Etched - Building AI Hardware to Make Inference Faster and Cheaper - [Invest Like the Best, EP.480]

21:53And at the same time, as I got to college and met a bunch of other people building cool tech, I got extremely excited by AI models, especially once GPT-3 came out. And I was like, wow, this is the first model that can kind of speak English. And these things are going to get really smart. What happened was when GPT-4 came out, there was GPT-4V, which was the first model with image uploading. So I went through my camera roll and I found a picture of my back with this bump on it before I was diagnosed. And I said, hey, chat GPT, pretend you're an expert doctor. A patient comes in and it says they have this bump on their back.

22:25What could it be? And it immediately says like, this could be a tumor. You should get an MRI immediately. Go to the doctor. And I just kind of sat there still. It was like, that took me six months. And yesterday, this feature wasn't there. Today, it's here. I go to show my parents and I got this notification being like, you're all out of image credits today. You need to get a pro plan. And I was like, holy crap, this is going to change everything. And we clearly don't have the infrastructure to serve it. And there's very few things you can work on that can actually bring this technology at scale to the world faster.

22:57I mean, there's like plenty of people that are super smart working on models. The fabs seem maybe unreachable to work on, but it seemed like the hardware was all designed before ChatGPT. Every GPU, every TPU, every AI chip that was serving these models were just fundamentally built before this and are retrofit to serve these modern models. There's going to be an entire new wave of architectures that came out. And what a more exciting thing to work on than bringing this to everybody. Very different angle at the same time. I was running a startup incubator called Prod, which has incubated a bunch of different companies.

23:26Some of the earliest ones being Cursor and AnySphere, which merged and Mercore and Etched went through it, a handful of others. At the time, as these models were getting smarter, it was 2022, I was realizing all of these companies are spending all the money they raise on compute. And I had this realization as I was working on some of my own stuff that like, oh my God, all the products I want to build are going to cost tens of millions of dollars a year in inference. This is not going to be tenable. The cost structure of every software company. And the COGS is not going to be like zero anymore for an incremental user.

23:55It's going to be like quite high and it's going to be a function of inference. And then the optics of every business is going to also be inference as people use more and more coding agents. So fundamentally, it seems like inference is going to be really important. And it feels like we are on a decade march for inference to become, you know, the biggest market in the world. So when you think about that, 10 years from now, there's going to be these giant projects where everything in that data center fundamentally hasn't been designed today. We should go pick something and work on it. That's kind of how it got started.

24:21Yeah, but I'm really excited for you to go back about as far, probably early in high school, maybe even earlier, and tell your favorite hash marks on the timeline that ultimately led to your ambition to drop out of Harvard and start this company. My first job ever was at a company called Exnor, where I did colonel development. I was 17, and a 17-year-old can't sign illegally binding contracts. So rather than go ahead and do a traditional CIA, they went ahead and sat me down and said, Gavin, don't share this information. And Xnore was one of the only companies that saw, hey, maybe this is a good trade, and got to work building kernels.

24:55And a number of other companies since. Xnore got bought by Apple for$200 million. Did the same thing at Octo. They got bought by NVIDIA for hundreds of millions of dollars. But when you do this sort of kernel's work, what you realize is that the math is relatively easy. But to get high-speed decode, the thing that matters is data movement. Almost all the work that you do is optimizing how do you move data around a single chip or across multiple chips. That's why we went ahead and built this cluster scale memory tech. We bring that interconnect time way, way lower. You can go do way more movement.

25:25And as a result, get a much faster time to generate each subsequent token and build these crazy things Rob's talking about for doing a year's worth of work in a month or more than that in the future. Can you talk about the competitive drive that's evident in some of the high school competitions that you participated in and won? We did a couple. For example, I was very active in the FTC robotics. I was lucky to have a very talented partner, Sanford. For a long time, we were part of a traditional school team where it was about 20 guys all working together as often typical at first tech challenge.

25:58And the goal is to go out and build a robot that scores the most points and a bunch of other things too. And first, they put a lot of emphasis around collaborating with other teams, around trying to do really good documentation, around trying to go ahead and get others inspired to go do the same thing. Sanford and I decided rather than go ahead and do it this way, we're going to win. And we did nothing else besides Boulder about the score the most points. As a two-person team. As a two-person rather than a 20-person team. That we were much, much smaller than almost every other team in the competition.

26:25We figured that if we were going to go specialize, if we were going to go out and do this, win the damn games really well, we wouldn't need to go ahead and advance based on the quality of our documentation or of our outreach. We were just going to go win. And so we did. We had branched off, built a two-person team that built a robot and decided we were going to go ahead and redesign it every three months. And we did. We actually had the world record for the highest score during this competition at one point. We were rated by OPR third in the world for software development. And it was a damn good machine.

26:54What from that episode can I translate as an analogy onto how you built Etch the company? With thinking about how you want to go ahead and do a full Rackscale product like this, there's a couple of key ideas. One of them is like velocity, velocity, velocity, that you win by shipping. You're not going to go out and win by having the best outreach or the best communications. You win by building the best product. And similarly, we think you can going to do it with a lot fewer folks. That if you're willing to go ahead and just focus on product, product, product, and parallelize relentlessly, you don't need 20 ,000 people like the big companies have.

27:31You can do the best product in the world with far fewer people. Yeah, there's a saying of the best part is no part. I think for us, it's also the best vendor is no vendor. As much as possible, we want to vertically integrate the entire product, both because we get more performance, but we can move way faster. So everything from the chips to the boards, to the cold plates, to the interconnects, to even the production. We want to do all of it as in-house as possible. I think we're the only startup right now that's building its own rack as well as its own chips. And we did it all at the same time.

27:58A couple of years ago, the last time we were public, at that point, we just started building our rack team. And we brought over Brian Loyler, who built all of NVIDIA's HGX and DGX systems, which is like 80 % of their revenue. And we said, we're going to build the rack at the same time. We actually went through multiple iterations of the rack before the chips even came back. Before the chips came back, we made thermal chips that had the exact same hotspots as we expected our chips to have so we could build the cold plates. We could overpressurize them and blow them up. We haven't had a single leak since our chips came back with the cold plates because we already validated them.

28:27We have a factory in Taiwan. We have a few dozen people out there. We built a clone of a bunch of the test stations in our office. We have a two megawatt data center on this floor and we did 24-7 development cycles. People are doing day shifts and night shifts to actually get the hardware up and running as quickly as possible. It's that extreme vertical integration and extreme parallelization of the schedule that lets you get products to market way faster. If you think about the building of the early team and what it required as two young guys building this company, there's lots of very talented young entrepreneurs out there, maybe for the first time of this scope or magnitude in a long time, all of whom probably could benefit from the lessons that you've learned, getting very sophisticated, talented people to come join you even after careers at the other great companies.

29:08If you were teaching this as a class, like here's how to get lead talent when you're young and inexperienced and naive, what would be the syllabus? We have a pretty bimodal talent philosophy. It starts with what we call the legends, which is when we're trying to solve an incredibly hard technical problem and generally do something that hasn't been done before, we need to find the very best person in the world. And often the number one guy in the world versus the number 10 guy versus the number 100 guy, huge difference in whether it's actually possible to solve the problem. We created this system we call project-based recruiting, where we map out all of the hardest technical problems across all industries that anyone has ever had to solve.

29:43We look at temporality. So who are the people who did the zero to one? Who is in charge, quote unquote, who actually did the work? We talk to as many people as possible, and then we just track it. And you'd be surprised by the amount of people who say yes after the first conversation is pretty low. But the amount of people who say yes after the 20th conversation is surprisingly high. You really got to keep at them. When you hear no from somebody who really is the best in the world, then that really means, hey, you should go ahead and come back when you have a few more milestones for proving out.

30:10I think it's one of the most convincing things to see is, hey, we make bold claims. And when you go ahead and hit those again and again and again, that is really belief-inspiring. When we decided we wanted to build a rack and not just a chip, we were looking at this and we're saying, you know, how many products have actually shipped that scale for a rack-scale system that actually have the power density that we're trying to solve? And we just kind of said, if we were going to wave a magic wand, what would the best possible person in the world look like? and be like, well, if we could find somebody who like started at NVIDIA and built the entire rack team through all their different generation, learned all this different stuff, but is still scrappy, still understands the startup culture, but has seen scale, like that would be the best possible person.

30:53So we mapped all of the different teams that related to all of the different rack scale products at NVIDIA. And we found three people that we thought could fit the bill. And we talked to all of them and two of them have just retired. And one of them was planning to do one more generation for NVIDIA and then retire. His name is Brian. And over time, we convinced him to join. Brian started the HGX and DGX team at NVIDIA, which was a majority of NVIDIA's revenue, tens of billions of dollars a quarter. And the other two guys ended up investing, by the way. But when you have somebody like that, they just know what good looks like because they've seen it.

31:25And there's so many times where we would talk to Brian and he'd just point to us and be like, that's a billion dollar, like a billion dollar lesson I learned, a billion dollar lesson I learned. Like, you know, that just saves us cycles. And you pair someone like Brian with somebody like Sanford. Do you have a name for them? So Brian's a legend. What's Sanford? Yeah, we say chips on shoulders, put chips in data centers. So Sanford and Gavin in high school were world robotics champions. And Sanford was finishing his senior year of college. And we called him up a couple years ago and we said, hey, can you come check out what we're doing?

31:56We need some help on the platform side. He comes for a week and we say, can you build a cold plate this week? and if you asked like any thermal engineer anything like that they would think you're just like totally naive right I mean these things take months to do and like to be clear they do but you can make real progress in a week if you if you put your mind to it and you think it's possible and he built a contraption in a week that like actually de-risked like a pretty key power question we had and you put those two together and they've done incredible things one is that possible without the other because you need the extremely driven people that just keep asking why and don't know where the bodies are buried to like take tons of aggressive risks.

32:33And then you need the people who've seen scale and still have the startup scrappy mentality to help them along the way. So it's really the legends plus some naivete, raw first principles type talent. It's not just that you have both in the company, it's that they're working together. That's right. If I think about that funnel, anything else more interesting to say about how much better you've gotten at recruiting and like why those metrics keep getting better? One of the shocking things is I wish you were being such a contrarian vet kind of self-selects. Right. that like you're the kind of person who is some opportunistic, she's going to go join whatever the hot company is, or rather than go ahead and do deep diligence, you will not come work here.

33:08And it's one of the things I worry about as we announce more and more of the product and the specs. We may lose some of this if we're not very careful. You kind of have to be sick in the head to join our company. When you think about it on paper, it's like you, a person who is probably a very accomplished engineer, making a good amount of money, it's liquid, it's predictable somewhere else, you're going to convince your family to move to San Jose and live in this apartment on this housing program for the semiconductor company run by two, what, 24-year-olds now, that's pre-product, that is going against the biggest companies in the world and the most supply-constrained environment ever created with a design that they're saying is not going to be like 10 % better, but it's going to be 10x better.

33:47Something must be wrong with you to do that. People are just wired differently here that they like really want to not prove people wrong who don't believe, but prove people right who do believe. They just take it personally. And, you know, that's really fun to find those people. And frankly, just the nature of the company makes it very easy to whittle out the people who aren't like that. One of the very first things you and I talked about, Rob, was I started asking about Sohu, which is the name of the first product here. And you said, we can talk about that in great detail. But the thing you should know is that we're really focused on is building a machine that can, at scale, produce these things and generations of them as efficiently at the highest possible quality level.

34:26So we want to build like the company or the machine that is the company is the thing that will produce this thing and then subsequent things. So I'd like to talk about a few principles or cornerstones of the company. We've alluded to some of them. You've said velocity, you've said vertical integration, have become more popular topics. Parallelization is something maybe that we should talk about. But I'm especially interested in your guys' willingness to take huge risk to go faster. Maybe tell your favorite story about why this is the philosophy, what it's allowed you to do that maybe other companies haven't done.

34:58There's a number of stories here. But one of my favorites is there was a time where we were getting close to taping out the chip. We realized, wait a minute. One of our vendors is way, way behind schedule. And we had two very bad options. One option is to keep the current vendor and push our downlines out on the order of a year. Another option was we switch vendors, start over, and also get push timelines out by a year. Neither of these was a good option. So we had to go look for option number three. And what that was was we figured out they're all in Bangalore. They're actually going and doing the work.

35:31We went out and shipped a dozen of our top engineers across the world to Bangalore for six months. I was there as well. I lived in Bangalore for four and a half months personally. And every morning, we'd go ahead and walk across the crazy busy Bangalore streets into the office. We'd be the first ones in. We'd go out and build a wide variety of tools, both things like, hey, auditing a huge amount of the code that was going in, building a bunch of tools as well to make this go even faster, making sure we're making the right design decisions on the spot right there. No 12-hour back and forth. Go ahead and decide immediately.

36:03And then at 1 a.m., we'd go walk back through the now empty Bangalore streets and do it all again the next day. We'd still have a bunch of the team in the U.S. we ran these 12-hour on each side handoffs where we had 24-hour development cycle. We're at 8 a.m. and 8 p.m. every day. We'd all get on the Zoom. We'd share all the data. We'd say, when I wake up, like, this must be done. Like, we must get this chip out. And it was extremely intense. At the same time, we saw other chips at the same stage as us with that same vendor that ended up taking years that still aren't out today, that still have even taped out today.

36:36And it's that level of extreme urgency that's required to bring products to market. What is the key to doing this well, this has become a trope because of Elon mostly, that like his special skill and others that seek to emulate him would try to do this too, is figure out like what the binding constraint is and just flood the zone personally on that thing, which is kind of like going to Bangalore or something. It seems like this is a central tenet of the business and of any business that's going to do this kind of vertical integration. What's the key to doing that well? Like, again, what have you learned about that specific act?

37:07For me, I think there are two key tricks to this. The first one is that you can't build a chip alone. It's got to be a team problem. And your most important job is to go get great people to go with you and great people to go ahead and be inspired and excited to go ahead and do crazy things like this. It is a huge ask to go say, hey guys, uproot your lives for six months or in one case, 12 months that we had sent one guy out well ahead. It sucks, but we're lucky to have team members who are in it for the right reasons. But I think the second big thing too is being able to make decisions very fast.

37:41That one of the worst items is when there's a factory or there's a vendor who is waiting for you to go ahead and make some call and has been just stalled. And this happens all the time, even for very small things. So send folks, dolly get a big amount of responsibility to them and say, make a reasonable call. Okay, if you're wrong every now and then, but I would much, much rather be right most of time they give an answer immediately, then wait every time for the perfect response. Speed wins. What about spending money to go faster? There's this learn by doing thing, which has become so interesting.

38:16And as the world has gone away from software and towards more hardware again, in the world of technology, that we've outsourced so much of the learn by doing, by shipping stuff overseas and effectively just being the ideal guys here in the US. Seems like that obviously is reversing. And you've adopted this way of learning by doing, You want to be in that iteration learning loop. Absolutely. And part of that is willingness to spend and take risk with dollars. Yeah. Can you talk about that a little bit? I think there's a great quote of like, the biggest risk is not taking risk. Very similar here, which is like, every day there's over a billion dollars of revenue in this category.

38:49And a lot of it's inference. So every day we don't ship, we're just leaving tons of opportunity on the table. So your willingness to spend money should be extremely high if you can get a very clear ROI out of it. So we have this concept that we call prefetching. which is when you're waiting for one thing to get done, when you know you're going to do other things once you have it, is there ways that you can parallelize the entire schedule? So for example, like we know our chip is going to come back on a certain date. We want it to be that everything possible that could be done without the chip is done before the chip lands.

39:20And this costs a lot of money. This means that like we want to build our entire software stack beforehand. Like we shipped racks to customer data centers without our chips in them with all the networking, all the CPUs, all the storage, all set up so we could bring all that data center software up before the chips came back. It meant that we took over 700 FPGAs and put the entire full reticle chip on an FPGA cluster and ran a dozen different models with our full inference stack on them before the chips came back. It means that we built a thermal chip to mock the thermal profile of our chip and built cold plates based on that before the chip came back.

39:53It means we had the entire production line ready. It means we did many revs of the circuit board. It means the entire product was ready to go before the chips came back. And this is what it gives you. There was another very famous AI chip company that took 10 months to go from getting their silicon back to having them running inference in a rack. And this was like publicly announced to their investors and it was a really big deal. We were able to do it in 40 days. And it's because by the time the chip came back, everything was boring. The software was already written, the rack was already there, the production line was already set up.

40:25We were just go, go, go, go, go, get everything together. You don't always catch everything. You make some tweaks on the fly and then off you go. In that particular case too, that was a big part of it. Also like the shift I think made a big difference too. We went out and literally had a day shift and a night shift. There were team members who would come in at 10 a.m. and leave at around midnight. Or she would come in at midnight and leave at 10 a.m. You're running around the clock to get to that 40 days. Yeah, I mean over half the company lives next to the office. So it makes it easier to do that type of thing.

40:54You pay them to do that, right? Pay them extra, do you still do that? The invisible hand does wonders. I mean, hey, it works for me, too. We're both there. I'd love to take one big step back and talk a bit about just the broader ecosystem here. The amount of shortages on the supply side, the exposure of risks in the global system and the supply chain around this stuff has become like everyday Wall Street Journal front page news. like the stocks that people are watching and investing in and excited about, about the memory stocks. These were, you know, boring commodity, like nothing burgers five years ago.

41:29And now they're the center of global attention. If you just assess because you've been building in it, the global connected supply chain that's required to make stuff like this possible, just riff on it. Like what scares you? What's working well? What needs to change? What do you hope you change by virtue of how you build this thing? What's your assessment of this story right now. I think that one of the most undervalued pieces of a supply chain story is almost none of these things are you buy them and they don't talk to the vendor again. You have to go collaborate. That is the most important part of being successful, I think, in ships with TSMC or with memory vendors.

42:07You need that partnership. I think that for TSMC in particular, people don't understand why it is so valuable. People look at the tech and the tech is the best in the world. But for me, the real value is all in the service. TSMC customer service is way, way better than I have seen at any other company in any other industry. It's the kind of thing where if you say, hey, you can approve your yield by making this change. You can go make them a recommendation and then we'll go run an experiment on their own dime in our case to see if they could actually get the higher yield. And when we found that we were right and the experiment it worked.

42:43They moved over the rest of the line. And that kind of thing doesn't happen in most industries. If I go to like the steelworks plant and say, hey, I want you to change the competition of the steel, and they'll say, screw you. Not TSMC. It is why they are the number one and why they're going to win. One of the things that matters a ton is power availability and time to power. And the problem is the more power you want, the more shortage there is. It's actually very similar to chip clusters, which is like, why is Colossus charging$12 an hour for Blackwells? It's because they're the only place you can buy 20 ,000 of them at once, right?

43:15Why is the 500 megawatt data center so hard to find? It's the exact same reason. One of the things we need to think about is like, how do we get way more juice out of each megawatt? People are looking throughout the entire stack, whether it's just improving the PUE, but also entirely new hardware to get the most tokens per megawatt to solve this problem. But fundamentally, building new buildings is hard. It's much easier to go from 100 megawatts to a gigawatt than from a gigawatt to 10 and 10 to 100. and we are pushing the limits of what's possible on these timelines. So there's a lot of people trying to scale in their data centers as much as trying to scale them out.

43:48One of the interesting things about a system like this is what it replaces. Yeah. If I think about a rack like this versus, I don't know, a set of Blackwells or something, or Rubens or whatever's coming next, how should I conceptualize that? It's not just Watts, it's also physical space. The Reber's talked about this in their recent readings called that literally this is a problem that there's literally no space to put the systems. How should I conceptualize what this represents or replaces in terms of other units of compute? Here's how customers think about deploying models generally, which is when I'm building a data center or I'm building a cluster, it's not like in the abstract of like, oh, I like these chips and like, yeah, this is the power footprint and so forth.

44:24It's like, I have a real production workload I'm trying to serve. And for my product to be useful, there's a certain speed I need to serve for that. And for certain products, it's really fast and certain products, it's really slow. Whatever the speed is, this is my speed. The question is, in a given amount of power, how many users can I serve while guaranteeing that speed? So another way to put it is ISO, what's called interactivity, what is my throughput? We are just finishing kind of the early innings of the AI infrastructure boom where people really just cared about speed. GPUs were not able to reach a lot of the speeds of other types of chips, like all these SRAM chips, thousands of tokens per second, and that enabled tons of new use cases that got people very excited.

45:02There's an entirely new wave of AI chips, us being one of them, they're all going to be able to hit these speeds. The question then is, if you're hitting these speeds, what is the number of users you can serve at the same time? And by proxy, if I have 100 megawatt data center, how many software agents can I run at the same time? So when people are doing that evaluation, our hardware is going to generally be able to get you an order of magnitude, more concurrency at a given level of interactivity. So that directly translates into tokens per watt, tokens per dollar, all the things people care about when they're actually serving these giant mixture of expert models at scale.

45:34There's these now famous interactivity curves. Not many people publish them, but you can see a Blackwell curve. You can see an A &B curve, which is a little bit worse than Blackwell's, and it's still an$800 billion company. So if you think about what then the impacts are of shifting that curve, not just a little bit further out, but much further out, what are the things that most excite you about what this will enable? I mean, I want to go out and solve some of the hardest problems, and I want to go solve these in much less time. There were things growing up that I was not sure I'd be able to live to see.

46:07For example, with the unidistic conjecture, one of the things that we talked about in college, and I was not sure I'd see that proven in my life. And this was done by an AI model, and it was done over a long period of time. But if you're able to run the same model 10 times faster, you can go shrink the time to go have these breakthroughs. And there's a huge number of other problems in math like this as well, that I worry it will take a thousand years to approve a thing like this, you can either have a much smarter model or a model of the same intelligence running much faster. You can then shrink that and I can see it.

46:36It's so cool seeing these breakthroughs get made. I am so, so excited to see much more of this happen. I think too often people think about tasks and applications and stuff in these very short time horizons. Doing a chat and it's like 50 % faster is nice, but it's not like game changing. as these agents go longer and longer time horizon and the models get more and more capable, you're going to see gigantic bodies of work that would take months of compute. And we think about this in wall clock time. Like if you talk to a pre-training researcher at a lab, they'll tell you that wall clock time often is one of the most important things that matters.

47:12And what wall clock time means is the time from starting your run to finishing it to actually get data back. If you can shrink this time from a six-month run to a two-month experiment, you're going to be able to do many more iterations and people will make changes on the model architectures to actually improve the wall clock time. Very similar here in terms of how we think about the use cases, which is the exciting part about super low latency decode is wall clock time on long horizon tasks becomes much shorter. So a year long compute build would now take a month and that month long compute build will now take three days and that three day compute build will now take seven hours and so forth and so forth.

47:49So that's the thing that I think is really hard to internalize because the models are just getting capable enough to do this stuff. I thought it was really cool months ago when Cursor published that they had a bunch of coding agents build an entire browser from scratch in a week. Totally nuts. And that will soon happen in under an hour. And there's going to be many of those types of things that are going to happen with these massive parallel agents all working on a given task. What are the ultimate limitations of these systems? Is it just It's like a physics question. Like how many times faster, cheaper can we get?

48:21Theoretically. There's a lot of room at the bottom, as they say. If you think about chip-to-chip latencies on an NVIDIA product, you're looking at 4 ,000 nanoseconds to go from one chip to another. We'll be able to do much better than that. What's the mathematical limit? Is speed of light. You can do it in just a handful, like two, three nanoseconds. And there are 4 ,000? 4 ,000 today. There is a lot of room at the bottom. The same thing for things like power efficiency. That, sure, we're able to go and shave a huge amount by bringing the voltage down by so much. But you can go lower. You can go much, much lower.

48:54It's very challenging. But when I think about 20, 30 years in the future, then I think it's inevitable. And also for economies of scale, for cluster scale up. For a long time, 8 chips was the biggest scale up domain. And they even had the NL72, bringing it to, well, 72. But you can be way, way bigger. You look at like a fab, for example. You have a$40 billion single monolithic building with only a handful of lines running through it. You could have the same kind of thing for some futuristic mega cluster. $40 billion,$100 billion as a giant mega token factory serving one or a handful of models for a massive number of users to get that same economies of scale thing.

49:37Same model, massive number of people. You mentioned kernels engineering and that being your first job. That has emerged as a thing that nobody had ever heard of in their lives to now something that you hear about all the time, the importance of it to eke more performance out of the bare raw metal. When will that just be something that AI doesn't entirely as well? Are humans still the best kernel engineers? Are they doing it with the assistance of AI systems? How far down will humans still be in the loop of designing these things? When will that go away? Today, it's all very hybrid. And the best kernels are still written by human AI collaborations.

50:14Also, any AI model is built with both these fundamental primitives, like matmoles, like convolutions, like chip-to-chip operations collectives. And making these overlap and making these really fast matters enormously. And it's the kernel designer's job to go ahead and figure out, or can I overlap? How do I allocate memory? How do I verify that if there's some issue, like a retransmite, it doesn't stall the whole pipeline? These things are very challenging. but they can go and make your model performance be, say, 3-4 % better per optimization. And you can do so many. And when we thought about our software stack, we wanted to go skate at where the puck is going to be.

50:48And three years ago, there were kind of two ways you could build software. One of them was to invest heavily in graph compilers. These things are not very performant, but they work out of the box. They don't require a human to go come in and tweak all the kernels. But we went the opposite direction. We are kernel's first programming, and that means that it, for a long time, did not work out of the box. But if you were kernel's expert, you could get incredibly, incredibly high performance. And the thing about this is that now, as the coding models get better and better, they're doing more and more of the kernel generation task.

51:21And when the models keep getting smarter, it'll eventually do all of it. It will become superhuman. So we're going to build for where the world is going. And even today, we think about our profiling tools or debugging stack. We think about it from how will the model use these tools more than we think about how will humans use these tools. We sometimes run experiments internally, and we had Codex actually get GPT-OSS running from scratch just based off of our docs completely by itself. And it did it, I think, overnight. We think about game selection a lot, and what we mean by that is making sure we're investing our energy in the right bets.

51:57Because regardless of what you choose to work on, it will take tremendous effort. And one of the things that we started with was, you know, the decision explicitly not to build an arbitrary graph compiler, not to support arbitrary PyTorch, not to support arbitrary CUDA, not to support arbitrary Onyx graphs. But instead, we envisioned a world where there was going to be under 100 models that actually mattered. And they were all going to look very similar from the underlying mathematical perspective. And that we were going to build primitives using physics that were going to accelerate these as much as humanly possible.

52:27and we were going to allow the most sophisticated customers to have direct access to the hardware and do whatever they want. And that has saved us a tremendous amount of time not having to build a compiler and that has allowed us to actually get much more performance. And funnily enough, when we started, a lot of people dismissed this idea and the only people that took us seriously were in high-frequency trading. They all hate compilers too. They all write their own kernels. And we've had dozens of people from high-frequency trading join the team because they saw this philosophy too. What are the limits to vertical integration?

52:55Like how do you know where to draw the line? And I'm starting with this question to talk a bit about the broader market. The circumstances of the broader market are really interesting to me where the vast majority of chips, of AI chips, get bought by a very small set of customers. Many of those customers are themselves trying to design their own AI chips. OpenAI announced jalapeno. It seems like this very funny circumstance for the most valuable thing in the world all kind of flows through a couple chip makers, a couple chip buyers. They all seem to be thinking about doing each other's job. And then you've got the circumstance where like, okay, then these things go in a data center and you've got neoclouds and inference providers in this other part of the stack.

53:33You've got model builders and providers. Like I can imagine a world where because you have the best hardware, you design models and you build data centers. You leak outside of your current vertical. So like how do you think about where to draw the lines for the business? We have a saying that production is the product. Ultimately, what matters here is we know inference is going to be the biggest market in the world. whoever produces the most tokens is going to be the most valuable company in the world. So all the decisions we make is how do we get the most token capacity online as possible? And part of that is building a really good product that has way more throughput, that can run at way better latencies and so forth.

54:10So we can, per chip we make, get way more tokens online. Another part of it is like not doing parts of the stack unless we absolutely have to, to get to giant scale. So there are parts that we decided to do because it was absolutely required to get to scale, like building the rack instead of just building the chips, like doing a CM model instead of doing a JDM model. But there are parts of it that are kind of noise to us right now. Like we're not going and building our own data centers today. That doesn't actually help us get more capacity online. In general, our customers are actually making power and moving their clusters around to get our chips online because they're such high throughput.

54:43If there was a world where other things were a constraint, we would totally go and integrate with them. But the reality is we're just purely focused on getting as many tokens online as possible. I think it just comes down to economies of scale again. At certain parts of the stack, there are huge economies of scale and others there aren't. For example, on designing models, huge economies of scale there. For chip fabrication, same story. But for example, if you think about building some small metal part inside of that rack, there's not that same effect. We think the natural boundaries are on the chip side, on the bottom, and the model layer at the top.

55:15And we'll fill the whole gap between. A few weeks ago, there was a guy who was running a next generation AI chip for one of the frontier companies. And he's trying to recruit one of our architects and actually kind of did an uno reverse card and started recruiting the person trying to recruit our guy. And within a week we hired him. And I was going on a walk as we were kind of finalizing the offer. I was like, you're leading this super important project. Why are you deciding to join? And his answer was super interesting, which was, it fundamentally is not existential for my company for this product to win.

55:45For Google with TPUs, the revenue comes from search. Google won't fail if TPUs fail. That's right. Meta won't fail if MTIA fails. Microsoft won't fail if Maya fails. And OpenAI won't fail if Jalapeno fails. Ultimately, this is our product. It is completely unsurprising that the best chip in the world is built by a company that only builds that chip. It's NVIDIA, right? And for us, it is completely existential for us to get as much token capacity online as possible. It recruits a set of talent and recruits a support from suppliers and from customers that view it with the level of intensity that we do.

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57:47As I think about you guys building the solution, the process of doing so is solving a sequence of really hard challenges. What has been the single episode that was the hardest to overcome? We were designing the chip. We built this massive, massive FPGA cluster to go out and verify that full chip worked as is. And FPGAs are digital entities. You can go test digital logic, but not analog logic. And it turns out that when the chip came back, we began to go see issues in our attention to computation of incorrect results. And we realized, wait a minute, there's a problem with a backpressuring logic across a clock domain crossing is failing.

58:23And this is going to cause the chip to produce wrong results. And it is very, very hard to solve. And we realized there was one and only one way to solve it, as we had to do a line up two clock signals on our chip to within 50 picoseconds. That is literally 50 trillionths of a second. And we had to go get the signals aligned to this super small granularity and do it on every chip 2 billion times a second. A lot of people said this was impossible. We had people quit. Yeah. That people literally were like, this problem is unsolvable. And best of luck, guys. Well, when you have a problem like that, step one is, okay, let's assume the problem is solvable.

59:02How would it be solved? Well, first, we realized what we have to be able to do is find a way to go ahead and move our clock phase by a picosecond, 10 picoseconds. And we had an idea. What if we had these two clocks? We had them just a little bit apart from each other. We figured out that, hey, if we go out and figure out the phase, if we then go out and use a drifting mechanism to go ahead and wait for just the right amount of time to get that always lined up, we can do this extremely reliably and then lock the phases exactly where they have to be. We can guarantee this never happens. People were, I think, blown away that this worked.

59:39It worked as well as it actually did, but we made it work. How long did that take? It was actually about two weeks. It was dark two weeks. It was a very scary two weeks. But it was the kind of thing where when that kind of thing happens, that is the most important time to go ahead and be investing effort. That is the hardest time to go do it when you feel like things are hopeless. But like the sooner you solve that problem, you can get back to building and scaling up production to mass volumes I think a lot of our story is like as Gavin says assume it is possible Like assume it is possible to have a chip with way more flops on it Assume it is possible to have a system with way lower latency between chips Assume it is possible to create a shared memory pool that can run at way higher bandwidth Like how would one do it a lot of the time when we do experiments?

1:00:22We will do dozens of experiments and all of them will fail But we only need one to work. There was multiple times. I mean, Gavin, I think, was leading the charge in our chip bring up with, I think, 30 different board experiments, and three of them worked. And all three of them were worth their weight in gold. This is one of the things that people come to me and say, Gavin, almost none of your experiments works. I only got to get lucky once. So one idea for one of these stories that I'm asking about, you know, difficult moments in the company's history is around the ability to raise capital to fund the thing.

1:00:50I think when you started it, you knew you'd need capital, but you did not know you'd need the quantum of capital that you've ultimately raised and are spending to build the solution. And you hadn't raised money before. These are all new things, right? There was moments where it was really, really difficult because I was there, I saw it. Moments where it was extremely difficult to raise the money that you did, that without which the company would not exist, it would have died. And like many great stories, there are many near death moments, but money specifically in this new world, this isn't software.

1:01:19You don't just need a little bit of money. Maybe you could tell the story about the true hardest part about raising money early on before you had something that you could show people and be so proud of and performance that you could show them blow their socks off. It was just you guys talking about an idea, but talk about the early difficulties raising money because it was pretty hardcore. We've had some intense moments. It reminds me of probably early 2024 before we raised our series A. And we were at this point where we We had done enough of the architecture, done enough of the design that we knew that the chip architecture was sound.

1:01:53We had to go build it. There was a lot more to do. We were ready to go into what's called the physical design stage. We needed to sign an agreement with a physical design vendor, which will cost you at least$40,$50 million. And then we had this realization as the models were getting bigger and bigger, and you're seeing these giant MOE models come out, that we were going to need to build the entire cluster, not just the chip, but we were going to need to build boards. We're going to need to build interconnects. We need to build cold plates. We're going to need to figure out all of the networking and everything.

1:02:20And if this was going to cost a lot more than the$15 million we had in the bank. And you're like, man, that was scary. Yeah. That like, you're sitting in that moment. You think, holy crap, we can't afford this. And I began looking at like, huh, how hard is it to go back to Harvard? At the end of 23, we put together this like memo. And we spent like 100 hours on this because we're like, we have no idea how people are going to believe us when we ask for the amount of money we're about to ask for. And it was like 30 pages. It was like extremely technical and in-depth of all the different things we needed to build and like all the milestones we needed to hit and how the market was going to evolve and all the new use cases and the cost per token and all this modeling.

1:03:00And then we went and talked to investors. And every major investor in the Valley passed immediately. They were just like, okay, two kids that just finished Harvard, haven't taped out a chip, no test chip, inference, who knows if this is going to be a big market. Everything's going to be training. The model's still hallucinating. This could all be a bubble. At the time, the biggest semiconductor fundraiser for a Series A was around like$40,$50 million. We were looking at this and we were just like tallying the bill. We're like, we think we're going to spend$100 million in the next 12 months. We really want to do this.

1:03:32If we want to actually get to scale and actually get the performance we're talking about. This is going to be extremely capital intensive. How the hell are we going to pull this off? I think that one of our key ways we got started in this process, we thought to ourselves, what is the cheapest possible way we go do this? Decided, well, made almost nothing. Yeah. And if I ate nothing but ramen, then we would go ahead and spend basically just the money for the mask, the 11-meter tape pouch, and that would be that. And if so, we could probably do it on$30 million, an obscenely low number. and that we actually went out and got a debt provider.

1:04:07They wanted to go ahead and lend us the money. We need to go across this chip threshold. And there, I think it was first to go catalyze a series of other, hey, maybe we can go do one more thing, one more thing, one more thing. Yeah, so we're at this moment where we're like, if we really want to build this company, because we're not going to half-ass it. We're not going to go do a test chip and spend years on it and let the entire AI market boom while we could be building the product. If we're going to do it, we're going to go all the way. we're going to need to find a way to get$100 million. I mean, I remember Gavin and I were like sitting down in the office in Cupertino late at night, just like looking at each other.

1:04:41And we're like, could we cut 500K here? Could we cut 100K here? How long could we convince everyone not to take a salary? And we're like, holy fuck, the math is not going to close. We really need to solve this. And there was a period of a few weeks where you kind of just go into survival mode and you call every person that could possibly know an investor and you're like, we need$100 million to do this. If we do this, we think this could be one of the most important companies of all time. Do you know somebody that wants to take an aggressive bet, that wants to believe in us? Here's all the information.

1:05:14We're an open book. Here's the team. It's great people. We've been working super hard. We've done these things in record time, but we have these 100 things to go. Do you want to do this? And the snowball starts and you get a million here and $2 million here and you're like, okay, we're not going to run out of money this month. You get a $5 million check,$10 million check. You're like, okay, maybe I can buy those FPGAs. And this snowball happened where we were very lucky that we ended up putting it all together. We had a board meeting. I show you the spreadsheet and we look at it and it's like$103 million.

1:05:46It's like, these are all like soft commits. And we all look at each other and we say, we're going to take it. And that was a series A. And it's been much easier since then. And we've raised almost a half dozen rounds since then, many of them from those investors just doubling and tripling down. That's allowed us to get to market so quickly. Like this rack would not be possible had we not have been so aggressive. I also think like suppliers too, I think deserve a little bit of a commendation here. Yeah. That TSMC was willing to work with us back before we raised any of that a hundred million dollars back when it was still really, really scary.

1:06:18Synopsis actually went ahead and let us get some of their emulators on extremely favorable terms where we pay over many years. Basically a big loan. It takes a lot of belief from your partners to go do this. But I didn't know that you come out with this very strong team, and all the folks who back you are not in it just out of pure financial incentive. They believe. Why didn't TSMC believe, do you think? This is a great story. Even before you joined in, there was a conference, a semi-event. And I was one of the only young CEOs of semiconductors. And I think that's kind of a novelty. Asked me to come in there and speak.

1:06:54I get to the semi-event. And I am the only speaker there under 40 and only person there under 30. I was at the time 22. I go up and I speak. There was a speaker's dinner afterwards. And by pure luck, I happen to be sitting next to this very senior TSMC VP. It was a very nice dinner. There's like the former CEO of Arm there. It's very bougie. Everyone's in a suit. And I'm there with this VP. And it turns out we both studied math in college. We go out and get a little piece of paper. We begin talking in great detail about how do modern AI models work at the actual per-tensor by-tensor level. And the guy just gets it.

1:07:28And we begin talking about, hey, how do you run this very effectively? Why is global such a critical technology to make this work? And the following day, I get an email from TSFC saying, Gavin, I want to work with Etched to find a way to make it happen. Crazy. They've been a great partner ever since. It's amazing to think about some of the tropes. And obviously, I should break the fourth wall here. I'm a big Etched investor. I've been involved for a long time. I think that's the world of you guys. So I'm incredibly biased in this conversation. I'm trying to ask questions that are broader and interesting and could be objections to what you're doing.

1:08:00And we'll keep doing that. But it's so interesting to me that when you read about investing, everyone cites this idea of contrarian and right as the quadrant that makes all the money. And it sounds really nice. But contrarian means everyone else thinks you're stupid. And so when you go and you get immediate no's from literally everybody, it is a fascinating quadrant to exist in before you become consensus. What was it like for you? I'm super curious. Well, it's interesting. At the time, it was the largest by a lot first check that I had written. Suffice to say, I am not a math expert or a semiconductor expert or an AI expert really at the time.

1:08:37And so it was much more of a believed in the concept of this market potentially being huge, you having made very, very clear bets on how the future was going to look, having positioned the company in order to attack those things in a hardcore way. And then just the two of you and what I felt about you was the majority of the reason why we made the bet when we did in 2023 or whatever it was. But at the time, it was the biggest. And I think the same thing you said about naivete applies to investing as does to maybe building a semiconductor startup, which is like, I didn't know what I didn't know.

1:09:10And when I called experts, they were basically like, this is stupid. They laid out in very logical terms, like why this wasn't going to work and why it was such a low probability bet. And I think one of the things I've learned from it, it's just like, you kind of have to damn the base rate. Like if you invested on base rates, you should do something other than what we and I do. There's always the index fund. Yeah. There's always an index fund. Exactly. So it's actually never been scary for me. I think probably most of that is because there's a lot I don't know. And if I knew more about what you guys have done and the difficulty, I probably wouldn't have done it.

1:09:41I don't know what that says about like maturing as an investor. Like maybe I don't want to know, you know, a lot more and have some of that healthy naivete. I don't know. Funny. I mean, a lot of the traditional semiconductor funds missed the entire AI chip, like all the AI chip companies and like all the coding experts missed all the coding companies. I think it's very hard to realize the constraints have changed. And when you've looked at tape outs for 20 years and you've seen so many of them not work on the first try or the second try or the third try, like you couldn't even run a workload. You totally forget that EDA tools are way better and that FPGAs exist today in a way that they didn't exist before.

1:10:17And all the types of validation you can do today just wasn't possible. For us, a lot of our believers, either they were kind of on two sides of it. They were just believers in the market and the team, or they were building chips today and extremely technical, like the high-fifty trading firms, where they would literally audit everything from the microarchitecture and the RTL to the board designs and the schedule and the software stack. And we would sit down with 10 of their people who build their own chips, and they're asking us such detailed questions that we're wondering, are they going to build the chip?

1:10:45It was really on either of those sides. And if you were anywhere in the middle, you just wouldn't understand it. In the investing world, I often talk about variant perception, something that you see or believe that others don't, right? And that perception creates the opportunity. I think I've invested, I don't know, five or so times in Etched. And every time when you do it, stakes are getting bigger and bigger. It does get a little scarier and scarier. And because you guys have been so quiet in the marketplace, I think it's very easy to dismiss you. As the stakes get bigger and bigger, those dismissals are harder to hear.

1:11:17I do think betting on something that you see when what you hear from the outside world is very different, that perception gap equals opportunity. The last thing I would say is the accumulated evidence of your guys' and your team's ability to solve seemingly impossible problems is one of the most interesting things a company can have. It's like a binary, like companies do this or they don't. That's the thing is a big advantage of people who have been here for a long time. You get some new joiners who are scared shitless. You see a thing like this and they're old timers who've been here for all of two years.

1:11:53Smoking cigars in the trenches. Another one. Yeah, there's definitely a find-away mentality. If you're here, you're here because you assume it's possible. So, like, we can't be saying it's impossible. Everything is solvable and we're just going to work at it until we figure it out. A favorite story I have, this guy who's kind of a legend in Silicon Validation who joined our team. and we were doing the early stages of what's called wafer sort. When your chips are coming out of fab, they go out on these wafers and you have this thing called a probe card that attaches to the wafer before you dice it with these probe pads and you send these electrical signals to basically test which chips are good and bad.

1:12:28So when I slice the wafer into a bunch of chips, I can package it and only package the good ones. We go through our first wafer. It's like 2, 3 a.m. because we're doing it with TSMC over the phone in Taiwan. We have the screen with the wafer that's all gray and each chip is gray. And then as you start running the patterns, the squares are supposed to turn green or red, and they all turn red. We're like, fuck. Like, this is really bad. Like, everybody's like, guys, take a breath. He leans back, he's like, the puzzle begins. You have to have the attitude of like, yes, you will go up, and you will go stare into the abyss, and you will go see scary things, and we'll solve them.

1:13:08When did you see the first green square? Within a day of that. But in the moment, it's extremely scary. And there's a certain type of person who just like is addicted to that feeling of just feeling the fear and solving it. And we are lucky to have a lot of those people here. If you think about applying all of this earned know-how from this last several years and now thinking ahead to Gen 2, Gen 3 and beyond. Sure. What will you be doing most differently as a result of everything that you've learned? Just like from a conceptual standpoint, like the way that you will attack designing and producing this next one based on what you learned doing it the first time.

1:13:43It took us a while to get to the primitives that we think are really what matters for scaling inference. We tried a bunch of things early on, from compilers that would turn different models into FPGAs, to burning weights in silicon, to splitting your HBM to KV cache and weights, and all of these different things. And there was a lot of cycles of learning until we got to the point that we realized that, like, fundamentally, if you want to run a majority of tokens in the world, you need to do three things. You need to build a chip with the most flops and a given power budget. You need to build a chip that has the lowest latency between other chips, so the biggest scale up domain possible.

1:14:16And you need to produce as much of it as possible. And I think probably in the first half of our journey so far, we learned the first two and that informed the design a lot. And that informs a lot about the bets we're making in the future with the low voltage inference and the cluster scale memory. But the production part, I think, in the past year has become extremely obvious. How much people want to deploy this stuff if you can have it available today? The best ability is availability. If I have a thousand chips today, someone's going to use them. And, you know, we need to build a chip that's not just like way better than what's been built before, but it needs to be available at many gigawatt scale.

1:14:50We need to be able to be building a product that is producible at gigawatts per month than the limit. As we think about that, a lot of the design decisions we're making with our next gen, which you've seen already, is just about simplicity. We're moving tons of parts, trying to assemble and disassemble the thing again and again, and learning how to make it as quick in the cycle times as possible in production, making sure it's going to be reliable, making sure it's going to be serviceable, and making sure it's going to be producible at gigantic scales. What about other problems in the ecosystem that are outside of your control, such as capacity at the leading nanometer at TSMC or availability of HBM4 memory or, you know, some of these other things where like everyone is fighting for a scarce unit of capacity or whatever.

1:15:32How do you face up against those realities when you're trying to produce as much as humanly possible? The people deploying the most computer in the world do think about supply a bit zero-sum, which is there's only so many wafers being produced on a given nanometer node on a given fab, right? And there's only so much memory being produced. And that's why actually for our first gen product, we built it on a different supply chain than the Rubens. We're on four nanometer, Rubens on three nanometer. We're on a different HPM than Rubens and so forth. So it actually is not a zero something. It's a positive something where more is more.

1:16:05So often when we're talking to people deploying at scale, it's not a decision between a gigawatt of a GPU and a gigawatt of us. It's two gigawatts. And I think as much as possible, thinking about supply chain early in the design decisions, because if you have the most performant product and you can't produce it, then you're just a podcast. That's the other big thing about vertical integration, too, is for certain things like for the chips and for the memory, you have to go ahead and partner. For most of the other stuff, those are also very highly in demand components. And the more that you build yourself, the more stuff you can go do on top of what the world can currently build.

1:16:38It is not, oh, you're taking availability with somebody else. You're adding way, way more. I think that's how you win. One of the things I realize we haven't talked at all about is the models themselves, which is kind of crazy, the things behind all of this demand. Anything interesting that you would say about the way that you see models progressing based on what we've seen so far? I guess I'm more interested in how you as thinkers about hardware think hardware might impact where the models themselves go in the future. One of the most important ideas that we believe in is that machines don't think like people think.

1:17:11You look at airplanes, for example. Airplanes don't fly like birds fly. That when you think about how mechanical devices have to work, it's often very different. And in much the same way, for people, storing data and loading memory is very cheap for neurons, and doing math is relatively expensive. And it is the exact opposite for chips. Generally, loading data is very expensive, and doing math is very cheap. And as time goes on, you'll end up finding that math gets cheaper at a rate that is faster than a memory gets cheaper. Due to this fundamental limit on any kind of DRAM device, you should go ahead and think about, how can I make my model use a huge, huge amount of compute?

1:17:54What if I had, for example, many copies running at the same time? What if I activated a huge number of experts? What if I had gigantic experts that I could go ahead and run on multiple server racks at the same time? That is how I think you'll build models that are the next generation of intelligence. And context, too. There's been a lot of work on, hey, very efficient inference. What if I don't load the full context in the memory? And most of the time, I think that makes a lot of sense. But you want to go build a super intelligence. Why can't you go look at a billion tokens of context? Why can't you spend a huge amount of compute to go ahead and read all of it super fast?

1:18:29I would love to be able to talk to a machine that was able to go attend to every book ever written, and short-term memory. And I think that's going to get to a point where you can. A theme in models right now is this focus on something called dynamism, which is this ability to control the level of computation and memory spent at a per-token or per-user level when doing attention, as well as this ability to dynamically, in your chip, on the fly, send data to other chips for different MOE models doing certain types of operations. And the reason is fundamentally, as we are scaling context length, as we're scaling model size, as we're scaling the amount of computation per user, we're looking for ways to be more efficient.

1:19:09So, you know, the first thing is like Gavin says, you know, mixture of experts, you know, architectures where, you know, maybe we don't need every parameter being used for every token, but maybe there's things where even at a token level, we can say, well, this token needs this context on this other token. They can share that memory. So we don't have to have overhead of using the memory as much. Maybe this token is really important. So we should spend more compute. We should have longer context on that token. So hardware that really accelerates these types of very dynamic computations is extremely important.

1:19:38And as you can imagine, current hardware that was designed before those types of architectures have lots of overheads in doing them. So you basically end up in these really bad worlds where you have inefficient hardware at doing this dynamism. So therefore, you can't run it very well. Or you have these very blocky architectures that are kind of applying blunt force to many different tokens that all need more or less computation. I have two questions about the future. We've talked a lot about what you've built so far and how you've built it. The first is about the new ways that people might start using these systems, the raw technology, log of runtimes, things of this nature.

1:20:13When inference gets much cheaper, faster, more accessible, there's more tool supply and it's better. What are the things that you think people will use that capacity to do that are the most interesting, exciting to both of you? Yeah, there was a viral tweet by Noam Brown, where you said that as these models are having longer and longer time horizons, they can do tasks that take, say, six months. And there's often not enough time to go and evaluate them for such a long period of time because by that point, you'll have a new model app. You'll want to go evaluate instead. And with tech like we built our cluster scale memory, you can go ahead and run that six-month job much faster.

1:20:50But there's a second piece of this, too. I've talked to Noah about it. He's now an angel as well, where it's not just the time. There's also the number of people or agents who are working on this. If you're trying to go and evaluate, can a human build a rocket? You will find that the answer is no. No one person can go build a rocket. Instead, you have to go put a team together. And I believe the same thing will be true of agents too. If you want to go ask, can an agent go out and build some crazy futuristic piece of software? You will probably need a very large team. Maybe that's 10. Maybe that's a million.

1:21:23You have to go and have this enormous amount of both cluster scale memory to go ahead and have that very short time per token and a huge amount of flops to be able to go run that whole fleet. I'm going to be a little futuristic. I firmly believe we are on a global march of inference becoming a majority of global GDP. It may take more than 10 years, but it's going to happen. And right now we measure productivity as a society as GDP per capita. But really, it's going to look much more like agents per megawatt or maybe agents per gigawatt by then. And while we're being futuristic, I think this is the second to last year where a majority of the workforce is going to be human.

1:22:02I think in 2027, you're going to see there's going to be more agents doing knowledge work than humans. And it's going to be extremely interesting to see what happens. You could imagine a world where, for countries, a majority of their energy ends up going into data centers doing inference. and the energy efficiency of those data centers basically governs how many agents and therefore how big their workforce is. So you're going to see, like as Gavin is saying, one agent or a team of five to 10 agents working on group projects for a couple of days. So you can do pretty cool stuff because they're smart, but it's not going to be civilization scale.

1:22:35What happens when you have countries that can have literally a billion concurrent agents, like a billion people in the workforce working 24-7 concurrently on the same stuff. It's just kind of unfathomable what's going to happen. And it's going to be the biggest proliferation of technology humanity's ever seen. I think as well, like when you have these huge, huge amounts of demand, you get this idea of economies of scale again. Yep. If you think about people, I have a brain. I'm not using the whole thing all at the same time. That's only part of it's going to be active. And this is the way healthy brains work.

1:23:07MOE models have worked much the same way. On MOE model, only a small fraction and the parameters is being used for any given token at any given moment. But if you have a large number of users on a piece of hardware, you can go kind of take that brain, cut it up into many different experts on many different servers, and run a huge amount of volume through it. So you'll have a bunch of different pieces of traffic. You'll have many of them using each part of the brain at any given point in time. And you'll also make the cost per thought, cost per token, way, way lower. So you're going to end up with these giant scale distributed brains.

1:23:38is the form factor of this is a big data center with a bunch of chips, a huge amount of flops, and a huge amount of scale up interconnect. You think we'll see a trillion dollar individual data center? Absolutely. It is a matter of time. It's like asking, well, you see a billion dollar fab or a 10 billion dollar fab or a hundred billion dollar fab. It is inevitable that the economies of scale don't stop at, oh,$40 billion is the magic number for fabs. No, the cost wafer keeps going down as you keep spending more money. And the same thing will be true of plants that go out and make steel or plants that go out and make tokens.

1:24:12A very smart alien lands on earth and wants to know from each of you how you would frame up this opportunity that you guys are tackling. What do you say to them? Framing it as thinking is really valuable, that every company in the world runs on thinking. And we are entering this really unique moment in time where you have machines that can go think almost as good and as soon as good and as soon better than the best humans can. Building these machines is going to be a huge opportunity. But more important than that, the way in which you go ahead and run this kind of thinking is going to be very, very different as demand goes higher and higher and higher and higher.

1:24:49There's a unique moment right now to go build a new set of solutions, a new roadmap for how do you run the future quadrillion parameter models for a billion people all at the same time on a gigantic scale-up cluster. We are in a new era of intelligence where the cost of producing intelligence is dramatically so much cheaper than the value of the intelligence that we are in a many-year, probably many-decade supply shortage of these tokens. And basically any chip or any system that can produce tokens is likely to be extremely valuable. And you should find some part of the supply chain of the token.

1:25:29I can be everything from model training down to what we're doing in silicon and otherwise to spend time on and push the frontier. And that the companies that are the largest are frankly going to be the companies that produce most of the global supply of tokens and own a majority of the supply chain of that token. And importantly, it's people who build systems that as they get more and more chips put together, get cheaper. The way you want this to scale is not that, oh, if I want to go serve 10 times more tokens, I buy 10 times more servers. It must be some solution where I only deserve 10 times more tokens, then I get some economies of scale benefit with my, say, cluster scale memory tech.

1:26:07It allows me to then not charge as much as 10 times more for those tech tokens. What a ridiculously exciting future that you guys are building to enable. When I did this with Gavin last time, I asked him my traditional closing question. So this time I'll ask you, what is the kindest thing that anyone's ever done for you? During my cancer treatment, there was a big decision I had to make. The doctors came to me and said, it's time for you to decide. Do you want to get surgery or do you want to get radiation? Here's the trade-off. If you get surgery, you're more likely to live, but you have to assume you'll never be able to walk again.

1:26:38If you get radiation, you'll be able to walk again, but it's not the same probability that you'll live. You may die. What do you want to do? And I was 16, and my parents said, you have to make this decision for yourself. I thought a lot about it for a long time and decided, I'm going to do the surgery. I get the surgery. one of the things you do when you get a tumor resection is they do something called a necrosis analysis, where they look at all the different cells and say, is a cell dead or alive? Because if you have a bunch of cancer cells that are alive, you have a problem. And they looked at it and they said, you know, you usually want 98, 99 % necrosis for us to say you're in the clear.

1:27:13You're below that. You should go get radiation. And there was only a few machines in the world that actually could do the type of radiation I needed. One of them was in Boston. I was in a wheelchair and I needed to move to Boston for multiple months. And both of my parents decided to move out and drop everything they were doing and live with me. And I'm eternally grateful. Peace. Beautiful. Thanks, guys. If you enjoyed this episode, visit Colossus.com. You'll find every episode of this podcast complete with hand edited transcripts. You can also subscribe to Colossus, our quarterly print, digital and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most.

1:27:51Learn more at colossus.com slash subscribe.

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