Full episode · 1 h 3 m
Andrew Feldman on Building Cerebras and the Future of Chips | Ep. 57
Andrew Feldman says chip startups need radically different architectures, not incremental gains, to challenge incumbents.
Uncapped · Jack Altman & Eric Vishria
- Andrew Feldman is cofounder and CEO of Cerebras Systems.
The best minutes
- 4:09Attacking Goliath requires a radically better productPro pick 6 min
- 9:37Failure analysis kept Cerebras going through 18 months of setbacks3 min
- 16:13Chip fabs are a rare, tightly coupled supply chain7 min
Also worth watching
- 43:57Data center delivery depends on power gear and site readiness6 min
- 51:57Faster inference can open new markets and increase throughput3 min
- 55:12Nvidia's long fight as a public company shaped its grit2 min
- 29:00Cerebras made architectural choices to adapt as AI changed4 min
- 12:55Cerebras is developing memory and optical links alongside compute3 min
Skip the opening investor backstory, the board and hiring discussions, supplier relations, and the closing childhood and education stories.
Summary · 4 min read
Andrew Feldman is the cofounder and CEO of Cerebras Systems, which he founded in 2016. Cerebras makes the Wafer Scale Engine, described as the world’s largest chip.
Andrew Feldman says Cerebras was built around a different architecture because incremental gains cannot dislodge incumbents that keep improving. He describes the technical and financial risk of wafer-scale computing, and says the board helped by financing the work without trying to solve engineering problems. Feldman explains how Cerebras moved from training toward inference as AI’s use became clearer, while chips, fabs, power and data centers remain constraints. He argues that speed can create new markets and credits NVIDIA’s grit, rather than CUDA or chip architecture, for its success.
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- 0:44A chip startup needs a radical edge
Feldman says he saw AI as a computationally intensive workload that might justify a dedicated chip, unlike the power-intensive rise of ARM processors for cell phones. Eric Vishria says his experience showed him how hard company-building is, and that investors need some naivete to attempt difficult ventures again.
- 4:09Incumbents demand a radical advantage
Feldman says a startup challenging a giant like NVIDIA cannot win by being slightly better or cheaper, since the incumbent can cut prices or bundle products. He argues for a 10-, 100- or 500-times-faster product and says Cerebras took control of the full stack, from chip and system to software and API, to pursue that advantage.
- 9:37Wafer-scale computing took 18 months to prove
Feldman says Cerebras spent about 18 months unable to make its wafer-scale design work, while spending about $8 million a month. The team analyzed each failure and made new mistakes rather than repeating old ones. In July 2019, after progress from wafers shattering in seconds to running for a day, he says they solved a problem no one had solved in 75 years of computing.
- 13:21Cerebras is pursuing three compute bottlenecks
Feldman breaks computing into calculation, memory and moving data to where it is useful. He says Cerebras is working on faster cores, memory capacity and speed, and I/O. Its programs include stacking HBM onto an SRAM-based wafer and putting optical switching close to compute.
- 15:52Fab capacity cannot catch fast demand
Feldman explains that advanced fabs cost $40 billion or $50 billion and take years to build, while ASML currently makes the lithography machines they use. He says demand has outpaced the capacity to build fabs, and that specialized manufacturing skills are scarce. He blames decades of U.S. policy for fabs and related suppliers leaving, and proposes a 20-year period of waiving local ordinances to encourage domestic fab construction.
- 23:49Hardware boards must respect long cycles
Vishria says his expertise did not include chip design, so he focused on what he could contribute. Feldman says good board members help in their areas of expertise, ask useful questions and support financing, while leaving technical choices to engineers. He says hardware companies can spend years building a chip before getting meaningful customer feedback.
- 29:00An inference pivot emerged step by step
Feldman says Cerebras made decisions one step at a time, revising its view as the market changed. As AI became more useful, he concluded inference would put heavy pressure on compute infrastructure. He says the first architecture accelerated the algebra underlying AI rather than specializing in convolutional networks, which let it handle transformers that had not yet been invented.
- 33:04Hardware teams need selective experience
Feldman says Cerebras promoted young product leaders from within when insight and ability mattered more than experience. For chip design and mechanical systems, he says years of experience building hardware are a better predictor of success. He contrasts this with software, where teams can often correct a decision quickly; chip mistakes can take years to fix.
- 39:22Hardware depends on durable supplier trust
Feldman says Cerebras’s long-standing relationships with TSMC and contract manufacturers mattered during a period of supply contention. He advises keeping commitments, sharing information early and treating new partners well. Vishria notes that hardware depends on dozens of specialized suppliers, unlike software companies that may rely on only a few major vendors.
- 42:13Data centers expose power and community gaps
Feldman says data centers are a constraint whether customers rent cloud capacity or their own facilities. He points to an aging U.S. grid and describes new interest in generators, batteries, fuel cells and nuclear power. He says the industry also failed to communicate with local communities about costs, water use, jobs and tax revenue.
- 47:05Data center construction has long poles
Feldman says a good builder can bring 50 megawatts online in 18 months from raw land and permits, while some operators are retrofitting old factories with existing power access. Generators, electrical transmission switches, cooling and electrical work can take time to secure. He expects the industry will still be chasing data centers and chips five years out.
- 52:47Speed can unlock new markets
Feldman argues that speed creates markets, using Netflix’s shift from mailing DVDs to making films as an example of what faster internet enabled. He says Cerebras is working with AMD and AWS on disaggregating inference workloads. He reports five times as much throughput with AMD at the same speed, with similar numbers from AWS.
- 55:20Feldman credits NVIDIA's grit
Feldman argues that NVIDIA’s success came less from CUDA or its chip architecture than from the grit and intensity it developed during a difficult decade as a public company. He describes Cerebras as a challenger energized by proving doubters wrong, from making wafer-scale chips to producing and packaging them in volume and winning government, sovereign cloud, OpenAI and hyperscaler customers.
- 59:49Feldman recalls an academic childhood
Feldman says he grew up on the Stanford campus, where his neighbors included William Shockley and Amos Tversky, and that intellectual ability was the neighborhood’s main currency. He expected to become an academic and was working toward a PhD, but says he got bored, went to Stanford’s business school and was drawn into business.
Published Sep 15, 2026.
Transcript · 46 min read
Uncapped, hosted by Jack Altman and Eric Vishria, with Andrew Feldman.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
You got board meetings every six weeks. All you've got to say is, still can't make it. Right? That's the board meeting. I mean, what else are we going to talk about? Still can't make it again and again. And Eric's like, should I get in? Can I do anything to help? That's right. They're like, can we help? I was like, no. But we always believed that if we could make it, there would be huge demand for it. All right. Super excited to be doing this today. Andrew, thanks a bunch for being here. So, Andrew, you're the CEO and founder of Cerebrus, which is obviously one of the most important chips companies.
And also really happy to have my partner Eric here, who's going to be kind of half host, half guest, who has been working with Andrew since the beginning. But thank you both for making time. What I want to start with is it's 2026 now, and obviously Cerebrus is very important in the sort of AI landscape. But you started the company in 2016, and AI was not what it is now back then. So I guess, what was the kind of headspace then? What was the idea, the insight? Like, what were you building when you started the company, and what was the thinking? Well, Jack, thanks for having me. It's always fun to hang out with Eric, and so I appreciate you having me.
I think as a computer architect, when you see a new workload on the horizon, you get excited, right? It's very, very difficult to win share in a mature market in compute. And so when something new emerges, you get excited and you lean forward and you say, well, can I make it faster? Can I build a chip that is better at this work? And then you ask the other question is, is there enough of this work to justify building a chip that's better? So you have two questions. Can I and should I? And what we saw with AI was an extremely computationally intensive workload. Unlike, for example, the rise of ARM processors for cell phones, they weren't computationally intensive, they were power intensive, right? We saw a problem that was going to be hard on compute, and we saw that what was being used was an architecture that was being remodeled for it, and that we could build something better. And that was sort of the the insight and then it was a a long hard slog before we go to the long hard slog eric obviously for you like as an investor you know you were not you know also in this ai era but you saw something that made you invest and you were i think you know previously not a chips investor and you've done more like and probably never will be again probably never will be again
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like would you do that every time again was there something that made it clear to you or was it what happened for you when you made the investment? I think one of the scariest things about venture, and I think about this all the time, is the more you work on companies and you see the challenges and you see how hard it is, you build up scar tissue. And then the thing is like, well, you should do it again, but will you do it again? And I ask myself that all the time. You actually need some naivete. um like you need to have this like oh can do attitude and this like naivete around it to attempt it like otherwise you just don't do it again and i've definitely built up a bunch of scar tissue um and i don't know had i actually had any idea how hard it was going to be for them like how much science and technology had to be built, how many innovations, how many times the company was going to come to the brain.
Don't tell your VCs how hard your shit is. The moral of the story is, tell them it's no problem. Keep it inside. Push it down. Don't tell them. I'm doing great. It's a really hard problem. All's good, man. Yeah. So what happened? So you basically, you know, you start the company, you get to the tape out, and then you're done, right? It's like that? No, never. So how's it go? So what happened? I think we had a, you know, this isn't our first chip company.
“You have to come up with a product that has a value proposition that even if the other guys give it away, they can't compete.”
“We hadn't run out of ideas. And each time we built it and it failed, we'd go through sort of good engineering practice.”
“And what this does is it enables HBM to behave like SRAM, which is exactly what everybody wants. You get the capacity of HBM and the speed of SRAM”
I want to go to a bit broader in the supply chain.
And I think it's kind of well understood right now that AI is very supply constrained. But I, like probably many other people, I wouldn't say I have a perfect understanding of the supply chain. And one of my favorite things to do on this podcast is you get a successful person like you,
“a fab and especially a fab that builds at cutting edge geometries is a modern pyramid. It's one of the greatest things humans make.”
And we got to do better. That's not smart. Yeah. So what do you think should happen there?
I think we should sit down with Global Foundries and TSMC and Samsung and have a 20-year period where we waive all local ordinances to allow them to build fabs. I think this is about building U.S. domestic fab capacity. yeah um i think i mean look when when a big ship right tried to parallel park in the suez canal right we were delayed in chips and we couldn't buy washing machines yeah right i mean if we lost our chip capacity it would be catastrophic for our for our industry not just for our industry but for the country for the country for the economy eric how did you as a board member when you were like learning this stuff as you went, you still obviously, you know, I don't think issues.
lying to me i think you've managed to be very helpful like what eric was extremely helpful so like how did you approach this um i have a very small circle of competence it is not any of the stuff that he talked about if he if he if you asked me to explain what packaging is right now i could not do it i certainly could not do it in an adequate way and andrew explains things to me all the time um and you know so i think it's important just to be like hey this is where you what you can do and this is what you can't do you know and there are periods of time in any company particularly if you're doing technological innovation where there's just like there's just you just have to let the engineers engineer and like and the scientists do their thing and and and stay out of the way and keep them financed maybe keeping them financed was very important i guess i think I think there are a couple things that made Eric a good board member, and I think that are foundational in being a good member.
You don't know about everything, and share and make us better in those domains where you're a real expert, and don't talk about those other domains, right? Sometimes there's a lot of words, and we were lucky. We had a really good board, and everybody knew what they were good at, and they helped us in those domains in which they had real expertise, right? I mean, one of the advantages of the venture world is you can see across an industry, right? When we're deep in it, we're going deep. They can see wide, right? But there weren't efforts by the board to try and solve technical problems. They didn't have, that wasn't their expertise.
Talking about how we might finance the company, thinking about all sorts of other things, they were enormously helpful, and they were patient. And I think they asked thoughtful questions, and our board meetings made us better. It's like, what should a board do in a hardware company in general, when you expect that there's going to be much less to contribute on the product? Is it financing and recruiting? I think the question when you've got a long, hard project, and this is the opposite of SaaS, right you raise money and then you spend two or three years to build one before you have any real idea what the customer is going to say you go and you talk to customers and they say yeah yeah that sounds great because who's not going to say yeah yeah that sounds great right and so the product management is unbelievably difficult you survey customers and because it's no cost and it's easy to say yeah yeah this is great nobody wants to to sort of put their foot on the throat of somebody else's idea, right? They say, yeah, yeah, it's great. It takes you three years to two and a half years to get to the point where you can bring it to a customer. I think what you can do as investors is understand that trajectory, understand that your first chip is very rarely a good one,
and it's a second or third one. I mean, even really strong teams like Google's TPU team, the fourth one was good. The first two were, oh, yeah, the fourth or fifth were really good parts. it takes years you have to to know that going in you have to know it's a long game right um ask questions related to the long game right are you hiring the right people right are you thinking about this in the is it have you made the right decision between uh specialization and flexibility right these are questions that can really help sharpen our thinking but you know whether to use this one technique or this other technique in design, whether you use one tool vendor or the other, you got to let that, you got to let the team pick that.
Did it feel really different for you versus like an infrastructure role? Yeah, totally. It was just very different. I mean, your time to revenue is so much longer. The revenue comes in like giant chunks. um the initial customers you know we had u.s government customers then we had you know huge sovereign cloud and g42 you know then you have now today obviously open ai and so you have these like things you're so your customer concentrations more it's just like every dynamic's different the investment in go-to-market so much less so i think it feels really really different The things that are the same, by the way, are you need really good people from a whole bunch of domains coming together who stay motivated over a really long period of time.
That part's actually the same. And then the market is very dynamic. I mean, think about it. Pre-Transformer, you started the company pre-Transformer. We did. At that time, TensorFlow was dominant. TensorFlow was dominant. ResNet was still a thing. Right. These were very small, very simple networks compared to where we are today. Yeah, the actual ML, the AI, looked nothing like it does today.
“the path to it, we knew, was unknowable. And so each time you get over a new mountain, you look around and you re-decide.”
interesting like on the you know on the backs of you know companies like spacex andrel cerebrus you know palantir maybe others but obviously like hardware is now hotter than hot and everyone's to fund hardware but it's it does just talking this through it is crazy hard it's really hard yeah it's hard and uh it requires enormous internal fortitude and it requires uh success has
historically been predicted by some experience in the field yeah different than like you know AI where you see the advantage to? I think in AI and social networking, a lot of the founders and the leaders were building tools for themselves and their classmates and their friends. And there they had unique insight. I mean, if you look at cognition, if you look at cursor, these are some of the best engineers, software engineers in the world. They're building tools for themselves, right? That's true. That's different from- I also wonder if this last topic probably plays a big role in it, which is when you can change your opinion the next day and it's fine that you were wrong yesterday, speed and decision-making speed trumps versus if you do something and then you have to live with that for a year.
Years. Years. You can't just be wrong and fix it tomorrow. I think in chips, we measure three times and cut once, right? It is really unforgiving to big mistakes. And so you have to be very, very sure that you get the big items right. One of the things that I think is interesting about how you built the company, which is you and you mentioned a lot of the early team have done multiple companies together. A lot of you guys have worked together for a long time. You're older and more experienced. I think you're not that old. Somebody called me a boomer. I'm just like, my dad's a boomer. A boomer?
You know, at some point you're looking up and it's all the same. When you're that low, you're looking up and it's just like when you're eight. He's really old, like 27. But you know what's interesting actually is if I look at the product leaders in your organization, if I look at some of the go-to-market leadership, they're actually very young. They are. and um and i'm just interested in like how was there intentionality around that not necessarily ageist obviously but just like how you combined the perspective of you know young at the cutting edge product leaders with seasoned hardware engineers i think we tried to think really hard about where experience mattered we're blistering intelligence matters um you know if you look at at our product organization um unbelievably smart i mean some of the best product people i've ever seen um all young all promoted from within um you know we we don't have big company rules you got to be in a job for this amount of time before you can get promoted right i mean if you're extraordinary we're going to give you more and more responsibility and more and more and if you do a great job with it there's no end to where we will we will take you um you know my co-founder sean uh you know one of the
five of us i mean in my last company we we hired him as an individual contributor and when we were acquired four years later the guy was 25 or 26 when we when we hired him in 2007 right when we were acquired by amd we made him a corporate fellow right there was just no end i mean and And now he's a founder and he's CTO of a public company. And we do that sort of ruthlessly. And there are some areas where to be exceptional requires a tremendous amount of experience. There are some areas where it doesn't require any experience. We don't have a long history of understanding what customers want in AI.
So their methodology, smarts, insight, trump experience. In other areas, in the making of chips, it's been my experience that some odd years of previously building chips is the best predictor of whether you're going to deliver exceptional chips. And that's also from the mechanical side on the system side. There's just not a lot of chance in college or in graduate school to actually build silicon. to actually build a machine. They're so expensive. They're so hard to build. They take so long that even in a doctoral program, you don't get a chance to tape out a chip, to deliver it, to bring it up, to put it on a board, to power it, to write the software for it.
And so it takes some time. Are the relationships something that are critical or can those be earned quickly by young people? I think the following. First, five is too many founders without question, except that we'd worked together before. And so everybody knew what the other folks were good at, right? And so there wasn't a lot of head-butting at all. I mean, we'd all worked together previously. We all had tremendous respect for what the others could do and some humility about what we couldn't do. And so we were able to do that. Your specific question is, does it take a lot of time to build trust?
I think within the six or eight weeks of working with someone, you can tell if they're extraordinary. I mean, extraordinary people, in the first email they send you, you go, whoa, that's exactly what I needed, right? Every list is in descending order of importance. There's not a lot of fluff. There's high signal. There's, you go, whoa, and then you see that again, and then you watch the way they run a meeting, and you go, whoa, and then you watch them deliver something. They motivate a bunch of people around them. They can work across the organization. You say, well, that's a person I need on the next important project, and then they just chew through work.
I always thought with recruiting, it's like if I came away learning real things and I wanted to have another interview or meeting just because I was like, I'm going to learn more stuff. I was like, that's my best. The best. The best. It's so good. The best. What about the external relationships? You've got to work with a lot of people outside your company. You've got to be in Taiwan. You've got to work with – It helps to bring those with you a little bit, some of them. We've been building chips with TSMC for decades. We'd been working with our contract manufacturers for decades, and so, especially in a time of contention, that those relationships had been in place for years that you'd been to your word, not once, not twice, not just in good times, but you'd been good to your word over good and bad times.
That's really, really important. I think you earn relationships with new partners in exactly the same way. You're good to your word. You get them information early. You write. I mean, it's, you know, write a thank you note. No, really. I mean, do what your mother said, right? Be a good person. Write a thank you note. Do what you say you're going to do. This, to me, was one of the big learnings, particularly during the COVID era and everything. If I think of the software companies, even software infrastructure companies we work on, it's really like their vendor that matters is AWS. And maybe now their vendor that matters is AWS and a foundational model company or something like that.
There's like two vendors that matter. This is like, I don't know what the vendor list is, but it it's it's insane it's dozens right it's it's it is the all of these components that go into the system all of these specialty manufacturers that build the cooling plate and part of the water system and like and so you have all of these things that have to kind of come together and and we just aren't used to that like that those meant that many dependencies in order to deliver our product or at least I wasn't – that was like a big thing and you have to kind of work with them on – Software guy coming to grips with a supply chain.
Whoa. Yeah, exactly. They call it a chain for a reason. And it really is. It is. And you're like, wait a minute. Holy cow. This – all of this stuff. And when you're growing exponentially, like then everything becomes even more complicated, right? Because it's like you're putting in – I never thought about this. You know, it's like AWS. We want more capacity. you know, you go online and you add capacity, like you want more capacity on, in terms of like number of wafers, like that's a 15 month lead time kind of decision and a huge amount of capital. And your vendor is also making a huge allocation decision. And so they have to buy into it too.
And so I, that, that was a big learning for me in just terms of like how much complexity there is in that one of the things we've been talking about a lot internally is that it feels like no matter how big you think it is and it is hard to wrap your head around the size of the ai build-out that's happening yeah and the capex going in you know i actually saw a chart this morning that was like one percent of gdp was spent per year on highway and telecom right and then it was two percent for railroads yeah and then this ai build-out is like three and a half percent of gdp it's like only one person got it right which was your brother no that's true yeah i mean the only person and everybody thought he was out of his mind i mean what what what sam's really good at and i think is so hard is he saw an exponential and wasn't afraid right you take that exponential out two or three or four or five years you go holy crap right he wasn't afraid Everybody else was afraid.
Now it's going to slow down. He was like, no. I remember the Stargate, like $7 trillion or whatever. Right. I mean, now the initial Stargate is sadly small. Yeah. Right? And it was mind-boggling. Yeah, people were laughing. They were laughing. And so all of us got it wrong except him. I think we got it wrong. I think TSMC got it wrong. I think NVIDIA got it wrong. Everybody, the memory guys got it wrong. We all got it wrong except Sam. Yeah. so you know you've got this insane build out going and you know the the demand for the demand for inference seems like it's going even faster than no matter what we're going to be able to build you know and so then there's a lot of questions about you know what will that mean for the price of compute and what will it mean in terms of how much will be available and token spend and all
“And often even the big sites unfold in 50 megawatt blocks. 50 megawatt blocks is 18 months.”
I was really, I happened to fly over, leaving Memphis last night, I happened to fly over macro hard and macro harder. It was astonishing. Like the size of- Nobody can build like Elon. It's unbelievable. The size of buildings and construction nearby and everything, I was like, whoa, that is tremendous. Which leads to an interesting question now, which is like, I could imagine a world where either it'll just never be enough data centers, and for 10 more years, it's always going to not be enough.
Or because of the dynamics of the lead time and what's going on right now, you could imagine like an overbuilt. And betting on which of those worlds you think is going to come out, there's probably some alpha there for whoever calls it. We won't have enough data centers. I'm just, yeah. We won't certainly, I mean, I, seeing out 10 years in our space right now is really hard, right? 10 years ago, transformers weren't running. People were running tiny little models and trying to identify faces or cats and chairs or whatever. I mean, it was, 2016 was not a big AI year, right? But five years, we will still be chasing data centers and we'll still be chasing chips.
And for those who use HBM, they'll still be chasing HBM. Yeah. It'll be interesting also how politics plays in all this, because now you've got probably more government interest and involvement in tech and AI specifically than we've ever had, obviously. What we need is more people who don't understand making decisions. That's clearly what we need. I mean, I think more people with absolutely no clue making important decisions. You know, I think this is an area where they're really thoughtful discussions to be had about what's good for the US and good for different municipalities, different rural areas.
And those aren't being had by the politicians, right? They're knee-jerk, they're sort of... Let me ask you a question on this. So don't move away from Paul. Let's say we do agree on this like compute thing the compute is
“And, you know, with AMD, we're seeing 5x additional throughput, I mean, five times as much throughput, while keeping the speed the same.”
“They look to CUDA. I don't think it's CUDA. They look to their chip architecture. I don't think it's their chip architecture.”
You know, we are, and I think this is what's interesting about Jensen too, is he sees himself still as the underdog. Now he's the big dog. but you know this is my my fifth startup you know we sold three and took one public previously and we've now taken this one public um i'm a i'm a professional david in the battle with goliath and i i wake up every day with that mentality and that we're now bigger uh we we have sort of bigger competitors right we have bigger challenges we have more people throwing stones at us. And so I hope we take that. I personally wake up every day with that passion and that drive. I mean, every day I think to myself, when we started, they said, it would never work. You can't do wafer scale. We made wafer scale. And then they said, all right, you did wafer scale, but you can't yield it in volume. And then we yield it in volume. They said, okay, you can yield it in volume, but you can't package it in volume production.
and then we packaged it in volume production. They said, okay, now you've packaged it in volume production, you only have a government customer. And we said, okay. Then we won a sovereign cloud. And then they said, you don't have a frontier lab. And then we won OpenAI. And then they said, okay, you've got government, you've got sovereign clouds, you've got a frontier lab, you don't have a hyperscaler. Then we won a hyperscaler. right each time they said then they said you've got all these cool customers but you couldn't you couldn't do big models now we're serving GPT what are they saying what's that now like what are they saying you can't do now the CEO's a boomer but each time I became a millennial and I shut down you know right and so I think when you're sort of a professional David when you are an entrepreneur at heart right each one of those fires you up right we're only interested in solving problems that other people can't solve that we're only interested in doing things that other people can't do that's why we get up every morning it's not the money it's not it's not notoriety it's because we love building cool things and we really like building cool things that are so hard that other
people can't build them Eric told me that a pretty cool and unique childhood I don't know if that fed in. to this at all but like how'd you grow up I grew up on the Stanford campus my parents were faculty and there's a little neighborhood where all your all your neighbors are other professors my neighbor was William Shockley that's crazy transistor so all we knew about him a you know we're 10 or 8 is that his wife gave out full-size candy bars at Halloween the inventory was at Bell Labs he invent he brought Silicon Valley right he but the movement of Shockley to the West Coast created the foundation for Silicon Valley and we're thinking he gives big three musketeer bars right but I think there were a couple things that were glorious first the only currency was intellectual horsepower right that That was, nobody cared if you were rich.
Nobody cared if you'd started a company. This was the 70s. What they cared about was, well, that dude's really smart. He does good work. My other neighbor was Amos Tversky. And for his work with David Kahneman, they got a Nobel Prize in economics. My dad's tennis match. There were six or eight guys in rotation. They played doubles on Saturday and Sunday. Somewhere in my mid-20s, I realized three had Nobel Prizes and one had a Fields medal. Right? He sucks at doubles. That's right. No, no. Let me tell you, it was some old man tennis. I mean, their serves were grim. Their physics was good. It's a crazy way to grow up.
It's a crazy way to grow up. And, you know, the neighborhood was safe. We'd get on our bikes and we'd just go all summer and come back when it was dark. And so did you think you'd be an academic? I did. Actually, I was working on a PhD. I got a little bored. I went to the business school at Stanford while I completed my qualifying exams for my Ph.D. And I sort of got sucked into this by mistake. And, you know, my dad still asks me, he's like, you're going to finish your Ph.D., Andrew. I'm like, Dad, all my professors are dead. Nobody left. Yeah, amazing. All right, well, Andrew, this was a blast.
Thanks a ton for doing this with us. And obviously, you know, you've built something extremely special. And it's been cool to just watch and learn vicariously through Eric. so thanks for everything. It's a pleasure to be here and chat with you guys and you know Benchmark was an extraordinary partner. I mean I think if you do hardware you're going to be in bed with your backers for a decade and pick good ones and I'm proud we did. Thank you.
