Dylan Patel & Jon (Asianometry) – How the Semiconductor Industry Actually Works

Today I'm chatting with Dylan Patel who runs Semiconductor and Jon who runs the Asianometry YouTube channel. Does he have a last name? No, I do not. No, I'm just kidding.

Why? Why is it? I'm Jon Why? Why is it only one letter? Because why is the best letter? Why is your face covered? Why not? Seriously, why is it covered? Because I'm afraid of looking myself get older and fatter over the years. But this is seriously, it's like anonymity, right? Anonymity. Okay. Yeah. By the way, so you know what Dylan's middle name is? Actually, no. I don't know. He told me. What's my father's name?

I'm not going to say it, but I remember. You could say it. It's fine. Sanjay? Yes. What's his middle name? Sanjay? That's right. So I'm the War Cache Sanjay Patel. He's Dylan Sanjay Patel. It's like literally my white name. It's unfortunate my parents decided between my older brother and me to give me a white name. I could have been Dwarkesh. You know how amazing it would have been if we had the same name? Like Butterfly effected all that we probably would have turned out the same way. Maybe it would have been even closer.

We would have met each other sooner. You know, yeah. War Cache Sanjay Patel in the world. Yeah. Yeah. Yeah. All right. First question. If you're a Xi Jinping and you're scaling pill, what is it that you do? Don't answer that question. John, that's bad for AI safety. 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. I want to know. I want to kind of like a AI lab foreigners or hardware foreigners. Honey potting up an AI. I would basically like this is totally off cycle.

But like this off the off the reservation. But like I was doing a video about Yugoslavia's nuclear program. Nuclear weapons program. Start it. 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 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 would get to everything. And then like, I shouldn't do that. I was getting 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 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. So, okay, let's talk about the espionage. So, what is the most like valuable piece of if you could have this blueprint like this, this, this, this like one megabyte of information. Do you want it from TSMC? Do you want it from Nvidia? Do you want it from a book?

What is the first thing you would try to steal? I mean, I guess like you have to like stack every layer, right? And I think like 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 ASMR for over five years. And you know, ASMR is kind of like, oh, it's fine. The Dutch government's really pissed off, but it's fine. Right. I think it's a they already have those files, right? In my view, it's just a it's a very difficult thing to build, right? I think 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 mixed employees are TSMC, you 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 clearly are still not scale-pilled in my view. So these people are, I don't know if you could like hire them, it would probably worth a lot to you, right? Because you're building a fab that's where 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 like foreign adversaries or do they just get poached by other companies?

Within the same industry, but in the same country. And then yeah, well, like why doesn't that like sort of drive up their wages? I think it's because it's very compartmentalized. And I think like back in the 2000s prior to TSMC before Smith got a big, it was actually much more kind of open more flat. I think after that, there was like after the Hong Kong and after all the Samsung issues and after all the the SMICs rise when they're you literally saw.

I think you should tell that story actually the the the TSMC guy that went to Samsung and Smith and all that. I think you should tell that story. There are two stories. There's a guy if 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 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 those 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 like a whole bunch. It's like a Konga line of like Taiwanese to go live just like they just get on the plane and fly over.

And generally that's actually a lot of like a lot of like acceleration points within China Semiconductor industry. It's from talent flowing from Taiwan. And then the second thing was like the Among Song. The Among Song was a is a nut and I've met him. I've not met him. I've met people who work with him. And they say he is a nut. He is 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 cares about. He worked from TSMC, literal genius, 300 patents or whatever, 285, goes 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. And he was like head of R&D, right? Or something? He was like one of the top R&D. He was like second or third place. And it was for the head of R&D position basically.

More of the head of R&D 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, Konga line goes to 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. So there goes the Samsung Chairman usually like like part of the family that owns Samsung. Correct. Okay. So it's like kind of relevant.

So it's a bit like he goes over there and he's like well on 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. Uh huh. Toss that out. We are going to the leading edge and that is it. They go to the leading edge. The guys like they win apples business. They win apples business. They went back from TSMC. Or did they win it back from TSMC? They went ahead a portion of the.

They had a big portion of it. And then TSMC, more as Tom is like at this time was running the company. And he's like I'm not letting this happen because that guy talks to work for as well. But also God damn brilliant. And also like very good motivating people. He's like we will work literally day or night. Set up what is called the nightingale army where you have they split a bunch of people and they say you are working R&D night shift. There is no rest at the TSMC fab.

You will go in there as you go in there will 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 army for like a year or two years. They finished Finffett. They basically just blow away Samsung. And at the same time they sue the Almond Song directly for stealing trade secrets.

Samsung basically separates from the Almond Song and the Almond Song goes to smick. And so Samsung like at one point was better than TSMC. And then yeah he goes to smick and smick is now better than well or not better but they caught up rapidly as well after very rapid that guy's a genius. That's the guy'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.

Like what is research to make the next process know to look like? Is it just a matter of like 100 researchers go in they do like the next N plus one. Then the next morning the next 100 researchers go in it's experiments. They have a recipe and they're what they do every recipe a TSMC recipe is the culmination of a long

long years of like research right. It's highly secret and the idea is that you're 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 better is a not is a better or not kind of a thing like that.

You're basically it's it's it's it's multi variable problem that each every single tool sequentially you're processing the whole thing you you turn up knobs up and down on every single tool you can increase the pressure on this one specific deposition tool or and what are you trying to

measure is it like does it increase the yield or like what is it that it's not it's yield it's performance it's power it's not just a one you it's not just better or worse right it's a multi variable search space and what are these people know such that they can do this is that

they'd understand the chemistry and physics so it's a lot of intuition but yeah it's it's PhDs in chemistry PhDs in physics PhDs in EE brilliant geniuses people and they all just and they don't even know about like the end chip a lot of times it's like oh I am an etch engineer

and all I focus on is how hydrogen fluoride like 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 I remember like I 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 but he told me basically it was like it's it's very apprentice master apprentice like you know in Star Wars cif there's only one right master

apprentice master apprentice in it used to be that there is a master there's a apprentice and they pass on this secret knowledge this guy knows nothing but etch nothing but etch over time the apprentices stop coming and then in the end the the apprentices moved to Taiwan and that's the

same way it's still run like a you have the NT and NTHU Ching huang university national Ching huang university there's a bunch of masters they teach apprentices and they just pass this secret knowledge now hmm who are the most agi people people in the supply chain is there anybody like the

like I got my phone call collect right now okay go for it sorry could you mention to the podcast and video has got I guess calling Dylan for the for to update him on the earnings call oh it's not that's not exactly but go for it go for it yeah so Dylan is back from his call with

Jensen Huang just not with Jensen Jesus what did they tell you huh what did they tell you but next year's earnings no it was just color around like a hopper black well like margins it's like quite boring stuff for most people I think it's interesting though I guess we could start time on and you know what they would be like a sign there's like a lot of points there all right we covered the chips themselves how how do they get like the the 10 gigawatts data center up what else do they

need so I think there is a true like question of how decentralized do you go versus centralized right and if you look in the US right as far as like labs and such the you know opening ixai you know inthropic and then Microsoft having their own effort inthropic having their own efforts despite having

their partner and then meta and you know you go down the list it's like there is a 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 by do 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 Xi Jinping and scale pill interesting you must now centralize the compute resources right because you have you have

sanctions on how many Nvidia GPUs you can get in now there's still north of a million a year right even post october last year sanctions they still have more than a million h 20s and and other hopper GPUs getting in through you know other means but legally like the h 20s and then on

top of that you have um you have your domestic chips right but those 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 slash over the next six months is a hundred

thousand GPUs yeah right open AI x ai right these are like 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 um you know no 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 a centralized as the us companies are and the us companies are quite decentralized if you're Xi Jinping in your scale pill do you just say xyz company is now in charge and every GPU goes to one place and then then you don't have the same issues as the us right in the us we have a big problem

with like being able to build big enough data centers being able to build substations and transformers and all this yeah that are large enough in a dense area China has no issue with that at all because their supply chain adds like as much power as like half of Europe every year right like or some some absurd statistics right um so they're building transformers 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 gorgeous 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 2627 in the or 26th year in the us 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 gorgeous 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 million chips that Nvidia shipping in q3 and q4 the h20 um let's just put them all in this one data center they just haven't had that centralization effort right you can argue that like 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 you know low and behold you have a little bit of a little worm in there suddenly what you're doing your big training run oh this GPU off oh this GPU oh no oh no oh no I don't know if it's like that is actually by the way just to be clear John is is East Asian East Asian

time of East Asian descent half tiny Taiwanese have Chinese right that is right but like I think I think I think I don't know if that's like as simple as that to like uh because because training systems are like fire like they're they're water is it water gated firewalled what is it called

not firewall I don't know there's a word for that where they're not like they're airgapped airgapped I think you're going through like the all the like a four elements they're like they're earth protected water if you're using big your scale pill you're like you might the air

vendors fuck their what fire vendors you know we got the avatar right like you have to build avatar okay um I think I think that's possible um the question is like does that slow down your research do you like crush like cracked people like deep seek uh who are like clearly like not

being you know influenced by the government and put some like idiot 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 but I mean in the in 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 do you can't experiment and like have different

people lead and try their efforts as much if you're less centralized a more more centralized so it's like there is a balancing act there the fact that they can centralize I didn't think about this with that is actually like uh because uh 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 sink trading run as big in America as if like China as a whole decides to do one together um the the ten gigawatts you mentioned near the three were just down

is it like literally like how how how widespread is it like a state is it like one wire like how I think like between not just the damn itself but like also all of the coal there's some nuclear reactors there I believe as well um between all of n n like renewables like solar and wind between

all of that in that region there is an absurd amount of concentrated power um 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 yeah is more more of like the uh correct way to frame it um and that's how the that's how the

labs are also framing it right like I think if they started right now like how long does it take to build the biggest uh the biggest AI data center that in the world 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 etc 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 what 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

right 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 stop making aluminum there and instead started like making you know AI's there or making AI there like 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 like there's many ways for them to

hide compute as well to the extent that you could just take out a 5 gigawatt aluminum refining center and like build a giant data center there then I guess the way to control Chinese AI has to be the chips because like everything else they they have so like how do you like they're just like walk

me through how many chips do they have now how many will they have in the future well the like how many is that in comparison to US and the rest of the world yeah so 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 etc because their supply chain is built for that right and then it's pretty easy to pivot that whereas the US adds so little power each year and Europe loses power every year though the western sort of industry for power is non-existent in comparison right but on

the flip side is quote unquote western including Taiwan manufactured chip manufacturing is 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 you have you have you have you have equipment way for 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 of the largest advanced packaging

company in the world was Hong Kong headquarters 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 if

it's packaging is useful for stacking memory stacking chips on coos 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 and 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 AI is 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 AI-pilled it was like is you want to restrict them

from having AI is worse than us right so starting on the right end it's like okay well if you want to restrict them from having better AI is 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 good better than what they can build internally but currently the restrictions are flipped the other way right they can build better chips in China then we restrict them in terms of chips that in video or AMD or an 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 John don't seem to just think the expert controls are kind of a failure do you do you do you agree with them or that is a very interesting question because

I think it's like why thank you like what do you you're so good yeah do our case 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 instead they're talking about AI right yeah when you do

sanctions like that it's you need like such a deep knowledge of the technologies you know just taking lithography right if your goal is to restrict China from building chips and you just like boil it down to like hey lithography is 30% of making a chip so or 25% cool let's let's sanction

lithography okay where do we draw the line okay let me ask let me ask let me figure out what where the line is and if I'm a bureaucrat from the lawyer at the Commerce Department or what have you well obviously I'm going to go talk to a smell and a smell is going to tell me this is the line because they know like hey well this this this is you know there's like some blending over there's like they're like looking at like what's going to cost us the most money right 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 is like chip manufacturing is like like 3d chast or like you know a massive jigsaw puzzle and that if you take away one piece China can be like oh yeah that's the piece let's put it in right

and currently this export say 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 break 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 they either have to go kind of like full bat to the freaking like table slash wall or or chill out right like and like you know let them let them do whatever they want

because the alternative is everything is focused on this thing and they've 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 like you go deeper into the tech tree or what have you 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 keep pulling a thread other things will start developing

to kind of close that loop and like I think it's it's it is that's why I say it's an art right I don't think you 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 and now they really believe it right to some extent the sanctions have made China believe in the importance of the semiconductor industry more than anything else so from an AI perspective what's the point of

export control then because even if like if they're going to be able to get these like if you were like concerned about AI and they're going to be able to build a 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 why you're going to say I'm big as Lee oh I think there's a couple in it oh I love a little

little bit of like you know no but I think there have been a couple where people have talked about the nationalization right but like if you have you know nationalization then all of a sudden you aggregate all the flops is 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-pilled right just from foreign ships uh for individual model in like in 2026 it's six they can train a 2027 like they can release a 2027 model by 2026 yeah

and then a 28 model you know 2028 model in the works right like they totally could just with foreign ships 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

centralized or does like anthropic and open AI and xai and 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

US right if that's possible and like or you know and what happens there but China could absolutely have it in 2627 if they just have the desire to and that's just from foreign ships right and then domestic chips of the other question right 600,000 of the uh

Ascend 910b which is roughly like 400 terra flops or so um 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 of the ascend 910b's are going right but I mean well there's like rumors about like some

they are being divvied up between the like major alibaba bite dance by do etc um and next year more than a million and it's possible that they actually do have you know 130 before the US because data center is not as big of an issue um 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 um even ignoring sort of even once

the centralization question has solved right because that's clearly not happening today uh for either party um 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 um so there is a possibility right yeah it seems

like a big question then is how much could smick either increase the product like increasing amount of waifers like how many more waifers could they make and how many of those waifers could be dedicated to the night because like I assume there's other things they want to do with these 7 kind of

yeah so so there's like two points parts there too right like so the way the US's sanctioned smick 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 smick is still buying a ton of tools

um that can be used for their seven nanometer and their you know call it 5.5 nanometer process or six nanometer process for the 910c which releases later this year right um they they can build as much of that as long as it's not in Shanghai right and Shanghai has uh anywhere from 45 to 50

high-end immersion lithography tools is is what's like believed uh by intelligence as well as like many other folks um that that roughly gives them as much as 60,000 waifers a month of 7 nanometer but they also make their 14 nanometer in that fab right um and so the belief is that they actually

have about like 25 to 35,000 of 7 nanometer capacity um waifers a month right yeah doing the math right of the chip die size and all these things because uh to probably also use this chiplets and stuff so they can get away with uh using less leading edge waifers but then their yields are bad

you can roughly say any you know something like 50 to 80 uh good chips per wafer um with their with their bad yield right with their bad yield because it's hard right you know they're you're uh even if it was like yeah even everyone's knows the number right so like a

thousand steps even if you're 99% per each like 98 or 98% like in the end you'll still get a 40% you know overall interesting 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 or 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 that's the most scientific measure dog shit percent yeah yeah as a multiplicative effect right yeah um so yields are bad because uh they have hands tied behind their back right like um a they are not getting to

use uh uv whereas uh on seven animators I'll never use duv but uh TSMC eventually started using uv initially they used duv right doesn't that mean they actually are succeeded because that they have bad yield because they have to use like oh both brand new classes again they still are

determined success is mean they stop they're not stomping going back to the yield question right like oh theoretically 60,000 waifers a month times 50 to 100 dyes per wafer with yielded we yielded dyes 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 at like let's make 200 million Huawei phones right like oh okay cool I don't care right like as as the west you don't care as much even though like western companies will get screwed like Qualcomm and like

you know uh and media tech Taiwanese companies um so so obviously there's that um in the same applies to the US but when you when you flip to like sorry I don't fucking know what I was gonna say nailed it we're keeping this in hey everybody I am super excited to introduce our new sponsors

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go to jane street dot com slash doorkesh to learn more and now back to dillan and john 2026 if they're centralized they can have as big trading runs as anyone you know US company oh the reason why i was bringing up shanghai they're building seven nanometer capacity

invasion they're building five nanometer capacity in Beijing but the US government doesn't care and they're importing dozens of tools into Beijing and they're saying to the US government in a smell this is for 28 nanometer obviously right this is not bad and then obviously you know like in

the background you know we're making five nanometer here right are they doing it because they believe in the air because they want to make Huawei phones uh you know Huawei was the largest TSMC customer for like a few quarters actually before they got sanctioned oh Huawei makes most of the

telecom equipment in the world right uh 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 seven and five nanometer do you

say do you think the dominant is a Huawei is actually a bad thing for the rest of the Chinese tech industry i think quality 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 what the hell is Nokia and like 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 um and 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 note that's equivalent to like four years old right or three years old so it's like wait so they actually engineered us with a worse process node you know so it's like oh wow okay like you know Huawei is

Huawei is like crazy cracked why do you think that culture comes from the military because it's the PLA it is that we it is generally seen as an arm of the PLA but like how do you square that with the fact that sometimes a PLA seems to mess stuff up oh like filling water and rockets i don't know

that was true there is there is like that like like like like crazy conspiracy I can't conspiracy I was like you you don't know what the hell to believe in China especially as a not Chinese person but like nobody know even Chinese people don't know what's going on in China

there's like you know like all sorts of stuff like oh they're filling water in their rockets clearly they're like in constant it's like like if I'm a 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 the shit right like drones and like no no no we're filling water in our missiles these are all fake we don't actually have a hundred thousand missiles that we manufacture in a facility that's like super hyper advanced

and rathy honest stupid as shit because they can't make you know missiles nearly as fast right like I think like that's also like a flipside is like how much false propaganda is there right because there's a lot of like no smick could never smick could never they have they don't have

the best tools but but but then it's like motherfucker they just shipped 60 million phones last year with this chip that performs only one year worse than like 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 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 I wonder if it's going on there 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 what do you mean espionage you mean yeah like obviously like East Germany in the Soviet industry was basically was just a 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 how how does he not explain how they can make things with different processes I don't think it's just espionage I think there's literally a lot of it 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 as in all 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 a send 910b looks nothing like a GPU looks nothing like a TPU it is like its own independent thing sure they probably learned some things from some

places but like it is just like they're good at engineering it's 996 like wherever that culture comes from they they they do good yeah they do very good I know well another thing I'm curious about is like yeah we're they're culture confirms 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 company but like it's hard to like be a big company and like stay good that is true I think it's like I think like but I think a lot

a word that I hear a lot in with regards to Huawei's a struggle right and China has a culture of like the Communist parties like 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 go crazy because they think that in five years that they're going to fight the United States and though so like literally everything they do every second is like their country depends on it right it's it's like it's the Andy Grovy and 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 researching kind of now's 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 gonna die oh fuck like you know they're gonna beat us our country depends on it we're gonna get the best people from the entire country that are like you know the best at whatever they do

and tell them you will if you do not succeed you will die or like you will die your family will die you're finally being slaved everything like terrible by the evil western figs right exactly Western yeah comment like like a couple or not capables they don't believe in cut they don't say

that anymore but some are like like you know everyone is against China China is being it's been defiled right and like they're saying like if you that is all on you bro like you can't do that then like you if you can't get that fucking radio to be slightly less noisy and like

transmit like 5% more data we have a great postfire 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 uh-huh uh why isn't there more vertical integration in this interconnected industry well like why are

they're like this subcomponent requires a southern component from this other company which requires a component from the other company like why is more of it not done in house the way to look at it today is it's super super stratified in 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 like in or the very beginning it was integrated right what happened was

it was the funniest thing it said like you know yet 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 as company goes back to his old company says I can sell you

a product spinner right and that's the beginning of what we call the semiconductor manufacturing and equipment industry like basically it was 70s right like everyone made their own equipment 60s 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 in your competitive

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 like more maybe more companies overall but like the like the final people like who make the make the way for users less and less and then it's interesting in a way

it's similar to like the AI foundation models where you need to use like the revenues from like a previous model in order or like your market share to like fund the next round of ever more expensive development 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 yet Malaysia with Sotera you have Singapore with chartered you had there was one there's white there's white semiconductor where I talked about earlier there's loans 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're going to 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 to fund the next node right so technically in the

sense that what is kind of is aggregating all this money of his profit to kind of fund this next node to the point where now like there's no room in the market for an N2 and or N3 like they're technically you could argue that economically you can make an argument that like 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 I'm sorry to like completely derail you but like there's this video where it's like there's an holy concoction of meat slurry

yes what 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 to use like the way he was describing two dead debuts is kind of like that right 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 like what's the point like why is through an intermediate or not justify I'm not saying N2 is like N2 specifically but say N2 is 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 unless unless more you know technology spawn like AI now makes yeah yeah yeah one nanometer or whatever yeah there was a long period of 60-N-A yeah yeah viable right so so like we're nice and viable and what's as in like it's like

money money worth it or like every two years you get a shrink right yeah like clockwork Moore's law and then five nanometer happened it took three years holy shit and then three nanometer happened it took three or no sorry is it three nanometer five it took three 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 three of or sorry when three nanometer finally launched they still only Apple only move half of the iPhone volume to three nanometer so this is like now they did a fourth year of five nanometer for a big

chunk of iPhones right and it's like oh is the mobile industry pedoring out then you look at two 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 they can funnel into like more and more expensive chips but finally like that was that was real running out right it was to how economically viable is two nanometer just for one player TSMC you know ignore intel ignore Samsung just in you know because in 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 probably equity money and now I know I'm not putting money in debt and subsidies people salaries yeah but like anyways like you know there's there's a strong argument that like

funding the next node would not be economically viable anymore if it weren't for AI taking off right and then generating all this humongous demand for the most leading edge chip so and how much how big is the difference between seven to five to three nanometer like is it like is it like

does it matter is it a huge deal in terms of like who can build the biggest cluster or so there's the there's this simplistic argument that like oh moving in process node only saves me x percent in power right and and that that has been pedoring out right you know when you move from like 90

nanometer to 80 something right or 70 something right it was like it was you got two x right denard scaling was still intact right but now when you move from five nanometer to three nanometer first of all you don't double density um sram doesn't scale at all logic does scale but it's like

30 percent so all in all you only saved like 20 percent 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 then just the individual transistors power efficiency benefit because you

know for example you're multiplying a matrix that's like you know 8000 by 8000 by 8000 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 uh but you

know the the AI really really really wants new processed nodes because of a you know power used as a lot less now yeah uh 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 like you you look at the scaling it's like well no everyone needs to go to the most recent process node as soon as possible I want to ask the normie question uh for like

everybody's world I want to phrase it that way okay I want to ask a question that's like a nori not for you nerds I think I think John and I could communicate to to look at the point where you wouldn't even know what the fuck time okay now it's supposed uh Taiwan is invaded or Taiwan has

an earthquake nothing is shipped out of Taiwan and from now on what happens next the rest of the world how would a field's impact a day in a weekend a month in a year and I mean it's 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 it leading edge people were focused on leading edge but there's a lot of trailing edge stuff that like people depend on every day I mean we all worry about AI the reality is you're not gonna catch your fridge you're not good to

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 depressing I think and I live there yeah I think day one market crashes a lot right you're gonna think about like I think I think the big like big six six biggest

companies magnificent seven whatever that gets called or 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 there they're they'll 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 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 with fuck all the environmental stuff like wars probably happening but like the

supply chain is trying to like figure out what the hell to do to fix it but six months in disupply 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 you are like 40% chips now like it's just chips on in the tire there's like there's like two thousand plus chips right every Tesla door handle has like four chips it 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 two thousand plus chips even in in in 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's some in

Europe there's some in the US there's some in Japan you're going to bring in a guy to work in on Saturday into four yeah yeah I mean yeah so so you have like TSMC always builds new fabs that old fab they get tweak production up a little bit more and more 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's been the most important player and not just TSMC there's UMC there are there's PSMC there's a number of other companies there Taiwan share of like total manufacturing has grown every single

process node so in like one thirty and a meter 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 his manufactured in Taiwan right by you know either PSMC 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 toothbrush 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 we're supposed to do this interview like many months ago and then I like have 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 but I feel like with AI it's like

it's not that like no you've just spent time you've spent time but like I also feel like it's like less complicated it 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 yes it's in this field like I the thing I hear somebody's 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 the tutorial on how

the transformer works or whatever it's like yes I mean like many layers of really difficult 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 open AI 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 you went straight to work it 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 a at least like a fricking

masters in chemistry right and probably a PhD right like like over 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 not to say anything bad about sholto he's not really but he's cracked he's like omega crack that 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 specialized like crazy too you can't just pick it up um you know like sholto I think what did he

say he like just started like he was a consultant in McKinsey and at like night he would like greed papers about robotics right and like run experiments and whatever yeah and then and then like he like was like like people noticed who's 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 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 archive is like a free thing um the paper publishing industry is like

out of warrant everywhere else and you just like cannot download i triple e papers or like SPI papers or like other organizations and then two at least up until like late 2022 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 and like what is state of the art is public that is not

the case in semiconductors semiconductors have been shut down since the 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

so then how do you guys keep up with it well we don't know it I don't personally I don't think I know it I don't I mean I you don't know it because like like there was a guy there's like I spoke to one guy is 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 people who know lithography you you done pretty good work in packaging right nobody knows anything they all have shelman Indonesia they're all in this like single well right they're they're 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 all stack the like the stratification of just like manufacturing is absurd like the tool people don't even know exactly what intel and TSMC doing production and vice versa they don't know

exactly how the tools optimize like this and it's like how many different types of tools there are dozens and each of those has like an entire tree of like all the things that we've built all the things we've invented all the things that we continue to iterate on and then like here's the

breakthrough innovation that happens every few years in it too so if that's the case of like nobody knows a whole stack then how does the industry coordinate to be like um uh you know in five in two years we want them to go to the next process which has gait all around and for that we need

X tools and next technologies developed by whatever that's really fascinating it's a fascinating social kind of phenomenal right you can feel it I went to Europe the earlier this year Dylan was like had allergies but like I was like talking to those people and you can just it's like gossip

it's gossip you start feeling the you start feeling people could coalescing around like a something right early on we used to have like Semitech where people all these American companies came together and talked and they came and they hammered out right but the Semitech in reality was dominated by

a single company right and 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 arising 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 but I think it's like God came and proclaimed it we will shrink density 2x every two years Gordon Morse he made an observation and then like it didn't go nowhere as it went way further than he ever expected because it's like oh there's a 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 not every single engineer didn't believe it but like 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 obstratification every single layer

and abstraction layers every single layer through the entire stack to where people it's it's it's an unholy concoction I mean you've said in this word but like you 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 below above you know what's going on and then like beyond that it's like okay I can like under try to understand but like not really like but I guess I didn't answer the question of like when I already asked or whatever I don't know was it 10 20 years

ago I watched your video about it where they're like we are UV is the now is like this is we're going to do UV instead of the other thing and this is the path forward if how do they do that if they don't have the whole sort of picture of like different constraints different tradeoffs different

blah blah blah 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 semi-conductors are so siloed and the data and knowledge within each layer is a not documented online at all

right documentation because it's all siloed within companies B it is 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

an amazing amount of intuition on what to do just when you see something to where like AI can't just learn semi-conductors 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 semi-conductors tools fabs runs on like windows XP right like the each tool has like a windows XP server on it or like you know like all the chip design tools like have like centa centa centa like version six 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 they're afraid to touch it yeah because it's an unholy amalgamation it's unholy a snappy work

it should not work this thing should not work it's literally a miracle 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 a thousand x velocity or a hundred thousand x velocity and and so some examples of where this is already like shown true is some of in videos

AI layout tools right and in 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 the there's a lot of like various like simulation

things design or is that manufacturing it's all designed right most of it's designed manufacturing has not really seen much of this yet although there's starting to come in inverse lithography maybe yeah I'll see and say maybe I don't know if that's AI that's not AI yeah 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 alphagos type stuff like

or like not rl from alpha go but like like five six seven eight year old rl techniques being brought in but like gender they i being brought in could like really revolutionize the industry you know although there's a massive data problem so and can you give those can you give the

possibilities here in numbers in terms of maybe like a flop per dollar or whatever the relevant thing here is like how much do you expect in the future to come from process and order improvements how much from just like how the hardware is designed because of AI if you like how to decide

I don't specifically for like GPUs yeah like we had to disaggregate future improvements I think I think you know it's first it's important to state that semiconductor manufacturing and design is the largest search space 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 one e 10 one e 11 right 100 billion transistors yeah on on leading edge chips right blackwell has 220 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 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 space that human is no sure the search like what are you trying to optimize over well useful compute right what is you know if you're if the if the goal is optimize intelligence per picojule right and intelligence is some nebulous nature of like the

what the model architecture is yeah uh but and then and then picojule is 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 h 100 does not go to compute and there are more efficient like

uh compute are you know a o u's or a thomacologic 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 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 compute there are 100x gains from architecture even if we like literally

stopped shrinking I think we could have 100x gains from architectural advancements what time period uh that the question is how much can we advance the architecture right yeah the the challenge the other challenge is like the number of people designing chips has not necessarily grown in a long time

right um yeah like company to company shifts but like within like the semiconductor industry in the u s and the us makes you know designs the vast majority of leading edge chips the number of people designing chips has not grown much um what has happened is the output per individual has sort

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 what happens when we bring this in as the question and and how you can solve this search space somehow uh with humans

and AI working together to optimize this so it's not most of the power is movement data movement and then the logic the the compute is actually very small to flip side um the compute is first of all compute can get like a 100x more efficient just with like design changes and then you can 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 uh power delivery helps you innovate that uh 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 uh to where like Nvidia agenda agenda gen will do more than 2x performance per dollar uh i think that's very clear and then like hyper scalers are probably

going to try and shoot above that but we'll see if they can execute the there's like uh two narratives you can tell here of how this happens one is that these AI companies were training the foundation models who understand the trade-offs of like how much is the marginal increase in compute

versus memory work to them and what trade-offs do they want between different kinds of memory they understand this and so therefore the accelerators they build uh they can make these sort of trade-offs in a way that's like most optimal or and also design like the architecture of the the

model itself in a way that uh uh uh uh reflects like what where are the hardware trade-offs another is Nvidia because it has like i don't know how this works but presumably they have some sort of like know how like they're accumulating all this like uh knowledge about how to better design

this architecture and like also better search tools sort of so on um 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 open AI and Microsoft and uh amazon and then throw up a core designing their

accelerators will keep getting better at like designing the accelerator i 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 you know the the optimal model for google to run on tp use given a given amount of dollars a given amount of uh compute is different architecturally than what it is for open AI with envidaged stuff right it is like absolutely different and then like even down to like

networking decisions that different companies do and data center design decisions that people do the optimal like if you were to say you know x amount of compute of tp 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 right we can ask about that real quick uh the um so earlier we're talking about how china has the uh h 20s or b 20s and uh there there's like much less compute per memory bandwidth and like the amount of memory right does that mean that chinese models will actually have like very different

architecture and characteristics than american models and the future so you can take this to like a very like large conclude like leap and it's like all you know neuromorphic computing or whatever is like the optimal path and that looks very different than like what a transformer does right um or

you could take it to like a simple thing which is like the level of sparsity uh that like of course greens kripar city like experts and all this sort of stuff um the arrangement of what exactly the attention mechanism is because there are a lot of tweaks uh it's not just like pure transformer

attention right or like hey demot like how wide versus tall the model is right that's like very important like demod versus you know number of layers right um these are all like things that like would be different like and i and like i know they're different between like say a google and an

open a i and what is optimal yeah but what really it really starts to get like hey if you were limited on a number of different things like uh like china invests humongously in computing memory um you know which is like basically the memory cell is directly coupled or is the uh the compute cell

right so these are like things that like china's investing hugely and you go to conferences like oh there's 20 papers from chinese companies slash universities are about compute and memory or like you know hey like because the flop limitation is here maybe in video pumps up the on ship

memory and like 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 newtard american chips right uh a newtard chips that go to the u.s but like it'll start to diverge more and more architecturally because

they'd be stupid not to make chips for china right um and while they 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 base of solutions right because this whole area is like blocked off it's really 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 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 right that is not the case for chinese model i assume

right uh because there's probably very different use cases for them uh china's crushes the west at video and image recognition right at i cml like albert go at you know of cartesian like state space models like every single chinese person was like can i take a selfie with you man was harassed

in the us like you see albert and he's like so awesome he invented state space models but it's not like state space models are like 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 that is further along and has better capabilities in right so it's like because of all the surveillance cameras there sorry because of all the surveillance cameras there yeah that's the the quiet part out

loud right but like there's already divergence in like capabilities there right like you know 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 yeah yeah and and everyone has constraints but the constraints different companies have or 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 black well and then what's like set about tpv6 right there i'm not going to say they're like converging but they are getting a little bit closer in terms of like how big is the

matmo unit size and like some of the like topology and like world size of like the scale up versus scale out network like there is some like convergence slightly like not saying they're similar yet but like already they're starting to but then there's different architectures that people could go down and path so you see stuff like 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-rhythmic matric intensity because the hardware we have is very high-rhythmatic intensity and transformers run really well on GPUs and tpv's and like 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 like usable fobs out of the mall out of your chip it's worthless because even if it's 30% you know compute efficiency when 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 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 text acts and regulations that differ in every jurisdiction in Japan for example a lot of people pay for online purchases by ticking 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 are made head of compute of a new AI lab if like SSI came to you the Elias Tesco ver new lab and they're like Dylan we give you one billion dollars you are head of compute like help us get get on the map we're gonna compete

with the frontier labs what is your first step okay so the the constraints are you or a us slash Israeli firm because that's what SSI is right and your your researchers are on the us in Israel you probably can't build data centers in Israel because power is expensive as hell and it's probably

like risky maybe I don't know so still in the US most likely most of the researchers are here so or a lot of them are in the US right like Paul Torrever so I guess you need a significant chunk of compute you obviously the like the whole pitch is you're gonna make some research breakthrough that's

like compute efficiency when data efficiency when whatever it is here makes them breakthrough but you need compute to get there right because your GPUs per researcher is your research velocity right obviously like data centers are very tapped out right under the 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 square the circle yeah I'm a question um I kid you not in my group house like group chat there like

there have been two separate people who have been like I have a cluster of 800s and I have like a long lease on them but I don't like I'm trying to get sell them off is it like a buyer's market right now because it doesn't seem like people are trying to get rid of them so so I think like um

for for the Ilya question is like a cluster of like 250 60 p's or even 4k gps is kind of it's kind of cope right it's not enough right um yes you're gonna make compute efficiency wins but with a billion dollars you probably just want the biggest cluster in one individual spot um and

so like 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 less or like vast or like foundry like or 100 different GPU resellers the cluster sizes are small now is it a is it a

buyers market yeah last year you would buy H 100s for like four dollars or three dollars like if you you know an hour an hour right at a first shorter term or midterm deals right now it's like if you want a six-month deal you can get like two dollars 15 cents or less right like and like

the natural cost if I as 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 dollar 40 and then you add on the debt because I probably took debt to buy the GPUs or cost equity positive capital gets up to like

dollar 70 or something right um and so you see deals that are like the good deals right like Microsoft renting from core weaver like a dollar 90 to two dollars 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 dollar 70 so like there's still a lot of profit when people are selling in the low twos like GPU companies yeah people are deploying them but it is a buyers 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 know the exact name of the law but um it's effectively more as law right every two years the cost of transistors have and yet the industry grew right every every six months or three months the cost of of intelligence you know like open AI and GPD GPD

for what February 2023 right 120 dollars per million tokens or something like that was a roughly the cost and now it's like 10 right so it's like the cost of intelligence is tanking partially because of compute um 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 get make it cheaper and scale up and make it cheaper right right right anyways what you're saying if you're ahead of a computer of SSI okay ahead of computer SSI there's obviously no free data center lunch right

in terms of you know and and you can just you know take that based on like the data we we have shows that there's no lunch free lunch per se like immediately today you need the compute um for 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 gonna go like we're gonna go um by a Memphis factory put a bunch of like generators outside like mobile generators usually you preserve for like natural disasters a test of battery pack drives much power as we can from the grid tap the natural

gasoline that's going to the natural gas plant like two miles away they get go out natural gas plant like just like send it and like get a cluster built as fast as possible now you're running 100 KGPs right I know and that cost that cost about five billion right four billion right not not not

not one billion so it's scale that SSI has as much smaller by the way right um 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 32 K 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 know it makes the most sense to build your own cluster one uh instead of renting it or get a very close relationship like a uh open AI Microsoft with corviv or open AI Microsoft with Oracle slash

crucio um the next step is Bitcoin right um so opening i has a data center in uh Texas right or or they're it's going to be their data center it's like the kind of contract and all that corviv there is a 300 megawatt natural gas plant on site um powering these crypto mining uh data

data centers from the company called core scientific and so they're just converting that uh 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 a hundred thousand GB 200 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 uh for a 32,000 GPU cluster that they're building and you're saying Nvidia is doing

that uh well they're going through partners right because the the the other interesting thing is the big tech companies can't do crazy shit like Elon did uh yes she oh interesting they can't just do crazy shit like because this actually do expect um Microsoft and Google and whoever to like

drop their net zero uh commitments as the scaling a picture intensifies yeah yeah um so so so so like this this like this like or what what xai is doing right it's 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 the there's no unequivocal way to say any of the power is because um 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 corbiv thing 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 pui 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 there there PUE is like 1.1 or lower right i.e 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 this like core scientific one is going to be like 1.5 1.6 i.e. even like 300 megawatts of generation on site i only deliver like 180 200 megawatts to the chips given how fast solar is getting cheaper and also the fact that like you know how the reason solar is difficult

elsewhere is like you know you're like you got to like power the homes at night um here i guess it's like theoretically possible to like figure out you know only like run the clusters in the in the day or something absolutely not that really that that's not possible because it's so

expensive to have these GPUs yes so so like when you look at the power cost of a large cluster it's trivial in an into some extent right like um you know like the 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 re or power so cheap in china in the us 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 uh for these uh uh activities is why it's really difficult um and the economic regulation around

that but the 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 a billion dollars and i already have a data center i already have all this stuff i'm paying regular rates for the data centers on paying through the nose or

anything paying regular rates for power not paying through the nose power sub 15% of the cost and it's sub 10% of the cost actually right the the biggest like 75 to 80% of the cost is just the servers right and this is on 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 a relevant 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 we're like what do they do when when there's droughts right they like sick like force people to not shower they basically reroute the power from when there was a when there was a power shortage in Taiwan they basically reroute a power from the

residential and this will happen in a capitalistic society as well most likely because like it's a fuck you like why are you're not gonna 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 let's discuss what would happen if the training regime changes and if it doesn't change so like you could imagine that the training regime becomes much more parallelizable where it's like about like coming up with some sort of

like search or something like 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 a wide area in that world how fast could we sk it like just like let's go through the numbers on like year after year and then what suppose

it actually has to be you would know more than me but like suppose it has to be the current regime and like just explain what that would mean in terms of like how distributed that would have to be and then how plausible it is to get clusters of certain sizes over the next two years. I think it like is not too difficult for Ilya's company to get a cluster of like 32k and like of Blackwell next year. Okay, let's look at our numbers.

Like 2025, 2026, 2026. Before I like talk about like the US I think it's like important to note that there's like a gigawatt plus of data center capacity in Malaysian next year now that's like mostly by dance but like there's like you know in power wise there's like there's the humongous damning of the Nile and Ethiopia and the country uses like one third of the power that that damn generates so there's like a ton of power there to like how much power does that damn generate?

Like it's like over a gigawatt and the country consumes like 400 megawatts or something trivial and it is like are people bidding for that power? I think people just don't think they can build a data center in fucking Ethiopia. Why not? I wonder if the dam is filled yet is it? I mean they have to like the dam could generate that power they just don't. Oh good. 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 an Ethiopia? Like you wanted 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 like easily I think. People talk a lot about it in the Middle East. There's 100 KGB 200 cluster going up in the Middle East right? And the US like there's like clearly like stuff the US is doing right? Like the you know G 42 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 open I wanted to use the data center from them but instead like the US forced Microsoft to like I feel like this is what happened is force Microsoft to like do a deal with them so that G 42 has a 100 K GPU cluster but Microsoft is like administering and operating for security reasons right? And there's like omnivine 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 buildouts over the next couple of years right? Like and you know go to every country it's like this stuff is happening but on the grand scheme of things the vast majority of the computer 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 the 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 in open AI I have figured out their actions. So Microsoft has signed deals north of 10 billion dollars with fiber companies to connect their data centers together. There are

some permits already filed to show people are digging you know between certain data centers. So we think with fairly high accuracy we can say we think that there's five data centers massive not just five data centers five like regions that they're connecting together which comprises of many data centers right? What will be the total power usage of the depends on the time but easily north of a giga lot right? Which is like close to a million GPUs? 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 121300 watts 1400 watts but next generation Nvidia GPUs are it's 1200 watts for the GPU but then it actions of being like 2000 watts all in right? So there's a little bit of scaling of power per GPU but like you already

have 100k cluster right? Open AI in Arizona XAI in Memphis and many others already building 100k clusters of H100s you have multiple at least five I believe GB 200 100k clusters being built by Microsoft slash Open AI slash the partners for them and then and then potentially even more 500k

GB 200s right? Is a giga lot 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 ads since 2022 instead of like the total capacity at each data center then you're still like north of multi-gig-a-lot right? So they're spending 10 plus billion dollars on at least fiber deals with a few fiber companies, Lumens, AEO 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 Corvieve in Texas or like this Oracle, Crusoe and Texas and then like in Wisconsin and Arizona and and you know a couple other places there's a lot of data centers being built up you know and and providers right? QTS and Cooper and like you know you go down the list there's like so

many different provide and self-build right? Data centers I'm building myself. So so uh uh uh giga was just like you give the number on like okay 2025 Ulon's cluster is gonna be the big like oh it doesn't matter who it is so so then there's the definition game right? Like Iwann claims he has the largest cluster at 100k GPUs because they're all fully connected and who it is like I just want to know like how how many like I don't know if it's better to

to to to nominate and 100,000 GPUs this year okay for the biggest cluster 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 things I hope are bound of that but anyways like you know it'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 300 to like 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. But then cluster. By the end of the year, yes. No, no, no, well, so one cluster is like, but you know, I mean, the wishy washy definition, right? Multi-site, right? Can you do multi-site? What's the efficiency loss when you go multi-site? Is it possible at all? I truly believe so. What is it, whether it's, well, what's the efficiency loss as a question, right? Okay, it would be like 20%

lost, 50% lost. Great question. This is where you need like the secrets, right? And anthropics got similar plans in the Amazon and you go down the list, right? People have been in the year after that. The 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? For Microsoft, the Microsoft

5-gigawatt thing happens in 2026. One 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're easily north of two, three gigawatts. And then the question is, can you start using the old chips with the new chips? And like the scaling, I think, is like, you're going to continue to see flop scaling like much faster than people expect. I think as long

as the money pours in, right? Like that's the other thing is like, 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 $5,200 billion. Which I think they will raise that like end of this year early next year. 50,200 billion? Yes. Are you kidding me? No. Oh my God. 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. We'll ship themselves to be a bottleneck to the scaling. Not in the near term. It's more going back to the concentration versus decentralization point. Because like the largest cluster is 100,000 GPUs. Nvidia's manufactured close to six million hoppers, right? Like across last year and this year. Right? So like, what that's fucking tiny, right? So then why is Sam talking about the $7 trillion to build foundries and whatever? Well, this is this,

you know, like draw the line, right? Like log, log lines. Let's fuck. Number goes up, right? You know, if you do, if you do that, right? Like you're going from 100 K to 300 to 500 K, 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 pacing. What is do that again? So like 2026, like the number of H-20

billion. If you try 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, 385 to 7x. And then you start, you get multi-site going better and better and better. You can get to the point where multi-million chip clusters, IE, they're kind of, even if they're like regionally not connected right next to each other, are right there. And in terms of flops,

like it would be 1E what? 2028, 2039. I think 2030 is like very possible. Like 2829. Wow. Yeah. And 1E 30 you said by 2829. Yeah. And so that is literally six orders of magnitude. That's like 100,000 times more compute than GPD4. The other thing to say is like the way you count flops on it on a training run is really stupid. Like you can't just do like pre-empt active parameters times tokens times six, right? Like that's really dumb because like the paradigm as you mentioned,

right? It's like and you've had many great podcasts on this like synthetic data and like RL stuff, post-training, like verifying data and like all these things generating and throwing it away, like all sorts of stuff. Search, like inference time compute, all these things like aren't counted

in the training flops. So you can't like say 1E 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 the for the pre-training may be you know way bigger or like the searched inference time may be like way way bigger, right? Right. But also the like because you're doing this sort of adversarial synthetic data where like the thing you're weakest that you can make synthetic data for that, it might be way more sample

efficient. So like even though the pre-training flops will be irrelevant, right? Like I actually don't think pre-training flops will be 1E 30. I think more reasonably it'll be like the total subnation of the flops that you deliver through the model across pre-training, post-training, synthetic data for that pre-training data, post-training data, as well as like some of the inference time compute efficiencies like could be like it's more like 1E 30, right? So suppose you really do

get to the world where like it's worth investing. Okay, actually if you're doing 1E 30, is that like a trillion dollar cluster, a hundred billion dollar cluster? 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. And then for 1E 30, right now I think 5% of TSMCs and 5 is in video or like whatever percent it is by 2028, what percentage will it be? Again, this is like a question of like how scale-pilled you are and how much money will flow into this and how you think progress works. Like will models continue to get better or does the line like not, does the line slow-over? I believe it'll like continue to like skyrocket in terms of

keeping that world. In that world, why wouldn't like, oh not a 5nm but like of 2nm A16, A14, these are the nodes that'll be in that time frame of 2028, used for AI, I could see like 60, 70, 80% of it. Like yeah, no problem. Given the fabs that are like currently planned and are currently being built, that is is that enough for the 1E 30 or will we'll be able to? So then like the chip

code is making sense. Because like the chip goes off about like we don't have enough compute there's no. So no, I think I think like the plans of TSMC on 2nm 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 2nm capacity they're building. They will not need A16, they will not need A14, 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 it accelerator. But you know, that's besides the point, in an 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 like that paradigm goes away, right? If you believe in scaling and you believe in like that models get better, the new models will generate, you know, infinite, not infinite, but like amazing productivity gains for the world and such on and so forth. And if you believe in that world, then like TSMC needs to act accordingly. And the amount of silicon that gets delivered needs to be there. So 25, 26, TSMC is like definitely there. And then on a longer

time scale, the industry industry can be ready for it. But it's going to be a constant game of like, you must convince them constantly that they must do this. It's not like a simple game of like, oh, you know, if people work silently, it's not going to happen, right? Like they're, they have to see the demonstrated growth over and over and over and over again on across the industry. And and to see investors or companies or more so like TSMC needs to see

Nvidia volumes continue to grow straight straight up, right? And and oh, and Google's volumes continue to go straight up. And you know, go on down the list. Chips in the near term, right? Uh, next year, uh, for example, are less of a constraint than data centers, right? Um, and likewise for 2026, uh, the question for 2728 is like, you know, always when you grow super rapidly, like people want to say, that's, that's the one bottleneck because that's the convenient thing to say.

And in 2023, there was a convenient bottleneck, coos, right? The pictures got much, much like cloudier and not cloudier. But we can see that like, no, HBM is a limit or two. Coos is as well, coos, especially, right? Um, data centers, transformer substations like, uh, all like power generation batteries, like UPS is like, uh, CRH is like water cooling stuff. Like all of this stuff

is now limitations next year in the year after. Fabs are in 2627, 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 four to 5% of total US, uh, 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, so, you know, so anyways, like there have been humongous, like, um, weakening of the industry. Um, 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. You're after that. Now we can produce three acts 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, when given the like 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 like, you know, blah, blah, blah,

to all of a sudden, oh, this is the sex, I'm in power. I'm like, this is the sexiest time to be alive. And like we're, we're going to do all these different plans and projects and people have all this demand and they're like begging me for another percent of efficiency advantage because that gives them another percent to deliver to the chips. Like all these things were 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 six months, 18 months, three 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, we pivot to like, 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 you 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. But because this is their plan. If sites are already planned and like the money's not there, right?

So, how do you like about plan? It's like without going. 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? Microsoft could pay for it on the current time scale.

Right? Oh, what's what's, you know, their catbacks going from $50 billion to $80 billion direct catbacks and then another 20 billion across like Oracle, Corbiv, 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 open AI. They may have doubts like Holy shit, we're taking on a 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 open AI that they'll take on all this credit risk. Now, now obviously, opening 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. This is intense. That's very intense.

John, if he's right, I don't know if it's not him, but like in general, if like the capabilities are there, the revenue is there. The revenue doesn't matter. Or revenue matters. Is there any part of that picture that still seems wrong to you in terms of like displacing so much of TSMC production waifers and like, uh, well, power and so forth. Does any part of that seem wrong to you? I can only speak to the semiconductor part, even though I'm not an expert, but I think the

thing is like TSMC can do it. Like they'll do it. I just wonder though he's right in that in the sense of 24 25 that's covered. Yeah. 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 like and that means is there money? Is there money by 24 25? How much haven't you done you do think the AI industry is a whole needs by 25 in order to keep scaling?

Doesn't matter. Compared to smartphones. Compared to smartphone. But I know he says it doesn't matter. I'll get to a lot. You keep, I know. What is smart phones like Apple's revenue is like 200 something billion dollars. So like yeah, it needs to be another smartphone size opportunity, right? Like even the smartphone industry doesn't drive this sort of growth. Like it's not crazy, don't you think? So today is so far right? The only thing I can really perceive. Yeah, I go for it. But like

that's a lot. But you know what I mean? It's is no. I want to reel and dev. So, so, so like few things, right? The return on invested capital for all of the big tech firms is up since 2022. Yeah. And therefore it's clear as day that them investing in AI has been fruitful so far, right? Wait, wait, wait. For the big tech firms. Return on invested capital. Like financially you look at the you look at meadows, you look at open Microsofts, you look at Amazon's, you look at Google's.

The return on invested capital is up since 2022. So it's on AI 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, right? Super messy. That's a 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 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. This is the deep technical analysis. You'll subscribe to send me an hour. This is just, this is just the real thing. Can you imagine what happens to the stock if sat, yes, starts talking about Pascal's wager? Don't know, but this is psychologically what's happening, right? This is a, if I don't and and Satya said it on his earnings call, the risk of

under investing is worse than the risk of over investing. He has said this word for word. This is Pascal's wager. I must believe I am a GI pill because if I'm not and my competitor does it, I'm absolutely fuck. Okay, other than Zach who, you know, pretty convinced. Sundar said this on the, on the, on the, on the earnings call. So, Zach 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 it. 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 UAE inside of me? Blast don't believe it. Like all these major companies and capital holders also believe it because they're putting their money here. But that's like, how can, like it won't lie. It can't last unless there's money coming in somewhere. Correct, correct. But

then the question is the, the simple truth is like, GPT-4 costs like $500 million to train. I agree. And it is generated billions in reoccurring revenue. But in that meantime, opening I 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 GPT-5 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, five billion dollars of revenue run rate. You just raised $5,200 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 the 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 three, four, five years if AI is not real, right? That's like basically what their 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 in 2026 to support this. You need the revenue in 2526 to support the $10 billion that open AI spent in 23 or Microsoft spent in 23 slash early 24 to build the cluster, which then they train the model in mid 24, you know, for early 24, mid 24,

which they then released at the end of 24, which then started generating revenue in 2526. I mean, like, not only I could say is that you look, you look at a chart with three points on a graph, GPT-1, 2, 3, and then you're like, even that graph is like the investment you have to make in GPT-4 or GPT-3 is 100x. The investment you had to make in GPT-5 or GPT-4 is 100x. Like 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 like increase exponentially, not just like, you know, of course, of course, I agree with you, but I also agree in dealing with that it can be a cheap ROI, like, semi conduct, TSMC does this, invest $16 billion and expects a ROI does that, right? That's, I understand that. That's fine. Lag all that. The thing that I don't expect is that GPT-5 is not here. It's all dependent on GPT-5 being good. If GPT-5 sucks, if GPT-5 looks like,

it doesn't blow people's socks off, this is all void. What kind of socks you're wearing, bro? Show them AWS. GPT-5 is not here. It's late. We don't know. I don't think it's late. I think it's late. 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've played it, we've already lost John. We've already accept that GPT-5 would be good. You got it. Bro, life is so much more fun when you just like are delusionally like.

You were just ripping balls, it's how are we? When you feel the AGI, you feel your soul. This is why I don't live in San Francisco. I have tremendous belief in GPT-5 area because what we've seen already, I think the public signs all show that this is very much the case. What we see with beyond that is more questionable and I'm not sure because I don't know what I don't know. We'll see how much they progress. But if 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 that amount of intelligence, the amount of usage increases massively. As you continue to push that curve out, that's what really matters. It doesn't need to be today, it doesn't need to be

a revenue versus how much cat-backs. In any time in the next few years, it just needs to be, did that last humongous chunk of cat-backs make sense for Open AI or whoever the leader was? How does that flow through? Or were they able to convince enough people that they can raise this much money? You think Elon's tapped out of his network with raising $6 billion? No. XAI is going to be able to raise 30 plus. Easily. I think so. You think Sam's tapped out?

You think Anthropics tapped out? Anthropics barely even diluted the company relatively. There's a lot of capital to be raised in just from call it FOMO if you want. But during the .com bubble, people were spending the private industry through like $150 billion a year. We're nowhere close to that yet. We're not even close to the.com bubble. Why would this bubble not be bigger? If you go back to the prior bubbles, PC bubble, semiconductor bubble,

mechatronics bubble, throughout the US, each bubble is smaller. You call it a bubble or not. Why wouldn't this one be bigger? How many billions of dollars a year is this bubble right now? For private capital, it's like $55, $60 billion so far. For this year, it can go much higher. Right? I think it will next year. Let me think about the wrong way.

At least finishing up and looping into the next question was like, prior bubbles also didn't have the most profitable companies that humanity's ever created investing. And they were debt finance. This is not debt finance yet. That's the last little point on that one. Whereas the 90s bubble was very debt financed. This is like. Exactly. For those companies. Yeah, sure. But it was so much built. You got to blow a bubble to get real stuff to be built. It is an interesting analogy.

Though the.com bubble, I've always been bursting a lot of companies when bankrupt. They in fact did lay out the infrastructure that enabled the web and everything. You can imagine in an AI, a lot of the foundation of the companies or whatever, like a bunch of companies were like, go bankrupt, but like they were unable to. You could enable the singularity.

During the 1990s, the turn of 1990s, it was a immense amount of money invested in like memes and like optical technologies because everyone expected the fiber bubble to continue. Right? That all ended at 2003 to the two of which. You went right and that started in 94. It hasn't been revitalization since. Right? Like that's, you could risk the possibility of.

Women, one of the companies that's doing the fiber build out for Microsoft, the stock, like fucking Forex last month or this month and then how's it done from 2002 to 2000? Oh no, horrible, horrible. But like, we're going to rip, babe. You could rip that bow. Maybe you could freeze AI for another two decades. Sure, sure, possible. Or people can see a badass demo from GPD5, slight release, raise a fuckload of money. It could even be like a dev in like demo,

right? Where it's like complete bullshit, but like it's fine, right? Like, should I should? No, it's fine. It's fine. It's fine. Dude, I don't really care. You know, it's, it's, the capital is going to flow in, right? Now, whether, whether their deflates are not as 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, is it, I don't even know,

it's Warren Buffett quote. You don't know who's, you don't know who's, who's going to be naked until the tide goes out. No, no, no, the one about like, um, the market is delusional far longer than you can remain solvent or something like that. That's not Buffett. That's not Buffett. Yeah, yeah, yeah. That's, uh, John Maynard Keynes. Oh, shit. That's that old. Yeah. Okay. Um, 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? Or we have some flows, but like, that's the world you're operating in. Um, I reckon that if an AI bubble hat, if AI bubble pops, each one of these CEOs lose their jobs. Sure. Or if you don't invest and you lose, it's a Pascalian wager and you're, uh, that's much worse across decades, the largest company at the end of each decade,

like the largest companies, that list changes a lot. Yeah. 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 M&N song, right? 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, um, John, like,

how, like, what, how did it begin? What were you doing when you started the product? You just said, like, you're a stock company. No way. Please, please. Wait, you have it? No, is he joking? I guess if he doesn't want to, we'll talk about it later. Okay, sure. I think like, I used to, I mean, the story's famous. I've told it a million times. It's like Asian industry start off as a tourist channel. Yeah. 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 what was the other company you started? It tells too much about me. Oh, come on. Um, 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, 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 Shaq 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 start to call me China nometry. I didn't like that. So I moved to other parts of Asia. And now, like, and then what year did you like start? Like what year was like people started watching your

videos. Let's say like a thousand views per video or something. Oh my gosh. That was not. I started the channel in 2017. And then it wasn't until like 2018 that 2019 that was actually, I labored on for like three years, first three years with like no one watching. Like I got like 200 views. And I'd be like, Oh, this is great. And then were you, were the videos basically like the ones you have? But I'm sorry, backing up for the audience who might not, I imagine basically everybody knows

Asian nometry. But if you don't, like the most popular channel about semiconductors, Asian business history, business history in general. We've like, juu politics, history and so forth. And yeah, I mean, it's like, honestly, I've done research for like different AI guests and different, like whatever thing I'm trying to be, I'm trying to understand like how does harder work? How does AI work? It's like, this is like my back. How does a zipper work? Did you watch that video?

No, I think it was a span of three videos was like Russian oil industry in the 1980s and how it like funded everything. And then when it collapsed, they were absolutely fucked. And then it was like, the next video was like the zipper monopoly in Japan. Not about it. It's not a monopoly. Strong, strong holding in a mid in a mid-tier size. There's like the luxury zipper makers. Asian nometry 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, like in the channel, for some reasons, 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 no, I should know this. But like, you know, in the end, it's, but then, you know, I just try my best to kind of bring interesting stories out. How do you make a video every single week? Because these are like two a week. You know how long he had a full-time job five years six years. Or sorry, eight textile business. And a, yes. And a full-time job. Wait, no, full-time job textile business and Asian nometry until like for a long, long time.

I literally just gave up the textile business this year. And like, how are you doing research and doing like making a video and like twice a week? I don't know. 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. Sorry. See, the difference is Dark Esh. You go to SF Bay Area parties constantly. Dark Esh is, I mean, John is like locked in. He's like locked in 24 seconds.

I believe that the agency work ethic and I've got like the Intel work ethic. I don't, I got the Huawei ethic. If I do not finish this video, my family is that it will be, will be pillaged. He's actually, it's really stressed about it, I think, like not doing something like on a schedule. Yeah. Is it very much like I do, I do two videos a per week. I write them both simultaneously. And how are you scouting out future topics you want to do research? And these

are just like, what, you know, you just like pick up random articles, books, whatever. And then then you just, if you find it interesting to make a video about it, sometimes what I'll do is that I'll Google a country, I'll Google an industry and I'll Google like what the 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, simple, simple, simple, nice, idea about it. I do. Literally. Is it like, do you like, keep a list of, like here's the next one, here's the one after that. I have a long list of like ideas. Sometimes it says big as like Japanese whiskey. No idea what Japanese whiskey is about. I heard about it before I watched that movie. And then so it's just like, okay, I should

do a video about that. And then eventually, you know, you get to a, you get to move that. I don't know. 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. I just finished a video about how IBM lost the PC. Yeah. So right now I'm de- I'm unstressing about that. But then I'll kind of move right onto, like the videos do kind of lead into others.

Like right now this one is about IBM PC. How IBM lost the PC now is next is how compact collapse. How the wave destroyed compact. So technically, I think that I'll do that. At the same time, I'm dualigning a video about two bits. I'm dualigning a video about the directed self assembly for semiconductor manufacturing, which I'll read a lot Dylan to work for. But then like, like a lot of that is kind of like, it's just, it's in the back of my head. And I'm like, producing it as I go.

Dylan, how do you work? How does one go from Reddit shit poster to like running, like a semiconductor research and consulting firm? Yes. Let's start with the shit posting. It's a long line, right? Like so immigrant parents grew up in rural Georgia. So when I was eight, I begged for, or seven, I begged for an Xbox and when I was eight, I got it, 360 or eight. They had a manufacturing defect called the Red Ring of Death.

There are a variety of fixes that tried them, like putting a wet towel around the Xbox, something called the Penny Trick. Those old didn't work, my Xbox still didn't work. My cousin was coming the next weekend and like, you know, he's like two years older than me. I look up to him, he's like in between my brother and I, but I'm like, oh no, no, we're friends, you know, you don't like my brother as much as you like me. My brother's more like a jockey type, so it didn't matter.

So like he didn't really care that the Xbox is broken, he's like, you better fix it though, right? Otherwise parents will be pissed. So I figure out how to fix it online. It ends up, you know, I tried a variety of fixes, ended up shorting the temperature sensor and that worked for a long enough until Microsoft had the recall, right? But in that, you know, I stayed, I learned how to do it out of necessity on the forums. I was an early kid, so I liked games, whatever.

But then like, there was no other outlet. One of those things, I was like, holy shit, this 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 twin teenager. And then like, you know, soon as I started making money, 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 and like I got my internship and like internships, I was like 18, 19, right? I started making money. I started investing in semiconductors, right? Like of course, this is the 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 just kept building and building and building? When a college did data science, the type stuff went to work on like hurricane, earthquake, wildfire simulation and stuff for a financial company, but before that, like 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 those is because like, oh, this technology, oh, it's made by them, you know, you kind of, do you have like friends and person who were into this shit? Or was it some like, I made friends on the internet, right? Oh, that's dangerous.

No, I've only ever had like literally one bad experience and that was just because you just drugged out, right? Like a one that exergents online or a group? Like meeting someone from the internet in person. Everyone else has been genuine. Like you haven't a 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? I know, 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 stopping you, right? And if you bang your head against the wall, you learn it, right? And then what, what were you doing this? Was there like, did you expect to work on this at some point? Or was it just like, you're interested?

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 cause that was like where the most advanced stuff was happening.

So it's 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 then I did data science, you know, you could, I said I did AI when I interviewed, but like, you know, it was like bullshit, multi variable regression, whatever, right? Those simulations of hurricanes are of course a lot of fire 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 YouTube 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 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, you know, also stayed in hotels and motels like three, four days a week. But I'd 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.

Like what kinds of people, investors, hardware companies, like, there were like, it was like, people who weren't in hardware that wanted to know about hardware 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 posts and like, hey, like can we end up blah, blah, blah.

And it was all sorts of like random, it was really small money. And then in 2020, like it really picked up and I just like, I don't know, I just arbitrarily make the priceway higher and it worked. And then I started posting, I made 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 fucking retarded, like, you know, there's what's actually right, 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, someone pay and like, you know, for a report or whatever, right? And it up, that ended up doing like, I went to sleep that night.

It was about, it was about photo-resist and like the developments in that industry, which is the stuff you put on top of the way from before you put in the LITO tool. LITOography 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 keep, let's post more paid sort of, 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 companies earnings for like, you know, since I was 18, I mean, 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 art chip architecture, photo resist. You know, AI NIRIPs, right? Like, you know, I see them all, like, how many conferences do you go to a year? Like 40. So you like live at conferences? Yes, yeah.

I mean, I've been a digital nomad since 2020 and I've basically stopped and I moved SF now, right? But like kind of kind of not really. You can't say that the government, the California government, no, no, no, no, no. I don't live at SF, come on. But I basically do now, right? Right for an internal revenue service. Do not joke about the staff. Like do not seriously do. They're gonna send you a clip of this podcast be like, 40, exactly.

I am in San Francisco, like sub four months a year, continuously, you know, exactly a hundred and whatever day. Exactly, 179 days, let's go, right? Like, you know, over the full course of the year. But no, like, you know, go to every conference, make connections at all these like very technical things, like, international, electronic device manufacturing. Oh, lithography and advanced patterning. Oh, like a very large scale integration, like, you know, old, you know, circuits conference.

So 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 wanna 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, we got better and then like, started hiring people in 2020 and early 2022 as well. Or might have been, 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 hyperscale, there's 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-A-SML all the way to like, X-Like Microsoft and like an AI company, right? Like, you know, like, and then through the stratification, you know, now there's 14 people here in like, the company and like all across the US, Japan, Taiwan, Singapore, France, US of course, right?

Like, you know, all over the world and across many ranges of like, and hedge funds as well, right? X 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, right? Are you just talking about a monstrosity? Like, and then an unholy concoction. But so like, when we sell, we sell, you know, we have data analysis, consulting, et cetera.

For anyone who like really wants to like, get deeper into this, right? Like we can talk about like, oh, people are building big data centers. But like, 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, right?

We try and track all of that, follow every server manufacturer, every component manufacturer, every cable manufacturer, like just like all the way down the stack tool manufacturer. And like, know how much is being sold where and how, and where things are and project out, right? 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 wanna have and sell, and you know, it's the validation is that hyper scalers purchase it and they like like get 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 trying to do the best, right? And trying to be the best. 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 could start a business today, somewhere in the stack, what would you pick? John, tell them about your textile business. I think I'd work in memory. Something in memory. Cause 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 to HBM exists because of limitations in D-Bram. 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, would change, could change the world in that scenario. 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.

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 like, for coming, right? Still more than 10% 15% a year, Kagger, of growth and density slash cost improvement. Memory has like literally been like since 2012, like really bad.

So, 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 well.

So there's a lot of like work there that 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 use, or utilize AI to build better and make everything more efficient in the world and produce more bounty and like iterate feedback loop, right? And there is more opportunity to today than any other time in human history in my view, right? And so like just go out there and try, right? Like 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 shit load of money. And if you have a passion for like B2B SaaS, I promise to God you'll make fuck loads of money, right? 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 like be successful, right?

I think that's really the view is not necessarily there's one specific spot because every layer that supply chain has, you go to the conference there, you go to the 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 cradle optimal, right?

Like there's so much more to go in terms of innovation and inefficiency. 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 the reminder, Dylan Patel, semianalysis.com, that's where you can find the technical breakdowns that we've been discussing today, Asian Nonmetry YouTube channel, everybody who already aware of Asian Nonmetry, but anyways. Thanks so much for doing this, this was a lot of fun. Thank you. Thank you.