Chips, Neoclouds, and the Quest for AI Dominance with SemiAnalysis Founder and CEO Dylan Patel

What would it take to challenge Nvidia? SemiAnalysis Founder and CEO Dylan Patel joins Sarah Guo to answer this and other topical questions around the current state of AI infrastructure. Together, they explore why Dylan loves Android products, predictions around OpenAI’s open source model, and what the landscape of neoclouds looks like. They also discuss Dylan’s thoughts on bottlenecks for expanding AI infrastructure and exporting American AI technologies. Plus, we find out what question Dylan would ask Mark Zuckerberg. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @dylan522p | @SemiAnalysis_ Chapters: 00:00 – Dylan Patel Introduction 00:31 – Dylan’s Love for Android Products 02:10 – Predictions About OpenAI’s Open Source Model 06:50 – Implications of an American Open Source Model for the Application Ecosystem 10:48 – Evolution of Neoclouds 17:26 – What It Would Take to Challenge Nvidia 27:43 – What Would an Nvidia Challenger Look Like? 28:18 – Understanding Operational and Power Constraints for Data Centers 34:48 – Dylan’s View on the American Stack 43:01 – What Dylan Would Ask Mark Zuckerberg 44:22 – Poker and AI Entrepreneurship 46:51 – Conclusion

• 0:06Speaker 00

  Hi listeners, welcome back to No Priors. Today, I'm here with Dylan Patel, the chief analyst at Semi Analysis, a leading source for anyone interested in chips and AI infrastructure. We talk about open source models, the bottlenecks to building a data center the size of Manhattan, geopolitics, and poker as a tell for entrepreneurship. Welcome, Dylan. Dylan, thank you so much for being here.

• 0:26Speaker 01

  Thank you for having me.

• 0:27Speaker 00

  I've been really looking forward to this conversation. You're such a deep thinker about the space. And then also, it's very odd, you clearly have the Samsung Watch.

• 0:35Speaker 01

  Yeah.

• 0:35Speaker 00

  The foldy phone.

• 0:36Speaker 01

  I got the bling.

• 0:37Speaker 00

  And the laptop.

• 0:39Speaker 01

  The fold, yeah, yeah.

• 0:40Speaker 00

  Tell me more.

• 0:41Speaker 01

  So, part of the origin story is that I was moderating forums when I was a child and my dad's first Android phone was the Droid, right?

• 0:48Speaker 00

  Okay.

• 0:48Speaker 01

  And for some reason, I was obsessed with messing with it, like rooting it, like underclocking it, improving the battery life, all these things, because when we were on a road trip, there's nothing to do besides like mess around on this phone. So, I posted so much about Android that I became a moderator slash R/Android on Reddit and like many other subreddits related to hardware and Nvidia and Intel and all this stuff. But because of that, I've just always had Android. Now, I've had work iPhones before, but I just really love Android. And then it's like, if you're gonna like technology, I'm not like someone who pushes it, but like get the best stuff. So, I have like the Ultra Samsung Watch, which I think looks cool, and the foldy phone, right? It's fun. It's obviously different and weird. No iMessage is a travesty.

• 1:28Speaker 00

  What is it dominate at? What is it better at?

• 1:30Speaker 01

  Um.

• 1:31Speaker 00

  Besides the openness of like the hackability?

• 1:33Speaker 01

  I don't even hack that much stuff anymore, right? It's like, what do you use your phone for? I think, I think the main thing is like you can have like Slack and an email app on two different parts of your phone. I think that's probably the main thing. Or like, you can actually use like a spreadsheet on a folding phone. You cannot use a spreadsheet on a regular phone.

• 1:49Speaker 00

  Okay.

• 1:50Speaker 01

  And that's not even an Android thing. Like, Apple's folding phone next year will be able to do that just fine and I'll have no argument then.

• 1:56Speaker 00

  Yeah.

• 1:56Speaker 01

  But I just like it, you know? People, people have their preferences. People are creatures of habit.

• 2:00Speaker 00

  You gotta look at the GPU purchasing forecast.

• 2:02Speaker 01

  Yes.

• 2:03Speaker 00

  On a sheet on your phone.

• 2:04Speaker 01

  Yes, I do, I do, no. Like, it's like someone's telling you numbers. You're like, wait, this is like slightly different than my number, right? Like.

• 2:10Speaker 00

  Okay, so we have a week of big rumored announcements coming up. Tell me your like reaction to the OpenAI open source model.

• 2:18Speaker 01

  In theory, it's gonna be amazing, right? Like, I assume this is releasing after it's released, or?

• 2:24Speaker 00

  Yes.

• 2:25Speaker 01

  So that's okay. The open source model is amazing, guys. I think the world is going to be really shocked and excited. It's the first time America's had the best open source model in six months, nine months, a year. Llama 3.1, 4.5B was the last time we had the best model. And then Mishral took over for a little bit, if I recall correctly. And then the Chinese labs have been dominating for the last like six, nine months. Right? So it'll be interesting. It'll also be funny because like the open source model probably won't be the best for just regular chat. Because it is like more reasoning focused and all these things. But it'll be really good at code and so I'm excited for that. Yeah. Like tool use, although that's like going to be confusing. Like, how do you use the tools?

• 3:09Speaker 01

  If you don't have access to OpenAI's tool use stuff, but the model is trained to do so? That'll be interesting for people to figure out. I think the last thing is like the way they're rolling it out is really interesting. They accidentally leaked all the weights, but no one in the open source has figured out how to actually run inference on it. Because there's just some weird stuff in the model with the architecture, like four bit and like the biases and all this other stuff. But what's interesting is other companies dropped the model weights and say, go make your own inference implementation. But OpenAI is like actually like dropping the model weights and like all these custom kernels for people to implement in inference. So everyone has a very optimized inference stack day one.

• 3:45Speaker 00

  And they work with partners on it, too.

• 3:47Speaker 01

  Yeah, working with partners on this. But this is very interesting because like when DeepSeek drops, it's like, well, together in fireworks are like, yeah, we're the best at inference. Because we have all these like people who are really good at low level coding, whether it be like fireworks with all their like former PyTorch meta people. Or together with like, you know, TreeDAO and all the, you know, Dan Fu and all these like super cracked like kernel people. They have like higher performance, right? Uh, but in this case, like OpenAI is releasing a lot of this stuff. So it's, it's, it's, it's interesting for the inference providers too. Like, how do they differentiate now?

• 4:16Speaker 00

  Yeah. I, I mean, my premise on this is, um, in the end, a lot of the model optimization performance layer is open source. And it's a commodity. And it will end up being like a fight at the infrastructure level, actually.

• 4:31Speaker 01

  Interesting.

• 4:31Speaker 00

  And so, you know, all of these inference providers, like, as you mentioned, you know, fireworks and together, base ten and such, they they compete on both dimensions. And the question is, what's going to matter in the long term?

• 4:41Speaker 01

  Why would these model level software optimizations all be open? They haven't been open so far and the advancements are so fast, right? Like.

• 4:48Speaker 00

  Oh, well, I think they, a bunch of them have been partially open. And I think OpenAI is also pushing for them to be open as well. Right? And so I think there's a lot of force in the ecosystem to open source from both like the Nvidia level up.

• 5:04Speaker 01

  Yes, yes.

• 5:04Speaker 00

  And from the model providers down.

• 5:05Speaker 01

  Agreed.

• 5:06Speaker 00

  And so I think today, these providers all fight on that dimension.

• 5:10Speaker 01

  Yeah.

• 5:11Speaker 00

  And they also fight on the infrastructure dimension. And I think infrastructure is going to end up being a bigger differentiator.

• 5:15Speaker 01

  That makes sense.

• 5:15Speaker 00

  You can't open source your actual infrastructure, right? You just have to have the network and you have to run it, right?

• 5:21Speaker 01

  Yeah, yeah, that makes a lot of sense. Although, like, I see today, the inference, like, providers have such a wide variance, right? Like, the ones you mentioned are on the, like, the leading edge, especially like Together and Fireworks, I think are on the leading edge of like their own custom stacks, all the way down to like, there's a lot of people who just take the out of box open source software.

• 5:38Speaker 00

  Yeah, I think there's no market for that.

• 5:39Speaker 01

  But those guys have just, yeah, I agree, there's no market. It's like commoditized.

• 5:43Speaker 00

  Yeah.

• 5:43Speaker 01

  They have really, really way worse margins than the people who are very optimized. When you see Nvidia trying to open source all this stuff around Dynamo and and in OpenAI and all these other people are trying to open source stuff, but the level of optimizations is also like really, really large, like caching between turns and caching tool use calls and all these other things. And it's not just like a single server problem. Like, it's like the DeepSeek implementation of inference is like 160 GPUs or something like that? Like, that's over $10 million of hardware. And then that's just one replica. And then you'll have a lot of replicas and you share the caching servers between them. So like, seems like just that orchestration of that, but also the infrastructure of that, it's a very large amount of infrastructure. I don't know, that's interesting thought that that will be completely commoditized optimization layer.

• 6:28Speaker 00

  Well, I think that there's optimization at the single node level. And then there's like the system software.

• 6:33Speaker 01

  Yeah.

• 6:33Speaker 00

  Where you can like orchestrate this. And and I think that owning the abstractions for it and having people use your tools and more sophisticated teams to do that optimization.

• 6:42Speaker 01

  Yeah.

• 6:42Speaker 00

  It's like a very ugly distributed systems problem. I think that will matter.

• 6:45Speaker 01

  Okay, I could agree with that. I could agree with that. Single node is not necessarily, yeah, I agree.

• 6:49Speaker 00

  Let's move, uh, uh. You know, out and a layer down, like, what does having access to an American open source model mean, or just more and more powerful, like, open source AI models mean for the application ecosystem?

• 7:02Speaker 01

  I mean, I know, like, a lot of people and some enterprises are really iffy about, like, using, like, the best open source model. They're, like, worried. It's like, there's nothing wrong with them today. There's nothing in them today, right? You know, there's the worry that one day they will-

• 7:14Speaker 00

  How do you check?

• 7:15Speaker 01

  I mean, you don't, but you can just vibes it out. Like, they're, like, competing with each other to just release as fast as possible, right? Like, like DeepSeek and Moonshot and all these other, you know, Alibaba, etc. Like, they're competing to release as fast as they can with each other. The Alibaba teams in Singapore are like, I don't think that they're, like, putting Trojan horses in these models, right? And like, there's some interesting papers that Anthropic did on, like, you know, trying to embed some stuff in models and ended up, like, being detectable pretty easily. Again, like, I don't know how to, you know, I'm not, I'm not too much into that space of interpretability and, like, evals, but I just don't think that they are, right? It's just a vibes thing. But some people are worried that they could be, or they're just, like, iffy, like, oh, I don't want to use a Chinese model. It's like, well, fine, but now you're gonna go use a service

• 7:59Speaker 01

  that is backed by a Chinese model, which is fine, like, you know, like, uh, but they, you know, they're fine with that. They just don't want to directly use the model. I don't know. I think, I think it's, it's interesting for some enterprises who are still stuck on MAMA, but it's mostly just really interesting because it continues to move the commodity bar up. Now with this tier being open source, and sure, like, probably won't be, like, drastically better than Kimi, but Kimi is so big, it's so difficult to run, like, people aren't running it, whereas the OpenAI models, like, relatively small, so you can run it without being, like, giga brain of infrastructure. You end up with that commoditizing so much more of the closed source API market. And I think that's just gonna be great for adoption, right? Like-

• 8:39Speaker 00

  Yeah, one of my, um, hopes is, uh- For our companies that are doing more with reasoning, it is like they're still blocked on cost and latency.

• 8:51Speaker 01

  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 what tokens, what models, where, et cetera, why? And it's very clear that people aren't actually using the reasoning models that much in API. Like Anthropic has eclipsed OpenAI in API revenue and their API revenue is primarily not thinking. It's called for, but it's not in the thinking mode. You know, code is code being the biggest use case that's skyrocketing. And the same applies to like OpenAI and DeepMind and from what we see, querying big users and other ways of like scraping alternative data because the latency issues, because the cost issues especially, right? The cost is just ridiculous.

• 9:31Speaker 00

  You're exactly. So I guess my view is you're not allowed to have a tech podcast without saying the words Jevon's paradox now. And I think like, I think the behavior is going to be like, we see a lot of people use reasoning because it's so much cheaper to run if you take out a big piece of the margin layer and you make it smaller. And so I think like, we have a lot of companies that are at scale who are using it, but it's so expensive that they restrain themselves.

• 9:54Speaker 01

  For a long time, OpenAI was charging more per token for the reasoning model, right? Oh, one and oh, three than they were for GPT four O, even though the architecture is like basically the same. It's just the weights are different. And there's like some reason for it to be a little bit more expensive per token because the context length is on average longer. But in general, like it made no sense for it to be like, was it like four X the cost per token? That didn't make any sense. And then finally they like cut it. But for a long time, not only was it like way more tokens output and it was also a way higher price per token, even though, and they were just taking that as margin.

• 10:26Speaker 00

  Because they could, right? Because they had the only thing out there.

• 10:28Speaker 01

  Yeah. And then, you know, DeepSeek dropped and Anthropic and Google and others started releasing models and it like, you know, commoditized quite a bit. But this is going to just like kneecap, like, like take, cut everyone off at the hip, right? And bring margins down again.

• 10:41Speaker 00

  Oh, who has an API business, you mean?

• 10:43Speaker 01

  Yeah, yeah. For API for models that aren't like. Like super leading edge.

• 10:48Speaker 00

  What do you think evolves in the sort of Neo cloud layer over time?

• 10:52Speaker 01

  It's funny, every day we still find a new Neo cloud, like we have like, like 200 now. And still every day we find new ones, right?

• 10:59Speaker 00

  Should they all exist?

• 11:01Speaker 01

  Obviously not, right? Like, so to some extent, it depends on what the Neo cloud business is, right? Like today, it's, there is quite a bit of differentiation between the Neo clouds. It's not just like buy a GPU, put it in a data center. Otherwise, you wouldn't have some Neo clouds with like horrible utilization rate. And you wouldn't have some Neo clouds who are like completely sold out on four, five, six year contracts. Right? Like CoreWeave, for example, who doesn't even quote most startups or they just give them a stupid quote. Cause they just like, I don't want your business or like they want a long-term contract, right? Which a lot of people don't want to sign. And so like, there's quite a bit of differentiation in, in financial performance of these Neo clouds. Time to deploy, reliability, the software they're putting on top, right? Like many of them can't even install Slurm for you. It's like, what are you doing like? And you should have some sort of like.

• 11:42Speaker 00

  So very low level hardware management.

• 11:43Speaker 01

  Yeah, yeah, it's like very, and it's like, to some extent, from the investor side, we see a lot more debt and equity flowing in from the commercial real estate folks. As commercial real estate has been really poor over the last couple of years, few years. They've been starting to pour money into cloud space. And obviously the return profile is quite different because it's like a short-lived asset versus like a longer-lived asset. But at the end of the day, like these companies, they're okay with a 10, 15% return on equity, right? And over time that falling, that is not okay for venture capital, right? And yet a lot of these Neo clouds are backed by venture capital. So a lot of these companies will fail either because it no longer makes sense for them to continue to get venture funding. Or they end up getting outcompeted because they just can't get their utilization up, unlike, you know, some other clouds, right?

• 12:28Speaker 01

  Like, like the, like the CoreWeaves and Crusoes and such of the world, right? So there, there's sort of like a rock and a hard place for 100 of these Neo clouds. And there's many of them who are like. Oh no, I purchased these GPUs. I have a loan. It costs me this much. And because my utilization is here, I'm like burning cash, right? And they should at the very least not be burning cash, right? And so some of them are like, you know, they're desperate to sell the remaining GPUs. So they go out to like, you know, companies and give them insanely low deals. There, there's some startups who I really commend because they like really figured out how to get the desperate Neo clouds to give them GPUs. But those Neo clouds are going to go bankrupt at some point because their cash flow is worse than their debt payment. But at the end of the day, like, there's going to be a lot of consolidation.

• 13:12Speaker 01

  There is going to be differentiation, right? There's a lot of software today, uh, but we have this like thing called ClusterMax where we review all the Neo clouds uh, and, and major clouds. And it's like, like actually some of these Neo clouds are better than Amazon and Google and Microsoft in terms of software.

• 13:26Speaker 00

  In terms of um uptime and availability or however you.

• 13:29Speaker 01

  Yeah, uptime, availability, uh, reliability, network performance. Like there's just a variety of things that they don't have all the old baggage. Uh, but the vast majority are worse. And we, we measure across like, you know, a bunch of different metrics, including the ones I mentioned and security and so on and so forth. But our vision of like ClusterMax is that it starts at like a really low stage today, which is like, does the cloud work and how long does it take the user to like get a workload running? Because you have Slurm installed or you have Kates installed and um, you know, your network performance is good. Your reliability is good and it's secure, right? Like, these are like table stakes, like what we consider gold or platinum tier today will be just like table stakes in like, you know, six months, a year, a couple of years. Uh, there will be a whole layer of like software on top. And then it's like, do Neo clouds build this software, right?

• 14:14Speaker 01

  And some of them are, right? Like Together, Nebius, um, are offering inference services on top, right? So they're, they're saying, hey, we actually want to provide an API endpoint, not just rent GPUs. And Corey rumored by the information to be uh, attempting to buy fireworks for the same reason, right? Like, do you move up or do you just slide down into like, I'm making commercial real estate returns? Or you have to go crazy, right? Like Crusoe is like, we're going to build gigawatt data centers, right? Like, okay, there's no competition there. There's like a few companies doing that, right? So it's very different. So you either have to go like really, really big or you need to move into the software layer. Or you just make commercial real estate, or you go bankrupt, right? Like, these are the paths for all NeoClouds, I think.

• 14:55Speaker 00

  I really have to believe there's a reason for Beam for these companies. And my, like, simple framework for it is, I think the software layer is really hard for people coming from this operation to, to try and build, right? There's actually a lot of very specialized software, so I think people will buy or partner into it.

• 15:11Speaker 01

  Yeah.

• 15:12Speaker 00

  But if you think about other inputs, it could be like, I'm very good at, like, finding and controlling power agreements.

• 15:17Speaker 01

  Yeah.

• 15:18Speaker 00

  Um, it could be like, I build at a scale other people are incapable of doing so.

• 15:21Speaker 01

  Yeah, yeah, which is, which is like, sort of what, like,

• 15:23Speaker 00

  Or like Nvidia wants me to exist, right? I can't, like, think of, like, a lot of arguments beyond that. And so I, I would agree with you, like, eventually, we're gonna see consolidation, uh, either in this layer or, you know, commoditization by the inference providers.

• 15:37Speaker 01

  But in the meantime, there is a lot of lunch to eat from Amazon, who continues to charge, you know, really, and, and Google and, and Microsoft, who continue to charge, like, absurd margins for their compute because they're just used to doing that in CPU world.

• 15:53Speaker 00

  Yeah.

• 15:53Speaker 01

  Right? And so, like, their ROIC is, like, extremely high on CPU and storage. And, and to assume that it can, like, translate over to GPUs is, is a bit of a fallacy, and which is why, which is why a lot of these companies are moving in, right? And it's like, okay, in standard cloud, there's a lot more software that, like, people can't just build out of nowhere. Yes, EC2 is a product that is, like, pretty simple, but, like, block storage and all these other things are actually quite difficult to do at scale well, like that Amazon does. And that's what makes them able to charge this absurd margin on standard compute. But now, like, it's like, well, the cloud doesn't actually generate, create any software that the user, end user actually uses, right?

• 16:33Speaker 01

  It's like, sure, I need summer communities, but then I'm just using PyTorch, which is open source, and I'm using a bunch of Nvidia software, maybe, or which is open source, or I'm using a bunch of open source models. I'm using, you know, VLM and SD Lang, which are open source. It's like, you just go down the list, it's like, there's actually no software that the cloud Can provide to deserve the margins that Amazon and Google's clouds do have today. If you're just infrastructure provider.

• 16:55Speaker 00

  I think that there is software that the cloud can provide.

• 16:57Speaker 01

  Yes.

• 16:58Speaker 00

  But the major clouds have not delivered that software.

• 17:01Speaker 01

  Agree, agree.

• 17:01Speaker 00

  Okay.

• 17:02Speaker 01

  Um, because it's really, it's really hard to do this stuff, right? Like, there is no reason that every single startup needs to have like multiple people dedicated to infra. And like figuring out how to run models and like their SLA, their reliability is just so low. Right? Like so many, so many random SaaS providers that are AI, like they, they have GPUs, they have open source model. It works great, except sometimes it fails and then it's down for eight hours and it's like, why? Uh, this shouldn't be a problem. It should be something you should just be able to pay away.

• 17:27Speaker 00

  I mean, I, I feel like the multi-trillion dollar question that you um have thought about for perhaps longer than um almost anyone else is like, what does it take to actually challenge Nvidia? You know, asking for a friend, what would it take?

• 17:40Speaker 01

  The like, you know, simple way to put it is like, it's a three-headed dragon, right? Like you have, you have, they're actually just really, really good at, you know, engineering hardware and GPUs. Like that is difficult. Um, they're really, really good at networking and then they're really, I would actually say they're like okay at software, but everyone else is just terrible. No one else is even close on software, but you know, and, and I guess in that argument, you can say they're great at software. But like actually like, you know, installing Nvidia drivers is not like, not always easy, right?

• 18:06Speaker 00

  Well, there's great and there's also just like, well, there's like 20 years plus of work.

• 18:10Speaker 01

  Yeah, yeah.

• 18:10Speaker 00

  Ecosystem, right?

• 18:11Speaker 01

  Uh, yeah.

• 18:12Speaker 00

  There's today's capability and like usability and there's just like mass of like libraries.

• 18:18Speaker 01

  Yeah. So I think Nvidia is really hard to take down because of those three reasons. And it's like, okay, as a hardware provider, can I do the same thing as Nvidia and win? No, they're an execution machine and they have these three different pillars, right? I'm sure they have a lot of margin, but like you have to do something different, right? Um, in the case of the hyperscalers, right? Google, Amazon with TPUs, Amazon with Trania, Meta with MTIA. They are making a bet of, I can actually do something pretty similar to Nvidia. If you squint your eyes now, like Blackwell and TPU is. Starting like the Nvidia architecture with TP architectures are actually converging, like same memory hierarchies and similar sizes of stallicer arrays. Like it's actually not that different anymore. It's still quite different, right? But hand wave view, it's like pretty similar and trinium and TPUs are very similar.

• 19:00Speaker 01

  Architecturally, the hyperscalers are not doing anything crazy, but that's okay because they can just like do the mass, the margin game. That's fine. But for a chip company to try and compete, they must do something very unique. Now, if you do something unique, it's like, okay, all your energy is focused on that one unique thing. But on every other vector, you're going to be worse. Like, are you going to be there at the latest process node as fast as Nvidia? No. Okay, that's like 20, 30%, right? On cost slash performance and power, right? Are you going to be on the latest memory technology as fast as Nvidia? No, you'll be like a year behind. Great. Same, same penalty. Are you gonna be the same on networking? No. Okay. You know, you just stack all these penalties up. It's like, oh, wait, your unique thing can't just be like 2 to 4X faster. It has to be like way faster. But then the problem is, if you really look at it simplistically, right? Like a flop is a flop, right?

• 19:44Speaker 01

  Again, like this is super simple, but like there is not 10X you can get out of doing a standard von Neumann architecture on efficiency of compute. In which case, do all of these things that Nvidia will engineer better than you because they have a team of 50 people working on, you know, just memory controllers and HBM and just like networking, or actually like thousands of people working on networking. But like each of these things, do they just cut you by a thousand? And that's like, oh, actually, what would have been 5X faster is now only like 2X faster. Plus, if I like misstep, I'm like six months behind and now the new chip is there, right? And you're screwed. So, or, or supply chain or like intrinsic like challenges with, okay, getting other people to deploy it now or rack deployments. There's all these supply chain challenges, right?

• 20:27Speaker 01

  Like literally in Amazon's most recent earnings, they said they're like, chip architecture is not aggressive. Their, their rack architecture is very simple. It's not that aggressive. They were like, yeah, we have rack integration yield issues, which is why we've had, which they like blamed their miss on AWS for their trinium not coming online fast enough because of rack integration issues. And when you look at the architecture, like we have an article on it. It's like, it's not like that crazy. Like, it's like what Google was doing like four or five years ago, right? It's like, oh, wait, supply chain is hard and Amazon couldn't get everything in supply chain to work. And so therefore they missed their AWS revenue by a few percent, right? Which caused the whole stock market to freak out. But it's like, there are so many things that could go wrong in hardware and the time scales are so long. And then the last thing is that like model architecture is not stagnant. If it was, Nvidia would optimize for it.

• 21:12Speaker 01

  But model architecture and hardware, right? Software hardware co-design is the thing that matters. Right? And these two things, you can't just like look at one an individual, right? Like, there's a reason why Microsoft's hardware programs suck, right? Because they don't understand models at all. Right? Meta, Meta, their chips actually work for recommendation systems and they're deployed for recommendation systems. Because they can do hardware software co-design. Google is awesome because they do hardware software co-design. Uh, why is AMD not catching up despite being awesome at hardware engineering? Well, yeah, they're bad at networking, but also they suck at software and they can't do hardware software co-design. You know, there's like much deeper reasons why you could get into this, but you have to understand the hardware and the software and they move in lockstep. And whatever your optimization is, doesn't end up working, right?

• 21:53Speaker 01

  So one example is all of the first wave AI company, AI hardware companies, right? Cerebrus, Grok, uh, Samba Nova.

• 22:00Speaker 00

  Yep.

• 22:01Speaker 01

  Uh, Graphcore.

• 22:02Speaker 00

  Yep.

• 22:02Speaker 01

  All of them made a very similar bet. No, they were very different, right?

• 22:06Speaker 00

  Some of these are architecturally pretty weird in relative.

• 22:08Speaker 01

  Right, they're architecturally pretty weird, but they made the same bet on memory versus compute. Right? We're gonna have more on-chip memory and lower bandwidth, right? Off-chip, right? Because that was the trade-off they decided to make. So all of them had way more on-chip memory, uh, than Nvidia, right? Nvidia, their, their on-chip memory has not really grown much from A100, H100, Blackwell, right? It's up 30% in like three generations, whereas these guys had like 10x the on-chip memory. Right? All the way back in like, when they were competing with A100 or even the generation before. But that ended up being a problem because they were like, oh yeah, we could just run the model on the chip, right? We can put the whole weight, all the weights on there. And then, you know, we'll be so much more efficient. And then the, the model just got way too big, right?

• 22:48Speaker 00

  Yeah.

• 22:48Speaker 01

  And Cerebrus was like, oh wait, but our chip is huge.

• 22:50Speaker 00

  Yeah.

• 22:50Speaker 01

  Oh wait, 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, right? Like some companies are like, oh, we're gonna make our like systolic array, your compute unit, super, super, super large. Because let's say Lama 70B is an 8K hidden dimension and your batch and all that. Like it's, it's a pretty large matmole. Oh, great. Okay, we'll make this chip. But then all of a sudden, all the models get super, super sparse MOEs, right? Like, the hidden dimension of deep-seeks models are like really tiny because they have a lot of experts, right? Instead of one large map mall, it's a bunch of small ones, you do route, right? Like, and all of a sudden, like, if I made a really, really large hardware unit, but I have all these small experts, how am I going to run it efficiently? You know, I, I, no one, they didn't really predict that the hardware would go that way, but then it ended up going that way.

• 23:35Speaker 01

  And this is like, this is actually the case with at least two of the AI hardware companies today. I don't want to, I don't want to, should talk to them just because, you know, it's a, let's be friendly. But like, this is like, like, clearly, like, what's happening, right? So it's like, you can make a decision, it's a hardware bet that will actually be way better on today's architectures. But then, architecture evolves in the generality of like Nvidia's GPUs, or even like TPUs and Tradium is like jet more general than like as an architecture, but then it doesn't beat Nvidia by that much, right? In which case, they're just going to destroy you with their six months or a year ahead on every technology because they have more people working on it and their supply chain is better, right? So you, you, you, it's kind of really tough to make the architecture bet, have the models not just go in a different direction that no one predicted, because no one knows where

• 24:20Speaker 01

  models are headed, right? Even like, you know, you can get Greg Brockman, and he might like have like a good idea, but like, I'm sure he doesn't even know where our models will look like in two years. So there's got to be a level of generality. And it's hard to like, hit that intersection properly. And so I'm very hopeful people compete with Nvidia. I think it'll be a lot more fun, there'd be a lot less margin eaten up by the infra, there'd just be a lot more deployment of AI, potentially, if someone was able to compete with Nvidia effectively. But Nvidia charges a lot of money because they're the best. And like, if there was something better, people would use it, but there isn't. And it's just really hard to get, be better than them.

• 24:55Speaker 00

  I mean, you have to give the first gen AI hardware companies some credit, because they like made a secular, correct decision about the workload. But then, the architectural decisions, like, ended up being hard to predict currently, right? Then you have the cycle of Nvidia innovation, which is really hard to compete with.

• 25:12Speaker 01

  Yep.

• 25:13Speaker 00

  Both hardware and also, as you said, supply chain issues.

• 25:16Speaker 01

  Even just putting together servers is hard.

• 25:17Speaker 00

  Yes. I think the thing that you point out, that like, people oversimplified was with maybe a current generation of AI chip startups, they're like, we're betting on transformers. And it's a lot more complicated than that, in terms of workload at scale and continued evolution in model architecture. And it's also not exposed, so that if you're not working with the SOTA labs at Like from the beginning and then you can't make predictions because nobody can make a lot of predictions right now. It's very hard to like, say, I'm gonna be better at the workload two years from now in a like, a very comfortable way.

• 25:52Speaker 01

  Yeah.

• 25:52Speaker 00

  With no other changes happening. Like, I can't make that bet right now.

• 25:55Speaker 01

  Yeah, and it's like, one of the interesting things about OpenAI's open source models, it's like all their training pipelines, but on a quite boring architecture, right? Like, it's not their crazy, like, cool architecture advantages that they have in their closed source models, which are, make it better for long contacts, or more efficient KV cache, or all these other things, right? They're doing it on a standard model architecture that's publicly available. They like intentionally made the decision to open source a model with a boring architecture that's pretty much open source, right? Already, like, people have already done all these things and kept all the secrets internal that they wanted to keep. And it's like, what's, what's in there, right? Are they even doing standard scaled dot product attention? Probably, but like, there's probably a lot of weird things they're doing, which don't map directly to hardware.

• 26:38Speaker 01

  And like you mentioned, right, like transformer chip architecture is like, there's a lot more complicated here than just like, oh, it's optimized for transformers, because like, so is an Nvidia chip and a TPU. And their next generation's more optimized for it. Like, they take steps towards it. They don't leap. But as long as they're like, close enough to where you are architecturally optimized for workload, they'll beat you because of all the other reasons.

• 27:00Speaker 00

  And I think your description of like, how might a, like, a chip startup win, or any vendor win by specializing? Like, that actually is really hard in this era. Like, generalization may continue to win to a degree.

• 27:10Speaker 01

  And it happened with all the edge hardware companies too. You know, we talk about the first gen AI hardware companies for data center, there were a handful. But for the edge, there were like forty, fifty. And like, none of them are winning because it turns out the edge is just, take a Qualcomm chip or an Intel chip that's made for PC or smartphone and deploy it on the edge, right? Like, that ended up being way more meaningful. So, so it ends up being like, the incumbents, they can take steps towards what you're going for. And if you didn't. Execute perfectly, or if the models didn't change their architecture away from what you thought it would be, you end up failing.

• 27:43Speaker 00

  If you had to make a bet that something becomes competitive, what is the configuration or company type that that does that?

• 27:49Speaker 01

  I don't want to show any company that I've invested in or anything like that. And so therefore.

• 27:53Speaker 00

  Not investment advice?

• 27:54Speaker 01

  No, no, but like I'd like, I like, I would just say like, I probably think that like AMD GPUs or Amazon's Tranium will be probably more likely to be a best second choice for people, or Google TPU, of course, but I think Google's just more interested in it for internal workloads. I just think that those will be much more likely options to succeed than a chipped hardware startup. Yeah. But I mean, I really hope they do because there's some really cool stuff they're doing.

• 28:18Speaker 00

  If we zoom out to the macro and we think about just the scale of hardware and data center deployment for these workloads, people talk a lot about the operational constraint on building data centers of this size, the power constraints. I think in particular on the power side, it's very interesting how that practically shows up. Is it generation at scale, at cost? Is it grid issues? It's like, how should, you know, more people in technology understand this?

• 28:49Speaker 01

  Yeah, so supply chain is always like fun because like people want to point at one thing is the issue, but always ends up being these things are so complicated. Like if one thing was solved, you could increase production another 20% and then something else would be the issue.

• 29:03Speaker 00

  You think it's a multi bottleneck?

• 29:04Speaker 01

  Yeah, or like, hey, for company A, it's actually because their supply chain is this, this is the issue. And for company B, it's this is the issue. But you know, that's sort of in generalities, but like, I think zooming out, right? Like Noah, Noah Opinion, like he had a really fun blog about like, is this AI hardware build out going to cause a recession? I think it's actually funny because you can flip the statement and be like, actually, the US economy would not be growing that much this year if it weren't for all the AI build outs and as a result, data center infrastructure, as a result, electrician's wages have soared, as a result, power deployments and other capital investments which have 15, 30 year lifespans are being made. And all of this capex is in turn actually growing the economy.

• 29:45Speaker 01

  And like, actually, maybe the economy wouldn't even be growing much or at all if it weren't for all of these investments.

• 29:51Speaker 00

  One thing that is perhaps looked over from the White House AI Action Plan was the view of like, we're going to build these AI data centers in the United States. We're actually going to need like a lot of general investment beyond the GPUs and the power, which are everybody's first two items into like labor, for example, right? So if you just, you know, for simplicity's sake, be like, it's the size of Manhattan and we have to run it. And it's a new system with changing topology and like very high degree of relatively novel hardware with failure.

• 30:21Speaker 01

  Yeah.

• 30:21Speaker 00

  And like lots of networking that I'm like, mm, like kind of feels like we need to have a bunch of new capacity, like from a labor or robotics.

• 30:29Speaker 01

  In like 23, it was very simple. It's like Nvidia can't make enough chips. Okay, why can't Nvidia make enough chips? Oh, COAS, right? Chip on way from substrate packaging technology. And I was like, oh, HBM, right? Like those were like, it was like very simple 23, 24, like, yeah, all these tools involved in that supply chain. It was great. But then it like very quickly became much more murky, right? Then I was like, oh, data centers are the issue. Okay, we'll just build a lot of data centers. Oh, wait, substation equipment and transformers are the issue. Oh, wait, power generation is the issue. It's not like the other issues went away, right? Like actually, you know, COAS is still a bottleneck and HBM is still a bottleneck. Optical transceivers are still a bottleneck, but so is power generation and data center, physical real estate, right?

• 31:12Speaker 01

  Like I mentioned, like 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, right? As you mentioned, labor, right? That's like one way they were able to remove a part of a constraint. They're still constrained on power and they had to delay the bring up of some GPUs in Ohio because the AEP, the grid in Ohio like had some issues, right? The utility, right? Would like bringing on a generator or something, right? Oh, okay, great. Well, we'll buy our own generators and put them on site. Oh, wait, now there's an eight-year backlog or whatever, four-year backlog for GE's turbines. Yeah. Oh, okay, great. Um, I'm Elon. I'm gonna buy a power plant from overseas that's already existing. I'm gonna move it in. Okay, great. Now there's like permits and people protesting against me in Memphis. Like, you know, there's like, there's like a bajillion things that could go wrong.

• 31:57Speaker 01

  And labor is a huge one. I've literally had people in pitches be like, no, no, we've already booked all the contractors. So no one else is going to be able to build a data center in this entire area of this magnitude besides us. Because we took all the people. We took all the people. They're gonna have to fly them in, but it's like, okay, fine. Like, you can fly them in, but it's like, there's just like not that many electricians in America. And as a result, we've seen the wages rise a lot for people building data center infra. There's a group of like these Russian guys who used to work for Yandex, Russia's search engine, who like wire up data centers, who now live in America and they get paid a ton, like, and they get paid bonuses for being faster and therefore they do like certain drugs to be able to finish the build outs faster.

• 32:38Speaker 01

  Because they get bonuses based on how fast they build it, right? Like, it's like, there is crazy stuff going on to alleviate bottlenecks. But it's like, there's bottlenecks everywhere. And it really just takes a really, really hyper competent organization tackling each of these things and creatively thinking about each of these things. Because if you do it the layman old way, you're gonna, you're gonna lose and you're gonna like, you're gonna be too slow, right? Which is why OpenAI and Microsoft, partially like Microsoft is not building Stargate for OpenAI, right? It's because it would have just been too slow and they're doing it the layman old way. You have to go crazy. You have to go, that's why Microsoft rents from CoreWeave a ton, right? Because, oh, wait, we, we, we need someone who can do things faster than us. And, oh, look, CoreWeave's doing it faster. And now like, you know, OpenAI is like going to Oracle and CoreWeave and, and others, right? N scale in Finland and all these other companies all around the world, the Middle East, right? G42, like,

• 33:23Speaker 01

  anywhere and everywhere they can get compute because you put your eggs in many baskets and whoever executes the best will win. And this infrastructure is very, very hard. Software is like fast turnaround times, like, you know, it's, it's still hard. Software's not easy. But it's like, the cycle time is very fast for like, try something, fail. Right? Try something else. It is not for infra, right? Like, what has XAI actually done to deserve their prior funding rounds? They haven't released a leading edge model, right? And yet their evaluation is higher than Anthropic today, right? At least Anthropic's raising, but whatever, right? Like, it's Elon, A, and B, they've tackled a problem creatively and done it way faster than anyone else, which is building Colossus, right? Like, and that's commendable because that is part of the equation of being the best at models, right?

• 34:05Speaker 00

  Yeah, besides the talent.

• 34:06Speaker 01

  Yeah, and Elon is known for being able to get talent. So it's like, it's like, there's, there's so much complicated on the infra that, you know, it'd be nice to say there's one thing, but yeah, like, the White House Action Plan lists a lot of things, but I want like, you know, how do we concretely like, solve the talent issue? It's like, there's not enough people in trade school. The pay will go up and that'll help, but the time scales and that are too slow. Like, do we somehow import labor, right? That's how the Middle East is building all their data centers. They're just importing labor. Or is there something more intelligent we could do? Robotics, right? I think, I, I just realized today, you told me just now, like, the company I seed or I angel invested in, you led the round, right? Like, it's really cool for data center opti- uh, automation, right? Like, there's all sorts of like, interesting problems on the infra layer that could be tackled, uh, and tackled creatively.

• 34:49Speaker 00

  Speaking of like, the policy and geopolitics implication here, like, what do you think about the, you know, White House implication that America needs to like, export the AI stack? Or like, needs to control important components of it? Like, it's better for us to be exporting Nvidia chips than to foster a new industry? It's better for us to have like, a globally leading open source model, et cetera? Like, what actually makes sense to you there?

• 35:15Speaker 01

  I want to tell a crazy story. I was in Lebanon, uh, for a week.

• 35:19Speaker 00

  This is a good start.

• 35:20Speaker 01

  Yeah, this is completely unrelated, but it just popped in my head. I think it'll be entertaining. I was in Lebanon. I was with a few of my friends. So it was like, two Indian people, two Chinese people, then a Lebanese person, right? And these like, 12-year-old girls ran up to the Chinese woman that was with us, like, my friend. And they were like, "Oh, my God, your skin's so beautiful. Do you like sushi?", right? It's like, fine, you just ignorant. But what was really interesting is like, when they asked where we're from, we're like, San Francisco. They're like, "Do people get shot in the streets?" Because their entire worldview was built from TikTok.

• 35:48Speaker 00

  Okay.

• 35:48Speaker 01

  Of politics. And it's like, when you think about the global propaganda machine that is Hollywood, and it's not intentional, just American media's pervasive, it built such a positive image of America. Now, like with monoculture broken, and it's more social media based, a lot of the world thinks America is like, people are getting shot all the time, it's like really bad, and it's like, bad lives, and people are working all the time, it's unsafe, and like, you know, like, Europe has a certain view of America, and like, I don't think it's accurate. Like, random Lebanese 12-year-old had a really negative view of some, like, they liked America, they loved Target for some reason, because some influencers posted TikToks about Target, but like, they had negative views of America. It's like, from a sense of like, what is important is like, the world should still run on American technology, right?

• 36:31Speaker 01

  And they generally do still, in terms of the web, although, you know, ByteDance TikTok has broken that to a large degree. But in this next age, do you want them to run on Chinese models, which now have Chinese values? Which then spread Chinese values to the world, or do you want them to have American models that have American values? Like, you talked to Claude, and it has a worldview, right? And it's like, I don't know if you want to call that propaganda or what, there's a worldview that you're pushing, right? And so I think it makes sense that we need that worldview espoused. Now, how do you do that, right? The prior administration, current administration had different viewpoints on this, right? Prior administration said, yes, we would love for the whole world to use our chips, but it has to be run by American companies. And so it was like, Microsoft, Oracle, we're cool with you building shitloads of capacity in Malaysia. We don't want random other companies doing it in Malaysia.

• 37:15Speaker 01

  So the prior diffusion rule had a lot of technical ways in which, like, you know, you could be, you could have these like licenses and all this. It was very hard for like, random small companies to build large GPU clusters, right? But it was very easy for Microsoft and Oracle to do it in Malaysia. Of course, the current administration tore that up, and they have their own view on things. I, I, I mean, I think there was a lot of things wrong with the diffusion rules, right? They were just too complicated, they pissed a lot of people off, etc. Now they have a different view, which is, like, what did they do in the Middle East, right, with the deal they signed? Well, actually, most of those GPUs are being operated by American companies, or rented to American companies, right? Either or, right? Like, G42 operating them, but renting them mostly to like, OpenAI and such, for a large part. Or, Amazon and Oracle and others are operating the GPUs themselves in the Middle East.

• 37:59Speaker 01

  So it's like, okay, that's effectively the same thing, but in a very different way. That is still, I think, a view, right? Which is like, we want America to be as high in the value stack as possible, right? If we can sell tokens, or if we can sell services, we should. Okay, but if we can't sell the services, at least sell them tokens. Okay, we can't sell them tokens, at least sell them like, Infra, right? Whether it be data centers or renting GPUs or just the GPUs physically. And it sort of like makes sense, right? The value chain, like, give them the highest value, highest margin thing where we capture most of the value. And like squeeze it down to where like actually for like the bottom of the stack, right? Like the tools to make chips. Maybe you shouldn't sell. And so like export, current export controls and policy dictate that, yes.

• 38:41Speaker 01

  You know, it's better to sell them services, but sell them both, right? Like give the option, let us compete. And don't let anyone else win. I think the challenge here is that like, how much are you enabling China by selling them their GPUs? Like, how much fear mongering around like Huawei's production capacity is there? Like, how realistic is it versus not because of the bottlenecks of like Korea sanctions that America's made Korea put on China? For memory or Taiwan on China for chips or, you know, US equipment on China, right? Like, there's a lot of different sanctions. Many of these are not well enforced slash have holes. But it's sort of like a, it's a very difficult argument on like, how much capacity of GPUs should be sold to China?

• 39:25Speaker 01

  A lot of people in San Francisco, frankly, don't sell China any GPUs. But then they cut off rare earth minerals and, you know, like ostensibly, most people think that like the deal was that you get, you get GPUs. And also EDA software because the administration banned EDA software for a little bit, just for like a few weeks, basically. Until China was like, okay, we'll ship rare earth minerals. You can't just ban everything because China can retaliate. If they banned rare earth minerals and magnets and such, car factories in America would have shut down. And the entire supply chain there would have had like hundreds of thousands of people not working, right? Like, you know, like there is like,

• 39:56Speaker 00

  Yeah.

• 39:56Speaker 01

  There is a push and pull here. There is a push and pull here. So like, do I think China should just have the best Nvidia GPUs? No, like that, that would suck. But like, you know, can you give them no GPUs? No, they're going to retaliate. Like, there is a middle ground and like Huawei is eventually going to have a lot of production capacity. But there's ways to slow them down, right? Like properly ban the equipment because it's not, there's a lot of loopholes there. Properly ban the subcomponents of like, of memory and wafers. Because Huawei is still getting, you know, wafers in Taiwan from TSMC through like shell companies, right? Like, it's like, you know, there's a lot of enforcement challenges because. Parts of the government are not funded properly or not competent enough. And it has never been competent, right? So it's like, how do you work within this framework? Well, like, OK, fine, we should sell them some GPUs so that they, you know,

• 40:41Speaker 01

  that kind of slows them down on a Huawei standpoint, although not really, right? But also gets us back the rare earth minerals, but don't sell them too many, right? Like, how do you find that massive gray line? Is what the administration's grappling with, in my view.

• 40:54Speaker 00

  Implied in that opinion is your belief of they are going to be able to build Nvidia equivalent GPUs eventually. If forced.

• 41:01Speaker 01

  Maybe not equivalent.

• 41:03Speaker 00

  Sorry, price performance competitive.

• 41:05Speaker 01

  There's like, interesting things here, right? Like, if China has a chip that consumes 3X the power.

• 41:10Speaker 00

  But they have 4X the power, then.

• 41:12Speaker 01

  Yeah, like, who cares, right? Like, you know, obviously, there's a lot of supply chain challenges with building that. And it's like, hey, maybe it's on N minus two technology. It's on five-year-old technology or four-year-old technology. Great. And it only consumes 3X the power because they're able to do a lot of software optimization, architecture optimization, et cetera. They end up with something that maybe costs a little bit more, but like, when you think about the value of a GPU today, right? Like, you know, the GPUs dominate the cost of everything. But over time, services will be built out which are high margin, right? Like, and you can go look at Anthropic or OpenAI fundraising docs and like, see that their API margins are good. API margins are nothing compared to what service margins will be for people who use these APIs to build services. And that's nothing compared to the like, net good to the economy

• 41:54Speaker 01

  from how much automation can happen and how much increased economic activity there is. So this is the argument of like, OK, even if their chips cost 3X as much, do you.

• 42:03Speaker 00

  They can subsidize that rationally.

• 42:04Speaker 01

  They can subsidize that rationally because the end goal is like, oh, wait, actually, we can deploy a lot of Chinese AI and make money and gather data because people are sending us their like prompts and all their databases and all this stuff to our models controlled by our companies, et cetera, right? Like, plus, we're just making money off of it. And they've done this in other industries, right? They rationally subsidize like solar, and now no one can even compete on solar. Or EV, and it's like, very close to no one can compete on EVs even, right? Besides like Tesla, really. And even Tesla is adopting a lot of like, Chinese supply chain, right? It is rational to say you want to have America have more AI prowess around the world. You know, so that random child in Lebanon doesn't think America is like bad, or they're using American products more than China, Chinese products.

• 42:48Speaker 01

  But like, how you get there is very difficult. And it's a, it's a hard thread to weave.

• 42:53Speaker 00

  Thread, you got it.

• 42:54Speaker 01

  I don't croquet, you know?

• 42:57Speaker 00

  Crochet.

• 42:57Speaker 01

  Crochet.

• 42:58Speaker 00

  You clearly don't.

• 42:59Speaker 01

  Croquet is.

• 42:59Speaker 00

  Amazing. Croquet's the game. I want to ask you like a wild card, a question to finish out. We're trying to get Mark to do the podcast.

• 43:08Speaker 01

  Zuck.

• 43:09Speaker 00

  Yes. You can ask him any question. What would you ask? Mark, you gotta do the podcast.

• 43:13Speaker 01

  I thought like the like, did you read the doc, the page they put up? I thought that was very interesting that they were like, we want AI to be your companion. So my question to him is not like around his infra stuff, because I feel like I know most everything. Like you can figure that stuff out from supply chain, like satellites, all this stuff. But like, the interesting thing I'm curious about is philosophically, what exactly like does the world look like? If everyone is talking to AIs more than other people? Or if they're interacting socially with the AIs more than other people? Do we lose our human element? Do we lose our human connection? It's not the same thing as, hey, I'm posting on social media and we're interacting with our social media posts, which that already breaks the brain of a lot of people. What happens when it's like always on your face? Like meta, you know, his worldview is like meta reality labs makes these like devices that you wear.

• 43:57Speaker 01

  And they're always, they have all this AI on them and you're talking to the AI companion all the time. How does that change the human psyche? Like this human machine evolution, like is, what are the negative ramifications of it? What are the positive ramifications? How do we, how are you going to make sure that there's more positive ramifications from this than like, you know, the slopification and like complete brain rot of like our youth, right? Which I like, love my brain rot, right? Like, it's like, okay.

• 44:23Speaker 00

  Obviously, the coding wars continue to be like very central. And we were talking about cognition's relevance and like how, how to think about the strategy here. But I do think it's really funny what flipped your bit on cognition. Can you tell the story?

• 44:38Speaker 01

  I, I thought cognition and GMI, right? Like, you know, like OpenAI, Anthropic, XAI, etc. They're just gonna make better code models, like, you know, they just have way more resources. General models will win. You know, I hadn't really met too many people there. It was just like a pure vibes based thing. And I, you know, I'd used a little bit of Devon, but I was like, whatever, right? Like, I was like, cloud code seems better and we use that internally. But like, I went to Code2's East Meets West event. It's an awesome event where there's people from Asia. Like there was like, you know, all these like CFOs and CEOs of like major Chinese companies, east coast of US, all these finance bros. Also, west coast, like a lot of tech people, right? So you and I were both there. There were people from governments and major companies. And Scott was there. I spoke with him like very briefly, but then. What was interesting is like, it's like, you know, they have a poker night one night and

• 45:23Speaker 01

  everyone gets blasted. The, the like leader of Kotu is like very good at poker. These hedge fund guys are just good at poker generally. People like poker as well. You know, there's a big poker culture in, in the Bay. I was playing. I'm okay. Right. Um, but I see, I see, I just look over at the super high stakes table. Scott's just dominating everyone, right? I'm like, what is going on? Like, how are you like, you're like taking chips from like CEO of major Chinese company. I don't want to name people's names because I think there's like some terms around them, like naming who's there. But like, you know, it's like, you're, you're like winning like a lot of chips from a lot of big people. And it's like, all of a sudden my vibes were like, I don't know, maybe like, maybe he can win. Maybe he can take from the lion, you know? Uh, so I was like very excited about that. Uh, you know, I thought it was funny. Uh, I still have zero, like, I, I have not done

• 46:07Speaker 01

  much due diligence on their code product. Like, you know, like, it's like, nor have I on like Claude code, besides the fact that we use it. But it's like, you know, cool.

• 46:14Speaker 00

  Well, I think WinSurf Acquisition Part Two is like a, a pretty good hand to play here. Um, and, uh, you know, as somebody who invests a lot at a, you know, violently competitive application level.

• 46:26Speaker 01

  Yeah.

• 46:26Speaker 00

  Poker game is live, man. Everybody, you just invested live players.

• 46:30Speaker 01

  Exactly. And, and so I, I just loved that, you know, that was how he, uh, he dominated everyone. It's like, it's like, it's such a stupid reason because I pride myself on being analytical. I'd like data driven. And it's like, you know, vibes.

• 46:42Speaker 00

  Correct. For any entrepreneurs listening, I think like, you know, Dylan might angel invest or we might back you fully if you, if you win the cognition poker game. Uh, and we'll host at conviction. Um, okay. If we got it? Good. Awesome.

• 46:56Speaker 01

  Yeah, thank you.

• 46:59Speaker 00

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