Jonathan Ross, Founder & CEO @ Groq: NVIDIA vs Groq - The Future of Training vs Inference | E1260

Jonathan Ross 0:00 We did not raise 1.5 billion, that's revenue. That's actually about 30% of the revenue of OpenAI. Your job is not to follow the wave, your job is to get positioned for the wave. You could almost say we're one of the best things that's ever happened to NVIDIA, because they can make every single GPU that they were gonna make, and they can sell it for training, high margin, gets amortized across deployment. You know, we'll take the low margin, high volume inference business off their hands, and they won't have to sell you the margin. We're growing faster than exponential. And when you are growing faster than exponential, there is no amount of profit that you can make that matters. What matters is getting a toehold in the market and becoming relevant.

Harry Stebbings 0:38 Ready to go? Jonathan, thank you so much for agreeing to do this in Paris. You look fantastic, by the way. I feel so underdressed, but you look great.

Jonathan Ross 0:58 Thank you. I, I could take the tie off if you want, but I'll never be able to tie it again. I don't know how to tie a tie.

Harry Stebbings 1:01 (laughs)

Jonathan Ross 1:02 No, literally, my Chief of Staff has to tie it for me, it's, and it's, like, a struggle 'cause, like, he's putting it on himself, he's tying it. I, I literally only bought this suit recently.

Harry Stebbings 1:12 Well, I mean, you look fantastic. I don't, I, I think, have a suit, so you're one up on me. I wanna split the show into two parts today. I wanna talk about the landscape where we're at, and then I wanna dive specifically into Grok, where you're at. You've announced a massive new deal that I think everyone's slightly misunderstanding-

Jonathan Ross 1:26 (laughs)

Harry Stebbings 1:26 ... what we were just talking about. Um, I just want to start on

Harry Stebbings 1:29 where we're at. In terms of, like, scaling laws, everyone says we are at the limits of scaling laws, and then there seems to be exponential innovation happening with the likes of DeepSeek and others.

Jonathan Ross 1:39 Yeah.

Harry Stebbings 1:39 Where are we at in terms of the limits of scaling laws?

Jonathan Ross 1:42 So scaling laws is a paper that was published by OpenAI, and what it does is it effectively says the more parameters your model has, basically the better it can absorb information. So you'll, you'll see these curves that they draw, and they're, they're amazing. You should show it if you can. But effectively, um, you have these sort of asymptotic drop-offs where y- you keep getting better and better, but you get a logarithmic improvement when you put a linear number of tokens in. This is why you see people doing 15 mil- uh, trillion tokens of training and whatnot. But they're misunderstood because, um, the assumption is that all of the, all of the data is the same quality. So, uh, you have a kid now, right?

Harry Stebbings 2:24 Mm-hmm.

Jonathan Ross 2:24 So eventually, you're gonna be training your kid, and you're gonna say, and play along with me here, "What's one plus one?"

Harry Stebbings 2:30 Two.

Jonathan Ross 2:31 What's two times three?

Harry Stebbings 2:33 Six.

Jonathan Ross 2:33 What's the second derivative of the square of the hyperbolic tangent?

Harry Stebbings 2:38 Yeah. Yeah, good question.

Jonathan Ross 2:39 But, but that's how we train these models. We give them really simple problems to solve, and then we give them these really hard ones. We, we don't really train them up. We don't do it smart. So what some people do is they will train on the dregs of the internet, and then they'll save some high-quality data for the end to make them better. But what you can do, and this is where I think everyone's getting confused, is it's sort of like with AlphaGo Zero, where it generated its own data and trained. You can have an LLM generate synthetic data, and when it generates the synthetic data, the data's better. You then train on that synthetic data. So what you do is, is you, you train, you train-

Harry Stebbings 3:19 Why is synthetic data better than real data?

Jonathan Ross 3:21 Because the model is smarter. So, you know, Reddit is great, but not necessarily as high-quality as talking to someone with a PhD in a topic.

Harry Stebbings 3:33 Sure.

Jonathan Ross 3:33 And so, just like with, um, more expert people who are more knowledgeable and more capable, if you have a better model, it generates better data. So you train the model, it gets better, you, you produce better data, and you produce a, a range of data here, and you get rid of all the parts that are wrong. So now it's the best part, so it's a little better than the model is, 'cause you're pruning it, 'cause you get to do this offline, right? And then you train the model, and the model comes up here, and then you do this again, and then you keep the better data. You train it again, you just keep moving up. So when you do that, the actual scaling laws don't look like these asymptotics. They actually improve-

Harry Stebbings 4:07 But there has to be a ceiling on efficiency. No?

Jonathan Ross 4:10 Does there? So there's a mathematical limit. So if you-

Harry Stebbings 4:15 (sighs)

Jonathan Ross 4:15 If you study computer science, you've probably heard of something called big O complexity.

Harry Stebbings 4:18 Yeah.

Jonathan Ross 4:19 Big O complexity is, um, you know, i- i- if I am solving a problem, and I look at how I solve it, I might need to take more steps if I solve it with one algorithm versus another. So for example, quicksort versus bubble sort. Quicksort, I need n log (n) steps. Bubble sort, I need n squared. What's the difference? If I'm sorting 1,000 numbers, n log (n) , that's 10,000 steps. But with, um, n squared, that's a million steps, because it's either 10 times 1,000 or 1,000 times 1,000. One of the reasons that these LLMs struggle to multiply large numbers is because, um, multiplying is not linear. These LLMs can do anything linear without, you know, needing to think. But just like on a piece of paper how you need to write out all those intermediate steps, these LLMs need that intermediate space in, in order, and, and those steps, in order to compute these things. It's a mathematical requirement. There's nothing, you cannot train a model enough so that it'll see any arbitrarily large number and just be able to multiply it. But you can choose bigger and bigger groupings of numbers for it to memorize, in which case it can do it in fewer steps. And effectively, as you are training the model on more and more data, it's seeing more and more examples, so now it just has the answer for more specific situations, so it doesn't need to do as much reasoning. But it still needs to do reasoning for some of these problems.

Harry Stebbings 5:49 So what does that mean for the next step?

Jonathan Ross 5:51 In terms of?

Harry Stebbings 5:52 What happens now? If we have no efficien- like, if we have no efficiency ceiling, what does that actually mean?

Jonathan Ross 5:57 But you need both. So the, the training of the model makes it more intuitive. It means that it can sort of just...... come up with the answer like that, more stream of consciousness. The reasoning part is different. The reasoning is the algorithm on top, right, the- the big O complexity portion. So it's system one, system two thinking, or thinking fast, thinking slow, like Daniel Kahneman's book. And so when you pair them together, when you make it more intuitive, it, you know, you get- you get better this way, right? But when you start adding in the system two portion, you start to get this, right? You- you hear the- the volume is very little, but when you do this, and so you get this, um, polylinear is the term, but you could think of it as geometrically increasing, improvement in the model when you combine it with that improved training, but also the improved what they call test time compute or runtime

Jonathan Ross 6:48 compute.

Harry Stebbings 6:49 Totally get that. So just so I understand. So when we think about bottlenecks, if we have synthetic data that powers the training...

Jonathan Ross 6:56 It gets more intuitive-

Harry Stebbings 6:57 Yeah.

Jonathan Ross 6:57 ... because it gets to the answer more quickly, sort of like a grandmaster in chess just seeing the right moves.

Harry Stebbings 7:02 Sure, but synthetic data is not constrained in terms of its supply size. If we think-

Jonathan Ross 7:07 That's true.

Harry Stebbings 7:07 ... if we think about the other bottlenecks, there is hardware, there is energy efficiency, there's algorithmic limits. What is the bottleneck?

Jonathan Ross 7:15 But- but if I'm tell, if- if your job is to get better at multiplying numbers-

Harry Stebbings 7:20 Yeah.

Jonathan Ross 7:20 ... and I tell you that I want you to be able to do it with fewer steps, more intuitively, for you to be able to multiply three-digit numbers versus two-digit, you need 10X the data, and you need 10X the examples, right? And so as you get better on the intuitive part, you need more examples to train on. Make sense?

Harry Stebbings 7:39 Sure. Totally. And so what is the bottleneck then there? Is it the hardware quality? Is it compute? Is it algorithms? 'Cause it's not data.

Jonathan Ross 7:48 It is the compute. It is the data. It is the algorithms. It's all three of them. But, uh, so people misunderstand the concept of a bottleneck. Compute has been more of a- a- w- less of a bottleneck and more of a- a sort of a, you know, soft neck or something, right, where when you provide even more compute, you can sort of overpower the lack of data, the lack of improvement in algorithms. So it's not a hard bottleneck, it's a soft bottleneck. But ideally, you would improve all three. You would be getting better data. You would be getting better algorithms. And the algorithm improvements are gonna be there. The- the data improvements are gonna be there, but compute has always been the easiest lever because it's so fungible. If I just give you more compute, it works better.

Harry Stebbings 8:34 Has DeepSeek not shown you that actually we don't need the compute and you can do more with less?

Jonathan Ross 8:39 Not exactly. There was an algorithmic improvement on that, and the algorithmic improvement, as I explained, w- you know, is this seemingly silly thing where they just wrote the answer in a box and then they knew what to look for rather than ha- having to have a human being check it or something like that, right? It was very simple. Um, but that was an algorithmic improvement, and it made it easier to generate the data that was then

Jonathan Ross 9:00 trained on.

Harry Stebbings 9:00 Can I ask, I think there's misconceptions around compute, data, uh, especially kind of synthetic- synthetic data, as you said there, algorithms. When you think about the biggest misconceptions that people have around AI and s- specifically kind of inference, what do you think they are?

Jonathan Ross 9:16 When we started, the first misconception, which people don't hold anymore, is that training was more expensive than inference. Um, at- at Google, anytime we would train a new model, we would end up using 10 to 20 times as much compute on the inference as the training. So we always ended up j- d- Inference was always the- the critical infrastructure piece that we needed. Um, but then after getting, you know, past that, now everyone understands inference is important. Um, I think one of the-

Harry Stebbings 9:48 Do you think they fully do? 'Cause when you look at NVIDIA's stock price post-DeepSeek, it was down 15%. If you understood the value of inference, shouldn't be down 15%.

Jonathan Ross 9:59 Well, and Jevons paradox and all that and, yeah, I- I don't agree that NVIDIA stock should have gone down for that. I think that was a- a mis-, um, understanding on most people's part. But it also shows, I think that shows more, like, everyone keeps saying NVIDIA stock can't possibly go higher, right? And they were looking for an excuse for, "Oh, now that's it. That's why we were wrong and we need to sell now." But that has nothing to do with the, that- that's just a- a sort of popularity contest side of the market. That had nothing to do with the weighing machine of the market.

Harry Stebbings 10:28 So should founders building today, should they build with the assumption that scaling laws will continue? Should they build with what we have today? How do you advise them on that?

Jonathan Ross 10:37 I would advise you to build based on things getting better, but I would also focus a little more on the- the sort of big quantum steps. So the analogy that I like is if you look at the information age, we went through, w- we had the printing press, we had the telephone, we had the telegram, we had the, um, internet, and we had smartphones, right? And if you had built Uber back when we had, um, uh, internet, it wouldn't have worked because you'd book a ride. You'd go somewhere. How do you get home?

Harry Stebbings 11:13 (laughs)

Jonathan Ross 11:14 Exactly, right? And, uh, we're in the same sort of space now. So w- we don't, the models hallucinate. So it would be hard to build a medical diagnosis company. It would be hard to build a legal company, right? However, if you were doing that and the algorithmic enhancements happen that get the hallucination rate down, you are perfectly positioned, just like Grok. We were around for seven years before we had product market fit, right? We were around. We, our bet was scaled inference, that inference was gonna be the bottleneck, that we were gonna need to run really big, heavy models. Like, everyone was assuming you would have a single PCIe card running inference 'cause training was the complicated part, right? The- the reality was we made the right bet ahead of time and then we were perfectly positioned. Your job is not to follow the wave. Your job is to get positioned for the wave. And that's the hardest thing to do because everyone is trying to talk you into coming on shore again.... right? Almost everyone was telling us, "Don't do LLMs. They're gonna be terrible for you." And we're like, "This is literally what we built for."

Harry Stebbings 12:17 Did you ever doubt yourself? Seven years is an incredibly long wait time.

Jonathan Ross 12:21 Well, it (sighs) ...

Harry Stebbings 12:23 Just doubt th- that-

Jonathan Ross 12:24 There was doubt, but there was never a pause, and the reason was... So even back m- before starting the TPU, I was concerned that AI was going to be a technology that would allow some people to have o- outsized control, outsized influence. If you allow that to just happen in potentially not the best hands, it doesn't really matter how rich you are, it doesn't matter... Uh, nothing matters. It, it's the most important technology. So, it didn't matter how hard it got, there was no choice but to be successful, and our goal is to preserve human agency in the age of AI, right? If, if we don't do that, we have failed, and so it wouldn't matter whether there was doubt or not. And yes, there was plenty of doubt. There was a point where we were so close to running out of money, we did this thing that we called Grok bonds. So you know, um, uh, war bonds from World War II?

Harry Stebbings 13:21 Of course. But for anyone that doesn't, what is a war bond?

Jonathan Ross 13:24 So, a war bond... World War II was funded with bonds. The US government, uh, uh, they had these posters. It was like, "Fund your troops," and, and whatever, and you'd buy them and, and they would pay you a return, and that funded the war effort. We were very close to running out of money at one point. Rather than trying to pretend to be strong, you know, we were vulnerable with our employees, and we said, "We're gonna run out of money. We need you to trade equity, salary for equity." We literally took pictures of the, the war bonds and we put Grok bonds on it instead, and we had an all-hands where we, we said this. And we were worried everyone was gonna leave. Uh, instead of leaving, about 80% of the employees participated, 50%, I think, went to the statutory minimum salary by law. When we finally raised, um, the first bid of our $300 million round, we had, uh, so little money in the bank left that it was less money than we saved doing Grok bonds. So, had we not done that, we would've literally run out of money. So, there were (laughs) some really hard times, and I know every founder has these, and from the outside it's so hard to understand. It's like watching a TV show. You're not in it, but you mult- y- you know, it's like when you are there, everything is 10 to 100 times more intense, because people left their jobs, they left their careers, their families are banking on this, and you have to make decisions like go out there... And what happ- what would have happened if we went out there and asked everyone to do Grok bonds and everyone quit? Then the shareholders would've been ho- like, you have all of these people depending on you, but if you lean towards that vulnerability, people are often gonna go with you

Jonathan Ross 15:12 on it.

Harry Stebbings 15:12 So, what is a world where inference is so crucial and 20 times more important than training? What does that world look like?

Jonathan Ross 15:20 Uh, I think the simplest way to understand it is equate an LPU or a GPU to an employee, right? If, if you have enough of them, uh, the LPUs or GPUs, you can do work just like with an employee, but it's, it's a little different in the sense that, uh, they can't quit and take another job. Uh, you don't have to retrain th- o- once you get a model to a certain capability, it'll always be at least that capability, right? It's not gonna regress. You, you, you know... um, so you get the consistency out of it. But now imagine that you're a startup and rather than having to go out and hire 100 people, you hire 10 and you buy the amount of compute equivalent to 90 employees' worth. That's a very different way of thinking about the world because now CapEx, or in, in some cases different types of OpEx, can, can be used instead of just employees. And so th- a- and in terms of inference, just to give you a sense of our scaling, we started 2024 with about 640 chips in production. We ended with over 40,000.

Harry Stebbings 16:29 Wow.

Jonathan Ross 16:29 This year we wanna be at over two million, and next year the number is much, much, much larger.

Jonathan Ross 16:35 So-

Harry Stebbings 16:36 Are we seeing constraints on chip supply? I mean, that is an unbelievable scaling story.

Jonathan Ross 16:40 Yeah. So, for us to hit our numbers next year, which I'm not sharing publicly, we're gonna need almost all of the capacity of the fab that we're, we're using. The, the biggest issue... So, 7 Powers. We love 7 Powers, right?

Harry Stebbings 16:55 Uh, by far. Yeah, yeah.

Jonathan Ross 16:56 Hamilton Elmers? Okay. Um, you don't normally think of tech companies as having a cornered resource, but NVIDIA has a cornered resource. They're a monopsony, the opposite of a monopoly, a single buyer-

Harry Stebbings 17:07 Mm.

Jonathan Ross 17:07 ... for HBM, and, and the interposer, the CoWoS.

Harry Stebbings 17:11 So, what is HBM?

Jonathan Ross 17:12 So, HBM is high-bandwidth memory.

Harry Stebbings 17:14 Okay.

Jonathan Ross 17:14 And w- w- one... GPUs are, and-

Harry Stebbings 17:18 And who produces HBM? I'm sorry for the dumb questions (laughs) .

Jonathan Ross 17:21 There's, there's three companies in the world that do this. Um, SK hynix, Samsung, and Micron.

Harry Stebbings 17:25 Okay.

Jonathan Ross 17:26 And it's a specialty memory. It's only used in high-end servers, so there's a limited quantity that's built. It's very expensive to ramp up. It's a very technically challenging type of memory to build, more so than others, and so there's a very limited supply. And GPUs are f- so fast computationally that if you were using regular memory, it'd be like drinking out of a martini straw.

Harry Stebbings 17:50 (laughs)

Jonathan Ross 17:50 It would just take forever. This is why you see people preferring to do, um, even inference, but especially training, on, um, GPUs rather than CPUs, because the memory bandwidth is too limited, and, uh, CPUs d- rarely use HBM. They're mostly regular memory.

Harry Stebbings 18:07 Mm-hmm.

Jonathan Ross 18:08 Um, our architecture... And so y- it's, the, the observation that we had when we started Grok...... everyone knows Moore's Law. Every 18 to 24 months, like clockwork, double the transistors. It means double the compute. But we noticed that AI was getting better faster, and it, it clearly wasn't the algorithms, 'cause algorithms have sort of discontinuous jump. It also, uh, didn't seem to be, um, the data, 'cause there wasn't that much more data, and the transistors were only doubling every 18 to 24 months. So where was all of this capability coming from? Turns out the number of chips was also doubling every 18 to 24 months. So rather than 2X, it was 4X. So the, the question we asked was, if you're effectively gonna have an unlimited number of chips, do you do something architecturally different? And the answer is absolutely. So rather than using external memory, we just use a large number of chips and keep all of the parameters of the model in the chips live, and then we just have this pipeline where the computation flows through it, sort of like an assembly line, right? So imagine if you were trying to build a factory, and the factory was only 1/100 of the size needed for the assembly line. So you'd run a bunch of cars through 1/100, tear it down, set up the next 1/100 assembly line. You'd just do this over and over again. That's the way a GPU works. LPUs, very different. We actually just have the, the computation flow through a whole bunch of chips. So rather than using, um, eight chips, we'll use 600 or 3,000 for a model.

Harry Stebbings 19:40 How does that change energy efficiency?

Jonathan Ross 19:42 It, it improves it about 3X, and the reason is-

Harry Stebbings 19:45 How does it improve it when you use more?

Jonathan Ross 19:48 So-

Harry Stebbings 19:48 'Cause you use less for more?

Jonathan Ross 19:49 Per token. So the footprint is higher. Think of it as the difference between a factory or a backyard, um, sort of garage. The backyard garage is not gonna be as efficient. However, it has a lower energy footprint. Or another example would be if you were trying to transport a ton of coal from one side of the city to the other, and you did it on mopeds or you did it with freight trains, which one would be more efficient? The moped would use less energy per trip, but it would need more trips and therefore would use more energy overall. In fact, this is one of the things most people misunderstand. They think that edge computing is lower energy. Actually, edge computing is less energy efficient than computing in the data center.

Harry Stebbings 20:31 Why is that?

Jonathan Ross 20:32 When you're computing in the data center, it's a little bit like that freight train. You're actually getting to do a whole bunch of jobs simultaneously. So the fact that we don't have to read from that external memory means that, um, we don't have to spend the energy doing that. Even with GPUs, you get to batch. But going back to why it's so energy efficient, the amount of energy used in a chip, there's, there's these physical wires, and the physical wires have a width, and when you look at the width and you look at the length, you charge that wire up to set it to a one, and then you discharge it to set it to a zero. Which means it's sort of like charging a capacitor and discharging a capacitor using energy. The longer that wire, the more charge. When you have HBM here and another chip here, you're actually having to charge a wire between the chips and then discharge it every time you send a bit. And so th- that's a long distance to travel, but also the wires are wider than the wires that are inside the chips. So you just use a lot more energy. When we keep that, um, memory in the chip, it's only traveling a little distance using much thinner wires, and therefore it uses a lot less energy.

Harry Stebbings 21:41 So do we see a world of LPU and GPU, GPU (object clatters) usage in combin- Like, how d- how does that-

Jonathan Ross 21:48 Yes.

Harry Stebbings 21:48 ... distribution look between LPU usage and-

Jonathan Ross 21:51 So-

Harry Stebbings 21:51 ... GPU usage?

Jonathan Ross 21:52 There's a couple of things. The first is, um, training should be done on GPUs. W- And actually, I think NVIDIA will sell every single GPU they make for training. Right now, about 40% of their m- you know, market is inference.

Harry Stebbings 22:05 All right.

Jonathan Ross 22:06 Um, I think if we were to, to deploy a lot of much lower cost inference chips, um, what you would see is th- that same number of GPUs would be sold, but the demand for training would increase, because the more inference you have, the more training you need and vice versa. Um, the other use case is we're actually so crazy fast compared to GPUs that we've actually experimented a little bit with taking some portions of the model and running it on our LPUs and letting the rest run on GPU, and it actually speeds up and makes the, the GPU more economical. So since people already have a bunch of GPUs they've deployed, one use case we've, uh, contemplated is selling some of our LPUs to sort of nitro-boost those GPUs.

Harry Stebbings 22:49 Well, this is my question, which is that, you know, people have bought GPUs so far ahead of time-

Jonathan Ross 22:53 Yeah.

Harry Stebbings 22:53 ... that by the time you get them, they're deployed and installed, they're almost out of date.

Jonathan Ross 22:57 Actually, we've, we've spoken with some customers that put orders in over a year in advance. They paid a year in advance and still haven't gotten them. Uh, the, the recent deployment we did in, in Saudi Arabia, uh, 51 days from contract to the first tokens being served in production in country.

Harry Stebbings 23:16 How are you able to do it so quickly? 51 days as an astonishing.

Jonathan Ross 23:19 Yeah. Um, part of it is architecturally, things are much simpler for us. We don't have a bunch of other hardware components. We actually don't use switches to communicate between our chips. We just plug our chips into our chips. Our chips are the switch. And we don't have all of this network tuning. Think about it this way. When you're going across town in, uh, France, how long does it take to get from one side to the other?

Harry Stebbings 23:44 A long time.

Jonathan Ross 23:45 A long time, but a variable amount of long time.

Harry Stebbings 23:48 For sure.

Jonathan Ross 23:48 If you do it in the middle of the night, it might be fast. If you do it in, you know, middle of the day during an event like we've got going on with AI Summit, it could be particularly slow.

Harry Stebbings 23:56 Rush hour, terrible.

Jonathan Ross 23:56 Yeah, exactly. But it's unpredictable.

Harry Stebbings 23:58 Uh-huh.

Jonathan Ross 23:59 However, certain modes of transportation, like trains, can be predictable. With what we're doing, it is, uh, 100% predictable.

Harry Stebbings 24:09 Given the energy efficiency-

Jonathan Ross 24:11 Yeah.

Harry Stebbings 24:11 ... given the predictability, why is NVIDIA not being more proactive on LPUs?

Jonathan Ross 24:17 Um, what makes you think that they don't want to be more proactive on it?

Harry Stebbings 24:21 ... it's, they don't talk about it.

Jonathan Ross 24:23 Well, why would they talk about it? That'd be like talking about something you don't have when you're trying to project strength rather than vulnerability.

Harry Stebbings 24:30 Well, I think if you wanted to protect shareholder value and you wanted to protect a, a Wall Street image of dominance and being ahead of the game, you'd at least say, "Ah, we are of course working on LPUs as well."

Jonathan Ross 24:40 But then until they had that abil- th- until they had LPUs, they would effectively be exposing that there's something missing. Like, if you look at the last GTC, there was an announcement that the latest GPUs were 30X faster than the previous generation.

Harry Stebbings 24:55 Yeah.

Jonathan Ross 24:56 And when you look at how it was done, there was this curve that looked kinda like this, and then it basically ended here, and then there was another curve that was kinda like this. Now, that 30X was from the end of this curve to this curve. If you moved it here, it would have been less than 30X. If you moved it here, it would have been infinite, so their chip is infinitely faster than the previous one. But that wouldn't have sounded reasonable, right? There's a history in this market of specsmanship. Because it's so hard to like get access to chip... And this is a, a lesson on enterprise sales. I think in enterprise sales, people rely on specsmanship

Jonathan Ross 25:37 because-

Harry Stebbings 25:38 Specsmanship is what?

Jonathan Ross 25:39 It's, well, my specs are better than your specs.

Harry Stebbings 25:41 Okay.

Jonathan Ross 25:42 My chip is faster than your chip. I get more teraflops per second than you do, right? But who cares, like, just tell me what the, the tokens per dollar is, and tell me what the tokens per watt is. Nothing else really matters. But people will find all of these other weird things to measure that they might be better on. Sort of like, I'll sell you a car with better RPMs. RPMs don't matter, right? What matters is miles per gallon and maybe the speed that you can drive at, although speed limits kind of render that, you know, moot, right? But in the case of, um, uh, enterprise sales, people often... Well, there, there was a time when the way that you would buy soap or you would market soap, the billboards would say, "Our soap has more bubbles than this other brand's soap." Who cares? And what they figured out was, let's put really happy people up on a billboard after they use the soap, and then maybe people will associate that happiness, right? Lifestyle marketing.

Harry Stebbings 26:40 Sure.

Jonathan Ross 26:42 For some reason, enterprise still hasn't learned this lesson. It's still, "We have more bubbles. We have more teraops. We have more whatever." Things that people just literally don't care about.

Harry Stebbings 26:52 So you think NVIDIA's, "Hey, we're 30 times faster," is not good marketing?

Jonathan Ross 26:56 I think it worked because it's what people are used to, but our counter was w- we did a press release to that, that said, um, "Grok, still faster." That was it. And, and people went gaga over it, right? Because it was just, w- we are, we're still faster, so who cares?

Harry Stebbings 27:15 (laughs) Uh, I, I totally get it. Do you think Wall Street understand that though?

Jonathan Ross 27:20 I think they're starting to.

Harry Stebbings 27:21 Yeah.

Jonathan Ross 27:22 But again, I, I, I don't think there's real competition here. I think if you are competing, you have done something seriously wrong. If you're competing, it means that you haven't found an unsolved customer problem. Because if you're competing, someone else has already solved the problem, so why are you spending time on it?

Harry Stebbings 27:41 So you don't view NVIDIA as a competitor?

Jonathan Ross 27:44 No, they, they don't offer fast tokens and they don't offer low-cost tokens. It's a very different product. But what they do very, very well is training. They do it better than anyone else and by such a wide degree, it's a solved problem. Why would we bother trying to solve a problem that's already been solved?

Harry Stebbings 27:59 So you're like cede the training market to them, we'll own the inference market?

Jonathan Ross 28:03 Yeah.

Harry Stebbings 28:03 And they're saying, "Fuck that. We also want the inference market."

Jonathan Ross 28:07 Of course. It's the way it always works.

Harry Stebbings 28:09 So what do we do now? So now we are competing in the inference market.

Jonathan Ross 28:13 But are we?

Harry Stebbings 28:14 Yeah.

Jonathan Ross 28:15 So w- we don't really have people saying, you know, "We're gonna buy GPUs instead of you." We do have people saying, "We're gonna buy both." That happens, but we don't care, because we al- Like, I showed a demo to someone, and he's like, "Should we just not buy any more GPUs?" I'm like, "No, you should buy every single GPU you can get your hands on." And he's looking at me very perplexed, and I'm like, "Well, how are you gonna do training? We don't do training. Buy the GPUs. Get every single one you can, because I want your models running on us to be really good."

Harry Stebbings 28:43 Totally, but for inference, they don't need to buy NVIDIA anymore.

Jonathan Ross 28:47 They don't need to buy GPUs for inference, but if you can get them, I mean they're a little expensive, but if you're used to it, why not? Plenty of, l- people still sell mainframes. But if you want lower cost and faster, then you want an LPU.

Harry Stebbings 29:03 How much lower cost is it?

Jonathan Ross 29:05 More than 5X lower.

Harry Stebbings 29:07 More than 5X lower?

Jonathan Ross 29:08 The, just the memory alone in the latest GPUs costs more than our fully loaded CapEx per chip deployed. And, and on top of that, so w- we talked about the energy efficiency. So we use about a third of the energy per token. About over a three-year period, one-third of our cost is the OpEx, which is mostly energy and data center rent, and two-thirds is the CapEx, which means that since we're one-third of the energy, the cost to run that GPU to produce the same number of tokens for inference is the same as our total cost. Just the OpEx for the GPU is the same as our CapEx plus our OpEx.

Harry Stebbings 29:42 Why is 40% of their revenue inference, then, and what, why have you not taken so much more of that?

Jonathan Ross 29:48 At the beginning of 2024, we only had 640 chips. At the end, we had 40,000. We're not at that scale yet. So you have to, uh, you have to provide quality, you have to provide low cost, you have to provide speed, but you also have to provide capacity. And so this is where that, that, um, most important part of not using HBM came in. It means that we effectively have no scale limits. So the GPU itself is actually, um, manufactured using the same process that, um, you use for your mobile phone, right? So the same silicon that's in your mobile phone is the same silicon for the GPU. In fact, they build the mobile phones first, uh, the mobile phone chips first, because they're smaller, so they yield better.So the, the, NVIDIA actually gets it after Apple. The, the difference is that memory. That's the only difference, but that memory is the hard part to manufacture, that's, that's limited in scale. So by us avoiding that, we effectively have almost no limit on how much we can scale up, and that's important for inference.

Harry Stebbings 30:49 What is NVIDIA's margin?

Jonathan Ross 30:52 70 to 80%.

Harry Stebbings 30:53 70 to 80%. So they can take 70 to 80% off and be radically more-

Jonathan Ross 30:58 Yeah.

Harry Stebbings 30:58 ... comparatively cost-effective compared to you.

Jonathan Ross 31:01 But-

Harry Stebbings 31:01 Like, you could destroy their margin.

Jonathan Ross 31:04 W- W- But why would... So, uh, w- a- in that same vein, you could almost say we're one of the best things that's ever happened to NVIDIA because they can make every single GPU that they were gonna make and they can sell it for training, high margin, right?

Harry Stebbings 31:17 Mm-hmm.

Jonathan Ross 31:17 Gets amortized across the deployment. And, you know, we'll take the low margin, high volume inference business off their hands, and they won't have to sell either margin.

Harry Stebbings 31:26 What's low margin?

Jonathan Ross 31:27 Anywhere from... Depending on the deal. Um, we do get some on the, the backside, but up front, it's about 20%.

Harry Stebbings 31:33 About 20%?

Jonathan Ross 31:34 Yeah.

Harry Stebbings 31:34 Okay. So theirs is 80, yours is 20-

Jonathan Ross 31:37 Yeah.

Harry Stebbings 31:37 But then you're looking at a 20X margin.

Jonathan Ross 31:38 But then we get more later off of it, so we take some of the risk.

Harry Stebbings 31:41 What do you mean you get more later side?

Jonathan Ross 31:43 So the deals that we do, um, y- the partner will off... 'Cause we don't deploy our... We don't spend money for our own CapEx. The partner will put up the money for us to deploy. We pay back with a m- you know, decent IRR, and, but we split, and most of it goes to the partner, and then once we hit the IRR, it flips the other way. So others are putting the CapEx up for us.

Harry Stebbings 32:04 What does it look like at the end then?

Jonathan Ross 32:06 It's a little... It, it's not like other business models. So we, we didn't just innovate on the chip. We also innovated on the business model, and, um, we're limited in how much money we can make based on how much we can deploy, not how much money we have, because the partners are putting that money up.

Harry Stebbings 32:22 Hm.

Jonathan Ross 32:22 So when I'm looking at what we can do, it's all about how much we can scale.

Harry Stebbings 32:26 What are the limits to your deployment? Is it purely chip constraints?

Jonathan Ross 32:29 Mostly. So you're asking about misconceptions in AI. I think one of them is about power. So it is true that there is a mismatch in the market between people with chips and people with power, but that's partially because you need a data center in the middle and there aren't enough data centers. Those aren't the hardest thing in the world to build. They're not easy, but they're not the hardest thing. It's harder to build up the power. Um, however, because of that mismatch, you have big hyperscalers going around and saying, "I need a gigawatt of power," and they'll say this to, to 60 different potential data center builders. And then all of a sudden you hear this echo, "Well, I heard that, you know, there's a, there's a gigawatt here and a gigawatt here and a gigawatt here," and all of a sudden there's like 60 gigawatts of demand and it's this echo from that first gigawatt. The thing is, I am aware of about 20 gigawatts of power that people wanna make available for data centers now. Right now, there's about 15 gigawatts of data centers worldwide, so more than double the current capacity. M- Concern that I have is that people are now building up more power, and what's gonna happen in the next three to four years is people are gonna be like, "I built up all this power and no one's using it, and this was, like, a complete waste and we're never gonna do this again." Then what's gonna happen, remember that doubling of chips every 18 to 24 months? Well, three to four years, you double that 15 gigawatts twice, and now you're talking about what, 120 gigawatts? There isn't that much power available. And then another one after that, now you're at 240. And so what's gonna happen is we're gonna overbuild slightly right now just because of that mismatch and the, the miscommunication that's going on right now, and then we're gonna dampen our building and we're gonna, you know, close down on that, and then we're gonna have the real need for the power. That's my big concern right now, because that, that power will become a hard bottleneck in three to four years.

Harry Stebbings 34:22 Okay, just so I understand, why will we have that data overs- or data center oversupply when we are moving into a world of inference which will be 20X larger than training?

Jonathan Ross 34:32 So the problem with data centers is everyone thinks that data centers are real estate, and a lot of people do real estate. Data centers are not real estate. Um, the, the common joke, y- in the industry now is someone says, "I'm gonna have, you know, 100 megawatts of capacity for you, and I'm gonna have it in three months. Are you willing to sign?" And then you ask a question like, "Well, um, what's your uptime?" And they're like, "I don't know. Whatever the, the, you know, power grid is." You're like, "Wait, what? Y- w- where are your generators?" "Oh, I haven't ordered those. I'll order them now." "You know that there's a 90-month lead time on generators right now?" "Oh, really?" And then the next-

Harry Stebbings 35:12 A 90?

Jonathan Ross 35:12 90. Nine, zero. Mm-hmm. And then the next question is, "So where are you getting the water from?" "W- Wait, data centers need water? I thought it was a bunch of chips. W- What do you mean water?" So there's a bunch of people who have no idea what they're doing going into it because they think it's real estate, and so those people are now building an oversupply of data centers, but they're not really building them. So they're, they're fake data centers that people think are real.

Harry Stebbings 35:40 What happens to those data centers? 'Cause they're not gonna be utilized, are they? Amazon is not gonna pay for a data center that doesn't have-

Jonathan Ross 35:46 Well, Amazon doesn't fall for this. Amazon has really good people.

Harry Stebbings 35:48 Mm-hmm. Whoever the, the buyer is, is not gonna pay for a data center that's got no water or got no power.

Jonathan Ross 35:54 Yeah.

Harry Stebbings 35:55 And so is it just wasted from your-

Jonathan Ross 35:57 Most of these projects will never be developed.

Harry Stebbings 35:59 Will we build them fast enough? You said about the data c- like, the oversupply.

Jonathan Ross 36:03 Yeah.

Harry Stebbings 36:03 It ta- it does take time to build a data center.

Jonathan Ross 36:05 Yeah, and, and it's, it's... So it's almost... Okay, if you train a model-

Harry Stebbings 36:10 Yeah.

Jonathan Ross 36:10 ... you really wanna amortize it for about six months. If you, um, if you deploy chips, you really wanna amortize it for three to five years, all right? We're more on the three-year side, others are more on the five-year side. Uh, if you build a, um, data center, you're probably talking 10 to 15 years. In a power plant, you're talking like 15, 20 years.So, the- the problem we have in the industry is not on- on this... And- and there's this mismatch between the- the sort of financing and the- the needs here. So, you have someone who wants to train a model, they're gonna be doing this for six months and they don't understand why people want three to five-year commitments on the chips, and then the people deploying the chips don't understand why someone wants a 15 to 20 year (laughs) commitment on the data ce- right? It's at seven years now on the data centers. And then the people building the data centers then need a long-

Harry Stebbings 37:03 So, 70 years commitment?

Jonathan Ross 37:04 Yeah. That's the kind of thing they're asking for.

Harry Stebbings 37:07 (exhales) .

Jonathan Ross 37:08 So, you've got this complete mismatch throughout the ecosystem, but the funny part about it is while they all want to take zero risk and have a committed, um, you know, sovereign wealth level, um, uh, sort of credit rating on the other side of it with long commits, the longer the payoff time, the more generic the infrastructure is. A model has a pretty specific use, but accelerators like LPs and GPUs can be used for other things besides generative AI or LLMs. Um, the data center can be used for other things besides the accelerators. The power can be used for anything. So, while they're looking for the least risk over here, it's the place where there is the least risk because if- if we don't use it for- for AI, we'll use it to power all of the electric cars.

Harry Stebbings 37:59 So, is this a case where incumbents win because they're one of the only ones who are able to match the durations required by data center providers?

Jonathan Ross 38:05 Well, and- and this is why we've partnered with, um, Aramco and this new, uh, entity in Saudi Arabia, because they have an enormous, um, ability to fund this over the long term, they have a very long-term perspective, um, and they have an amazing credit rating.

Harry Stebbings 38:23 I mean, this in the- the- the... So, when you say they have an ability to fund it, they... and this is where the m- misconception was, people think it's a funding round of a billion and a half. It's not a funding round of a billion and a half.

Jonathan Ross 38:32 No, we d- we- we did not raise 1.5 billion. That's revenue. That's a- that's actually about 30% of the revenue of OpenAI.

Harry Stebbings 38:39 Can you just walk me through how that deal is structured?

Jonathan Ross 38:41 Yeah. So, we started off last year, right? And- and we got to, um, 19,000 of our chips deployed and we did that in about 51 days. And the question was, "What could we do this year?" So, they've gone off, they've collected up a bunch of power in- in the country, and the deal is structured so that they will put up the capex for us to deploy our- our chips in that data center, or those data centers, and we pay back based on the money that we make. So, it's- it's sort of, um... it- it's a little bit different than debt in that they participate in the upside, um, but it- it's similar in nature, but it is revenue because we actually make profit upfront.

Harry Stebbings 39:29 How does that change what you can do?

Jonathan Ross 39:30 Well, we are not limited by capital anymore. And one of the unique reasons we can do this... Th- there's a... there is one misconception around Grok. Um, there was a paper that was written that said that, um, w- we couldn't be profitable while being lowest price. We could charge more, but actually, we have a very positive contribution margin right now. Um, and so as far as we know, we're the only ones that are actually making money running these open source models because with the open source models, everyone's sort of competing with VC dollars trying to take market share Uber style, right? And meanwhile, we're sitting here going, "We could do this all day long because we're making money and we're- we're able to even pay off an IRR and- and- and make our partners money." So, the- the difference here is... W- there's another part of the model. So, we're also working with some proprietary model providers, so we, um, uh, actually showed off the first one at Leap, uh, on Sunday, um, where, uh, we did a voice model with Play AI. That one is also a rev share, but the thing is they get to make money off of that, whereas most others in the industry are losing money because of the commoditization of the models.

Harry Stebbings 40:45 So, do you have cheaper pricing over time-

Jonathan Ross 40:49 Yeah.

Harry Stebbings 40:49 ... as you bluntly have, uh, less monopoly power, or do you have higher prices as your monopoly increases?

Jonathan Ross 40:57 Well, we want the... we want the margin to stay about the same, but we want the prices to go down because then we get into Jevons paradox-

Harry Stebbings 41:04 Mm-hmm.

Jonathan Ross 41:04 ... and- and life gets great 'cause we're gonna scale and our focus is on getting to scale, right? To preserve human agents in the age of AI, we need to be one of the most important compute providers in the world. And our goal by the end of 2027 is to be providing at least half of the world's AI inference compute. We think we could be further than 2X, um, given that we don't have all the constraints, but in order to get there, we do need to be very aggressively building out and we need to give people no excuse for not running their models on us and- and using the models that are on us by charging extra. And- and what I keep telling the team over and over again, 'cause you have to remind them sometimes, is we are growing faster than exponential. And when you are growing faster than exponential, there is no amount of profit that you can make that matters. What matters is, you know, getting a toehold in the market and becoming relevant.

Harry Stebbings 41:58 What would prevent that?

Jonathan Ross 41:59 We used to be worried that someone would try and price below us-

Harry Stebbings 42:03 Mm-hmm.

Jonathan Ross 42:03 ... and then we realized that wasn't a concern because there's so much money going into this that people are going to want to lose less money by running on us, so that isn't a concern. But that was the big one early on until we realized that.

Harry Stebbings 42:18 When we see Zuck investing $65 billion in data centers, what does that actually mean? That means he's internalizing all of the margins that he would've had to spend on data centers with the providers that we mentioned earlier, and Facebook is doing full stack. Wh- what does that mean?

Jonathan Ross 42:32 So Meta is doing 65 billion a year. Um, I think Google said 70 or 75, and Satya said Microsoft's doing 80, and then you've also got Stargate.... right? Yeah, these are crazy sums of money.

Harry Stebbings 42:45 And this is all for data center build-out?

Jonathan Ross 42:47 No, it also includes the stuff that goes in. Includes the chips as well, the systems, everything.

Harry Stebbings 42:52 Okay. We've never seen money like this, no?

Jonathan Ross 42:55 No. There's never been anything like this, but there's never been a case where it was so clear that there was gonna be value at the end, right? If- if you knew how successful, um, search was gonna be, right? Remember, Google stayed private as long as they did because they were afraid that Microsoft would figure out how much money search was making and then would try and replicate it, and the moment that they went public, Bing, right? They- they called that perfectly. Everyone knows how much money there is in AI, so everyone's going after it.

Harry Stebbings 43:24 Do you think that value is distributed amongst many players or concentrated towards one or two? I completely agree with you in terms of the clear value when assigned, but is it e- distributed to some levels evenly or concentrated?

Jonathan Ross 43:37 It's a power law. And the more value there is in the economy, the more risk there is of a single entity being so far on one end that they just dominate, and you see this with the Mag Seven, right? And it's predict- just the bigger the economy gets, the more you will have, uh, you know, big swings in- in the economic outcomes. Right now, the hyperscalers are all sort of even in their market caps. It's strange. You would expect one of them to just be killing it and taking it much further, and so I don't understand why they're so closely

Jonathan Ross 44:17 grouped.

Harry Stebbings 44:18 So, when we think about that distribution, like how do we think about changing that then? Like obviously with a growth, you know, you want to be one of the Mag Seven, you want to be one of the most important companies in the world. How do you see that?

Jonathan Ross 44:29 There's... So the way that you get there and the way that you stay there are two very different things.

Harry Stebbings 44:34 Mm-hmm.

Jonathan Ross 44:35 And the- there's a l- there's sort of a circle of life that happens in startups. The first circle or the first stage is solve an unsolved problem, right? That's how you go viral. That's how you do well. The second stage is the marketing stage, which is now other people are trying to copy what you've done, right? 'Cause they can't think of something themselves, and now you have to fight it out in- in advertising and marketing and whatnot. And you see CPG companies often get stuck there, right? And it becomes more about where on the shelf they are than- than anything else. And then the final stage is the seven powers. It's once you've found some of those and you've really started improving it and you have, you know, sort of systemic advantages, and then what happens is someone solves an unsolved customer problem and the whole cycle of life continues, right? Now Google, uh, has to redo this because LLMs are better than search, right? So, the way that you start off to become a Mag Seven is you solve that unsolved problem. The way that you stay there is first you find one of those seven powers or- or multiple, but then you have to be ready for when you get disrupted to continue fighting back and- and solving customer problems.

Harry Stebbings 45:46 Kent, we mentioned the different huge amounts of money that's being spent here. Is this a good bubble that bluntly lays the foundations for an incredible next 10 to 20 years where bluntly the capital actually turns out to be productive but not seemingly so on paper? Or is it where actually just a huge amount of money is incinerated on depreciating assets?

Jonathan Ross 46:06 I can guarantee you that a huge amount of money will be incinerated, but I also bet that in total more money will be made than will be put in. And so this is the problem. You- you have to look at it either in aggregate or individual bets, right? When everyone is making investments in the market, some people are gonna lose money 'cause not every company's gonna be successful. So, what you always see is when there is some real tech improvements or s- things coming, you've got the things that were early that people are investing in heavily that are super successful, and then everyone else wants to get in on it, and you know, it goes from you have AI chips and AI models to now you've got AI, you know, T-shirts, and next thing you know you've got AI thermal grease, right? It- it just, like people just start applying AI to everything. Next thing you know you'll have an AI condo, right?

Harry Stebbings 47:01 Sure.

Jonathan Ross 47:02 Yeah, and so the- the trick is discerning what is real and what isn't. You're always gonna have all of these really obnoxious charlatans coming in whenever there's something real, and that's unfortunate, but eventually they get cleared away once people start to understand the technology and what's real and what isn't. And so the job is to start educating, and the more educated people are, the less they'll invest in AI thermal grease.

Harry Stebbings 47:32 (laughs) What is the largest individual bet that will lead to the largest incineration of cash?

Jonathan Ross 47:37 I'm not gonna call anyone out in particular, but I actually think it will happen across every single discipline. The...

Jonathan Ross 47:45 Are you aware of the Keynesian beauty contest?

Harry Stebbings 47:47 No.

Jonathan Ross 47:48 Okay. So, um, John Maynard Keynes, the economist, he has this great, um... This will explain everything you need to know about VC.

Harry Stebbings 47:56 (laughs)

Jonathan Ross 47:57 So-

Harry Stebbings 47:57 I'm nervous, but keep going. (laughs)

Jonathan Ross 47:59 So, um, take a magazine full of models, human models, like, you know, good looking models, and have a whole bunch of VCs in the room, and they're allowed to make bets on who the- the most beautiful model is, and in the end, whoever has the most money on them is the winner, and based on the proportion that you put on that particular model's face, you get the share of all of the money. So if you, if you put money on one that isn't the most beautiful by dollars, then you lose your money to the- the people who bet on that one. And that was sort of the- the bet that SoftBank was making, which was they could win the Keynesian beauty contest. "I'm just gonna put more money in and I'm gonna win." That is problematic when you have true technological advantages as opposed to marketing. When you're solving customer problems, it's a weighing machine. Once the customer problem has been solved, you then get into this sort of popularity contest of marketing.Now, something unusual has happened this time around, which I don't think has ever happened in, in VC before, which is, y- you see people raising billions of dollars, who, who have competitors who've raised billions of dollars. It... Usually, there is a clear winner in the Keynesian beauty contest. You don't have this, like, fight where, you know, i- i- it's, it's sort of like, "Well, I gotta put a little more money in." "I gotta put a little more." "I gotta, you know, put 10 billion in." "No, I'm gonna put 20 billion." "I'm gonna put 500 billion in!" Right? Because the Keynesian beauty contest has gone completely amok (laughs) . And, um, this has never happened before, and so now people don't even understand how to react because it used to be if someone had raised a billion dollars, you're like, "Oh, they're the winner."

Harry Stebbings 49:46 'Cause-

Jonathan Ross 49:46 Now, it's like there's three or four competitors who have a billion dollars.

Harry Stebbings 49:49 So who wins and who loses? Like, is Meta gonna incinerate the largest amount of cash ever?

Jonathan Ross 49:53 I think the Keynesian beauty contest no longer applies here, because there's so much money available, being spread out, and, and I think you're... You're gonna see that the people who have the best products are actually gonna be the winners because everyone can be capitalized. But there will be problems for the winners because of this. The problems are gonna be of the sort... You, you had this employee that you were gonna hire, and someone offered them a ridiculous amount of money. Yeah, you see this all the time now.

Harry Stebbings 50:22 Yeah.

Jonathan Ross 50:22 And they could have gone and contributed to the winner, but now they're contributing to a competitor that shouldn't even exist, right, or is equally likely to win, and now you're splitting the talent.

Harry Stebbings 50:31 What do you also do when you have such high salaries? We've seen a million, two million for kind of junior to mid-level in some of these companies, and they are living an amazing life, actually, in great places. You think they're living that amazing life in Guangdong when they're working for DeepSeek or any other Chinese alternative? I don't think so. I think they're actually getting paid much less, working their fucking ass off 20 hours a day, and not getting kombucha and being paid two million a year. Fair?

Jonathan Ross 50:59 Not only fair, we have a policy that we never offer the highest because we want people to choose us, not choose the salary. If we win in a bidding war, then that means the next time someone comes along with a higher salary, that's it. They're just gonna go take that other job. There's no loyalty. They don't believe in the mission. Instead, we focus on, "Look, we're gonna build this. This is your opportunity. You're gonna get to work with amazing people. Um, spend some time with the team. Are these the people you wanna be working with? Because frankly, you're gonna make so much cash, it doesn't matter." But bet on the equity, the outcome, right? "Help us make this thing valuable." And people who buy into that, they're so much easier to manage because they're mission-oriented. They all wanna do the same thing. They're not there because they want the kombucha, and they're not gonna complain because the, the cappuccino machine is broken. They'll just go and buy their coffee next

Jonathan Ross 51:52 door.

Harry Stebbings 51:52 Will you and NVIDIA move into the model layer? Everyone talks about model providers becoming application providers. Will infrastructure providers become model providers?

Jonathan Ross 52:01 We have decided that we're not gonna train our own models. We'll do a little fine-tuning for specific cases or whatnot, but we don't wanna compete. And that's really important because, uh, people are putting their models with their weights on us, right? And, and, and they don't want us to, to learn from and take that stuff for our own benefit. This is the problem you have when you work with a hyperscaler because you know they're also doing everything that you are doing. So we've decided model providers, you make the model, we don't do that. I think there's also the data side of the users and the queries. So the other thing that we could do that we do not do is log the queries, and then we've got data if we wanted to train. We don't train. We have no reason to hold the data. So we, we only temporarily store things in the DRAM so there's no persistent storage. If the power went out, everything's gone. And DRAM is limited, so we can't hold things for a long time. So you know that we don't have your data. Now, people who are building businesses on top of us, you can obviously keep the data from your customers if you want. We have no control over that. That's fine. But we don't take any data.

Harry Stebbings 53:09 Do you think NVIDIA move into the model providing?

Jonathan Ross 53:12 It's possible, but I think... I mean, if I was them, I would avoid it because I wouldn't wanna give the customers of mine... I mean, NVIDIA is great at training, right? It, it, it's crazy. It'd be like, you know (laughs) , um, being a, um, automotive, uh, uh, uh, a car company and then creating your own taxi service. You're now competing directly with your customer, right? And I think tech companies love to do this. We, we have a management philosophy and it's based on big O complexity, and we only do things that require a sublinear number of employees. So what I mean by that is if someone comes to me and says, "I need 10 people to go do this thing," a lot of people would say, "Well, why can't you do it with five?" I would say, "Okay, you're supporting customers. If we double the number of customers, do you need 20 or do you need 11?" Because I wanna know what's that growth rate. Are they automating everything, right? We completely automated our compiler. We completely automated everything that we... or, you know, large portions of our cloud, and that means that we can scale with a small team. We have 300 people. With 300 people, we built our own chip, we built our own networking hardware and software, we built our own runtime, we built our own orchestration layer, um, we built our own compiler, we built our own cloud. We built all this with 300 people. Now, we would only be able to do this with a small number of people because you don't have the communication overhead. But if we... If... You have to decide what your constants and variables are. What are the things that you wanna preserve? And one of the constants is talent density. We wanna stay small, we wanna stay nimble. And the, the other side of this is growth is a problem. So we measure our growth in what I call problem units. So a problem unit, every time you triple something, you have about the same number of problems as the last time you tripled.... going from 100 employees to 300, 300 to 1,000, 1,000 to 3,000. Each one of those has the same number of problems. We scaled from 620, uh, or 640 LPs last year at the beginning to 40,000. That's four problem units. That's four triplings of the number of chips. If we were also tripling the number of employees, that would be another problem unit. Management bandwidth is limited, you can only solve so many problems.

Harry Stebbings 55:32 Mm-hmm.

Jonathan Ross 55:33 So you have to decide where you're gonna allocate them. If you build things really well from the beginning and you can scale up with the number of employees you have, then you can scale over here. If you wanna triple the number of customers, there's another problem unit that you have to solve.

Harry Stebbings 55:46 What's the biggest challenge when you are scaling at that rate, but then the team is not scaling in conjunction with it?

Jonathan Ross 55:52 There's this common belief that the people that you have early on are right for the job, and the people that you get later, maybe they're better in a s- sort of more corporate environment.

Harry Stebbings 56:01 Yeah.

Jonathan Ross 56:02 I don't think that's the case. I think you should always try and get generalists. Um, otherwise, you, you get stuck and ossified in a particular way of doing things because that's what that one person knew how to do. But there, there are people who burn out. In a s- being in a startup is hard. Like, there are people who just literally burn out. There's also people who were the best that you could get at the time, and then there are people who are just unmanageable wild children and they should go off and start another startup, and they shouldn't be, you know, scaling with you. That's, that happens, but it's the rarer of them. I think saying that you're, you're gonna hire B players because you've gotten large enough is, is laziness and an excuse, and it, it's a lack of creativity in your business model and how you're going... and the algorithm of how you're gonna scale. Think of it this way, Walmart versus Amazon. Walmart wants to double the number of customers, they have to double the number of stores and employees. Amazon does not need to double the number of websites. That's a fundamental advantage. But Amazon still has to double and improve the logistics, right? They don't have as many problems where they have to scale linearly, but they have some. You wanted to disrupt Amazon, what you would do is you'd build a completely robotic logistic system and bring the comple- the, the overhead and complexity of that down, and then you could outmaneuver them, right? That's how you need to impro- Don't just say, "I need more people." Focus on the algorithm of your business.

Harry Stebbings 57:30 And the last time we spoke, we discussed DeepSeek. I think more has come out over the last few weeks about, bluntly, their innovations, some of the distillation that they used. Where is China better than us today?

Jonathan Ross 57:41 Well, uh, as we discussed, they're more willing to use things that maybe they shouldn't be using. Um, you know, they, they distilled the OpenAI model. Uh, a lot of people have the opinion, "Well, OpenAI was scraping the internet, so you know, good one for DeepSeek." But whether that's right or wrong, most of the model providers had considered that a red line they didn't wanna cross. I don't know if that's gonna change, but, but it might. But the other thing-

Harry Stebbings 58:08 Given the open source nature of DeepSeek, OpenAI now benefit from the innovations that they did also have.

Jonathan Ross 58:13 Well, and they also probably have all the data that DeepSeek paid them to generate.

Harry Stebbings 58:19 So?

Jonathan Ross 58:21 Yeah, I... But, but they also were clever, they, they innovated. I think the biggest thing is this is a shot in the arm for morale in China, and it gives them a sense... But, but again, this, you know, as I said, is Sputnik 2.0. It's also woken up the US.

Harry Stebbings 58:36 It totally has. How do you compare Stargate to the $128 billion that China have now permitted?

Jonathan Ross 58:42 So China has a more complicated situation and a simpler one at the same time. The, the, the s- problem is they don't have the technology that we have in terms of the chip efficiency. On the other hand, they have scale. If they wanted to deploy 150 nuclear reactors, as I think the plan is, no big deal, they just do it. So if the chips aren't as efficient, they can just deploy more of them. On the other hand, if they wanna go out into the world and deploy chips like they did with Huawei and networking gear, that's gonna be complicated because people aren't gonna have the power around the world to run more expensive accelerators. The- At home, I don't think anything's a problem. I only think as they're trying to expand, it's gonna be an issue.

Harry Stebbings 59:30 China is quite opaque in everything.

Jonathan Ross 59:33 Yeah.

Harry Stebbings 59:33 What do we not know about China that we would like to know?

Jonathan Ross 59:36 I think the most important thing to understand is where they're gonna end up on the, the censorship and privacy of these models. We come from democratic countries. We have an expectation that companies can build something that says anything. Are they going to be permissive and allow models to make mistakes and hallucinate, or are they gonna shut it down? Because I think if you know that, you know whether or not China has a shot. One of the biggest nightmares that they have is free speech. It's the exact opposite of that vulnerability we talked about earlier. Can you imagine Xi Jinping going out and saying, "Country, we've lost our advantage in AI. I need your help." Never ever. It's always gonna be, "We're the greatest, we're the best." Everyone's gonna know differently, but they're all gonna have to t- toe the party line. Right now, because of that, I think it's really hard for them to just allow these models to, to say anything, say, you know, "The US is great and better at AI." That, that's a bad thing for them, and so that's gonna really tell you a lot about the AI story in China.

Harry Stebbings 1:00:47 And so if they aren't permissive of, uh, more open, truthful models, then they're inherently disadvantaged you're saying?

Jonathan Ross 1:00:55 Well, look what, um... I, I forget if it was... Was it Jack Ma who got in trouble with the CCP?

Harry Stebbings 1:01:01 Yeah.

Jonathan Ross 1:01:02 Yeah. Um-... if they aren't more permissive, then if you're running a Chinese tech company, your fear is that you become Jack Ma. That's really gonna stifle innovation.

Harry Stebbings 1:01:14 (exhales)

Jonathan Ross 1:01:14 If I was in China right now, I'd be looking for the exit for how I could do ... Like, if, if your craft is AI, I would wanna do that some place that's supportive.

Harry Stebbings 1:01:23 Do you really buy that they don't have access to Blackwell? This is China. You think Xi Jinping's like, "No, sorry. No Blackwell." (laughs)

Jonathan Ross 1:01:31 Well, I, I don't, I don't think it matters on whether or not they physically have it, because right now most of the cloud providers are happy if you swipe a credit card to rent it to you.

Harry Stebbings 1:01:39 But there is limits to renting, no? I don't, I'm-

Jonathan Ross 1:01:42 Maybe.

Harry Stebbings 1:01:43 ... I'm naive.

Jonathan Ross 1:01:43 I, I think if you ... So, um, one of the concerns right now is about, um, uh, Malaysia or Singapore, that region over there being a place where people are deploying GPUs with the wink, wink, like, "We're not gonna rent it to China." Right?

Harry Stebbings 1:01:59 (laughs)

Jonathan Ross 1:01:59 Um, but that's, that's a belief that a lot of people are doing that. Otherwise, that's a lot of GPUs for that region. That feels like it's even more of a safety net just in case the tap ever gets turned off at the hyperscalers, because right now, um, you could just write a check to any of the hyperscalers and say, "I need these chips." They'll deploy them, and you can run on them. Doesn't really matter where you're coming from. I mean, if you're a sanctioned country, no. China's not sanctioned.

Harry Stebbings 1:02:25 Okay. So we have China, where China is obviously in terms of innovation and actually proving that they, they are in the race, we have the U.S., and then we have Europe, which, which feels like it's languishing.

Jonathan Ross 1:02:36 Yeah.

Harry Stebbings 1:02:36 Is this the ultimate nail in Europe's coffin?

Jonathan Ross 1:02:38 We talked about how Grok almost died.

Harry Stebbings 1:02:40 Yeah.

Jonathan Ross 1:02:41 But we had the right technology all along. We were just waiting for the thing, for, for the LLMs to arrive. And I think Europe's very similar. I think Europe has amazing talent, amazing talent. Um, but that talent leaves and goes to the U.S. or other places.

Harry Stebbings 1:03:01 (exhales)

Jonathan Ross 1:03:02 So the question is, how do you have Europe's LLM moment? How, how do you position yourselves? And it's, it's not that complicated. The problem is when you surround yourself, y- y- you become the average of your five closest friends, right? If your five closest friends are like, "That'll never succeed. Ah, you should just keep your job. Ah, startups, they're terrible," then you're gonna be risk averse. But if your five closest friends say, "You should do it. That's great. I support you," then you're gonna be more likely to do a startup. And even in Silicon Valley, people make that transition from the big tech company to the startup. And it's hard, right? They're, you know, comfortable, right? They're making those crazy salaries.

Harry Stebbings 1:03:48 Mm-hmm.

Jonathan Ross 1:03:48 The, the big companies take care of them. And they have a fiduciary obligation to their family. How do they make that, that leap? And it's because you've got tons of entrepreneurs trying to hire them, and they hear the pitch all the time, and they get used to it, right? They also see the success around them. And then VCs come in and, and try and close some of the candidates in the early stage too, right? Europe needs the same thing. You need a place where people are surrounded, just surrounded by entrepreneurial people who are risk on and who aren't gonna try and talk people out of joining a startup.

Harry Stebbings 1:04:25 From a regulation perspective, Europe is, you know, unbelievably efficient and the masters of regulation. You know, I was speaking to someone the other day in the EU who supposedly hired 1,500 people for AI safety and policing. Um, what would you do if I put you in charge of European AI regulation?

Jonathan Ross 1:04:43 Well, I wouldn't waste my time regulating something that doesn't exist. Instead of regulating, what are you gonna promote? You wanna promote risk-taking. You wanna promote that enclave of people who are risk on. So, I was just visiting Station F yesterday. Amazing. Macron was there. It was, like, full of people, right? Vibrant, you feel it. I would ... And I was talking to the, the person who runs Station F, Roxanne, um, and Xavier Niel. And with Roxanne, we were talking about, what about a City F? What about, uh, a place where there's ... You start off with, like, 10,000 people in, in the center, right, a little radius. And then once that's full, you expand it. Once it's full, you expand it, and so on, to get to, like, a million people in Europe who are all risk on, a little Silicon Valley here. And I would give it special economic dispensations. I would allow everything that employers need. I would make it simple, and I would say, "You know what? If you don't wanna buy into that, that's fine. Go to other regions in France. Go to other regions in Europe. But if you want to participate in what is going to be the biggest technological revolution in human history, this is the city for you."

Harry Stebbings 1:05:53 You're not inherently punishing incumbents then? And what I mean by that is if we are talking about, uh, I'm just using this as an example, uh, AI insurance underwriters, startups. Yeah. There's many companies that are going after insurance underwriting in AI, and you are giving them benefits like that. You are inherently punishing some of the biggest providers of insurance in your region. You're inherently punishing people who hire 200,000 people. That feels unfair.

Jonathan Ross 1:06:18 So, there is no right to be an incumbent, especially a slothful incumbent that is not reacting to disruption.

Harry Stebbings 1:06:26 Fair.

Jonathan Ross 1:06:27 And you want to encourage disruption. And this is one of the things in Silicon Valley. You can move from one place to another. There, there are no ... I mean, we had non-solicits when I started, but even that's gone, right? So, that free movement of people is very important.

Harry Stebbings 1:06:42 Are you allowed to start work straightaway?

Jonathan Ross 1:06:45 Straightaway, but not before. If you start before, that's bad.

Harry Stebbings 1:06:48 Yeah, but we have months, months, like six months.

Jonathan Ross 1:06:51 There's no such thing. And so in that region, I would say you can immediately start, like literally the next day.

Harry Stebbings 1:06:56 That is so good. We have to wait six months.

Jonathan Ross 1:06:59 It's not good. If you are a company right now, it, it feels like, "Okay, well, it's harder to poach." But what does that do? It suppresses wages.It's harder to hire someone, they're less likely to move. There's less competition, it suppresses wages.

Harry Stebbings 1:07:11 And by the way, the company has to pay for the six months anyway. (laughs) It's ... It makes no sense at all. I, I, so I, I totally get you and understand there. Um, can I ask you, you know, you mentioned, like, what would you promote? Uh, a lot of people would promote ... I loved the way you said risk-on. Um, being a European, I actually thought first safety and regulation, uh, but specifically safety. So, uh, sticking with that, all that Dario will talk about these days is safety. Is he losing a step by being so focused on safety when, bluntly, his competitors are talking about product?

Jonathan Ross 1:07:45 So, safety matters in AI. It's a little bit like nuclear power. Lots of pros, lots of cons. I'm worried about different things than, I think, um, than Dario is, is worried about. I'm more worried about people voluntarily giving up their decision-making authority-

Harry Stebbings 1:08:04 Mm-hmm.

Jonathan Ross 1:08:04 ... because it's so easy, and this is what I mean by preserving human agency in the age of AI. Good analogy is, you, you probably know plenty of wealthy people and the struggles they have bringing up children with wealth. I refer to it as financial diabetes. Right? You, you have children who aren't incented to, to ... They're not, they're not gonna strive to succeed. I was very fortunate when I was growing up, and so I, I actually just told this story for the first time today, so n- no one's heard it, but, um, I was fortunate because my father lost all of his money multiple times. Just ... And I've heard you say the same thing.

Harry Stebbings 1:08:43 Mm-hmm.

Jonathan Ross 1:08:43 Yeah. And he would sell a billion-dollar life insurance policy, and he would get all the commissions from that, and he would have tons of money, and then he would spend it all. And so there was one time we were living in a $20 million mansion, and there was a couple of times where we ordered food. He would talk to the delivery guy, and he would convince him to, to give us the food, and he would pay him back later 'cause we'd get money later. But, um, this time, he was, like, so despondent, he sort of locked himself in his office and wouldn't come out. My little brother came to me and said I had to go and talk to the Chinese food delivery guy and convince him to, uh, to give us the food. And I was, like, mentally preparing how to, like, convince him to do it, and I, I walk out, and I walk up to him, and I'm, like, getting ready to do my whole spiel, and he hands it to me, and I'm like, "Uh, I don't have the money right now." He's like, "Oh yeah, pay me later." I didn't have to ... I fortunately didn't have to do anything. He just trusted 'cause we were living in a $20 million mansion. But that happened multiple times, and when that happens multiple times ... Like, I have a friend who was homeless once for a couple of weeks, and he'd almost been homeless a couple of times, and he said the best thing that ever happened to him was that he was homeless for a couple of weeks because he survived it, and it's like, I've been through it.

Harry Stebbings 1:09:55 (laughs)

Jonathan Ross 1:09:55 I, I always viewed this as the worst thing that could ever happen in the world. But now that I've been through it, I can survive it. I'm not worried anymore. I think we live incredibly comfortable lives, way too comfortable. Most people don't have to go through that. And so we have this sort of financial diabetes as a society, and I think it's gonna get worse with AI. I think we're really going into an age of abundance. Very few people have to worry about food security now. But what happens if you don't need to worry about home security or anything? What happens if you can just live a life without working? And what is that gonna do to your psychology? And so as we enter an age of abundance, how do we get people to still be making their own decisions and have a full life?

Harry Stebbings 1:10:40 Do we get better, or do we get accepting of good enough? And what I mean by that is, you know, now, bluntly, with, with the majority of schedules, we will start with OpenAI, and we will do deep research, and then we will kind of use different prompts depending on different guests, and then we supplement it with a huge amount of research from speaking to Chamath and speaking to Scooter and speaking to everyone in between. We care about it being good enough first and then fucking great later with all the references. Most people will actually just be happy with good enough and get away with it. Do we, as a human society, get happy with good enough?

Jonathan Ross 1:11:17 When we hire, we hire for something that we call booking the win early. So, one of the most important driving forces for people is loss bias. When you have something, you don't wanna lose it. People are less likely to go after something that they, they've already had. And you, you did grow up in a, in a family that was well-off, and then you lost that. That might be part of the drive because you wanted to get back to it. When we have an engineer that we're hiring, and there's a room full of people who are saying, you know, "Uh, if, if we do this thing, we could be twice as fast," I want that engineer to hear, "Wait, if we don't do that, we're gonna be half the speed we could've been." The loss bias, right? Book the win early.

Harry Stebbings 1:12:01 (laughs)

Jonathan Ross 1:12:01 Because it's possible, it must be done. I think that's a smaller segment of the population. Those are the people who deliver amazing things that no one else is gonna do because everyone else is like, "That's good enough." However, I think with AI, it's so easy to create a prototype to stand out, you're gonna need to do that. And one of the things that's happened with the ability to communicate more freely and, and see what other people are doing, like, can you ... Think back to what the restaurant experience was 20 years ago versus what it is now. The average restaurant is better than what high-end restaurants were 20 years ago because people see all of the, the stuff that others are doing, the best, and they start to expect that. And you have less localization, more globalization. You have to compete at the highest ends, and AI is no exception. There's gonna be 40 people creating that app. You know that you have to polish it in order to stand out.

Harry Stebbings 1:12:58 Listen, dude, I could talk to you all day. I do wanna do a quick-fire. What do you believe that most around you disbelieve? (laughs)

Jonathan Ross 1:13:03 I'm gonna go with anti-founder mode here.

Harry Stebbings 1:13:07 Mm-hmm.

Jonathan Ross 1:13:07 I'm anti-founder mode. I believe in delegation. I think when you are telling people how to do their job-... that is an indication that it's not a, not necessarily a problem with you, it could just be that that person is not right for that job. And it's much easier to just direct them than to go find someone else competent. But it also means you probably haven't aligned them. So, we, we align people through this challenge coin.

Harry Stebbings 1:13:32 Sure.

Jonathan Ross 1:13:33 Um, so everyone at Grok carries this, um, 25 million token per second challenge coin.

Harry Stebbings 1:13:39 Huh.

Jonathan Ross 1:13:40 And what this is, is it tells everyone what we're doing. It's an alignment. And I can't tell you how many people I've showed that to who are like, "That's awesome." And yet no one else is making them. And it-

Harry Stebbings 1:13:52 It's heavy.

Jonathan Ross 1:13:52 It's heavy. But, but also, you know, you, you, you like to say, you know, the greatest things in life, um, you know, the heaviest things aren't, uh, gold or whatever.

Harry Stebbings 1:14:01 Yeah.

Jonathan Ross 1:14:01 Yeah.

Harry Stebbings 1:14:01 I own no gold, but I made decisions.

Jonathan Ross 1:14:03 Exactly.

Harry Stebbings 1:14:04 Yeah.

Jonathan Ross 1:14:04 Well, this was a very made decision because I had to consolidate everything we were doing into one very simple message, that we're gonna get to 25 million tokens per second, and then engraved it on a coin, on this tiny amount of space right here, and gave it to everyone at Grok. And now whenever we're in a meeting and something doesn't help with this, they can just (knocks on wood) tap their coin on the table and be like, "No, no, no, that's not the way this is gonna go."

Harry Stebbings 1:14:29 So, is everyone wrong on founder mode then?

Jonathan Ross 1:14:31 I think that's what you do when you don't have the quality of people working for you. You need a... You need the right gearing ratio between you and your direct reports.

Harry Stebbings 1:14:39 Uh, it's a really unfair question, but I have to ask it. How do you analyze Elon's attempt to buy Twitter? Uh, not to buy Twitter, to buy OpenAI?

Jonathan Ross 1:14:48 So I was sitting at the Elysee Palace or however-

Harry Stebbings 1:14:52 Yeah.

Jonathan Ross 1:14:52 ... you pronounce it, um, at the dinner with Macron and Sam Altman, uh, so it was, it was Macron, it was J.D. Vance, and it was Sam Altman. And frankly, I think, um, I think Elon was a little jealous that Sam Altman was sitting next to J.D. Vance and it wasn't him. And 'cause it was right around the time that Sam Altman was speaking that he announced it. And frankly, I thought Sam's Tweet response, part of it was pretty good. I would've probably said instead of whatever he said about 9 billion, I would've said, "Yeah, I'm gonna take Twitter public at $420 a share." It was just a... It was attention grabbing, right? Some people can't stand to not be getting attention. And so my revenge on this is to give as little attention as possible, so let's move on.

Harry Stebbings 1:15:42 (laughs) What would you do if you, you knew you couldn't fail?

Jonathan Ross 1:15:45 I would put in 100% of the orders for every single chip we could possibly manufacture because right now the demand is unlimited, but every time you triple, you find the same number of problems. And so you gotta keep... You gotta do it a little judiciously. But if I knew that no matter what problem was gonna come up, that we didn't need to be safe at all, I would just go, "Great. We're gonna go build 20 million chips." Done.

Harry Stebbings 1:16:14 In 10 years time, is NVIDIA 3X bigger, 10X bigger, or 50X bigger?

Jonathan Ross 1:16:19 I, I think they will be bigger. I couldn't tell you a number. Training will become more important. I wouldn't be surprised if they were 3X bigger. I also wouldn't be surprised if they stayed around the same.

Harry Stebbings 1:16:32 Wow.

Jonathan Ross 1:16:33 Uh, it's, it's so hard to, to tell where things are going 'cause remember, a lot of assumptions in, in the investment in NVIDIA were that they were gonna run away with the entire market.

Harry Stebbings 1:16:43 Mm-hmm. Including the inference market.

Jonathan Ross 1:16:45 Including the inference market. And they just haven't built the right thing for inference.

Harry Stebbings 1:16:48 Mm-hmm.

Jonathan Ross 1:16:48 I do think that as a weighing machine, they should increase in value. But there is so much popularity contest applied to it that I don't know if, if they're... It... They might need to grow to get to, to the, where they are. Uh, you know, they might need to grow their revenue to get to where they are, but it's a pretty fair multiple given everything going on. So I couldn't tell you. It... Like, the popularity contest skews everything.

Harry Stebbings 1:17:13 What's a crazy AI prediction you have that everyone else think is science fiction?

Jonathan Ross 1:17:18 I would assume that in the next 10 years, and I know this is gonna be crazy, but you, you, you saw that picture of me and my weight loss, right?

Harry Stebbings 1:17:27 Unbelievable, dude. I mean, really amazing.

Jonathan Ross 1:17:29 70 pounds. 70 pounds, yeah. But I was on Mounjaro. So if you know anyone who's overweight and it's hurting their health, get them on Mounjaro as soon as you can. It works.

Harry Stebbings 1:17:38 Sorry, what is Mounjaro?

Jonathan Ross 1:17:40 It's one of those, uh, GLP inhibitors, one of the, the weight loss drugs-

Harry Stebbings 1:17:43 Yeah.

Jonathan Ross 1:17:43 ... that have become popular recently.

Harry Stebbings 1:17:45 Huh.

Jonathan Ross 1:17:45 It works. But my crazy AI belief is that if it is possible, if it is possible to significantly slow or stop aging, I think that you will have a Mounjaro moment in maybe the next 10 years. It... Because that came outta nowhere.

Harry Stebbings 1:18:04 Yeah.

Jonathan Ross 1:18:04 All of a sudden, you know, you, you could just lose weight. Something finally worked. And it's worked for a bunch of people. You probably know a bunch of people who've lost weight. Yeah, exactly. And I don't know if it is possible to slow or stop aging, right? Some, some wear and tear is a real thing and it might just be impossible. But if it is impossible to slow or stop aging, if, then I think in the next 10 years we will do it and it will be sudden. It'll be like the Mounjaro, um, um, and the other one as well. The other... It, it'll be like that moment.

Harry Stebbings 1:18:35 I don't see how it