Foundation Capital's Steve Vassallo: Cerebras' first investor on five-startup-in-one engineering risk and the Solana connection

Speaker 2: Yeah.

Speaker 1: Congrats to everyone.

Speaker 2: Yeah. Let's do it again soon.

Speaker 12: Nasdaq. Thank you guys. We'd love to do that.

Speaker 2: Thank you. Take Goodbye. Up next we have Steve from Foundation Capital. He's Cerebras' first term sheet investor, also the first investor in Solana and a bunch of other great companies. So we will bring in Steve from Foundation Capital from the waiting room. Steve, how are doing?

Speaker 1: There he is.

Speaker 11: Doing great. Are Sorry keep

Speaker 2: you waiting. Congratulations. Thank you so much for taking the time to come chat with us. How are doing?

Speaker 1: It's just another another another day. You're not here. Are you you didn't are you at the Nasdaq or you're calling in from home?

Speaker 11: Yeah. Exactly. No. Just another day. No. I am I'm at my hotel on my way to the dinner that Eric's also headed to momentarily.

Speaker 2: Fantastic. Okay. We won't keep you too long but I would love to hear the story of you meeting Andrew Feldman in February from there, how you wound up working together.

Speaker 11: Yeah. So I showed actually Andrew's email last night over dinner, but yeah, he and I and Gary met in October 2007. They were raising money for the company that they started prior to Cerebras, which was called C Micro. Yeah. And it was kind of broadly in sort of new server architecture. So these guys have been thinking about these kinds of problems for a long time. But I passed on the investment, but stayed close. We we really connected in that meeting. And then, when I saw them get acquired by AMD, it was about four or five years later, I was like, guys, Andrew in particular, you guys are not going to stick around this company for too long, so let's start riffing on some new ideas. And that began basically a two year conversation about a whole bunch of ideas. Actually, it all started really in kind of this concept of warehouse scale computing. Yeah. We were looking at companies like Mesosphere, ended up actually doing a small investment there.

Speaker 2: Oh, yeah.

Speaker 11: CoreOS and a whole bunch of others. And Andrew came in in November year of 2014 and shared his ideas with our enterprise team. And then basically we riffed on ideas in the 2016, so it was like March timeframe, we started telling them, look, we we want to be your first term sheet. We've been like courting each other for for a while here. And, yeah, we got him a term sheet to lead that first financing. And then Eric stepped in and we changed the terms a little bit to to make room and co lead along with Eric and and Pierre from Eclipse. Yeah. Yeah. And then they started started it right in our office.

Speaker 1: That's amazing. Can

Speaker 2: you can you talk to me about there's, you know, crypto and AI feel like two wildly different technologies, but there's a ton of overlap everywhere you see from crypto miners pivoting to neo clouds. There's a lot of movement back and forth. And I'm wondering like what in your mind the similarities differences are like why you've been drawn to both over your career Where where the gap is? Where there's similarities?

Speaker 11: So what I would say the similarities which are probably in retrospect somewhat obvious. Mhmm. I would say the hardest problems of software and systems live in the area that we're working on in AI. So the AI infrastructure, the Frontier Labs as well, all the work they're doing there. And the same thing is also true at the bottom of stack, the layer ones Mhmm. And the very hardest technologies over in crypto. You know, the folks that are attracted to both of those areas tend to to be very technology driven. They're they love distributed systems. They love the hard problems around cryptography and elliptical curve cryptography. They love low latency computing. Like they're they're they're quite similar in terms of being systems thinkers. And so those are the those are the ways in which I would say that the problems are quite similar. And in fact, here's a funny anecdote related to this. So Anatolyak O'Laughlin, co founder of Solana, part of the reason why he chose to work with us back in March 2018, so about two years after we invested in Cerebras, was because we were investors in Cerebras.

Speaker 2: Oh, no way.

Speaker 11: He was like, you guys you guys take hard problems seriously. He had spent twelve years at Qualcomm.

Speaker 2: That's right. Yeah. Distributed systems. And the

Speaker 11: Brew Operating System. Exactly. Yeah. So he and then was at Dropbox and understood those challenges. And so he said, wow, you guys care about these kinds of hard problems and that matters to us. So we ended up doing fair bit more diligence and writing actually a larger check into that very first Solana financing. So

Speaker 2: Yeah. Can you take us back to earlier in your career, pre investing, obviously fascinating hard problems, but like where does all that come from? Does it start in high school, college, early career? Walk me through some of the early days.

Speaker 11: That's why I studied robotics and embedded systems, sort of the intersection between mechanical and electrical engineering Yeah. In undergrad, and then came to graduate school and and did more of that. And then my very first Friday at Stanford, met David Kelly who's the founder of IDO, is a product development consulting firm that worked with the very best kind of fortune 1,000 companies. When they would hit a snag, a hard problem, or want to invent a new product, and they didn't often know how to wrestle those challenges to the ground, they would call us. Mhmm. And so we did a lot of work for Apple. We did a lot of work for Cisco. Yeah. We did a lot of work across every industry from healthcare to consumer devices to, you know, really hard problems in in systems. And so I worked there for five years designing products. In fact, one of my other earlier today on the desk at the Trading Floor in NASDAQ.

Speaker 2: Oh, wow.

Speaker 11: It was Cisco's voice over IP phones which I worked on now twenty eight years ago. No way. So just working on cool cool things, hard problems. Mostly where it feels like if if you solve that problem it was worth solving. There's a there's a there's a real prize at the end.

Speaker 2: Okay. I want to

Speaker 9: take that's how

Speaker 11: I got started.

Speaker 2: Yeah. Want to take this full circle then because robotics is sort of having a moment but it still feels like it's early in terms of as a consumer, as optimistic as I am, I just don't think I'm going to have a humanoid robot walking around my home this year. Most people we've talked to have said, yeah, it's maybe five, six, eight, ten years away. But that's like the perfect timeline for a venture capitalist to start getting involved. You don't want to be trying to build custom AI chips today. You want to start ten years ago like Cerebras did. So how are you thinking about the like pulling your experience from robotics into the modern era? Because if the boom isn't already here, it's probably going to be here in a decade. If not a decade, two decades like it's coming. Robots are going to be real. So how are you thinking about it?

Speaker 11: So we've done a fair bit of work in embodied intelligence in terms of research and as I'm sure you're familiar, it's always a little tricky to invest in an area that you have some operating experience. Yeah. It tends to bring some scar tissue. Yeah. And so you might be more circumspect than than if you'd had kind of a beginner's mind.

Speaker 2: Sure.

Speaker 11: I would say I am generally not a big believer in the humanoid approach.

Speaker 2: Sure.

Speaker 11: I think there are use cases for example in the home companionship. Yeah. And even in that case it's a bit of a stretch. I think you need to think about robotics more broadly and think about industrial automation Mhmm. And then look at the problems that are not necessarily a kind of the, you know, the consumer level use cases. Yeah. But you walk the factory floor and you see people moving around pallets. And the human form factor is not good for moving pallets around.

Speaker 2: Yeah.

Speaker 11: And so you wouldn't actually build a humanoid robot if you were trying to deal with that use case. So I think when I zoom out and I say what are robotic systems? Robotic systems are basically ways of automating automating human labor. And so, and and in fact the greatest compliment for most of these systems is when you stop calling them a robot. You actually call them a forklift you call it a washing machine.

Speaker 2: Oh, that's a great

Speaker 11: And it's when that technology diffuses into the background and you just focus on what is the application. So that's how I look at it through kind of the product lens as opposed to the technology lens.

Speaker 2: Yeah. Yeah. I was I was you know, you see these demos of humanoids loading washing machines and I've been thinking in the back of my head every time interacting with my washing machine like, is it time just for a ground up first principles rebuild of what a washer and dryer stacked is. Like if you if you constrain it to like you have this dimension but now you have all the modern technology and your goal is to just take in dirty clothes and put out clean clothes like can you do something better than just a big tumbler and then another tumbler, one with water, one without? And I'm excited by that. Is the implication of that that almost you would be open to talking to entrepreneurs who are maybe thinking a little bit narrower, thinking a little bit smaller at least in the interim? And then how would you guide someone towards long term messaging around their company if they are finding a wedge, but then they want to grow at some point?

Speaker 11: Yeah, so I think it is exactly what you just described, which is, and again, the sort of applications do matter here, but the notion that you would start with something that is, let's call it sort of big enough to matter, but small enough to win. Yeah. And in hardware technology, being more focused is actually a huge advantage or huge point of leverage. Mhmm. And so, and then as you continue to build, you want you want to be able to access larger opportunities in markets. Mhmm. And so I I really do believe that that is the way you get started with hard technologies and hardware in particular. I think there's another thing that we do, and I will just say this kind of brings it to Cerebras again for a minute, is we look at we look at workloads. And so one of the reasons why we backed Andrew and Gary and Sean and team back in in 2016 was it was quite clear, and we saw this through the lens of our portfolio, that the AI workloads at that time was more ML. They were ramping very, very steeply. And whenever you see computing workloads that are doing something new and different, and this you know you're talking about in the robotics context, and we'll get to that in a second. But when you see a workload that is spiking hard, there's often an opportunity to basically replace the compute layer. In other words, there's often sort of purpose built silicon that should exist here. And so in the case of personal computers, very clear. Serial programming, and you were very well suited to the x 86 platform. It was actually something we saw go on and on for decades. As soon as you started to see the need for much better graphics, of course you would build a graphics processing unit that's really good at rendering graphics, at doing floating point math, at managing lots of multiple cores, and then of course take the mobile era. And then you say, okay, wait a minute, what's going on here? I need low power, I need a smaller form factor. And so when you look at these workloads, oftentimes there is this sort of transformative opportunity, and that's exactly what we saw in 2016 was, wait a minute, like there should be purpose built silicon for this ML and AI workload. At first of course we started with training, back to your point around how do you start small, and then seven years in was actually a board meeting when Sean, one of our co founders said, we got to go after inference. It's just, it's exploding. And so again, this point, you start small and then rotate towards the much larger opportunity.

Speaker 2: Yeah. I mean, talked to Andrew about all the ups and downs, a classic overnight success with tons of moments on of, intense tumult. But I'm curious about were you ever worried or hesitant that the company might narrow down too much? And because you've heard like YouTube has custom silicon for video encoding and there was probably an opportunity at some point to narrow the focus even more to do chip development for one specific company, be less generalized and maybe ramp the revenue a little bit faster. But was there a tension there that you were observing and like how did you get through those moments?

Speaker 11: I'd say that the primary tension that relates to your question Mhmm. Was probably around making sure we would not silo ourselves into use cases that were traditionally just high performance computing use cases.

Speaker 2: Sure.

Speaker 11: So those workloads are valuable, and those markets are actually still relatively interesting, but they're not growing anywhere close to the rate of the inference, and specifically the reasoning part of inference where Yeah. You start chaining workloads together. Yep. So we we worried a little bit about that being, you know, a niche that was not interesting enough for us to build, you know, a really nodal company. If I zoom back from that, and you asked sort of what are the things we really worried about in those early scary days, I mean there were, I don't know if Andrew shared this, and there were like five startups were the hard problems for us to go after. I mean, I mean it was absolutely, there were moments, I was joking with one of the other founders last night, where you would you would come back from a board meeting and you weren't quite sure whether we were going to figure out our way through a very fundamental, you know, thermodynamics challenge.

Speaker 2: Okay. So when you say five problems, you're not talking about fundraising, hard negotiation with TSMC, talking to a supplier. You're

Speaker 11: talking All of about that too. All of Okay.

Speaker 3: All of

Speaker 11: that truth. I'm talking about the actual hard problems Yeah. Meaning hard technology problems.

Speaker 9: Yeah. Yeah. Yeah.

Speaker 11: And you know, the ones that are sort of more physical, know, where you have laws of physics and thermodynamics to obey. Yeah. And you don't get to negotiate. Andrew's a very good negotiator, but he's also learned that he can't negotiate with the second law of thermodynamics. Yeah. So no, these were this was how do you yield a semiconductor that's the size of a dinner plate? How do you power it? How do you cool it? How do you maintain continuity across thousands of connections? How do you put it in a system and integrate it and then in a data center and then put put 65 over 64 of them in a data center together. So it was those kinds of very hard challenges where I say five startups in one. And and they were of course also stacked which means that the risks are now combinatorial. Yeah. So even more dangerous.

Speaker 2: So you've been through taking companies public, you know, being involved with public companies several times. A lot of times, the founders that you're backing is their first time becoming a public company. What are you telling them? What advice can you share with a founder, not Andrew specifically, but any founder who's going public? How will the company change? What are you telling them as they become the CEO of a public company?

Speaker 11: Yeah. So there's there's a few things that come to mind. One is buckle up because it it it's going to be particularly in markets like the one we're in right now where I mean you see the headlines change every every few days. I mean there'll be another drop of another model tomorrow that could, you know, upend the public markets.

Speaker 2: Yep.

Speaker 11: And so you don't have a lot of control over what the world thinks about your share price. And so you've got to coach your teams and your engineers in particular to know that like when when the when the share price is moving, it very often has nothing to do with what you're doing in the day to day. Mhmm. And and you just need to steal your sense, yourself against that. I think there's also a piece which is you just have to grow up. Like there's there's a cadence to these businesses. Orderly unfortunately, I wish they were longer. Where, you know, Andrew and Bob are going hop on an earnings call very soon. And they're going to have to start talking about the business of the business, not necessarily the technology of it. And that requires a level of discipline and planning that oftentimes founders don't, you know, don't have their stuff together well enough in order to be able to sort of manage through that transition. And then the last thing I would say is actually the flip of it, which is don't forget what made you special.

Speaker 2: Mhmm.

Speaker 11: Because when you get into this quarterly cadence, and you start to think, well, how do I meet the next quarter? Mhmm. You oftentimes lose sight of the long horizon that was the larger opportunity for you to go after, you know, not just you know, the opportunity right in front of you, but there's much much larger opportunities. And we're, you know, building systems for the next gen, and the gen after that, and the gen after that. And so you can get tricked into being in a kind of quarterly mindset. Yeah. And it's one of the most toxic ways to kill a company that's built around innovation. So you just want to, you want to make sure that, you know, there's that horizon that's still calling, that's where we need to go.

Speaker 2: I love it. Thank you so much for coming on Breaking It Down. Sorry for running long. I'll let you get to the celebratory dinner. Say hello to everyone and have a great day.

Speaker 11: Awesome. Thanks so much. Talk to

Speaker 2: you soon. Have a good one.

Speaker 9: Bye.

Speaker 2: That's our show folks. Leave us five stars on Apple Podcasts and Spotify. Another one. Sign up for our newsletter at tbpn.com. See you tomorrow at 11AM Pacific Time and have a great rest of your day. Goodbye.