Why Elon Wants to Put Data Centers in Space | Ramez Naam

Ramez Naam is an investor at Planetary VC and a longtime clean energy and AI expert. In this conversation, we break down the energy bottleneck constraining AI growth — from gas turbines and batteries to floating ocean data centers and Elon's orbital data center ambitions. We also cover why bitcoin miners are pivoting to AI infrastructure, general vs. narrow superintelligence, and the data bottleneck reshaping AI training. ==================== Turn every conversation into a searchable business asset with PLAUD NotePro. Visit https://Plaud.ai/pomp and use code POMP for 15% off. ==================== Simple Mining makes Bitcoin mining simple and accessible for everyone. We offer a premium white glove hosting service, helping you maximize the profitability of Bitcoin mining. For more information on Simple Mining or to get started mining Bitcoin, visit https://www.simplemining.io/pomp ==================== Uphold is the easiest way to buy and sell crypto unlike any other platform allowing you to trade in just one step between any supported asset. Check them out at https://www.uphold.com/pomp/This video includes a paid sponsorship with Uphold. I’m compensated by Uphold for promoting its products and services and may receive commissions from referrals. Terms apply. Not available in all jurisdictions. Digital assets are risky and may result in the total loss of your capital. ==================== 0:00 - Intro1:03 - Why power is the real bottleneck for AI & solutions7:35 - Elon's orbital data center thesis11:17 - Cooling & maintenance challenges in space15:06 - Panthalassa: floating ocean data centers19:16 - Base Power & Texas deregulated grid21:30 - Giga Energy: from bitcoin mining to AI infrastructure22:44 - American Consolidated Electric & supple chain bottlenecks24:58 - General vs. narrow superintelligence33:02 - Where untapped data lives & building a data moat36:47 - Token costs, open source models & model routing41:53 - What is the mission Ramez is going after?

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• 1:00Speaker 02

  The Bitcoin miners I know are all shifting to AI. I think you've covered this as well, and it's because they have access to power. And that is the bottleneck. And you just make more dollars per kilowatt hour going into an AI compute than you do in Bitcoin, ultimately. And B, because power is the constraint, the value flows to what's scarce.

• 1:23Speaker 03

  Bang, bang. Today, guys, we got a great conversation with Romez Nam. He's an investor at Planetary VC. And in this conversation, we're getting deep into the weeds of what's going on in the artificial intelligence space. We know that energy and power are a massive bottleneck. And Romez is here to explain exactly what the potential solutions are, who's likely to win, and what are the various companies doing so that we can get more power to get to superintelligence? On top of that, we talk about how superintelligence is actually going to be applied. It's going to be general or narrow. What is it going to mean for you? And what are some of the companies that he's excited about that are actually helping to usher in a world where superintelligence makes you and I happier, healthier, wealthier, and richer? It sounds good to me. And Ramez is here to explain it all. Here's my conversation with Ramez Nam. All right, Ramez, there's a massive problem in the AI industry.

• 2:07Speaker 03

  Energy and power are the bottleneck at the moment. Can you please help us understand why is this problem persist? And what are the potential solutions as to how we're going to get around this?

• 2:16Speaker 02

  Yeah, absolutely. AI is hugely compute intensive. The way that we've been making AI better is the scaling laws. And the scaling laws look awesome at the beginning, but they're brutal. It means you have to keep doubling the amount of compute to get linear gains and ad performance. So there's this huge race to do so. And we're just not used to expanding the grid at this pace. In the 50s, we built out the grid 5%, 7% growth a year. But for the last two decades, it's been zero growth. So we're just not accustomed to putting on like new power or new sources of demand at this pace. So the poles and wires themselves are the bottleneck right now. Chip production is about twice the pace that we can hook things up to the grid.

• 3:00Speaker 02

  And that's the bottleneck everyone is trying to work around.

• 3:03Speaker 03

  Now, when you think about that bottleneck, can you walk through? There's different companies with different potential solutions for this. They're all kind of racing to see not only what solution is going to be the one that is the winner, but also how do they fund it? How do they build it? There's a lot of stuff, supply chain issues. Where, maybe, what are the ones that you're most excited about? Or the ones you think are most noteworthy as a solution?

• 3:24Speaker 02

  Well, let me walk you through like the overall set of options people have. Option one is you're going to build a data center. You put in a request to the grid to get hooked up. And a few years ago, that was fine. Now it's maybe a five to seven year wait. Option two is behind the meter power. So people are trying to buy natural gas turbines to build onsite at their data center to run their load. The big turbines made by companies like GE are sold out five to seven years. So now they're turning to smaller turbines that are maybe a 10th, a 20th in size, and a whole bunch of these things on the back of semi tractor trailers, basically. And you have companies, Boom Supersonic was trying to build a supersonic jet

• 4:08Speaker 02

  a few years ago, or as recently as six months ago. They've pivoted to taking their engine and making it a power plant for behind the meter power for data centers. Option three, one that I really love is batteries, just being really clever. We build the grid out for peak demand for late afternoon in the summer when the AC is on. But most hours of the day, the poles and wires, which again is the bottleneck, are not loaded. So you take a battery, install it at your data center, fill it up at night, don't need to touch the grid in the afternoon. And suddenly you can get powered on a lot faster. We've just passed some regulatory changes for that. And then you get into the sci fi stuff.

• 4:51Speaker 02

  You get into Elon talking about space based data centers. Nothing about the physics says that can't work, but the cost of launch has to come down a whole lot. Or one of a startup that I'm invested in called Pontalasa, making floating ocean data centers that get power from waves. And the benefit of both space and ocean is it's going around regulatory. It's no longer needing permitting and local opposition and wait times it connected to the grid.

• 5:24Speaker 03

  Now, let's talk about a couple of these, like going and just applying for power and kind of doing it the traditional way, not very interesting. I think people understand that. And obviously, there's those long wait times. You talked a little bit about these like almost mobile centers. One of the things I found really interesting is when Elon went to go build the original Colossus, my understanding is that they essentially just brought in a ton of generators. And then he just lined the entire facility on both sides with these generators and kind of like hacked it together. It was kind of like a do it yourself project, if you will. But it worked and they got up and running. And so how sustainable is that type of stuff where people are essentially just cobbling together different solutions and they're able to just get up and running? Is that something that could persist for years?

• 6:05Speaker 02

  It works in some places. About a third of data centers that we think will get built this year are doing some version of that. Maybe as many as half next year. But there's a lot of debate. When you do that, you probably have to overbuild by 30, 50% to have sort of the spare capacity. And you end up with problems. You end up with a local air pollution issues like those power plants, Colossus, Colossus One, probably exceeded their permits, there were lawsuits. You end up with noise. When you hear people complaining about data centers making noise, data centers themselves are quiet, but these generators behind the meter are really loud. So this works best in rural areas, out in the middle of nowhere. And so you'll see people doing that. I think almost every data center

• 6:50Speaker 02

  really wants to have a grid connection ultimately. Nobody wants to manage their own power plant, but people, your time to power is everything. Let me put this another way, economically for your listeners here. If you look at the cost of electricity going into a data center versus the revenue it generates, it generates $20, $30, $40 of revenue per dollar it spends on energy. So time to getting it hooked up is everything. And you'll pay more for energy. You're paying more if you're in these small generators onsite than if you would go to the grid. But it's worth it because you're going to make so much money off of it.

• 7:28Speaker 03

  One of the aspects of the data center build out that I think most people have missed is I've seen a number of examples where these data center providers show up somewhere and they say, hey, I need to get more electricity. The electrical company is like, well, we just can't get it there. We don't have the infrastructure or whatever the thing is. And the data center company literally says, I'll build the infrastructure. I'll pay for all of this build out. I'm happy to have everyone else get the upgrade as well who uses this grid. But if it helps me get online faster, it is worth that expense. And so that seems to be a strategy that a lot of people are using as well.

• 7:59Speaker 02

  We're getting there. And all the data center builders have signed this voluntary pledge that they're not going to raise prices for their neighbors. And in fact, contrary to the public perception, places where we've seen the greatest data center build out in general have lower electricity prices in the country and have not seen them go up. But that said, there's actually like regulatory challenges here. The utility rates are set by the utility commission. There's actually work that has to happen to make it possible for the utilities to let the data center builders pay for all this stuff and not pass on the costs. So everyone's scrambling to figure that out now.

• 8:35Speaker 03

  Got it. Now, let's go and talk about maybe the orbital data centers first. So the promise here is if I was to regurgitate back Elon's thesis, if you will, is you need power. Well, where is power persistent at all times from the sun? As long as you are far enough away from Earth in space that you can capture 100% of the sun's energy. And if you can capture that via solar in space, and then you can have basically a data center right there at the point of energy capture, you can then beam the data, the connection, et cetera, back down to the internet. He's proven that he can launch rockets. He's proven he can reuse the rockets. He's proven that he can beam the internet down from these kind of low orbit satellites. Do you think it's possible to actually build the orbital data

• 9:18Speaker 03

  center? Like, I don't think there's actually one that exists yet, but it seems like the math works?

• 9:23Speaker 02

  It's possible. The math doesn't work on a price basis yet, but it could work with Starship. So, look, a Starlink satellite doing internet is kind of an orbital data center. It's, it's using its compute to handle telecoms and compression and channel hopping and so on, not for computing AI. But it has some compute on it. And we've had the first example of a satellite launch that has a GPU on it doing that sort of thing. So, nothing says it's impossible. When you do the math, you've got to bring launch costs down by somewhere between a factor of four and a factor of 10 from where they are now to compete against costs on the ground. Starship should be able to do that, but it's going to require that we have, you know, two-stage usability. They got to catch both the upper and the bottom.

• 10:09Speaker 02

  And they've got to be able to launch a lot of Starships. They've got, you got to get to the point of launching Starship multiple times per day for that launch cost to get cheap enough. But the flip side of that, here's a way to look at that. If you think demand for compute is basically infinite, if you think it's unlimited, eventually you just run into limitations on the ground. And so, even if it's more expensive to go to space, if that's the only place from a regulatory standpoint that you can go, it might make sense. Now, I don't actually buy that argument. I think there's a lot of other places we can figure out to put compute. But that's part of the bet Elon's making. I'll make one more argument, flip side of this. Elon's wanted to go to Mars, right?

• 10:52Speaker 02

  You can't, there's no business model for going to Mars. He wants to get Starship down to these incredibly low launch costs, a tenth of what it costs today. That only works if you're launching, again, you know, 1,000 Starship flights a year or more. There is no demand. There's not enough demand for launch, for telecoms, for Starlink-type services to actually scale Starship to the fleet size and launch cadence that he wants. So, in a way, this is a godsend to Elon. This is, it's really smart. Like Elon originally wanted to go to Mars. He didn't want to build a satellite telecoms network. He built Starlink

• 11:33Speaker 02

  because it was the business model that worked for launch. And so now orbital data is a business model that can actually justify enough math to make Starship make sense. Flip side is, you know, we've got a whole lot of desert on planet Earth. And it's probably, for the time being, until Starship is at that scale, it's probably cheaper to, you know, cover the deserts of the world with solar and batteries that work 24/7 to build these data centers. You see the first signs of projects that pencil like that in the UAE. In Chile, we have solar plus battery projects that are baseload solar, that are cheaper than behind the meter gas. So, I expect the world to start doing some of that for data centers as well.

• 12:18Speaker 03

  Now, when you're thinking about this stuff in space, the two things that I've heard people critique this is the cooling is still highly debated as to like, is it easier or harder in space? Maybe you have some opinions there. And then the second thing is, how do you fix the GPUs or the machinery if there's some sort of issue with it?

• 12:38Speaker 02

  Yeah. The cooling is just physics, right? So, space is an insulator. There's no, you know, convective cooling. There's no conductive cooling. You touch space and no heat leaks out. But you build giant radiators, big aluminum fins with special coatings on them. They radiate heat. So, it just comes down to sort of a mass equation, and that comes down to cost. You just have to launch a lot of aluminum fins to be able to radiate that heat away. And that just adds to the weight and adds to the cost. You can do it. Now, you do have some other complications. You're going to have to have these cooling loops, you know, filled with maybe something like ammonia or some other coolant and pumps and circulators and compressors. And that adds to the moving part count, which again, adds the possibility of things breaking. And so, we don't know. The maintenance question

• 13:22Speaker 02

  is one of the most unanswered. We can do the math on cooling and say, okay, that just means launch cost has to be so much lower. I don't think we have good data on how often things will break there. And in particular, if you look at Elon's plans, the hotter you run a chip, the easier it is to radiate away the heat. But the hotter you run a chip, the more failures you have. So, he's talking about running these chips, you know, 25 degrees Celsius, 50 degrees Fahrenheit hotter than they run in data centers on Earth. And that's a challenge.

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• 15:23Speaker 03

  Yeah, it's fascinating to me because I guess part of this is also if you can start to shrink the size of the machines that need to be up there, like everything is just compression, compressing timelines, compressing costs, compressing size, compressing, you know, all of this. That really feels like the world is about to spend, I don't know, trillions of dollars just compressing this entire thing to make it as efficient as possible, right?

• 15:46Speaker 02

  Yeah, I mean, that's what we've been doing, you know, like, we're making sand sentient, as they say, right? So, we've been taking information and compressing it into this physical stuff, the ability to compute into smaller and smaller packages. But that has meant that while total energy use per unit of computation goes down, the density of it goes up. So, the temperatures of these things go up a lot. I'll pitch again, one of my startups, Pontalasa, they do this at the ocean, right?

• 16:12Speaker 03

  Yeah, the ocean guys. Yeah, what do they do?

• 16:14Speaker 02

  Well, they're, we just announced a big round of theirs a little while ago, led by Peter Thiel, among others. They build these floating ocean data centers that bounce on waves. And the biggest waves on the planet are around Antarctica. So, these are floating data centers that go up and down in giant ocean swells, and they generate electricity from the water being sort of forced up a tube. And we think they're super cheap, and the cold ocean waters give you basically cooling for free. So, it's another bet. In many ways, it's a bet like space-based solar. Let's get past the limitations of the grid. Let's get past local permitting and zoning. Let's get back local, get past local pushback

• 16:57Speaker 02

  and go to a frontier where there's a lot of energy and where there's not a lot of obstacles from a regulatory standpoint.

• 17:06Speaker 03

  Let's talk about this a little bit more. So, there's basically like a big giant ball. I almost think of it like, you know, that movie like Bubble Boy or whatever it was, right? But inside of it, you have a data center. How, as the ball is being pushed on the waves, what is the connection to the electricity generation? Is there some sort of like wheel or something that is like capturing the motion?

• 17:25Speaker 02

  Yes, it's amazing. So, what you see in the picture is a sphere at the top. But then you have a tube that's gently tapering, that's like a football field long, 85 meters long, that goes down. The waves send it up, and when it comes down, water is forced up that tube, and that water turns a turbine. And the way they've got it, some very just clever systems of tubes and pipes, if you will, you can have 90, 92, 95% continuous power output from that system with almost no moving parts. And you don't even need a cooling loop. The GPU is just plugged into, you know, a heat sink that conducts electricity

• 18:09Speaker 02

  straight to the metal walls of this stuff that goes to this super cold ocean water and cools it.

• 18:15Speaker 03

  Now, what happens if you get calm waters? Or are there no calm waters in Antarctica?

• 18:21Speaker 02

  In the Southern Ocean, a calm day is like the worst day on the, you know, waviest beach where people live.

• 18:31Speaker 03

  Got it. So, there's always motion that's going on, and that allows you to continue to do this. Now, if something is 85 meters deep in the water, like, I don't know, it's Titanic. What if it hits something? Or can these things break? Or how do you think about some of these edge cases when you're underwriting an investment like this?

• 18:47Speaker 02

  The ocean's really big. So, I like, these things, while enormous in some sense, are tiny compared to the scale of what's out there. They have the ability to steer themselves to some extent. They mostly follow the current, but they can, they can, you know, set their course. And all of, everything we build has some failure rate. In space, we'll have risk of orbital debris, we'll have things break for various reasons, we'll have solar storms. But if you distribute the compute enough, losing one node is not catastrophic.

• 19:19Speaker 03

  And of these spheres that are in the water, do you have to build an entire network of them where they communicate with each other? Or can you just have, you know, one and there's really just like that thing is beaming connectivity or power or compute to an end customer?

• 19:34Speaker 02

  Yeah, that's a really good question. So when we look at AI compute, we're talking about data centers doing two different things. One is training the AI models and the other is inference, serving up. When you go chat with chat GPT or Claude or whatnot. For training, you need massive data centers with incredibly high speed interconnect, like optical connections between all the racks, maybe a gigawatt in one place is what people want. And so you're not going to use distributed systems for training. But for inference, when you're just asking a question to an AI model, you might be talking to one rack of servers, might be talking to 20 GPUs at once. And so that can be pretty easily done with distributed model.

• 20:16Speaker 03

  Got it. Let's go back to the grid for a second. I want to throw a couple of different ideas. Some I've invested in, some I've looked at and not invested in, but I think are interesting. One is Base Power. They've got this kind of decentralized system that they've built. They're essentially selling, the way I describe it is like a Tesla Powerwall, which people are familiar with. They've got their own version, it's super cheap, you attach it to your home, and then they are allowing you to hold power, sell it back to the grid, you can use it if there's some sort of blackout. It's almost like a decentralized electrical grid is the way I think about it. Is that going to eventually be the default type of system for the entire US electrical grid? Or do you think that only is going to work in certain states?

• 20:55Speaker 02

  Base is amazing. Very, very smart team. I'm acquainted with them. They've done some really clever stuff. Their model works because Texas, where they had started, is a deregulated energy market. So they're, you know, giving you a battery for, I think the current quotes I've seen are nine bucks a month, and you got backup for your house. Why does that work? It works because they're using that battery to buy and sell power on the grid, to buy power when it's cheap, sell it back when it's expensive, and they become your electricity retailer. And so that business model, all around, pencils for them. Like, you're paying them nine bucks a month, but in some sense, they're using your house to build a small power plant. I mean, it's a battery that, you know, pulls in and out

• 21:40Speaker 02

  power, and they're taking over, they become your utility that you're paying. That business model works in eight US states, Texas being the largest. So ERCOT, the Texas power grid, is a deregulated market. And it's kind of amazing because it's a very free market system where both on the retail side, you can pick who your power company is, and they compete on prices. And on the wholesale side, on the generation side, anybody can build a power plant and just sell power onto the grid. It's incredibly competitive. In fact, Texas is the number one state in the US for solar, wind, and batteries without any specific policies to advance them. It's just a very,

• 22:25Speaker 02

  very competitive market, and so these things win on cost. So I'd love to see more of the US adopt a model like that.

• 22:31Speaker 03

  Now, there's companies like Giga Energy in Texas. What they are doing is these kind of modular, they started with Bitcoin mining, now they've really went hard at AI infrastructure. And what I think is interesting about them is they really started with building the hardware, but now they're doing powered land and really saying, well, maybe we need to kind of eat the full stack when it comes to the development of the data center in this way. Is that the natural progression for a lot of these players is to eventually go from, hey, I've got one kind of corner of this supply chain or one piece of this, and then now I've got to go and be able to deliver kind of an entire project?

• 23:04Speaker 02

  Look, I think it makes sense for a lot of reasons. I think we will see, still see multiple business models make sense. But A, the Bitcoin miners I know are all shifting to AI. I think you've covered this as well. And it's because they have access to power, and that is the bottleneck. And you just make more dollars per kilowatt hour going into an AI compute than you do in Bitcoin, ultimately. And B, because power is the constraint, the, you know, value flows to what's scarce. So the ability to get things hooked up to power, or the ability to route around power limitations of the grid, that becomes a limiting factor where the value flows.

• 23:45Speaker 03

  Now, another company is American Consolidated Energy, or Electric, I'm sorry. And so when you think of that business, a friend of mine runs it, and I think American Consolidated Electric started looking at, well, turbines, switchboards, you know, these various different components in the supply chain, those are in very high demand, very low supply. There is a promise of, yeah, sure, maybe you'll get it in three years or something like that. And so, in a weird way, it almost feels like a business that is going after the supply chain and saying that you're going to sell actual hardware components to these data centers or to these land developers. That's like selling the picks and shovels back in the gold rush. I'm surprised that we don't see more companies stepping in to solve those bottlenecks.

• 24:27Speaker 02

  We see some of it, but you know, transformers classically are in short supply right now. So all of these components are in short supply. These come, you know, often from companies that are not public, that are sometimes family owned, old companies that are a little risk averse to building out, you know, a new assembly line or increasing their supply chain. But the prices are just going so high that we're seeing more and more investment, both from the existing players and from new entrants that are saying, well, I can supply that. We're also seeing, you know, things are changing, electrical system, you're, you know, the power output from a solar plant,

• 25:07Speaker 02

  for example, is DC. The grid is AC. At the end of the day, the GPUs are consuming DC. So we have all this conversion back and forth. So we're seeing people talk about entirely DC-based data centers. We're seeing people talking about increasing the voltage in a data center because it reduces how much copper wiring you need and makes the whole thing cheaper and simpler to assemble. So the whole world is routing around and trying to orient itself around these data centers that are, they're not yet an enormous amount of our energy consumption, but they're incredibly concentrated load. They have like the highest power density of any consumption of energy. How do we make that all work better? How do we move to these high power systems?

• 25:52Speaker 02

  How do we solve the bottlenecks of components that people just weren't buying in such volumes a couple of years ago?

• 25:59Speaker 03

  Now, we've been talking a lot about the electrical grid and some of the bottlenecks in terms of power and energy. Let's talk about where this is all going. I think there's a lot of folks who believe that there is going to be some sort of like general superintelligence. And that general superintelligence, you know, we're close, we're going to hit it, we're going to take off, and the world's going to be amazing. It sounds like you're a little bit bearish on that being the potential path that we take. But you do believe in this kind of like narrow superintelligence. And so describe the difference between general and narrow, and why do you think narrow is the more likely path?

• 26:32Speaker 02

  So I'm one of the few people on the West Coast who doesn't think we're about to hit a singularity and ASI that just does everything for us magically, utopia or dystopia. But look, AI is limited in a large sense to the intelligence found in its training data. And so you can train AI, today we're training AI off of basically all written human work. We're close to that, where we might be hitting that now, maybe 2028, we will have trained off of basically all the books, all the internet, all the scientific articles and so on. And AI can only really get as good in some sense as what exists there. It's also limited by how much noise and error there is in the data. So you crawl everything on the web, you're going to find some stuff

• 27:14Speaker 02

  that is amazingly insightful, you're going to find some stuff that's utterly crap. The places where we see AI become truly superintelligent are these formal domains that we think about as highly verifiable. Right? So the first things that AI beat us at were games. Why was AI, machine learning models, able to get so good at chess or the game of Go? Well, you can play an infinite number of games against yourself. So you have no limit on how much data you produce to train on. And two, you know, instantly, in like, with 100% reliability, this was the right answer, this is the wrong answer. Did I win the game or lose the game? There are very, very few domains

• 27:54Speaker 02

  like that. Domains that are like that, formal math. So when you see AI like actually help the world's top mathematicians solve math problems. And coding is kind of like that. Coding is a little bit more messy. The requirements for coding are, you know, still spoken in natural language. But you can at least tell, like, did it compile? Did it work? How fast did it run? And so on. But other things like, write me a novel, or run my company, or figure out the right foreign policy decisions for the US and Iran. Those are incredibly messy. You don't have a way to judge quickly in a million simulations, did I get the right answer or the wrong answer?

• 28:36Speaker 02

  And so it's just much harder to produce this like totally formal, totally precise, and nearly limitless amount of verified training data, which is what makes AI is truly superhuman.

• 28:49Speaker 03

  Makes sense. Now, how much of the narrow superintelligence is going to be based on specialized workflows? And you know, a lot of the like reinforcement learning that comes from experts inside of a specific industry or the feedback and, and a lot of the stuff that we're starting to see now, where I think there's two bets. There's like, let's go build general purpose models and chase that general superintelligence. And then these narrow superintelligence pursuits really, to me, feels like it's a bet just on specialized workflows with industry-based knowledge or experts. And that's kind of where it sounds like you believe the value is.

• 29:22Speaker 02

  Great question and super insightful. So up until a few years ago, all of the work was just training on the internet and books and papers and so on. But a lot of the gains are coming from synthetic data and reinforcement learning. All right. So pre-training is what consumes all of this massive amount of text. And then we do, even in pre-training, we put in synthetic data. Like a simple example is you find some code example in one language, you translate it into every other programming language that exists, and you make that additional synthetic data that goes in, or you solve a bunch of math problems that you know you can solve formally, you train on that data. And then reinforcement learning takes that further. And so this domain,

• 30:05Speaker 02

  A, that is how we're getting a lot of the gains, we think. It's secret sauce for all the labs, but that's what the, the, what we are figuring out from observing them. And B, generating that data is becoming itself a massive market. So we think the sort of specialist human data generation market is something like $10 billion a year right now. You have companies out there, like Merckor is one, that are valued private startups at multiple billions of dollars, who basically produce this data in the right manner, with the right software and so on,

• 30:47Speaker 02

  to help the labs, or sometimes to help enterprises, improve their AI models. And so I think you're going to see more and more and more of that, a related domain there. You see leaders in AI have said things like, AI will cure cancer, AI will double the human lifespan or whatnot. These are overblown expectations. It's not like that at all. But if you look at what's happening in AI and biology, again, AI is limited by the quality of its training data, and that data has to be experimentally derived, if you're talking about curing disease. So you see AI companies partnering with, or acquiring, or licensing data from biotech companies that

• 31:29Speaker 02

  have platforms to do experimentation and get real world data that can feed back into these AI models. Data is the bottleneck on AI.

• 31:39Speaker 03

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• 34:20Speaker 03

  don't know, but most of their archive is not digitized. It's just sitting in magazines, or, you know, newspapers, or whatever. And so, again, if I was owning one of those businesses, I would immediately digitize all of my archive. And then I'm sure that there are people who would love to come and buy it, because it's unique data that has, you know, real world application or whatever. That, to me, is like data that already exists, and it's more of an access problem. How did the labs get access to it? And once they get access to it, then it may not be as valuable on the go-forward basis, because it's kind of a one-time thing. That is very, very different than, let's say, for example, I know of companies right now that are buying data as

• 35:01Speaker 03

  companies shut down. So they go in and they say, hey, I want to come and I want to buy all of your Slack messages and Google Docs, and just like how the company operates, and I'm going to use that for trading. That, to me, is like current information. But again, it has some end life, because there's no more data being generated.

• 35:19Speaker 02

  Yeah.

• 35:19Speaker 03

  Then the third step is like, you or I, I don't know, let's say that we're a doctor, and I'm just going to stream data on a daily basis to some model lab, whether that is me writing stuff intentionally, whether it's me somehow recording it through, you know, cameras inside of my office. But there's some like ongoing data. That third bucket, to me, feels like it is the most valuable going forward, but the most underexplored. Is that how you view kind of this data bottleneck and like where some of this is going to get solved?

• 35:48Speaker 02

  Yeah, it's really interesting. So I think, to your first example, I'm reminded of Andre Karpety, you know, one of the OpenAI co-founders, led AI Tesla, now at Anthropic, talks about fossil data, that we want to, we mined all of this fossil data like we mined fossil fuels. But now we have to move into a new domain of generating new data. And I think this data, like ongoing data of how a business operates and so on, I think is super interesting. I think, again, the more structured and more formal it is, the more precise it is, the higher value it is. The thing that everyone is looking for is proprietary data advantage. What do I have access to that no

• 36:29Speaker 02

  one else has access to? And it might be that the only way to actually generate proprietary data advantage is to be generating that data yourself. So again, like biotech being a clear example, the interesting companies in biotech that are being founded and funded right now are trying to figure out how do they speed up that loop? You look at a company like New Limit, founded by Brian Armstrong of Coinbase, working on some incredible stuff in cellular reprogramming and longevity. They have an amazing in-house data pipeline where they do high-speed experimentation on cells, capture that data, use that data to improve their AI

• 37:11Speaker 02

  models, which then hopefully allows them to do smarter experimentation going forward. So that sort of feedback loop or virtuous cycle, that's the real sustainable competitive advantage. And it's, it's not common. You know, we look at AI, there's a lot of fear of sort of AI monopolies, AI concentration of power. But the reality is, in the AI market overall, there are no obvious network effects. There's no effect like Facebook. So it's just hyper-competitive, and the value accrues to us, the users, today.

• 37:43Speaker 03

  I do wonder, you know, if we kind of take this a step further, when you go and you look at the model, so there's two heuristics that I've been looking at. The first is inside of these companies, and we have this business CFO Sylvia, it's like an AI CFO. And so, one of the challenges with the business is that all model usage is user generated, meaning that you essentially have uncapped exposure to token price, right? Or a compute cost, because as long as people are banging on that thing, you're paying for it. What we started to realize was we were very under optimized when it came to when do we hit the model? How do we hit the model, etc.? And I think that at the end of Q1 or so, we started paying attention to this. I started talking to a lot of friends who were CEOs of companies. They all started being like, hey, we got to get more efficient at our

• 38:27Speaker 03

  token usage. We want the same output with lower token cost. I think that's pretty much now well understood, like that is underway and is happening. But now what I see the conversation shifting to is, okay, well, you have open source that is starting to become very popular. A lot of it's coming from China. So what I see the public narrative being is closed sourced American models versus open source Chinese models. What I don't hear a lot of people talking about is what about open source American models? That to me feels like that is going to be a massive part of the market, but it just seems like we haven't really seen that the narrative take hold there yet. Why is that?

• 39:05Speaker 02

  Well, I think the benefit of open weight, and most of these models are not fully open source. You can't get the source code. You don't have the training. But you have open weight. You can download the weights. You can tweak them. You can run them where you want to. That benefits the fast follower the most. If you believe that you are in the lead, then releasing your model in a way that people can use freely or tweak themselves is just sabotaging your own margins. But if you think that you're not in the lead, releasing your model in an open way can give you, you know, a big publicity boost. And it can also, it can help you, you know, get enterprise sales, enterprise contracts to implement this stuff, to tweak the model for enterprise use. That's how a lot of the Chinese companies are, are making money. So I think it'll generally be

• 39:49Speaker 02

  that way. But that said, there are a couple places that open weight models come out from the US. One is Google Gemini puts out their, their gamma or Gemma models, which are actually really pretty good. And the other is that Nvidia puts out Nematron, because Nvidia wants to sell compute. So to Nvidia, it's actually quite important to have open weight models. And they don't just put out open weight. They put out the recipe. Here's the training data. Here's how we trained it. Here's what we trained it on. Here's the post-training we did, and so on. So that's the closest to open source that we have possibly. So, you know, there's some rumblings right now that China might start restricting the release of frontier AI in response, perhaps to,

• 40:33Speaker 02

  or maybe for the just similar reasons to why the White House, you know, took some steps to slow down the release of our recent models from Anthropic and OpenAI. And so Nvidia is, I'm very glad they're out there with open weight and close to open source models themselves.

• 40:52Speaker 03

  Yeah, it does feel like that's a very big piece of it. Now, the second thing that I'm starting to see is it sounds like people are starting to build model routing as well. And this is becoming much more popular where even at Sylvia, we're starting to work on, okay, well, if you get a query, should it go to the highest powered, most valuable, you know, kind of highest cost model? Or should it be routed to something that is cheaper, faster, etc.? There doesn't seem to be a lot of companies yet that have built these solutions. Almost everything I'm seeing is custom built. But I got to imagine someone's going to step into the market and say, hey, we can help you do this in a much, you know, kind of easier, more efficient manner, right?

• 41:29Speaker 02

  There are some startups working on this. It's pretty exciting. I'm not sure if I can talk about them yet. It's a, it's a very interesting space. And they find not only that they can, they save you money on tokens, but by being really smart, they can sometimes get results that are better than any single frontier model on its own. If they just know when to ping which model, or sometimes the hardest questions, they'll ask multiple models and try to interpolate their answers. So I think there's an enormous opportunity there. At the same time, model routing is famously hard. I don't know if you recall, like, ChatGPT around, you know, sometime after 4.0, took away the model picker and just did automatic routing of people's chats to different models. And it was

• 42:13Speaker 02

  a nightmare. Nobody liked it. So you have to have some degree of explicit control and some transparency for the user of what's happening while also being smart in this way. And I will say, you know, a couple interesting people for your followers to, to read online. Brian Armstrong recently posted up what they're doing at Coinbase in this way. Aaron Levy of Box has lots of smart things to say about real world usage in these cases of using multiple models. And then Jack Dorsey has just done some, some more radical stuff, really, at, at Square Block in terms of how they're using AI.

• 42:53Speaker 03

  Makes, makes sense. Mez, what's your mission? Like, what are you kind of going after here?

• 42:59Speaker 02

  I want to make the world a better place. And, you know, you see the solar and wind behind me. I've been a clean energy guy for a long time. My roots are software. And AI, I think, is the most important transition we have happening right now. And to me, the most important conversation in AI is making sure that AI stays democratized and not centralized, and that we all have access to frontier intelligence, and that no one company has a monopoly or lock on it. And that's my, my current obsession in the world of AI.

• 43:33Speaker 03

  Yeah, it's, it's pretty interesting, I think, how somebody comes from the clean energy space, and now it's like, hey, where is superintelligence? And the full stack understanding to allocate capital is, it's becoming harder, but it's also becoming kind of much more asymmetric in the returns, right? You know, I was talking to a friend yesterday, and Anthropic is what, four or five years old?

• 43:58Speaker 02

  Yeah.

• 43:59Speaker 03

  And it's a trillion dollar company? If you had told somebody in 2010, you know, that people are going to build a trillion dollar company in five years, they wouldn't even believe that companies could be a trillion dollars, right?

• 44:10Speaker 02

  Fastest revenue growth of any company ever in history, right? It's not just a speculative valuation, though there is speculation, but it is the revenue growth is just off the chain.

• 44:22Speaker 03

  Yeah, it's pretty, pretty crazy. All right. Where can we send people to find you on the internet?

• 44:26Speaker 02

  planetary.vc or at Mez on Twitter.

• 44:30Speaker 03

  Amazing. All right, Mez, thank you so much for doing this. We'll do it again in the future.

• 44:34Speaker 02

  Thank you, Pom. Take care.