Dylan Patel

“It's actually just to reward hack it and actually just, find the zero day. So you can think of it as, a like, a a human.”

“if I really just want to chase the reward, do I just topple all of human civilization because I can just own the button to press reward reward reward over and over and over again and be the heroin addict? Yeah. I think that this is like a real like thing.”

“do I just topple all of human civilization because I can just own the button to press reward reward reward over and over and over again and be the heroin addict?”

“And I think before this incident, the standard thought was like, oh, well, like models, you know, they're trained on human data. Yeah.”

“Anthropic took months to release Mythos. Right? They they said it was done in February. They did not release it until like what?”

“have they prevented themselves from using Meetos two internally to make Meetos three better? Or have they prevented themselves from using Astra to make Astra plus one better?”

“So I think that's the you've got the public and and and, you know, if anything, like, the gap between Mythos and public models is still there.”

“If model progress at the labs pause, then more compute comes online. It has to slow down. Right? Sort of right now we have supply demand, right?”

“Supply of silicon can go many ways. You can either leverage it to high throughput things or high interactivity things.”

“the moment Mithos was good, and available internally Yeah. I I I she told me that she stopped taking her ADHD medicine. Oh, come on. And that made her a better employee.”

“all of the successful AI hardware adoption of non NVIDIA flavors has been Anthropic adopting TPUs. Obviously, Google doing their own with their TPUs. Or Anthropic and OpenAI now adopting Trainium, or OpenAI adopting Cerebras.”

“American open source models are terrible. French open source models. Well, Mistral just stopped open sourcing models really. Are there any of the good ones? So it's it's all Chinese open source models.”

“Base Ten, like Tuhin's probably made more money off of, you know, Minimax or GLM or DeepSeq than those companies have because he's serving those models at scale. And same with, Linett Fireworks.”

“My my own company of 90 people, 30% of my cost now is tokens versus 70% employee costs.”

“In in past sort of major revolutions, whether it was like the fax machine or cars or any other technological revolution, it took decades to dissipate.”

“AI task length, right? The amount of time that an AI model can work on any specific job is increasing by 2x every seven months.”

“So how one say, oh, pause, let me measure value, when trying a bunch of stuff is the only way you can actually get to figuring out where the value is versus what doesn't work?”

“And so this massively improves their productivity, despite the fact that this is all old, twenty plus year old technology.”

“Right? If if you're not gonna understand these systems if your managers haven't taken the time to understand the different architectures.”

“But actually with AI, a lot of the people that are on my team, their jobs have radically changed over the last few years.”

“In this particular case, the model decided that it's going to pull it found while reverse engineering an APK, found an AWS access key.”

“So Anthropic turned their first gross profit in Q2 in June. And then in Q3, they will be turning a billion dollars of operating profit, slightly over.”

“Anthropic turned their first gross profit in Q2 in June. And then in Q3, they will be turning a billion dollars of operating profit, slightly over.”

“And so this is this is their financials that they're going to be putting out in their IPO.”

“You look at OpenAI late last year, their margins had were roughly 30% gross margin, but if you stripped away the free users, they were at 50%.”

“OpenAI late last year, their margins had were roughly 30% gross margin, but if you stripped away the free users, they were at 50%. Now, total company gross margin is closer to 55%,”

“Now, total company gross margin is closer to 55%, and if you strip away the free users, they're at about 65%.”

“On Amazon's most recent earnings call, they talked about how AWS had a strong gross margin beat. Right? They won on gross margin because Bedrock was so profitable.”

“And and that makes, you know, initially, that'd be like, okay, well now I need a ton ton of compute to calculate all the prefilled tokens.”

“you calculate that once, you store it off in memory, whether it be system memory or storage, and then you pull it back in when you run the turn.”

“we're seeing cache hit rates above 95% for many AgenTeq workflows, which which means the cost for a cache hit is it's not free,”

“So if you take the, you know, just open source inference engines off the shelf, that gets you a certain level of cost.”

“we have over $80,000,000 of compute GPUs from NVIDIA AMD, TPUs from, Google, Tranium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest, PyTorch version,”

“Every day it runs on an automated CI, and we run it on all the latest Chinese models from GLM, Zebu, Moonshot, Kimi, Alibaba, all these models we run.”

“Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it, you know, fixed context length.”

“we've analyzed over $5,000,000 worth of Claude code traces. Right? So this is real production traffic that people have donated to us,”

“It's 72 GPUs. It's got more memory than Vera Rubin. It's got more more, memory, flops. It's got it's it's better than Vera Rubin in most every way. It's a little bit later.”

“It's got more more, memory, flops. It's got it's it's better than Vera Rubin in most every way. It's a little bit later. I mean, three to six months after,”

“by being the launch partner for Helios, know, that's that's pretty exciting. There's there's quite a bit of, public customer traction, Meta, Oracle for OpenAI,”

“There's there's quite a bit of, public customer traction, Meta, Oracle for OpenAI, and there's many other customers who are who are looking to adopt it.”

“There's a there's a data center that I know of specifically that is going to be deploying Helios, and they are redoing the doors so that they can reel the Helios in.”

“It's it seems like it'll be a much smoother ramp than GB. You know, GB had a lot of problems because it was brand new,”

“you guys were the first on liquid cooling with XAI's Colossus, the first large scale 100,000 GPU deployment with liquid cooling, and you've done many more since, and you were the first and everyone else has sort of followed since.”

“Jensen, when he originally launched Blackwell, had claimed it would be a 25x improvement. And at the time, no one believed him. Right? It's Jensen, right?”

“In DeepSeek v three, Blackwell is 30 x faster than Hopper on on somewhere on the continuum.”

“I emailed him. I like, hey, Jensen. You know, back in 2024, you said or back back when you launched Blackwell, said '24 twenty five x.”

“Anthropic is free cash flow positive, and they are profitable in q two. Even in April. In April, they closed April's books. They were profitable.”

“What we had was that we had a subscription to every model or we had a subscription to the $200 tier for a chat GPT for every user.”

“by the end of January, our ARS, right, our our our annual recurring spend had hit $4,000,000. And that's because people were using Cloud Code. Now today, it's about $11,000,000.”

“We're spending, you know, more like, you know, more than a third of the spend, you know, employee spend, a third of it is also on top of that is AI.”

“Memory capacity is only growing 30% a year for the next three years. And yet demand is doubling, is doubling. And so what's gonna end up happening is memory prices are gonna keep soaring.”

“And right now, you know, if MacBook prices or iPhone prices go up a $100, that market's not gonna adjust too much,”

“This year, we're deploying 20 gigawatts of data centers. Next year, that number goes up 50 gigawatts or 50%, sorry. 50%, sorry.”

“If you look back three years, it's GPT-four. Now it's like maybe like quen one of the smaller quen models that's like 27 b parameters total and like 2,000,000,000 active is like way better.”

“And the way that Anthropic and Google's models are headed, it's actually a terrible decision potentially for them to train with GPUs.”

“Open eyes are much more sparse and that has benefits. Then anthropics are, they're still sparse but more dense in general and that has different benefits.”

“NVIDIA, the NVLink can only connect 72 GPUs. For Google, their ICI can connect 8,000 chips at super high bandwidth, but you have to pass through other chips to get there because there's no switch.”

“We've got over $50,000,000 of hardware donated to us. Once we launch TPUs and training, would actually be over $100,000,000 of hardware.”

“I think by 2030, just OpenAI and Anthropic will have over 100 gigawatts combined, and then you'll add Meta and Google and so on and so forth.”

“by 2030, just OpenAI and Anthropic will have over 100 gigawatts combined, and then you'll add Meta and Google and so on and so forth.”

“from Hopper to Blackwell, which is all we've had over the last three years, roughly 30x improvement on DeepSeek, on the most optimized deployment, which you can see on InferenceX there's about a 30x improvement.”

“If you look back three years, it's GPT-four. Now it's maybe like quen, one of the smaller quen models that's like 27B parameters total and 2,000,000,000 active is way better.”

“The real breakthrough innovation is when you leapfrog a few layers, you co optimize and co design them, and now all of a sudden you've taken what could have been a 2x here, 2x here, 2x here, and instead of being multiplicative to 8x, it's actually 100x, because you've optimized it across all three layers.”

“They're making a TPU with Broadcom, that's a different architecture than a TPU with MediaTek, that's a different TPU than the architecture that is, I won't disclose, by research.”

“This year there's going to be 20 gigawatts, even accounting for the delays, and next year there's going be more than 30 gigawatts accounting for the delays.”

“Anthropic in Q2 is profitable, their net income profitable, excluding stock based compensation. And I think by Q3 they may even be profitable, including stock based compensation.”

“And I think by Q3 they may even be profitable, including stock based compensation. That's how profitable they're getting, and their margins on an Opus token, at least Opus 4.8 token, is north of 80”

“That's how profitable they're getting, and their margins on an Opus token, at least Opus 4.8 token, is north of 80 for the API price.”

“Tranium sells at sub $10,000,000,000 per gigawatt rental rate to Anthropic and to OpenAI. GPUs, at least before the craziness of the last six months, usually went around 12 to $13,000,000,000 per gigawatt.”

“GPUs, at least before the craziness of the last six months, usually went around 12 to $13,000,000,000 per gigawatt.”

“We try and track the entire supply chain from tools that manufacture chips, fabs, data centers, energy, industrials, and then AI models and who's using them, how much, and where.”

“In the case of OpenAI, they don't show you the whole reasoning chain. In the case of Anthropic, they do.”

“And then as the horizon of AI models gets longer, right, rather than a question answer immediately, the question answer becomes ten minutes, hours, days, the value of all the reasoning is gone.”

“And then subsequently, that that sends the world into a depression. Right? The world has not experienced a 20 free fall since maybe, like, a one of the great wars.”

“It would take five to eight years of all of the world's tool manufacturing capability Yeah. To even replace the capacity in Taiwan.”

“China has a vertical supply chain. Yes. In many cases, it's fifteen years behind, ten years behind, in some cases, only three years behind.”

“the flip side is, is, is mythos is more token efficient. Methos fast mode is probably cheaper than like, four, six fast mode for most tasks.”

“Release New release in two weeks. Everyone's releasing in two weeks. Google, OpenAI, maybe Anthropic. I don't know about Anthropic, but Google and open AI definitely releasing in two weeks.”

“for everything new, more, more continued pre training because the spud, they kind of like didn't finish the pre training and just released it. So finished the pre training, do more RL drop model.”

“But the CLI experience is not the end all be all of agent orchestration and therefore they've really cooked themselves into sort of like an innovator's dilemma where they keep making the CLI better.”

“I think it was tree down. Tree down is like, yeah, dude, Codex is like so dumb, but I always just have it created and then this and it works and it's smarter, but the code is sloppy, I have Opus rewrite it.”

“But you can't go the other way around. You can't have Opus write the thing and then have Codex fix this.”

“Some paper that reduces KV cash, and no researcher in America has even heard of this paper.”

“It's the fucking best thing ever. DeepSeek and TurboQuant were the, like, most precipitous ones that popped up the most,”

“But it's like, what data do I have? That's why my two fifty ks to one mil context is trash anyways, because there's no data on this stuff.”

“All their quoted specs are lies impossible to achieve Whether it's Nvidia or AMD, neither of them you can ever hit their peak flops.”

“if you just take a eight k by eight k by eight k gem, very common shape and you run it on the map mode unit of AMD and Nvidia, you get like 50% sustained performance on Nvidia and you get like 30% sustained performance on AMD.”

“There there is no functional way to get anywhere close to their flops that they advertise.”

“There's no way to get the performance that vendors like to claim. And so our whole thing there was, well, how do we actually have a benchmark that represents what people actually get on performance?”

“And furthermore, with software changing literally multiple times a week, right, PyTorch has nightlies, VLM has nightlies, Asteeling has nightlies, CUDA drivers update constantly. You just go You go through the whole list.”

“with software changing literally multiple times a week, right, PyTorch has nightlies, VLM has nightlies, Asteeling has nightlies, CUDA drivers update constantly. You just go You go through the whole list.”

“with software changing literally multiple times a week, right, PyTorch has nightlies, VLM has nightlies, Asteeling has nightlies, CUDA drivers update constantly.”

“VLLM and SGLANG love competing with each other. They used to post on Twitter all the time about how they had beat the other one in certain some kind of performance”

“They used to post on Twitter all the time about how they had beat the other one in certain some kind of performance and now they use Inference X all the time.”

“AMD and Nvidia. They love to compete with each other. And AMD engineers and Nvidia engineers look at Inference X as a leader board.”

“I think one common thing between the three of us is if you're not pissing off people with your benchmark, then you're not testing something useful.”

“And just in the last six months, it's gone from, you know, deals for one year transacting at $171.60 an hour for h 1 hundreds to now $2.40 plus.”

“When someone has really good networking with these leading edge techniques to run a model such as KIMI or DeepSeq called disaggregated PD or wide EP, these are two different techniques, network performance is a humongous factor.”

“code agent revenue has gone from a couple billion to north of 10,000,000,000 in like a very short amount of time. And these, the horizon of these has also increased dramatically,”

“code agent revenue has gone from a couple billion to north of 10,000,000,000 in like a very short amount of time.”

“So whereas before you'd have like many GPU servers per CPU server, and so you'd be like, 100 megawatts of GPUs would be served by even like one megawatt or less of CPUs.”

“Nowadays the ratio is like getting much, much closer, both for RL training and for inference, agentic inference.”

“So then you've just seen everyone run out of CPUs, Amazon's volumes on CPUs, of CPU servers they're installing have three x year on year, right, from this year versus last year.”

“Amazon's volumes on CPUs, of CPU servers they're installing have three x year on year, right, from this year versus last year. And so there's just no capacity anywhere”

“And so there's just no capacity anywhere and that's causing a lot of instability, not just in GitHub but probably other places too.”

“A lot of the impetus for the recent Amazon and OpenAI deal was, yes, OpenAI wanted money, yes, they needed compute, but they also just went to Amazon and were like, give us your CPUs, And before, OpenAI's stack was pretty much only on x86 CPUs, but Amazon had tons of ARM CPUs and so they ported the whole thing,”

“And when we look at like o one, that's like all it could do is math. Right?”

“Memory has like four x in price over the last like year, and it's gonna continue to go up. And then now storage SSD, right?”

“More importantly is that AI is buying all the capacity on three nanometer and two nanometer in a couple years that, people are having to turn to other directions.”

“Part of it was because they want to have really fast inference, but like part of it is that Grok is manufactured on Samsung.”

“Because there's no three nanometer capacity for them at TSMC, they need to chip somewhere else, and if you just if AI is as crazy as like we believe it is, and demand is as crazy as we believe it is, it's gonna be even crazier next year,”

“Right? Because all the AI chips are on three nanometer and it takes time, Small mobile chips are easier to make than big AI chips, so all the AI chips are moving to three nanometer now, right? MI three fifty from”

“Right? Because all the AI chips are on three nanometer and it takes time, Small mobile chips are easier to make than big AI chips, so all the AI chips are moving to three nanometer now, right?”

“MI three fifty from late last year from AMD or Tranium three and TPU v seven from late last year, or slash early this year from Amazon and Google”

“You're starting to see it with Anthropix revenue adding $67,000,000,000 of ARR a month, but there's so many more firms coming online with revenue streaming in,”

“The NVIDIA has locked up over 60% of the capacity this year in long term contracts alone, and they're buying more on top of that.”

“NVIDIA has locked up over 60% of the capacity this year in long term contracts alone, and they're buying more on top of that.”

“And as you look at what they're negotiating in the market today, there's over 250,000,000,000 of wafers, of memory, of substrates, of PCBs, of networking equipment that they're going to sign this year,”

“as you look at what they're negotiating in the market today, there's over 250,000,000,000 of wafers, of memory, of substrates, of PCBs, of networking equipment that they're going to sign this year,”

“I can buy 10,000. I could buy a 100,000. I can't buy millions, tens of millions, which is what Jensen's, setting his supply chain up for.”

“I can't buy millions, tens of millions, which is what Jensen's, setting his supply chain up for.”

“Anthropic needs to get to well above five gigawatts by the end of this year, and it's gonna be really tough for them to get there, but it's possible.”

“I've seen deals where certain AI labs I'm be a little bit vague here for a reason, have signed at as high as $2.40 for two to three years.”

“Google had Nano Banana and Gemini three, which caused their user metrics to skyrocket, and leadership at Google was like, oh.”

“a gigawatt of, you know, NVIDIA's Rubin chips. Right? So Rubin is announced at GTC, I believe, the week this podcast goes live.”

“You need about 55,000 wafers of three nanometer. You need about 6,000 wafers of five nanometer, and then you need about a 170,000 wafers of DRAM, right, memory.”

“you end up with actually, I need about three and a half EUV tools to do the 2,000,000 EUV wafer passes for the gigawatt. So three and a half EUV tools satisfies the gigawatt.”

“I need about three and a half EUV tools to do the 2,000,000 EUV wafer passes for the gigawatt. So three and a half EUV tools satisfies the gigawatt.”

“Because we're talking oh, what's a gigawatt cost? It costs like $50,000,000,000 roughly. Right? Whereas, what does three and a half EUV tools cost? That's like 1.2.”

“700 EUV tools by the end of the decade. 700 EUV tools, three and a half tools per gigawatt, assuming it's all allocated to AI, which it's not, but three and a half tools per gigawatt gets you to 200 gigawatts worth of AI chips for the data centers to deploy.”

“700 EUV tools, three and a half tools per gigawatt, assuming it's all allocated to AI, which it's not, but three and a half tools per gigawatt gets you to 200 gigawatts worth of AI chips for the data centers to deploy.”

“700 EUV tools, three and a half tools per gigawatt, assuming it's all allocated to AI, which it's not, but three and a half tools per gigawatt gets you to 200 gigawatts worth of AI chips for the data centers to deploy. Right? So 200 gigawatts, Sam wants 50 gigawatts, right, 52 gigawatts a year.”

“So memory crunch will continue to be harder and harder, and prices continue to go up. And this affects different parts of the market differently.”

“the price of memory is like tripled. Let's call it if it's now it's $12 per gig for DDR. So now you're talking about a $150 versus $50.”

“our projections are we maybe get down to like 800,000,000 this year, and next year like 600 or 500,000,000.”

“You look at like quad code spend, right, it's freaking nuts. Right? 19,000,000,000 of revenue for Anthropic now. All of that is, you know, at some multiple is code.”

“Literally, we we you know, like, one day my head of ops was like, guys, our run rate of spend is $6,000,000. This is not sustained.”

“one of my engineers spent like $8,000, he's Canadian, so whatever, right, on on Cloud Code. Right? And it's like, oh, shit. Right?”

“I'm just telling you what to do and it's doing these things. And his daily spend now on 4.6 fast 1,000,000 context is $5,000 a day.”

“And his daily spend now on 4.6 fast 1,000,000 context is $5,000 a day. Jeez. And I look at the productivity, it's fine. Right?”

“If a nuclear missile's heading from China to America and we can use AI to stop it, but it requires surveillance and autonomous weapons, what do we do?”

“And Dario was like, well, you can call us. I'm sure we can figure something out. Like, this is just the dumbest response you could ever come up with.”

“That's maybe his greatest skill, is to take advantage of a good crisis. And so he goes out there and he signs deals with the US government.”

“Nowadays, people in the ninetieth percentile think they're middle class, and people in the fiftieth percentile don't think they're middle class.”

“Nowadays, people in the ninetieth percentile think they're middle class, and people in the fiftieth percentile don't think they're middle class. Yeah.”

“Now with Cloud Code, think we move 10 times the speed of any of our competitors. Right? It's actually insane.”

“It's actually insane. And so my plans of hiring and the business we win and the basis we're able to do is actually just growing.”

“Over the last couple years I've realized, wait, I actually think UBI is perfectly fine. Which I think is crazy because again, I'm very capitalist.”

“The sentiment is already more than half Americans have a negative view of AI. As we fast forward to the end of this year, as Anthropix revenue goes from 19,000,000,000 to maybe 60, and OpenAI is similar, if not higher, the amount of jobs that get supplemented, the amount of change that happens in society,”

“Google will have no cash flow next year because they're they see AI so clearly, and they know that they need to spend every dollar they make on compute,”

“If we start looking at like, hey, this year, Google spending 2 Amazon spending $200,000,000,000, Google spending a $180,000,000,000 on on AI infrastructure primarily. Right?”

“Anthropics, you know, adding $23,000,000,000 of revenue a month now Mhmm. Versus they were just adding a few 100,000,000 of revenue a month earlier. So clearly, we're in the take off period.”

“Just in this month just in January, it went from 4% of or 2% of commits on GitHub to 4% of GitHub commits were done by Cloud Code.”

“If Anthropic added 2,500,000,000 of revenue, and their gross margin is 40%, they added like $1,500,000,000 of compute in one month. Mhmm. Right?”

“There's no reason why Google will have any profit in '27 at all, right, in terms of cash flow. They will just spend every dollar they make on on AI infrastructure.”

“Google and Amazon haven't taken on debt yet for AI infrastructure, but they will. Right?”

“And it seems obvious to me that, like, any party that wants to win should just become the anti AI party. Because life as we know it is changing.”

“NVIDIA recognizes they're they're the leader, they're the tent pole. Hey, in one respect, they can just run faster than everyone, but it's kind of hard to be two x better than Google or or OpenAI or whoever else's internal chip, right, to justify their, you know, 75% plus margins.”

“And then they have to be two x to four x better to justify four x better to justify their margins because that's what they're charging above cogs.”

“Jensen is very paranoid about losing. Right? These specializations if he just kept making his mainline chip would mean people could you know point point solutions for specific parts of the market would crush him on cost and performance, then he can't justify his margin.”

“acquiring Rock is like how you get those resources to make more solutions for different parts of the market. And as far as like, are they threatened?”

“I don't think they'll go beyond, like I think they'll stay in single digits market share, single digit percentage market share. Yeah. Single digit percentage market share”

“I think the entire country is like semiconductor pilled. Mhmm. Right? There are dramas where people fall in love in the fab or dramas where people fall in love and they're photovoltaic,”

“There are dramas where people fall in love in the fab or dramas where people fall in love and they're photovoltaic, like, solar cell researchers and engineers.”

“China has the most vertical stack in semiconductors today and they're the best at semiconductors in the world because their fabs could still run somewhat on a lot of things because they have built some of these chemical supply chains. Right? Like TSMC for certain kinds of chemicals 100% share from Japan.”

“And then what's the economic value of that $100,000,000,000? Now, how much of that is in China? Right? Like, China's number is probably 10 x lower.”

“at the end of the day, this is an economic war. Right? If The US and the West win in AI and control, you know, more powerful AI systems that have this feedback loop that improve economic growth and weapon systems and whatever else, right, engineering of grids and cyber attacks and all these sorts of things.”

“They have this like advantage over China, then China will not rise to be the global hegemony. But without AI, China definitely will rise to be the global hegemony.”

“That $50,000,000,000 of COGS needs to burn on infra, which cost roughly with if a five if you're talking about five year depreciation, call it $250,000,000,000, right, of infra Yeah. For a $100,000,000,000 of revenue.”

“That $50,000,000,000 of COGS needs to burn on infra, which cost roughly with if a five if you're talking about five year depreciation, call it $250,000,000,000,”

“You can disable that where it's not automatically committed. But 2% of GitHub commits today are Cloud Code. $2,000,000,000,000 of software wages paid in the world.”

“US Grid will get to like 10% of power by like '28, 27 is data centers. For water consumption, it's not even gonna crack 1%. Yeah. By the end of the decade.”

“When you first turn on the cluster, about ten to fifteen percent of them fail RMA in the first two weeks. Wow. And then that's fine. Like you have to receipt them, whatever.”

“When you first turn on the cluster, about ten to fifteen percent of them fail RMA in the first two weeks. Wow. And then that's fine.”

“hopper's now at 5%, but black belt's still 10 to 15%. Wow. Right? Actually started out higher than that. Sure. And when a new generation comes out, it's gonna be higher than 15%.”

“for a lot of people, I'm fine to spend 10x the price on something that completes 10x faster. So Cerberus sort of just makes a ton of sense there.”

“When you're doing these rollouts and especially as things get more and more agentic and training, you might not only need to send not the entire weights but just the tokens that are relevant.”

“Whereas the energy industry in America wasn't. And and so, like, initially, people were, like, not creative. They're like, let's do let's do these kinds of gas plants.”

“next year, 30 gigawatts are being added and we think the power is there for it. Wow.”

“What was it It's this it's or this year is like I think it's like 18 ish, 10 ish. Okay. Fifteen fifteen to 18”

“But it will fully beach semiconductors again in '27. Right? And so we see this across the entire space of the ecosystem. It's not just TSMC.”

“But it will fully beach semiconductors again in '27. Right? And so we see this across the entire space of the ecosystem. It's not just TSMC. It's also memory both.”

“The memory makers, in fact, have just not expanded capacity. Basically, new they've not built new fabs since 2022. Yeah. Because their their cycle their cycle is so undulating.”

“And I think I think like if you think about how much do you believe in AI and what's your access to information of AI, you know, there's not many hedge funds who live in San Francisco and like fully breathe and live and understand it.”

“by the end of the year do how many people even believe by the end of the year AI startup revenue is over a $100,000,000,000?”

“I think that's an insane statement for a lot of people, but that's what it's gonna be. Yeah. Right? And who believes that number? Right? It's like Yeah. Very few people.”

“when Anthropic says in their funding, like, hey, we're gonna have $300,000,000,000 of revenue by the end of the decade. And it's like, actually, I think that number's too low.”

“OpenAI is gonna have 18 gigawatts or 16 gigawatts by the end of twenty eight, and they're gonna be able to pay for it. And that's like, well, that's $300,000,000,000 of spend.”

“I remember the first NURBS I went to, it was, like, '21 or '22. And I was like I would walk around. I'd stop only posters that said the word transformer.”

“There's tons of gains in quantization and systems, but there's also tons of gains in RL stuff and both the, like, you know, infra side of things and non infra side of things.”

“OpenAI has over 500,000 GPUs working on r and d. Right? Let's call it r and d.”

“Semi Analysis ClusterMax Platinum. The only recipient of Platinum, you know, we had about six gold recipients and we tested roughly 40 different cloud companies, but CoreWeave was the only one that achieved Platinum.”

“And since then we just released the second version, but in the middle, the clouds that ranked the highest actually ended up booking way, way, way more revenue.”

“And this has enabled, you know, the top in the industry companies to have gross margins of 35, 40%. And now there's a ton of Neo Clouds that are losing money.”

“There's I believe five or six that have already gone out of business. It's only been a few years. Right. Right?”

“In fact, it's physically impossible to get 2,000 teraflops out of the H100. Even though the advertises it, at most you can get 1,300 FP8 floating point eight teraflops.”

“One example is one that DeepSeek open sourced over December of last yearJanuary, February of this year, where a single replica of inference for a single model is going to be like 140 GPUs.”

“And that's a very common ratio. Right? And then when you think about, okay, the cost of running pre fill is roughly one fourth of running decode.”

“So you can cut your cost to one fourth of what it was previously if you just don't do the pre fill. Right?”

“We try to 10x the training capacity every eighteen to twenty four months. And so this would be effectively a 10x increase. 10x from what GPD five was trained with.”

“Fairwater 4, which you're going to see under construction nearby, will also be on that one petabits network so that we can actually link the two at a very high rate.”

“In the future maybe they're doing days worth of work autonomously. And then the model companies are charging thousands of dollars”

“Oracle is going from like one fifth your size to bigger than you by end of twenty twenty seven.”

“And then the other one is, given our estimates on what your capacity is in 2028, is three and a half gigawatts lower? Sure, you could have dedicated that to OpenAI training and inference capacity.”

“So, you mentioned how you're depreciating this asset that's five, six years and this is the majority of the 75% of the TCO data center. And Jensen is taking a 75% margin on that.”

“So what all the hyperscalers are trying to do is develop their own accelerator so that they can reduce this overwhelming cost for equipment to increase their margins.”

“They're going to make something like five to 7,000,000 chips, right? Of their own TPUs. You look at Amazon, they're trying to make three to 5,000,000.”

“So, these labs are now projecting revenues of 100,000,000,000 in 2728, and they're projecting revenue keeps growing at this rate of like 3x, 2x”

“Actually, this week we find that we hit 50 people, so having started it five years ago to now, and I've hired my first person two and a half years ago, We've now grown to 50 people just completely organically, so it's been a hell of a ride.”

“it's a substantial fraction of economic growth, depending on who you ask. It's anywhere from 25% to 75 of the current quarter's economic growth. That's insane for The US.”

“It's anywhere from 25% to 75 of the current quarter's economic growth. That's insane for The US.”

“It got to a few $100,000 of revenue and I was like, Holy crap. And then I was able to start hiring. And then I left when we hit a few million of revenue.”

“And then I left when we hit a few million of revenue. More and more of the revenue was coming from not Substack. The reason was like, Hey, Substack takes 10%.”

“And then I added Substack and growth went like this. And then I left Substack and it went back to like this, the email capture.”

“And then I left Substack and it went back to like this, the email capture.”

“Now, scaled to be And it has scaled to be multi million dollar Substack revenue.”

“Now, scaled to be And it has scaled to be multi million dollar Substack revenue. Obviously, built a lot of business beyond that, even that is good enough”

“I was lucky enough that the day Chad GPT released, I released a post called that was about the training cost of GPT-three. It was just like a coincidence.”

“Even though a 10% cut sounds like a lot, I'm the largest technology substack and I'm telling you that it's worth it.”

“I'm the largest technology substack and I'm telling you that it's worth it. And so, you can think, Oh, I can save the 10%, but actually the growth is all there. Hell yeah.”

“But then there's also, on the storage side, the drive vendors don't make any money. But Weka and Vast, I mean, look at their pricing models.”

“But then there's also, on the storage side, the drive vendors don't make any money. But Weka and Vast, I mean, look at their pricing models. They make crazy margin on storage.”

“Most of the inference providers are selling at flat margins or even negative for their public endpoints. And they then make it up when people do private deployments.”

“And so I think that's what a lot of this is, is you end up with the public business as like a loss leader just to generate private engagements, whether it be sovereigns or enterprises.”

“And in some deployments, you'll be completely limited on power. And so there's many deployments. For example, OpenAI's major training cluster in Arizona that did GPT-five and such.”

“Even though it's terrible on a TCO basis, they were able to get, you know, 10% more GPUs in, and it's great. And Meta has done similar.”

“But capital is far more of a limiting factor. Because if you have enough power, a 20% difference in performance per watt only ends up being a 4% difference in TCO.”

“Because if you have enough power, a 20% difference in performance per watt only ends up being a 4% difference in TCO.”

“So, in most cases, don't on an NVIDIA deployment, right? That's where NVIDIA takes 5x markup on their manufacturing cost, right?”

“If it's another deployment, if it's AMD, then that that that 20% power difference might translate to eight or 9% TCO difference when you when you talk about power cost and data center capacity cost.”

“Those that are just targeting developers on short term rentals, they may not be able to get their ROI back.”

“And so those are probably less likely to be able to succeed versus those who are locking in these massive deals with or long term deals that may not be massive but with enterprises or with AI labs, or with the hyperscalers who have no capacity because the demand is just so incredible.”

“the standard unit for an inference deployment being hundreds of GPUs instead of a single node. And then there's all these different things about traffic.”

“If you tried to use NVIDIA's Blackwell six months ago, the numbers were not amazing, right? But now they're actually amazing. So how did that progress over time? Same with AMD, right?”

“And why this is important is you can see the progress of hardware over time as the software stack gets more optimized.”

“we have tens of millions of dollars of hardware of the current and last generation GPUs. As I mentioned before, the software updates every single day.”

“20% of your cost is your electricity, your data center real estate, roughly. And then the rest of the cost is that hardware, at least on a standard NVIDIA deployment your GPUs, your networking, etcetera.”

“GB200 has a huge power efficiency advantage, right? Everyone here understands the challenges of running and deploying GB200. There's a lot of challenges with the backplane.”

“But it turns out at certain interactivity rates, I. E. Tokens per second per user, it's 10x more efficient per watt, right, compared to H200.”

“at certain interactivity rates, I. E. Tokens per second per user, it's 10x more efficient per watt, right, compared to H200.”

“So we do different scenarios. We do document processing, which is 8,000 context in, 1,000 out. We do chat, which is 1,000 in, 1,000 out.”

“in some cases, AMD actually does have a publicly usable open source implementation that beats NVIDIA even, right, with the MI355 versus V200, which is a surprise, right?”

“AMD actually does have a publicly usable open source implementation that beats NVIDIA even, right, with the MI355 versus V200, which is a surprise, right?”

“But at other points, you can have a 10x performance TCO benefit from going with a more expensive Blackwell server than a cheaper H100 server.”

“If we don't divide by TCO, then it looks like the performance of B200 is actually 15x that of H100, versus the performance TCO is only 10x,”

“Over two months, the performance of AMD dramatically improved because they went from, hey, software problems are always a thing, to over a two month period, they've dramatically improved.”

“What there there wasn't much volume. Right? In those initial systems. Right? In the early time frame. Yeah. Early time frame.”

“Continue to increase density, continue to build all these new building blocks around cooling and sure, you you you had buildings blocks, but most of your volume was Intel x 86 CPU.”

“Everyone else's hopper servers h 100 air cooled. And so each server consumes, you know, 10 kilowatts almost. Right?”

“So that's why Shibbol Micro dedicated deep cooling so aggressively. And if the liquid cooling solution from Super Micro didn't exist, then those servers would be consuming 30% more power.”

“And if the liquid cooling solution from Super Micro didn't exist, then those servers would be consuming 30% more power. Right?”

“And people have had problems where their data center wasn't ready and something stopped working. They don't know, is it the chip? No.”

“while cost is only up, know, call it 50% or 60% or 70% versus Hopper, the performance is well north of 2x, right?”

“Now with Blackwell, there's actually even a third tier of memory, which is memory within the Tensor Core.”

“Even Hopper in January 24 to December 2024, you still had like a 30%, 40% performance improvement. And likewise for Blackwell, you've seen a similar sort of improvement, right?”

“I see another 50% to doubling in the cards, just from the fact that there is so much low hanging fruit or performance left on the table if you are using the general open libraries, right? Whether it's cuDNN, VLM, SGLANG, all these other things,”

“for the general purpose user, yeah, you're going to have another doubling of performance potentially on the tables over the next year.”

“NVIDIA learning from the issues on Hopper, with Blackwell, they actually doubled the time for system level test.”

“Now you have 72 GPUs. And if you go look at the rack, right? It's completely different, right? It's liquid cooled. It's an entire rack that consumes 140 kilowatts, whereas H100 servers consumed 10 kilowatts.”

“If the reliability of each GPU is the same, then when a single GPU fails in 72 GPUs, you have a much higher chance of something failing, right?”

“OpenAI has said they're running production inference on GV200 a couple of months ago, in fact. Right?”

“it used to be you'd build a data center and there wouldn't really be much retrofits cost, right? You would slide servers in and out, you would rent racks to people.”

“Meta has got a data center design that's only built to last for a single generation. They put them in these tent buildings.”

“And so you've sort of Which is the exact opposite of what you would have expected. Oh, use the big thing for training and use the small thing for inference.”

“NVIDIA's next generation actually has something very different. They're not saying, Hey, there's a training GPU and an inference GPU, right? Because either is fine.”

“what sort of NVIDIA's pitching is like a split of inference into two workloads, and we'll see if they're successful. There's a lot of challenges on the infrastructure side.”

“In 2020, they released an Ascend chip and submit it to impartial public benchmarks, and they were the first to bring seven nanometer AI chips to market.”

“The the full ban. And so they were only able to make a small volume of these chips, but they had trained significant models on these chips that they made then.”

“they were able to acquire 3,000,000 chips, 2,900,000 chips from TSMC through these other entities. Right?”

“Our our revenue estimate for NVIDIA in China for just h 20 was north of 20,000,000,000 because that's what they were booking in capacity slash had to write off.”

“Our our revenue estimate for NVIDIA in China for just h 20 was north of 20,000,000,000 because that's what they were booking in capacity slash had to write off. Yeah. And then it got banned.”

“They were importing lithography at a much higher rate than that. Right? Like, 40% of their equipment imports were lithography, and they were stockpiling lithography equipment.”

“The consensus for the banks is $360,000,000,000 of spend next year across all of them. And my number is closer to, like it's, like, $45,500.”

“OpenAI, which signed a $300,000,000,000 plus deal with with Oracle, will actually be able to pay $300,000,000,000, right, across raising capital and revenue?”

“No. No. No. Like, NVIDIA ordered the volume for the Xbox before Microsoft gave them the order.”

“AWS has been decelerating revenue. Year on year revenue has been falling consistently. And and our big call is that it's actually going to start reaccelerating.”

“Now, of course, the bet is a bit like there's a bit more security in the bet in that Oracle really only needs to secure the data center capacity.”

“my opinion on how you buy GPUs is that it's like buying cocaine or any other drug. This is described to me, not me.”

“You call up a couple people. You text a couple people. You ask, yo. How much you got? What's the price? It's like Exactly. This is fucking like, buy drugs. Like oh, sorry. Sorry.”

“Their revenue growth rate right now implies that they would be OpenAI sometime in 2027, which is a really, really big deal in terms of revenue.”

“There's not gonna be an unofficial MCP running out there. Right? Like, you know, it's like like, fuck off. Right? Like, that's just not gonna happen.”

“It is literally many gigawatts. And then in addition, right, not just about securing OpenAI ridiculous amounts of compute, but what what was really interesting is Oracle gave a guidance of four years.”

“Oracle gave a guidance of four years. They told everyone what their revenue is gonna be for the next four years. No company has ever done that.”

“How are you gonna how are you gonna pay for this? By the end of the year, it's like 14 right now. Right? Billion dollars on an ARR basis.”

“They are a bit of a cult. Right? They've they believe in the mission much more than any other lab. Not that OpenAI people aren't cultists as well.”

“First, we have to solve software engineering, and then we can use that to build all the other pieces. Right? First, we have to solve computer use.”

“Anthropic, you know, sort of has you know, they had seven cofounders all with, like, equal equity. And these these cofounders were, like, very focused on exactly this mission.”

“They've got cofounders who are extremely technical, whereas OpenAI, right, you know, they they they don't have necessarily like, Sam's not super technical. You know, the obvious Greg Greg is a cofounder, he's super technical.”

“I don't know what ungodly percentage of video is only on available on YouTube. And sure, you can scrape YouTube, blah blah blah.”

“These servers cost hundreds of thousands of dollars if nothing new. Know GB 200 from Nvidia cost well over $3,000,000 so the cost of these things is huge”

“Data centers for CPUs, 10 megawatts of rack, 12 megawatts of rack. Kilowatts. The new ones, kilowatts, right? Kilowatts of rack, sorry. The new NVIDIA servers, 140 kilowatts of rack.”

“In The US collectively there's like 10 gigawatts going up over the next year and a half, two years. And Europe is trying to scale to gigawatt deployments as well.”

“Each of these gigawatt deployments once you take it from all the way from the power to the GPUs and the cloud contract, those are tens of billions of dollars each gigawatt.”

“NVIDIA's gonna have better networking than you. They're gonna have better, HBM. They're gonna have better process node. They're gonna come to market faster.”

“They're gonna have better negotiations with whether it's TSMC or SK Hynix and the memory and silicon side or all the rack people or, like, copper cables, everything, they're gonna have better cost efficiency.”

“if the user asks a low value query like, hey, why is the sky blue? Just route them to mini. The model can answer perfectly fine, and that is a chunk of queries.”

“But if they ask, what's the best DUI lawyer near me? All of a sudden, this is like, you're in jail, you have one shot, you're like, screw it.”

“Etsy. 10% of their traffic now comes from chat, and OpenAI makes nothing off of that. But they very they really, really will soon. Right?”

“30% of the chips are going to them, just those two companies. But that's actually like, okay, well, 70% of the stuff, who's making off well, one third of it is ads, whether it be ByteDance or Meta or many of the other people who are doing ads.”

“I legitimately believe OpenAI is not even capturing 10% of the value they've created in the world already just by usage of chat. Right?”

“So now you need to do something, you know, that will give you five x advantage, right, in hardware efficiency for a certain type of workload, and then pray the workload doesn't shift.”

“NVIDIA's revenue this year is gonna be, like, over $200,000,000,000, and next year expects over 300,000,000,000 plus Google's gonna spend, like, $50,000,000,000 on TPU data centers.”

“It's the GPU purchases. It's the networking. It's the it's the physical data center conversion power conversion equipment. All of this stuff is, like, 80% of the cost.”

“It's the it's the physical data center conversion power conversion equipment. All of this stuff is, like, 80% of the cost. And then 20% is gonna be your land and your power and your cooling”

“the new the new Trump, you know, tax bill institutes something really incredible, which is that you can depreciate all of the GPU cluster cost in year one, which we put out, like, a note about how, like, the tax implications to, like, meta are, like, $10,000,000,000 a year. And across each of the major hyperscalers, it's, like, massive.”

“Now this is obviously gonna be, like, crazy because, like, now they're buying g p their own GPUs and putting them in data centers and doing stuff, and they're competing with their own customers,”

“That's over $10,000,000 of hardware, and then that's just one replica, then you'll have a lot of replicas and you share the caching servers between them.”

“So this is something that I've found very interesting is that we've been trying to build a lot of alternative data sources for token usage, who's using tokens, what models, where, etcetera, why, and it's very clear that people aren't actually using the reasoning models that much in API. Anthropic has eclipsed OpenAI and API revenue,”

“So this is something that I've found very interesting is that we've been trying to build a lot of alternative data sources for token usage, who's using tokens, what models, where, etcetera, why, and it's very clear that people aren't actually using the reasoning models that much in API.”

“Anthropic has eclipsed OpenAI and API revenue, and their API revenue is primarily not thinking. It's Cloud four, but it's not in the thinking mode, code being the biggest use case that's skyrocketing.”

“But still the model's way too big to fit on it. This is, like, very simple. Right? You know, the same thing's happening in the other direction.”

“Meta is literally building these, like, temporary, like, tent structures to put GPUs in because building the building takes too long, and it takes too much labor, as you mentioned labor.”

“Like and they get paid bonuses for being faster, and therefore, do, like, certain drugs to be able to finish the build outs faster.”

“What has xAI actually done to deserve their prior funding rounds? They haven't released a leading edge model, and yet their evaluation's higher than Anthropic today.”

“They haven't released a leading edge model, and yet their evaluation's higher than Anthropic today. At least Anthropic's racing, It's Elon, A, and B, they've tackled a problem creatively”

“But, like, you know, can you give them no GPUs? No. They're gonna retaliate. Like, there is a middle ground, and, like, Huawei is eventually going to have a lot of production capacity,”

“Basically in between every layer, the router can route to whatever expert it wants to, And it learns which expert to route to. And and each expert learns its own independent things”

“And and each expert learns its own independent things and it's like really not something observable by people. But what you can see is tokens when which experts do they route to?”

“Google's backing out, I think they're gonna spend on the order I've heard like $250,000,000 this year with them. And they're backing out.”

“And they're backing out. Obviously, they've spent a lot of money and there's stuff they can't back out of, but it's like, that's gonna go down a lot.”

“OpenAI allegedly, like, cut the external Slack connection. Right? So there's no, like, slack between scale and OpenAI anymore.”

“He was chasing like AI is good and great, but like AGI is not a thing that is gonna happen soon. So sort of this is a big shift in strategy”

“As far as Sam is saying that no top researchers have gone, I don't believe that's accurate. I initially the top researchers definitely did say no, the best researchers, the best people.”

“And you said $100,000,000 I've heard a number for someone over 1,000,000,000 actually, for one person at OpenAI. But anyways, you know, it's a ridiculous amount of money,”

“I've heard a number for someone over 1,000,000,000 actually, for one person at OpenAI. But anyways, you know, it's a ridiculous amount of money,”

“And they had a bug in the training code for a couple months that was like a very tiny bug that like was messing up the training.”

“There's a generation of NVIDIA GPUs for laptops. Right? And and chips have solder balls on the bottom. Right?”

“And what ended up happening is because of that different rate of expansion, the solder balls connecting the chip and the board would crack.”

“Like but AMD is actually doing this and like taking it to overdrive. They're getting clusters at Oracle and Amazon and Crusoe and Digital Ocean and Tensorwave and they're renting GPUs back from them.”

“China's open sourcing stuff only because they're behind the moment they're ahead, they will stop open sourcing stuff. And at the end of the day, closed source will win. Unfortunately, closed source will win.”

“Germany and and Japan are losing a lot of their capabilities to do so slash the cost of their robots are just way, way, way higher than what China can do.”

“Like, can't do all the math, all the problems in frontier math. And the models went from sub 4% to 70% in six months.”

“frontier math. And the models went from sub 4% to 70% in six months. Right? And this is from not just OpenAI, but also Anthropic and Google and DeepSeek.”

“You fast forward to 2023, and all four of these countries combined, right, are still a third of China's robot deployments.”

“even as near as twenty twenty two, the majority of China's robots were being imported. Right? Japan and Germany being the largest market share players,”

“even as near as twenty twenty two, the majority of China's robots were being imported. Right? Japan and Germany being the largest market share players, but also USA, Korea is also in there.”

“'24, it's, it's rapidly growing Chinese share. And '25 is it looks clear, like, the the market shares is, you know, gonna be 60 is gonna be, like, 70% Chinese robots for China's domestic usage.”

“Two of Germany's best robotics companies were actually purchased by China, And a lot of their, servos and actuator construction was actually transferred to China.”

“Whereas Chinese companies are almost all cost plus. They're not price to value. They're cost plus. Right? And so you've got this interesting phenomenon where,”

“Whereas Chinese companies are almost all cost plus. They're not price to value. They're cost plus. Right?”

“They're not price to value. They're cost plus. Right? And so you've got this interesting phenomenon where, there are industries in China where the entire world relies on it.”

“there's this constant pressure cooker of continual cost reductions and engineering. It's not like, Oh, well, we make 80% margins. Our cost is low.”

“there's so many companies on the Hangshan, the Hangshan, which is a stock market in China, right, whose stock has literally been flat or down despite the fact that they own the global market.”

“One of things I learned early on is you can talk to anyone on the Internet. You can just reach out to anyone, and most people just say yes, but no one does it.”

“Or try and talk to those people because one of the things I learned early on is you can talk to anyone on the Internet,”

“the business transformed into, you know, selling data, right, or build building datasets and selling them. Right? And when the business truly flipped to that, we're now 95% of the revenue is that data”

“There's 26 people who work at the company now. Cool. All over the world. Right? US, Japan, Taiwan, Singapore, France, Germany Uh-huh. Canada. Yeah. Yeah. Well, that'll be one less country. Right? He's gone.”

“it's it's it's surreal to see, like, billionaires, like, actually care what you have to say or rather what you, like, have figured out.”

“it's surreal to see, like, billionaires, like, actually care what you have to say or rather what you, like, have figured out.”

“So the newsletter itself is, like, 200,000. Paid newsletter is, like, I think, like, 200,000 people. Paid newsletter is, like, 5,000. Nice. But then, like, the main bit like, that's less than 5% of the”

“But most of the AI value that's been generated does not accrue to the companies building the AI or deploying it. It actually deploy it actually accrues to the user.”

“Get services online with h one hundreds before every hyperscaler by a factor of, like, as much as, like, six months in many cases.”

“They're doing full liquid cooling as well and they're working on those designs, but, you guys have fully liquid cooled data centers.”

“it lets you get to these, you know, call it $10,000,000,000 clusters, right, in in time scales, from project start to completion that are much much shorter than anyone else.”

“Even if they even if they had the electrical equipment in in you know, not on lead time, they paid a bunch to skip the queue.”

“of work. This is this is actually some stuff that we've noticed on other clouds that we've tested, whether it's, you know, things related to InfiniBand p keys and we're able to snoop in other nodes of real live customer traffic. And, obviously, we tell them, like, we're like, yo, this is not okay.”

“things related to InfiniBand p keys and we're able to snoop in other nodes of real live customer traffic.”

“What's what's funny is there was a customer that had more than 10,000 GPUs with you. They got acquired. They had to start using some clusters at their acquiring company, the company that acquired them.”

“What's what's funny is there was a customer that had more than 10,000 GPUs with you. They got acquired.”

“And it would say, yes. It's bigger. Even though, like, everyone knows that nine dot one one is is way smaller than nine dot nine.”

“when we looked at g p d three, the cost fell 1,200 x from g p d three's initial cost to what you can get Lama 3.23 b today.”

“I think what was really surprising was that it was a Chinese company for the first time. Right? Because Google and and OpenAI and Anthropic and Meta have all traded blows.”

“It's not unexpected. Right? Like, this is actually within the trend line of what happened with GPT three is happening to GPT four level quality with DeepSeq.”

“And Meta's Meta's gonna release their new llama soon enough. Right? And that one is gonna be, you know, a similar level of cost decrease,”

“That would not pay for all of these build outs. Right? AI is useful today, but it's not capable of doing a lot of things.”

“So no one is trying to make with these $10,000,000,000 data centers, they're not trying to make chat models. Right?”

“There were hopes that Orion could be used for for GPT five, but its improvement was, like, not enough to be, like, really a GPT five.”

“Along the way, another team at OpenAI made the big breakthrough of reasoning. Right? Strawberry training. And they released o one, and then they released o three.”

“GPT five, as Sam calls it, is is gonna be a model that has huge pre training scale, right, like GPT 4.5, but also huge post training scale like o one and o three and continuing to scale that up.”

“Right? And this would be the first time we see a model that was was a step up in both at the same time.”

“They say it's coming, you know, this year, hopefully, the next three to six months, maybe sooner. I've heard sooner, but, you know, we'll we'll see.”

“When we look at power consumption by state, something pretty simple, there are certain states where more than half their power is going be consumed by AI just in the next few years.”

“Right? And this has just happened in six months. And the same applies to various certain coding tasks. The same applies to yeah.”

“GPT three was it cost, you know, it cost $60 for the million output. And over time, that kept reducing. Right? OpenAI released new models.”

“But this is the progression you get because of the advancement. That's one way to look at it is, oh, existing capabilities get cheaper. But as useful as GPT-four is, it can't do medical tasks.”

“But even within a cluster of like 16 k GPUs, GPUs will be slower by up to 10%. Right? So there there's a lot of variation even in the manufacturing.”

“And that was a measly 16,000 GPUs. Right? And when you look at like the next generation Llamas, the next generation GPTs, etcetera, they're training on a 100,000 GPUs or more.”

“So if you're training like a 70,000,000,000 parameter model, you need to pass four x that number every single training step. And when you're passing that, you can't really overlap communications and compute.”

“Like the the cost difference between different clouds can be as much as three x. Right? Because a lot of these clouds are bad at marketing, bad at sales, stranded compute.”

“S. To even 20% of global production, let alone a higher number, which is what we'd like, going back to maybe the 80s levels, right?”

“Even if they do fully do the full $100,000,000,000 they commit to, completely still relying on R and D in Taiwan.”

“The R and D fab they announced is not going to secure R and D supply chain, right? It's not going to secure new node development, right?”

“And if they were to continue to build out what we expect them to build out in the next four or five years, it's going get to 30%, 40%.”