Cerebras CEO on Why Raise $1BN and Delay the IPO

Harry, I’m okay with any questions, naive or otherwise. Happy to do it anytime. I I read your your LinkedIn posts, your Twitter posts.

done in our category. It was done at the highest valuation and with the premier investors. At late stage investing, you’re looking for the likes of Fidelity. They are the, what would the English call it, the sort of Oxford or Cambridge of investing, right? I mean, they are the premier public market investors. And 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 the treaties to lead the round. And then we were able to get enormous participation from Tiger Global, from Valor, from 1789.

So that’s point one. I think point two is that we now have sort of the dry powder to really push, take the opportunities in front of us to build out our manufacturing to the scale and scope we want, to add new data centres. We added five this year in The US to add more data centres. And we have more big ideas. I think incremental improvements, make believe gains achieved by dropping from, you know, eight bit to four bit, those aren’t gonna get us to the promised land in AI.

We’ve got real work to do as a community. I think this funding puts us in the catbird seat for that.

stage venture guy, you’re like, who are the public guys?

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

The first thing, Harry, is we are in a stage of the market where the claims are enormous, where tens of billions of dollars are being done here and there and nobody’s reading the fine print that it’s over five years and it’s up to. This is the great sort of CYA word in in marketing history is it will be up to 100,000,000,000 over five years. Right? Well, up to means it could be 30, it could be 12, it could be 40. Right? He could pick a lot of big numbers and it won’t be bigger than.

And so as you read these deals, I think you have to really think about the timeframe over which they’re being done. You have to think about whether anybody is actually counting. Lots of people are saying they’re going to bring hundreds of billions of dollars of jobs to The US and this and that. In eight months, has anybody got a little spreadsheet like nine jobs plus one factory? I mean, who who who holds anybody to account? And the answer is nobody. 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. We have customers coming to us and saying, we would like between five and forty million queries per second. Well, how do you not know by a factor of 35,000,000 queries per second where your demand’s gonna be? How are you unsure by an order of magnitude of what your queries are gonna be? The answer is things are moving at a rate that six, eight, twelve months out, everybody’s unsure. It’s so fast. It’s so big. And so you should think about sort of these announcements as sort of options on the future.

Right? That’s really the 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 the future rights to have some capacity. So that’s a way to think about it.

question about in extraordinarily rapidly moving environments, what the right planning cadences. What you really need is good planning changing rules rather than good planning. Right? I mean, we have to make big bets. We’re making five and seven year investments in data centre capacity. We are making hundreds of millions now in term of billions of dollars of bets in supply chain. Those are not three month bets. 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. You pay a little price to secure some capacity. And if you don’t use it, you just you go, alright. That was sort of a way to manage uncertainty about the future.

I’ve been wrong. If you would have said a year ago, two years ago, pick pick a time at which it would have been conceivable that OpenAI would get the valuations they’re getting. Right? It wouldn’t have been conceivable. Three months ago, six months ago, nine months ago, when was it? Like, the day before. Right? I think that’s true with the demand we’re seeing. It’s true with the valuations on companies we’re seeing. It’s true with the rate of

I would say that there are always grumpy people who they’re like, it’ll never work, and you’ll never beat Goliath. And truth is most things don’t work, and most of the time, Goliath wins. 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? Of course, if NVIDIA keeps growing at the rate they’re currently growing, eleven years from now, everybody on Earth works for them. Right?

Do the math. Right? However, is it possible that our economy looks very different in five years? Is it possible that the things we value are very different? That we have reorganized around AI, we’ve seen a major bump in labor productivity, we have benefited dramatically, and the economic pie is much larger. I think that’s not

public market stocks. I don’t like picking public market stocks. 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. As an entrepreneur, as a David in the battle with Goliath, I wanna make money when when we build a great company, period. But 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.

This is something that historically large companies have done as they feared for their technical prowess.

You start buying business as opposed to winning business. We saw that with Cisco who emerged it as a dominant position, you know, ’99, 2000, 2001. That that has been one of the strategies. Another strategy you you see is this predatory preannounce, where you announce B300s before anybody can get B200s. You start talking about Rubin before B200s 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.

I think these are the strategies of very large companies using their strengths. And I I think that’s what you’re beginning to see unfold with NVIDIA.

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. You wanna 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. It wasn’t designed for you or other analysts to anchor on different things. And and that’s a very reasonable thing for both of them, but it’s just it’s not an analyzable thing. Beyond the fact that NVIDIA has chosen to try and lock up a portion of OpenAI’s demand by investing in them.

That’s about as much as you can say.

name? Hold on. Wait a sec. Let’s go back to the 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 well, I could never call her. I mean, that would be just mom

Nothing there. The truth is is there may be and there are likely a huge number of moving parts that make it impossible to clearly describe without giving out more than they wanted to give out.

We are in unprecedented waters. People are clearly still getting value from H100s, and that’s more than two years. Right? So if you say it’s a two year depreciation, you’re empirically wrong. And I think people are still getting value from A100s, though not on the cutting edge. And so that’s closer to three or four years, and could be as long as five or six. The question of depreciation is how much faster are future generations than the current generation? That’s the actual question on depreciation. Because with depreciation, you’re saying, at some point, it’s no longer worth using a part that’s fully paid off because there is a new part that’s so much faster, uses so much less power, that it’s better for me to retire it.

That’s the actual the underpinning to the depreciation question. If I have a data centre and it’s 50 megawatts and I have this much capacity in it, at some point, even though my chips in it have been depreciated and I’m running them at at zero cost, right, Power plus zero depreciated cost. It makes sense to move them out because the new chips are so much faster, so much better, use so much less power. I get so much more dollars per. And so that’s the question. 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.

The question is, in that case, whether you read people’s marketing material or the actual performance results. Certainly, people’s marketing material would lead you to believe that generation over generation, there are huge gains. A little bit of engineering digging probably leads you to the conclusion that you’re getting two, two and a half x per meaningful generation move, not more. If you compare apples to apples, eight bit to eight bit, four bit to four 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 two x, 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 doesn’t move as far forward, it becomes the new bottleneck. It doesn’t matter how how many flops your chip has. If you can’t get data onto and off of the chip, those are wasted. The question isn’t how much how much faster is the chip, it’s how much faster is the solution. 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.

Not only is it fair, it’s the reason we went to Wafer Scale. 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 AMD’s, 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, half your real estate’s available for compute. 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 four, five thousand chips. What a mess. You know how many cables that is? You know the impact to the AI? It’s a horrible mess. And it limits you from doing things you want to do with the AI, like speculative decode. It has all sorts of painful challenges. The other hand, use one of these or two or four. And it’s simple, it’s easy, and this is what your friends 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 them again in the future.

Well, what we were missing is that for seventy five years, nobody could do it. 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 seventy five year history of the compute industry, and many people had tried and failed. After we did it, Elon tried at Dojo, and they failed. It’s really, really hard. Our strategy had never been done before, never been successfully yielded.

And so while it was obvious, it was hard.

No. We’re faster on both, but the software challenges in training are real. What does that mean? It means that when a new model is built and everybody reads about it in a publication, it was done on a GPU. For everybody to to train it, 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 another dedicated chip like ours. You have to move it, and that’s a harder software lift.

In inference, truth is nobody cares about CUDA. Nobody even cares about PyTorch. What they want is an API. It’s literally 10 keystrokes to move from a GPU based solution on OpenAI OSS one twenty b to our solution. It’s 10 keystrokes. That’s it. It’s nothing. The answer 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 a thousand b two hundreds, you got to get a thousand b two hundreds.

You need to train the model for four weeks or six weeks. You got to stand up a cluster of our machines. You got to it’s a bigger lift. I think the market right now is easier to move people off GPUs in inference, and the number of people doing inference is vastly higher than the number of people doing training.

I think it’s really hard for the mind to wrap itself around geometric growth or exponential growth. Right? I think there is nothing confusing about the greater growth of inference. Greater growth of inference is the number of people who use it times the frequency of use times the amount of compute needed per use. It is three 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 wanna do with it is bigger and more complicated, so it uses more compute. And so you have three variables, the size of the market’s a product of the three, all growing fast. We knew that going in. We see that, and it still takes your breath away. That that is really, really interesting. I

think we’ve seen anything

I agree. I I think the reason I’m a 100% sure that that we are underestimating the market is because of that premise.

in 1988, Robert Sallow, who won a Nobel Prize in economics, he asked this question. He said, we see computers on every desktop and everywhere we look except in the productivity statistics. And you say, well, that’s a really interesting 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 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. 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 nineties, he 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. And all these things used compute differently in ways that had never been consumed before. And you got this massive jump in productivity. If you use OpenAI in a various of their competitors, 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 reorganize ourselves around AI, you’re gonna see massive productivity gains. If we use AI to replace things we’re already doing, Google or something else, you’re not gonna see very big jumps at all. And so that transition takes time. And what he pointed to was a demographic. 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, an operating system for life.

Yeah, it’s feasible. 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 it’s not where we have people or where we have fiber optic cable. 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. The problem is 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 centre. The second observation is 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, and that’s on all of us. I think we have an obligation to deliver drugs that are more efficacious, to deliver better healthcare, to make aging less painful, make the looking after of aged parents or sick parents.

You go through society’s ills and woes. If we are going to consume this amount of power, the burden is on us to deliver value for it. If we use it and don’t do that, then it’s not

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 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, right, you can point to, you know, thousand poppy strategy, which is what a market is.

Right? 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 they blew up. You can always say that after the fact. Look at that. They’re using a ton of energy for that. That’s not useful. Let’s see.

And so I 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, we are sure that we’re giving these to disproportionately to projects that that matter to society.

confusing. On net, it’s probably more to help. The Biden administration was misguided and afraid. The Trump administration, I think to his credit, they surrounded himself with some smart people in the AI space. On net, it’s been positive.

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

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 that 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 a tooth, if you stopped and thought and marched, you might get further over a thirty or sixty

I I think the the risk there is not that they consume that much that they are that much value. That’s not the risk. Think they’re that much value because we believe the future economy will reward that. I think the issue is is that people then think the S and P is a safe investment or a safer investment than it might be. The risk is the mismatch in the mental model people have. Risk comes in financial markets where people fundamentally underestimate risk. When risk is priced properly, then what happens is your outcomes are not surprising.

If people continue to think the S and P is sort of an index of the global economy or and it’s not. It’s 30% or fifty percent seven 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. That seems to me to be a challenge in the new world order. And all the advice that sort of 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 because of consolidation,

I think they’ve proven themselves to be the greatest company in the first quarter of the twenty first century. They’ve proven themselves to be extraordinary company in the first quarter of the century. And I I I don’t know if 4,000,000,000,000 is right, but I think a very big number, maybe it’s too low, is

I think if you go back to planning, if you’ve got customers demanding five x at your capacity, I mean, probably didn’t get your planning right. Right? You probably should have should have planned better. I think there are bottlenecks at every level that are real and meaningful. I I think the first one is expertise. We have fundamental limitations in AI expertise. We’re not making enough AI practitioners. We’re not making enough data scientists who understand data pipelines. Our universities aren’t minting enough. Our challenges in The US with immigration don’t help that.

We have historically sucked the best and the brightest, first on j ones to come to our schools and h ones to stay. If that is not our policy, we need to make them. If the government decides that that is not the way they want to build a workforce, instead they want to build it 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-twelve. We need to do a better job of educating them in our universities in order to to make the number of of engineers we need to meet this demand.

That’s a bottleneck. And it’s why the best and the brightest are are getting such extraordinary compensation.

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. Are they to be paid more than world class soccer players? I have no idea. Maybe. Maybe not.

That’s what we have to think about. I mean, we’ve paid Charlie Sheen $2,500,000 an episode for 2.5 men. I’m pretty sure that there are lots of people who are whose net productivity to society is above that. And he still spent it all. He is. I saw that. I just saw the show on was it Netflix or Prime? What a sad story of somebody 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 I don’t spend a minute worrying about whether we’re paying extraordinary people too much. No company ever went bankrupt by paying extraordinary people too much. If you wanna go bankrupt, pay mediocre people too much. That’s how you mess up. Nobody’s ever struggled by paying truly extraordinary people too much. What’s the other bottleneck? You said expertise is one. TSMC can’t build fabs fast enough. The truth is is that these are both for for TSMC and Samsung. These fabs are the most amazing manufacturing plants on the planet.

You know, these are $30,000,000,000 $50,000,000,000 factories. Their ability to build them quickly enough is very much limited. That in turn limits and keeps the supply below where it would like to be of chips, not just our chips or NVIDIA, but everybody’s chips, below where it might otherwise be, keeps the cost up. Right now, there’s a shortage of data centre capacity. There’s a huge amount of investment that has gone into that. There’s a lot of words. But where are these gigawatt facilities that everybody’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 six months, eight months. And for the rest of the world, it takes a year and a half, maybe longer.

that they really understand. Right? It looks like a bond to them. It looks like a piece of real estate. You you you get a tenant. They pay rent every month. You you can loan against that. You you get an investment grade tenant that’s basically a bond. It has the advantage of 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. Core Weave and some of their sort of financial engineering and innovations there help the world to see that.

You know, like many things, lots of people will enter. The smart will make money. The less sophisticated will will lose money. Building data centres is not for everyone.

I think if the best can build them for 8,000,000 a megawatt and you’re spending 12 or 14, that’s how you lose money. You lose money because it 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 do you have real access? It gets you fast permitting. Once it becomes a construction project, can you keep control of your costs? Once it’s finished, can you keep good tenants in it? The ways to lose money in property are large and many.

There’s no free lunch there either. When you are trying to go unbelievably quickly, it’s harder and harder to be disciplined and not make mistakes.

It is completely unclear. The most successful two companies to date, OpenAI and Anthropic, neither are vertical. OpenAI used Azure, a 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, but it’s clear that it’s not the only strategy. That there are plenty of working models where you are not fully integrated from chip through system, data centre, through software, all the way to the top.

I think that there is a long history of software companies failing to build chips. The list is is very large. Whether 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. Companies at the size of Microsoft have been unable to deliver chips. There are plenty of examples as you look across the FANG group where chips were tried. Mean, probably the most successful is Google, and they’re ten years in. Modern software does not fit well in a chip making framework.

Weekly sprints don’t work well on two year long projects. 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 six months and tens of millions of dollars. 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. Google acquired the talent from a collection of companies and then set it in a BU that was a side and under somebody who who had enormous respect in the organization and oars and had a ten or fifteen year view.

These are things that have been challenging in in many companies. Chip building is an MBA nightmare. Your analysis that says Intel had, you know, between 2,000 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, it’s impenetrable. And the answer is this is really hard. Very small sort of mental model differences produced tremendously different results.

How did every leader miss the largest compute market in the first part of the twenty first century? How did AMD miss it? How did how did how did ARM win it? All the leaders missed it. And then you say, alright. Maybe there’s something in the guts here that I don’t understand. Right? You gotta really get in there, and it’s not on a PowerPoint. It’s not in a two by two. 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.

You know, we are lucky at Cerebras. We’ve got one of the top six or eight teams in the world. Other startups don’t.

Which part of the chip market? Are talking about AI silicon or we’re talking about silicon in general or we’re talking about I I

Yeah. Absolutely not. One takes 90%. You know, even at the at Intel strength, they had dominance in x86 and zero market share on the cell phone and almost no share in the switching market. Broadcom had dominance in the switching silicon market, which is a form of processor in silicon, and no share in x 86 or, other forms of compute. Yeah. It it will not all accrue to

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. And others who were out looking for mark, looking for money had negative margins. As you prepare for being a credible public company, people do look at your margins. That’s a really important part of of moving from being an idea to being a real company. What are

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

We’ve seen that again and again.

Well, I think right now, sovereignty plus the fact that we deliver their inference through the fastest hardware on Earth makes their product, the LeChat product, really compelling. They’re using their their advantages to compete. There were, in Europe, too few AI labs that are, doing interesting work. They sort of looked around and and sort of used strategic advantage. You know, we wanna be the Europe’s leader, you know, played that card really well. Hats off to them,

in. The arms race certainly didn’t help either The US or Russia in the late eighties and nineties. We we both spent money we wish on weapons, we wish would have been spent on infrastructure, people, or or other things. We will be much stronger if we can find ways to peacefully engage. Before these issues, we we knew the guys at Didi and ByteDance 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.

We had a huge opportunity in 2019 to do a deal in China, and I decided to pass because I I didn’t think it was the right thing to do. Long before Department of Commerce limited exports to China, I I didn’t think it was right, and I was concerned about how the technology would be used. The realpolitik right now is that they’re better at making drones. They’re better at making robots. 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. Imagine that, Harry. Imagine how much money you could make if the government of the UK offset some of your losses from AI companies that didn’t work out. You know, we have real work to do in The US.

China thought long and hard about their power infrastructure. Their form of government allowed them to to plan strategically. Our decentralized form of government has left us with sort of a patchwork of power infrastructures. Even if the federal government wants to support you, there are local regulations, like at the at the city and county level of towns that can interfere with a project and 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 eight months, ten months, and caused them to redesign the fab. That’s a problem that we have to sort of collectively work through. We have the premier universities, historically drawn talent from around the world. If you look at the great CEOs in our industry, Jensen Huang, Tan, Lisa, I mean, you go down the list, Sundar, at Microsoft, they came and their parents came. We gotta take that really seriously.

I am sure that in every government program, there’s abuse. Was there more abuse in the h one than in in other areas? 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 J1, which is a student visa, and then enter the H1B lottery through the approved process to get a green card and become citizens, and this is how my parents did it. I think it’s one way to bring an extraordinary amount of talented people to The US.

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

We will have peace in The Middle East in our lifetimes. Why do you believe that? Having visited and spent time now in in The UAE, in Saudi, in Qatar, I think the returns to moderation, the economic gains, someone said, we’re we’re too busy to hate right now. We’re too busy building. 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 is the path to the future.

I think in the S1 it says, maybe the first half of twenty four, we haven’t published others, but a lot, 75, 80%.

the answer your question, no. I went there to do business as a Jewish guy before we had any business done. What I found surprised me. 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. It may well sort of be coloured by the fact that I spend time in Abu Dhabi and I spend time in Dubai and I spend time in Riyadh and I spend time in Doha. Sure, it’s coloured by those things.

But I I think I think

They they bought so much, they consumed and, know, the the data I gave you was through the first half of twenty four. They placed such big orders, they consumed all our manufacturing capacity. They are building at such an extraordinary rate that through the first half of twenty four, consumed an enormous amount of our manufacturing capacity.

everybody’s expectations. You can be a professional salesperson in Silicon Valley for twenty or thirty years and not see a $500,000,000 order. 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 we started doing business with G42, nobody heard of them. Everybody in the world heard Do

They’re all mistakes in retrospect. 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.

My bets here have have been pretty good. To go to Wafer Scale, solve a problem that nobody had previously solved. Jean Amdahl, one of the fathers of our field failed. IBM failed. TI failed. Everybody’s failed at this. We had a period of about fifteen months between about 2017 and early two thousand nineteen where we couldn’t make one. And we were running a burn of about 6,000,000 a month, 7,000,000 a month. And we stayed with it and our board stayed with it. The result was Did

Yeah, we did. And we weren’t running around like chickens without our heads. We were going through engineering Each failure was, you know, we did a full FA, a failure analysis. Each time we fixed the cause, we did another one, didn’t work, did another one, didn’t work. And each time we got little better. And we got better and better and better. Then we solved it. 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 chiller outside and pour it in.

And when we had it running, the founder 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 seventy five years, the smartest people in our industry had been unable to solve. And we stood there for like half an hour and it was one of the highlights of my career. Pretty cool. Alright, I’ll give it to you. Fair enough. That was a big, big bet.

That was

You know what? It it felt like that. And it was, you know, the brainchild of my co founders. It was their invention. It was a physical manifestation of their ideas.

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. The number of different relationships that are necessary. You need a relationship with the fab. You need a relationship with the EDA toolmaker. You need back end design engineers. You need logic design engineers. You need IP relationships with IP providers. It has been an extremely difficult road for young CEOs. On the other hand, young CEOs in many of the markets you invest in have done extraordinarily well, particularly where they look like their customer.

The reason that the entire social networking world was built by young founders is that they were building a product for their friends, and that is an advantage. The reason that AI startups who are doing tools for other students, for coders, are are young is because they understand the needs and demands of their target customer base extraordinarily well. That’s an area where people are gonna get clobbered is to take a mentality that said, it’s enough to be smart in this field to build a good chip. That that has historically not been the case, that there are real returns to having done 15 or 20 of these in the past.

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 a leader in the cleaning and tokenization of data, and these are extraordinarily important roles. Many AI projects fail on those fronts, have nothing to do with the AI. They fail because the data was a disaster. They fail because everything except the AI was a failure.

That’s an area that that profoundly underinvested in.

I don’t know. That is a very curious market. Why is it curious? Well, scale sort of pioneered it. Turing was in a different market completely and pivoted to it and found great success in it. There are these collections of others. I think clearly the provisioning of value added tagged or evaluated data is really important. Whether it’s durable, whether we get machines that do it every bit as well as people is a question that that’s really hard to answer right now. That’s why it’s curious. It’s that clearly it’s important now.

And the question is, will it clearly be important in three years? That’s a question I don’t know the answer to. Maybe I’m

way.

wrong. I think economic dislocation isn’t resolved in very short periods of time. That might be true in fifteen years, but in the three to five year time frame, I certainly don’t believe that will be the case. The adoption of AI or the diffusion of AI into the economy, it will nibble its way in. Let’s ask this question. AlphaFold solved one of the hardest problems 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 AlphaFall is four years old now.

This was a massive breakthrough for which the inventors were given Nobel Prizes. Where’s the drug? Show me the the medical benefit. It will get there. It will be important. Continuations of the model will have fundamental impact, but where are the X-ray crystallographers who are displaced because of it? That was what X-ray crystallographers were doing only physically. They’re not out of work. In fact, there’s more demand for them. I think that it will have really interesting effects on the way we educate children, and that’s an interest of mine.

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, you made a set of mistakes in your math work, the result wasn’t read on a paper.

Oh, look. You 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. Imagine that. Nobody does it. 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 I think the way we teach will change a great deal.

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, 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. I mean, always thought that was a terrible way to spend an extraordinary year twenty two to twenty four 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

don’t I’ve never thought of that. I 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. Fifteen 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, you know, the the the scariest spin bowler, the scariest speed bowler. I enjoy that. That that is something that in a quiet moment, you sit back and say that that I’m competing with every disadvantage against the absolute best in the world every every single day at work, and I love that. And 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, you know, maybe he can beat them.

That’s the life

Well, I’m happy to to jump on. My view of your LinkedIn post, by the way, is there there are two, exactly two things I’ve read in my life that 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 your LinkedIn posts. They have the feel of what my life is. This notion that that somehow you can achieve greatness, you can build something extraordinary by by working thirty eight hours a week and and having work life balance, that is mind boggling to me.

It’s not true in any any part of life, and your willingness to jump in and say, no. That’s not how it’s done, guys. 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 thirty, forty, fifty hours a week. It’s it’s every waking minute. And, of course, there are costs. You know, it’s probably true for world class athletes too if you listen to 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. Faster. This isn’t thirty hours a week or forty hours. These guys are the best in the world at everything.