The GPU Myth: State of AI Compute 2026 | Stephen Balaban

32:29Okay. So like that complex coordination between we've got a bunch of bare metal systems to, Hey, we've got a virtualized system that has, you know, what's called RDMA, you know, RDMA remote direct memory access that allows them to read and write quickly, not just from the disks, but from each other's memory, the GPU's sort of HBM memory, and allow them to do that sort of direct memory access, allowing it to go directly from a GPU to another GPU without getting copied to the CPU, for example. Having that all work is an immense, immense software undertaking. And going back to the original question, it's like, well, what are people not getting about neoclouds?

33:19Well, first of all, the answer is that most neoclouds don't have this kind of technology. Most neoclouds have not made the really it's like kind of high tens to hundreds of millions of dollars of software investment that you need to make to build a real cloud system that can partition a high performance computing environment like this. And so like that is, and then to have it all work with the storage. Anyways, I guess that sort of summarizes the steps that you need and kind of, you can think about all the different moving parts of a modern, like how does an AI data center work? People talk about AI data center, but really you have to kind of go down that one next level down, which is, because if you were By the way, if you were to ask an AI data center landlord, a traditional one, what's going on inside of the data center, they'd be like, well, look, we're real estate people.

34:15And we really outsource this to the GC, but the GC doesn't know, of course, anything that's going inside. It's their tenants who know. So this is what's actually happening inside of an AI data center. And then it serves the result. Also, going back to the community stuff. If people knew a lot more about like, well, this AI data center is actually just serving the chat GPT requests that I'm giving it. Right. Like sometimes they don't even realize that that's actually what an AI data center does. So you mentioned tenants. Do you rent them? Do you also own some, building some? And what does that fit in the overall strategy?

34:54Yeah. So, you know, initially we started off as being primarily a renter and we've actually started to get into the business of financing some of them, the construction of them ourselves, as well as, you know, we're going now into full vertical integration where we are identifying land, coming to the table with a basis of design, which is basically all the engineering diagrams to construct the data center. financing and constructing that data center, putting the servers in, and then associating that with a long-term offtake agreement with one of the major compute consumers in the world and financing it all.

35:37So we're getting to full vertical integration at Lambda. And it's been great because we've been able to kind of, again, bring that engineering mindset to this problem, which was historically mostly run by people in real estate. In your own data centers, are you the sole tenant or is part of the idea that you can also rent some to others? In a lot of our data centers, we are the sole tenant. In terms of the data centers that we're planning on constructing, we don't yet have any plans to lease that space to others. So we're not trying to get into the leasing data center business. Maybe that's something that you can imagine down the road.

36:17I wouldn't write it out completely. But for now, we have to focus on providing Lambda with the compute that we need to service the market. How international are you, by the way? I'd say that we're very much focused on North America. And so we have data centers in Canada, United States, and Mexico. We're very much, like I say, primarily focused on North America, but really within the United States, obviously. And we haven't had this desire internally to try to go and expand into Europe or too far into Asia. We've done some partnerships with some of our great investors like SK Telecom. And we have a data center that we've operated in Korea and Seoul.

37:06And so we have some experience with international. But right now, we're just like, look, let's focus on the U.S. market. it's where the opportunity is. Do you need for performance reasons to be close to the customer the way you need to have regions in cloud? You know, it's super interesting. I get this question a lot and people, they're like, well, does latency matter? Does, so I'll tell you what matters and what doesn't matter. You can look at your own utilization of whether it's chat GPT or Claude or Grock or Gemini and you can see, hey, a lot of the things that I'm doing, I kind of shoot it off.

37:45I come back later and there's a research report for me. Maybe it's a long running agent workflow. In those cases, latency doesn't matter at all. The only thing that matters is your cost per token. That's all that matters. And so that's been a really interesting change. I think that, you know, the old school, traditional legacy cloud business was so latency focused because of some of the applications. But this new fleet of AI applications are far less latency sensitive. So that's one. But there is the caveat, which is this governance and data governance is becoming an important thing. And a lot of countries are wanting to have the AI compute that their citizens are using be run out of their own country so that they can, you know, at least have their own perception of control or whatever and the you know that is that is another that is an element to it but i'd say that from the latency there's no technical reasons let's talk about the financing stack so presumably it's a commission of equity and debt how does it all work yeah so um the way that it works is that you you know you can really fragment it into this two parts which is like financing your on-demand cloud versus financing an offtake agreement, which is like a longer term commitment.

39:08And on the on-demand cloud, you're kind of looking at Lambda's credit quality. On the offtake agreement, you're kind of looking at the credit quality of the end customer who's paying the bill. And so what you do is you just take your offtake agreement, you take this chunk of GPUs that you're deploying, you take a lease or the property and you kind of put it into a box and you can go to the private credit markets and you can come up with an asset-based loan. There's a variety of different methodologies for financing it. Most of it is just some sort of special purpose vehicle that's designed to finance this particular deployment with a very known and easy to underwrite, which is basically just a fancy way of saying the finance term for just assessing the risks and the downsides of a particular credit investment.

40:08And there's a vibrant private and there's a vibrant credit market for that. On the on-demand cloud side of things, it's not quite as mature as when there's, for example, an investment grade off-taker agreement. But it's becoming more and more mature. And in general, creditors and lenders are really starting to understand the value of an NVIDIA chip. Because, you know, you actually look at the chips that we deployed in 2023, H100s. We're now leasing those out at a higher rate now than we were originally in 2023. So these creditors are starting to look at these assets and say, wow, this is an asset that is very valuable and also easy for us to underwrite.

41:02And of course, while they are underwriting towards the actual cash flows that are coming out of that agreement, just as an asset class overall, people are realizing that this is a really great opportunity. And so creditors are starting to flock to these deals. You're renting an 800 at a higher rate because, why? Because the demand for compute is so rabid that people will take any or the technical depreciation of the product is slower than people thought? What drives that? Well, what's driving it, I mean, certainly it's the demand being high increases the price that you're able to get in the market.

41:41There's no question about that fundamental law. Again, going back to what people didn't understand about this market. There was people who were saying, oh, well, there's a five-year lifetime or three-year lifetime. I even heard some people say three-year lifetime for these GPUs. This is completely false. We have GPUs that we've commissioned, and we're one of the earliest neoclouds. In fact, we're probably the only neocloud that actually has GPUs in our fleet that are fully depreciated from an accounting perspective, which is most people are adapting around a six-year accounting depreciation schedule.

42:17But that's not the usable life. The usable life is longer than the accounting depreciation schedule. And what really matters is the economic usable life. And so what we're starting to see is that like the people who are the naysayers, oh, this is going to be you're going to throw these GPUs out in five years, are completely wrong. They're completely wrong and they've been wrong the entire time. Do you think there is going to be or do you already see happening some kind of financial market for compute units, you know, with trading and derivatives? Is that happening? I'm starting to see some people start to examine what a maybe vibrant spot market – first, you need to have a spot market for something before then you can establish a derivative like a future or other more exotic things.

43:07I'm starting to see that. But fundamentally, I think that the asset class is just starting to mature and creditors are starting to become very comfortable with investing in the credit side of buying NVIDIA GPUs and deploying them into data centers. And we don't need to get too fancy with it. That's kind of like part of my opinion is that I think that that market is starting to mature, that maybe an eventuality is having more complex securities that surround GPUs. But I think for right now, people are starting to realize that it's a great credit investment. And that's what's changed, I'd say, over the last year, is that people have started to really treat it like a more mature asset class.

43:54Maybe quickly just go back to the very origin, because I think you've been effectively in the AI world the whole time, but are coming from a very different angle with multiple pivots. What did you start with and when? Well, you know, with the complexity of the business, you can now see the complexity, the capital intensivity, just the sort of not fitting into a box. And you can see why we've oftentimes not had a lot of traditional venture investors in Lambda. And, you know, all of our investors have done exceptionally well, but they've kind of come from more often than not outside of traditional, let's say, mainline Silicon Valley VCs.

44:34And so just going back to the origin story, I started Lambda in 2012, and we were a facial recognition software company. So I was training convolutional neural networks to do face and image recognition. And we eventually hosted that on API. I was training those ConfNets on a 4X NVIDIA GTX 580 workstation that I had bought from a friend who had built it, actually. And this was, you know, really pretty avant-garde stuff at the time. Most people didn't really believe in what was called the field called deep learning at the time. And that was inspired by the ImageNet 2012 moment or that was even before that?

45:24The ImageNet moment, you know, I pulled the CUDA ConvNet repo off of Google Code. That's how you know how old Lambda is, is that Google Code was still around. And I pulled the CUDA ConvNet code base and was like playing around with it. I got very lucky that the AlexNet paper had been published the same year that Lambda was founded. It's not a coincidence at all. It's not a coincidence at all. We launched this face recognition API, got a couple thousand users, but it wasn't really generating a ton of cash. And sort of as part of that complex story of startups, you know, in parallel, I was sort of I found these guys who had just graduated from their PhD programs.

46:08programs, the gentleman, Zach and Nico. And they had said, hey, we're going to start a company. I said, hey, let me help you guys out. I'm going to work with you for a year. I'm going to learn a little bit more about neural networks. And I helped them out on this company. I helped them get a company called Perceptio started. And I was the first employee there while I was running Lambda. And we were running these confinets locally on the iPhone. And again, this is 2013. So we were using the GPU image library and just straight open GLES shaders, like the shaders that are used for rendering. We were using those to run the confinets on the iPhone.

46:53And eventually, I kind of left to go continue to work on Lambda full time. and probably about a year or so later, they got acquired by Apple. And so if you know the feature on your iPhone where you swipe up on an image and you can recognize faces and search through your library, that's maybe some of the stuff that eventually got integrated into iOS through that acquisition. And then Lambda, we continued on. We had a variety of different products, everything from Lambda Hat, which was a baseball cap that took a picture every 10 seconds with a camera embedded in the tip of the brim for gathering data sets for image and face recognition.

47:30Which is fascinating because fast forward to today and that's a whole segment, right? Like capturing everyday life to train the AI. It goes to show you have to, you know, it's one, it's important to be able to see the future. It's also important to get your timing right as well. Right. And now it all worked out, right? Despite maybe that Lambda hat product not being great, but it taught me a lot about how to build hardware. where I lived in Shenzhen for a little bit, working on the PCB and spinning the PCB and designing the actual hardware product. And it taught me how to make consumer electronics.

48:08And that was actually a huge, huge skill because it totally opened my mind to new ways of doing business that aren't just making apps, right? And eventually, we had this product called DreamScope, which became really popular in 2015 and 16. And it was basically using the Google Deep Dream methodology of using a ConvNet to generate images. It's like an early version of MidJourney or whatever. And Deep Dream and the Leon Gatte's style transfer algorithm allowed you to turn a photo into a painting, basically. And we got like a million users on that process, tens of millions of images, maybe 15 million images or something like this.

48:54And that caused us to have a huge AWS bill. It was like$40 ,000 a month or something. And so to replace that, we'd ended up building a little cluster out of workstations. And then there was a$60 ,000 CapEx that we were terrified to make, by the way. We were so, so scared that doing this CapEx was going to put us out of business. We made it out of workstations because we thought, oh, well, worst case scenario, we can just sell them. And so lo and behold, we did end up, you know, turned it online and it brought the bill down to zero. So it paid itself back in a month and a half. And we thought, oh, this is like, we're saving more money than we're making.

49:32Maybe we should be in the business of providing compute to other AI researchers. And thus, we started selling workstations and servers and started developing our cloud platform, maybe did$3 million of revenue in 2017, that first year selling workstations, then 10 million in 2018, then 30 million in 2019. we grew the hardware business over the next couple of years to probably about$200 million run rate. And then the cloud business, we really started in 2019. And we started development before then, but we started really marketing it. And it kind of was slow to grow, to be honest, because not a lot of people in 2018 and 19 and 2020 wanted a bunch of AI compute.

50:16There was a pretty niche market for it. But eventually, you know, our cloud business continued to grow. And now it's at, you know, a little bit under a billion dollar revenue run rate. We've fully exited the hardware business. And so, yeah, Lambda's got a absolutely wild founding story to summarize. Are some of the people that were there at the beginning still around? I think you started the company with your brother. Is that right? And your brother is still at the company? Yeah. And so in terms of like the early people, basically, it's not, I mean, not even basically of the four people who are making DreamScope, me, Michael Balaban, my co-founder and fraternal twin brother, Chuang Lee, who's our chief scientific officer, and then Steve Clarkson, who's an engineering leader at the company and, you know, has a bunch of folks reporting into him.

51:07Now, you know, they're all still at the company. the next hire one of those gentlemen named Mitesh Agrawal who was one of the next hires in that team he was with the company for maybe eight years or something like this

51:25and then he eventually left and joined another former Lambda team member Thomas Summers to start Positron which is an accelerator company and they're like now valued at over a billion dollars and so not only has like the original team stuck around but we've already started to kind of see what like a Lambda alumni a Lambda mafia network looks like in in the world Lambda lab member alumni How did you keep the band together during the difficult times? Just when you're running a startup company that's this capital intensive, working capital intensive as well. Like it's just you get a lot of shocks to the system as you're growing.

52:16And then COVID. What COVID, I mean, in April of COVID, software companies were feeling great because they could ship software and there was so much more demand. Hardware companies, the docks were closed. You couldn't ship revenue. in April and March. And so, I mean, I remember all these things really distinctly. I think I remember just getting in front of the team like, hey, look, it's really tough right now. And there's certainly a feeling that we're not sure if we're going to make it through this, but the only thing to do is just to suck it up and enjoy the pain, run through it and come up with the solutions to the problems that you're presented with, all in the service of delighting customers.

53:04Because fundamentally, I mean, the big thing is just aligning people towards the only reason we're all here is to build something that people want and they love so much that they tell their friends about it and they give you money. And then everything else, it just follows from that customer experience of delighting customers with what you do. When we do onboarding, for example, I used to do this thing in what's called Lambda 101. And we would show a picture of like a Linux penguin. And he was like on a Lambda workstation. And he was reading the GPT-2 paper and training at a loss curve, which is like what you see and you look at if you're doing machine learning research.

53:45I was like, look, just put yourselves in the shoes of the penguin who's training using this workstation or cloud service to train a neural network and just think about what's going to delight them. You know, whether it's, you know, people on our shipping team who said, hey, let's put some T-shirts inside of the boxes. And so every workstation came with a lambda T-shirt, you know, or members of the data center operations team said, hey, you know what? we should do a white rack because that'll kind of like set us apart and make everything look good. And we'll be really proud to showcase that. And, you know, those are the types of things that as you kind of imbue your company with the kind of delight the customer first mentality that I think helps you get through the hard times.

54:29Recent evolution in that journey is that you just brought on a new CEO and my fellow French countryman, Michel Combe, to run the business. Walk us through the thinking and what led you to make the decision and how that equips the company for the next chapter. It's a huge honor as a founder to get to the point where the company can afford to bring on amazing talent like Michelle in that seat. Because if you think about it, I'd say most companies, it's not uncommon for somebody to say, Hey, look, a lot of people sometimes maybe there's a component of ego involved where they have to be the founder, CEO.

55:14I've never really personally had that. I care about the technology, as you can tell. I care about building a great generational company. And I think there's so many different seats to do that from. And so getting to the point of maturity where we can afford to bring on a CEO like Michel, who has experience, obviously, previously SoftBank International CEO, Sprint CEO. Alcatel. Alcatel. He's on the board of some really amazing companies, including McLaren, which is a fun one. I always did the fundraising and capital formation and day-to-day business management as a necessity and not because that's what I really love doing, for example.

56:02And I think there's plenty of founder CEOs who absolutely love every aspect of their CEO job. I think that privately, and it'll be very hard for you to get this out of any founder CEO oftentimes, but like secretly when I talk with founder CEOs, I'm always like, yeah, so like, how much do you hate? I find it shocking that people don't find speaking to VCs all day exciting, but I will take your word for it. It's been like an amazing experience for me that I, to be able to form a team around the company and to just see everybody flourishing in the things that they love to focus on. So for example, now that I'm the CTO, one of the main things I'm focused on is what does rapid data center deployment look like at the company?

56:53And, you know, kind of working to say like, hey, I want Lambda to be this sort of vertically integrated high velocity powerhouse that. So when you look at the world, you say, all right, there's two people in the world that can and two companies in the world that can do high velocity deployments, SpaceX AI and Lambda, where we're just extremely focused on how do you cut every little piece out of the process to stand up compute faster. and that's like something I've just been diving into and really enjoying with my new time as a CTO. What was XAI's record like when they launched? I think it was like 200 and something days.

57:39Yes, and you think that can be matched or exceeded at a repeatable pace? I think it can be matched or beat, yeah. And that's process mostly? I think it's everything from the site selection process, the set of constraints that you use in a site selection process, the MEP pipeline, the way that you construct the data center. How do you make it so that the end customer will consume that compute? And how do you cut out a lot of stuff out of the process? Because oftentimes, you know, the people who've been designing these data centers have really kind of been real estate people, as I've mentioned, who've been kind of grabbed by the scruff of their neck by a hyperscaler.

58:22They're like, go and build this design. Here, go. Go get a GC runoff. And they don't know anything about what goes inside of it. And so, and the hyperscalers, on the other hand, have been really building towards traditional cloud services. I mean, if you look at a modern region in any of the clouds, they have hundreds of services. I mean, everything from satellite base stations to tape storage to spinning disk to face recognition APIs. I mean, these are all the services and each of those services requires a different SKU and has different parameters about what you're kind of servicing. and in fact, you know, you might have somebody who's trying to run an ATM backend on one of these things.

59:08That's a pretty different design space and design constraint than an AI data center that could maybe, you know, have a lower availability and uptime, right? And so that's kind of where I think Lambda is able to build a lot of really unique value and, you know, through this kind of targeted AI first approach. You had a quote where you said that AI won't write software, it will become the software. What do you mean by that? So that's in my sort of like idea around what I call neural software and or, you know, a neural computer, neural operating systems. And the best way to kind of get this experience is to go to your chat GPT or your cloud and say, hey, just, you know, render for me an ASCII art desktop interface.

1:00:03Okay. So you're working in purely in the domain of text. And I want you to just pretend to be an operating system for me. I'm going to say, click on this, you know, open up this and I want you to just behave like a computer. So give it that prompt. Okay. And what you're going to see, I think, is that you're going to see that sort of future of the large language model becoming the software and not generating the software. And this results in an extremely sort of squishy and flexible way of interfacing with a computer where it's not possible to have a bug, only a misunderstanding about the prompt and what you've asked for.

1:00:48And I think that for a lot of the pieces of software on your computer, you might see that taking over where, you know, you can get the glimpse of the future with this ASCII art, and then eventually it'll also have a multimodal network that's generating every pixel on your screen, as well as every audio waveform that comes out of your speakers. The advantage to this is that you can really sort of dream up software. The only the part that is being experienced by you and is actually implemented, if that makes sense. It could have whatever feature it is if you ask it. And that's like a really powerful way to interact with a computer, I think.

1:01:31So it's not like you give simple instructions to the LLM, suddenly the LLM is the software. I guess make the analogy, Like, you know, vibe coding takes in a prompt and then outputs human readable, writable, compilable code that runs on normal human software programming language substrate, right? You know, it outputs C code, which gets put through a compiler, or it outputs Python code, which gets put through a Python interpreter. That software is static. Once it's been generated, it can't change, right? you can vibe code it again and maybe you know vibe code on the fly there's like a couple different stages of the gradient between traditional human written software and then you go to like maybe vibe coded software then you go to just in time vibe coded software where like it's a live creation of the software application but still software but then you go to the next step which is just you're interacting with the LLM and it is emulating kind of how software might behave.

1:02:33And that's the difference between Vibe coding and a neural operating system or neural software. Neural software, there is no code that's running. It's just modifications of the feature activation space and the context in the mind of the neural network. And how far do you think we are from that? Is that something? I mean, we have prototypes of it today. So we have prototypes of it today. And when you say you, is that Lambda? Or is that... Yeah, Lambda's developed a prototype. There are multiple other companies that develop prototypes of this. There's academic research that has outlined what this might look like.

1:03:16And how far are we from mass adoption? I would say that generally speaking, when I'm early on something, I tend to be about a decade to a decade and a half early. So I would say that between a decade and 15 years, we will see mass adoption beginning or otherwise happening for neural software. I mean, you already have it. So here's another example, by the way, you already have, So you can think of a Tesla self-driving car or any type of end-to-end neural network and then large model that's doing autonomy as a form of neural software. And people understand that aspect, which is it's seeing video, it's making decisions about what to output.

1:04:09Now, the user experience is the driving experience. that said that is an example of neural software i would i would argue and so we already see that today now the question is when is your everyone's computer is going to adopt that i'd say a decade do agents change anything from your perspective as a compute provider and if so in what way to understand what needs to change on the computer we understand need to understand what's changing with the user so when you're doing vibe coding with agents one of the things you'll notice is that your wall clock time in the world is mostly spent on running tests, gathering data, searching through code base.

1:04:54A lot of the time is spent not just inferencing a neural network, but it's actually spent doing other things. And it's actually very much similar to how software engineers spend some of their time, right? You know, the old XKCD cartoon of compiling where they're sword fighting on the office chairs. And someone says, what are you guys doing? Compiling. And so now there's a bunch of time spent compiling. There's a bunch of time spent running tests because the part of the way that the agent 24-7 loops really work well is when you are constantly banging against a nice suite of automated tests to make that the code you're writing is good.

1:05:38And so, well, what does that mean? It means that every single cloud service needs to start doing a lot more traditional CPU workloads. They need to focus on a great environment, a secure environment to host your Claude code instance on. And then you need think about security from the perspective of you need to think about how this massive influx of new applications are going to be secured how do you use the agents internally well i mean a lot of the engineers at lambda are already you know doing a fully agent driven workflow i mean if you just go to clod code and say hey use advanced workflows or you know spin up agents, you can do that.

1:06:33So that's like step one. I've demoed internally and some folks have adopted what I kind of call self-assembling software. And so self-assembling software is this idea where you kind of tie in to a 24-7 running agent fleet product requirements and constant user feedback that's coming off of the system. So you have a very clear and tight loop to go from submitting, hey, this is a bug or this is a feature request. And there's a fleet of agents who are implementing that live for you. And that sort of cycle I call self-assembling software is because you kind of say, hey, this is what the software is for.

1:07:18But most of the development for it is going to happen after the software is launched and the users start to interact with it and customize it for themselves collectively. And I think that that is kind of maybe the future paradigm of where a lot of the agent-driven development is going to go towards. The other side of that, eventually, once the models get smarter, I think that they're quite not quite there yet. But, you know, tying that back into, hey, I need help. And I'm not talking about the human, I'm saying the agent's going, hey, I need a human to help me. I need you to plug in a thousand GPUs for me, or I need you to give me an API key to a particular service.

1:08:01I need you to go sign up for something for me. Can you go please negotiate this? And I think that that's actually how you're going to start to see it happen, which is product user feedback gets implemented by the agents. The agents then also ask the people at the company to go and do things for them, all in the service of delighting customers and making money. You've talked about gigawatt scale factories. Is that what you were describing earlier around like setting up a beginning super good at creating data centers very quickly, but it's also making them bigger? What is that concept? It's an AI factory, which is a basically land data center servers inside that is generating tokens.

1:08:42And a gigawatt scale means that it's consuming a thousand megawatts or a billion watts which um is a lot of power sort of like maybe you can think of it for context new york city is something like five gigawatts you also talked about one person one gpu is that uh your vision for the future unpack that for us so um you know before people really believed in the ai thesis i you know when i was pitching our series b and c I would kind of talk a lot about the similarities between, let's say, the computer industry and the AI industry. I really felt like AI was forming a set of generational companies.

1:09:26And there was going to be a set of generational companies that got minted with the changes that were coming with AI. And this is like in 2020, 2021. And if you read about the history of Apple, for example, in the early days, the motto and the sort of the credo at Apple was one person, one computer, one person, one computer. And, you know, there's a sense of humility that's embedded in this one person, one GPU, which is the one person, one computer. You think about how visionary Steve Jobs was. That was, you know, Apple was, you know, what founded in 1976 or something like this. The Macintosh came out in 1985 or 1984, excuse me.

1:10:081984, you know, whatever. Eight years or so after founding. Is that one person, one computer yet? No, not even close. All right. So 1984 to 1994. All right. Well, is it one person, one computer? Well, we're just starting to have the internet boom. So we're not quite there yet. 2004, we finally have broadband internet access. And maybe for the first time in the United States, there's not quite one person, one computer, but there's certainly like one person, one family, one computer, you know, or, you know, something like this. It's like getting close to it. You don't have until 2014. So 74, 84, 94, 2004, 2014, 40 years after one person, one computer, do you have probably truly one person, one computer?

1:10:57And you get actually beyond one person, one computer because people have laptops and cell phones and I would consider a cell phone a computer. So and then and then finally, you didn't have e-commerce penetration until 2024. Fifty years after the founding or so of Apple Computer, when e-commerce starts to actually penetrate because of COVID. I think that the reason I really wanted to choose that one person, one GPU is because, one, I believe that in the future, everybody in the United States will need the computational power of one GPU or more to just do their daily work, enjoy life, whether it's getting access, whether it's getting entertained, whether it's being productive, whether it's being creative.

1:11:45And I also recognize that it took Steve Jobs and Apple, one of the best companies in the history of capitalism, half a century to accomplish their goal. And so I think this is not it's not just like an overnight. Let's, you know, quickly get to one person, one GPU. So that's that's that's what that means to me. To close, are you ready for a couple of quick hot takes? Sure. What is one idea in AI that is overhyped? I think a lot of the sort of agentic workflows for things that are not software engineering, I think, tend to be overhyped. And I'll tell you that the reason for that is because one of the ways that you get an agentic workflow working really well is that it needs to have very concrete feedback mechanisms, which are done brilliantly through automated testing.

1:12:32It's not at all done brilliantly for like going by a site. there's no traction to give a model to go and iterate over a long period of time on. So I think agentic workflows for things that aren't readily verifiable. Now, I wouldn't say as far as everything that's not software engineering, because there's plenty of readily verifiable fields. CAD, computer-aided manufacturing, finite element analysis, computation fluid dynamics. There's a bunch of fields where you can really do a great agentic workflow. I mean, and simulate it and then go and iterate. It's not the case for, hey, Claude, make me a billion dollars.

1:13:12Make no mistakes, you know. Sadly, or maybe not. Okay, fascinating. Inflation would be, it wouldn't be inflation. It would actually be just value creation in the economy. Deflationary even. What is one idea in AI that is underrated? Yeah, I really think that the neural OS thing and, you know, also some of the aspects of self-assembling software. I still think people... You know, the funny thing is I'll give the same answer. Agent-based workflows for software development. I think that most people don't understand. They literally don't understand because they've never tried it. They've never gone to Claude.

1:13:48Go to Claude, go say maximum effort, use the latest model, and then go and build whatever you wanted to build and say, you know, spin up 10 agents to go and do it. I think a lot of people still haven't done it yet. Well, Stephen, it's been wonderful. Thank you so much for spending time with us. Matt, thank you so much for having me. Appreciate it. Hi, it's Matt Turk again. Thanks for listening to this episode of the Mad Podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from.

1:14:22This really helps us build a podcast and get great guests. Thanks and see you on the next episode.