Cerebras CEO, Andrew Feldman on Why Raise $1BN and Delay the IPO & Why NVIDIA’s Worried About Growth

Things are moving at a rate that 6, 8, 12 months out, everybody's unsure. It's so fast, it's so big. There is unbelievable demand and nobody knows where it will go in the future. The question of depreciation is how much faster are future generations than the current generation? That's the actual question on depreciation. People often say we don't have enough power in the US, and this is strictly wrong. We have plenty of power. It's in the wrong places. Risk comes in financial markets where people fundamentally underestimate risk. No company ever went bankrupt by paying extraordinary people too much. Ready to go?

Andrew Feldman, dude, it is so lovely to have you back on. I so enjoyed our first show. You put up with my naive questions enough to agree to do a round 2.

Man, I must be Harry, I'm, uh, I'm okay with any questions, naive or otherwise. So I'm, uh, happy to do it anytime. I, I read your, your, uh, your LinkedIn posts, your Twitter posts. Uh, I, uh, I'm rooting for your mom. I mean, all good, all good, dude.

You are too kind. Uh, listen, I want to start with the billion-dollar raise that you just announced yesterday. Um, can you just talk to me about the billion-dollar raise, why it's important, why now, and what it means for the company?

Well, look, it It was the largest raise ever done in our category. It was done at the highest valuation and with the premier investors. So, at late-stage investing, you're looking for the likes of Fidelity. They are the— what would the English call it? The Oxford or Cambridge of investing, right? They are the premier public market investors. When they choose to lead a round, it brings Wall Street a great deal of confidence. We were really happy to partner with them and with Atreides to lead the round. Then we were able to get enormous participation from Tiger Global, from Valor, from 1789. That's point one. I think point two is that we, we now have sort of the dry powder to, to really push and to take the opportunities in front of us, to build out our manufacturing to the scale and scope we want, to add new data centers. We added 5 this year in the US, to add more data centers. And we have more big ideas. I, I think incremental improvements, make-believe gains achieved by dropping from, you know, 8-bit to 4-bit, those aren't gonna get us to the promised land in AI. We've got real work to do as a community. And I think this funding puts us in the catbird seat for that.

On the fidelity side, it's actually interesting. I had Brian Halligan, the CEO of HubSpot, on the show recently, and he actually taught me the importance of specifically getting fidelity in both pre-IPO and when you IPO, just because of the signal that it sends. And I didn't actually realize as a venture guy, the weight that's placed on—

As an early-stage venture guy, you're like, who are the public guys? I know. Okay.

Fidelity, like whatever. T. Rowe, sure. They're all the same, right? And I was like, no, no, they're not. Like Fidelity are the monster in the room.

They're the monster.

And the importance of getting them is very high. Can I ask you, dude, like why not go public? It was, 'cause it was rumored that you guys were gonna go public. Why do this pre-public round?

We, we still have every intention of going public. I think it's very common in, uh, late stage to do a pre-IPO round. I, if you can get it done very quickly, if it doesn't distract you and, and keep moving. I, I think there were so many opportunities in front of us that, uh, get gathering the capital so that we could continue to prosecute these opportunities was sort of a no-brainer.

You said about the real work to be done, I think it's quite difficult for everyone who's not really in the market to understand what the hell's going on, given all the news that we see. Can you help us just with the lay of the land in the last 3 months of where are we at now? What's changed? Let's start there.

The first thing, Harry, is we, we, we are in a, a stage of the market where the claims are enormous, right? Where tens of billions of dollars are being done here and there and Nobody's reading the fine print that it's over 5 years and it's up to. This is the great sort of CYA word in marketing history is it will be up to $100 billion over 5 years. Right? Well, up to means it could be 30, it could be 12, could be 40, right? It won't be bigger, right? He could pick a lot of big numbers and it won't be bigger than. And so I think as you read these deals, I think, you know, you have to really think about the timeframe over which they're being done. You have to think about whether anybody is actually counting, right? Lots of people are saying they're gonna bring hundreds of billions of dollars of jobs to the US and this and that. I mean, in 8 months, has anybody got a little spreadsheet like 9 jobs plus 1 factory? I mean, who holds anybody to account? And the answer is nobody. And so I think that's number one. I think number two, what this signals more than anything is that there is unbelievable demand and nobody knows where it will go in the future. That it's so big and happening so quickly that they don't know. I mean, we have customers coming to us and saying, We would like between 5 and 40 million queries per second. Well, how do you not know by a factor of 35 million queries per second where your demand is going to be? How are you unsure by an order of magnitude of what your queries are going to be? The answer is, things are moving at a rate that 6, 8, 12 months out, everybody's unsure. It's so fast. It's so big. And so I think you should think about sort of these announcements as sort of options on the future, right? That's really the way to think about it is in an unknown environment, how can I take an option on the future? I don't know if I'll use it all, but I'll pay something for, for the future, uh, rights to, to, to, to have some capacity. So that's a way to think about it.

Given that it's so fast, it's so big, how do you think about planning for that uncertain future?

It's brutal. I mean, I think there's a very interesting question about, in extraordinarily rapidly moving environments, what the right planning cadence is, right? And what you really need is good planning changing rules rather than good planning, right? If you can only— I mean, we have to make making big bets. We're making 5- and 7-year investments in data center capacity. We are making hundreds of millions, now in terms of billions of dollars of bets in supply chain. And those are not 3-month bets, right? And so, I think what you need to do is use different rules that have historically been used. You plan more frequently. You have a shorter view, you take options on the future. And if the future moves against you, you lose the premium on the option, right? You pay a little price to secure some capacity. And if you don't use it, you just, you go, all right, that was sort of a way to manage uncertainty about the future.

What do you think the chances are that you are still underestimating even your wildest of demand expectations.

100%. I've been wrong. I mean, look, if you would've said a year ago, 2 years ago, pick a time at which it would've been conceivable that OpenAI would get the valuations they're getting. Pick a time, right? It wouldn't have been conceivable, right? 3 months ago, 6 months ago, 9 months ago. When was it a reasonable, like the day before, right? And I think that's true with the demand we're seeing. It's true with valuations on companies we're seeing. It's true with the rate of ideas entering the community.

How much of this do you think is sustainable? Everyone lobbies that, oh, it's not sustainable. A lot's experimental. It's not enduring. How much do you think is sustainable?

You know, I would say this. I would say that there are always grumpy people who, who like, it'll never work, and, you know, you know, you'll never beat Goliath. And, you know, truth is, most things don't work, and most of the time Goliath wins, right? But there's no alpha in that. There's no money made for you or me betting on the biggest of the big dogs to continue not to lose. I mean, how uninteresting is that? And so I think what— of course, right, if NVIDIA keeps growing at the rate they're currently growing, 11 years from now, everybody on Earth works for them, right? Do the math, right? I mean, however, is it possible that our economy looks very different in 5 years? Is it possible that, uh, the things we value, uh, are very different, um, that we have reorganized around AI? We've seen a major bump in, in labor productivity. We have, uh, benefited dramatically, and the economic pie is much larger. I, I think that that's not only Likely, it's almost certain.

You mentioned that if Nvidia continues to grow the way that they do, everyone will work for them.

Well, you just, you know, you keep doubling at that rate and you multiply that. You can't keep doing that.

To what extent is it just completely unshakable at this point for them? Like where the scale and the size of money is like, you know, Jonathan Ross from Groq said on the show, they will unwaveringly get $10 trillion within a 5-year timeline.

I hope he's long on them then. Um, I, I, I don't pick public market stocks. I, I, I think, uh, I don't like public picking public market stocks. I think in the public market you can lose money on good companies, you can make money on shitty companies. And that for me doesn't sit well, right? Uh, a, a, a, as an entrepreneur, as a David in the battle with Goliath, I, I wanna make money when, when we build a great company. Period. But, um, can they continue to grow? I think we are seeing some things that big companies do as they begin to worry about growth. I think use your balance sheet more and your technology less, right? This is something that historically large companies have done as they feared for their, their, their technical prowess.

And when you say that, you kind of referring to investments in your OpenAI's $100 billion and ElevenLabs and everyone in between.

You start. Buying business as opposed to winning business. And I think that we saw that with Cisco, who emerged as a dominant position, you know, '99, 2000, 2001. That has been one of the strategies. Another strategy you see is this predatory pre-announce, where you announce B300s before anybody can get B200s. You start talking about Rubin before B-200s are technically finished. You don't talk about the field failure rate of your products, which are massive. Rather, you sort of keep talking about the future in an effort to convince people to wait to make a good decision rather than go with technology that's better and present. And so I, I think these are the strategies of, of very large companies using their strengths. Um, and I, I think that's what you're beginning to see unfold with, uh, with NVIDIA.

Specifically on the— I do want to talk about actually kind of the speed of chip development.

Yeah.

Uh, specifically, but speaking of the $100 billion into OpenAI, how did you analyze that? For me reading that, I didn't really know how to analyze it. It's so unprecedented?

Well, I, I think it was designed for nobody to understand it. If one wants to make something very clear in an investment, we've invested this amount of this valuation, the deal's done now. If you want to make something more difficult, it's up to this amount over an unbound specified amount of time at no valuation given, or evaluation specified, but it can change, right? It wasn't designed for you or, or other analysts to, to anchor on different things. And, and that's a very reasonable thing for, for, for both of them, but it's just, it's not an analyzable, uh, thing. What, what would— beyond the fact that, that NVIDIA has chosen to try and lock up a portion of, uh, OpenAI's demand by investing in them. That, that's about as much as you can say.

Totally get you. And I'm glad that it's meant to be confusing because I was confused looking at it going, what price was this? How much are they buying?

Like, I don't know if it was meant to be confusing. I think it was meant to be—

you know, my mother goes shopping and I ask her, it's a lovely dress, Jules, how much is it? Well, it doesn't matter. It doesn't matter.

You call your mother— wait a sec, let's go back to the important important thing. You call your mom by her first name?

Oh yeah, Jules.

Okay, you don't call her mom? I've never called my mom Shirley. I mean, I, I could never call her— I mean, that would be mom or something else, but not—

no, no. But when I, when I get the like price on application, I'm like, oh, Andrew Feldman. Oh, that, that's what this felt like. That's what I was like, really? No, nothing there.

I certainly don't think it was The truth is, is there may be, and there likely a huge number of moving parts that make it impossible to clearly, uh, describe without giving out more than they wanted to give out.

But you mentioned pre-announcements that like B300s, B200s, timings of such. Are we thinking about chip depreciation in the right way? I just, you know, again, I just did a show with Jonathan. He's like, hey, we actually think about them on like an 18-month time cycle to maybe 2 years. And I was like, wow, that's It's quite quick. Are we thinking about it the right way? And how should we be thinking about the amortization of chips?

We are in unprecedented waters. Um, I think, uh, people are clearly still getting value from H100s, and that's more than 2 years, right? So, uh, you know, uh, if you say it's a 2-year depreciation, you're empirically wrong, right? I mean, they are Uh, and I think people are still getting value from A100s, though not on the cutting edge. And so that's closer to 3 or 4 years, uh, and could be as long as 5 or 6. The question of depreciation is how much faster are future generations than the current generation? That's the actual question on depreciation, because what you're— with depreciation, you're, you're saying at some point it's no longer worth using a part that's fully paid off because there's a new part that's so much faster, uses so much much less power, that it's better for me to retire it. That's the actual sort of, uh, the underpinning to the depreciation question. If I have a data center and it's 50 megawatts and I have this much capacity in it, at some point, even though my my chips in it are, uh, have been depreciated and I'm running them at zero cost, right? Power plus zero depreciated cost. It makes sense to move them out because the new one, the new chips are so much faster, so much better, use so much less power, I get so much more dollars per. And, and so that's the question. And, uh, if we don't as an industry continue to build extraordinarily sort of better parts generation after generation, then people don't move from one generation to the next. They last longer, you depreciate them longer.

Where are we? I feel very naive for asking this. Where are we in the performance improvement pathway for chips? Are we like in there, we've got 90% and we're incremental gains, or are we at the— we are still at the super early stage and we have 90% of the gains to be made?

I think the question is in that case whether you read people's marketing material or the actual performance results. I mean, certainly the, uh, people's marketing material would lead you to believe, uh, that generation over generation, they're huge gains. Um, a little bit of engineering digging, uh, probably leads you to the conclusion that you're getting 2, 2.5x, uh, per meaningful generation move. Um, not more, right? If you compare apples to apples, 8-bit to 8-bit, 4-bit to 4-bit, if you compare actual performance, you know, you might have more flops on the chip, but your memory bandwidth didn't improve more than 2x, so you can't get to them. These chips are a solution. And if you make one part fast, it's a system. You make one part fast and the other part you don't move, doesn't move as far forward, it becomes the new bottleneck. And it doesn't matter how many flops your chip has. If you can't get data onto and off of the chip, those are wasted. And so the question isn't sort of, oh, how much faster is the chip? It's how much faster is the solution? And that includes memory, which has, for inference, is the fundamental limiter for the GPU architecture. And so it doesn't matter how much faster the chip goes. It matters how much faster the memory bandwidth is.

On this, I was chatting to a founder in the space and he said that what everyone fails to understand is that although SRAM sounds great in terms of having memory, and SRAM is obviously, you'll describe it much better than me and hate me for this, but SRAM is obviously memory on-chip versus off-chip.

Yep.

Seemingly great, but he said it's completely unable to handle scale. And so although it may be quicker, for anyone who wants to do large scale, it is incapable at present of doing that. And that's a fundamental need and requirement of any of the large providers. Do you think that's fair? And how do you think about that?

Well, I, I, not only is it fair, it's the reason we went to WaferScale. So let me explain. What your friend said is strictly true in that SRAM is blazing fast and low capacity. HBM is a flavor of DRAM. It has high capacity. And it's very slow. Now NVIDIA and all GPUs, including, uh, AMDs, chose a big capacity memory that is slow because it's perfect for graphics. You don't have to go to memory very often. You can hold a lot, you don't go very often. SRAM is blazing fast but it can't hold very much. So the problem on traditional chips is if you put memory on the chip, you are using space that could be otherwise used for compute. You have a fixed amount of real estate. And so if you put half memory, then you have half your real estate's available for compute. And so Our idea was that if we, we sort of, if we built a chip that was the size of a dinner plate, we could stuff it to the gills with fast SRAM, overcoming the limitation of SRAM, which is it doesn't store very much, by putting a huge amount down, by using more silicon area. Now, if you're an SRAM solution today in a normal-sized chip, and you're trying to do a trillion-parameter model, use 4,000 or 5,000 chips. What a mess. You know how many cables that is? Do you know the, the impact to the AI? It's a horrible mess, right? And it limits you from doing things you want to do with the AI, uh, like speculative decode. It has all sorts of painful challenges. The other hand, Use one of these, or two, or four, right? And it's simple, it's easy. And this is what your friend said exactly right, and the reason we went to build a bigger chip so we could fill it with this fast SRAM, so we could get over the traditional limitations of SRAM that couldn't store very much by using a lot of space, by using a huge amount of, of silicon area. So your friend is exactly right. You might listen to him again in the future.

Question to you, no offense, that seems a little bit obvious. Like, okay, increase the real estate, shove more SRAM on.

It does, doesn't it? Yeah.

Is it as obvious as it seems? Am I missing something here?

Well, what we were missing is that for 75 years nobody could do it. That building a bigger chip had proven impossible before we did it. Nobody in the history of the compute industry had been able to build a chip bigger than about 840 square millimeters in the 75-year history of the computer industry. And many people had tried and failed. And after we did it, uh, Elon tried Dojo and they failed. Um, and so, uh, it's really, really hard. And, uh our strategy, uh, had never been done before. It'd never been successfully, uh, sort of yielded. And so, uh, while it was obvious, uh, it, it was hard.

So when we think about like where the market is today in terms of training and inference, do we agree then that actually NVIDIA's chips are much better for training than yours are, but yours are much better for inference than theirs are? And the market splits in that respect?

No, we're faster on both, but the, uh, the software challenges in training are real.

What does that mean?

It means that when a new model is built, all right, and everybody reads about it in, in a publication, it was done on a GPU. Everybody to train it. They, they take the recipe that was originally done for the GPU and they have to move it to the recipe for their hardware, whether that's a TPU, whether that's an AMD GPU, whether that's a, a, another, uh, dedicated chip like ours. You have to move it, and that's a harder software lift. In inference, the truth is nobody cares about CUDA. Nobody even cares about PyTorch. All right. What they want is an API. And so it's literally 10 keystrokes to move from a GPU-based solution on OpenAI OSS 120B to our solution. It's 10 keystrokes. That's it. It's, it's nothing. And so I think the answer is, is that while we are faster at training and we are faster at inference, it's easier to demonstrate inference, right? You just put up a side-by-side to show that you're faster than 1,000 B200s. You know, you got to get 1,000 B200s, you need to train them for, for 6 weeks, train the model for 4 weeks or 6 weeks. You got to stand up a cluster of our machines, you gotta— it's a bigger lift. So I, I think, uh, uh, the, uh, the market right now is easier to, to move people off GPUs in inference. And the, uh, number of people doing inference is vastly higher than the number of people doing training.

Can I ask you, when you look at the inference market today, how has it developed in a way that you did not expect?

I think it's really hard for the mind to wrap itself around geometric growth or exponential growth, right? I think the, the, the— there is nothing confusing about the rate of growth of inference. The rate of growth of inference is the number of people who use it times the frequency of use times the amount of compute needed per use. Right? It is 3 different variables multiplied by each other. The problem is they're all growing fast, and that produces some mind-numbing effects. More people are using AI. Once they start using AI, they use it more frequently, and what they want to do with it is bigger and more complicated, so it uses more compute. And so, you have 3 variables, the size of the market's a product of the 3, all growing fast. And we knew that going in, we see that, and it still takes your breath away.

I don't think we've seen anything yet.

I agree with that. No, no, I agree. I think the reason I'm 100% sure that we are underestimating the market is because of that premise.

I, I think Sam Altman said it very well in terms of how people use ChatGPT, but he said, you know, essentially the majority of people use it like Google, a Google replacement, and actually the younger people use it as an operating system for the future, which is the right way to do it in his mind.

Absolutely right. I, I think, and, and this is, uh, there's a famous, uh, there was a I was just going to put up a post on this. Um, in 1988, Robert Solow, who won a Nobel Prize in economics, he asked this question. He said, uh, we see computers on every desktop and everywhere we look except in the productivity statistics. And you say, well, that's a really interesting to stay— thing to say. And there was another economic historian who jumped into the fray and he did this huge study. His name was Paul David, and he wrote a very famous paper called The Computer and the Dynamo. And what he studied was the adoption of electricity in the manufacturing sector between about 1880 and 1955. And what he showed was at the beginning, electricity produced very little productivity gains. It was basically used as a backup for belt-driven systems. And it wasn't until they reorganized the shop floor to take advantage of electricity that you got this huge jump in productivity. And if you roll that forward to the computer, what he was saying is, look, we used computers to do things we're already pretty good at. That we replaced a typewriter, we replaced general ledger accounting with spreadsheets. We were good at those things. You didn't get a big jump. And what he predicted, and then happened immediately thereafter— by the mid-'90s, you had a huge jump in productivity. We had begun tying them together. We built the internet. We had the, the first parts of a cloud. We— and all these things used compute differently in ways that had never been consumed before. And you got this massive jump in productivity. Productivity. If you use OpenAI and various of their competitors the way you use Google, you'll see a very modest jump in productivity. If you use them in a fundamentally different way— that was Sam's point— you'll see a huge jump. And if we use— if we reorganize ourselves around AI, you're going to see massive productivity gains. If we use AI to replace things we're already doing, Google or, or or something else, you're not going to see very big jumps at all. And so what I know is that transition takes time. And what he pointed to was a demographic, right? Younger users are using it in a different way than older users. Older users are replacing something they already had. Younger users are using it in a way that never existed before as, uh an operating system for life.

If we're going to see that transition that you mentioned there, the data, sorry, the energy requirements are just insane. I mean, Sam said a trillion dollar spend. He needs the energy of Japan or more, to be blunt.

Yeah.

Is this feasible, Andrew Feldman?

Yeah, it's feasible. Um, is it desirable or good for society is a different question. It's feasible. People often say we don't have enough power in the US, and this is strictly wrong. We have plenty of power. It's in the wrong places, right? It's not where we have people or where we have fiber optic cable, right? We have a ton of power in West Texas in natural gas. We have a ton of power in upstate New York in hydro. We have a ton of power in lots of places. We don't have people there. And so the problem is one of a mismatch. Between where all the power is and where the people are, or where the buildings are, or where the telco fiber is that we need to get data to and from the data center. So that's the first observation. The second observation is sort of one of a community, and that's that to the extent we consume this extraordinary amount of power, we have an obligation to deliver amazing things, right? And that's not all of us. Right? I, I, I think we have an obligation to deliver drugs that they're more efficacious, to deliver better healthcare, to, to make, uh, aging less painful, make the looking after of aged parents or sick parents. I mean, you go through society's ills and woes. Um, if we are going to consume this amount of power, the burden is on us to deliver value for it. And if we use it and don't do that then it's not a gain for society.

But do you think that's controllable? Like, you know, creating Ghibli or Ghibli images— I get it wrong— like, isn't particularly value-inducing, but it churns a huge amount of, you know, compute and energy. Can we control that? That's like, it's a very hard question.

It's a very hard question. And I, you know, I'm one voice in this. The problem with markets is that they do a lot of things that aren't productive in order to get one that is very productive. Ghibli may or may not have been net societally gain, but maybe the technology that is, uh, used in Ghibli is used for X-ray crystallography and later is fundamental to finding major scientific breakthroughs. I mean, that's the messiness at any given point in time. You can point to in a thousand poppy strategy, which is what a market is. A market has a lot of bad ideas to get a few good ones. That's your business. Your business is investing behind a lot of big ideas, most of which fail. And the market is that writ large. And so, in that environment, you can always point to, "Oh, that was a dipshit investment, Harry." Why'd you invest with them? I mean, they blew up. You can always say that after the fact. Oh, look, look at that. They're using a ton of energy for that. That's not useful. Let's see. And so I think the answer is we need to be sure that at a societal level where we use government dollars, where we use tax breaks, where we use permitting breaks, Uh, we are sure that we are using— we're giving these to disproportionately to projects that matter to society.

Do you think Trump's done more to help or to hurt the U.S. AI effort?

I think it's confusing. I think on net it's probably more to help. The Biden administration was misguided and afraid, and I, I think Uh, uh, the Trump administration, I think to his credit, they surrounded himself with, uh, some smart people, uh, and, uh, in the AI space. And I, I think they have, uh, on that it's been positive.

When you look at what is required in terms of energy, is nuclear unavoidable or the sole solution to be the providing force of energy for this next generation of AI?

No, it's not unavoidable. It's, it's a very reasonable decision for countries that don't have lots of alternatives. I mean, you know, Canada has more falling water than anywhere else on Earth, right? The opportunity for Canada to develop, uh, the cheapest power on Earth is, is mind-boggling. Um, there is cheap power in lots of places, uh, but in order for, uh, countries that, that wish to and don't have the natural resources of Finland that has geothermal, or Iceland that has geothermal, Canada that has falling water, um, nuclear is a very reasonable and cost-effective strategy, especially over a several-decade view.

Can I ask what worries you most today, Andrew Feldman?

I do think of this idea that to consume the resources we're consuming, we have to be sure that we produce some extraordinary outcomes. I worry the opportunity is so big that as a community, we're running sort of helter skelter at it. That actually, sometimes instead of running where you trip and fall and graze your knee and chip tooth. If you stopped and thought and marched, you, you might get further over a 30 or 60 or 90 day period.

Do you worry about the concentration of value in Mag 7? They now make up more of the S&P than they pretty much ever have done in history. And that concentration of value is very real. If AI hits a speed bump in any way, the market could derail significantly. And the multiplier effect of that is felt by everyone.

The risk there is that not that they consume that much, that they are that much value. That, that's not the risk. I think they're that much value because the future economy that we believe the future economy, uh, will reward that. I think the issue is, is that people then think the S&P is a safe investment or a safer investment than it might be. The risk is the mismatch, right, in the, uh, in the mental model people have, right? Risk comes, uh, in financial markets where people fundamentally underestimate risk. When risk is priced properly, all right, then what happens is, uh, your outcomes are not surprising. But if people continue to think the S&P is an index of the global economy, and it's not, it's 30% or 50%, 7 companies, then they're exposed to sector risk that they weren't signing up for. They thought they were diversified. And in fact, they're heavily dependent on a very narrow sector. And that's a risk. Do you know what I mean? That, yeah, that, that seems to me to be a, a, a challenge in the new world order. And all the advice that sort of the, the pundits give, you know, diversified portfolio— when the world changes and that portfolio is not— you keep holding it and it's not diversified anymore. 'cause of consolidation, then there's real risk.

Do you think the risk is priced properly when you look at NVIDIA at $4.5 trillion?

Look, I think they've proven themselves to be the greatest company of the first quarter of the 21st century, right? 21st century, right? I mean, they've proven themselves to be the extraordinary company in the first quarter of the century. And I don't know if $4 trillion's right, But I think a very big number, maybe it's too low, is right because of what they've achieved.

When we look at, we've said before about the insatiable demand, the demand that we cannot predict or anticipate, what are the bottlenecks today in your mind? Again, we had Jonathan McCrock on and he was like, actually, I had someone come and demand 5 times the supply that I have in total. And I only, that was from one customer, um, suppliers mine. How do you think about the bottlenecks that we have to reach the insatiable demand that you mentioned?

I think if you go back to planning, if you've got customers demanding 5x your capacity, I mean, you probably didn't get your planning right, right? Oh, you probably should have, you should have planned better. I think there are bottlenecks, uh, at every level that are meaning real and meaningful. I think, uh, I think that the first one is expertise. I, I think, uh, we have, uh, fundamental limitations in AI expertise. We're not making enough AI practitioners. We're not making enough data scientists who understand, uh, data pipelines. We are not— we're not— we're just— our universities aren't minting enough And our, our challenges in the U.S. with immigration, uh, don't help that. We have historically sucked the best and the brightest, first on J-1s to come to our schools and H-1s to stay. If that is not our policy, we need to make them right. If the government decides that that is not the way they want to build a workforce, instead they want to build it, uh, out of people who live here We need to do a better job of training those people. We need to do a better job of teaching them in K-12. We need to do a better job of educating them in our universities in order to, to make the number of, uh, of, uh, of engineers we need to meet this demand. That's a bottleneck, and it's why the, the best and the brightest are getting such extraordinary compensation.

Is the war for talent completely out of control? You're seeing your Zucks of the world spend hundreds of millions on one person. Do you think that's a blown-up anomaly, or do you see the war for talent being unprecedented?

There are engineers who have skills that no number of other engineers working together can achieve. There are scientists who do, who have ideas and who have brains that are— can't be replicated by lots of other talented people working together. Ought they to be paid more than world-class soccer players? I have no idea, right? Um, maybe, maybe not.

Uh, but I mean, inherently, yes, from an economic rationale standpoint, yes, the value generated from a chief scientist at OpenAI, if they add $50 billion of enterprise value to pay them $1 billion, is worth it.

That's what we have to think about. I mean, we've I don't know, we paid Charlie Sheen $2.5 million an episode for Two and a Half Men. $2.5 million an episode, right? I'm pretty sure that there are lots of people who are, uh, uh, whose net productivity to society is above that.

Um, and he still spent it all.

I just saw the show on— was it Netflix or Prime? What, what a sad story of somebody who who was so self-destructive and so talented. But, you know, should we be paying soccer players or basketball players? I have no idea. And I don't spend a minute worrying about whether we're paying extraordinary people too much. I think no company ever went bankrupt by paying extraordinary people too much. You go— if you want to go bankrupt, pay mediocre people too much, right? Uh, that, that's how you, how you mess up. But No, nobody's ever struggled by paying truly extraordinary people too much.

What's the other bottleneck? You said expertise is one. What's another?

TSMC can't build fabs fast enough. I think the, the truth is, is that these are both for, for TSMC and, uh, and Samsung. These fabs are the most amazing manufacturing plants on the planet. And, you know, these are $30 billion, $50 billion factories. Um, and the, their ability to build them quickly enough is, is very much limited. Um, I, I think that in turn limits and keeps the supply, uh, below where it would like to be of chips, not just our chips or NVIDIA chips, but everybody's chips. Below where it might otherwise be. It keeps the cost up. Um, I think, uh, uh, right now there's a shortage of data center capacity. Um, and I, I think, uh, there's a huge amount of investment that has gone into that. Um, there's a lot of words, but where are these giga gigawatt facilities that everyone's been talking about, everybody's committing to them. Where are they? Well, they're not up yet.

How long does that take?

You know, somebody like— for Elon, who's the fastest in the world and maybe the best at building plants and large construction projects, it takes 6 months, 8 months. And for the rest of the world, it takes a year and a half, um, maybe longer.

Are we investing enough in data center buildout? I mean, it is one of the most insanely, um, hot categories now in terms of investment properties. I'm coming from like a pure Wall Street mindset. Like every Wall Street guy wants to be in data centers.

Yeah, it, it, it has a structure that they really understand, right? It looks like a bond to them. Um, it looks like a piece of real estate. You, you get a tenant, they pay rent every month. You can loan against that. You get an investment-grade tenant that's basically a bond. It has the advantage of falling into a category or a pattern that is really well understood in the debt market and in the capital markets. And that's an advantage. And I think CoreWeave and some of their sort of financial engineering and innovations there help the world see that. Um, uh, I, I think, uh, you know, like many things, lots of people will enter. The smart will make money, uh, the less sophisticated will, will lose money. And I, I think building data centers is not for everyone.

How will you lose money building data centers?

Look, I, I think if the best can build them for $8 million a megawatt and you're spending $12 or $14, that's how you lose money. You lose money because it begins as, can you get access to low-cost power? It then continues to, once you have access, can you get permitting? Does that take long, or can you— do you have real access? It gets you fast permitting. Once it becomes a construction project, can you keep control of your costs? All right. Once it's finished, all right, can you keep good tenants in it? Um, the, the ways to lose money and property are large and many. It had, you know, that there's no, uh, there's no free lunch there either. And so, uh, I, I think, uh, when you are trying to go unbelievably quickly, you, it's harder and harder to, to be disciplined and not make mistakes.

To what extent is it important to be fully horizontal? You know, we hear about Zuck wanting the data center build out to be just immense in terms of size and scale. To what extent does it need to be horizontal versus vertical?

It is completely unclear, right? The, the most successful two companies to date, uh, OpenAI and Anthropic, neither are vertical. OpenAI used, uh, Azure 100% infrastructure for years, and Anthropic has used a combination of AWS and, and Google. And so neither are vertically integrated to date. Now, whether that's the right strategy going forward, whether they'd make those decisions again, uh, who knows? But it's clear that it's not the only strategy. There are plenty of working models where you are not fully integrated from, uh, chip through system through data center through software all the way to the top.

Totally understand that in terms of the different models there. Again, sorry to cite it, but it's kind of handy having just done it. Jonathan said that you would definitely have OpenAI and Anthropic build out their own chips because then they would have control of their own destiny. Do you think OpenAI and Anthropic build their own chips so they don't have self-reliance on NVIDIA in the way that they do today?

I think that there is a long history of software companies failing to build chips. The list is, is very large. I think whether, uh, OpenAI can do it, whether they can do it through partnership with other vendors, with Broadcom, with smaller, more innovative companies, is an open question. Um, but I, I think that, uh, you know, companies at the size of Microsoft have been unable to deliver uh, chips, right? I think, uh, uh, there are plenty of examples as you look across the FAANG, uh, group where chips were tried. I mean, probably the most successful is Google, and they're 10 years in, right? Maybe longer. You know, soft— modern software does not fit well in a chip-making framework. I mean, weekly sprints don't work well on 2-year-long projects. You know, move fast, break things often is not the way you think in the chip world. The way you think in the chip world is measure twice before you cut once, because your bugs cost you 6 months and tens of millions of dollars. And so it, it's a very different mentality. And where there's been success, it has frequently been acquired. Apple got into the chip business through buying PA Semi. Amazon got into the chip business through acquiring Annapurna. Um, Google acquired the talent from a collection of companies, uh, and then set it in a BU that was aside and under somebody who, who had sort of enormous respect in the organization and ours, had a 10 or 15-year view. Um, these are things that, that have been challenging in many companies. Chip building is an MBA nightmare, right? Your analysis that says, look, Intel had in, you know, between 2000 and 2010, some of the world's leading architects, the world's leading fabs, and proved completely unable to build a working cell phone part. And you ask yourself why? They had everything they needed. And, you know, you do an MBA chart and it's like you cannot— it's impenetrable. And the answer is, this is really hard. And the very small sort of mental model differences produce tremendously different results. How did every leader miss the largest compute market, right, in the first part of the, the 21st century? They— how did AMD miss it? How did— how did every— how did Arm win it, right? And all the leaders missed it. And then you say, all right, maybe there's something in the guts here that I don't understand, right? You got to really get in there. And it's not on a PowerPoint, it's not in a 2x2, it's not at some sort of consultant level. It is deep in the DNA of the small number of people who can build these things. And, uh, you know, we are lucky at Cerebras, we've got one of, one of the top 6 or 8 teams in the world, and, uh, other startups don't.

What does that market look like, do you think, in 10 years' time? I know 10 years is a huge amount of time given where we're at, but in 10 years' time, is it a monopoly market with one taking 90%? Is it like CloudWare?

Which market?

Specifically the chip market.

Which part of the chip market? We talk about AI silicon, or we're talking about silicon in general, or we're talking about—

I would say silicon in general.

Yeah, absolutely not. One takes 90%, you know, even at the— at Intel strength, uh, they had dominance in x86 and zero market share on the cell phone. And, uh, uh, and almost no share in the switching market. And Broadcom had dominance in the switching silicon market, which is a form of processor and silicon, and no share in x86 or other forms of compute. Yeah, it will not all accrue to one or two companies.

How do you think about the importance of margin today? As a business is Cerebras.

I think the reason we were able to raise at a higher valuation and from better investors and, and more money is because we had them right. And others who were out looking for money had negative margins. And I, I think, uh, as you prepare for being a, uh, sort of a credible public company, I think, you know, people do look at your margins and I, I think, uh, that, that's a really important part of, of moving from being an idea to being, uh, a, a real company.

What are NVIDIA's margins today?

Extraordinary. Some of the, the highest in history for a hardware company.

How do you think about that? Is that just pricing power, which they are taking advantage of?

Absolutely. I mean, the, the short answer is, why does it make sense for, uh, AWS to, to build a Trainium part? Well, because they want to get rid of the 78% gross margin that NVIDIA is charging. That's why it makes sense. Just like that. It might be on the high-end chips 85%. And I, I think people don't like that historically. Historically, people sort of put that in the back of the mind and they remember And, you know, when Intel stumbled, the number of people who came out of the woodwork to kick them when they were down was extraordinary. And it was sort of years of pent-up sort of frustration came out when the giant stumbles. And I think we've seen that again and again.

Speaking of that giant stumbling and being built, do you think sovereignty will be a big enough reason why incumbents are built. You know, we have like Mistral, a model provider in, in Europe, and the kind of sovereignty is their core play. Do you believe that is a sufficient enough core play to be a giant?

Well, I think right now, sovereignty plus the fact that we deliver their inference through, uh, the fastest, uh, hardware on earth makes their, uh, product, uh, the La Chat product really compelling. I think they are doing— they're using their, their advantages to compete, right? There were in Europe too few, if you want my opinion, too few AI labs that are doing interesting work. And they sort of looked around and sort of used strategic advantage. You know, we want to be the Europe's leader, um, and, and, you know, played that card really well. Hats off to them. And then they raised at a huge valuation.

Final one, just in terms of geography, you know, Deepseek obviously had that moment and it kind of solidified the concerns around China. How do you feel about China today as a pressing concern towards the US in terms of the race towards AGI between the two? Do you hate the way that it's is posited as like China versus the US, the AI race. How do you feel about that?

I think it benefits neither, the position we're in, right? I think, uh, the arms race certainly didn't, uh, help either the US or, or Russia, uh, in the '80s and '90s. We, we both spent money we wish— on weapons we wish would have been spent on infrastructure, people, or other things. I think, uh, we will be much stronger if we can find ways, uh, to peacefully, uh, engage. Uh, before these issues, we knew the guys at Didi and ByteDance and, and, and Ali and Baidu extremely well. They're talented engineers trying to build cool stuff. I think our governments are at loggerheads, and that's a problem. And I, I think, uh, we of course made choices. Um, we had a huge opportunity in, in 2019 to do a deal in China, and, and I decided to pass because I didn't think it was, uh, the right thing to do. Um, long before, uh, uh, the Department of Commerce limited exports to China, I didn't think right. And I was concerned about how the technology would be used. Um, but I, I think, uh, the realpolitik right now is that they're better at making drones, they're better at making robots. Uh, they are— their government has an extraordinarily aggressive policy in AI. For years, they, they backstopped their venture groups, right? So if you lost money in an AI company, the government would make you whole. Um, imagine that, Harry. Imagine how much money you could make if the government of the UK, right, offset some of your losses from AI companies that didn't work out. Um, and I, I think, uh, you know, we have real work to do in the US. And, um, you know, they spent—

what work do you have to do that you haven't done? What would you like to see?

I think, uh, You know, China thought long and hard about, uh, their power infrastructure, and their form of government allowed them to plan strategically. Our decentralized form of government has left us with sort of a patchwork of power infrastructures, um, you know, where even if the federal government wants to support you, their local regulations, like at the city and county level of towns, right, that can interfere with the project and set a project back billions of dollars. I mean, Samsung built a fab in Texas, and they had to change the design of a fab because of a local fire ordinance. And the US government worked for years to get deployment of billions of dollars in Texas, and a local fire ordinance set them back 8 months, 10 months, and caused them to redesign the fab. I mean, right, that, that's a problem, and, uh, that's a challenge that we have to sort of collectively work through. I, I think we have the premier universities, we have historically drawn talent from around the world, um, you know, if you look at, uh, see the great CEOs in our industry Janssen, Hock Tan, Lisa. I mean, you go down the list at Sundar, at Microsoft. I mean, they came, their parents came. I mean, we gotta take that really seriously.

You don't buy the whole, well, actually a load of people actually just abused H-1s and we'll just move to O-1s, which people were using anyway, and the average salary for an H-1 was $120,000, and so it's a good thing and people will just use O-1s?

We have H-1s and we have O-1s. I am sure that in every government program there's an amount— there's abuse, right? I mean, I'm not saying there's no abuse. Um, was there more abuse in the H-1 than in, in other areas? Um, I, I don't think so. Having the best and the brightest come to your universities, and once they benefit from sort of our great institutions, to want to stay and contribute, first with a J-1, which is a student visa, and then enter the H-1B lottery through the, through the, uh, approved process, right, to get a green card and become citizens. And this is how my parents did Right. I think it's one way to bring an extraordinary amount of talented people to the US.

Is there anything else you'd change? You said the power infrastructure and the permitting around it.

Power infrastructure is, uh, ends up at the local level, which is not necessarily where big ideas, uh, and sort of strategy is well knitted together. I think we, we have starved our universities of compute. If you want to interesting training work at a university. Very hard to get enough compute to do that. We're just not set up for that. We have power, people. Those are two dimensions. I think the Trump administration's done a good job in generally relaxing some of the regulations that were painful.

Andrew, I want to do a quickfire with you, so I'm gonna pummel you with quick questions and you got to give me your immediate thoughts.

You know, that's hard because I only have long answers. Harry, so it's totally fine. You'll be—

honestly, um, what do you believe that most around you disbelieve?

We will have peace in the Middle East in our lifetimes.

Why do you believe that?

Because I believe, uh, having visited and spent time now in, in the UAE, in Saudi, in Qatar, I, I think the returns to being, uh, to moderation the economic gains. Uh, someone said, we're too busy to hate right now, we're too busy building. I, I think those have been writ large so clearly in the UAE with the rise of Dubai and the UAE in return for making peace with Israel, in return for a more moderate position. I, I really believe that that, that is the path to the future.

How much of your revenues are from the UAE?

I think in the S-1 it says, uh, uh, and that was for, uh, maybe the first half of '24. We haven't published others, but a lot. I think 75, 80%.

I mean this in the nicest way, do you not have to say nice things about it then?

Like, if someone's giving me a question, no, I I went there to do business as a Jewish guy, uh, but before we, uh, before we had any business done, right? I, I— what I found surprised me. And we, we don't do much in Saudi, and I think they're making great strides. And we don't do anything in Qatar right now. I think they're making great strides. So I, I don't think it's just— it, it may well sort of be colored by the fact that I, I spend time and in Abu Dhabi, and I spent time in Dubai, and I spent time in Riyadh, and I spent time in Doha. Um, and, uh, sure, it's colored by, by those things.

Why are your revenues concentrated there? Is it just because they're like more willing to embrace innovation, new relationships, new vendors?

No, I, I think they, they bought so much, they consumed. And you know, the, the data I gave you was through the first half of '24. Uh, they placed such big orders, they consumed all our manufacturing capacity. I mean, it was— they are building at such an extraordinary rate that through the first half of '24, they consumed an enormous amount of our manufacturing capacity.

I think their orders exceed— did their orders exceed your expectations of their orders?

I think their orders exceeded everybody's expectations. You can be a professional salesperson in Silicon Valley. For 20 or 30 years and not see a $500 million order. Really, I think you can go around the Valley right now and talk to VPs of sales or EVPs of sales at dozens of public companies who've never seen an order of that size. They were bold and they were early. And you know, when we started doing business with G42, nobody heard of them. Now everybody in the world—

Do you think that was a resource planning mistake from you? I mean that nicely.

Oh, like he said, was it a resource planning mistake? I, I mean, they're all mistakes in retrospect, right? I mean, they're— if we hadn't won them and we had the resources for it, that would have been a resource planning mistake. I mean, I'm, I'm in the business of, of making big bets and making lots of mistakes.

Harry, what's the biggest bet you've made with Cerebris that didn't work out?

My bets here have, have been pretty good. You know, we— to go to wafer scale, to solve a problem that, uh, nobody had previously solved. Gene Amdahl, one of the fathers of our field, failed. IBM failed. TI failed. Uh, everybody's failed at this. We had a period of about 15 months between about 2017 and, and early 2019. Where we couldn't make one. And we were running a burn of about $6 million a month, $7 million a month. And we stayed with it and our board stayed with it.

Did you have signs that it would work?

Yeah, we did. And we weren't running around like chickens without our heads. We were going through engineering process. Each failure was, you know, we did a full FA, a failure analysis. Each time we fixed the the cause. We did another one, didn't work. Did another one, didn't work. And each time we got a little better, and we got better and better and better. Uh, and then we solved it. And when the founders— the first one that worked, the founders were in a tiny little lab that was a converted conference room that for cooling we had the windows open and we'd blown a hole in the wall so we could get external, a chiller outside and poured in. And when we had it running, the founders stood there together and stared at the box running, which is about as interesting as watching paint dry. And we stood there and we couldn't believe it. It was like, we have just solved a problem that for 75 years the smartest people in our industry have been unable to solve, and we have done it. And we stood there for like half an hour, and it was, it's one of the highlights of of my career.

Pretty cool. All right, I'll give it to you.

Yeah, so that was a big, big bet. That, that was, uh, fair enough.

I had the $6-7 million a month burn. I'm like, all right, fair. I swear I'm almost picturing like angels singing and it's like tears coming down your face.

You know what, it felt like that. And it was, you know, the brainchild of my co-founders, um, Gary and Sean and, and JP and Michael. It was their invention and it was a physical manifestation of their ideas.

Where are people investing today where they will completely lose their shirt?

The silicon industry is not a place for 25-year-old CEOs, no matter how smart you are. The returns to having built parts before in what we do are enormous. And the number of different relationships that are necessary, uh, you need a relationship with the fab, you need a relationship with the EDA toolmaker, you need backend design engineers, you need logic design engineers, you need IP relationships with IP providers. Um, it has been an extremely a difficult road for, uh, young CEOs. On the other hand, young CEOs in many of the markets you invest in have done extraordinarily well, particularly, particularly where, uh, they look like their customer. The reason that, uh, the entire social networking world was built by young founders is it they were building a product for their friends, and that is an advantage. The reason that, that AI startups who are doing, uh, tools for other students, for coders, are young is because, uh, they understand the needs and demands of their target customer base extraordinarily well. And so I, I think that's an area where people are going to get, get clobbered, is to take a mentality that said you know, it's enough to be smart in this field to build a good chip. That has historically not been the case, that there are real returns to having done 15 or 20 of these in the past.

Where are people not investing enough where they should be investing more?

I think there are this collection of extremely unsexy things that are causing tremendous pain across the industry. Data cleaning, your data pipeline. These things are, you know, nobody puts on their LinkedIn data pipeline expert, and yet these are some extraordinarily valuable cats. Nobody leads with, you know, a leader in the cleaning and tokenization of data. And these are extraordinarily important, uh, roles. Um, I, I think, uh, most— many AI projects fail on those fronts, have nothing to do with the AI. They, they fail because the data was a disaster. They, they fail because everything except the AI was a failure. Um, I, I think that's an area that profoundly underinvested in.

What do you think the data provision market looks like? We see Surge, Macaw, uh, Invisible, Turing, uh, Handshake moving into it more and more. What does that market look like in, in 5 years? All of them are above $100 million ARR. What the fuck happens there, Andrew Feldman?

I'm different from some of your guests. There's a lot of stuff I don't know. I'm not afraid to just tell you, um, You know, that, that is a very curious market. Um, you know, why is it curious? Well, Scale sort of pioneered it. Um, I, I think, uh, Turing was in a different market completely and pivoted to it and found great success in it. Um, there are these collections of others. I think clearly, uh, the, the provisioning value-added tagged or, or or evaluated data is really important. Whether it's durable, whether we get machines that do it, uh, every bit as well as people, is a question that's really hard to answer right now. That's why it's curious, is that clearly it's important now, and the question is, will it clearly be important in 3 years? And that's a question I don't know the answer to.

Maybe I'm, you know, could go either What's your craziest prediction in terms of how AI reshapes the future in 5 years? So for example, Jonathan said, hey, I think AI will create massive labor shortages. It will create so many jobs for so many people that we will have massive labor shortages.

In 5 years? Absolutely wrong. Economic dislocation isn't resolved in very short periods of time. That might be true in 15 years, but, uh, I, I think, uh, in the 3 to 5 year time frame, uh, I, I certainly don't believe that will be the case. Um, I, I think, uh, AI will, uh, the adoption of AI or the diffusion of AI into the economy, it will nibble its way in, right? I mean, let's ask this question, uh, AlphaFold solved one of the hardest problems in, uh, in chemistry, a problem that had been open for years. Name a drug that's resulted from it. Not one. Now, I believe there will be, but AlphaFold's 5— what, 4 years old now? 3 years old, right? This was a massive breakthrough for which the, the inventors were given Nobel Prizes. Where's the drug now, right? Show me the, the, uh, the medical benefit. It will get there. It will be important. Continuations of the model will have fundamental impact. But sort of, where are the X-ray crystallographers who who were displaced because of it, right? That was what X-ray crystallographers were doing, only physically. Um, they're not out of work. They're— in fact, there's more demand for them. Um, so I, I think, uh, uh, I think what it means to— I think it will have really interesting effects on the way we educate children. And that's an interest of mine. Um, you know, we've sort of been educating children the same way since Alexander the Great was tutored by Aristotle, right? And it was sort of like, get a smart person, they're older, they stand behind you, they tell you what to do— read, you read it, you talk to them about it, they correct your paper. This form of instruction has been sort of unchanged. Maybe YouTube changed it a little bit in that you had different instructors. But imagine a system where, for example, uh, you made a set of mistakes in your math work and the result wasn't read on a paper. Oh, look, you got it wrong here. But They compared the type of mistakes you made to the type of mistakes thousands of other students made and said that for this group of mistakes, we have found that the following workbook is extremely effective at remedying this hole in their thinking. Nobody differentiates and modifies the training based on the type of error the students are making. And that's exactly what you ought to do. So I think the way we teach will change a great deal. I think what it means to be entry-level in a company will change a great deal because what entry-level has generally meant in consulting firms and at investment banks has been doing shit work, in particular being really good at spreadsheets and writing summaries. Of other people's research. AI will be better at that. That will change a great deal. But I don't think— I mean, I always thought that was a terrible way to spend an extraordinary— your 22 through 24 years. You are coming out of top schools. There's so much to be learned. There's so much you can contribute. But to do huge hours doing spreadsheets I think there's vastly more productive thinking those students, those young people are capable of and more learning that they can do and therefore be vastly more productive in the following years. And I think AI will change that.

Final one for you, Andrew. What would you do if you knew you wouldn't fail or couldn't fail?

I guess I don't— I've never thought of that. I look at it the other way. Is that every day I go to battle with Goliath. Every day, every dollar we sell is a dollar that if we didn't work at it, if we didn't think, if we didn't invent, if we weren't 10 times better, would default to NVIDIA. And before I competed with NVIDIA, I competed with Cisco for 15 years. And every dollar that we sold there, if we didn't build better product, if we weren't more aggressive, if we weren't more creative would have defaulted to the market share leader. And I think for me, in my career, I take great pride in facing every day the most wicked curve, the curveball pitcher for your cricket example, the scariest spin bowler, the scariest speed bowler. I enjoy that. That is something that in a quiet moment, you sit back and say that I'm competing with every disadvantage against the absolute best in the world every single day at work. I love that. What's more, everybody's betting against me except a very small group of people who you named, who stood up early on and said, maybe he can beat them. That's the life I've chosen and the career I love.

Dude, I absolutely love that. That is such a good end as well. You know, I do obviously many shows and there are some ends where we can't end like that. That is like a depressing end. That's a fantastic end. I so appreciate that. I so appreciate you. You know, you're my go-to when I'm trying to understand what the fuck's going on. And so thank you so much for explaining this to me today, dude. Why?

Look, I'm happy to jump on. You know, I, I, my view, uh, of, of your, uh, LinkedIn post, by the way, is there, there are two, exactly two things I've read in my life that, uh, feel like they understand what entrepreneurship is. And the first is Ben Horowitz's book, The Hard Thing About Hard Things, and the other is your tweets and your your LinkedIn posts. I think, uh, they have the feel of what my life is. This notion that somehow you can achieve greatness, you can build something extraordinary by working 38 hours a week and having work-life balance. Um, that is mind-boggling to me. It's not true in any part of life. And your willingness to jump in and say, no, that's not how it's done. Guys. I mean, you can have a great life, you can do many really good things, and there are lots of paths to happiness. But the path to build something new out of nothing and make it great isn't part-time work. It isn't 30, 40, 50 hours a week. It's, it's every waking minute. And of course there are costs. And, and, you know, it's probably true for world-class athletes too. If you listen to, uh, what Ronaldo talks about I mean, he worries about everything he puts in his body. He trains every single day. They work on rest, right? Rest isn't rest. Rest is something you work on so your body rejuvenates faster. I mean, this isn't 30 hours a week or 40 hours. These guys are the best in the world at everything.