Expanding AI chip capabilities beyond Nvidia with Modular CEO Chris Lattner | E1808

0:00If you go back in time, I built a technology called LLVM, which is this fairly obscure compiler technology that then is probably on your phone today and on many of your laptops and in your consoles and things like this, that technology helped unify a generation of compute around CPUs in particular. And so LLVM was great for hardware people because they could integrate with LLVM and then they got all the C++ and all the Swift and all the other languages and Rust and Julian and things like this for free. But machine learning doesn't have that. And so what modular is building is it's building that thing that once you plug into it, you have a full AI stack.

0:36For a hardware maker, that's a very powerful thing. This Week in Startups is brought to you by Roots. Invest in the only real estate investment trust that creates wealth for you and its residents at investwithroots.com slash twist. SuperGut is the only nutrition brand clinically proven to improve digestion, balance blood sugar, sustain energy, and manage weight. Save 25 % on the delicious shakes, bars, and prebiotic mix at supergut.com with code TWIST and LinkedIn marketing. To redeem a free$100 LinkedIn ad credit and launch your first campaign, go to linkedin.com slash thisweekinstartups. All right, everybody.

1:24Welcome back to This Week in startups. Really excited for today's guest because he's worked at some of the biggest technology companies in the world and working on AI. His name is Chris Latner. His company is modular. He's worked at Apple. He's worked at Tesla. He's worked at Google. And now he's got his own startup, as I just said, modular. So as we all know, NVIDIA is dominant right now in the AI MySpace,$16 billion in revenue in Q3. That's 2x year over year. They're wildly profitable. Stocks doubled since 2023. But as we've said on this pod and all in, and there's going to be competitors coming, right?

2:03Of course. And some startups are going at NVIDIA on the hardware front. We had Light Matter on recently, episode 1787. And they're trying to use optics, photonics-based chips, basically, to move data around. It's going to make things cooler in data centers and help with these large AI jobs. Well, Chris is taking a different approach at Modular. They're going to make it easier for developers to run AI modules on non-NVIDIA hardware. And they just raised$100 million, as AI companies are apt to do in 2023. Chris, welcome to the show. Well, quite the introduction, Jason. Thank you for having me. It's great to be here.

2:46yeah uh great to have you and um you are in the thick of it one of the things i hear over and over again uh from people deep in the ai space i had a conversation with elon about this not recently um and we see it at open ai and other places is only a small amount of the hardware that's being purchased is being used at any given point in time when ai jobs are running so for people who are technical but maybe not working in the specific field why is it that when we push a job you know we're doing chat gpt5 or claude 7.0 whatever people are doing they're doing a lambda or a llama i mean there's just so many different things on hugging face right now why is it that so the the hardware is not optimized to these jobs why are we find ourselves in this and then what is the actual percentage of the hardware being used, whether it's an H100, A100, or my M2 on my MacBook Pro.

3:43Yeah, so it's super interesting. If you zoom into what is AI these days, right? So many people focus on training. You have to start with the research, you have to start with the models, models are changing all the time. I mean, just follow what's happening. It's hard to keep up with the pace of innovation in the model architectures. But then there's also the inference side of things and the deployment side of things. And so these two markets, these two problems are actually completely different. So what you're talking about is you're actually referring to the training side of this. And modern training jobs, as many people know, have gotten huge, right?

4:14You get tens of thousands of nodes, thousands of GPUs. These are monstrous jobs. And so because of that, what you get is these time-sharing systems. And so it's super funny. Like we went from personalized computers all the way back to the mainframe or the job sharing. Like I'm going to put in my punch cards. That was Perot systems. Yeah. And so we're back on somebody's mainframe. Well, yeah. So we're back in those days. And so the actually better analogy, if I'm not joking about it, is HPC systems. And so if you go back 10 years ago or something, you'd get one of these massive supercomputer systems that a national lab would install.

4:50And then researchers would have to walk up and allocate time against it. Right. And so the big question then is how do you amortize the spend for the hardware across a lot of work that happens on any one of these massive supercomputers. And training systems today, they're massive supercomputers in every way, shape, and form. The programming models are very different. The workloads end up being a bit different. And so there's some differences, of course, but the way they get managed is very similar. Now, what I've seen is different groups that own these things manage them sometimes better, sometimes worse, right?

5:22And one of the challenges you'll see is that, for example, the big research teams may allocate 20 ,000 GPUs or something. But then the question is, how do you fully utilize it? This is one of the cases where timesharing, like clouds, are actually really great because often you're not training models all the time. Your model training is actually proportional to the research cycle that you've got going on. And so if you're one of the massive companies like Google, where you have thousands and thousands of researchers, what you'll do is you'll have this big hardware pool, and then you'll have the researchers that are all effectively putting in their slot so they can use the machines when they come up and then they run their batch job for perhaps hours, perhaps days, perhaps months, right?

6:05And they get allocation for it. But if you get these smaller groups where sometimes they're on cloud and so they're just renting by the hour, sometimes they build their own data centers. And then the problem they have is, okay, cool, you have all this hardware. How are you utilizing it? Is it being productively used? And so these are major questions I think that the entire industry is struggling But if you go just adjacent to that, that's training, that's where the models come from. If you go to production, the character is completely different. And so here, you're not talking about supercomputers.

6:36Here, you're talking about the fact that, you know, you may have tens of researchers that train a model and they use a massive amount of hardware to do so. But then you need to deploy that model. When you deploy the model, the problems are completely different, right? Here, the problem is you have a billion users. and a lot of queries and then a lot of follow-up queries and people want to i guess i'm not sure what it's called when you well there's prompt engineering and the prompts are getting more sophisticated so all that creates load on the system yep and the load on that system is really different instead of it being one massive computer that is then batch scheduled what you need is you need scale out and so any one of those systems is actually a single node often but now you need thousands and thousands and thousands of these nodes and those are fully utilized right because you got users in 24 time and all the time zones, right?

7:25And so that's actually a very different problem. And it's super interesting. And so if you look at AI today, it's super fascinating to me how much energy has been put into the training side. Everybody's always talking about the research models and the training and the training and the training. Few people talk about what it takes to get that thing into production. Yeah. And one of the big challenges that we as an industry are facing today is that, you know, the, these systems that people build with like tensorflow and pytorch and these kinds of things were always built by the research team for training and so getting that model in production is super difficult and this is almost an unsolved problem these days and one of the challenges there in particular is it's not just about cloud right often you want to train a model and then put it on a phone right and so that's a very different problem space and it's much harder than um some i mean it's very both of these problems really cool but it's explain to folks after all the training has been done and then you have this language model um and uh you then want to load it onto a phone how does that all work what is the output and how would you explain it to you know a lay person of hey we built the model but now we want to distribute the model to a bunch of different places and then let you play with it but what is required there so um i don't think that it would be in good taste to talk about how we do this because it is so complicated and nasty and un and horrible that we cannot go into all the details but i'll give you a sense yeah because that that's that's how i am right so so if you take a traditional enterprise that's building ml into their products right often they're not building one model into one product right so they have some they have many different kinds of models, some recommender models for like, hey, maybe you should look at this in your shopping cart next.

9:07You have classification models. So you're looking at, okay, well, you like that shirt? Like maybe you should pick this shirt. There's many different kinds of products. They then get matrixed into many different kinds of things that they're deploying into. So often cloud is a big deal, but then you have mobile apps and a lot of other things. And so what has ended up happening is that deploying ML today involves building this entire matrix of all these point solutions because there's no one thing that allows you to span across all of these things and so what you end up using is like this catastrophic array of like 15 different tools and all these tools have different problems like so i i'm i'm a apple sort of an apple alumni i uh have a ton of friends there yeah the uh easy to use programming language for building apps and so so i love apple and i love the apple folks but uh to deploy ml onto an apple platform you have to use their point solution called CoreML.

10:02And CoreML is not compatible with all the models. And so there's all this friction just to get onto an Apple device, right? And so Apple devices are pretty common out there. And if that's hard, you just think about what it means for this wide spectrum of different things. And one of the challenges here, the fundamental, the incentive structure problem is that hardware makers like Apple, like many other hardware makers, always want to build a solution for their hardware. And nobody's trying to build something that scales across everything. And so this is what we're focused on. Hey, everybody. Today, I'm joined by Roots CEO Dan Dorfman.

10:35Dan, welcome to the show. Thanks for having me, Jason. Tell everybody here in the audience, what is Roots? And what makes it different than the other real estate investing platforms? I'm a complete neophyte. Roots is a REIT with a little twist. Sorry, I had to do it. We are the first real estate portfolio that we know of that builds wealth for both our investors and our residents. And we've created a unique win-win model that creates partners and not tenants. Am I as an investor, if I wanted to put money into this, getting dividends or am I just getting the growth of it? How does all that work?

11:07When you invest with us, you get to participate in two ways. One is through the distributions of profits generated at the company. And we pay those out quarterly. Over the last 12 months, that's equated to about a 6 % cash on cash return to our investors just in distributions. And then the other way everybody participates is each quarter, we reevaluate what's called our net asset value. And as that ticks up our unit price or our share price of our portfolio goes up as well. And that's how you would basically be able to sell your share at any point and liquidate your investment and move on to your next piece or leave it in and keep growing with us.

11:49head to invest with roots.com slash twist to sign up and start investing today. That's invest with roots, no spaces, no dashes.com slash twist to sign up today. Because Nvidia has CUDA, right? That's their software for writing their machine learning apps. Apple has theirs. And these two things are just Google has theirs. Tesla has theirs, is like everybody builds their own thing so um if you go back in time um why does everybody build their own things is it just because it didn't exist before or because its customization is necessary to get the you know end result they want well because they don't have a choice functionally right and so it's super interesting i mean ai is so important to what we do right nobody takes a step back and says if ai is so important for the industry why is all the ai software so bad right and so you look at that is it a function of time we just were so young in the game yeah that's that's a big aspect of it so i the the analogy i give to people is that ai is like an adolescent like it's like a teenager right it's it's uh it has some it's very exciting it's overconfident it's got some wins under its belt it sometimes rolls over its parents car and causes a mess right but what's happening right now is everybody just wants AI to grow up.

13:07People want to build AI into their products. They want to not mess with the AI infrastructure. They want to actually be able to deploy things and build AI-enabled products. Right now, if you're one of the FANG companies, for example, you can take a team of 50 people and brute force it. But if you're many other people that should be using AI in their applications, it's so much more difficult. To your question, why does everyone build their stack? They don't have a choice. All of the technologies that exist today are built for a particular piece of hardware, or they're built by a research team, the stuff is not production quality.

13:41And if you go back in time, I built a technology called LLVM, which is this fairly obscure compiler technology that then is probably on your phone today and on many of your laptops and in your consoles and things like this. That technology helped unify a generation of compute around CPUs in particular. And so LLVM was great for hardware people because they could integrate with LLVM and then they got all the C++ and all the Swift and all the other languages and Rust and Julian and things like this for free. But machine learning doesn't have that. And so what Modular is building is it's building that thing that once you plug into it, you have a full AI stack.

14:19For hardware maker, that's a very powerful thing. And what's NVIDIA's take on what you're doing? Are they supportive of what you're doing? Or do they feel like what you're doing they're not supportive of because it's going to help you know people maybe port to other hardware platforms and maybe take away their dominance or do you get the sense that they care about their dominance at this point i mean they seem to have run away with it right now yeah well great question so i mean there's this narrative in the industry that we're here to hurt nvidia or something nvidia is one of our most important partners right and and and one of the things that i think people forget about is nvidia is really invested in building some really crazy exotic next generation products yeah right and so what we're interested in doing is we're interested in expanding the developer ecosystem that can use those products so we're on a very complementary set of missions here right and so what we're doing is we're looking at saying okay well this whole ai thing it evolved rapidly again it's very high potential but it's all a mess like the people who do it as you know are wicked smart you're some of the most brilliant people in the industry but there's other good people too that have good ideas right and so if we expand out the developer community if we 10x the number of people that can participate think about the amount of innovation that can happen there think about the new use cases and applications yeah right now people don't actually know this but a lot of what's happening in ai is limited to people who can code in um cuda cudo what is it uh yeah cuda yep and then i guess some people write in c sharp or c plus plus what are the other ways people generally get ai code you know down the hardware sack because you're you're building mojo i know which is yeah yeah you know more python like i think yeah well we'll talk about that um so it really it really varies and again ai is not one thing this is another thing that i think people get sometimes distracted by but it's not like transformers are one thing for example and so if you look at a lot of model or uh like stable diffusion which is a unit model which is a very different architecture what you get is a lot of python on the outside the python handles what's called tokenization of converting input text into something the model can understand you then get something like pytorch or tensorflow involved which is itself a gigantic complicated thing that is awesome in some ways but also challenging in other ways you get custom cuda kernels as you're saying so you want to get high performance out of one accelerator and so when you get c plus plus because sometimes python is really slow and so what ends up happening is a developer building one of these next generation models, you have to know all of these different things.

16:55And so, practically speaking, no sane humans actually can do that. And so this is why you need teams of experts. And these teams are super experts in every single different one of these parts of the problem, where somebody knows model architecture and differential equations, somebody knows Kuda, somebody knows C++, somebody knows all these things. And so only that is what's able to bring these things together. Which we've seen this movie before. in the early days of the web setting up a web server itself getting a sun microsystems you know server you know it wasn't like today uh obviously uh and uh remember when we had apps come out even pre-iphone if you were trying to build something for nokia or docomo or any of these other platforms around the world it was really hard and there was a limited number of people who could do it which meant you just didn't see a lot of apps they would come very slowly a couple of apps a year they were super interesting uh but then and they're expensive too right because the development costs are so high yeah which means something that's fun or interesting like the idea that there would be an app for skiers like i have an app on my phone for skiers called slopes there's like probably a half dozen of the fact that there's a solo developer or two person development team on their weekend hustle building an app it's just a crazy thought i mean you were at apple when this happened the concept that an app could be made by one person in their spare time and get to a million dollars in revenue or even a hundred thousand revenue ten thousand revenue was just there were so many hurdles to that you had to actually do deals with the carriers you had to put up servers yourself you had to figure out how to get distribution on yeah getting the app the distribution on people's phone was a roadblock you just think about the genius of steve jobs the app server distribution the the payment rails uh for people buying it and then the you know there's really lightweight easy uh app discovery and the ability to write them so you're working on Mojo.

18:45This is a programming language. Well, just before we move on from Apple, right? So my job at Apple was to lead the developer tools team, right? I mean, I had many hats, but by the time I left, I was running the developer tools team with Xcode, the whole iOS app development ecosystem, built the Swift programming language, also supported all of the internal hardware, which Apple has very fancy, very exotic, and next-gen hardware that they're building. And a major part of the job is to make people more productive. Make it so more people can participate, exactly as you're saying, because so many people have good ideas for apps, right?

19:20And so if you get more people involved, like the move from Objective-C to Swift, massively simplified things, made it much easier to learn. That was a huge movement that then enabled entirely new categories. And so many people today tell me, you know, I was able to become a programmer because of Swift, right? And so ML, I believe, has got exactly the same thing going on, right? Where it's absolutely possible for the most advanced teams to achieve things, right? But first of all, like complexity, which is really our enemy here, complexity, like if you fill your head with accidental complexity, you don't have space for other stuff.

19:53And so by relieving the accidental complexity, you make the teams of experts even more productive. But then you're also more inclusive to other people who have good ideas, but either are, you know, repelled by the complexity. What are the strategies for getting rid of complexity? I mean, I'm just thinking about playing chess. You kind of learn some heuristics, you know, some basic sets of moods, chunks of moves that you can apply in different places. Or, you know, we have co-pilots, which, you know, and we have open source. We have a lot of different ways to help people with complexity. But when you look at complexity in the world, what do you think of?

20:28Do you have a playbook for reducing complexity? Yeah, absolutely. So, and this is one way that modular is very different than pretty much everybody else in space, but complexity comes through abstraction or reduction of complexity comes through abstraction and through getting people to be able to work together. Okay. And so the idea here is that you look at all the domains of people that are involved, including all the people putting together the transistors on the chip, right? There's so many different specialities that the details can't fit in any one head. So success comes from teams of people, right?

21:02And then composing on other people's work. And so a lot of what I think software has been successful, I mean, you've built some pretty epic systems, right? Yep. It comes from being able to take things that other people built that you don't have to understand and then build new things on top of it, right? And so what a lot of folks are doing today in ML systems and ML ops and a lot of these things, they say, okay, well, there's so much complexity out here. What are we going to do? Well, we're going to throw a layer of Python on top of the stack. And then you'll deal with our layer and look, look how simple it is.

21:35Therefore, you don't need to know about any of this complexity. Now, there have been dozens or hundreds of attempts at this. I mean, there's a lot of stuff out there. Some of it's really good. But the challenge with that is if you're building on top of something like TensorFlow or PyTorch or, you know, you're trying to get onto novel kinds of hardware and like a TPU or something like that. Well, you actually get exposed to all this accidental complexity because it all leaks. and so yeah you get this cool demo but you can't fix performance or scalability or programmability or security or like these core problems that people struggle with by adding a layer of python on top of systems that are fundamentally broken yeah the facade doesn't work and in a way what we've seen happen in the modern web uh over time you know you have cloud computing abstracting away putting up servers uh and that and then storage got abstracted i mean gps got abstracted away there's a software development kit and sdk for anything there's an api for anything and then even building glue between systems um has gotten easier used to call it middleware i guess back in the day i don't know if there's still a term for that but enterprise java beans yeah it was like weird stuff to try to get you to move data from one system to the other it seems like comical now let me just talk about the complexity in the world writ large and in the technology stack because you've been at this for a couple decades it is pretty amazing when somebody's coming in now a 20 year old developer in school who is like building stuff how much do they know about what's actually going on beneath you know you know you see the little tip of the iceberg which are they even aware of like the complexity underneath yeah well so i mean again And it's hard to make generalizations about all 20 year olds because there's some variance there.

23:23But on the average 20 year old, on the average 20 year old, they know Python. Yeah. They know if you go into computer science, you know how to train a neural network, for example, but you don't know how to deploy it. Right. You get exposed to some other programming. Maybe you'll get a little bit of C++ or something like that. But most of most people coming out of a computer science degree know Python. and pretty much everybody that is not designed to be a computer scientist. So there's a lot of other fields out there. No Python. Right. And so Python is great because it's super high abstraction.

23:57It's like the ultimate duct tape language where you can bolt together these very powerful libraries. But Python also has certain challenges when it comes to performance or dealing with hardware or a lot of the things that inhabit the AI space. And so running Python on a service with a billion users is not always great, right? And so there are challenges there. And so, I mean, if you come back to what is modular doing about this, what we're tackling instead of adding layers of Python on top of existing systems, we're saying, let's go explode those systems. Let's do the hard thing. Let's go build the system from the bottom up.

24:32And this starts at the hardware, right? The hardware, there's a lot of really good hardware out there. To your point, nobody knows how it works. I mean, the people that built it do, but most application developers don't know how it works. And what has happened is that right on top of the hardware, there's all these different layers of effectively middleware, just like you say, right? But each piece of hardware has a different layer of middleware. And so that means that when you get to the top layer, the part that anybody actually wants to work on is super fragmented. And it makes sense. It's the insane structure of the people building the hardware.

25:03They want to build a thing for themselves. But the losers are all of us trying to get our jobs done. Many people in ML don't want to care about the hardware. They're made to care about it. You've heard me talk about Supercut a bunch. This has been a key part of my health journey. It's an awesome nutrition company that my bestie, David Friedberg from the All In Podcast started. I love their bars. I love their shakes, especially the gut balancing chocolate brownie bar. It is delicious. They also have an unflavored prebiotic mix you can add to anything. I like to put it in my coffee. You can put it in your own mail.

25:36their products are super helpful for weight loss. Why? Well, Superguts products mimic the effects of Ozempic by boosting your GLP-1 hormone. This helps quell hunger and boost your metabolism, which is a great, great combination, obviously. And Superguts prebiotic fiber, that actually alleviates digestive issues. And obviously, the products all taste great. The best part, the team at Supergut actually put the work in and scientifically proved their products work. They conducted a placebo-controlled clinical trial with Stanford last year. That's been published in the medical journal Diabetes, Obesity, and Metabolism.

26:12The results were amazing. The participants in this study, they lost weight, they lowered their blood sugar, they improved their metabolic health, and they had improved digestion and so much more. Whether you want to improve your gut health, maybe drop a few pounds like I did, or just feel better throughout the day, and listen, you're busy, you're traveling, I like to bring SuperGut with me, go to SuperGut.com and use the code TWIST. You get 25 % off. Go to supergut.com and use the code TWIST to get 25 % off. I've been on this health journey. I've lost 40 pounds. A big part of that, sincerely, was me using SuperGut.

26:40So go to supergut.com and use the code TWIST for 25 % off. What is this hardware going to look like in five or 10 years? Because we're at this point in time where what OpenAI did with, I think, 3.5 really kind of captured people's imagination and being able to actually play with it, inspired a lot of developers to maybe get in there um and so here we are everybody buying up sovereign wealth funds you know governments countries uh you know individuals companies startups everybody buying up all this hardware racking it data centers and um it seems to me um having watched this happen with fiber yeah uh you know we overbuilt fiber massively and then all the fiber companies world comm etc uh there were a ton of these uh went bankrupt they became worth literally 98 99 percent less than they were when they went public and all that wound up getting bought by google and other people at auctions are we at a similar moment right now where we're building up massive capacity or do you think there's enough jobs here to actually use this hardware and then the second part of the question so there's something about like this moment in time uh where does this all wind up if we're sitting here five years from today are we looking and going hey wow there's somebody just leapfrogged nvidia or there's three choices you can go just like you do android or you can do an iphone or you can you know pick aws azure or google or rackspace or yeah right on down the line yeah well so great question so there are really two different questions two different questions one question there is the today problem right and today problem everybody's talking about nvidia and the stockouts of nvidia and wouldn't be great if there are other options it's super funny because the majority of spend by many by many metrics is actually on the inference side which is still very dominated by cpus yeah and again like we we talk about the the pain point well the pain point is people trying to build these massive systems and there are not enough gpus to go around but meanwhile so much ai is in our life that's all being served in cloud a lot of that's happening some of it's on gpus and cloud but a lot of that's on cpus right and it works totally fine it works totally fine if you're on amazon and it's showing you some additional products like the one you're looking at in all likelihood that is in machine learning job that's being done on a cpu that was written five years ago or 10 years or if you do google search query there's dozens of models all talking and doing weird things and and there's this intricate dance right and so and so it's really interesting if you look at that like the your question about is there going to be an oversupply and overabundance i have no way to know right my goal is increase consumption by creating new categories right and so and it has nothing to do with h100 or nvidia it's just about ai and the applications of it are like a good thing it makes people's worlds better and so if we can increase the number of cool things and make our lives better that that seems good to me now your question about where do we go from here right so forget about forget about cloud for a second like so i've been working in the harbor software boundary for decades now.

29:46And the thing that when I zoom out and I look at this time, it's been super interesting. People talk about Moore's Law ended, you know, whatever. And what is Moore's Law? Well, different nerds will argue pedantically what that means. But it really means, you know, back in the day, we'd get a new laptop and every year would be, you know, 18 months to be twice as fast. Your Pentium chip was twice as fast. Absolutely. On the same code, right? And so And so what ended up happening, I don't know, 10 years ago-ish, is we had multi-core CPUs. Ah, we have more than one of these to deal with. And then we had GPUs come on the scene.

30:21Yep. Right? You look to now, we have massive GPUs. We have really dedicated AI chips like the Google TPU and Gaudi from Intel and all these things. There's tons of these things. And we still have CPUs. But these days, CPUs have like 100 cores on it. Right? Right. And so to me, again, many people are laser focused on the today problem. Yeah. But what happens when you look out five years or 10 years? Yeah. Right. And to me, I look at this is driven by physics. This is not a question about software or things like this. Physics is forcing hardware to get weird. And more importantly, specialized in the rise of wearables, the rise of personal computing, the rise of all like AR, VR, like all these things are a straight line towards very customized chips.

31:08and so that's very interesting yeah yeah and so we're gonna have all i mean we're gonna have even more crazy hardware in five years than we do today and this is where you start to say like how can we scale the software right nobody's going to be able to rewrite everything for every new generation of hardware that that doesn't work and this is this is why we're focused on solving this problem what do you think of the open source um risk five and you know amd licensing models and then hardware being built by other folks obviously nvidia outsources their hardware in terms of how it's being and they're a designer as well but it's proprietary and it's closed um so is what happened with python and other open source and you know everything we've seen in the open source community is is that likely to happen with hardware um or is that um you know great question so immediately before modular i worked at a company called sci-fi and they are the inventors of RISC-V.

32:05Yeah. RISC-V is an open source instruction set. And so what RISC-V allows you to do is it allows any hardware maker to create a member of the RISC-V family. And what that means, most importantly, is you get software. And so that is huge. Traditionally, you'd have, for example, ARM owns the ARM instruction set and only ARM and its licensees can build ARM-compatible chips. Yeah. Or XA6, you can have Intel and AMD, and they're the only ones allowed to build XA6 ships. And so with RISC-V, it allows you to go build. Arbitrary people can invent new things and play there. And I think that this is causing an explosion of innovation.

32:44And again, the challenge with that, and the good thing about that is you get an explosion of innovation. The challenge with that is you get all this crazy hardware, right? And so there's no software. And so you need software that can scale on to all this innovation. and so that's really where kind of the the industry is at loggerheads in yeah and so amd and these folks they they have blueprints but they own those blueprints they're they're their patents you can't just take them and build a house with them if we're just using an analogy here but if you take the risk uh five uh do they call it risk five or risk v that's five the nerdy on that is that there's four things before it so yeah yeah no i kind of got that i've heard somebody say risk v and i'm like are you sure it's risk v or it sounds like it was five um it's definitely five it's it's basically caught up to arm i think in terms of throughput or it's close enough um so so with any of these things it completely depends on what you measure there's advantages to arm there's advantages to risk five it's all super nuanced and a lot of people want to make overly simplified does this is this thing better than this and in tech it's never really that simple.

33:51And so ARM has got a very strong position. They certainly have some challenges. They've got to stay on their toes. But really, the innovation is the piece that I care about, right? And I want to make it so that once these people invent really cool RISC-V-based silicon, or ARM-based silicon, or whatever, right, that they can actually do something about that, right? Because having cool hardware that nobody uses is really kind of a problem right now. All right, listen, when you're selling to business-to-business buyers, you really want to get your pitch in front of decision makers. Why? Because upper level execs are usually the ones making purchasing decisions.