Eric Vishria - A Decade of Lessons Investing in Software & Hardware - [Invest Like the Best, EP.486]

24:38They learned it all. They're excellent, but somehow it's not translating. And there's some impedance mismatch between the AI founders potentially, the needs of the business and what these people are bringing. And I saw it really abruptly with a particular sales leader who we hired, who's like, hey, we can't hit any of these things because the way that software sales has been taught forever is a quota capacity model. You have a quota capacity model, each rep does this. In the early days of a company, the quotas are 1.2, 1.5 million, maybe the ISRs are at 750 or 850. and then over time it scales up and enterprise gets to 2.5 million.

25:15That's how all these financial models are built. You start with the quota capacity model, you take a discount on attainment, this is what we can do, boom, boom, boom. Fundamentally, without realizing it, everybody was implementing something that was based on pushing demand, not pulling demand. And for so many of these companies, they're operating here, these customers, and there's a new AI-enabled product that comes along and it's just fucking magic. So these companies are selling magic. Well, it turns out if you're selling magic and you're the first one there, you're going to sell a lot more than 2 million.

25:51The whole notion of a quota capacity model and that being working, it's not that it doesn't matter. It matters kind of, but it's definitely not the first order thing or constraint. So you have these execs come over with this, like, here's our math and here's the territory assignment and here's what we would do. And first you do West Coast and then you do East Coast and then you do Central, like all of these things. Oh, wait, no, no, it doesn't work like that at all. One of my big things that I started to realize is last time I'm interviewing these folks and talking to them is just like, hey, you need to check everything at the door.

26:19Check it all, which is probably good practice anyway, but check all the baggage. Check everything that you learned and just learn this from first principles. How is it working? What are really the bottlenecks on delivery? What are the bottlenecks on demand? Because it turns out that in a lot of these companies, you have reps doing 10 or 20, 30 million. I saw 50 recently. It turns out that's different. What is like the best salesperson that you've seen that's doing it in a de novo way doing? Honestly, the best salesperson in any of these companies is a founder. And what they're doing is bridging the jagged edge to what the customer's capability is.

26:53And that's it. It sounds so simple, but it's not. But that's what they're doing. The market is just so big. Just like we were talking about with cloud. I think the biggest mistake everybody made was they just undersized the market. And it turns out the market's just really, really big. And this market is fair. It feels like right in this moment in time, actually just this morning, you and I are in this great group chat together where the discussion is the demand for intelligence seems kind of unlimited. And it seems like the smarter the thing gets, the more demand there is. Maybe the bottleneck is just capital.

27:21The world just feels like it needs to take a breath. The RSI concept, if you apply it across technology, doesn't need to breathe. The agents don't get tired. but the world feels kind of like, oh man, it sure would be nice to have three months just to like digest this a little bit and for capital to form and evaluate its prospects and the scale is getting so big. Does it feel to you like this can just keep going or are we just going to get tapped out of money that can be invested in these things when it seems like we could consume like any amount of money to build any amount of stuff and serve any amount of inference?

27:54I'm not a macro economist. I am worried about energy. If you think about the models as translating compute into intelligence, quite simply, what do models do? They very effectively translate compute into intelligence. What's the demand for intelligence? Well, it seems like a lot. So then it follows that we will continue to have more and more demand on compute. I'm saying compute broadly, not chips, not storage, not whatever. But then like, what do we need for compute? We need energy, a lot of energy. There's all this topic of distillation and Chinese open source and all these things. But the bigger thing to me is that I think China's bringing on 10 times as much energy next year as we are in the US.

28:36If energy is what you need for compute and is the bottleneck, and there's unlimited demand for intelligence, then it stands to reason that if we have a lot less energy, then we will have a lot less intelligence or a lot less tokens or a lot more expensive tokens. If we have a lot more expensive tokens than supply demand, you're going to end up with less. And that seems very bad. To me, I think the energy bottleneck, however that is, it'll manifest in 20 different ways. Gas turbines go up and down, natural gas up and down and solar, whatever, all these different things, rare arts. That to me is probably more concerning.

29:15And if I'm thinking about it from a regulatory perspective or government perspective, and I think the administration is doing some things around this, fostering investment and development of all energy, solar, nuclear, gas, like whatever, do it all. We should do it all. And it'll work itself out. This is one of these things where, yes, one will be relatively better than the other. And I don't know, and I'm not smart enough to predict which one's which, but it'll all work. Speaking of compute, I would love to hear the Cerebra story. I haven't heard you tell the full version of this. The reason I'm asking about it is I'm deeply interested in compute.

29:48I have bigger investments in compute and I'm fascinated by it. It's just like the most magical thing to watch happen. It's mind boggling when you get close to one of these things, what humans have been able to do on these chips and in these systems. I think you invested in 2016 or thereabouts. I think it was your first foray into like extremely difficult hardware type investment. Shit's so hard. And now the world is full of opportunities like this. Whereas back Back then, it was a one-off. Teach me everything you've learned about hardware investing through Cerebris. Mostly, it's really hard. It's an amazing example to me about the naivete required.

30:24The company came in in 2016. It was five founders and a deck. I did not want to go to the pitch, but it's my job. Because I'm like, why are we going to go to hardware investment? It's crazy. It's been 10 years since we've made a semi-investment. I think basically the team was excellent. And then the kind of first slide was just like, GPUs actually suck for deep learning. They just happen to be 100 times better than CPUs. You have to remember, this is pre-Transformer. OpenAI is this weird research lab at this time. NVIDIA was worth like$40 billion, not$4 trillion. The TPU hadn't been announced.

30:57None of that. So this is early. But the whole idea, as soon as you said it, I was like, oh, shit, of course. Of course. Like, why? I had spent the last 18 months trying to figure out applications of deep learning. and looking at the security thing and looking at this medical imaging thing and like all this other stuff, thinking like, hey, there must be something here that's going to be really transformed by this stuff. Anyways, we go through this whole journey. We end up investing, which was amazing. We first met on Wednesday, part of meeting on Monday, had a bunch of meetings in between, just built a lot of conviction that this was a great swing.

31:31And I'll tell you what I understood, and I really just understood so little, but basically there are three things that we know how to do to speed up deep learning and hardware. Still till this day. Increase the number of cores, increase the communication between cores, bring the memory closer to compute. Those are the three things. That's it. Those are only three dimensions that we know in hardware. My articulation of what they said to me, honestly, all I understood was, let's just take all three of those things to their logical maximum. You have a wafer scale chip. At that time, you would have 450 ,000 cores on it.

32:02You'd have something like 20 gig of SRAM on the chip. So you never have to go off chip to get to memory. And because they were all in the same wafer, the communication between cores is maximized. So this is the best you could do on that process. And I think for the first chip was seven enemy or something. You're like, okay, that's it. That's what we do. And it turns out that in software, if you have that logical block diagram of like why it works and everything else, you're kind of 80 % of the way there. And it's a matter of go-to-market execution. In hardware, you're like 2 % of the way there.

32:33There's things like physics, entire supply chain of vendors. There's, of course, TSMC, which everyone knows, but it's not just TSMC. There's 30 other vendors that matter and putting all this stuff together and everything else. I didn't know any of that. Fast forward from 2016 to 2019, I think they got their first parts back. You go through this bring up, and then it's like, bring up. Oh, yes, we got a part back. And then it's like, bring up. Yeah, and then you got to go through, like, wait, bring up. And there's like 14 steps of bring up and everything else. And then by 2020, 20, we had our first thing that works.

33:07Then it's just this march of actually getting it to work. One of the lessons that I've learned on this stuff is basically you go through all these sims and everything else in hardware and semis in particular. That basically is your roofline. The best it's ever going to be is what that is. And then every bit of software and reality and compilers and kernels takes away from that roofline. You might start at 10 % of the roofline. Once you bring it up, you guys are grinding for months and years to get closer and closer and closer to the roofline. It's really different. It's really hard. I'm astonishingly bullish if I kind of rewind.

33:46Part of the reason we made the investment was if you looked at the four prior generations of compute in my lifetime. So you had CPUs. You had graphics, the networking, the mobile. There was a new workload each time. So you had multipurpose compute and it led to the CPU. you had massive parallelism led to the graphics processor. Graphics processor offered massive parallelism, which led to graphics. Then with networking, you needed really low latency chips, and so you had low latency chips. And then with mobile, you needed really power efficient chips. And in each case, we ended up with a new$100 billion company.

34:20The first question, going back to 2016, was, is AI that big a new workload? Because there've been many, many other attempts for specialized chips for other things that really just didn't end up mattering. There's some fine outcomes, but they just didn't really end up mattering, et cetera. Right, exactly. And so it's just like, well, okay, well, you need something that's a really, really big workload. Okay, so that's one. We had a lot of conviction on that. And then two, was the nature of the workload, did it introduce a new constraint or problem in it? What I learned was basically the AI workload benefited from the parallelism of GPUs massively, but GPUs didn't solve the core-to-core communication, basically the layers of the network problem.

35:00You're like, okay, wait a minute, there is a new constraint, which is communications, communication bound problem. Then you're like, okay, is AI a new giant workload that is going to have specialized chips? Everything that I just said was the entire, everything I knew at that time. That's obviously played out. And in each prior generation, we got Intel, we got NVIDIA, we got Broadcom and Vago, we got Qualcomm and ARM in each of these generations. And there will be these giant winners, standalone winners. Obviously, the TPU itself is a winner. Traneum is a winner. You've had Grok and Cerebrus etch.

35:35It'll keep getting fought out, but I think that wound up being big. And actually, I think there's a new sixth one that's coming, generation, which is, and I'm really excited we've made an investment that's unannounced in this, but I think that for the first time in a long time, there's actually room for a new CPU approach. The thing that's happening right now, and you see this reflected in all the semi-stocks and everything else, is the LLMs, which are running on accelerators and GPUs, generate code. The code runs on CPUs. And right now it's running on classic CPUs we've had around forever. But there's a whole bunch of constraints on CPUs that have existed and CPUs have dragged all this baggage forward that you might not need to anymore.

36:20And so I'm actually really excited about that possibly. The next category. The next category. Does the experience with Cerebris make you want to do a lot more investing in companies? Fuck no. But why not? In 2019, we're sitting in a board meeting and this thing is melty. It's like fucking melty. Okay. And we've raised$500 million or something. And it's like, wait, what? It's melting or burning or something. And like, we're looking at it. And I'm like, holy shit. I realized like$500 million isn't that much in today's era. But I was just like, we're going to lose all this money. This is not going to work.

36:56And what that team did, insane. They're built differently. And I have so much respect and thanks to them for what they've done. There are efforts that make you really proud to be a venture capitalist because you're funding something that makes a difference and matters. I'm an investor because that's a means to work with companies, not because I fundamentally love investing or something like that. I like working with the companies. That's my favorite part of it. Working with teams like that and companies like that is so special on these giant, ambitious efforts. And I said this well before 2018 or whatever, is whether Cerebus worked or didn't, I think it was an effort that was worth venture capital.

37:36That was the kind of thing you should do. You should try to build something that people have tried for 50 years and have been unable to, but now we think we could do and there's a reason and application for it and everything else. I love that. I do like those kinds of things, joking aside. And we do have a robotics company and then this CPU project that we're talking about. I think these kinds of things are actually really fun and interesting and good use of vendor capital, but they definitely aren't easy. The productive naivete that you described, Rare, is probably a virtue that you didn't know more than you knew, otherwise you wouldn't have done it.

38:06This is certainly my experience with Edge. Like in any of these fields, you ask experts, they're going to tell you, don't do it. It sucks. It's too hard. Base rate's too low. Young people can't deal with the 47 reasons. Is there anywhere where that is just a bridge too far? That could be like bio or something like this, where you just are unwilling to invest if you're naive. You know, what's so funny is the hard thing about investing is most of the time, all of these stereotypical statements are correct. They're not correct three times out of four. They're correct 19 times out of 20, maybe 99 times out of a hundred.

38:38Like they are correct. The thing that Bruce, one of our founders, always says, what could go right? We have to ask ourselves, what could go right? And do we see that path? Yeah. Young people can't build chips, or you shouldn't do another selling company, or you shouldn't do this, or whatever. All of that stuff is actually totally right, except when it isn't. Apparently, there's a saying that someone said to one of my partners, which was, if it doesn't work, it'll be for all of the reasons that your partner said. If it does work, it will be because those reasons didn't matter. It's just such a good example of this whole thing, which is, yeah, most of the time when we lay out the reasons that a company won't work, it's right.

39:17But then sometimes they just don't matter. You and I are both interested in robotics. It's not controversial that if robotics works, it might dwarf what we're currently living through. What do you think has to be true for it to work? Obviously, it's exciting. I want a robot in my house, folding my laundry. It sounds great. It's kind of one of these classics. It's always 10 years away and it's been that way for a long time. What do you see happening? What has to happen for this to actually be a thing in the near to medium term? We've had classics robotics forever and they're all over the place and they're on assembly lines and manufacturing lines and all those things where you're doing repetitive tasks in controlled environments.

39:54Repetitive tasks in controlled environments is like more or less solved and like, you know, that'll continue to happen. But having tweaked tasks in real world environments, that's where you need the AI. That's where you need the AI plus the robots. The trick with it all is you need a model that can do that. Of course, the first problem is there is no internet scale data to bootstrap the whole thing, right? LLMs are all bootstrapped on the internet, which is a ton of human knowledge. and the equivalent for that for robots doesn't exist. And people are trying different things with videos and simulations and teleoperation.

40:37So like there's a lot of different ways, but if you kind of think about, take teleop as an example, how much you need to teleop robots to get to an internet scale data. This first step is like getting a good set of data to bootstrap the model. One of the, I think, insights that you can have is with the internet, there's like a lot of slop data. Even before AI generated all this stuff, there was also a bunch of junk data. And some data was more valuable than others. Like you may value, okay, certain things on Reddit, more valuable than other things. You might value Wikipedia more than like other forums.

41:12You might value GitHub more than other things. And all of the model companies did that, right? They prioritized data that was more valuable and less valuable and ran that through the model. I think one of the most interesting things that these AI robotics companies are doing are saying, okay, well, let's just go after the high value data to start. If we go out for the high value data, then we can kind of bootstrap this model. Now, once you do that, then can you through the pre-training process, get to a place where you can have very small auxiliary examples of data that you add in post-training and all of a sudden it works for that.

41:48That's the magic we have with LLMs is you You have this giant pre-trained base, and then you add a little magic on it in RL and post-training, and you teach it a new thing that it wasn't in the pre-training set, and you kind of go from there. And I think the exact same thing has happened in robotics. We're investors in Sunday Robotics, which is going after this exact pipeline. It's a cool company. It's a cool company, and they're doing household robots. But the key part, before you get to household and all that stuff matters much less, actually, than can you get this training pipeline to work, and how do you do that?

42:20And one of the lessons I learned, and I look back, I try to learn from history because it doesn't repeat, but it rhymes. And if I look at autonomous vehicles as an example, which is they're robots, basically. They're AI plus robots. You look at Waymo and you look at Tesla. They were both designed vertically integrated in their own way. You have a Tesla. You had a fleet of Teslas that everybody owned. It was collecting data on the Teslas, and it was used to train the models to drive the Teslas. The same thing with Waymo. I think part of the lessons is they gather very, very high quality data. They did their pre-training and then they worked off of that.

42:58That was a simplification to allow you to get to a complete product or a complete solution, which of course is going to continue to improve and ultimately will generalize. I'm sure it will generalize in some way. So you'll be able to strap it on any car and everything else. So I think the same thing is happening in robotics where you have companies like Sunday and others who are using techniques where they're vertically integrating the robot and the model, the data collection around the robot. Sunday uses gloves that are designed with the robot hands. So they're perfect. So you get very good data transferability from one to another.

43:37You do this pre-training and you have a great pre-training data set. You have a good model. And then you start adding these examples and URL and post-training on top. And you get cool emergent behavior. Do you have a most visceral moment? When we invested in Sunday the first time we saw them, partner Peter arranged a demo. We went down into the basement at Stanford Lab. And they had like this totally janky cardboard glove thing. You see a few evolutions of it. The last time we saw a demo, we went down in the basement of their now office building. and there was like a dozen robots just folding arbitrary laundry.

44:15It wasn't in the demo. It was just trial and error. And then they have people like taking the clothes and then measuring them to make sure that they were folded properly and creating a rigorous baseline and evaluation criteria. And I was like, oh my God, this is happening. Do you ever worry about how to pick the right customer for these companies? Every one of these things folds laundry, which I don't think Anyone likes folding laundry. So it seems like a good use case, but it feels like we don't actually understand the demand for what these things will be used to do. You're worried about that, that we're sort of building solutions that will then be in searches of problems?

44:50I don't, and I'll explain why, and it's certainly informed by watching the LLM evolution. Some of the people who were involved in the early LLM at OpenAI really understood that code was going to be important. And obviously, you know, the Enthropic team had this perspective that you could get to RSI if you've got code generation going and automating AI research and whatnot. If you think about it, the first use cases were very much language oriented. They were very much essay writing and editing and marketing. I think the first application that really took off was Jasper, which was just writing marketing copy.

45:25I think it's going to evolve a lot, basically. I think laundry is kind of a good task because it's arbitrary, it's complex, it requires dexterous manipulation. It isn't time sensitive. If it takes three times longer, so be it. Who cares? It doesn't matter. Just let it run all day. So I think it has like some of those properties. I don't think the task is actually that important. What I think is much more important is, are you pre-training an amazing model than being able to post-train on top of it and get that flywheel going? If you get that flywheel going, then the task capability will just keep multiplying.

45:59Maybe this question will be annoying or slightly uncomfortable for you, but if you ask basically every founder and critically other investors of your type, almost everyone, if I ask like who's the best board partner, we'll say you. You come up way more often than anyone else that I've come across. And I'm curious why you think that is. What it is that you're doing that other people, the incentives are there to do a great job as a board partner. What do you think you're doing on the boards of these companies, partner with the founders that's actually different from other really talented investors who are also nominally doing the same job, but don't come up nearly as often when asked that question?

46:38One of the things that I've realized is we each are attracted to different types of entrepreneurs where we have chemistry. I'm an investor second, and I try to be a partner first. We'll see companies come in. We had one come in yesterday, and it's what I would call an investment-grade opportunity. You can invest. It probably works. you make money, it's good. And an investor would do that. A partner wouldn't because that's not sufficient for a partner. Unless you have real chemistry with that person where you feel like you're going to be able to work together really effectively and I'm going to learn a ton from them and they're going to learn something from me.

47:15Together, we're going to just feed each other's loops. Unless you feel that way, you can't be a partner. And so you pass on that. That's a really important fit element to me. It kind of starts with this mutual selection, actually, weirdly. They want to partner with us, and I want to partner with them. I'm really looking forward to working with them together. And I'll give you a really good example of where this comes into play for me. If I take Saji and Benchling, Benchling is life sciences SaaS, company's absolutely crushed, done really, really well. And then, of course, you have this biotech crash and everything else.

47:52and the company became grindy. The company had never had any churn for the longest time, such that even on their reports for every SaaS company, you have this like, okay, gross ARR added, churn line, net ARR added, like everybody does the same thing. They never had a churn line, never reported it for the first of like six years that I worked with the company. So then they got seven years of churn in like 12 months. Turns out life sucks when you get seven years of churn in 12 months. Through that grind and through it all, and related to the whole, wait a minute, the goalpost moved, we have to do something different, everything else.

48:29They kept thinking about how to apply AI for these biotech and pharma customers, which they're very close to. How can we make it better for them? How can we apply these models in their world in a way that they're excited about and continue to iterate? And it was grindy. And I was there for it. You're excited to work with that person And because like one, of course, you think it's a really special opportunity and there's a way out. There's a path and we can find it. But two, because of the joy of the game, the relationship and like everything else, like that's part of it. It's very different. I've seen different models and lots of different models of venture capital work.

49:04Moritz was a writer. Door was a sales guy. Gurley was an engineer. Peter's a career venture capitalist. They're all different. Working with them, it's like when I call these people, I learn something and they push back on me. And then I ask them questions. And what I've realized is like so much of my job is they know the answer. They know what they want to do. They know the answer. And it's maybe asking questions of them to maybe help solidify their conviction or solidify their articulation of what they want to do and why. And you just keep doing that. Through that process, hopefully we get 1 % better a few times a year.

49:42We make a 1 % better decision, 2 % better decision a few times a year. And if you do that over a decade, that compounds to real results. One of the questions that I ask myself before making an investment is, there are all these people that I care about through my life, like you care yours. Could I talk one of them into going to this company and honestly, intellectually, honestly to myself, explain to them why this could be their life's work? And if I can't do that, I should not invest. It just means that the project doesn't line up in that way. And so as long as we have one of those things, it doesn't matter that much what it is to me.

50:22It's important. It could make a big dent. And if it can make a big dent and it's a special person, I'd love to work on it. Are there any other questions like that that you ask yourself before investing? That's a particularly good one. The other question, if this person calls me at 9 p.m. on a Saturday night, will I pick up the phone? Calls that the green button test. Yeah, yeah, yeah. It's a chemistry thing and they have to feel the same way, obviously. One of the other ones is if it's right, does it matter? Which is different than this first one, but there's so many things that we could be right on as a business.

50:55I looked at one last week and I told an entrepreneur, I was like, I really think that you can build an amazing company here and you just shouldn't raise venture capital. But there's so many things that you can be right on, but they ultimately just don't matter. Nobody cares. That's a better way to say it. If we're right, we'll let many care. If they won't care, then you're just not going to build enough equity value. That's another useful one. One of the coolest things that's happening right now is all of that that you just described has higher stakes and more leverage attached to it, which is manifested most simply in more dollars and higher prices.

51:25You and I have talked about this notion of what high multiple on invested capital investing is like and what it has been like and what it's moving into. You did something recently, which was you raised a growth fund for the first time in a long time. I think that is related to this concept. these companies need more capital, the prices are higher, the outcomes are bigger. Maybe we can earn the same multiple on a billion-dollar entry price that we could on a$50 million entry price 10 years ago or whatever. Can you talk through that evolution, talk through the partnerships, way of thinking about it and talking about it, what you believe to be true that results in this decision to do this?

51:58Just go back to why did LPs, starting with Swenson and everyone else, why did they start investing in venture capital? And fundamentally, it wasn't because they thought they could beat the NASDAQ or the index by like three percentage points a year, five or whatever. It's because there are situations where venture capital could drive these insane multiples on invested capital. From a financial perspective, that's what they're seeking. For the longest time for most of the industry's history, two things were synonymous, early stage investing and high cash on cash multiples. The way to get high cash on cash multiples was to do early stage.

52:35That's it. Those two circles in the Venn diagram almost perfectly overlap. The thing that's changed is recently, relatively recently, in the last few years, because outcomes have gotten so much bigger and these markets are bigger and everything else, the circle of high cash on cash multiple opportunities is bigger than just early stage. and it's not so, so big that there's a gazillion new companies in there that you can generate 100Xs on. That's not true. But there are certainly many outside of early stage where you can generate high returns. That's it. I think that's what we want to go after.

53:13You could argue we're a few years late. I think I'd take that criticism. But I think that opportunity exists on a go-forward basis. We should go do it. But everything else that we represent, which is the high conviction, high commitment partnership, that has to still be there. As part of that discussion, what were like the other sides of the debate, such as maybe we would have said the same thing in 99 and halfway through 2020. As markets get exciting, the possibilities, we all do this extrapolation error. What were the counter arguments to like, let's, despite all that, still not do it? I think the biggest counter argument that really made this time right versus two years ago or whatever was you need the team that can do it.

53:53It's just a different mentality. There are differences in how you evaluate and think about things. All the other stuff or there's, you know, why not change and stay within your circle of competency and all those things are true. But that was like the biggest one. We had several examples over the last couple of years where we had, I think, the right intuition on a company or an opportunity. we didn't do it. Because it was outside the buy box. Because it was outside the box. That's obviously dumb. And I think it is quite different than a lot of than she does. We are really chasing these very rare special companies that have like very high cash on cash opportunities where we think there just can be runaway successes and we can invest in them.

54:34Is there any lesson to be pulled from the many, let's call it 20 to 100 X's that you personally have observed? I mean, it doesn't pencil in the beginning. You can't make something pencil if it was that clear. The price would be different. What have the 20 to 100xs taught you in aggregate, if anything? Work with really special people. You want to work really hard. You want to work smart and get lucky. And you need it all. It really all has to come together. There's a lot of things that are timing dependent. You have no control over as a company. And you take the Cerebris example as a good one, which is, this is our second, you know, we took it public this time in May, but we tried to take it public in 2024.

55:16And it would have been taken public at a much, much lower valuation. And it didn't work out because of CFIUS and all this stuff. So timing matters. The advancement of that 18 months made all the difference in the world. For a bunch of things that were honestly outside of our control, there were some things that were in our control, getting inference running and everything else. But there was a lot of stuff that was outside of our control. These are all the classic things. We just got to focus on what you can control. That's one of the things that's really different than software companies. With software companies, aside from like building on AWS or whatever, you pretty much own your whole stack.

55:46And so you're really fully in control of your destiny in that way. With hardware companies, you don't. There's an entire supply chain and all of this other stuff. HBM's a thing, DRAM's a thing, and TSMC's a thing, and a lot of those cross geopolitical borders. And so geopolitics gets involved and that makes that complicated. That's a really big difference. And so you got to get lucky on the timing and macro and other stuff. But I think it really starts with working with these crazy people with unbounded opportunities. And if you work with these crazy special people on unbounded opportunities, you get lucky from time to time.

56:19You're bound to. When I was 20 years old, I was working at an investment bank. Ben Horowitz, Mark and Ben had started LoudCloud. It was still in stealth. And Ben gave me an offer to be his assistant. I was talking to this associate who seemed like this elder at the time. He was probably 25. He said this thing to me, which stuck with me. He was like, do you golf? It's like classic banking question, do you golf? No, I don't like golf. But he's like, with golf, you keep on practicing, keep getting the ball in a three-parter, like close to the pen, close to the pen, close to the pen, close to the pen.

56:47You keep getting the ball close to the pen and you keep practicing. That's hard work. That's like working smart. That's what you want to keep doing. Getting the hole in one, that's luck. I kind of love that framing I use with my kids actually. What it says is like, yeah, there's luck involved and there really is luck involved. But there is actually a way to increase your luck. And the way to increase your luck is get a lot of balls close to the pin. Eventually one will drop. I like that. Each of us invests in one to two companies a year. I think in my 12 years, I've invested in 18 companies total.

57:17Crazy. Which is a relatively small number. So it's a very high conviction and very high commitment. I have a lot of skin in the game. I believe in these companies. If you keep on working with these very special people and these opportunities, magic can happen. What have you learned about the best reasons and conditions for going public? Bedford does these Monday night dinners. And so we had a Cedia last night, multi-hundred billion dollar private company. And we had this whole conversation. So it's kind of fresh. I think that ultimately when you go public, you have a range of new opportunities in what you can do.

57:58Public trust, because there's some transparency that comes with being a public company. You obviously have a currency that you can do things with that ends up being there. You have an unbelievable ability to raise capital, which I think is why the labs will ultimately go. Although I think the trust thing is actually a really important element of why they should go, and it's beneficial to the world and to America if they do go public. It's like - See what's going on. Yeah, see what's going on. Everyone can see it. I think that's a really beneficial setup. There's another element of it, which is what does a collegiate athlete want to do?

58:28Go pro. They want to play at a higher level. Is it harder? Yeah, it's harder. Is the competition tougher? Yeah, the competition's tougher. They move faster. They're tougher. They're bigger. They're stronger. The stakes are bigger. The stage is bigger. The scrutiny is bigger. All of that's true. It's kind of the same thing with companies. There are a handful, and it really is a handful, three, four, whatever, that can get to this tremendous scale without going public because things have gone through their execution and excellence. Lots of free cash flow. Dave, lots of free cash flow, and they've done really well over a really long time.

58:59And I think that's fantastic. good for them. But in general, for everyone else, get out there. The other thing that I would tell you is there are windows for a particular type of company. The SaaS companies that went public in 2021, a whole boat of them have struggled and it's been tough in the public markets because their stocks ripped to this multiple compression issue. They were trading at 30 times. They've 4X'd in size, but now they're trading at six times. It turns out you're under still. That's a tough place to be. I will also tell you that there There's 500 something, probably SaaS companies that are between 100 million and 500 million that are private.

59:39What happens? Those employees never got a chance to sell. Those employees don't have annual tenders. Those employees don't have an opportunity to exit. Those investors don't have an opportunity to exit. They're stuck. I don't think they're all going away. And as I said, I don't think they're all getting vibe coded and everything else. but ultimately the AI natives with their growth rates have sucked all the oxygen out of the room and all the interest and the window was missed. That's tough. What are the biggest debates right now inside of the partnership? I always love coming here and talking to you guys when there's something interesting going on because you debate, it's healthy, it can be really fun to watch and I learn a lot from it.

1:00:16What are those debates today? There's a ton of debate around in this AI, Right. Infra apps for foundational models, infra apps ecosystem. Where does value accrue? And how does it accrue? And where are the modes? And how do we think about that? But also the business model innovation. I think one of the things people don't understand about why SaaS did so well versus traditional software was it wasn't just that it was a better delivery model and everything else. There was actual business model innovation on it. You really did have this subscription element that ended up being fantastic for both the company and the customers.

1:00:51It was a win-win situation. That same thing actually exists in AI and selling by outcome and that piece of it. But then wrapped up into that debate discussion is how much value just accrues to the labs. Yes. How much of the value just accrues to the semis? That's a real discussion. What do you think? I'm like, I am of the view that it all works. It's a very weird thing. Will the CSPs do well? Yes. Yes. Not all of them, but will some of these neoclads do well? Yes. Will the fireworks of the world do well? Yes. Will NVIDIA do well? Yes. Will these chip startups do well? Some set of them. Yes. Are we going to have edge inference on our phones?

1:01:34Yes. Are we going to have near edge inference on Pops? Yes. Are we going to have big models and data centers? Yes. There's so much zero-sum thinking, which is just like, okay, how do we cut up this pie and they're going to eat this much? And like, oh, no, no, no, no, no. Anthropic or whomever is going to eat 98 % of the value and they're going to do all the drug discovery. And I was like, come on. No, that's not what's going to happen. When I say like, I think everything's going to work and I listed off all these other things, it's really important to understand that doesn't mean that every company that's doing every one of those things is going to work.

1:02:04It actually means quite the opposite of that. Most companies in each of those areas are not going to work. And it's actually more important than ever to have real differentiation, to really take each of these thoughts to their logical extreme and understand, wait a minute, you got to go all the way on these things and really be differentiated on it. Is there anything that you have your eye on, whether it's in the funding market, in the technology world, anything at all that you really are watching carefully? It's actually funny to me. Some of the people are so, so smart, and yet they're in this tech world where they're reaching these deterministic, almost conclusions of mass unemployment and all of these different things.

1:02:46I'm going to give you a really concrete example, which I think is just so good. Take Jeff Hinton in radiology. I think it was 2016 where he was like, we should stop training radiologists. AI is going to do it all better. Jeff Hinton's three orders of magnitude smarter than I am. Could not have been more wrong. But the actual thing that led him to make that statement and that conclusion was 100 % correct. If you look at these radiology images, we should be able to train AI to do a better job reading these things than humans. And that's probably true. And actually, I think the studies and areas have shown that to be true.

1:03:20And we have an investment in a company called New Lantern, which is approaching this. But the big hurdle and the big thing that it articulated was like, wait a minute, first off, all of the aggregated training data set doesn't exist anywhere. What you see is companies going after like chest CTs or like very specific elements. But your typical radiologist looks at a whole variety of things every single day from x-rays to CTs to MRIs of all parts of the body and everything else. And so an AI climbing in specific areas like test CTs is very marginally helpful because it's only doing that one thing, which could be one of 20 things or 40 scans that they read that day.

1:03:57Problem number one, you don't have the data, just like we talked about in robotics and everything else, to train the AI. Problem number two, the whole healthcare industry is oriented around reimbursing doctors for making readouts. How is that going to work? And there's liability associated with that. And there's repercussions of getting something wrong or missing something. And there's medical malpractice and everything else. How are we going to avoid that? And how are we going to get around that? Problem number two, real-world stickiness. We're going to end up with this application where AI really does help radiologists.

1:04:29It helps radiologists get more and more higher, higher throughput because AI can do some parts and the radiologists do some parts and they're checking each other and everything else. And you You kind of weirdly end up in this co-pilot situation for some time. And then you're going to slowly have the AI read more and more of the scans and build up and build up and build up. But the actual duration to get from here to there is going to take a long time. In the ensuing time, we need more radiologists, not less, because, oh, by the way, everyone's getting more imaging than they used to get because the cost of imaging is going down in a Javon's Paradox kind of way.

1:05:02My point on it is you have someone very, very smart who really understands the capabilities, really understands what's happening, has the right data, but by not thinking of that data in the real world application comes to the wrong conclusion. And that's how I think of the unemployment thing. I think it's just, it's almost the exact same setup. Eric, I love talking about markets and companies with you. An absolute blast. Thanks for the time. Thank you. If you enjoyed this episode, visit Colossus.com. You'll find every episode of this podcast complete with hand edited transcripts. You can also subscribe to Colossus, our quarterly print, digital, and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most.

1:05:43Learn more at colossus.com slash subscribe.

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