Semiconductor Synergy: The Next Order Improvements

• [58:01–64:55] The search space for chip improvements is near-infinite. While process node improvements are slowing, big jumps can still come from architectural and system-level changes—potentially 100x or more efficiency, especially as AI is used to optimize chip design and layout.
  • “There are 100x gains from architecture, even if we literally stop shrinking.” – Dylan Patel [60:41]
  • “Hardware has a huge influence on the model architecture that’s optimal.” – Dylan Patel [63:53]

12. Divergence of Model Architectures Between China and US

• [64:34–68:03] Different hardware constraints (memory bandwidth, compute/memory tradeoffs) and business needs will increasingly shape divergent model architectures in China and the US, resulting in models tuned for different densities, types of data (e.g., China’s lead in video/image), etc.

13. Scaling AI Clusters: Limits, Power, and Surging Investment

• [70:28–87:46] The demand for compute is exploding. Building ever-larger clusters is bottlenecked more by data center capacity and power delivery than by chip production in the near term. US tech titans are in an arms race, with cluster sizes and total global compute scaling by multiples each year. Funding is set to soar, with forecasts for hundreds of billions in capex being raised.
  • “We’re not even close to the dot-com bubble. Right. Why would this bubble not be bigger?” – Dylan Patel [107:05]
  • “If you believe in scaling...I could see like 60, 70, 80% [of leading edge] for AI, like, yeah, no problem.” – Dylan Patel [91:52]

14. Market Bubbles, ROI, and Pascal’s Wager

• [99:14–109:48] The panel draws parallels to past tech bubbles, noting big tech’s willingness to operate according to “Pascal’s Wager”: the risk of underinvesting in AGI is perceived as existentially higher than overinvesting, especially among company leadership.
  • “The risk of underinvesting is worse than the risk of overinvesting.” – Satya Nadella, paraphrased by Dylan Patel [101:08]

15. Personal Histories & Building in the Industry

• [110:19–129:32] John and Dylan share origin stories—John’s transition from tourist videos to deep dives on business history; Dylan’s rise from forum “shitposter” in Georgia to international semiconductor consultant and newsletter founder. Both describe years of obsession, relentless research, and an outsider’s perseverance.
  • “You can just buy engineering textbooks, right, and read them. If you bang your head against the wall, you learn it.” – Dylan Patel [118:48]

16. Opportunities for Entrepreneurs

• [126:10–129:32] If you want to break in, both suggest looking for weak spots in the stack. Memory (DRAM/HBM) is suggested as a key frontier, but success comes from relentless curiosity and efficiency more than picking a precise spot.
  • “There is more opportunity today than any other time in human history, in my view. ...If you have a passion for copper wires, I promise to God, if you make the best copper wires, you’ll make a shitload of money.” – Dylan Patel [128:54]

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Notable Quotes

• “Chip manufacturing is like 3D chess ...if they [regulators] like, you either have to go full bat to the table or chill out ...or they make the missing pieces domestically.”
  — Dylan Patel [23:18]
• “If I do not finish this video, my family will be pillaged.”
  — John Y [114:07] (on Asianometry’s work ethic)
• “I have tremendous belief in like GPT-5, Why area? Because what we've seen already...every time you increment the cost down of that intelligence, the amount of usage increases massively.”
  — Dylan Patel [105:20]
• “Nobody knows the whole stack.”
  — John Y [52:53]

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Selected Timestamps for Key Sections

• [08:43] How process innovation, “master-apprentice” system works
• [15:22] Centralization vs. decentralization in Chinese and US AI labs
• [23:15] Why export controls are like removing pieces from a puzzle
• [33:15] Why Huawei is so dominant — and the role of “struggle”
• [45:53] The global shock if Taiwan’s semiconductor fabs go offline
• [49:31] Why semiconductor knowledge is tacit and not open, unlike AI
• [54:33] Moore’s Law as the industry’s “Word of God”
• [60:41] Room for 100x hardware efficiency gains even without new nodes
• [64:55] How hardware constrains and shapes future AI model architectures
• [99:14] Pascal’s Wager and why tech firms over-invest in AI/compute
• [110:41] Asianometry’s channel origin story
• [118:46] Dylan’s journey from internet shitposter to industry consultant
• [126:59] Entrepreneurial opportunity in memory and foundational hardware

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Tone and Style

This episode is conversational, fast-paced, and threaded with meme-y, self-deprecating humor—blending deep technical expertise with cultural commentary and personal anecdotes. The guests are unfiltered, excited, and quick to debate; a must-listen for anyone wanting non-dogmatic, first-principles thinking on arguably the world’s most important industrial sector.

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Further Resources

• Dylan Patel: semianalysis.com
• Asianometry YouTube: YouTube Channel
• Dwarkesh Podcast: www.dwarkesh.com

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End of summary.

Transcript

[Dwarkesh Patel]

Today I'm chatting with Dylan Patel, who runs Semianalysis, and John, who runs the Asianometry YouTube channel.

[Dylan Patel]

Does he have a last name?

[John Y]

No, I do not.

[Dwarkesh Patel]

No, I'm just kidding.

[John Y]

John Y.

[Dwarkesh Patel]

That's right. Is it.

[John Y]

I'm John Y.

[Dylan Patel]

Wait, why is it only one letter?

[John Y]

Because Y is the best letter.

[Dylan Patel]

Why is your face covered?

[John Y]

Why not?

[Dwarkesh Patel]

Seriously, why is it covered?

[John Y]

Because I'm afraid of looking myself get older and fatter over the years.

[Dwarkesh Patel]

But so, seriously, it's like anonymity, right? Anonymity.

[John Y]

Okay. Yeah.

[Dwarkesh Patel]

By the way, so you know what Dylan's middle name is?

[John Y]

Actually, no, I don't know. He told me.

[Dylan Patel]

But what's my father's name?

[John Y]

I'm not going to say it, but I remember.

[Dylan Patel]

You could say.

[John Y]

You could say it.

[Dylan Patel]

It's fine.

[Dwarkesh Patel]

Sanjay.

[Dylan Patel]

Yes. What's his middle name?

[Dwarkesh Patel]

Sanjay. That's right.

[John Y]

Wow.

[Dwarkesh Patel]

So I'm Dwarkash Sanjay Patel. He's Dylan Sanjay Patel. It's like literally my white name.

[Dylan Patel]

Wow. It's unfortunate. My parents decided between my older brother and me to give me a white name. And I could have been Dwarkish. Like, you know how amaz it would have been if we had the same name, like Butterfly Effect and all that. Probably would have all wouldn't have turned out the same way, but, like, maybe.

[Dwarkesh Patel]

It would have been even closer. We would have met each other sooner. You know, who else is named Dwarkesh Sanjay Patel in the world?

[Dylan Patel]

Yeah. Yeah, yeah, yeah.

[Dwarkesh Patel]

All right, first question. If you're a Xi Jinping and you're scaling pilled, what is it that you do?

[Dylan Patel]

Don't answer that question, John. That's bad for AI safety.

[John Y]

I would basically be contacting every foreigner, I would be contacting every Chinese national with family back home and saying, I want information. I want to know your recipes. I want to know.

[Dwarkesh Patel]

I want to kind of like AI Lab foreigners or hardware foreigners. Foreigners.

[Dylan Patel]

Honey potting OpenAI.

[John Y]

I would basically like, this is totally off cycle, but like, this is off the reservation. But like, I was doing a video about Yugoslavia's nuclear program. Nuclear weapons program started. Absolutely nothing. One guy from Paris and then one guy in Paris, he showed up and he was like. And then he had. Who knows what he did. He knows a little bit about making atomic nuclear weapons. But like, he was like, okay, well, do I need help? And then the state secret police is like, I will get you everything. And then like, I shouldn't do that. I must get you everything. And for like a span of four years, they basically, they drew up A list. What do you need? What do you want? What are you going to do? What is it going to be for? And they just, State police just got everything. If I was running a country and I needed catch up on that, that's the sort of thing that I would be doing.

[Dwarkesh Patel]

So, okay, let's talk about the espionage. So what is the most valuable piece of. If you could have this blueprint, this one megabyte of information. Do you want it from TSMC? Do you want it from Nvidia? Do you want it from OpenAI? What is the first thing you would try to steal?

[Dylan Patel]

I mean, I guess you have to stack every layer, right? And I think the beautiful thing about AI is because it's growing so freaking fast, every layer is being stressed to some incredible degree. Of course China has been hacking ASML for over 5 years and ASML is kind of like, oh, it's fine. The Dutch government, government's really pissed off, but it's fine. Right. I think it's. They already have those files, right. In my view, it's just a, it's a very difficult thing to build. Right. I think the same applies for like fab recipes, right? They can poach Taiwanese nationals very like, not, not that difficult, right? Because TSMC employees do not make absurd amounts of money. You can just poach them and give them a much better life. And they have. Right. A lot of smics employees are TSMC know, Taiwanese nationals. Right. A lot of the really good ones, high up ones especially. Right. And then you go up like the next layers of the stack and it's like, I think, I think, yeah, of course there's tons of model secrets. But then like, you know, how many of those model secrets do you not already have and you just haven't deployed or implemented, you know, organized. Right. That's the, that's the one thing I would say is like China just hasn't they, they clearly are still not scale.

[John Y]

Pilled in my view.

[Dwarkesh Patel]

So these people are, I don't know if you could hire them. It probably worth a lot to you, right? Because you're building a fab that's worth tens of billions of dollars. And this talent is like they know a lot of shit. How often do they get poached? Do they get poached by foreign adversaries or do they just get poached by other companies within the same industry but in the same country? And then, yeah, why doesn't that sort of drive up their wages?

[John Y]

I think it's because it's very compartmentalized and I think back in the 2000s, prior to TS, before SMIC got big, it was actually much more kind of open, more flat. I think after that there was like, after the among Song and after all the Samsung issues and after all the SMIC's rise, when there, you literally saw.

[Dylan Patel]

I think you should tell that story, actually. The TSMC guy that went to Samsung and SMIC and all that. I think you should tell that story.

[John Y]

There are two stories. There's a guy, he ran a semiconductor company in Taiwan called Worldwide Semiconductor. And this guy, Richard Chang was very religious. I mean, all the TSMC people are pretty religious. But like, he in particularly was very fervent and he wanted to bring religion to China. So after he sold his company to tsmc, huge Cooper tsmc, he worked there for about eight or nine months. And he was like, all right, I'll go to China. Because back then the relations between China and Taiwan were much more different. And so he goes over there at Shanghai, says, we'll give you a bunch of money. And then Richard Chang basically recruits half of a whole bunch. It's like a conga line of Taiwanese line. Just like, they get on the plane, they fly on over. And generally that's actually a lot of acceleration points within China's semiconductor industry. It's from talent flowing from Taiwan. And then the second thing was Liang Mongson. Liang Mongsong is a nut. And I've met him. I've not met him. I met people who work with him and they say he is a nut. He is probably on the spectrum and he's. He does not care about people. He does not care about business. He does not care about anything. He wants to take it to the limit. The only thing, that's the only thing he cares about. He worked from TSMC. Literal genius, 300 patents or whatever, 285 works all the way to like the top, top tier. And then one day he decides he loses out on some sort of power game within TSMC and gets demoted.

[Dylan Patel]

And he was like head of R and D, right? Or something.

[John Y]

He was like one of the top R and D. He was like second or third place.

[Dylan Patel]

And it was for the head of R and D position.

[John Y]

Basically correct. More of the head of R and position. He's like, I can't deal with this. And he goes to Samsung and he steals a whole bunch of talent from tsmc. Literally, again, conga line goes and just emails. People say, we will pay. At some point, some of these people were getting paid more than the Samsung chairman, which not really Comparable, but, like, you know what I mean?

[Dylan Patel]

So they're going, isn't the Samsung chairman usually, like. Like, part of the family that owns Samsung?

[John Y]

Correct?

[Dylan Patel]

The movie. Okay.

[John Y]

Yeah.

[Dylan Patel]

So it's like, kind of irrelevant.

[John Y]

Yeah. So it's a. But then he goes over there and he's like, well, I'm like, we will make Samsung into this monster. We forget everything. Forget all of the stuff you've been trying to do. It, like, incremental. Toss that out. We are going to the leading edge, and that is it. They go to the leading edge.

[Dylan Patel]

The guys, like, they win Apple's business.

[John Y]

They win Apple's business. They win it back from tsmc, or did they win it back from tsmc?

[Dylan Patel]

They had a portion of the.

[John Y]

They had a big portion of it. And then tsmc, Morris Tang is like, at this time was running the company, and he's like, I'm not letting this happen. Because that guy, toxic to work for as well, but also goddamn brilliant and also, like, very good at motivating people. He's like, we will work literally day or night, sets up what is called the Nightingale army, where you have. They split a bunch of people and they say, you are working R and D night shift. There is no rest at the TSMC fab. You will go in. As you go in, there'll be a day shift going out. They called it the. It's like you're burning your liver. Because in Taiwan, they say, like, if you get old, like, as you work, you're sacrificing your liver. They call it the liver buster. So they basically did this Nightingale armory for like a year, two years. They finished Finfet. They basically just blow away Samsung, and at the same time, they sue Neon Mongson directly for stealing trade secrets. Samsung basically separates from Nel Mongsong, and Nel Monsong goes to smic.

[Dylan Patel]

And so Samsung, like, at one point was better than tsmc. And then, yeah, he goes to smic. And SMIC is now better than. Well, or not better. But they caught up rapidly as well, after.

[John Y]

Very rapid. That guy's a genius. That's the guy who's a genius. I mean, I don't even know what to say about him. He's like 78 and he's, like, beyond brilliant. Does not care about people.

[Dwarkesh Patel]

What is research to make the next process node look like? Is it just a matter of 100 researchers go in? They do the next N plus 1, then the next morning, the next hundred researchers go in.

[John Y]

It's experiments. They have a recipe and what they do every recipe. A TSMC recipe is the culmination of a long, long years of research. It's highly secret. And the idea is that what you're going to do is that you go, you look at one particular part of it and you say experiment, run experiment. Is it better? Is it not? Is it better or not? Kind of a thing like that.

[Dylan Patel]

You're basically, it's multivariable problem that each, every single tool, sequentially you're processing the whole thing. You turn up knobs up and down on every single tool. You can increase the pressure on this one specific deposition tool.

[Dwarkesh Patel]

And what are you trying to measure? Is it like, does it increase yield or what is it that?

[Dylan Patel]

It's not. It's yield, it's performance, it's power. It's not just a one, it's not just better or worse, right? It's a multivariable search space.

[Dwarkesh Patel]

And what do these people know such that they can do this is they understand the chemistry and physics.

[Dylan Patel]

So it's a lot of intuition. But yeah, it's PhDs in chemistry, PhDs in physics, PhDs in EE.

[John Y]

Brilliant geniuses people. And they all just, and they don't.

[Dylan Patel]

Even know about like the end chip. A lot of times it's like, oh, I am an engineer and all I focus on is how hydrogen fluoride etches this, right? And that's all I know. And like if I do it at different pressures, if I do it at different temperatures, if I do it with a slightly different recipe of chemicals, it changes everything.

[John Y]

I remember like someone told me this when I was speaking, like, how did America lose the ability to do this sort of thing? Like etch and hydrofluoric and acid. All of that I told them like he told me basically was like, it's, it's very apprentice, master, apprentice. Like, you know, in Star Wars Sith, there's only one, right? Master, apprentice, master apprentice. It used to be that there is a master, there's an apprentice, and they pass on their secret knowledge. This guy knows nothing but etch. Nothing but etch. Over time, the apprentices stopped coming. And then in the end the apprentices moved to Taiwan. And that's the same way it's still run. Like you have NTU and nthu, Tsinghua University, National Tsinghua University. There's a bunch of masters, they teach apprentices and they just pass this sacred knowledge down.

[Dwarkesh Patel]

Who are the most AGI pilled people in the supply chain? Is there anybody that said, like the.

[Dylan Patel]

Podcast, I gotta have my phone call with Colette right now.

[Dwarkesh Patel]

Okay, go for it.

[Dylan Patel]

Sorry, sorry.

[Dwarkesh Patel]

Could we mention that the podcast that Nvidia has got is calling Dylan for the. To update him on the earnings call?

[Dylan Patel]

Well, it's not this. Not exactly, but go for it.

[John Y]

Go for it. Yeah.

[Dwarkesh Patel]

Dylan is back from his call with Jensen Huang.

[Dylan Patel]

He was not with Jensen. Jesus.

[Dwarkesh Patel]

What did they tell you?

[Dylan Patel]

Huh?

[Dwarkesh Patel]

What did they tell you about next year's earnings?

[Dylan Patel]

No, it's just color around like a Hopper Blackwell, like margins. It's like quite boring stuff for most people. I think it's interesting though.

[Dwarkesh Patel]

I guess we could start talking about Nvidia.

[Dylan Patel]

You know what, Before, I think we.

[Dwarkesh Patel]

Should go back to China.

[Dylan Patel]

There's like a lot of points there.

[Dwarkesh Patel]

All right, we covered the chips themselves. How do they get like the 10 gigawatt data center up? What else do they need?

[Dylan Patel]

I think there is a true question of how decentralized do you go versus centralized, right? And if you look in the U.S. right, as far as like labs and such, the, you know, OpenAI xai anthropic, and then Microsoft having their own effort, anthropic having their own efforts despite having their partner. And then meta and you know, you go down the list, it's like there is quite a decentralization and then all the startups, like, interesting startups that are out there doing stuff. There's quite a decentralization of efforts today in China. It is still quite decentralized, right? It's not like Alibaba Baidu, you are the champions, right? You have like deep seek, like, who the hell are you? Does government even support you? Like doing amazing stuff, right? If you are zingping and scale pilled.

[Dwarkesh Patel]

Interesting.

[Dylan Patel]

You must now centralize the compute resources, right? Because you have, you have sanctions on how many Nvidia GPUs you can get in now. They're still north of a million a year, right? Even post October last year, sanctions, we still have more than a million H20s and other Hopper GPUs getting in through other means. But legally, like the H20s. And then on top of that, you have your domestic chips, right? But that's less than a million chips. So then when you look at it, it's like, oh, well, we're still talking about a million chips. The scale of data centers people are training on today over the next six months is 100,000 GPUs. OpenAI XAI. Right? These are quite well documented and others. But in China, they have no individual system of that scale yet, right? So then the question is like, how do we get there. You know, no company has had the centralization push to have a cluster that large and train on it yet, at least publicly, like well known. And the best models seem to be from a company that has got like 10,000 GPUs right. Or 16,000 GPUs right? So it's not, it's not quite a, quite as centralized as the US companies are. And the US companies are quite decentralized. If you're Xi Jinping and you're scale pilled, do you just say XYZ company is now in charge and every GPU goes to one place and then you don't have the same issues as the U.S. right? In the U.S. we have a big problem with being able to build big enough data centers, being able to build substations and transformers and all this that are large enough in a dense area. China has no issue with that at all because their supply chain adds as much power as half of Europe every year, right? Like, or some, some absurd statistics, right? So they're building transformer substations, they're building new power plants constantly. So they have no problem with like getting power density. And you go look at like bitcoin mining right around the Three Gorges Dam. At one point at least There was like 10 gigawatts of like Bitcoin mining estimated, right? Which you know, we're talking about, you know, gigawatt data centers are coming over, you know, 26, 27 in the, or 26 in the U.S. or 27, right. You know, sort of this is an absurd scale, relatively. Right. We don't have gigawatt data centers, you know, ready, but like China could just build it in six months, I think around the Three Gorges Dam or many other places, right? Because they have, they have the ability to do the substations, they have the, they have the power generation capabilities. Everything can be like done like a flip of a switch, but they haven't done it yet. And then they can centralize the chips like crazy right now. Oh, oh, a million chips that Nvidia shipping in Q3 and Q4, the H20, let's just put them all in this one data center. They just haven't had that centralization effort.

[John Y]

Well, you can argue that the more you centralize it, the more you start building this monstrous thing within the industry, you start getting attention to it and then suddenly, lo and behold, you have a little bit of a little worm in there suddenly while you're doing your big training run. Oh, this GPU off. Oh, this gpu. Oh no, oh no. Oh no.

[Dylan Patel]

I don't know if it's like Chinese.

[Dwarkesh Patel]

Accent by the way, just to be clear.

[Dylan Patel]

John is, is East Asian.

[John Y]

He's Chinese. I am of East Asian descent.

[Dylan Patel]

Half Taiwanese have Chinese. Right?

[John Y]

That is right.

[Dylan Patel]

But like, I think, I think, I don't know if that's like as simple as that to like, because, because training systems are like fire. Like they're, they're water. Is it watergated? Firewalled? What is it called? Not firewalled. I don't know. There's a word for that where they're not like they're gapped. Air gapped. I think you're going through like the.

[Dwarkesh Patel]

All the, like the four elements of dirt. They're earth protected.

[Dylan Patel]

Water, fire. If you're using pig and you're scale pilled, you kind of like unite the air benders, fire benders. You know we got the avatar, right? Like you have to build the avatar. Okay, I think, I think that's possible. The question is like, does that slow down your research? Do you like crush like cracked people, like deep seek who are like clearly like not being, you know, influenced by the government and put some like idiot.

[John Y]

Like, you know, idiot bureaucrat at the top. Suddenly he's all thinking about like, you know, all these politics and he's trying to deal with all these different things. Suddenly you have a single point of failure. And that's a, that's, that's bad.

[Dylan Patel]

But I mean, on the flip side, right, like there is like obviously immense gains from being centralized because of the scaling loss, right? And then the flip side is compute efficiency is obviously going to be hurt because you can't experiment and like have different people lead and try their efforts as much if you're less centralized or more centralized. So it's like there is a balancing act there.

[Dwarkesh Patel]

The fact that they can centralize. I didn't think about this, but that is actually like. Because, you know, even if America as a whole is getting millions of GPUs a year, the fact that any one company is only getting hundreds of thousands or less means that there's no one person who can do a single trading run as big in America as if like China as a whole decides to do one together. The, the 10 gigawatts you mentioned near the Three Gorges Dam, is it like literally, like how widespread? Is it like a state? Is it like one wire? Like how.

[Dylan Patel]

I think like between not just the dam itself, but like also all of the coal. There's some nuclear reactors there, I believe as well, between all of and like renewables, like solar and wind. Between all of that in that region, there is an absurd amount of concentrated power that could be built. I don't think it's like, I'm not saying it's like one button, but it's like, hey, within x mile radius, right. Is more, more of like the correct way to frame it. And that's how the, that's how the labs are also framing it. Right. Like, I think in the us, if.

[Dwarkesh Patel]

They started right now, like, how long does it take to build the biggest. The biggest AI data center that in the world?

[Dylan Patel]

You know, actually, I think, I think the other thing is like, could we notice it? I don't think so. Because the amount of like factories that are being spun up, the amount of other construction, manufacturing, et cetera, that's being built, a gigawatt is actually like a drop in the bucket, right? Like, a gigawatt is not a lot of power. 10 gigawatts is not an absurd amount of power. Right. It's okay. Yes. It's like hundreds of thousands of homes, right? Yeah. Millions of people. But it's like you got 1.4 billion people. You got like, most of the world's like, extremely energy intensive, like refining and like, you know, rare earth refining and all these manufacturing industries are here. It would be very easy to hide it, really. It'd be very easy to just like shut down. Like, I think the largest aluminum mill in the world is there. And it's like, it's like north of 5 gigawatts alone. It's like, oh, what, what could we tell if they stopped making aluminum there and instead started like making, you know, AIs there or making AI there. I don't know if we could tell. Right. Because they could also just easily spawn like 10 other aluminum mills, make up for the production and be fine. Right. So there's many ways for them to hide compute as well.

[Dwarkesh Patel]

To the extent that you could just take out a 5 gigawatt aluminum refining center and build a giant data center there, then I guess the way to control Chinese AI has to be the chips, because everything else. So how do you just like walk me through how many chips do they have now? How many will they have in the future? Is that in comparison to us and the rest of the world?

[Dylan Patel]

Yeah. So in the world, I mean, the world we live in is they are not restricted at all in like the physical infrastructure side of things in terms of power, data centers, et cetera, because their supply chain is built for that. Right. And it's pretty easy to pivot that. Whereas the US adds so little power each year and Europe loses power every year, the Western sort of industry for power is non existent in comparison. Right? But on the flip side is quote unquote Western including Taiwan manufacture. Chip manufacturing is way, way, way, way, way larger than China's. Especially on leading edge where China theoretically has, you know, depending on the way you look at it, either zero or a very small percentage share. Right? And so there you have equipment, wafer manufacturing, and then you have advanced packaging capacity, Right. And where the US can control China. Right. So advanced packaging capacity is kind of a shot because the vast majority, the largest advanced packaging company in the world was Hong Kong headquartered. They just moved to Singapore. But like, that's effectively like, you know, in a realm where the US can't sanction it. Right. A majority of these other companies are in similar places, right? So advanced packaging capacity is very hard, Right? Advanced packaging is useful for stacking memory, stacking chips on co ops, right? Things like that. Then, then the step down is wafer fabrication. There is immense capability to restrict China there. And despite the US making some sanctions, China in the most recent quarters was like 48% of ASML's revenue, right? So, you know, and like 45% of like applied materials. And you just go down the list. So it's like, obviously it's not being controlled that effectively, but it could be on the equipment side of things. The chip side of things is actually being controlled quite effectively, I think, Right? Like, yes, there is like shipping GPUs through Singapore and Malaysia and other countries in Asia to China. But you know, the amount you can smuggle is quite small. And then the sanctions have limited the chip performance to a point where it's like, you know, this is actually kind of fair. But there is a problem with how everything is restricted, right? Because you want to be able to restrict China from building their own domestic chip manufacturing industry that is better than what we ship them. You want to prevent them from having chips that are better than what we have. And then. Or. And then you want to prevent them from having AIs better. The ultimate goal being, you know, and if you read the restrictions like very clear, it's about AI, even in 2022, which is amazing. Like at least the Commerce Department was kind of a. I pilled. It was like, is is you want to restrict them from having AIs worse than us, right? So starting on the right end, it's like, okay, well if you want to restrict them from having better AIs than us. You have to restrict chips, okay. If you want to restrict them from having chips, you have to let them have at least some level of chip that the west also, that is better than what they can build internally. But currently the restrictions are flipped the other way. They can build better chips in China. Then we restrict them in terms of chips that Nvidia or AMD or intel can sell to China. And so there's sort of a problem there in terms of the equipment that is shipped can be used to build chips that are better than what the Western companies can actually ship them.

[Dwarkesh Patel]

John Dylan seems to think the export controls are kind of a failure. Do you agree with him or.

[John Y]

That is a very interesting question. Because I think it's like, why. Thank you. Like, what do.

[Dylan Patel]

You're so good. Yeah.

[John Y]

Darkness, you're the best. I think it's. I think failure is a tough word to say because I think it's like, what are we trying to achieve? Right? Like, and say they're talking about AI, right?

[Dwarkesh Patel]

Yeah.

[John Y]

When you do sanctions like that, you need such a deep knowledge of the technologies.

[Dylan Patel]

Just taking lithography, right? If your goal is to restrict China from building chips and you just boil it down to like, hey, lithography is 30% of making a chip or 25%, cool, let's sanction lithography. Okay, where do we draw the line? Okay, let me ask, let me figure out where the line is. And if I'm a bureaucrat, if I'm a lawyer at the Commerce Department or what have you. Well, obviously I'm going to go talk to asml and ASML is going to tell me this is the line. Because they know, like, hey, well, you know, this, this, this is, you know, there's like some blending over.

[John Y]

There's like, they're, they're like looking at like, what's going to cost us the most money, right?

[Dylan Patel]

And then they constantly say, like, if you restrict us, then China will have their own industry. Right? And, and the way I like to look at it is like chip manufacturing is like, like 3D chess or like, you know, a massive jigsaw puzzle in that if you take away one piece and China can be like, oh, yeah, that's the piece. Let's put it in. Right? And currently this export restrictions, year by year by year, they keep updating them ever since like 2018 or so 19, right when Trump started and now Biden's, you know, accelerated them. They've been like, they haven't just like, take a bat to the table and like, Brick it, right? Like, it's like, let's take one jigsaw puzzle out, walk away. Oh, shit, let's take two more out. Oh shit, right? Like, you know, it's like instead, if they like, you either have to go kind of like full bat to the frickin, like table wall or chill out, right? And let them do whatever they want. Because the alternative is everything is focused on this thing and they make that. And then now when you take out another two pieces, like, well, I have my domestic industry for this. I can also now make a domestic industry for these. You go deeper into the tech tree or what have you.

[John Y]

It's a very. It's art, right? In the sense that there are technologies out there that can compensate. Like if you believe the belief that lithography is a linchpin within the system is. It's not exactly true, right? At some point, if you keep pulling a thread, other things will start developing to kind of close that loop. And I think that's why I say it's an art, right? I don't think it can stop Chinese semiconductor industry, for the semiconductor industry from progressing. I think that's basically impossible. So the question is, the Chinese government believes in the primacy of semiconductor manufacturing. They used. They've believed it for a long time, but now they really believe it. Right?

[Dylan Patel]

To some extent, the sanctions have made China believe in the importance of the semiconductor industry more than anything else.

[Dwarkesh Patel]

So from an AI perspective, what's the point of export controls then? Because even if, like, if they're going to be able to get these, like, if you're like concerned about AI and they're going to be able to build.

[Dylan Patel]

Well, they're not centralized though, right? So that's the big question is, are they centralized? And then also, you know, there's the belief. I don't really, I'm not sure if I really believe it, but like, you know, prior podcasts there have been people who talked about nationalization, right? In which case, okay, now you're talking about.

[Dwarkesh Patel]

Why are you referring to this ambiguously?

[Dylan Patel]

Well, I think there's a couple. I love Leopold. No, but I think there have been a couple where people have talked about nationalization, Right? But like, if you have, you know, nationalization, then all of a sudden you aggregate all the flops, it's like, no, there's no fucking way. Right? Yeah, China can be centralized enough to compete with each individual US lab. They could have just as many flops in 25 and 26 if they decided they were scale built. Right. Just from Foreign chips for individual model.

[Dwarkesh Patel]

Like in 2026 they can train a 1E27. Like they can release a 1E27 model by 2026.

[Dylan Patel]

Yeah. And then a 28 model, you know, 1E28 model in the works. Right. Like the. They totally could just with foreign chip supply. Right. Just a question of centralization then the question is like do you have as much innovation and compute efficiency wins or what have you get developed when you centralize or does like anthropic and OpenAI and Xai and Google like all develop things and then like secrets kind of shift a little bit in between each other and all that. Like, you know, you end up with that being a better outcome in the long term versus like the nationalization of the U.S. right. If that's possible and like, or, you know, and what happens there. But China could absolutely have it in 26:27 if they have the desire to. And that's just from foreign chips. Right. And then domestic chips are the other question. Right. 600,000 of the Ascend 910B, which is roughly like 400 teraflops or so, you know, so. So if they put them all in one cluster, they could have a bigger model than any of the labs next year. Right. I have no clue where all the 7910Bs are going. Right. But I mean, well, there's like rumors about like some they are being divvied up between the like major Alibaba, bytedance, Baidu et cetera and next year more than a million. And it's possible that they actually do have, you know, 1E30 before the US because data center is not as big of an issue. 10 gigawatt data center is going to be. I don't think anyone is even trying to build that today in the US like even out to 2728. Really, they're focusing on like linking many data centers together. So there's a possibility that like hey, come 2028, 2029, China can have more flops delivered to a single model, even ignoring sort of even once the centralization question is solved. Right. Because that's clearly not happening today for either party. And I would bet if AI is like as important as, you know, you and I believe that they will centralize sooner than the west does. Yeah, so. So there is a possibility, right?

[John Y]

Yeah.

[Dwarkesh Patel]

It seems like a big question then is how much could SMIC either increase the product, like increase the amount of wafers, like how many more wafers could they make and how many of those wafers could Be dedicated to the night because I assume there's other things they want to do with these semiconductors.

[Dylan Patel]

Yeah, so. So there's like two points parts there too. Right. Like so the way the US has sanctioned SMIC is really like stupid kind of is that in that they've like sanctioned a specific spot rather than the entire company. And so therefore. Right. SMIC is still buying a ton of tools that can be used for their 7 nanometer and their call it 5.5 nanometer process or 6 nanometer process for the 910C which releases later this year. Right. They can build as much of that as long as it's not in Shanghai. Right. And Shanghai has anywhere from 45 to 50 high end immersion lithography tools is what's believed by intelligence as well as like many other folks that that roughly gives them as much as 60,000 wafers a month of 7 nanometer. But they also make their 14 nanometer in that fab. Right. And so the belief is that they actually only have about like 25 to 35,000 of 7 nanometer capacity wafers a month. Right, yeah. Doing the math, right, of the chip die size and all these things because probably also uses chiplets and stuff so they can get away with using less leading edge wafers. But then their yields are bad. You can roughly say any, you know, something like 50 to 80 good chips per wafer with their, with their bad yield. Right. With their bad.

[Dwarkesh Patel]

Why do they have bad yield?

[Dylan Patel]

Because it's hard, right. You know they're. You're.

[John Y]

Even if it was like, you know, even everyone's knows the number, right? Like a thousand steps. Even if you're 99 for each like 98 or 98 like in the end you'll still get a 40 yield overall.

[Dwarkesh Patel]

Interesting.

[Dylan Patel]

I think it's like even it's like 99. If I think it's like. I think, I think it's. If it's six sigma of like, of like perfection and you have your 10,000 plus steps, you end up with like yield is still dog shit by the end, right? Like yeah.

[John Y]

This is a scientific measure. Dog shit percent.

[Dylan Patel]

Yeah, yeah. As a multiplicative effect. Right, yeah. So yields are bad because they have hands tied behind their back. Right. Like a. They are not getting to use EUV. Whereas on 7 nanometer until never used UV. But TSMC eventually started using EUV. Initially they used duv. Right.

[Dwarkesh Patel]

Doesn't that mean the export control succeeded? Because that they have bad yield because they have to use like successes again.

[John Y]

They still are determined Successes mean they stop. They're not stopping.

[Dylan Patel]

Going back to the yield question, right? Like, oh, theoretically 60,000 wafers a month times 50 to 100 dies per wafer with yielded. Yielded dies. Holy shit. That's, that's millions of GPUs right now. What are they doing with most of their wafers? They still have not become skill pilled, so they're still throwing them out like, let's make 200 million Huawei phones, right? Like, oh, okay, cool. I don't care, right? Like as the west, you don't care as much. Even though like Western companies will get screwed, like Qualcomm and like, you know, and MediaTek, Taiwanese companies. So, so obviously there's that. And the same applies to the U.S. but when you, when you flip to like, sorry, I don't fucking know what I was going to say. Nailed it.

[Dwarkesh Patel]

We're keeping this in.

[Dylan Patel]

That's, that's fine, that's fine.

[Dwarkesh Patel]

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[Dylan Patel]

Oh, the reason why I was bringing up Shanghai, they're building 7 nanometer capacity in Beijing, they're building 5 nanometer capacity in Beijing. But the US government doesn't care. And they're importing dozens of tools and into Beijing. And they're saying to the US government and ASML, this is for 28 nanometer, obviously, right? This is not bad. And then obviously, you know, like in the background, yeah, we're making 5 nanometer here, right?

[Dwarkesh Patel]

Are they doing it because they believe in AI or because they want to make Huawei phones?

[Dylan Patel]

You know, Huawei was the largest TSMC customer for like a few quarters actually before they got sanctioned. Huawei makes most of the telecom equipment in the world, right? You know, phones, of course, modems, but of course accelerators, networking equipment, you know, you go down the whole like video surveillance chips, right? Like you kind of like go through the whole gambit. Yeah, a lot of that. Could use 7 and 5 nanometer.

[John Y]

Do you, do you think the dominance of Huawei is actually a bad thing for the rest of the Chinese tech industry?

[Dylan Patel]

I think Huawei is so fucking cracked that like it's, it's hard to say that, right? Like Huawei out competes Western firms regularly with two hands tied behind their back. Like, you know, like, what the hell is Nokia and Sony Ericsson like trash, right? Like compared to Huawei. And Huawei is not allowed to ship sell to like European companies or American companies. And they don't have tsmc and yet they still destroy them. Right? And same applies to like the new phone, right? It's like, oh, it's like as good as like a year old Qualcomm phone on a process node that's equivalent to like 4 years old, right? Or 3 years old. So it's like, wait, so they actually out engineered us with a worst process node, you know, so it's like, oh, wow, okay. Like, you know, Huawei is, Huawei is like crazy cracked.

[John Y]

Why do you think that culture comes from the military?

[Dylan Patel]

Because it's the pla.

[John Y]

It is the we We. It is generally seen as an arm of the pla. But like, how do you square that with the fact that sometimes the PLA seems to mess stuff up.

[Dylan Patel]

Oh, like filling water and rockets.

[John Y]

I don't know if that was true. I'm denying, I'm not denying that There.

[Dylan Patel]

Is, there is like that like, like, like crazy conspiracy, not cons. Like you don't know what the hell to believe in China, especially as a not Chinese person. But like, nobody knows.

[John Y]

Even Chinese people don't know what's going on in China.

[Dylan Patel]

There's like, you know, like all sorts of stuff like, oh, they're filling water in their rockets. Clearly they're like incompetent. It's like, look, if I'm the Chinese military, I want the Western world to like believe I'm completely incompetent because one day I can just like destroy the fuck out of everything, right? With all these hypersonic missiles and all this shit, right? Like drones and like no no, no, no. We're filling water in our missiles. These are all fake. We don't actually have 100,000 missiles that we manufacture in a facility that's like super, super hyper advanced. And Raytheon is stupid as shit because they can't make, you know, missiles nearly as fast, right? Like, I think like that's also like a flip side is like how much false propaganda is there, right? Because there's a lot of like, no, SMIC could never, SMIC could never. They, they don't have the best tools, blah, blah, blah. And then it's like, motherfucker, they just shipped 60 million phones last year with this chip that performs only one year worse than like what Qualcomm has. It's like proof is in the pudding, right? Like, you know, there's, there's a lot of like cope, if you will.

[John Y]

I just wonder where it comes from. I do really do just wonder where that culture comes from. Like there's something crazy about them where they're kind of like everything they touch they seem to succeed in. And like I kind of wonder why they're making cars.

[Dylan Patel]

I wonder if it's going on there.

[John Y]

I think I like if like supposedly, like if we kind of imagine like historically, like, do you think they're getting something from somewhere?

[Dwarkesh Patel]

What do you mean?

[Dylan Patel]

Espionage, you mean?

[John Y]

Yeah, like obviously like East Germany and the Soviet industry was basically, was just. It was like a conveyor belt of like secrets coming in and they're just use that to run everything. But the Soviets were never good at it. They could never mass produce it.

[Dwarkesh Patel]

How would espionage explain how they can make things with different processes?

[Dylan Patel]

I don't think it's just espionage. I think they're just like literally cracked. It has to be something else. They have the espionage, without a doubt, right? Like ASML has been known to been hacked a dozen times, right? Or at least a few times, right? And they've been known to have people sued who made it to China with a bunch of documents, right? Not just asml, but every fucking company in supply chain. Cisco Code was literally in like early Huawei, like routers and stuff, right? Like you go down the list, it's like everything is. But then it's like, no. Architecturally, the Ascend 910B looks nothing like a GPU. It looks nothing like a TPU. It is like its own independent thing. Sure, they probably learn some things from some places, but like it is just like they're good at engineering.

[John Y]

It's nine, nine, six. Like wherever that culture comes from, they they, they do good. They do very good.

[Dwarkesh Patel]

Another thing I'm curious about is like, yeah, where that culture comes from. But like, how does it stay there? Because with American firms or any other firm, you can have a company that's very good, but over time it gets worse, right? Like intel or many others. I guess Huawei just isn't that old of a company. But it's hard to be a big company and stay good.

[John Y]

That is true. I think it's like, I think a word that I hear a lot with regards to Huawei is a struggle, right? And China has a culture of like the Communist Party is really big on struggle. I think like Huawei in the sense they sort of brought that culture into some, into their, in the way they do it. Like you said before, right? They, they, they, they go crazy because they think that in five years that they're going to fight the United States. And those are like literally everything they do every second is like their country depends on it, right?

[Dylan Patel]

It's like it's the Andy Grove in mindset, right? Like shout out to like the based intel. But like only the paranoid survive, right? Like paranoid Western companies do. Well, why did, why did Google like really screw the pooch on a lot of stuff and then why are they like resurging kind of now is because they got paranoid as hell, right? But they weren't paranoid for a while. If Huawei is just constantly paranoid about like the external world and like, oh fuck, we're going to die. Oh fuck, like, you know, they're going to beat us. Our country depends on it. We're going to get the best people from the entire country that are like, you know, the best at whatever they.

[John Y]

Do and tell them you will, if you do not succeed, you will die or you will die, your family will die, your family will be enslaved and everything.

[Dylan Patel]

Like, it'll be terrible by the evil Western pigs, right?

[John Y]

Capital or capitalist, they don't believe in, they don't say that anymore. But something like, like, you know, everyone is against China. China is being, is being defiled, right? And like they're saying like if you, that is all on you, bro.

[Dylan Patel]

Like if you can't do that, then like you, if you can't get that radio to be slightly less noisy and like transmit like 5% more data.

[John Y]

It's like the great palace fire all over again. The British are coming and they will steal all the, all the, all the trinkets and everything. Like that's on you.

[Dylan Patel]

Uh huh.

[Dwarkesh Patel]

Why isn't there more vertical integration in the Semiconductor industry. Well, like, why are there, like, this subcomponent requires this other subcomponent from this other company, which requires a component from another company. Like, why, why is more of it not done in house?

[Dylan Patel]

The way to look at it today is it's super, super stratified. And every industry has anywhere from one to three competitors. And pretty much the most competitive it gets is like 70% share, 25% share, 5% share in any layer of like, manufacturing chips, anything, anything. Chemicals, different types of chips. But it used to, used to be vertically integrated.

[John Y]

Like, and the very beginning, it was integrated, right?

[Dwarkesh Patel]

Where did that stop?

[John Y]

What happened was the funniest thing was, like, you know, you had companies that used to do it all in the one. And then suddenly sometimes a guy would be like, I hate this. I think I know, I know how to do better. Spins off, does his own thing, starts his company, goes back to his old company, says, I can sell you a product that's better, right? And that's the beginning of what we call the semiconductor manufacturing equipment industry.

[Dylan Patel]

Like, basically in the 70s, right, like, everyone made their own equipment.

[John Y]

Like, they spin off all these people. And then what happened was that the companies that accepted, you know, these outside products and equipment got better stuff. They did better. Like, you can talk about a whole bunch, like, there are companies that were totally vertically integrated in semiconductor manufacturing for decades. And they are, they're still good, but they're nowhere near competitive.

[Dwarkesh Patel]

One thing I'm confused about is, like, the actual foundries themselves, there's like fewer and fewer of them every year, right? So there's maybe more companies overall, but the final people who make the wafers, there's less and less. And then it's interesting in a way, it's similar to the AI foundation models, where you need to use the revenues from a previous model in order or your market share to fund the next round of ever more expensive development.

[John Y]

When TSMC launched the foundry industry, right? And when they started, there was a whole wave of like, Asian companies that funded semiconductor foundries of their own. You had Malaysia with Subterra, you have Singapore with Chartered, you had. There was one. There's Worldwide, There's Worldwide Semiconductor, where I talked about earlier, there's one from Hong Kong, bunch in Japan, bunch in Japan. Like, they all sort of did this thing, right? And I think the thing was that when you go into leading edge, when the thing is that, like, it got harder and harder, which means that you had to aggregate more demand from all the customers and to fund the next node Right. So technically, in the sense that what it's kind of do is aggregating all this money, all this profit, to kind of fund this next node to the point where now there's no room in the market for an N2 or N3, technically, you could argue that economically, you can make an argument that N2 is a monstrosity that doesn't make sense economically and which should not exist in some ways without the immense single concentrated spend of like, five players in the market.

[Dylan Patel]

I'm sorry to, like, completely derail you, but, like, there's this video where it's like, there's an unholy concoction of meat slurry. Yes.

[Dwarkesh Patel]

What?

[Dylan Patel]

Sorry? There's like a video that's like, ham is disgusting. It's an unholy concoction of, like, meat with no bones or collagen. And like, I don't know, like, it was like the way he was describing two Dandy meter is kind of like that, right?

[John Y]

It's like the guy who pumps his right arm so much and he's like, super muscular. The human body was not meant to be so muscular.

[Dwarkesh Patel]

Like, what's the point? Like, why. Why is 2 nanometer not justified?

[John Y]

I'm not saying N2 is like N2 specifically, but say N2 as a concept. The next node should technically. Like, right now there is a. There will come a point where economically the next node will not be possible, like, at all. Right.

[Dylan Patel]

Unless. Unless more, you know, technology spawn, like AI now makes. Yeah, you know. Yeah. One nanometer or whatever, you know, it.

[John Y]

Was a long period of time. Yeah, yeah.

[Dylan Patel]

Viable. Right, so.

[Dwarkesh Patel]

So, like, viable and what's as in.

[John Y]

Like, like money worth it or every.

[Dylan Patel]

Two years you get a shrink, right? Yeah. Like clockwork. Moore's law. And then 5 nanometer happened. It took 3 years. Holy shit. And then 3 nanometer happened. It took 3 or. No, sorry, is it 3 name. Or 5 took 3 years. Holy shit. Like, is Moore's Law dead, right? Like, because TSMC didn't. And then what did Apple do? Even on the third year of 3 of. Of. Or, sorry, when 3 nanometer finally launched, they still only. Apple only moved half of the iPhone volume to 3 nanometer. So this is like now they did a fourth year of I of 5 nanometer for a big chunk of iPhones, right? And it's like, oh, is the mobile industry petering out? Then you look at 2 nanometer and it's like, going to be a similar, like, very difficult thing for the. For the industry to pay for this. Right. Apple, of course they have, you know, because they get to make the phone, they have so much profit that they can funnel into like more and more expensive chips. But finally like that was, that was running out, right? How economically viable is 2 nanometer just for one player? TSMC, you know, ignore intel, ignore Samsung, just, you know, because Samsung is paying for it with memory, not with their actual profit. And then intel is paying it from it from their former CPU monopoly private equity money and now private equity money and debt and subsidies people's salaries. But anyways, there is a strong argument that funding the next node would not be economically viable anymore if it weren't for AI taking off and then generating all this humongous demand for the most leading edge chip.

[Dwarkesh Patel]

And how big is the difference between 7-5-3nm? Is it a huge deal in terms of who can build the biggest cluster?

[Dylan Patel]

So there's the simplistic argument that like oh, moving a process node only saves me x percent in power, right? And that has been petering out, right? You know, when you moved from like 90 nanometer to 80 something, right? Or 70 something, right? It was like it was, you got 2x right? Denard scaling was still intact, right? But now when you move from 5 nanometer to 3 nanometer, first of all you don't double density. SRAM doesn't scale at all. Logic does scale, but it's like 30%. So all in all you only saved like 20% in power per transistor. But because of like data locality and movement of data, you actually get a much larger improvement in power efficiency by moving to the next node than just the individual transistors power efficiency benefit. Because you know, for example, you're multiplying a matrix that's like, you know, 8,000 by 8,000 by 8,000. And then like you can't fit that all on one chip. But if you could fit more and more, you have to move off chip less, you have to go to memory less, et cetera, right? So the data locality helps a lot too. But the AI really, really, really wants new processed nodes because of a power used is a lot less now higher density, higher performance of course, but the big deal is like well, if I have a gigawatt data center, I can now how much more flops can I get? If I have two gigawatt data center, how much more flops can I get? If I have a 10 gigawatt data center, how much more flops can I get? Right? And you look at the scaling and it's like well, no, everyone needs to go to the most recent process node as soon as possible.

[Dwarkesh Patel]

I want to ask the normie question for everybody. I want to phrase it that way, okay? I want to ask a question that's.

[Dylan Patel]

Like, nori, not for you nerds. I think John and I could communicate at the point where you wouldn't even know what the fuck we're talking about.

[John Y]

Okay?

[Dwarkesh Patel]

Suppose Taiwan is invaded, or Taiwan has an earthquake. Nothing is shipped out of Taiwan. From now on, what happens next? The rest of the world? How would it feel, its impact, a day, in a week, in a month, in a year, in.

[John Y]

I mean, it's a terrible thing. It's a terrible thing to talk about. I think it's like. Can you just say it's all terrible? Everything's terrible. Because it's not just like leading edge. Leading edge people. We're focused on leading edge, but there's a lot of trailing edge stuff that people depend on every day. I mean, we all worry about AI. The reality is you're not gonna get your fridge, you're not gonna get your cars, you're not gonna get everything. It's terrible. And then there's the human part of it, right? It's all terrible. Can we, like it's, it's depressing, I think. And I live there.

[Dylan Patel]

Yeah, I think day one market crashes a lot, right? You got to think about like, I think, I think the big, like big six, six, six biggest companies, Magnificent Seven, whatever the heck it's called, are like 60, 75% of the S&P 500 and their entire business relies on chips, right? Google, Microsoft, Apple, Nvidia. You know, you go down the list, right? They're, they all meta, right? They all entirely rely on AI. And you would have a tech reset, like extremely insane tech reset, by the way, right? Like so market would crash a week, a day in a couple weeks in, right? Like people are preparing now. People are like, oh shit, like, let's start building fabs. Fuck all the environmental stuff. Like war's probably happening, but like the supply chain is trying to like figure out what the hell to do to refix it. But six months in the supply of chips for making new cars. Gone. Or sequestered to make military shit, right? You can no longer make cars and we don't even know how to make non semiconductor induced cars, right? Like this unholy concoction with all these like chips, right?

[John Y]

You are like 40% chips now. Like it's just chips on in the tire.

[Dylan Patel]

There's like, there's like 2,000 plus chips. Every Tesla door handle has like four chips. And it's like, what the fuck? Like why? Like, like. But like it's like, it's like shitty like microcontrollers and stuff. But like there's like 2000 plus chips even in an, in an ICE vehicle, like internal combustion engine vehicle. Right? And every engine has dozens of, dozens of chips. Right. Anyways, this all shuts down because not all of the production. There are some in Europe, there are some in the us, there's some in Japan.

[John Y]

Yeah, the Europe, they're going to, they're going to bring in a guy to work on Saturday until four.

[Dylan Patel]

Yeah. I mean, yeah. So. So you have like TSMC always builds new fabs. That old fab tweak production up a little bit more and more and new designs move to the next, next, next node and. And old stuff fills in the old notes, right? So, you know, ever since TSMC has been the most important player. And not just tsmc, there's UMC there, there's PSMC there, there's a number of other companies there. Taiwan's share of like total manufacturing has grown every single process node. So in like 130 nanometer there's a lot. And including like many chips from like Texas Instruments or Analog devices or like NXP, like all these companies, 100% of it is manufactured in Taiwan, right? By you know, either psf, TSMC or UMC or whatever. But then you like step forward and forward and forward, right? Like 28 nanometer, like 80% of the world's production of 28 nanometers in Taiwan. Oh fuck. Right? Like, you know, and everything in 28 nanometers. Like what's made on 28 nanometer today? Tons of microcontrollers and stuff. But also like every display driver I see, like, cool. Like even if I can make my Mac chip, I can't make the chip that drives the display. Like, you know, you just go down the list. Like everything. No fridges, no, no automobiles, no, no weed whackers. Because that shit has my two feet. Brush has fucking Bluetooth in it. Right? Like why? I don't know. But like, you know, there's like so many things that like just like, poof, we're tech reset.

[Dwarkesh Patel]

We were supposed to do this interview like many months ago and then I kept like delaying because I'm like, I don't understand any of the shit. But like, it is like a very difficult thing to understand where I feel like with AI, it's like it's not.

[Dylan Patel]

That, no, you've just spent time. You've spent time.

[Dwarkesh Patel]

But like, I also feel like it's like less conflict. It feels like it's a kind of thing where, like in an amateur kind of way, you can like, you know, pick up what's going on in the field, in this field. Like the thing I'm curious about is like how. How does one learn the layers of the stack? Because the layers of the stack are like, there's not just the papers online. You can't just like look up the tutorial on how the transformer works or whatever. It's like, yeah, it's like, I mean, like many layers of really different things.

[Dylan Patel]

There are like 18 year olds who are just cracked at AI, right already, right? And like there's high school dropouts that get like jobs at OpenAI. This existed in the past, right? Pat Gelsinger, current CEO of intel, went straight to work. He was, he like grew up in the Amish area of Pennsylvania and he went straight to work at intel, right? Because he's just cracked, right? That is not possible in semiconductors today. You can't even get like a job at like a tool company without like at least like a fricking master's in chemistry, right? And probably a PhD, right? Like, like, of the like 75,000 TSMC workers, it's like 50,000 have a PhD or something. Insane, right? It's like, okay, this is like there's like some, there's like a next level amount of like how specialized everything's gotten. Whereas today, like you can take like, you know, Sholto, you know, he. When did he start working on AI? Not that long ago.

[John Y]

Not to say anything bad about Sholto, but he's cracked.

[Dylan Patel]

He's like omega cracked at like what he does, what he does. You could pick him up and drop him into another part of the AI stack. First of all, he understands it already. And then second of all, he could probably become cracked at that too, right? Whereas that is not the case in semiconductors, right? You, you. One, you like specialize like crazy. Two, you can't just pick it up, you know, like Shelter, I think. What did he say? He like just started like he was.

[Dwarkesh Patel]

A consultant in McKinsey and at like night he would like read papers about robotics, right? And like run experiments and whatever.

[Dylan Patel]

Yeah. And then, and then like he like was like, like people noticed. He was like, who the hell is this guy and why is he posting this? Like. Yeah, I thought everyone who knew about this was at Google already, right? It's like, come to Google, right? That can't happen in semiconductors, right? Like, it's just not like conducively. Like it's not possible, right. One, arxive is like a free thing. The paper publishing industry is like abhorrent everywhere else. And you just like cannot download IEEE papers or like SPI papers or like other organizations. And then two, at least up until like late 2022 or really early 2023 in the case of Google, right? I think what the palm inference paper. Up until the palm inference paper before that, all the good, best stuff was just posted on the Internet. After that, you know, it's kind of a little bit clamping down by the labs, but there's also still all these other companies making innovations in the public that. And like, what is state of the art is public. That is not the case in semiconductors.

[John Y]

Semiconductors have been shut down since 1960s, 1970s, basically. I mean, like, it's kind of crazy how little information has been formally transmitted from one one country to another. Like the last time you could really think of this was like 19, maybe the Samsung era, right?

[Dwarkesh Patel]

So then how do you guys keep up with it?

[John Y]

Well, we don't know it. I don't. Personally, I don't think I know it. I don't. I mean, I.

[Dwarkesh Patel]

If you don't know it, what do you mean?

[John Y]

Because, like, like there is a guy. There's like. I spoke to one guy, he's like a PhD in etch or something the world, one of the top people in Etch. And he's like, man, you really know, like, lithography, right? I'm just like, I don't feel like I know lithography. But then you've talked to the people who know lithography. You've done pretty good work in packaging, right? Nobody knows anything.

[Dwarkesh Patel]

They all have Gelman amnesia.

[John Y]

They're all in this, like, single. Well, right? They're digging deep. They're digging deep for what they're getting at. But they, but, you know, they don't know the other stuff well enough. And in some ways, I mean, nobody knows the whole stack. Nobody knows the whole stack.

[Dylan Patel]

The stratification of just like manufacturing is absurd. Like, the tool people don't even know exactly what intel and TSMC do in production and vice versa. They don't know exactly how the tool is optimized like this. And it's like, how many different types of tools there are? Dozens. And each of those has an entire tree of all the things that we've built, all the Things we've invented, all the things that we continue to iterate upon. And then here's the breakthrough innovation that happens every few years in it too.

[Dwarkesh Patel]

So if that's the case, if nobody knows the whole stack, then how does the industry coordinate to be like, in two years we want to go to the next process which has gate all around. And for that we need X tools and X technologies developed by whatever.

[John Y]

That's really fascinating. It's a fascinating social kind of phenomena, right? You can feel it. I went to Europe earlier this year. Dylan was like, had allergies, but like, I was like talking to those.

[Dylan Patel]

And you.

[John Y]

It's like gossip. It's gossip. You start feeling the. You start feeling people are coalescing around like a something, right? Early on we used to have like Sematech, where people, all these American companies came together and talked and they came and they hammered out, right? But Sematech in reality was dominated by a single company, right?

[Dylan Patel]

And.

[John Y]

But then, you know, nowadays is a little more dispersed, right? You feel, you feel like it's like, it's like, it's like a, it's a blue moon or rising kind of thing. Like they are going towards something, they know it. And then suddenly the, the whole industry is like, this is it, let's do it.

[Dylan Patel]

I think it's like God came and proclaimed it. We will shrink density 2x every two years. Gordon Moore, he made an observation and then like, it didn't go nowhere. It went way further than he ever expected because it was like, oh, there's line of sight to get to here and here. And like. And he predicted like seven, eight years out, like multiple orders of magnitude of increases in transistors. And it came true. But then by then, the entire industry was like, this is obviously true. This is the word of God. And every engineer in the entire industry, tens of millions of people, like, literally this is what they were driven to do. Not every single engineer didn't believe it, but people were like, yes, to hit the next shrink, we must do this, this, this, right? And this is the optimizations we make. And then you have this stratification. Every single layer and abstraction layers, every single layer through the entire stack to where people. It's an unholy concoction. I'm gonna keep saying this word, but no one knows what's going on because there's an abstraction layer between every single layer. And on this layer, the people below you and the people above you know what's going on. And then beyond that, it's like, okay, I Can try to understand, but not really.

[Dwarkesh Patel]

But I guess that doesn't answer the question of when IRDs or whatever. I don't know, was it 10, 20 years ago? I watched your video about it where they're like we are EUV is like this is, we're going to do EOV instead of the other thing and this is the path forward. How do they do that if they don't have the whole sort of picture of like different constraints, different trade offs, different blah blah blah.

[John Y]

They kind of, they argue it out, they get together and they talk and they argue and basically at some point a guy somewhere says, I think we can move forward with this.

[Dylan Patel]

Semiconductors are so siloed and the data and knowledge within each layer is A, not documented online at all. No documentation because it's all siloed within companies. B, there's a lot of human element to it because a lot of the knowledge like as John was saying is like apprentice, master, apprentice master type of knowledge or I've been doing this for 30 years and there's an amazing amount of intuition on what to do just when you see something to where like AI can't just learn semiconductors like that. But at the same time there's a massive amount of talent shortage and ability to move forward on things, right? So like the technology used on like, like most of the like equipment in semiconductor tool fabs runs on like Windows xp, right? Like each tool has like a Windows XP server on it or like you know, like all the chip design tools like have like CentOS, CentOS like version 6, right? And like that's old as hell, right? So like there's like so many like areas where like why is this so far behind? At the same time it's like so like hyper optimized. That's like the, the tech stack is so broken in that sense, they're afraid to touch it.

[John Y]

They're afraid to touch it.

[Dylan Patel]

Yeah, because it's an unholy amalgamation.

[John Y]

It's unholy. It should not be work. It should not work. This thing should not work. It's literally a miracle.

[Dylan Patel]

So you have all the abstraction layers, but then it's like one is, there's a lot of breakthrough innovation that can happen now stretching across abstraction layers. But two is because there's so much inherent knowledge in each individual one, what if I can just experiment and test at 1000x velocity or 100,000x velocity? And so some examples of where this is already shown true is some of Nvidia's AI layout tools, right? And Google as well, like laying out the circuits within a small blob of the chip with AI. Some of these like RL design things, some of these, there's a lot of like various like simulation things.

[John Y]

Design or is that manufacturing?

[Dylan Patel]

It's all design, right? Most of it's design. Manufacturing has not really seen much of this yet. Although there is starting to come in inverse lithography maybe. Yeah. ILT and maybe I don't know if that's AI. That's not AI. Anyways like there's like tremendous opportunity to bring breakthrough innovation simply because there is so many like layers where things are unoptimized, right? So you see like all these like oh single digit, mid, you know, low double digit like advantages just from like RL techniques from like alphago type stuff like or like not from alphago but like like five six, seven, eight year old RL techniques being brought in. But like generative AI being brought in could like really revolutionize the industry. You know, although there's a massive data problem.

[Dwarkesh Patel]

So and can you give those, can you give the possibilities here in numbers in terms of maybe like flop per dollar or whatever? The relevant thing here is how much do you expect in the future to come from process node improvements? How much from just like how the hardware is designed because of AI? If you like how to disaggregate. We're talking specifically for GPUs. If you had to disaggregate future improvements.

[Dylan Patel]

I think it's important to state that semiconductor manufacturing and design is the largest search base of any problem that humans do because it is the most complicated industry that anything that humans do. And so you know, when you think about it, right, there's, there's 1e10, 1e11, right? 100 billion transistors on, on leading edge chips, right. Blackwell has two 20 billion transistors or something like that. So what is. And those are just on off switches and then think about every permutation of putting those together. Contact ground, etc. Drain source, blah blah blah. With wires, right? There's 15 metal layers, right, Connecting every single transistor in every possible arrangement. This is a search space that is literally almost infinite, right? You could like the search space is much larger than any other search base that humans know of.

[Dwarkesh Patel]

The search like what are you trying to optimize over.

[Dylan Patel]

Well, useful compute, right? What is, you know, if you're, if the, if the goal is optimize intelligence per picojoule, right? And intelligence is some nebulous nature of like the what the model Architecture is. But and then, and then picajoules like a unit of energy. Right. How do you optimize that? So there's humongous innovations possible in architecture. Right. Because vast majority of the power on a H100 does not go to compute. And there are more efficient like compute, you know, alu's or arithmetic logic unit like designs. Right. But even then, the vast majority of the power doesn't go there. Right. The vast majority of the power goes to moving data around. Right. And then when you look at what is the movement of data, it's either networking or memory. You know, you have a humongous amount of movement relative to compute and a humongous amount of power consumption relative to compute. And so the. So how can you minimize that data movement and then maximize the computer? There are 100x gains from architecture. Even if we literally stop shrinking, I think we could have 100x gains from architectural advancements over what time period? The question is how much can we advance the architecture? Right. The other challenge is the number of people designing chips has not necessarily grown in a long time. Yeah. Company to company, it shifts. But within the semiconductor industry in the US and the US makes, you know, designs the vast majority of leading edge chips, the number of people designing chips has not grown much. What has happened is the output per individual has soared because of eda Electronic Design Assistance Tooling. Right now this is all still like classical tooling. There's just a little bit of inkling of AI in there yet. Right. What happens when we bring this in is the question and how you can solve this search space somehow with humans and AI working together to optimize this. So it's not most of the power is data movement and then the compute is actually very small. To flip side, the compute is, first of all, compute can get like 100x more efficient just with design changes. And then you could minimize that data movement massively. Right. So you can get a humongous gain in efficiency just from architecture itself. And then process node helps you innovate that there. Right. And power delivery helps you innovate that system design, chip to chip networking helps you innovate that. Right. Like memory technologies. There's so much innovation there and there's so many different vectors of innovation that people are pursuing simultaneously to where like Nvidia, Gen to Gen to Gen will do more than 2x performance per dollar. I think that's very clear. And then like hyperscalers are probably going to try and shoot above that, but we'll see if they can Execute.

[Dwarkesh Patel]

There's like two narratives you can tell here of how this happens. One is that these AI companies who are training the foundation models who understand the trade offs of how much is the marginal increase in compute versus memory worth to them and what trade offs do they want between different kinds of memory, they understand this and so therefore the accelerators they build, they can make these sort of trade offs in a way that's most optimal and also design the architecture of the model itself in a way that reflects what are the hardware trade offs. Another is Nvidia because it has, I don't know how this works, but presumably they have some sort of know how, like they're accumulating all this like knowledge about how to better design this architecture and like also better search tools for so on. Who has basically like better motier in terms of will Nvidia keep getting better at Design, getting this 100x improvement or will it be like OpenAI and Microsoft and Amazon and Anthropic who are designing their accelerators, who will keep getting better at like designing the accelerator?

[Dylan Patel]

I think that there's a few vectors to go here. Right. One is you mentioned and I think it's important to note is that hardware has a huge influence on the model architecture that's optimal. And so it's not a one way street. That better chip equals the optimal model for Google to run on TPUs given a given amount of dollars. A given amount of compute is different architecturally than what it is for OpenAI with Nvidia stuff, right? It is absolutely different. And then even down to networking decisions that different companies do and data center design decisions that people do the optimal, like if you were to say X amount of compute of TPU versus GPU compute optimally, what is the best thing you'll diverge in what the architecture is. And I think that's important to know.

[Dwarkesh Patel]

Right. Can I ask about that real quick? So earlier we were talking about how China has the H20s or B20s and there there's like much less compute per memory bandwidth and the amount of memory. Right. Does that mean that Chinese models will actually have like very different architecture and characteristics than American models in the future?

[Dylan Patel]

So you can take this to like a very like large conclusion like leap and it's like oh, you know, neuromorphic computing or whatever is like the optimal path and that looks very different than like what a transformer does. Right. Or you could take it to like a simple thing which is like the level of sparsity, like coarse grains varsity that is like experts and all this sort of stuff, the arrangement of what exactly the attention mechanism is because there are a lot of tweaks. It's not just like pure transformer attention, right? Or like, hey, how wide versus tall the model is, right? That's like very important. Like D mod versus number of layers, right? These are all things that would be different. I know they're different between say a Google and an OpenAI and what is optimal. But it really starts to get like, hey, if you were limited on a number of different things, like China invests humongously in compute and memory, which is basically the memory cell is directly coupled or is the compute cell, right? So these are things that China's investing hugely. And you go to conferences like, oh, there's 20 papers from Chinese companies, universities about computed memory or hey, because the FLOP limitation is here, maybe Nvidia pumps up the on chip memory and changes the architecture because they still stand to benefit tens of billions of dollars by selling chips to China, right? Today it's just like neutered American chips, right? Neutered chips that go to the US but like it'll start to diverge more and more architecturally because they'd be stupid not to make chips for China, right? And Huawei obviously, again, like has like their constraints, right? Like where are they limited on memory? Oh, they have a lot of networking capabilities and they could move to like certain optical like networking technologies directly onto the chip much sooner than we could, right? Because that is what's optimal for them within their search space of solutions, right? Because this whole area is like blocked off.

[John Y]

Really interesting to see, to think about like the development of how Chinese AI models will differ from American AI models because of the, because of these changes or these.

[Dylan Patel]

And it applies to use cases, it applies to data, right? Like American models are very important about, like, let me learn from you, right? Let me be able to use you directly as a random consumer. That is not the case for Chinese model, I assume because there's probably very different use cases for them. China crushes the west at video and image recognition, right? At icml, like Albert Gu at of Cartesia, like state space models. Like every single Chinese person was like, can I take a selfie with you? Man was harassed in the U.S. you see Albert and he's like, it's awesome. He invented state space models. But it's not like state space models are like here. But that's because state space models potentially have like a huge advantage in like video and image and audio, which is like stuff that China does more of and is further along and has better capabilities in. Right. So it's like there are.

[John Y]

Sorry because of all the surveillance cameras there.

[Dylan Patel]

Yeah, that's the quiet part out loud.

[John Y]

Right.

[Dylan Patel]

But like there's already divergence in like capabilities there, right. Like you know if you look at image recognition China like destroys American companies right on that, right? Because, because the surveillance you have like this divergence in tech tree and like people can like start to design different architectures within the constraints you're given.

[John Y]

Yeah, yeah.

[Dylan Patel]

And everyone has constraints but the constraints different companies have are even different. Right. And so like Google's constraints have shown them that they built, they built a genuinely different architecture. But now if you look at like Blackwell and then what's like said about TPV6, right they're. I'm not going to say they're like converging but they are getting a little bit closer in terms of how big is the matmul unit size and some of the topology and world size of the scale up versus scale out network. There is some convergence slightly not saying they're similar yet, but already they're starting to. But then there's different architectures that people could go down and paths. So you see stuff from all these startups that are trying to go down different tech trees because maybe that'll work. But there's a self fulfilling prophecy here too, right? All the research is in transformers that are very high arithmetic intensity because the hardware we have is very high arithmetic intensity and transformers run really well on GPUs and GPUs and you sort of have a self fulfilling prophecy. If all of a sudden you have an architecture which is theoretically it's way better but you can get only like half of the usable fops out of your chip, it's worthless. Because Even if it's 30% compute efficiency win it took twice, it's half as fast on the chip, right. So there's all sorts of like trade offs and like self fulfilling prophecies of what do, what path do people go down?

[Dwarkesh Patel]

John and Dylan have talked a lot in this episode about how stupefyingly complex the global semiconductor supply chain is. The only thing in the world that approaches this level of complexity is the byzantine web of global payments. You're stitching together legacy tech stacks and regulations that differ in every jurisdiction. In Japan for example, a lot of people pay for online purchases by taking a code to their corner store and punching it into a kiosk stripe abstracts all this complexity away from businesses. You can offer customers whatever payment experience they're most likely to use wherever they are in the world. And Stripe is how I invoice advertisers for this very podcast. I doubt that they're punching in codes at a kiosk in Japan, but if they are, Stripe will handle it. Anyways, you can head to stripe.com to learn more. If you were made head of compute of a new AI lab, if, like, SSI came to you, the Ilias discover a new lab and they're like, Dylan, we give you $1 billion, you are ahead of compute. Help us get on the map. We can compete with the Frontier labs. What is your first step?

[Dylan Patel]

Okay, so the constraints are you are a US Israeli firm, because that's what SSI is. Right. And your researchers are in the US And Israel. You probably can't build data centers in Israel because power is expensive as hell and it's probably risky. Maybe. I don't know. So still in the U.S. most likely, most of the researchers are here, or a lot of them are in the US Right. Like Palo Alto or whatever. So I guess you need a significant chunk of compute. Obviously, the whole pitch is you're going to make some research breakthrough that's like, compute efficiency, win, data efficiency, win. Whatever it is, you're making some breakthrough. But you need compute to get there. Right. Because your GPU is, per researcher, is your research velocity. Right. Obviously, like, data centers are very tapped out. Right. On terms of tapped out. But like every new data center that's coming up, most of them have been sold, which has led people like Elon to go through this, like, insane thing in Memphis. Right. I'm just trying to, like, I'm just trying to. I'm just trying to square the circle.

[John Y]

Yeah.

[Dwarkesh Patel]

On that question, I kid you not. In my group house, like group chat, there have been two separate people who have been like, I have a cluster of H1 hundreds and I have a long lease on them, but I'm trying to sell them off. Is it like a buyer's market right now? Because it does seem like people are trying to get rid of them.

[Dylan Patel]

So I think for the Ilia question, it was like a cluster of 256 GPUs or even 4K GPUs. It's kind of cope. Right. It's not enough. Right. Yes. You're going to make compute efficiency wins, but with a billion dollars, you probably just want the biggest cluster in one individual spot. And so small amounts of GPUs probably not like, you know, possible to use. Right. Like, for them. Right. Like. And that's what most of the sales are, right. Like you go and look at like GPU last or like vast or like foundry, like or 100 different GPU resellers. The cluster sizes are small. Now is it a, is it a buyer's market? Yeah. Last year you would buy 100 for like $4 or $3. Like if you, you know, an hour, an hour, right. For shorter term or mid term deals. Right. Now it's like if you want a six month deal you could get like $2.15 or less. Right. Like, and like the natural cost if I is if I have a data center, right. And I'm paying like standard data center pricing to purchase the GPUs and deploy them is like a $40 and then you add on the debt because I probably took debt to buy the GPUs or cost equity cost of capital gets up to like $70 or something. Right. And so you see deals that are like the good deals, right? Like Microsoft renting from Coreweaver, like $1.90 to $2. Right. So people are getting closer and closer to like there's still a lot of profit, right? Because the natural rate even after debt and all this is like A$70. So like there's still a lot of profit when people are selling in the low twos, like GPU companies, people deploying them. But it is a buyer's market in a sense that it's gotten a lot cheaper. But cost of compute is going to continue to tank, right. Because it's like sort of like I don't remember the exact name of the law, but it's effectively Moore's Law. Right. Every two years the cost of transistors halved and yet the industry grew. Right. Every six months or three months, the cost of intelligence, like OpenAI and GBD GPT 4, what, February 2023. Right. $120 per million tokens or something like that was roughly the cost and now it's like 10. Right. It's like the cost of intelligence is tanking partially because of compute, partially because the model's compute efficiency wins. Right. I think that's a trend we'll see. And then that's going to drive adoption as you scale up and make it cheaper and scale up and make it cheaper, Right, Right.

[Dwarkesh Patel]

Anyways, what you're saying, if you're head of computer vessels.

[Dylan Patel]

Okay, head of computer vessel, it's very intense. There's obviously no free data center lunch, right. In terms of, you know, and you can just, you know, take that based on like the data we, we, we have shows that there's no lunch, free lunch per se, like immediately today you need the compute for a large cluster size or even six months out, right? There's some, but like not a huge amount because of what X did, right? X AI is like oh shit, we're going to go like, we're going to go buy a Memphis factory, put a bunch of like generators outside, like mobile generators usually reserved for like natural disasters. A Tesla battery pack, drive as much power as we can from the grid, tap the natural gas line, that's going to the natural gas plant like two miles away, the gigawatt natural gas plant. Like just like send it and like get a cluster built as fast as possible. Now you're running 100k GPUs, right?

[Dwarkesh Patel]

I know.

[Dylan Patel]

And that cost, that cost about 5 billion, right? 4 billion, right? Not, not, not, not 1 billion. So scale that SSI has is much smaller by the way. Right. So, so their size of cluster will be, you know, maybe one third or one fourth of the size. Right. So now you're talking about 25 to 32k cluster right there. You still don't have that. Right? No one is willing to rent you a 32k cluster today no matter how much money you have. Right. Even if you had more than a billion dollars. So you now it makes the most sense to build your own cluster 1 instead of renting it or get a very close relationship like a OpenAI Microsoft with Core Weave or OpenAI Microsoft with Oracle Crusoe. The next step is bitcoin. Right. So OpenAI has a data center in Texas. Right. Or it's going to be their data center. It's like they've kind of contracted all that. Coreweaver. There is a 300 megawatt natural gas plant on site powering these crypto mining data data centers from the company called Core Scientific. And so they're just converting that. There's a lot of conversion but like the power is already there, the power infrastructure is already there. So it's really about like converting it, getting it ready to be water cooled, all that sort of stuff and convert it to 100,000 GB200 cluster. And they have a number of those going up across the country, but that's also like tapped out to some extent because Nvidia is doing the same thing in Plano, Texas for a 32,000 GPU cluster that they're building is that Nvidia is doing that. Well, they're going through partners, right? Because this is the other interesting thing is the big tech companies can't do crazy shit like Elon did.

[Dwarkesh Patel]

Why?

[Dylan Patel]

Esg.

[Dwarkesh Patel]

Oh, interesting.

[Dylan Patel]

They can't just do crazy shit like, because this actually.

[Dwarkesh Patel]

Do you expect Microsoft and Google and whoever to drop their Net zero commitments as the scaling a picture intensifies?

[Dylan Patel]

Yeah, yeah. So, so, so, so like this, this, like this, like what Xai is doing, right, is like, it's not, it's not that polluting, you know, on the scheme of things, but it's like you have 14 mobile generators and you're just burning natural gas on site on these like, mobile generators that sit on trucks, right? And then you have like power directly two miles down the road. There's no unequivocal way to say any of the power is. Because two, two miles down the road is a natural gas plant as well. Right? There's no way to say this is like green. You go to the core, we think is a natural gas plant is literally on site from core scientific and all that, right? And then the data centers around it are horrendously inefficient, right? There's this metric called P, which is basically how much power is brought in versus how much gets delivered to the chips, right? And like the hyperscalers, because they're so efficient or whatever, right? Their, their p is like 1.1 or lower, right? That is, if you get a gigawatt in 900 megawatts or more gets delivered to chips, right? Not wasted on cooling and all these other things, this core scientific one is going to be like 1.5, 1.6, I.e, even have 300 megawatts of generation on site. I only deliver like 180, 200 megawatts to the chips.

[Dwarkesh Patel]

Given how fast solar is getting cheaper and also the fact that the reason solar is difficult elsewhere is you got to power the homes at night here, I guess it's like theoretically possible to like, figure out, you know, only like run the clusters in the day or something.

[Dylan Patel]

Absolutely not. Really? That's not possible because.

[Dwarkesh Patel]

Because it's so expensive to have these GPUs.

[Dylan Patel]

Yeah. So. So, like, when you look at the power cost of a large cluster, it's trivial and to some extent, right, like, you know, like the meme that like, oh, you know, you can't build a data center in Europe or East Asia because the power is expensive. That's not really relevant. What's the. Or power so cheap in China and the U.S. that's where the only places you can build data centers. That's not really the real reason it's the ability to generate new power for these activities is why it's really difficult and the economic regulation around that. But the real thing is like if you look at the cost of ownership of a GP of, of an H100, let's just say you gave me, you know, $1 billion and I already have a data center. I already have all this stuff. I'm paying regular rates for the data centers. I'm paying through the nose or anything. Paying regular rates for power, not paying through the nose. Power is sub 15% of the cost and it's sub 10% of the cost actually. Right. The biggest like 75 to 80% of the cost is just the servers. Right. And this is on a, like a multi year, including debt financing, including cost of operation, all that. Right. Like when you do a TCO, total cost of ownership, like it's like 80% is the GPUs, 10% is the data center, 10% of the power. Rough, rough numbers, right. So it's like kind of irrelevant, right, whether or not you like, like how expensive the power is, right? Yeah. You'd rather do what Taiwan does, right, when like power, like what do they do when, when there's droughts, right. They like force people to not shower.

[John Y]

They basically reroute the power from when there was a. When there was a power shortage in Taiwan. They basically rerouted power from the residentials.

[Dylan Patel]

And this will happen in a capitalistic society as well, most likely because like fuck you. Like why you're not going to pay X dollars per kilowatt hour. Because to me the marginal cost of power is irrelevant really. It's all about the GPU cost and the ability to get the power. I don't want to turn it off eight hours a day maybe.

[Dwarkesh Patel]

Let's discuss what would happen if the training regime changes and if it doesn't change. So you could imagine that the training regime becomes much more parallelizable where it's about coming up with some sort of search or synthetic. Most of the compute for training is used to come up with synthetic data or do some kind of search. And that can happen across the a wide area in that world. How fast could we scale? Let's go through the numbers on year after year and then suppose it actually has to be. You would know more than me. But suppose it has to be the current regime and just explain what that would mean in terms of how distributed that would have to be and then how plausible it is to get clusters of certain sizes over the next few years.

[Dylan Patel]

I think it is not Too difficult for Ilya's company to get a cluster of 32K and like of, of Blackwell for next year.

[Dwarkesh Patel]

Let's. Okay, okay, let's like 2025-2026-2025-2026.

[Dylan Patel]

There's before I like talk about like the U.S. i think it's like important to note that there is like a gigawatt plus of data center capacity in Malaysia next year. Now that's like mostly bytedance, but like there's like, you know, in power wise there's like, there's the humongous damming of the Nile in Ethiopia and the country uses like one third of the power that that dam generates. So there's like a ton of power there to like power does that dam generate like it's, it's like over a gigawatt and the country consumes like 400 megawatts or something trivial and is like.

[Dwarkesh Patel]

Are people bidding for that power?

[Dylan Patel]

I think people just don't think they can build a data center in fudgeing Ethiopia.

[Dwarkesh Patel]

Why not?

[John Y]

I don't think the dam is filled yet. Is it?

[Dylan Patel]

I mean they have to like the dam could generate that power, they just don't. Oh, got it. Right. Like there's a little bit more equipment required, but that's like not too hard. Why don't they. Yeah, I think there's like, like true security risks, right? If you're China or if you're the US lab, like to build a fucking data center with all your IP and fucking Ethiopia. Like you want AGI to be in Ethiopia. Like you want it to be that accessible. Like people you can't even monitor. Like, like being the technicians in the fucking data center or whatever, right? Or like powering the data center. All these things. Like there's so many like, you know, things you could do to like you could just destroy every GPU in a data center if you want. If you just like fuck with the grid, right? Like pretty, pretty like easily.

[Dwarkesh Patel]

I think people talk a lot about the Middle East.

[Dylan Patel]

There's 100 KGB 200 cluster going up in the Middle east, right? And the U.S. like there's like clearly like stuff the U.S. is doing, right? Like the, you know, G42 is the UAE data center company, Cloud Company. Their CEO is a Chinese national or not a Chinese. He's Chinese, basically Chinese allegiance. But open. I think OpenAI wanted to use a data center from them, but instead like the US forced Microsoft to like. I feel like this is what happened is forced Microsoft to like do a deal with them so that G42 has a 100k GPU cluster. But Microsoft is like administering and operating for security reasons, right? And there's like Omniva in like Kuwait, like the Kuwait like super rich guy spending like five plus billion dollars on data centers, right? Like you just go down the list like all these countries, Malaysia has you know, you know, 10/plus billion dollars of like data center, you know, AI data center build outs over the next couple of years, right. Like, and you go to every country, it's like this stuff is happening but on the grand scheme of things, the vast majority of the compute is being built in the US and then China and then like Malaysia, Middle east and like rest of the world. And if you're in the, you know, going back to your point, right, like you have synthetic data, you have like this search stuff, you have like you have all these post training techniques, you have all this, you know, all this ways to soak up flops. Or you just figure out how to train across multiple data centers, which I think they have. At least Microsoft and OpenAI have figured. OpenAI's figure figured out.

[Dwarkesh Patel]

What makes you think they figured it out?

[Dylan Patel]

Their actions. So Microsoft has signed deals north of $10 billion with fiber companies to connect their data centers together. There are some permits already filed to show people are digging between certain data centers. So we think with fairly high accuracy, we think that there's five data centers, massive, not just five data centers, sorry, five regions that they're connecting together which comprises of many data centers, right.

[Dwarkesh Patel]

What will be the total power usage of the.

[Dylan Patel]

Depends on the time, but easily north of a gigawatt, right?

[Dwarkesh Patel]

Which is like close to a million GPUs.

[Dylan Patel]

Well the, each GPU is getting more power, higher power consumption too, right? Like it's like, you know, the rule of thumb is like GPU H100 is like 700 watts. But then like total power per GPU all in is like 1200, 1300 watts, 1400 watts. But next generation Nvidia GPUs are, it's 1200 watts for the GPU but then it actually ends up being like 2000 watts all in, right? Like so there's a little bit of scaling of power per GPU. But like you already have 100k cluster, right? OpenAI in Arizona, XAI in Memphis and many others already building 100k clusters of H1 hundreds. You have multiple at least 5, I believe GB200, 100k clusters being built by Microsoft, slash, OpenAI slash their partners for them and then potentially even more 500k, GB2 hundreds, right. Is a gigawatt, right? And that's like online next year, right? And like the year after that. If you aggregate all the data center sites and like how much power and you only look at net adds since 2022 instead of like the total capacity at each data center, then you're still like north of multi gigawatt, right? So they're spending ten plus billion dollars on these fiber deals with a few fiber companies, Lumen Zayo, like you know, a couple other companies and then they've got all these data centers that they're clearly building 100k clusters, right? Like old crypto mining site with core weave in Texas or like this Oracle Crusoe in Texas and then like in Wisconsin and Arizona and you know, a couple other places there's a lot of data centers being built up, you know, and providers, right. Qts and Cooper and like you know, you go down the list, there's like so many different provider and self build, right. Data centers I'm building myself.

[Dwarkesh Patel]

So, so gigawatts. Let's just like give the number on like okay 2025. Elon's cluster is going to be the big like it doesn't matter who it is.

[Dylan Patel]

So, so then there's the definition game, right? Like Elon claims he has the largest cluster at 100k GPUs because they're all fully connected rather than who it is.

[Dwarkesh Patel]

Like I just want to know like how many, like I don't know if it's better to denominate and 100,000 GPUs this year.

[Dylan Patel]

Okay, right.

[Dwarkesh Patel]

For the biggest cluster.

[Dylan Patel]

For the biggest cluster next year, next year, 300 to 500,000 depending on whether it's one side or many. Right. 300 to like 700,000 I think is the upper bound of that. But anyways, like you know there's, it's, it's, it's about like when they tear it on, when they can connect them, when the fibers connect it together anyways. 3, 300 to like 7, 500,000 let's say. But those GPUs are 2 to 3x faster, right? Versus the 100k cluster. So on an H100 equivalent basis, you're at a million chips next year in one cluster by the end of the year. Yes. No, no, no. Well, so one cluster is like.

[Dwarkesh Patel]

But you know what I mean, the.

[Dylan Patel]

Wishy washy definition, right? Multi site, right? Can you do multi site? What's the efficiency loss when you do a multi site? Is it possible at all? I truly believe so. What Is it whether. It's whether. What's the efficiency loss as a question, right?

[Dwarkesh Patel]

It would be like 20% loss, 50% loss.

[Dylan Patel]

Great question. This is where like, you know, this is where you need like the secrets, right, of like. And Anthropic's got similar plans with Amazon. And you go down the list, right, like people.

[Dwarkesh Patel]

And then the year after that, the.

[Dylan Patel]

Year after that is where this is 2026. 2026, there is a single gigawatt site and that's just part of the like multiple sites, right?

[Dwarkesh Patel]

For Microsoft, the Microsoft 5 gigawatt thing.

[Dylan Patel]

Happens in 21 gigawatt, one site in 2026. But then you have, you know, a number of others. You have five different locations, each with multiple, some with multiple sites, some with single site. You, you're easily north of 2, 3 gigawatts. And then the question is, can you start using the old chips with the new chips? And the scaling I think is like you're going to continue to see flop scaling much faster than people expect, I think as long as the money pours in, right? That's the other thing is there's no fucking way you can pay for the scale of clusters that are being planned to be built next year for OpenAI unless they raise like 50 to 100 billion dollars, which I think they will raise that end of this year, early next year.

[John Y]

50 to 100 billion. Are you kidding me?

[Dylan Patel]

No.

[John Y]

Oh my God.

[Dylan Patel]

This is like, you know, like Sam has a superpower. No, like it's like, it's like recruiting and like raising money. That's like what. He's like a God at will.

[Dwarkesh Patel]

Ships themselves be a bottleneck to the scaling.

[Dylan Patel]

Not in the near term, it's more again back to the concentration versus decentralization point because like the largest cluster is 100,000 GPUs. Nvidia's manufactured close to 6 million hoppers right across last year and this year, right? So like what? That's fucking tiny, right?

[Dwarkesh Patel]

So then. But why is Sam Talking about the 7 trillion to build foundries and what.

[Dylan Patel]

This is this, you know, like draw the line, right? Like log log lines. Let's number goes up, right? You know, if you do, if you do that, right, like you're going from 100k to 300 to 500k where the equivalent is a million. You just 10x year on year. Do that again, do that again or more, right? If you increase the pace, what is do that again.

[Dwarkesh Patel]

So like 2026, like the number of H100 try.

[Dylan Patel]

And you know, if you Increase the globally produced flops by like 30x year on year or 10x year on year and the cluster size grows or the cluster size grows by you know, 3 to 5 to 7x. And then you do your start, you get multi site going better and better and better. You can get to the point where multi million chip clusters I. E. They're even if they're like regionally not connected right next to each other are are right there.

[Dwarkesh Patel]

And in, in terms of flops, like.

[Dylan Patel]

It would be 1E what 130 is like very possible. Like 2829.

[John Y]

Wow.

[Dylan Patel]

Okay.

[Dwarkesh Patel]

And 1E 30 you said by 2829.

[Dylan Patel]

Yeah.

[Dwarkesh Patel]

And so that is literally six orders of magnitude. That's like 100,000 times more compute than GPT4.

[Dylan Patel]

The other thing to say is like the way you count flops on a training run is really stupid. You can't just do active parameters times tokens times six. Right. That's really dumb because the paradigm as you mentioned and you've had many great podcasts on this synthetic data and RL stuff, post training, verifying data and all these things, generating and throwing it away, all sorts of stuff, search, inference, time, compute, all these things aren't counted in the training flops. So you can't say 1 8:30 is a really stupid number to say because by then the actual flops of the pre training may be X, but the data to generate for the pre training may be way bigger or the search inference time may be way way bigger. Right, right.

[Dwarkesh Patel]

But also because you're doing the sort of adversarial synthetic data where the thing you're weakest that you can make synthetic data, it might be way more sample efficient. So even though coming up pre training.

[Dylan Patel]

Flops will be irrelevant. Right. I actually don't think pre training flops will be 1e30. I think more reasonably it'll be like the total summation of the flops that you deliver to the model across pre training, post training synthetic data for that pre training data and post training data as well as some of the inference time compute efficiencies could be like it's more like 1e30.

[Dwarkesh Patel]

Right, interesting. So suppose you really do get to the world where like it's worth investing. Okay, actually if you're doing one E30, how is that like a trillion dollar cluster, $100 billion cluster?

[Dylan Patel]

Like I think it'll be like multi hundred billion dollars and then, but then like it'll be like I like truly believe people are going to be able to use their prior generation clusters and alongside their new generation clusters and obviously like, you know, smaller batch sizes or whatever. Right, like, or use that to generate and verify data, all these sorts of things.

[Dwarkesh Patel]

And then for 1,030 right now I think 5% of TSMC's N5 is Nvidia or whatever percent it is by 2028, what percentage will it be?

[Dylan Patel]

Again, this is a question of how scale pilled you are and how much money will flow into this and how you think progress works. Will models continue to get better or does the line slope over. I believe it'll continue to skyrocket in terms of capabilities in that world. In that world. Why, why wouldn't like of not a 5 nanometer but like of 2 nanometer, a 16, a 14. These are the nodes that'll be in that timeframe of 2028 used for AI. I could see like 60, 70, 80% of it, like yeah, no problem.

[Dwarkesh Patel]

Given the fabs that are like currently planned and are currently being built, that is. Is that enough for the 1e30 or will.

[Dylan Patel]

I think so, yeah. So.

[Dwarkesh Patel]

So then like the chip code doesn't make any sense because like the chip code stuff about like we don't have enough compute.

[John Y]

There's no.

[Dylan Patel]

So no, I think, I think like the plans of TSMC on 2 nanometer and such are like quite aggressive for a reason. Right? Like to be clear, Apple, which has been TSMC's largest customer, does not need how much 2 nanometer capacity they're building. They will not need a 16, they will not need a 14. Right. Like you go down the list, it's like Apple doesn't need this shit, right? Although they did just hire one of Google's head of system design for tpu. But you know, so they are going to make an accelerator, but you know, that's besides the point. An AI accelerator, but that's besides the point. Like Apple doesn't need this for their business, which they have been 25% or so of TSMC's business for a long time. And when you just zone in on just the leading edge, they've been like more than half of the newest node or 100% of the newest node. Almost constantly that paradigm goes away. Right? If you believe in scaling and you believe in the models get better. The new models will generate infinite, not infinite, but amazing productivity gains for the world and so on and so forth. And if you believe in that world, then TSMC needs to act accordingly and the amount of silicon that gets delivered needs to be there. So 25, 26 TSMC is definitely there and then on a longer timescale, the industry can be ready for it. But it's going to be a constant game of, you must convince them constantly that they must do this. It's not a simple game of, oh, if people work silently, it's not going to happen, right? Like there has. They have to see the demonstrated growth over and over and over and over again on across the industry and investors or companies or more so like TSMC needs to see Nvidia volumes continue to grow straight up, right? And oh, and Google's volumes continue to grow straight up and you know, go down the list. Chips in the near term, right? Next year, for example, are less of a constraint than data centers, right? And likewise for 2026, the question for 27, 28 is like, you know, always when you grow super rapidly, like people want to say that's the one bottleneck because that's the convenient thing to say. And in 2023, there was a convenient bottleneck. COAS, right? The picture's gotten much, much like cloudier. Not cloudier, but we can see that like no, HBM is a limiter too. COASS is as well. Coass l especially, right? Data centers, transformers, substations, like all like power generation, batteries, like UPS is like crhs, like water cooling stuff. Like all of this stuff is now limitations next year and the year after fabs are in 26, 27, right? Like, you know, things will get like cloudy because like the moment you unlock one, oh, like only 10% higher, the next one is the thing. And only 20% higher the next one is the thing. So today, like data centers are like 4 to 5% of total US of total US. When you think about like as a percentage of US power, that's not that much. But when you think US power has been like this and now you're like this. But then you also flip side, you're like, oh, all this coal's been curtailed. All these like, oh, there's so many like different things. So like, power is not that crazy on a, like, on a national basis, on a localized basis. It is because it's about the delivery of it. Same with the substation transformer supply chains, right? It's like these companies have operated in an environment where the US power is like this or even slightly down, right? And it's like kind of been like, you know, like that because of efficiency gains, because of, you know, you know. So anyways, like, there have been humongous, like weakening of the industry. But now all of a sudden, if you tell that industry, your business will triple next year if you can produce more. Oh, but I can only produce 50% more. Okay, fine, year after that, now we can produce 3x as much, right? You do that to the industry. The US industrial base as well as the Japanese as well as like, you know, all across the world can get revitalized much faster than people realize. Right? Like I truly believe that people can innovate when given the need to. It's one thing if it's like this is a shitty industry where my margins are low and we're not growing really and blah blah, blah, blah blah to all of a sudden, oh, this is the sexy, I'm in power and I'm like, this is the sexiest time to be alive. And we're going to do all these different plans and projects and people have all this demand and they're begging me for another percent of efficiency advantage because that gives them another percent to deliver to the chips. Like all these things or 10% or whatever it is. Like you see all these things happen and innovation is unlocked. And you know, you also bring in like AI tools, you bring in like all these things. Innovation will be unlocked. Production capacity can grow. Not overnight, but it will on 6 months, 18 months, 3 year time scales. It will grow rapidly. And you see the revitalization of these industries. So but I think like getting people to understand that, getting people to believe because you know, if we pivot to like, you know, I'm telling you that Sam's going to raise 50 to $100 billion because he's telling people he's going to raise this much, right? Like literally having discussions with sovereigns and like Saudi Arabia and like the Canadian pension fund and like not these specific people but like the biggest investors in the world and of course Microsoft as well. But like he's literally having these discussions because they're going to drop their next model or they're going to show it off to people and raise that money because this is their plan.

[Dwarkesh Patel]

If these sites are already planned and.

[Dylan Patel]

Like they're not there, right?

[Dwarkesh Patel]

So how do you plan, how do you like plan a site without.

[Dylan Patel]

Today Microsoft is taking on immense credit risk, right? Like they've signed these deals with all these companies to do this stuff, but Microsoft doesn't have, I mean they could pay for it, right? Micro Life could pay for it on the current timescale, right? Oh, what's, what's, you know, their capex going from $50 billion to $80 billion direct capex and then another 20 billion across like Oracle Core weave, you know, and then like another like 10 billion across their data center partners, they can afford that, right? To next year, right? But then that doesn't, you know, like, this is because Microsoft truly believes in OpenAI. They may have doubts, like, holy shit, we're taking a lot of credit risk. You know, obviously they have to message Wall street, all these things, but they are not like, that's like affordable for them because they believe they're a great partner to OpenAI, that they'll take on all this credit risk. Now, obviously OpenAI has to deliver, they have to make the next model right? That's way better. And they also have to raise the money. And I think they will, right? I truly believe from like how amazing 4.0 is, how small it is relative to 4. The cost of it is so insanely cheap. It's much cheaper than the API prices lead you to believe. And you're like, oh, what if you just make a big one? It's like very clear what's going to happen to me on the next jump. That they can then raise this money and they can raise this capital from the world.

[John Y]

This is intense, Dylan.

[Dwarkesh Patel]

That's very intense, John, actually, if he's right or I don't know if not him, but in general, if the capabilities are there, the revenue is there, revenue doesn't matter.

[John Y]

Revenue matters.

[Dwarkesh Patel]

Is there any part of that picture that still seems wrong to you in terms of displacing so much of TSMC production, wafers and power and so forth, does any part of that seem wrong to you?

[John Y]

I can only speak to the semiconductor part, even though I'm not an expert. But I think the thing is TSMC can do it, they'll do it. I just wonder. He's right in that, in the sense that 24, 25, that's covered, but 26, 27, that's that secret point where you have to say, can the semiconductor industry and the rest of the industry be convinced that this is where the money is? Like, where's money is? And that means is there money? Is there money by 24, 25?

[Dwarkesh Patel]

How much revenue do you think the AI industry as a whole needs by 25 in order to keep scaling?

[Dylan Patel]

Doesn't matter.

[John Y]

Compared to smartphones. Compared to smartphones.

[Dylan Patel]

I know.

[John Y]

He says it doesn't matter.

[Dylan Patel]

I'll get to a lot.

[John Y]

You keep. I know.

[Dwarkesh Patel]

What is smartphones like? Apple's revenue is like 200 something billion dollars.

[John Y]

So like, yeah, it needs to be another smartphone size opportunity, right? Like even the smartphone industry didn't drive this sort of growth. Like it's crazy. Don't you think?

[Dylan Patel]

So today, so far, right?

[John Y]

Only the only thing I can really perceive, AI girlfriend. But like. But you know what I mean?

[Dylan Patel]

It's. No, I want a real one, damn it. So few things, right? The return on invested capital for all of the big tech firms is up since 2022. And therefore it's clear as day that them investing in AI has been fruitful so far, right? For the big tech firms, return on invested capital, like financially, you look at Metas, you look at Microsoft's, you look at Amazon's, you look at Google's, the return on invested capital is up since 2022. So it's in particular. No, just generally as a company. Now, obviously there's other factors here, like what is Meta's ad efficiency? How much of that is AI?

[John Y]

Super messy.

[Dylan Patel]

That's a super, super messy thing. But here's the other thing. This is Pascal's wager, right? This is a matrix of like, do you believe in God? Yes or no? If you believe in God, yes or no. Like hell or heaven, right? So if, if you believe in God and God's real and you go to heaven, that's great, that's fine, whatever. If you don't believe in God and God is real, then you're going to hell.

[Dwarkesh Patel]

This is the deep technical analysis you'll subscribe to semi analysis for.

[Dylan Patel]

This is just be ripping.

[John Y]

Can you imagine what happens to the stock if Satya starts talking about Pascal's wager?

[Dylan Patel]

No, no, but this is psychologically what's happening, right? This is a. If I don't. And Satya said it on his earnings call, the risk of underinvesting is worse than the risk of over investing. He said this word for word. This is Pascal's wager. This is I must believe I am AGI pilled, because if I'm not and my competitor does it, I'm absolutely fucked.

[John Y]

Okay. Other than Zuck, who. No, no, pre convergence.

[Dylan Patel]

Sundar said this on the, on the, on the earnings call. So Zuck said it, Sundar said it. Satya's actions on credit risk for Microsoft do it. He's very good at PR and like messaging, so he hasn't like said it so openly. Right. Sam believes it, Dario believes that. You look across these tech titans, they believe it. And then you look at the capital holders. The UAE believes it, Saudi believes it. How do you know? The UA believes it. Like all these major companies and capital holders also believe it because they're putting their money Here.

[John Y]

But like, how can, like, it won't last. It can't last unless there's money coming in somewhere.

[Dylan Patel]

Correct, Correct. But then the question is the simple truth is, like, GPT4 costs like $500 million to train.

[John Y]

I agree.

[Dylan Patel]

And it has generated billions in reoccurring revenue. But in that meantime, OpenAI raised $10 billion or $13 billion and is building a, you know, a model that costs that much, effectively. Right, Right. And, and so then obviously they're not making money. So what happens when they do it again? They Release and show GPT5 with whatever capabilities that make everyone in the world like, holy fuck. Obviously, the revenue takes time after you release the model to show up. You still have only a few billion dollars or, you know, $5 billion of revenue. Run rate. You just raised 50 to $100 billion because everyone sees this like, holy fuck, this is going to generate tens of billions of revenue. But that tens of billions takes time to flow in. Right? It's not an immediate click, but the time where Sam can convince. And not just Sam, but like, people's decisions to spend the money are being made are then. Right. Like, so therefore, like, you look at the data centers people are building, you don't have to spend most of the money to build a data center. Most of the money is the chips. But you're already committed to like, oh, I'm just going to have so much data center capacity by 2027 or 2026 that it's. I'm never going to need to build a data center again for like 3, 4, 5 years if AI is not real. Right. That's like basically what they're all their actions are. Or I can spend over $100 billion on chips in 26, and I can spend over $100 billion on chips In 27. Right. So this is, these are the actions people are doing and the lag on revenue versus when you spend the money or raise the money, raise the money, spend the money, build. You know, there's like a lag on this. So this is like, you don't necessarily need the revenue in 2025 to support this. You don't need the revenue 2026 to support this. You need the revenue in 2526 to support the $10 billion that OpenAI SPEN Microsoft spent in 23, early 24 to build the cluster, which then they trained the model in mid 24, early 24, mid 24, which they then released at the end of 24, which then started generating revenue in 25, 26.

[John Y]

I mean like the only thing I can say is that you look at a chart with three points on a graph, GPT 1, 2, 3 and then you're like.

[Dwarkesh Patel]

And even that graph is like the investment you have to make in GPT4 over GPT3 is 100x. The investment you had to make in GPT5 over GPT4 is 100x. Like so, so revenue currently the ROI could be positive. But like. And this very well could be true. I think it will be true. But like the revenue has to increase exponentially. Not just like.

[John Y]

Of course I agree with you. But I also, I agree in Dylan that it can be achieved ROI. Like semiconductor TSMC does this invest $16 billion and expects ROI, does that right, I understand that that's fine. Lag all that. The thing that I don't expect is that GPT5 is not here. It's all dependent on GPT5 being good. If GPT5 sucks, if GPT GPT5 looks like it doesn't blow people's socks off, this is all void.

[Dylan Patel]

What kind of socks you wearing, bro? Show them AWs. Show them AWs.

[John Y]

Show them AWS. GPT5 is not here. It's late, we don't know.

[Dylan Patel]

I don't think it's late.

[John Y]

I think it's late.

[Dwarkesh Patel]

I want to zoom out and like go back to the end of the decade picture again. So if you're, if this picture, you.

[Dylan Patel]

Know, we've already lost John.

[John Y]

We've already accepted GPT5 would be good. Hello.

[Dwarkesh Patel]

But yeah, you got it, you know.

[Dylan Patel]

Like bro, like life is so much more fun when you just like are delusionally like you were just.

[John Y]

We're just ripping bong vids, are we?

[Dylan Patel]

When you feel the AGI, you feel your soul.

[John Y]

This is why I don't live in, in San Francisco.

[Dylan Patel]

I have tremendous belief in like GPT5. Why area? Because like what we've seen already, I think the public signs all show that this is like very much the case. Right. What we see with beyond that is more questionable and I'm not sure because I don't know what I don't know, right? Like I don't know. We'll see how like how much they progress. But if like things continue to improve, life continues to radically get reshaped for many people. It's also like every time you increment up the intelligence, the amount of usage of it grows hugely. Every time you increment the cost down of that amount of intelligence, the amount of usage increases massively as you continue to Push that curve out. That's what really matters. And it doesn't need to be today. It doesn't need to be a revenue versus how much capex in any time in the next few years. It just needs to be did that last humongous chunk of capex make sense for OpenAI or whoever the leader was or. And then how does that flow through? Right? Or were they able to convince enough people that they need to they can raise this much money? Right. Like you think Elon's tapped out of his network with raising $6 billion? No. Xai is going to be able to raise 30 plus, right. Easily, right? I think so. You think Sam's tapped out? You think Anthropic's tapped out? Anthropics barely even diluted the company relatively. Right. Like, you know, there's a lot of capital to be raised in just from like call it FOMO if you want. But like during the dot com bubble people were spending. The private industry flew through like $150 billion a year. We're nowhere close to that yet.

[John Y]

Right.

[Dylan Patel]

We're not even close to the dot com bubble. Right. Why would this bubble not be bigger? Right. And if you go back to the prior bubbles, PC bubble, semiconductor bubble, mechatronics bubble throughout the U.S. each bubble was smaller. You know, you call it a bubble or not, why wouldn't this one be bigger?

[Dwarkesh Patel]

How many billions of dollars a year is this bubble right now for private capital?

[Dylan Patel]

Yeah, it's like 55, 60 billion so far for this year. It can go much higher. Right. And I think it will next year.

[Dwarkesh Patel]

Okay, so let me think of the.

[Dylan Patel]

Bong rip, you know, at least like finishing up and looping into the next question was like, you know, prior bubbles also didn't have the most profitable companies that humanity has ever created investing and they were debt financed. This is not debt financed yet. Right. So that's the last like little point on that one. Whereas the 90s bubble was like very debt financed. This is like disastrous for those companies. Yeah, sure, but it was so many, so much was built. Right. You got to blow a bubble to get real stuff to be built.

[Dwarkesh Patel]

It is an interesting analogy where like with, even though the dot com bubble obviously burst and like a lot of companies went bankrupt, they in fact did lay out the infrastructure that enabled the web and everything. So you could imagine in AI it's like a lot of the foundation model companies or whatever, a bunch of companies will go bankrupt, but they will enable the singularity.

[John Y]

During the 1990s at the turn of the 1990s, there was an immense amount of money invested in MEMS and optical technologies because everyone expected the fiber bubble to continue. Right. That all ended at 2003. 2002 issue went right.

[Dylan Patel]

And that started in 94.

[John Y]

Hasn't been revitalization since. Right. Like that's. You could risk the possibility of Lumen.

[Dylan Patel]

One of the companies that's doing the fiber build out for Microsoft. The stock like Forex last month or this month and then how's it done.

[John Y]

From 2002 to 2003?

[Dylan Patel]

Oh no. Horrible, Horrible. But like we're going to rip, babe. You could rip that bottle, baby.

[John Y]

You could breeze AI for another two decades.

[Dylan Patel]

You sure? Sure, possible. Or people can see a badass demo from GPT5, slight release, raise a fuckload of money. It could even be like a Devon like demo, right. Where it's like complete bullshit. But like it's fine. Right? Like, should I should edit that out? No, it's fine. It's fine, dude. I don't really, I don't really care, you know, it's. It's. The capital is going to flow in right now. Whether whether it deflates or not is like an irrelevant concern on the near term. Because you operate in a world where it is happening and being, you know, being, you know. What is the Warren Buffett quote, which is like you can be. I don't even know. It's Warren Buffett.

[John Y]

You don't know who's who. You don't know who's will be naked until the tide goes out.

[Dylan Patel]

No, no, no. The one about like the market is delusional. Far longer than you can remain solvent or something like that.

[John Y]

Buffett.

[Dylan Patel]

That's not Buffett.

[John Y]

Yeah, yeah, that's John Maynard Keynes.

[Dylan Patel]

Oh, shit. That's that old.

[John Y]

Yeah.

[Dylan Patel]

Okay, okay. So Keynes said it right? It's like you can be. Yeah. So this is the world you're operating in. Like it doesn't matter, right. Like what, what exactly happens? There will be ebbs and flows. But like that's the world you're operating in.

[John Y]

I reckon that if an AI bubble, if the AI bubble pops, each one of these CEOs lose their jobs. Sure.

[Dylan Patel]

Or if you don't invest and you lose, it's Pascalian wager and you're. That's much worse across decades. The largest company at the end of each decade, like the largest companies. That list changes a lot.

[Dwarkesh Patel]

Yeah.

[Dylan Patel]

And these companies are the most profitable companies ever. Are they going to let that list. Are they going to let themselves like lose it or are they Going to go for it. They have one shot, one opportunity, you know, to make themselves into, you know, the whole eminence. Right.

[Dwarkesh Patel]

Like, I want to hear, like, the story of how both of you started your businesses, or you're, like, the thing you're doing now. John, like, how. Like, how did it begin? What were you doing when you started the podcast?

[Dylan Patel]

You're going to tell me. A textile company?

[John Y]

Oh, my God. No way.

[Dylan Patel]

Please, please. Joking. If he doesn't want to, we'll talk about it later.

[Dwarkesh Patel]

Okay, sure.

[John Y]

I think, like, I used to. I mean, the story's famous. I've told it a million times. It's like Asian Omni started off as a tourist channel.

[Dwarkesh Patel]

Yeah.

[John Y]

So I would go around kind of like, I was. I moved to Taiwan for work, and then doing what I was. I was working in cameras. And then, like, I told.

[Dylan Patel]

What was the other company you started.

[John Y]

It tells too much about me.

[Dylan Patel]

Oh, come on.

[John Y]

It's like, I worked in. I worked in cameras. And then basically I went to Japan with my mom, and mom was like, hey, you know, like, what are you doing in Taiwan? I don't know what you're doing. I was like, all right, Mom, I will go back to Taiwan and I'll make stuff for you. And I made videos. I would, like, go to the Chiang Kai Shek park and be like, hi, Mom. This park was this. This. Eventually, at some point, you run out of stuff. But then it's like a pretty smooth transition from that into, like, you know, history of Chinese history, Taiwanese history. And then people started calling me Chinametry. I didn't like that. So I moved to other parts of Asia. And now, like. And then.

[Dwarkesh Patel]

So what year did you, like, start? Like, what. What year was, like, people started watching your videos? Let's say like, a thousand views per video or something.

[John Y]

Oh, my gosh. That was not. I started the channel in 2017, and it wasn't until, like, 2018, that. 2019, that it. Actually, I labored on for, like, three years. First three years with, like, no one watching. Like, I had got, like, 200 views, and I'd be like, oh, this is great.

[Dwarkesh Patel]

And then were you. Were the videos basically, like the ones you have, by the way, so, sorry. Backing up for the audience who might not. I imagine, basically everybody knows Asianometry, but if you don't. The most popular channel about semiconductors, Asian business history. Business history in general, even, like, geopolitics history and so forth. And yeah, I mean, it's like, honestly, I've done research for different AI guests And different. Whatever thing. I'm trying to. I'm trying to understand how does hardware work? How does AI work? It's like this is like my zipper work.

[Dylan Patel]

Did you watch that video?

[Dwarkesh Patel]

No, I haven't watched that one.

[Dylan Patel]

It was like, I think it was a span of three videos is like Russian oil industry in the 1980s and how it like funded everything. And then when it collapsed, they were absolutely fucked.

[John Y]

Yeah.

[Dylan Patel]

And then it was like the next video was like the zipper monopoly in Japan. Not a video was about asmr.

[John Y]

Not a monopoly. Yeah. Strong, strong holding in a mid. In a mid tier size. There's like the luxury zipper makers. Asianometry is always just kind of like stuff I'm interested in. And I'm like interested in a whole bunch of different stuff. And I like, like. And then the channel, for some reason people started watching the stuff I do and I still have no idea why, to be honest. I still feel like it's. I still feel like a fraud. I sit in front of like Dylan and he's. I feel like a fraud, legit fraud. Especially when he starts talking about 60,000 wafers and all that. I'm just like. I feel like I should be know. I should know this. But like, you know, in the end it's but. But that, you know, I just try my best to kind of bring interesting stories out.

[Dwarkesh Patel]

How do you make a video every single week? Because these are like two a week.

[Dylan Patel]

You know how long he had a full time job?

[John Y]

5 years, 6 years.

[Dylan Patel]

A textile business and a. Yes, and a full time job. Wait, no, full time job. Textile business and as anometry. Until like, for a long, long time.

[John Y]

Yeah, I literally just gave up the textile business this year.

[Dwarkesh Patel]

And like how are you doing research and doing like making a video and like twice a week?

[Dylan Patel]

I don't know.

[Dwarkesh Patel]

I like do these fucking. I'm like fucking talking. This is all I do. And I like do these like once every two weeks.

[Dylan Patel]

See the difference is dwark. You go to SF Bay Area parties constantly. John is like locked in.

[Dwarkesh Patel]

Yeah, yeah.

[Dylan Patel]

He's like locked in 24 7.

[Dwarkesh Patel]

He's got like the TSMC work ethic and I've got like the intel work ethic.

[John Y]

I don't. I got the Huawei ethic. If I do not finish this video, my family is. It will be. Will be pillaged.

[Dylan Patel]

He actually gets really stressed about it, I think, like not doing something like on his schedule. Yeah.

[John Y]

Is it very much like I do, I do two videos per week. I write them both simultaneously and how.

[Dwarkesh Patel]

Are you scouting out future topics? You want to do research? Is it just like, you know, you just like, pick up random articles, books, whatever, and then you just. If you find it interesting, you make a video about it?

[John Y]

Sometimes what I'll do is I'll Google a country, I'll Google an industry, and I'll Google like what a country is exporting now and what it used to export. And I compare that and I say, that's my video. Or I'll be like, or. But then sometimes also just as simple as, like, I should do a video about ykk. And then it's also just. But then it's also just a simple zipper is nice.

[Dylan Patel]

I should do a video about it.

[John Y]

I do. I do. It literally is.

[Dwarkesh Patel]

Do you keep a list of like, here's the next one, here's the one after that?

[John Y]

I have a long list of ideas. Sometimes it's as vague as Japanese whiskey. No idea what Japanese whiskey is about. I heard about it before. I watched that movie. And then so I was just like, okay, I should do a video about that. And then eventually, you know, you get to a.

[Dwarkesh Patel]

You get, how many research topics do you have in the back burner? Basically, like, you're like, I'm kind of reading about it constantly. And then like, in a month or so, I'll make a video about it.

[John Y]

I just finished a video about how IBM lost the PC. So right now I'm unstressing about that. But then I'll kind of move right on to like, the videos do kind of lead into others. Like right now, this one is about IBM PC, how IBM lost the PC. Now it's next is how Compaq collapsed, how the wave destroyed Compaq. So technically, I'll do that. At the same time, I'm dual lining a video about qubits. I'm dual lining a video about directed self assembly for semiconductor manufacturing, which I'll read a lot of Dylan's work for. But a lot of that is kind of like it's in the back of my head and I'm producing it as I go, Dylan, how do you work?

[Dwarkesh Patel]

How does one go from Reddit shit poster to running a semiconductor research and consulting firm?

[Dylan Patel]

Yes, let's start with the shitposting. It's a long line, right? Like, so immigrant parents, grew up in rural Georgia. So when I was 8, I begged for 7. I begged for an Xbox. And when I was 8, I got it. 360, right? They had a manufacturing defect called the red Ring of death. There are a variety of fixes. I tried them, like putting a wet towel around the Xbox, something called the penny trick. Those all didn't work. My Xbox still didn't work. My cousin was coming next weekend and like, you know, he's like two years older than me. I look up to him. He's in between my brother and I, but I'm like, oh, no, no, we're friends. You don't like my brother as much as you like me. My brother's more like jockey type. So it didn't matter. He didn't really care that I broke, that the Xbox was broken. He's like, you better fix it though, right? Otherwise parents will be pissed. So I figure out how to fix it online. I tried a variety of fixes, ended up shorting the temperature sensor. And that worked for long enough until Microsoft did the recall, right? But in that, you know, I stay. I learned how to do it out of necessity on the forms. I was a nerdy kid, so I like games, but whatever. But then, like, there was no other outlet once I was like, holy shit, this is Pandora's box. Like, what just got opened up? So then I just shit posted on the forums constantly, right? And, you know, for many, many years. And then I ended up like, moderating all sorts of reddits when I was like a tween teenager. And then like, you know, as soon as I started making money, you know, you know, grew up in a family business but didn't get paid for working, right? Of course, like yourself, right? But like, as soon as I started making money at like, and like, I got my internship and like, internship, I was like 18, 19, right? I started making money, I started investing in semiconductors, right? Like, I was like, of course this is shit I like, right? You know, everything from like, and by the way, like, the whole way through, like, as technology progressed, especially mobile, right? It goes from like very shitty chips and phones to like very advanced. Every generation they'd add something and I'd like, read every comment, I'd read every technical post about it. And also all the history around that technology. And then like, you know, who's in the supply chain? And it just kept building and building and building. Went to college, did data science type stuff, went to work on like hurricane, earthquake, wildfire simulation and stuff for a financial company. But before that, like, but during college, I was still, like, I wasn't posting on the Internet as much. I was still posting some, but I was like following the stocks and all these sorts of things. The supply chain all the way from like, the Tool equipment companies. And the reason I like like those is because like, oh, this technology, oh, it's made by them.

[Dwarkesh Patel]

You know, you kind of like friends in person who were into this shit or was it just online?

[Dylan Patel]

I made friends on the Internet, right.

[John Y]

That's dangerous.

[Dylan Patel]

I've only ever had like literally one bad experience and that was just because he was drugged out, right?

[Dwarkesh Patel]

Like one bad experience online or like.

[Dylan Patel]

Meeting someone from the Internet in person. Everyone else has been genuine. Like you, you have enough filtering before that point, you're like, you know, even if they're like hyper, mega, like autistic, it's cool, right? Like, I am too, right? You know. No, I'm just kidding. But like, you know, you go through like the, you know, the layers and you look at the economic angle, you look at the technical angle. You read a bunch of books just out of like, you know, you can just buy engineering textbooks, right, and read them, right? Like what's, what's, what's stopping you, right? And if you bang your head against the wall, you learn it, right?

[Dwarkesh Patel]

And then while you were doing this, was there like, did you expect to work on this at some point or was it just like pure interest?

[Dylan Patel]

No, it was like, it was like obsessive hobby of many years and it pivoted all around, right? Like at some point I really like gaming and then I got moved into like, I really like phones and like rooting them and like underclocking them and the chips there and like screens and cameras and then back to like gaming and then to like data center stuff like, because that was like where the most advanced stuff was happening. So it was like, I liked all sorts of like telecom stuff for a little bit. Like it was like, it like bounced all around, but generally in like computing hardware, right? And I did data science. You know, you could. I said I did AI when I interviewed, but like, you know, but it was like bullshit multivariable regression, whatever, right? Those simulations of hurricanes, earthquakes, wildfire for like financial reasons, right? Like anyways, you move. I moved up to like, you know, I was still, you know, I worked. I had a job for three years after college and I was posting and like, whatever. I had a blog, anonymous blog for a long time. I'd even made like some YouTub videos and stuff. Most of that stuff is scrubbed off the Internet, including Internet Archive, because I asked them to remove it. But like in 2020 I like quite quit my job and like started shit posting more seriously on the Internet. I, I moved out of my apartment and started traveling through the US And I went to all the national parks, like in my truck slash, like, tent slash. Also stayed in hotels and motels like three, four days a week. But I'd to like, like, I started posting more frequently on the Internet. And I'd already had like some small consulting arrangements in the past, but it really started to pick up in mid-2020, like, consulting arrangements from the Internet, from my Persona.

[Dwarkesh Patel]

Like, what kinds of people? Investors, hardware companies.

[Dylan Patel]

Like, there were like. It was like. It was like people who weren't in hardware that wanted to know about hardware. It would be like some investors, right? Some couple of VCs did it, but some public market folks. You know, there was times where like, companies would ask about like, three layers up in the stack, like me because they saw me write some random post and like, hey, like, can we? And blah, blah, blah, right? So those sorts of like, random. It was really small money. And then in 2020, like, it really picked up and I just like, like, why don't I just arbitrarily make the price way higher? And it worked. And then I started posting. I made it a new. I made a newsletter as well. And I kept posting. Quality kept getting better, right? Because people read it, they're like, this is like, you know, here's what to actually write. Or, you know, like, you know, over. Over more than a decade, right? And then in 2021, towards the end, I made a paid post because someone didn't pay and like, you know, for a report or whatever, right? Ended up. That ended up doing like, I went to sleep that night. It was about. It was about photoresist and like the developments in that industry, right? Which is the stuff you put on top of the wafer before you put in the litho tool, lithography tool. Did great, right? Like, I woke up the next day and I had like 40 paid subscriptions. Like, what? Okay, let's keep going, right? And let's. Let's post more paid. Paid, sort of like, like partially free, partially paid. Did like all sorts of stuff on like, advanced packaging and chips and data center stuff and like AI chips. Like all sorts of stuff, right, That I like, was interested in and thought was interesting. And like, I always bridged economically because I read all the company's earnings for like, you know, since I was 18. I'm, you know, 28 now, right. You know, all the way through to like, you know, all the technical stuff that I could 2022. I also started to just go to every conference I could, right? So I go to like 40 conferences a year. Not like, not like trade show type conferences, but like technical conferences. Like, like an ARC chip architecture photoresist, you know, AI neurips, right? Like you know, icml.

[Dwarkesh Patel]

How many conferences do you go to a year?

[Dylan Patel]

Like 40.

[Dwarkesh Patel]

So you like live at conferences?

[Dylan Patel]

Yes. Yeah, I mean I've been a digital nomad since 2020 and I've basically stopped and I moved to SF now. Right? But like kind of, kind of not really.

[John Y]

You can't say that. The, the government, the California.

[Dylan Patel]

I don't live at sf. Come on.

[Dwarkesh Patel]

But I basically do now for an Internal Revenue Service.

[Dylan Patel]

Do not joke about this, guys. Like, do not seriously joke.

[John Y]

They're gonna send you a clip of this, of this podcast. Be like 40%, please.

[Dylan Patel]

I am in, I am in San Francisco, like sub four months a year contiguously. And you know, exactly 100 and whatever it is, exactly 179 days. Let's go, right, like you know, over the full course of the year. But no, go to every conference. Make connections at all these very technical things like international electron device manufacturing. Oh, lithography and advanced patterning. Oh, very large scale integration circuits conference. You just go every single layer of the stack. It's so siloed. There's tens of millions of people that work in this industry, but if you go to every single one, you try and understand the presentations, you do the required reading, you look at the economics of it, you, like, are just curious and want to learn. You, like, you can start to build up like more and more. And the content got better and like, you know, what I followed got better. And then like started hiring people in 2020, in early 2022 as well. Or might have been. Yeah, yeah, like mid, mid 2022 started hiring, got people in different layers of the stack. But now today, like you fast forward now today, right? Like almost every hyperscaler is a customer. Not for the newsletter, but for like data we sell, right? You know, most many major semiconductor companies, many investors, right? Like all these people are like customers of the data and stuff we sell. And the company has people all the way from like X Simer, X asml, all the way to like X, like Microsoft and like an AI company, right? Like you know, like and. And then through the stratification, you know, now there's 14 people here and like the company and like all across the U.S. japan, Taiwan, Singapore, France, U.S. of course, right. Like you know, all over the world and across many ranges of like. And hedge funds as well, right? Ex hedge funds as well, right. So you got kind of have like this amalgamation of like, you know, tech and finance expertise. And we just do the best work there, I think.

[John Y]

Are you still talking about a monstrosity.

[Dylan Patel]

Like an unholy concoction? So like, and we sell, we sell, you know, we have data analysis, consulting, et cetera. For anyone who really wants to get deeper into this, we can talk about, oh, people are building big data centers, but how many chips is being made in every quarter of what kind for each company? What are the sub components of these chips? What are the sub components of the servers? We try and track all of that, follow every server manufacturer, every component manufacturer, every cable manufacturer, just all the way down the stack, tool manufacturer, and know how much is being sold, where and how and where things are, and project out all the way out to like, hey, where's every single data center? What is the pace that it's being built out? This like, this is like the sort of data we want to have and sell. And you know, it's the validation is that hyperscalers purchase it and they like, like it a lot, right? And like AI companies do and like semiconductor companies do. So I think that's the sort of like how it got there to where it is is just like, try and do the best, right? And try and be the best.

[Dwarkesh Patel]

If you were an entrepreneur who's like, I want to get involved in the hardware chain somewhere, like what is. Like what is. If you, if you could start a business today, somewhere in the stack, what would you pick?

[Dylan Patel]

John, tell them about your textile business.

[John Y]

I think I'd work in memory. Something in memory. Because I think like, if you, if this concept is like there, like you have to hold immense amounts of memory, immense amounts of memory. And I think memory already is tapped, like technologically. HBM exists because of limitations in dram. I said it correctly. I think like, it's fundamentally we've forgotten it because it is a commodity, but we shouldn't. I think it's breaking memory is going to. Could change the world in that scenario.

[Dylan Patel]

I think the context here is that Moore's Law was predicted in 1965. Intel was founded in 68 and released their first memory chips in 69 and 70. And so Moore's Law was a lot of it was about memory. And the memory industry followed Moore's law up until 2012, where it stopped, right? And it became very incremental gains since then, whereas logic has continued and people are like, oh, it's dying, it's slowing down. At least there's still a little bit of coming, right? Still more than 10%, 15% a year CAGR of growth and density cost improvement. Memory is literally been, since 2012, really bad. And when you think about the cost of memory, it's been considered a commodity. But memory integration with accelerators like this is like something that. I don't know if you can be an entrepreneur here though that's the real challenge is because you have to manufacture at some really absurdly large scale or design something which in an industry that does not allow you to make custom memory devices or use materials that don't work that way. So there's a lot of like work there that I don't. So I don't necessarily agree with you, but I do agree it's like one of the most important things for people to invest in. You know, I think there's, it's really about where is your, where are you good at and where can you vibe and where can you like enjoy your work and be productive in society, right? Because there are a thousand different layers of the abstraction stack. Where can you make it more efficient? Where can you utilize AI to build better and make everything more efficient in the world and produce more bounty and iterate feedback loop, right? And there is more opportunity to today than any other time in human history, in my view, right? And so just go out there and try, right? What engages you? Because if you're interested in it, you'll work harder, right? If you're like, have a passion for copper wires, I promise to God, if you make the best copper wires, you'll make a shitload of money. And if you have a passion for like B2B SaaS, I promise to God you'll make fuckloads of money, right? I don't, I don't like B2B SaaS, but whatever, right? It's like, whatever, you know, whatever you have a passion for, like, just work your ass off, try and innovate, bring AI into it and let it. You try and use AI yourself to like make yourself more efficient and make everything more efficient and I promise you will be successful. Right? I think that's really the view is not necessarily that there's one specific spot because every layer of the supply chain has. You go to the conference there, you go to talk to the experts there. It's like, dude, this is the stuff that's breaking. And we could innovate in this way or like these five extraction layers. We could innovate this way. Yeah, do it. There's so many layers where this is. We're not at the Pareto optimal right. Like, there's so much more to go in terms of innovation and inefficiency.

[Dwarkesh Patel]

All right. I think that's a great place to close. Dylan, John, thank you so much for coming on the podcast. I'll just give people another reminder. Dylan Patel, SammyAnalysis.com, that's where you can find the technical breakdowns that we've been discussing today. Asianometry YouTube channel. Everybody will already aware of Asianometry. But anyways, thanks so much for doing this. This was a lot of fun.

[Dylan Patel]

Thank you. Yeah, thank you.