Uncapped #52 | Mike Volpi from Hanabi Capital

Mike Volpi is a General Partner at Hanabi Capital, with a background that spans senior operating roles and nearly two decades of investing. Mike currently sits on the boards of several innovative companies, including Scale AI, ClickHouse, Ferrari, and Confluent, where he is known as a thoughtful sounding board and a steady presence through the highs and lows of startup life. Mike is a retired partner at Index Ventures, where he led investments in category-defining companies across AI, software, and infrastructure. Earlier in his career, he held leadership roles at Cisco, including as Chief Strategy Officer and SVP/GM of Cisco’s routing business, giving him firsthand experience in building products and teams at scale. We discussed what it takes to build a great venture firm in the AI era, why many of venture’s traditional rules are breaking down, and how AI is reshaping software, investing, and company building. We also explored the future of frontier AI labs, robotics, defense tech, and the mindset founders and investors need to adapt to a rapidly changing world. --- Timestamps: (0:00) Intro (0:39) Building a venture firm for AI (4:02) Designing Hanabi (5:52) Why stage matters less (9:38) Building a venture brand (13:58) The role of board seats (17:06) Attributes of enduring firms (20:44) The future of AI labs (23:14) Open-source and neolabs (32:49) The compute race (36:51) The future of software (45:49) Investing in defense (47:50) From operator to investor (50:35) Thriving founders today --- Links: https://x.com/mavolpi https://www.hanabi.com/ https://x.com/jaltma https://uncappedpod.com/ --- [email protected]

• 0:00Speaker 00

  The kind of classical marketing efforts that have been done for brand building, particularly in venture, sound off key to your average 22 year old entrepreneur. I think what does resonate is inside knowledge, tips, connections, network, all those things that are kind of indirect but organic ways of building brand. Yes, brand is important, but I think you want to build it organically.

• 0:24Speaker 01

  All right. I am really excited to be here today with Mike Volpe, who is probably one of the most successful venture capitalists of the last couple of decades and now building your own new firm. And I'm really looking forward to learning from you. So thanks for doing this with me.

• 0:35Speaker 00

  Are you kidding? It's a pleasure and I've already fooled you.

• 0:38Speaker 01

  Well, that's one. One of the things I want to start with talking to you about, which I thought would be kind of interesting, just because I also gave my shot at it. It's just like building a new venture firm. You obviously had had this incredible run at a great firm at Index for a long time. And now you're building your own firm, which you've been doing for the last, I don't know, year?

• 0:57Speaker 00

  Year and a half.

• 0:57Speaker 01

  Year and a half. Obviously, you've been really thoughtful about how you want to do it and what the trade offs you want to make are and what the firm design is going to be and what's the strategy. So I guess I want to start when you were thinking about creating a new firm, what did you sort of start by thinking about? What were like the key considerations that you got going?

• 1:14Speaker 00

  The first is somewhat obvious but very important, which is it's very hard to disrupt a market, to break into a market unless there's something macro that's happening that's an enormous change. Obviously, AI is that. So the first thing is to take a firm that's new and deadly focus it on whatever gigantic wave is hitting the industry right now. Because if you tend to do the, you know, spread the peanut butter thing, it's just never going to work. So first, A, identify that there is a massive trend to absolutely focus on it. The third thing I would say is gather people who are not only fluent, but just have grown up and live whatever this new trend is. I do think that there is a big generational shift right now

• 1:59Speaker 00

  between sort of classic entrepreneurship, classic venture and this new generation of AI venture. And some people are more predisposed to it than others. The last thing I would say is you have to be very careful about your past success. This is, I would say, Very true for individuals, for yourself, and it's also very true for firms. And the more success a firm has had, the more, to use an AI term, reinforcement learning there is of how things were done. And if the world shifts to a place where things are done a little bit differently, that reinforcement could be applied very incorrectly.

• 2:37Speaker 01

  I actually think this like past success reinforcement loop thing explains why a lot of, for example, like execs from pre-AI SaaS are like, that it's really difficult to adapt.

• 2:46Speaker 00

  Yeah.

• 2:46Speaker 01

  Because you learned a whole set of things that don't make sense anymore.

• 2:49Speaker 00

  Yeah, the whole concept of software is changing. Basically, most venture capital firms are focused on making money on software companies. And that foundation is based on the idea that software is complicated, expensive, and takes a long time to build. And it makes, it costs very little to make lots of it, but it costs a lot to make the first edition of it. And so then you're in this world of, I have a high fixed cost thing, which I need to sell to as many people as possible. Every business model starts looking like that. Then you move into an AI era, which takes the cost of making software way down. You're completely shifting the core assumptions on how a business is built.

• 3:30Speaker 00

  And that then extends into everything from go-to-market, engineering, fundraising, which customers you target first, how do you target your customers? Should it be a product-centric company or a service center? All these assumptions kind of blow up. So if you've had sort of a firm as an investor that's been structured to invest in a certain type of company with a certain type of people, and that base assumption changes, the cost of making software is now super low, you blow up everything. And so if you get stuck with the old way of doing it, you're probably going to invest in the wrong companies.

• 4:02Speaker 01

  You obviously invested in a certain way over the last, you know, years at Index, and you had a certain, I don't know, ownership model, stage you like to do, all the rest of it. When you came in with Hanabi, and you're like, I've got a blank, blank canvas now, and I need to not train on what I did wrong, I want to learn from the good stuff, I want to not like, you know, bring the bad stuff. What did you think about from a firm design perspective when it comes to like, let's say, the types of deals you were going to do, the stage, like the ownership, the size checks, the dispersion of all that stuff?

• 4:29Speaker 00

  Well, let's start with the people. First of all, I thought, you know, AI is a relatively complex and deeply technical field. And therefore, the people that I have at the firm should be native speakers of that new dialect. The number of people that were focused on AI and machine learning pre 2018, 19 is incredibly tiny. So essentially, the volume of people that are, I'll call it, they have native human beings, have a professional depth of five to six years max.

• 5:06Speaker 01

  How big of an advantage is it if you got into AI in 2018 versus 2024, let's say?

• 5:12Speaker 00

  Reasonably substantial, but not massive, I would say.

• 5:15Speaker 01

  Like you can catch up?

• 5:16Speaker 00

  You can catch up. You can catch up. But you know, you got to have a foundation like, how does it work? Why does it work? What are the flaws? How quickly does it evolve? Do you understand compute? Can you talk semiconductors? What's a GPU versus a CPU? Why? Memory, high bandwidth, like all this stuff. Like unless you sort of are genuinely fluent in tech, these are not conversations that you're going to easily have with your counterpart, the entrepreneur. So you got to look for people that are pretty damn techie and are pretty damn young. You know, when you build from scratch, the complexity is finding such people in this industry that want to be VCs. It's not the easiest thing in the world. But A, you look for that. The second kind of idea that's very important is in venture capital, we've talked a lot about stage centricity. We are an early stage

• 6:00Speaker 00

  firm. We are a mid-stage firm. We do growth. We do pre-public. We're crossover. Everything is defined by stage. I don't think that idea applies in this point in time because I could invest in a company at 10 billion in valuation and three years later, it could be worth 380 billion. In that sense, what you have is a suite of companies that we would now consider growth stage, but that represent venture-like return characteristics.

• 6:27Speaker 01

  It's also probably the flip side of that is, you know, there was probably a point in time when investing in something that was worth 10 billion, the downside risk was also probably very different than it is today.

• 6:36Speaker 00

  Yeah, but honestly, like you can't sit around VC and worry about downside risk. I mean, if you're gonna lose your money, you're gonna lose your money.

• 6:40Speaker 01

  No, I'm just thinking about you. There were growth investors who at one point in time could, you know, they could, I'm gonna get my three Xs when it works and I'm gonna get my money back when it doesn't. And now it seems like there's still venture outcomes from much later stages.

• 6:52Speaker 00

  Absolutely. I mean, when I, when I got started in this industry, growth investors would be like, oh, I'd like a three X lick pref. And I might triple my money and whatever. And now I think you can genuinely go into a company at, you know, you would get into Anthropic at 60 billion and you're gonna get like 10 X plus return on it. So I think you have to be ignore stage, observe the size of the opportunity, the magnitude of it and so forth. And in some cases, you're finding two people in a garage and you're helping them build the business. And in some cases, you're piling in Anthropic at 380 billion. And they, they have literally all of those have 10 X plus possible outcomes. So I think you, you just don't pay attention to that.

• 7:35Speaker 01

  I want to stick with that topic for a second. I think it is an idea that is maybe still not the mainstream idea, but definitely more people are operating in this way where, you know, you can, you could sort of choose what boundaries you create. The stage one has gone away in favor of other things. Do you think that you're able to do that as a result of experience? Or do you think a early career investor is also equipped to both traffic in pre-seed first money investments and also make assessments at the growth stages?

• 8:00Speaker 00

  Let me phrase it this way, on the assessment side, so being able to effectively value very different stage companies can be done by the same person. It's not that hard. Like, you know, looking at a spreadsheet is not rocket science for the average engineer. What is trickier, one of the things that I still think in this era and empire era is important is the proximity or affinity you have with the founders. If you're investing in two kids coming out of OpenAI right now and they're 22 years old and you're a young new investor, you can definitely do that. It's not likely that you'll be able to establish that relationship with Dario. So I think the advantage I have is that I can play that, you know,

• 8:43Speaker 00

  relationship game with some pretty senior folks because of my experience, but also appreciate that I need to build that relationship with a 25 year old. Now, some of the younger people on my team don't quite have the ability to go up to Dario or Sam and establish a relationship with them, which is understandable. My job is to start to open up that pathway for them so they can. But I do think the assessment of the fact that, you know, Cerebras is an interesting investment at the, at the price that it was done, or that, you know, OpenAI might still be, that anybody can do is just look at the math and see what it looks like and expect some level of multiples. It's not that hard to make the decision. I do think it's hard to get in at a growth stage if you don't quite have the reputation.

• 9:27Speaker 01

  When you thought about, you know, building this, not just as, you know, creating great funds, but also, you know, sort of building a firm, what are the other things that you've thought about? Like, have you thought about, you know, we've talked a little bit about team, the stage and focus and things like that. Have you thought about things like brand, infrastructure, you know, sizing of the funds over time? Like, what are some of the other attributes when you think about building like a new, real venture capital firm?

• 9:49Speaker 00

  I still believe brand is super important. Brand is probably even more important when your purchaser, the, i.e. the entrepreneur, is not incredibly well informed. It's like, you know, what is brand? Brand is basically a way of summarizing the value proposition that a product has to the prospective buyer. Because the prospective buyer is an expert. You know, you buy a Mercedes Benz, why? I don't know anything about the engine or the reliability or whatever.

• 10:20Speaker 01

  You just know it's good.

• 10:21Speaker 00

  But you just know it's good.

• 10:22Speaker 01

  Yeah.

• 10:22Speaker 00

  Right? That's what brand is. And if you think about entrepreneurs being younger. And having less network in the world of capital, brand is very important. So I do think you have to be super conscious of brand. However, the creation of that brand has to be done very differently, which is to say, in the old world of brand, you know, you, you could use marketing, good stuff. Like, you know, you put out super professional content and banners, and you sponsor this conference and so forth. I think in this modern era, there's an enormous amount of transparency. And so, brand gets conveyed in a much more organic way,

• 11:05Speaker 00

  person by person, reference by reference. So it's less about, did I sponsor this conference? Or is my banner over here?

• 11:12Speaker 01

  Yep.

• 11:13Speaker 00

  And it's much more about what impression do you convey to a person that you interact with, which spreads, and then people get to know you.

• 11:21Speaker 01

  Yes.

• 11:21Speaker 00

  Right? So I think the kind of classical marketing efforts that have been done for brand building, particularly in venture, sound off key to your average 22-year-old entrepreneur. They're just, it doesn't resonate.

• 11:35Speaker 01

  Yeah.

• 11:35Speaker 00

  I, I think what does resonate is inside knowledge, tips, connections, network, all those things that are kind of indirect but organic ways of building brand. So yes, brand is important, but I think you want to build it organically.

• 11:50Speaker 01

  Playbooks aren't the right thing. I heard somebody did like a survey of college students and what VC firms. And interesting on this point, Thrive was ranked really highly. And they don't do a lot of, you know, they're not, it's not loud. There's not tons of blog posts. There's not all the other stuff and.

• 12:04Speaker 00

  Yeah, I think Josh and his team over there are probably the prototypical, built a brand on the down low versus the, you know.

• 12:13Speaker 01

  Totally. There's also when you feel like you're in on a secret, you know, when you, when you know about it, and if you know, you know, brand, that's worth a lot more to people.

• 12:20Speaker 00

  A hundred percent.

• 12:21Speaker 01

  But how does that get cultivated? I guess is one of the questions, you know, like.

• 12:24Speaker 00

  Well, it's, you know, over time, success, you know, Hermes doesn't exactly do big brand ads.

• 12:28Speaker 01

  Yeah.

• 12:28Speaker 00

  You just know it.

• 12:29Speaker 01

  I also think, I'm sure you've probably felt this just over the course of your career, but probably over the long term, making good investments is probably the number one thing. You know, being able to sit with founders and say, I've invested in these companies.

• 12:40Speaker 00

  I think that helps. I, I, look, I, I think, at least from my own experience, what I find is like, having made some good investments, i.e. I know what I'm doing, is a good one. But also just people saying, look.

• 12:52Speaker 00

  So and so was very, very helpful in these particular moments in my company's evolution. When this happened, they were helpful in this way. When this happened, they were helping. You know, you're on the board of somebody and they say, who's your best board member? And if you're that person?

• 13:04Speaker 01

  Yeah.

• 13:05Speaker 00

  And the reason it's, I think, particularly tricky in VC is because people are always like, okay, tell me about what your value proposition is as a venture capitalist. And you're like, well, I do recruiting and we do marketing and we introduce you to customers.

• 13:15Speaker 01

  In that voice, yeah.

• 13:16Speaker 00

  You know, this kind of stuff.

• 13:18Speaker 01

  Yeah.

• 13:19Speaker 00

  And that's what every single venture firm does. But the real, the real, in my experience, the real thing that you do with entrepreneurs is whenever they get stuck,

• 13:28Speaker 01

  Yes.

• 13:29Speaker 00

  you have an answer for them. And they get, you know, entrepreneurs, you've been one, you get stuck in all sorts of ways. I don't get along with my co-founder anymore. What should I do? I've got the series B coming up and should I do it as a two-stage series B? Like all this.

• 13:41Speaker 01

  And you can't sell this, though. It's like one of those things that just has to come through references, I think.

• 13:45Speaker 00

  Exactly.

• 13:45Speaker 01

  Like, you can't be like, what's your value prop? Oh, I'm there for you when it's hard. It's like, okay.

• 13:49Speaker 00

  That reference, in due course, combined with some level of investment success, becomes brand. And then it's highly unassailable because you can't throw banners at that.

• 13:57Speaker 01

  How do you think about leading in board seats then, in contrast to participating? And, you know, maybe having like a slightly less engaged relationship? Do you do both?

• 14:07Speaker 00

  NABE is a small fund and, you know, we manage $175 million. So, leading a series A in this day and age, which probably ranges between 15 on the low end to 2530, not broadly generalizing, very hard to do with my size fund. So for now, we generally lead seeds, co-lead a few series As, deploy some capital against big companies that we think are going to be even more successful. I do think that at some point in the future, maybe with a little more capital, we'll try to lead some things. But I also think that the idea of classic lead may not be as relevant in the future. Meaning, you

• 14:51Speaker 00

  know, again, this is classic VC rule. I want to own 15% of the company or 20% of the company at series A. Okay, that's, that's nice. First, it's probably incongruent with what the entrepreneur wants. But then again, would you own 20% of some schmo company or 1% of Anthropic? So I think there too, you sort of need to relinquish this need to like lead, not lead, whatever, and deploy capital into good companies. If you can deploy

• 15:17Speaker 00

  As a, you know, 4% owner or an 8% owner, just deploy the capital. And then these companies creates a disproportionate value over time. Just pour more money in, more money, more money, more money, even at higher valuations. Just put more of it in because they're great companies.

• 15:33Speaker 01

  Yeah.

• 15:33Speaker 00

  And so I think that's one of the kind of historical VC rules that we have to let go a little bit of like, I must own this much in the beginning, own a reasonable amount.

• 15:42Speaker 01

  And buy more.

• 15:42Speaker 00

  And then buy more over time. Now, what does that mean in terms of board membership, not board membership? It's a badge of honor for a VC to say, I'm on the board of that company. In my case, you know, I've done this for a while. That's not, I don't need that badge of honor anymore. But I will commit to spending a lot of time. And so basically, almost every investment that I've made as part of an AB, I will do weekly, bi-weekly, or at least monthly check-ins with founders, go on a walk with them, spend an hour with them. In some cases, for the younger companies, it's literally like weekly. A, I find that a lot higher bandwidth in board meetings. I, I've done enough board meetings to last a few lifetimes at this point, and I don't find them as productive for knowledge extraction.

• 16:25Speaker 00

  And I think it helps the entrepreneur more with what matters to them in the moment.

• 16:29Speaker 01

  A lot of it is like managing their execs and things like that a lot of the times.

• 16:31Speaker 00

  Yeah, there's, there's so much.

• 16:32Speaker 01

  There's all sorts of stuff.

• 16:33Speaker 00

  Everybody's got like, you know, when you're a founder, like, okay, this week I got this issue. Next week, I got a different issue. Next week, I gotta do it. So, yeah, once a quarter, you prepare a deck and you sit around and read the deck. And, you know, all the three guys that are observers want to say something useless, like, you know.

• 16:48Speaker 01

  Once they're big, they're useless, I think. A small board meeting is a good forcing function to get everybody together, but once they're big, it becomes a different thing.

• 16:54Speaker 00

  I have done some board seats. I don't make it in any way mandatory, but I do make it mandatory to invest the regular time, one-on-one or one-on-two, depending on how many founders they have.

• 17:06Speaker 01

  When you think about going from, you know, a good early fund, couple of funds, what do you think will allow for a new firm to break through into the sort of like, multi-decade, this is now a truly established firm? You know, like, do you think that'll be a function of the brand ascending to a certain place? Is it a function of getting to a certain size? Like, what do you think is the thing that leads to, all right, this is now one of, you know, the institutional firms?

• 17:32Speaker 00

  When you take this job of a venture capitalist and really distill it down to its. It's barest bones attributes. I think there's four things that matter. You need to find opportunities. So discovery sourcing is important. You need to have good judgment to decide whether something is good or not. You need to convince the entrepreneur to take your money. So you've got to be a salesman. And then lastly, you've got to help them develop their business. So there'll be a reference so that the other things work. Those four things matter. I think a lot of the other decorations that VC has become, where you have like 600 people at a firm to do spreadsheets, and by the way, nobody's going to do in spreadsheets anymore.

• 18:14Speaker 01

  That's true too. That's over.

• 18:15Speaker 00

  I think you can have actually very compact organizations of people that do those things very well. Doing those things very well take actually a fairly diverse skillset. So, once you start trying to over-specialize, like, you know, this VC is a good sourcer, but they have bad judgment. This one's a great salesperson, but they can't source. You know, this one's a great operating partner.

• 18:39Speaker 01

  Now, you're trying to stitch together like five people to do one deal.

• 18:41Speaker 00

  No. So, I would say the firms of the future are probably leaner and more concentrated to groups of people that are well-rounded at that suite of four skillsets.

• 18:52Speaker 01

  On that point, the way you described it, you know, was the loop of the strong reference from being a great board member loops back. You can't then transfer it to somebody else doing the next deal. Like, it almost has to be one person.

• 19:02Speaker 00

  No, I agree. Venture capital has gone from this sort of boutique business to this scale business of like, I administer $20 billion. So, if you want to call that venture.

• 19:13Speaker 01

  Administer is a funny way to say it.

• 19:15Speaker 00

  If you want to call that venture, okay, then you have all sorts. But in terms of actually helping companies grow and develop in true startup mode, then I think it's smaller firms with that set of skills demonstrated by as many people in the firm as possible.

• 19:32Speaker 01

  I suppose also, there's going to have to be something around the sort of like, the generational transition ability of the firm has got to be like the thing that makes it last beyond the founders and.

• 19:41Speaker 00

  Yeah, but I mean, you know, that's the trickiest thing at every single venture firm. This fact doesn't change, which is the loop in VC is very long, right? So, you're a brilliant investor at Benchmark. You're making investments today. Benchmark will raise a fund, the next fund you do, not because of the work you did, but your predecessors. So is the credit due to them or is the credit due to you?

• 20:04Speaker 01

  Them.

• 20:05Speaker 00

  And this is, well, I don't know, because you're going to generate value for the capital that you're raising now. So I think when legacy investors get greedy on the economics in a firm, things go poorly. That is the quintessential failure mode of every venture firm, which is legacy partners who no longer do useful investments.

• 20:25Speaker 01

  Yeah.

• 20:25Speaker 00

  Take the disproportionate portion of a promote.

• 20:28Speaker 01

  It seems to me, it's almost like it was clearly the last generation's work that earned the next fund, but you have to give it all away anyway if you want the thing to

• 20:37Speaker 00

  Yeah, exactly. To live on. Yeah, you get a bit on the future.

• 20:39Speaker 01

  And you have to just say, I made enough. And now, you know, something was handed to me, I'll hand it to the next people kind of thing.

• 20:44Speaker 00

  Yeah.

• 20:44Speaker 01

  I want to talk about AI now. Obviously, you know, I know you're fully AI-pilled, the whole firm's around AI. So I want to talk about, you know, a few of the ideas here. What I want to start with is just sort of level setting on your overall, I guess, assessment of the state of play of AI. And maybe let's start with kind of like what you think is happening at the labs, at the sort of like core intelligence. And, you know, I want to talk about sort of where you think the, you know, advantages and edges are, what you think around sort of like compute and capital and scale of these things. Like, what's your read of sort of like, you know, the state of AI at this sort of central lab level first?

• 21:19Speaker 00

  The first thing I would say is my mental model of AI is a stack of things that start at the bottom with foundries and then semiconductors and then core models and the infrastructures that's associated with building those. And then there's sort of, you know, middleware and application layer software that sit all stacked on top of each other. I think you were referring to the big labs themselves,

• 21:43Speaker 01

  Right.

• 21:43Speaker 00

  which sort of sit right in the middle of that pie. My perspective right now is that the winners are the winners and we already know. I think, you know, clearly OpenAI, Anthropic, Google, I would throw Meta in the mix right now. And if Elon can get his act together at X.

• 22:01Speaker 01

  Yeah, those are the five.

• 22:02Speaker 00

  Those are the five.

• 22:03Speaker 01

  Yeah.

• 22:03Speaker 00

  Why those five? And that's because if you think about what are the vectors? What are the forces that make one model company better than the other one? Right now, compute and accessibility of compute and chips dominates. Compute is very highly correlated with capital. And you're talking about a group of companies that spend fifty to a hundred billion conservatively a year on compute. Now you show up and you're a new company X and you're like, hey, you know, I raised two billion. And it's like, yeah, nicely done. You're about an order of magnitude, if not more, at a disadvantage than your competitor.

• 22:43Speaker 00

  So I don't see a scenario until model architectures change, and they may change. It's the bitter lesson to me right now. Even if you came up with a far better model and you could argue that the way OpenAI and Anthropic do their things are a little more clunky, they can just throw compute at it and blow you away. I don't see that landscape changing. They are the dominant force in LLMs. Now, there are other kinds of models that could be kind of interesting, but at least in that world, game over.

• 23:12Speaker 01

  Okay, so two follow-ups on that. The first is, what do you think about then open source? And so like, obviously in the wake behind the frontier, you have open source, you know, let's call it six, whatever, nine months behind. Obviously, smaller models, cheaper, you know, served by, you know, inference companies. There's obviously a lot of utility there. Is that going to be an important part in your view of the next few years? Do you think that that's going to sort of be at the edges no matter what?

• 23:39Speaker 00

  Open source is a relevant phenomenon, but not a business in AI.

• 23:46Speaker 00

  If you look at the overwhelming amount of prompting that's happening, it's against the most advanced models. And that's the most monetizable, right? So advanced models being queried over and over again is where the dollars are. And that will sustain. Open source can exist, in my view, in the context of, you know, somewhat far behind and somewhat reliant on various forms of distillations and techniques that the big guys have already done. So what that tends to do is if you think about the monetization curve, it commoditizes the tail end of the curve, but not the front end of the curve. And the big dollars are still in the front end of the curve. So I think there will be.

• 24:30Speaker 00

  The asterisk there is that as if you're creating an open source model that's very close to the front end, you're by definition using a lot of compute. And you need to have capital. You need to have money to get that compute. And you're seeing even the most staunch open sourcey firms not be so open sourcey anymore. The latest Quen model is closed. Muse Spark is closed. That's not a coincidence. They're spending a lot of money training those models. They're not just gonna like open them up.

• 24:59Speaker 01

  Do you think the frontier labs will stop allowing third party to use their frontier quite as much because of the compute crunch stuff?

• 25:07Speaker 00

  It is likely that that will be the case.

• 25:09Speaker 01

  Let's say that that happens. Then what are the impacts of that? If, you know, the if the big labs start to say, hey, you can sort of, you know, you can access our old models, but the new stuff is only first party?

• 25:19Speaker 00

  Well, I mean, what it'll do is it'll cause the open source stuff to be even further behind. And by the way, temporally, you can have blips. So like, you know, Jensen can get the idea that he really wants an open source model. So you can go to reflection or whatever and give them $10 billion so that one generation they can catch up and then they'll fall behind.

• 25:34Speaker 01

  But it's a one time thing.

• 25:35Speaker 00

  It's a one time thing. Yeah, you I don't think it's continuous. I do think that you'll have, I mean, open source will be relevant. It's relevant in one more context, which is at least in today's world, and I don't know if this will sustain, but in today's world, if you post-train a lesser model enough, it will perform a small number of tasks just as well as the big models can, right? Essentially, open source can exist as the underlying layer of companies that are post-training them for specific personalized or business or corporate use. That exists, and there may be a business model somewhere out there, if these post-training kinds of companies are quite successful, that they can provide financing for open

• 26:18Speaker 00

  source to be closer in to the big models. But open source out of the kindness of your heart, which was the Quen, the Deep Sea, Kimi, Lama.

• 26:28Speaker 01

  Doesn't make sense.

• 26:29Speaker 00

  No. I mean, who's gonna go spend $50 billion to give it away? I mean, this doesn't make sense.

• 26:33Speaker 01

  It made sense for a minute, or what do you think was the argument for it at the time?

• 26:37Speaker 00

  I don't think people forecasted how much money it would take to train these bigger models.

• 26:41Speaker 01

  Yeah.

• 26:42Speaker 00

  I don't think people expected it. I think, you know, decisions like, why was Lama free? My guess is the folks at Meta looked at all these people and said, we can't exactly monetize this, but we'll try to commoditize their market by putting out something that's free. And I think that that's past.

• 26:57Speaker 01

  Yep.

• 26:58Speaker 00

  That's past, clearly.

• 26:59Speaker 01

  Yeah. I mean, it seems like once we saw that it was tapping into labor budgets for real instead of like software spend, at that point, it's not really about price, it's about value.

• 27:07Speaker 00

  Yeah, exactly.

• 27:08Speaker 01

  That's the open source side. And then what about the NeoLab side? Because that's like obviously the other potential vector here, and I would argue that's probably like one of, if not the hottest venture segments right now, that is getting backed at, you know, huge dollar amounts, huge prices. Like, what's your read on why that's happening?

• 27:24Speaker 00

  Yeah, well, we largely have not invested in NeoLabs because of my thesis that the big labs are gonna win. It is difficult to imagine that there will be an algorithmic improvement.

• 27:36Speaker 00

  That will allow a new lab to be competitive with the capital that the big labs are throwing at it, not to mention the fact that the big labs, they're not sitting there clueless, like thinking like, oh yeah, there's no other algorithms here. They're doing their own research of what the next generation will be. I don't see a scenario where there's a more clever architecture to a model that outperforms dramatically the big guys. Now, there are a couple of areas that I find somewhat interesting and those usually sit in pockets where there is a set of proprietary data that the model can be trained on that is not available to the big labs. So if you think about the core of the training that

• 28:19Speaker 00

  happens for the large models, it's the internet, the internet and the internet is largely available to everybody. If there's pockets of the internet that are not available, you can pay for it and open it up and get that stuff. Any model that sort of bases its training on internet available data is likely to not be as competitive with the big labs. However, there are pockets where you need, you need or have proprietary data. We're investors in periodic labs. Why is that interesting to us? They create their own data through their own labs and they do training and reinforcement based off of that. We are interested in robotics. Why? Well, robotics data is not broadly available on the internet. However, you can either teleop it or have

• 29:01Speaker 00

  remote workers create data, but you have to basically generate the data that your model trains on. So long as that data universe is reasonably private and not available to everybody else, then the people who have the right data strategy can actually stand out. So there are pockets where I think different kinds of models can be used for different kinds of orthogonal directions than the classic core models. But the NeoLab that comes along and says, you know what? I'm really good at RL.

• 29:30Speaker 01

  Right.

• 29:31Speaker 00

  And you know, because we do RL better, we're going to beat the other guys. And I'm like, you know, OpenAI got the memo on RL. They didn't miss that memo.

• 29:38Speaker 01

  Spending a lot of money on RL. On the robotics example, let's say, so you know, just to run with that, like data is like the thing that could differentiate it. How do you then think about investing in, let's say, scale for robotics data? That's going to be a data company selling to the big labs versus I'm going to invest in a new lab that is going to get that data? Or do you think both are workable?

• 29:59Speaker 00

  As a longtime investor in scale, what I would say is the

• 30:05Speaker 00

  The business of providing labeled data to large labs is a tough business to be in. At scale, we were there early and we served the labs well. But every single time a new contractor come up, it's a dogfight. It's scale, it's surge, it's Mercur, Mircor, it's handshake, it's Turing, whoever it is. Because ultimately, the job of annotating that data isn't that hard. It's a low barrier to entry job. And what the labs want is that data provided to you at low cost. I think essentially the same will be true for robotics data that is provided to the labs. It's going to be very competitive, low barrier to entry. And it's be largely dependent on who bids lowest.

• 30:47Speaker 01

  So then what would you need to believe to back a new robotics model?

• 30:51Speaker 00

  One is the architecture of the post pre-training is important to understand in robotics. Because generally, like all other things, if you do heavy doses of post-training in robotics, the robot will do one task really well, but that doesn't generalize. In order to do have robots that generalize, you need a lot of pre-training data. And that pre-training data is very important on the specific embodiment of the robot. So like what physical shape that robot takes is very important. So companies that generate a lot of their own pre-training data, often created in-house, is actually a material differentiator, in my view. Purchase data is nice, but not a material differentiator. So if your robotics company says, I get all my data from scale, I get all my,

• 31:36Speaker 00

  not that you should buy some of that data, please do, because I'm still on the board at scale. But I don't think that's the differentiator. Differentiator is how and what kind of data do you collect in-house? And you don't share with anybody else. And you created largely, I think, around embodiments which are explicit to your robot, your robot, your architecture, particularly since the hardest tasks in robotics right now are largely manipulation tasks or dexterity tasks. Like what kind of gripper do you have? Is it three fingers, four fingers? Is it a little claw? Like those things matter a lot in the pre-training.

• 32:08Speaker 01

  Do you think a company that makes robots would be a good source of that data for themselves? Like, is that a good way to back in? Like if somebody's building a robot, they have them out in the wild. They're presumably collecting a lot of potentially proprietary data. Like, is that one of the vectors?

• 32:22Speaker 00

  Absolutely. Yeah, I mean, one of the advantages I'm pretty convinced that Elon will have with Optimus is that he has data collection mechanisms. All over.

• 32:31Speaker 01

  Let me look at self-driving. It's just like, once you're in the wild, you start getting the flywheel going.

• 32:34Speaker 00

  Absolutely. And, you know, we're investors in a company called Mind Robotics, which is the robotics spin out of Rivian. Same thing. Their own factory will be their data collection instrument and they will have a proprietary funnel, which will be materially differentiated than other people.

• 32:49Speaker 01

  What's your read on the overall compute situation? So like, one framing of this is that basically like the demand for AI is just going absolutely vertical much faster than can compute can get online. What do you think is like the likely ways this plays out? You know, if like the labs, you know, Anthropic adds 10 billion of ARR, you know, a few more months. It's just like, how do you think this ends up shaping, you know, maybe down through the stack? You know, we were talking about the labs, but like, what will the ripple effects be? And like, how do you think about this dynamic if demand really is surging at this rate?

• 33:18Speaker 00

  It used to be that the demand for GPU compute was largely on training. Now with this explosion in inference, you have the twin effect of bigger models being trained and tons and tons of inference running on the same systems. So that means that the load or the demand for compute is kind of skyrocketing and it's likely that it will continue for some time. The supply side of that is basically TSMC wafer starts.

• 33:47Speaker 01

  Yeah.

• 33:48Speaker 00

  There's only so many of those. So anybody that bought compute historically probably got it cheaper than anybody that will buy it in the future. And I think one of the very smart things that opened, I did, is they bought a lot, a lot, a lot of compute ahead of time. As a result, I think, Google aside, because we talk about TPUs in a second, but Google aside, they probably have the lowest cost of compute of any player in the market right now.

• 34:14Speaker 01

  Yeah, there's the price of it. And then there's also just the availability.

• 34:17Speaker 00

  The availability.

• 34:17Speaker 01

  You know, money doesn't get you more wafers kind of thing. You just gotta wait.

• 34:20Speaker 00

  Yeah, exactly. On the other hand, the value, particularly on the inference side, that the value that people are seeing from the, the inferencing of these models is very high. Like, you know, you sit there and you do a spreadsheet and you ask Claude to do the spreadsheet for you and you go, wait a minute, I'll pay triple the amount of money that I'm paying for. So in some sense, we're getting into the fact that you'll be able to sell that, the output of that compute at a much higher price point in the future, given how productive and useful it is. Today, we live in a world where Nvidia is the gatekeeper to all compute. Again, TPUs aside, because that's a whole interesting side conversation of what Google decides to do with TPUs. I don't see that sustaining. And I do see more specialization in the kind of compute.

• 35:03Speaker 01

  You don't think Nvidia's dominance will sustain?

• 35:05Speaker 00

  No.

• 35:05Speaker 01

  What I think will happen?

• 35:06Speaker 00

  I think you're going to have compute that is oriented towards specialized tasks that are not necessarily dependent on Nvidia. So for example, I'm a big fan of Cerebras. Why do I like that one? It's not a particularly good training chip, although Andrew will argue with me that that's not true, that's not true. But, you know, let's leave that as where it is. In inference, it's a godsend. It's super fast and it's made certain compromises that make it very, very good for that. I think that's one. I think we're going to see more of that style of architecture that's very good for inference, for example. I think you're going to see more of these. There's even further evolutions of, you know, back in the old days, we used to call them ASICs. But if you look at Etched or Talus, these

• 35:49Speaker 00

  are companies that are fundamentally baking in some parts of the neural network weights into the chip itself, which will make it even faster than Cerebras, even less flexible than Cerebras. So I think that you're seeing this evolution towards mission-specific silicon rather than general-purpose silicon. And that will happen and will take the load off of everything being Nvidia. But I do think that we're going to do a lot of work in that bottom layer of the infrastructure to liberate ourselves as an industry from a stranglehold of chips.

• 36:21Speaker 01

  Yeah, I feel like the US probably can't invest enough fast enough in this.

• 36:25Speaker 00

  Already, some, a bunch of the GPUs are being built in Arizona. Like, I think some of the Cerebras chips are made in Arizona. I know Intel is trying to come online with their fabs to do independent silicon. I don't know that that's still worked yet. But I do think we're going to see a huge amount of capacity. And then obviously, there's a geopolitical issue of Taiwan. And, you know, I can't imagine that that same situation will be true in five, six years.

• 36:50Speaker 01

  That would be great.

• 36:50Speaker 00

  Yeah.

• 36:51Speaker 01

  What about now flipping to the other side? You know, we've been at the bottom of the stack, like the application layer. Obviously, over the last few years, there've been a bunch of amazing application companies built. Do you feel like there's durability there? And if so, what do you think the source of it is? Or, you know, what are the places that you think are risky? Not talking about pre-AI assassins, talking about like an AI native, you know, seeming application. I guess let's start there.

• 37:12Speaker 00

  In a given business, there are two things that are fundamentally proprietary. One is, we talked about data. Every business has a bunch of data and it's their most relevant data. And the second one is business workflows. So how you do and conduct business. I think companies that can capture the data and the workflows that exist inside of a business or in a given industry vertical, that the big labs are unconcerned about or don't have access to those things can survive as application companies. If your basic concept, though, is, you know, I take a document, I send it over to OpenAI API

• 37:55Speaker 00

  or Anthropic API, it comes back and I show it to you this way.

• 38:00Speaker 01

  It's not enough.

• 38:00Speaker 00

  That's not enough. What will also happen is, over time, the OpenAI's, Anthropic's and Google's of the world will look at the most interesting Tams and saying, wait a minute, I'm going to do that.

• 38:12Speaker 01

  Yes.

• 38:13Speaker 00

  So, you know, there's Anthropic for legal or Anthropic for finance or whatever.

• 38:17Speaker 01

  Yeah.

• 38:18Speaker 00

  This is not their core business, so they're going to have varying degrees of success.

• 38:21Speaker 01

  But it'll do 80% of the job.

• 38:22Speaker 00

  But it'll do the damage. Business verticals, like if you're in construction, there's a bunch of workflows in construction that it's not likely that OpenAI or Anthropic or Google will care about.

• 38:33Speaker 01

  Also, I personally, I don't know, maybe this is something I need to let go of, but it would be surprising to me if people no longer wanted UIs. But we'll see if everything just became chatting.

• 38:43Speaker 00

  Uh, no, I'm, I'm in that camp. You know, it was interesting when you were talking to Brett, he was talking about like systems of record and how systems are record important. I actually think one of the reasons why Salesforce sustains is because every salesperson knows how to use Salesforce. So actually, the human interaction with the system is the single highest moat that, that, that anybody has from, from breaking in. That might change over when agents do the work versus humans, but as AI pilled as I am, I think that takes a while.

• 39:12Speaker 01

  Yeah.

• 39:13Speaker 00

  And so that, that retains some, the UI does retain some level of sustainability for some period of time.

• 39:18Speaker 01

  What do you think about AI like services as software, fully do the work type of things where, you know, instead of selling you, you know, accounting software to an accounting firm, we're just going to sell you completed accounting work kind of thing. Like, do you think that's where stuff ends up? Are you more excited about that? Are you more excited about the, you know, software looking companies?

• 39:37Speaker 00

  Well, I answer the question two ways. One, I think it's a high bar today for AI systems today to actually offer accounting as a service. You still, you, you, you may not need the entry level, lower level, white collar work, but you still, in today's world, need a lot of contextual and other forms of knowledge that accountants have about their clients, about the systems, about how all things work. So I think we're still in an era where purely delivering a, a sort of service from AI with no humans involved. I think we're a little early for that.

• 40:13Speaker 01

  Particularly because in some of these, you need the verification of a human stamping. It is actually part of the value.

• 40:17Speaker 00

  You know, I think customers obviously will want some human to have supervision over it. And then humans, we have some very interesting sporadic contextual knowledge about things. That's actually surprisingly helpful. To the point earlier about VCs and what makes us different than introducing you to customers and, and employees. There's just like random little bits of knowledge here and there that we don't like over time. You never know what it's going to be. I think that exists and persists for a while. I will say though, that SaaS business models, like classic product models, are going to go through a very important transformation, which is when you spent a lot of money developing a piece of software and then sold it to many, many people, you architect your business to be a product business. And VCs for years have looked at things and said like, oh, I want a product business, not a service business.

• 41:02Speaker 00

  But now if the cost of software goes way down, I think you go in one of two directions. Either you're a low cost provider, you're like the Amazon of. Software, low margin, available to everybody's, you know, good delivery and so forth, or what you offer is a more customized edition of the software that either has agents associated with it, or is really plugged in and integrated with your system in a particular way. I think what that has done is sort of recast, you know, 10 years ago, when I was doing VC and we said like, oh, they have a professional services business model. You're like, oh, no, no, no, pass, pass, not that one. Now they're like called FDEs. They're really cool.

• 41:40Speaker 01

  Count your medicals.

• 41:41Speaker 00

  Super cool. And the interesting thing of, you know, what I, what do I think of an FDE is it is a job that is a go between, between a business problem and a technical problem. That's the fundamental value of an FDE. Now, your business problem tends to be pretty specific to your business. Your technical problem is something that's built on a stack and structured and so forth. And somebody needs to say, Jack, I understand your business problem. This is the way how we solve it. And that's the software company of the future. In some cases, you will solve it by populating a framework or an infrastructure with agents. In some cases, still plug in a human here and there. But that's what a software company of the future, I think, looks.

• 42:21Speaker 01

  And one of the interesting things, just from a business model perspective there is these deal sizes are so gargantuan that an FDE just doesn't matter for the, for the COGS relative to the compute costs and other things. If you're doing a $10 million enterprise deployment, who cares?

• 42:36Speaker 00

  A hundred percent. But, you know, keep in mind, like, I started my career peddling routers and switches, right? And that's when you used to go to the network engineer on the other side. It goes like, well, my switch has 24 ports. Wow. You're, his only has 16 ports and therefore, and like, for that difference, you could charge $100,000 a year. But when you're talking about the CEO of T-Mobile saying like, I need to reduce my churn by 2%. And you do that for them, that's hundreds of billions.

• 43:05Speaker 01

  That's right.

• 43:05Speaker 00

  Right? And no surprise, the contract values that these people are willing to offer are huge. Why? Because you're actually solving a business problem for them.

• 43:13Speaker 01

  Yeah.

• 43:13Speaker 00

  You're not providing them with the technology. And that's why, that's why the stuff, the FD stuff makes so much sense.

• 43:18Speaker 01

  So that's a bit about like the sort of end native stuff. Just quickly, I'm curious, your sort of pulse on the pre-AI SaaS. The sort of public software companies have obviously gotten, you know, hit in the last year or so. And every time the labs release some new thing, you know, it's like the stocks get traded down further. Do you think it's like oversold? Do you think it's undersold? How do you feel about it?

• 43:39Speaker 00

  You know, I'm not a very good public investor, so it's hard for me to say if things are over or undersold. But I do think all of those companies have like one of two paths forward, right? Which is, one is hang on to what you've got, grit your teeth, fire more people.

• 43:53Speaker 01

  Just get out of it.

• 43:53Speaker 00

  Increase your EPS and just, you know.

• 43:56Speaker 01

  Make the most profit available for their duration.

• 43:59Speaker 00

  Fire half your staff and increase the EPS.

• 44:01Speaker 01

  Private equity essentially to yourself.

• 44:03Speaker 00

  But there's going to be a set of companies that will be able to embrace the AI thing and do kind of a transition of their business into more of an AI centric business model. I don't think the market has really differentiated that because if you look at the multiples, everybody's sort of sitting at five times, four times.

• 44:23Speaker 01

  Everyone's saying that.

• 44:24Speaker 00

  Yeah. And I mean, look, I think there's a big difference between, I don't know, Figma and Workday.

• 44:29Speaker 01

  Yeah.

• 44:30Speaker 00

  Right? I, I think the chances that Dylan's going to figure something out are

• 44:33Speaker 01

  Much higher.

• 44:34Speaker 00

  A lot higher. And I think his, well, I'm an interested stakeholder, obviously, but I do think that some of those stocks have been oversold. Now, I don't, I can't tell you if they should be trading at a 12 times multiple or a six, but it just feels a bit oversold right now. Given the capability of the founders. The beautiful thing about founders and leaders is that however the business looks today, it doesn't have to look like that in five years.

• 45:03Speaker 01

  Yeah.

• 45:03Speaker 00

  Right? If Tesla traded at car company multiples.

• 45:06Speaker 01

  It would look different.

• 45:07Speaker 00

  It would look very different. The reason it doesn't do that is because people look at Elon. Even though

• 45:12Speaker 01

  Space sucks will be the same, of course.

• 45:13Speaker 00

  Even though there are no cars that are, well, I mean, there's a handful of cars that are self-driving and there's no robots in the market. People go like, he will successfully transition the company at that stage. Therefore, the company is worth more. I think SaaS is like the car business right now. Everybody thinks the car business sucks. Everybody thinks SaaS sucks. Some of those leaders

• 45:31Speaker 01

  Yeah.

• 45:32Speaker 00

  will be able to transform their companies by hook or by crook into something like what Elon did. And I think you have to look into the management of that company and say, do they get it? And then I think you'll see differentiation of the good ones and the bad ones in the SaaS universe.

• 45:48Speaker 01

  I completely agree. And then how about outside of AI? Is there anything that you're investing in that's not AI?

• 45:54Speaker 00

  Right now, it's like 90% plus AI, but I would say defense tech is an interesting area. In defense tech too, there's a lot of AI mixed into it.

• 46:03Speaker 01

  Sure.

• 46:04Speaker 00

  But I do think that we're seeing geopolitical shifts that are reasonably permanent for some time. Take, for example, the dynamic between the US and Europe and defense spending. I don't see European defense spending being reduced. It's probably going to be dramatically increased. And so there's opportunities there. It's the right tailwinds. I do think that with companies like Anduril and to a degree Palantir being successful at breaking into the market of selling into the US defense infrastructure will be an inspirations for both sides.

• 46:44Speaker 01

  I was gonna say, it does both things. It both, it unlocks capital and talent, but then it also sort of warms up the DOD to believing that, you know, these companies can work with them and take their understanding seriously and all that.

• 46:55Speaker 00

  Absolutely. And I don't think that's going back, you know, this sort of like the, you know, Pandora's box has been open on that front. So I do think that that's an interesting sector. You know, it's interesting because there's always these philosophies like, oh, do you invest in weapons and not invest in weapons? And when I was at Index, we didn't do defense investing at the time. And I remember looking at Anduril and thinking, this is going to be a winner. And I had a conversation with Trey Stevens and I was like, hey, I don't think we can do this because we don't do weapons. And he was like, I don't think you get the mix here. Weapons are a system that deters violence because the more weapons one side had as the other side have it, the less likely as they are to go to war. Now, this president has disproven

• 47:37Speaker 00

  that. But leaving that aside, I think the logic was pretty strong. And so now I'm more of the belief that I think the right kind of defense investing is the right thing to do.

• 47:49Speaker 01

  Yeah, I think so, too. Before you joined Index, you were like a founder, you're an operator. Do you feel like that mattered, looking back? Like, was it valuable to you to have done that? Do you think, would you have been an even greater investor, had you just been an investor the whole time? Or do you think that if you sort of like, think back and reflect on those chapters, that it, like, the net effect was, that it made you a better investor than just more experienced one of?

• 48:13Speaker 00

  First of all, I really enjoyed being an operator. It was super fun to be at a fast growth company, to see things explode and grow. You know, I joined Cisco, we were a few hundred people. I left, we were 55,000.

• 48:25Speaker 01

  Oh, my God.

• 48:26Speaker 00

  You know, like-

• 48:26Speaker 01

  In how long?

• 48:27Speaker 00

  I was there for 13 years.

• 48:29Speaker 01

  Wow, that's crazy.

• 48:30Speaker 00

  But it grew to that size in about 10.

• 48:32Speaker 01

  Wow.

• 48:32Speaker 00

  And by the way, you feel somewhat like you participated in something that's reasonably momentous, like-

• 48:37Speaker 01

  Yeah.

• 48:38Speaker 00

  You know, I got there when nobody had the internet, and because of the products that we made, everybody got the internet.

• 48:43Speaker 01

  Wow. I mean, that's only, you know, you can count the companies on one hand that have had that headcount growth.

• 48:48Speaker 00

  Yeah, yeah, yeah. Not just the headcount growth, but the societal impact of what was created.

• 48:54Speaker 01

  Yeah.

• 48:54Speaker 00

  Just because of the pure enjoyment of having been there, I'm happy I did it. How does that, how is that relevant in the context? I do, of the rest of my life, I do think that the old Steve Jobs things applies, which is you got to connect the dots, looking back. Now that I understand VC better, and by the way, I still get, I still learn every day, but I understand it a little bit better. I didn't actually realize how important having that card of having been an operator was. Because there's a lot of VCs, everyone's smart. Everyone's got a degree from a good school. Everyone's got a big fund. You got to differentiate yourself, right? Like, when an entrepreneur is selecting a VC, I am the product.

• 49:36Speaker 00

  What makes this product different than any other product in the market? And in my case, having had that experience of growth, recruiting, customers, relationships, all that was enormously useful for me. I'm not of the camp that you have to have been an operator because, you know, you look at Peter Fenton, the biggest thing he's operated was the lemonade stand.

• 49:59Speaker 01

  Shout out, Peter.

• 50:01Speaker 00

  But, but he is, he is one of the greatest VCs of all time.

• 50:04Speaker 01

  He is.

• 50:04Speaker 00

  And so we all, as a venture capitalist.

• 50:07Speaker 01

  And there's many other examples like that, too.

• 50:08Speaker 00

  Absolutely. I mean, Michael Moritz was a journalist. John Doerr was a sales guy. So we all come at it from different angles. I think the important thing is to recognize that you have to have an angle and that that angle has to be relevant to the entrepreneur. And so for me, having been an operator was mega.

• 50:27Speaker 00

  But it's, I don't, I don't view it as like, it's a must. It's an approach that happened to help me.

• 50:33Speaker 01

  And it was, most importantly, it was a fun way to do it.

• 50:35Speaker 00

  Yeah, absolutely.

• 50:35Speaker 01

  When you think about what types of founders are thriving today, has it changed versus before this AI thing? Is it, are there different mindsets, different types of educational backgrounds, different work experience, different age? Like, have there been updates that are material to you that change what kind of founders you're interested in?

• 50:53Speaker 00

  I think the biggest difference is the level of maturity that some of the younger founders have relative to even 15 years ago. There is so much content available, so much peer community available, that you take the average 20-year-old, 21-year-old, and they're showing up with so much knowledge compared to a generation ago.

• 51:15Speaker 01

  I know. I was 21 15 years ago. And when I meet a 21-year-old today who's like a founder of, I'm just like, oh my God. Like, I wasn't there when I was 27.

• 51:23Speaker 00

  Dude, I was 21 in the Stone Ages.

• 51:25Speaker 01

  So, yeah, there you go. But I'm just like, it's crazy what people know now. And I think it's going, I think it's accelerating. Cause I think, you know, now a 12-year-old today is going to be using AI tools. And by the time they're 20, they're going to be. Eight years into this.

• 51:38Speaker 00

  And it's not just like book knowledge or AI knowledge. There's also a lot of just commercial knowledge.

• 51:42Speaker 01

  Yeah. What do you think that's about? Is that from the online content? Where does that from?

• 51:46Speaker 00

  Yeah, I mean, it's all of the above. You know, we're absorbing content from all sorts of sources. It's PR groups, it's online, it's stories that we tell, podcasts that we listen to.

• 51:57Speaker 01

  Here we are.

• 51:58Speaker 00

  Especially yours.

• 51:58Speaker 01

  That's right.

• 51:59Speaker 00

  But I think people accumulate knowledge from all these sources at a very young age.

• 52:03Speaker 01

  It's incredible.

• 52:03Speaker 00

  It's extraordinary. I mean, it's absolutely extraordinary.

• 52:05Speaker 01

  Can I ask you an offensive question?

• 52:06Speaker 00

  Yeah.

• 52:07Speaker 01

  So all these young founders, you're not old, but you're not, you're not a young VC exactly. And so, and you're doing it very successfully. What is the mindset? Like, what do you have to do to be not a 25 year old VC?

• 52:19Speaker 00

  First of all, thank you for calling me old.

• 52:21Speaker 01

  You're not old.

• 52:23Speaker 01

  If you were, let's pretend, yeah.

• 52:24Speaker 00

  Look, I, I think the, I think the challenge that we old dudes run into, especially when we've had some amount of success, is that we think we know better. You've got to approach these things with a beginner's mind. Because every 21, 22 year old knows a whole lot of stuff that I don't know about. If you approach the relationship with the like, hey, I'm the all knowing grandfather. Let me tell your kid how this is.

• 52:53Speaker 01

  Yeah.

• 52:53Speaker 00

  It's not gonna work. It's not gonna work. You just have a conversation with people and you say, that's interesting. Let me give you my first principles answer to what you're saying. I think this is the right approach, but if you don't agree, tell me why. And let's have a conversation as equals. So I would say, forget your age, treat the person like an equal. And sometimes you might have an experience that you can pull on and say, well, you know, five years ago, I saw this. It might be wrong today, but I'm just gonna throw it out there for you to consider. And if they like it, great. And if they don't, let's move on to the next topic, right? But it's treating people like equals is the biggest thing.

• 53:25Speaker 01

  And this is so hard for people to do. Cause I mean, one of the things I think makes that particularly hard. By the time somebody's, you know, been working for 30 plus years, they've had successes. You don't want to relearn everything, you know? I, I think it takes a certain mindset to do it. But when you look at some of like the best tech executives, you look at a bunch of the best investors. I mean, I look at like, you know, take Jeff Bezos, take Vinod. You take a lot of people. I do think that if people are able to reset in beginner's mind all the time, it does seem like the most effective people are both extremely experienced and, you know, are able to think from the beginning again.

• 53:59Speaker 00

  Totally. I mean, not to get philosophical about it, but we all start as younger people. Maybe you didn't because you're a very confident man. But I started with a lot of lack of confidence, like, you know, can I do this? Will I be able to do this? And then over time, you sort of like, oh, I figured this out. I figured that out. And then you get to be 59 like I am and you're like, hey, I want to feel confident on this foundation.

• 54:20Speaker 01

  I thought you were 49, by the way. I didn't realize.

• 54:22Speaker 00

  Very nice of you to say. I wish I was 49. But, uh, you know, you get on this foundation of self-confidence, which is built on the fact that you think you know. At that point, being able to tear it all down and say, actually, I don't know, is a very hard thing to do. It breaks the entire structure of what gives you comfort to be the person that you are today.

• 54:42Speaker 01

  Do you feel like you've got that successfully broken down?

• 54:45Speaker 00

  Not always, but I try hard.

• 54:47Speaker 01

  I would imagine it feels good. Like, I would imagine it feels kind of freeing.

• 54:51Speaker 00

  It doesn't always feel good to people. It doesn't feel good to feel dumb.

• 54:55Speaker 01

  New.

• 54:56Speaker 00

  New.

• 54:56Speaker 01

  Yeah.

• 54:57Speaker 00

  It's a, it's very naked, right? It's a very uncomfortable feeling.

• 55:00Speaker 01

  But it's permission to like explore and have it all be light again.

• 55:03Speaker 00

  I agree with you if you can get there.

• 55:05Speaker 01

  Yeah.

• 55:05Speaker 00

  But I think for a lot of people, that's a lot of breaking down of layers and layers and layers of stuff that's been built up. I do think it's the ultimate expression of self-confidence is when you can accept the fact that you don't know something.

• 55:15Speaker 01

  That's what I think, too. I think being able to like raise your hand in a conversation with a group of people, say, what was that word you just said? Super confident thing to be able to do that. People don't want to.

• 55:24Speaker 00

  No, I agree. But that's a tough thing for people to go through.

• 55:26Speaker 01

  Does it change for you the types of people that you want to build your firm with at Hanabi? When you think about, you know, the sort of dynamics of the market, does it make you want like a wide range of teammates, experiences and ages and backgrounds and all of that? Or do you think it's more about this mindset's at the core?

• 55:43Speaker 00

  I think this mindset is absolutely the core forever. But I do think that a modern day fund. Is structured with a lot of people that are cohort relevant with the current entrepreneurs. I am not, and I try my hardest to be cohort relevant through the things that we talked about. It's much easier to teach somebody of the current cohort a little bit about VC, rather than teach an old dog how to, how a 25 year old thinks.

• 56:10Speaker 01

  Well, Mike, this was a total pleasure. I really appreciate you doing this with me. I learned a ton. Thank you.

• 56:15Speaker 00

  Super fun. Thank you. Have me back anytime.

• 56:17Speaker 01

  Great.