The Decade of Data (Tomasz Tunguz)

Speaker A: What's really exciting for crypto is you have the entire US stock market, which is the largest stock market in the world, and then you have crypto, which is large but not as large. And all of a sudden those pools of capital should fuse, which is great. I think software startups will have a much faster path to IPO as a result of crypto than they've had in the last 10 years. Speaker B: How does this change end up accelerating the timeline to IPOs for some of these software companies, do you think?

Speaker A: We have made it so expensive to go public that if you're a microcap software company, if you're worth somewhere between $1.5 to $2 billion, it's not economic to go public. Most of those companies get taken out by PE right away. And so wouldn't it be amazing if you had a venue where small-cap, high-growth software companies were publicly traded? Speaker C: Hey, I'm Mario, and this is The Generalist Podcast. As the saying goes, the future's already here. It's just not evenly distributed. Speaker B: On this show, I sit down with the founders, investors, and thinkers who are living in the future.

To help you see it earlier, understand it better, and capitalize on it. Today, I'm speaking with Tomáš Tungus, the founder and managing partner of Theory Ventures. In just 2 years, Theory has raised nearly $700 million in capital and backed promising startups across AI, crypto, data analytics, and data infrastructure. As Tomáš shared, his goal with Theory isn't just to build another venture firm, but something closer to an investing corporation. In our conversation, we discuss what it takes to build a modern venture firm and how Theory is accelerating deal flow analysis and market mapping with AI.

Ethereum's existential moment, how the blockchain risks losing its dominance the same way that AWS lost ground to Microsoft Azure as the result of AI. And the 100-agent future of work, why knowledge workers might soon manage an army of AI agents each. I walked away from this conversation with new ideas on how AI might reshape work, how crypto is changing IPOs, and why we're living through what Tomáš calls the decade of data. This is a new podcast, so if you like it, I hope you'll consider subscribing and joining us for some of the incredible episodes we have coming up.

Now, here's my conversation with Tomáš. Speaker C: This episode is brought to you by Brex. Fred Adler, the influential venture capitalist of the 1970s, was known for displaying decorative pillows in his office that featured a signature business philosophy: "Corporate happiness is positive cash flow." In today's post-SERP environment, Adler's wisdom feels particularly relevant as founders need to make every dollar work harder. That's exactly what Brex delivers. Their modern finance platform was built specifically for startups like yours and designed to help extend your runway when capital efficiency matters most. With Brex, you get global corporate cards with up to 20x higher credit limits and no personal guarantee required.

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I've been an admirer of your, your writing and your public thinking, uh, basically since I first learned I learned about what venture capital was, uh, which is now almost a decade ago. And so, yeah, it's great to have you here. I'm super excited. And maybe we can begin with a little bit of a, an introduction to your career in venture. How did you end up here and, uh, running Theory Ventures? Speaker B: All right, uh, Tomáš, it's so good to have you here. I've been an admirer of your, your writing and your public thinking, uh, basically since I first learned I learned about what venture capital was, uh, which is now almost a decade ago.

And so, yeah, it's great to have you here. I'm super excited. And maybe we can begin with a little bit of a, an introduction to your career in venture. How did you end up here and, uh, running Theory Ventures? Speaker A: Yeah, I, uh, when I was 17, I started a company with my dad. It got me into startups. I was fascinated that you could start a company. Uh, and then what after college? Oh, I studied mechanical engineering, computer science. And then I went to work for a startup that was founded by 3 alums from my school that ultimately ended up going public and was an early employee there and fell in love with it more, understood a bit more about it.

Went to work at Google, built some large-scale machine learning systems with a phenomenal team for ad targeting. And then I've been a venture capitalist ever since. And I remember, I remember I was walking along the Embarcadero, which is the part of San Francisco that butts the water. And I saw, I an angel investor talking to a founder outside at a restaurant. It's like, oh my gosh, I can't believe that's a career. I hope I get to do that one day. And I was very lucky that I had the opportunity to start and now have a firm of our own called Theory.

Speaker B: And Theory, I think, is 2 and a half years old. Is that about right? Speaker A: Exactly right. Yeah. Okay. Speaker B: And, you know, one of the things that I really admire and find really interesting about your approach to investing is you both invest in, in data companies, but also take like a very data and thesis-driven approach to venture, which especially, you know, at the earlier stages isn't particularly common, especially not in the way that, that you guys do it. How did you sort of like land on, on that approach as being the right one for you and the place where you felt you had a real edge?

Speaker B: And Theory, I think, is 2 and a half years old. Is that about right? Speaker A: Exactly right. Yeah. Okay. Speaker B: And, you know, one of the things that I really admire and find really interesting about your approach to investing is you both invest in, in data companies, but also take like a very data and thesis-driven approach to venture, which especially, you know, at the earlier stages isn't particularly common, especially not in the way that, that you guys do it. How did you sort of like land on, on that approach as being the right one for you and the place where you felt you had a real edge?

Speaker A: Yeah, I think there are many different strategies that make money in venture, and the one that appeals to me the most is to go is to perform really deep research. And that's the kind of work I really enjoy. It's gratifying because it helps us build, as a firm, it helps us build networks of people who might be helpful to portfolio companies later. It helps us understand what's happening within those ecosystems so we can be helpful board members. And over time, we think that knowledge is a compounding advantage, right?

The nuances of one wave, if you have them in a particular place stored, let's say in the organizational memory, is a huge advantage later on. Insight Partners is famous for this. They have a database of all the pitch decks and metrics over their entire existence. And that was a big inspiration. And, you know, I look at some of the hedge funds that, that have done really well. They build information asymmetry. It compounds over time. I think the same thing is true in venture. The datasets are obviously quite different, but if we build the internal systems of knowledge the right way, then, then I think over long periods of time it compounds.

Speaker B: That's really interesting. I would say that, you know, one of the maybe more common, uh, ways of thinking about venture would be that these asymmetries get sanded away super fast because things just get, you know, uh, competed away really, really quickly. What are the, like, types of asymmetries that you see as actually being compounding, uh, or having the ability to compound for, for you guys? Speaker A: The real asymmetry is just, is getting there earlier. I mean, you have the venture capital asset class has grown. From $8 to $250 billion in 15 years, let's say.

And the amount of competition has increased dramatically. The number of different financial products that are offered to founders has grown. And that's wonderful for the ecosystem, right? I think the bigger startup land, broadly speaking, is the better. The goal for us is to be able to carve out a niche. It doesn't have to be very large, but it has to be one where we think we can create a great business and drive strong multiples. And so to be able to do that, What we do is someone comes in on Monday and I'll give you a story from our first intern.

His name is Alex, who went on to start a company. And Alex, a brilliant guy, walks in on Monday during the investment committee and comes in with a 10-page paper. And he says, I don't believe GPUs are the future of AI. It'll all be ASICs, application-specific integrated circuits, which are chips that are designed to perform one operation like Bitcoin mining, but in this case, transformer architectures. Now there are startups that are doing that. And so he walks in that Monday and we debate. And I think that kind of investing is really fun.

It's really fun because for half an hour every Monday, we sit there and, and within our team, we have a head of AI who is head of AI at 3 Unicorns. And there's Lauren who built the healthcare practice at Palantir and sold contracts worth tens of millions of dollars to the largest healthcare companies in the world. And then we have two sales leaders from unicorns, and then we have investors. And so there's this composite team with many different backgrounds and experiences, and they each have a different perspective on the opportunity.

And that composite view and that initial debate really leads us to decide, should we go and invest time here? Is this an investable opportunity? Is this within our scope? Does it meet our, our requirements? And that, that kind of investing is fun because out of that Monday, there's a lot of energy, right? If we find a theme or an idea that we're really interested in, there's a lot of energy. The whole firm pulls its resources, its network, and we can go and start a diligence process and a market map and really understand that space.

And we follow up on those market maps every Monday. And then every, every month we review and we present to each other what we've learned. And so there is both a product for the organization, which is all of that research that we keep. And then there's an education of the entire firm. And so with time, we should all become increasingly sophisticated investors or increasingly sophisticated on many different spaces. Speaker B: So many interesting threads that I want to dive into there. One of which almost a bit of a meta point, but it's interesting to me that your investment team sounds like so many of them are former operators have really like been in the weeds in industry.

Uh, how did you think about like building out the team that way and having sort of these more functional experts versus, you know, folks that maybe have longer investment track records? Like, why is that the right approach for this style of firm? Speaker A: So I think the future of venture capital looks an awful lot like the future of— they look like investing corporations. They don't look like partnerships. Why are venture capital firms partnerships? Well, it's because of our history. Silicon Valley started with 6 or 7 men who would get together every Tuesday.

At a particular restaurant and each write a $50,000 check into a company. And that was the first syndicate, right? And so, and out of that, 2 or 3 of them got together and said, let's start a firm. We can raise some institutional capital. Capital markets were not very supportive. So the funds were small. And then there were 10 or 15 of these firms up until the late '90s. And then all of a sudden there was kind of this institutionalization of the asset class post-GFC, post the global financial crisis. And the asset class started to explode.

Right. But we have kept this notion of a partnership and there are many different reasons for that. But I think ultimately we are offering, if you look at like hedge funds, they're structured as investing corporations. If you look at private equity firms, they're, they have investing teams and operations teams. And so, and if you look at most major financial companies, they have different kinds. There's a marketing team and there's a product team or technology team. And, and so we think that's the future. Uh, and in addition to that, it leads to better investment decisions because you have these different perspectives, right?

We want our sales leader's perspective on how effective a founder is at selling. We want our head of AI and our technical team's perspective on the technical depth of a company. And we need those conversations and that understanding to happen very quickly. We can't have a lot of additional meetings. Ideally, we help the companies and give them guidance if we can, and we have a perspective on their market or their sales strategy, and it helps inform our investment decision. And so I think it just gives us like a mosaic or a better triangulation of understanding a particular business or market.

Speaker B: Super interesting. I'm also interested to dig in a little bit deeper onto this thesis development piece. It sounds like, you know, on the one hand, you are constantly generating these theses as an organization and sort of having these Monday meetings and chasing down new leads. But also you've sort of organized the firm a bit around sort of a few mega theses that you're deciding to like really go deep on, sort of, uh, data, AI, crypto. There you have, you know, much, uh, sharper lenses around those different things. But, um, how did you sort of like land on those as, you know, let's say the sort of super narratives that you're, you're following?

And how do you sort of like convince yourself that these are the places where we really have to go deep so you don't get stuck? Chasing a dead end or chasing something that's so, so broad that you would really actually end up having no edge. Speaker A: All of them are underpinned by data. Modern data stack, databases, visualization, that's data. AI, machine learning, all of that's data. And blockchains, well, that's just a different kind of database. Right? So they're just data systems broadly written. They are sold to different buyers. Speaker B: That's really interesting.

So then what is the deep interest in data systems there? Like why is that the thing that you're like, this is the big opportunity for us? Speaker A: Well, I love data. Speaker B: Yes, part of it is personal, right? Speaker A: That's a prerequisite. So number one is I love data. I think the second thing that's, they create really big companies. I mean, Databricks and Snowflake and the modern data stack are both decacorns. Maybe we'll see them becoming centacorns. Within the world of AI, right? You have Anthropic and OpenAI and many others that would be crowned decacorns.

And then within the world of blockchains, I mean, you have Ethereum is worth $350 billion, Solana is worth $90 billion. So you have incredibly large outcomes. That's one. Two, there's a combination. So all of these markets have technical innovation, faster databases, in-memory databases, better forms of decentralization, new AI architectures. Will it be transformers? Will it be diffusion models? Are they state-based models? And so there's a technical component that is fun to understand. And then there's a go-to-market component, right? And so that's fascinating and it filters, right? There's less competition in very technical domains just because it requires a particular set of expertise and a desire to understand it.

Um, and so the combination of those two things, really, really large markets, barrier to entry for complex topics. And then the last thing I'd say is all of these categories are like of the moment, right? Like they're, they're very interesting categories. They're capital efficient. And so that makes it fun. Plus the replacement cycles are fast. So in venture capital, we might invest in a company and, and work with that company for, for 5, 10, maybe 15 years. But ideally the cycles of, it's not like nuclear reactors, right? Let's say awesome space, but a nuclear reactor takes 10 years to build.

And then the next generation is probably 50 to 60 years from now. So you, you might be able to invest in one nuclear reactor company in your lifetime, but you can invest in many data points in Coin's investment career. Speaker A: Well, I love data. Speaker B: Yes, part of it is personal, right? Speaker A: That's a prerequisite. So number one is I love data. I think the second thing that's, they create really big companies. I mean, Databricks and Snowflake and the modern data stack are both decacorns. Maybe we'll see them becoming centacorns.

Within the world of AI, right? You have Anthropic and OpenAI and many others that would be crowned decacorns. And then within the world of blockchains, I mean, you have Ethereum is worth $350 billion, Solana is worth $90 billion. So you have incredibly large outcomes. That's one. Two, there's a combination. So all of these markets have technical innovation, faster databases, in-memory databases, better forms of decentralization, new AI architectures. Will it be transformers? Will it be diffusion models? Are they state-based models? And so there's a technical component that is fun to understand.

And then there's a go-to-market component, right? And so that's fascinating and it filters, right? There's less competition in very technical domains just because it requires a particular set of expertise and a desire to understand it. Um, and so the combination of those two things, really, really large markets, barrier to entry for complex topics. And then the last thing I'd say is all of these categories are like of the moment, right? Like they're, they're very interesting categories. They're capital efficient. And so that makes it fun. Plus the replacement cycles are fast.

So in venture capital, we might invest in a company and, and work with that company for, for 5, 10, maybe 15 years. But ideally the cycles of, it's not like nuclear reactors, right? Let's say awesome space, but a nuclear reactor takes 10 years to build. And then the next generation is probably 50 to 60 years from now. So you, you might be able to invest in one nuclear reactor company in your lifetime, but you can invest in many data points in Coin's investment career. Speaker B: Uh, I think to a certain extent everyone's interests are sort of inscrutable even to themselves.

Like it's hard for, for, you know, if someone asked me why do I like writing, it would not be an easy question to answer. But to the extent that you can articulate it, like, why do you love data? Like, what is it that speaks to you about that as something to spend your time on? What is the elegance of these business models that like really animates something in you? Speaker A: Well, it's all understanding. I remember being in, so I had this amazing professor in grad school. His name was Minh Pham and he I studied control systems and our two final exams, one was building the autopilot system for Boeing 747, given only six sensors.

And then the other one, which was a real use case was imagine you have to send, you have to receive a signal from a satellite and there's a pad that has six actuators and that six actuators move the receiver. And you have to move that receiver in real time to maximize the signal, uh, given earthquakes and the earth and the earth moving. And so, you know, whatever, like, like cool technical problems. We learned very basic control systems. And I was just fascinated by the fact that we could take these sensor data and actually make something that was magical, right?

Like a plane can fly by itself. I drive in a car that Well, I don't really drive the car anymore. The car drives itself. And all of that is as a result of a whole bunch of sensor data that's coming off some telemetry. And the same thing, you know, he was doing something in the stock market. And so it's kind of like fracking, or it's kind of like oil, where you take this thing that comes out of the ground and then you can do everything. I mean, you can make gasoline to put in a car from oil.

You can make plastics to have a Tupperware container to save your applesauce, or you can make clothes from oil, right? And so I think it's this unbelievably useful raw ingredient and we're getting better and better at it, right? The whole world of data before, say, 2010 was focused on this, on the very finest grade of oil, Brent, let's say, which was structured data, stuff that looks like it's in Excel or could fit in a structured database. And then over the last, say, 15 years, we've been learning how to frack data.

We've been taking this raw, unstructured data, the Word documents, the conversations that you and I have, the podcasts and blog posts, and we're able to run it through an extremely inefficient system, but actually get something useful out of it with AI. Speaker B: And this is why I think, or at least certainly one of the main reasons you wrote about the 2020s being the decade of data. What, what are sort of some of the other things that like make you feel like we were really at a potential turning point? Potential inflection point for, for this magical material?

Speaker A: Well, I mean, just look at the CapEx spending of Facebook and, or Meta and Google, 300, 250 to 300 billion in data center spend. And you read the public earnings of all of those companies, they're capacity constrained. There's way more demand for AI consumption than they can supply. Meta is building one that's larger than Manhattan. One of them that's slated to be produced will consume more electricity than the country of Ireland. Speaker B: Wow. Speaker A: And so, wow. When you get a sense of this scale of the computers, the data centers, but really computers that we're building, I was reading a research report.

We think in 5 years, 15% of American electricity, which is growing the demand, 15% will be used to feed data centers. That gets me goosebumps. I'm so excited because it just tells you how valuable all of these data pipelines are and the demand for those kinds of insights. Speaker B: Given, you know, what you said about your intern Alex, who was talking about, uh, you know, the things down to the chip level, do you look at, at companies like in the hardware stack and, you know, down to the chip level?

Like, is that something that you would theoretically invest in or no, too far field? Speaker A: By exception, there's a very particular set of expertise that you need at the chip level. I mean, there's a whole field of EDA, which is CAD drawings for these chips. There's the assembly of chips on one side and power management on the other. It's a fascinating domain. I think the hard part, there are a couple of challenges, at least for a fund of our relatively small scale. The first is the amount of capital required for a lot of these chip companies is pretty significant.

The second is if they make a mistake on tape-out, which is kind of the final rendition, it's extremely expensive. And then the third is the number of potential buyers is ultimately pretty limited. I mean, you look at like the EDA market, the software market, there are 2 or 3 publicly traded companies that Synopsys is one, Cadence is another, that could buy these kinds of companies. There will be breakouts, there is no doubt, but I think it's hard for us. And then the other dynamic there is you have all of the major hyperscalers investing in their own silicon.

So you have Google with the TPUs, and that's not going away, and Amazon with the Inferentia chips, and I forget the name of their training chips, and Microsoft has theirs, and I'm sure Meta has created open-source architectures that they've released. So I think it's a big boys' game. Speaker A: By exception, there's a very particular set of expertise that you need at the chip level. I mean, there's a whole field of EDA, which is CAD drawings for these chips. There's the assembly of chips on one side and power management on the other.

It's a fascinating domain. I think the hard part, there are a couple of challenges, at least for a fund of our relatively small scale. The first is the amount of capital required for a lot of these chip companies is pretty significant. The second is if they make a mistake on tape-out, which is kind of the final rendition, it's extremely expensive. And then the third is the number of potential buyers is ultimately pretty limited. I mean, you look at like the EDA market, the software market, there are 2 or 3 publicly traded companies that Synopsys is one, Cadence is another, that could buy these kinds of companies.

There will be breakouts, there is no doubt, but I think it's hard for us. And then the other dynamic there is you have all of the major hyperscalers investing in their own silicon. So you have Google with the TPUs, and that's not going away, and Amazon with the Inferentia chips, and I forget the name of their training chips, and Microsoft has theirs, and I'm sure Meta has created open-source architectures that they've released. So I think it's a big boys' game. Speaker B: Yeah, hard to, hard to break in as a, as an insurgent.

On the crypto side, you know, you've been interested in crypto for a long time, and you really are interested in it for the sort of core technology rather than, uh, you know, a lot of the, the mania that, that surrounds it. You know, a decade, decade and a half in, depending on how you want to, you know, uh, judge it, how do you think about the progress crypto has made as an industry? Like, where do you sort of see it in the cycle of development? Speaker A: I think it's broadly underappreciated and it's been extraordinarily massive.

I mean, you look at Bitcoin for a new generation as gold. It's the equivalent of gold. People trust it more. And that I think would've been— that's a pretty phenomenal statement to say we've had an asset that has existed for thousands of years in gold that people trust as an ultimate store of value. and now we have a digital equivalent. And I think it's, you know, less than 50 basis points of global wealth is in Bitcoin, and most of the projections say it's going to 2 or 3. So that's pretty significant.

I think the second thing that's really happened within the world of crypto that we're seeing now is the broad acceptance within the US of stablecoins. Speaker B: Yeah. Yeah. Speaker A: Absolutely. I mean, by the end of the year, I bet most major large banks in the US have their own stablecoin project, a pretty significant fraction. 5 to 10% of US dollars moving through systems will likely be through stables. And I think 2 years ago that would've been unfathomable. Speaker B: Yeah, that seems like it's happened insanely fast. Like I remember writing about it just, yeah, probably about 2 or 3 years ago at this point.

And it still felt like there was a certain group of people in crypto for who it was so obvious that it was almost a boring thing to write. And then for a large swath of the world, like, still very, very strange and hard to wrap your head around. Speaker A: Right. Jamie Dimon said very famously, uh, crypto is rat poison. And now JP Morgan has an internal blockchain where they move $1 to $10 billion a day on it for international settlement. So we've come a long way. You know, and then I think the, the, the third trend that we're paying a lot of attention to is the tokenization of stock.

So Robinhood is doing this and Coinbase is doing this. And there's, I think there's two really interesting axes here. So typically when you buy a stock, you buy it on the New York Stock Exchange or the NASDAQ, that's called a venue. And if you wanted to buy a token, let's say you wanted to buy Solana or Ethereum, you would buy it another venue. You might buy it on Coinbase, which is a centralized exchange. You might buy it on a decentralized exchange like Uniswap. But you can't, you can't buy stocks using crypto exchanges and you can't buy tokens using stock exchanges, but the brokerages are fusing them.

So Robinhood today, you can buy crypto and you can buy stocks. Where the venue is, where the asset underlying, it doesn't matter. And then now what you can do is you can buy synthetics. Robinhood is offering shares of OpenAI that you can buy. And so, and it's on a blockchain. It's a tokenized stock. There's a lot to unpack there. Speaker A: Right. Jamie Dimon said very famously, uh, crypto is rat poison. And now JP Morgan has an internal blockchain where they move $1 to $10 billion a day on it for international settlement.

So we've come a long way. You know, and then I think the, the, the third trend that we're paying a lot of attention to is the tokenization of stock. So Robinhood is doing this and Coinbase is doing this. And there's, I think there's two really interesting axes here. So typically when you buy a stock, you buy it on the New York Stock Exchange or the NASDAQ, that's called a venue. And if you wanted to buy a token, let's say you wanted to buy Solana or Ethereum, you would buy it another venue.

You might buy it on Coinbase, which is a centralized exchange. You might buy it on a decentralized exchange like Uniswap. But you can't, you can't buy stocks using crypto exchanges and you can't buy tokens using stock exchanges, but the brokerages are fusing them. So Robinhood today, you can buy crypto and you can buy stocks. Where the venue is, where the asset underlying, it doesn't matter. And then now what you can do is you can buy synthetics. Robinhood is offering shares of OpenAI that you can buy. And so, and it's on a blockchain.

It's a tokenized stock. There's a lot to unpack there. Speaker B: Yes. Speaker A: But, um, what does this mean? Well, it means that what's really exciting for crypto is you have the entire US stock market, which is the largest stock market in the world. And then you have crypto, which is large, but not as large. And all of a sudden those pools of capital should fuse, which is great. The other thing it means is that startups should have a much faster path. I think software startups will have a much faster path to IPO as a result of crypto than they've had in the last 10 years.

Speaker B: Oh wow, interesting. I want to unpack both of those, uh, quickly. But the first part, like you say, they should fuse. Can you explain to folks like why that almost is, uh, sort of an inevitability that, you know, these things are going to come together from, from like an architectural advantages standpoint? Speaker A: Yeah, I mean, there's no real difference between a token and a stock, right? What do you have with the stock? You have a voting right, depending. You have a share in the company. You have a dividend right.

Let's just say it's those three and the ability to trade it, right? So you have those three things. Well, token is the same thing. I have a dividend right. It can produce yield for me. I can vote and I can trade it. And so, okay, securities law, I think will ultimately catch up and say both of these things are the same thing. So legally it's, and there are already significant volumes of debt like bonds that are on crypto exchanges that are traded just as they would be in bond, on bond markets.

So that market was the, the bond market was the first one to normalize relations, let's say. And the equities market is coming. The other thing that's happening is publicly traded software companies trade on multiples, right? We talk about forward multiples, EV to NTM. And for a long time, crypto tokens did not. I mean, you looked at some of these multiples, they were in the tens of thousands. Of times of revenue. Speaker B: Yes. Speaker A: And over the last 6 months, certain categories of crypto tokens are now trading at 40 to 60 times revs.

So still elevated, but we're now achieving normalization. And so if I'm a software investor and I have a dollar to invest, and it's just as easy for me to invest in the equity market as the token market, and I have a reasonably good proxy of financial statements between the two. Now all of a sudden my dollars can flow freely and I'm satisfying my fiduciary responsibility to do diligence on both of them, which is lacking today in the token world. There's no GAAP, there's no S-1, right? But it's coming. Speaker B: Yes.

Speaker A: And over the last 6 months, certain categories of crypto tokens are now trading at 40 to 60 times revs. So still elevated, but we're now achieving normalization. And so if I'm a software investor and I have a dollar to invest, and it's just as easy for me to invest in the equity market as the token market, and I have a reasonably good proxy of financial statements between the two. Now all of a sudden my dollars can flow freely and I'm satisfying my fiduciary responsibility to do diligence on both of them, which is lacking today in the token world.

There's no GAAP, there's no S-1, right? But it's coming. Speaker B: How does this change end up accelerating the timeline to IPOs for some of these software companies, do you think? Speaker A: How much does it cost to go public? I'm not talking about the dilution or the, you know, the amount of money raised. I'm talking about the legal fees, investor relations, the regulatory fees, what do you think it costs to take a software company public? Speaker B: Gosh, I honestly have no idea. I would put it in those sort of like single-digit millions.

Speaker A: $15 to $25 million. Speaker B: Okay. Wow. I'm off by a, by a good chunk there. Speaker A: Okay. So if you're a $100 million revenue company, is it rational to raise a round of financing that costs you $15 to $25 million? Speaker B: Yeah, totally. Wow. Speaker A: So to put it into perspective, if you were going to raise a Series X, very late stage round. Speaker B: Yeah. Speaker A: Your legal fees might be in the hundreds of thousands of dollars. You might touch a million depending on how big your cap table is.

You know, Series A is $30K, Series B is $40K, Series C is $50K. So all of a sudden you have the step up of like 3 or 4 orders of magnitude in just your cost, your transaction cost to go public, which is why it used to be in the late '90s, you need like $25 million of trailing revenue to go public. And today, no, no, no, you can't go public unless you have $250, maybe $500 million in revenue, because as an expense, it'd be bananas. What, just raise it in the private markets?

And so that's why you look at the average growth of publicly traded companies, it's asymptoting to 10% basically, which is software inflation, uh, because it's so, it's so expensive to go public. So why is it so expensive? Well, there's just a lot of regulation, right? Sarbanes-Oxley, all that kind of stuff. And so if you look at crypto exchanges, the regulation there is much less. There's no S-1, there's no audit. And so we, some of those components are important, but the point is we have made it so expensive to go public that if you're a, call it microcap software company, if you're worth somewhere between $1.5 to $2 billion, it's not economic to go public.

Most of those companies get taken out by PE right away. Wouldn't it be amazing if you had a venue where small-cap, high-growth software companies were publicly traded? And so that's what, that's what gets me so excited. And these exist. You look at ChiNext in China or the ASX in the UK or TSX or the rumors of the Texas Stock Exchange, which would be a new venue for trading. All of them, all of those venues are focused on high-growth small-cap technology companies where the disclosure requirements are significantly less than the NYSE, than the New York Stock Exchange or the NASDAQ.

Speaker B: Yeah, totally. Wow. Speaker A: So to put it into perspective, if you were going to raise a Series X, very late stage round. Speaker B: Yeah. Speaker A: Your legal fees might be in the hundreds of thousands of dollars. You might touch a million depending on how big your cap table is. You know, Series A is $30K, Series B is $40K, Series C is $50K. So all of a sudden you have the step up of like 3 or 4 orders of magnitude in just your cost, your transaction cost to go public, which is why it used to be in the late '90s, you need like $25 million of trailing revenue to go public.

And today, no, no, no, you can't go public unless you have $250, maybe $500 million in revenue, because as an expense, it'd be bananas. What, just raise it in the private markets? And so that's why you look at the average growth of publicly traded companies, it's asymptoting to 10% basically, which is software inflation, uh, because it's so, it's so expensive to go public. So why is it so expensive? Well, there's just a lot of regulation, right? Sarbanes-Oxley, all that kind of stuff. And so if you look at crypto exchanges, the regulation there is much less.

There's no S-1, there's no audit. And so we, some of those components are important, but the point is we have made it so expensive to go public that if you're a, call it microcap software company, if you're worth somewhere between $1.5 to $2 billion, it's not economic to go public. Most of those companies get taken out by PE right away. Wouldn't it be amazing if you had a venue where small-cap, high-growth software companies were publicly traded? And so that's what, that's what gets me so excited. And these exist. You look at ChiNext in China or the ASX in the UK or TSX or the rumors of the Texas Stock Exchange, which would be a new venue for trading.

All of them, all of those venues are focused on high-growth small-cap technology companies where the disclosure requirements are significantly less than the NYSE, than the New York Stock Exchange or the NASDAQ. Speaker B: Wow, how interesting. Um, you also mentioned, you know, the Robinhood OpenAI example, which is more like, you know, secondaries trading, which feels like an exciting, you know, other set of, another asset class that sort of, uh, needs to be opened up. But there's all these sort of complexities about like, are these essentially options? Is it, you know, owned through an SPV?

And some, so like, who owns it? How do you see that ending? Ending up playing out? Speaker A: Well, I've been reading online trying to understand because Robinhood mentioned the OpenAI tokenized stock and OpenAI said we did not authorize this. So in most secondary transactions, the board must authorize those transactions. And so my understanding, which is only based on what I've read online, is that Robinhood is creating a synthetic asset that tracks the value of OpenAI stock and they're backstopping it. And maybe there's an SPV underneath. Anyway, it's not clear.

Speaker B: Unclear. Yeah, we'll see how it all shakes out, I suppose. Speaker A: But I do think the notion of tokenized secondaries or secondaries that float, or even tokenized venture firms where LPs can trade underlying positions, is important. Let me reframe venture capital the way that PE works today. I started a seed fund. I find a company, two people and a dog, and I help them get to a Series B, and I work with them for 4 years, and then I sell the entirety of my position. And then a Series B specialist fund that is focused on scaling go-to-market buys that entire position and grows the company from a million in ARR to $25 million in ARR and gets the benefit of both the revenue growth and the multiple expansion associated with the revenue growth.

And then sells it to a Series E investor. And I'm just making things up. And that Series E investor is really good at preparing companies to go public. They have the right Rolodex of executives. They know the investment bankers and they go from Series E to IPO. That's the way that a large part of the private equity market works. And there are a couple of benefits. The first is the time for investors to get their money back. The DPI, dollars to paid in, is much faster, which makes subsequent fundraising much faster.

Their specialization on individual stages. Um, and so the, the cycle of money basically just moves a whole lot faster. We're not, we're nowhere close to that, right? We talked about venture capital is a $250 billion asset class. In the last 3 years, the totality of all secondary funds is $6 billion. So it's, you know, we're talking maybe a basis point. We're not there yet. Um, but I wonder, one of the questions in my mind is, I wonder if we get to a place where seed funds are liquidating positions after 3 or 4 years, Series A funds are liquidating, maybe not the entirety, but some fraction thereof.

Speaker C: This episode of The Generalist Podcast is brought to you by our very own Generalist Plus, the premium newsletter that's redefining how investors and builders navigate the technological frontier. Generalist Plus delivers a mini MBA to your inbox at just a teeny fraction of the cost. Just $22 a month or $220 annually. So what's included? 1, tactical interviews where elite founders and investors reveal their actual strategies and decision frameworks. 2, comprehensive guides that distill hundreds of hours of research into actionable insights on investing and company building. 3, an exclusive database of emerging startups poised for for significant growth.

And finally, complete access to our archive of meticulously crafted case studies. All of this comes wrapped in the distinctive storytelling and incisive analysis that readers have come to expect from The Generalist. We've designed Generalist+ to level up your capabilities as an investor and operator through knowledge that matters, delivered with precision and depth. So join a community of strategic thinkers who are gaining an edge in understanding markets, technology, and business fundamentals by visiting com. That's com. Speaker B: You know, we were talking about some of these crypto innovations and, you know, I'm reminded of a piece I think you wrote in early 2024 where you talked about the most profitable software startup in the world is Ethereum.

Um, and I thought that was such a, was such a good framing and such an interesting way that you, you talked about it. It feels like it's been a really sort of tough cycle for Ethereum in general. There's lots of soul searching, lots of discussion about where, where things head. Speaker B: You know, we were talking about some of these crypto innovations and, you know, I'm reminded of a piece I think you wrote in early 2024 where you talked about the most profitable software startup in the world is Ethereum. Um, and I thought that was such a, was such a good framing and such an interesting way that you, you talked about it.

It feels like it's been a really sort of tough cycle for Ethereum in general. There's lots of soul searching, lots of discussion about where, where things head. Speaker A: How have you sort of looked at that? Speaker B: How do you see its place in the ecosystem? You know, I do think there's such a clear story around Bitcoin, as you said, and, and a clearer one around Solana. But, you know, are people, uh, too down on Ethereum? Like, how do you think about its potential today? Speaker A: Yeah, I love— you had a brilliant point in there.

You said, is Ethereum soul searching? So just for the audience, Solana's nickname is Soul. Speaker B: There you go. That's right. Right. Speaker A: And so the major competitor to Ethereum is Solana. So I think Ethereum is kind of the, the, the grandparent of all of these chains and the volumes. I mean, just to kind of put it in perspective, 50 to 60% of all stablecoin volume is on Ethereum. And another big chunk is on Tron. So it's really the vast majority part of the market. And then 60% plus of the decentralized finance or DeFi market is on Ethereum.

So it's the big dog. And Ethereum, it plateaued, right? There was not a lot of innovation. They allowed other companies to innovate on top, which are called Layer 2s, like Arbitrum and Optimism. And they were solving, those L2s were solving performance challenges for the ecosystem. And so they were basically allowing you to move money faster and cheaper than Ethereum and saving you 90%, just kind of give you a sense of the relative efficiencies of those systems. And then Solana came around and said, we're going to architect in a very different way.

We'll be more centralized. It will be faster and cheaper. And then you have another one called Sui, which is lucky enough to be an investor that also has very, very significant performance improvements. That's the ex-Meta blockchain. Team, one of the two. And so within the span of 3 years, you had 2 orders of magnitude improvement in blockchain performance relative to Ethereum. Let me ground that in an example. If I wanted to send you a CryptoKitty, it would cost me $50 to $70 to send it to you on Ethereum. If I use, so if I used an L2 like Arbitrum, it might cost me $2.50.

And then if I use a more modern blockchain like Solana or Sui, it might cost me 20 cents, right? So very significant reduction in transaction costs. And so as a result over time, well, developers will build on the systems that are more efficient, much better price performance characteristics. Nobody wants to pay those fees. And so that's where we found ourselves with ETH maybe 3 to 4 months ago. And then people started all of a sudden, they're like, yeah, dissatisfaction, the community decided. okay, we need to, we need to react. And so they raised $150 million and Vitalik is more involved and they're at this crux, this critical moment in, in the blockchain's development where they need to figure out what their competitive advantage is.

It's a little bit like, like AWS before AI, it was the standard. Everybody, I mean, it was not even a question if you were a startup. Which platform you're, you're building on AWS. And then AI came around. No, Azure became the dominant platform. Why? Because they had a special relationship with OpenAI. And so Solana has a special relationship with Stripe, right? And Robinhood has a special relationship with Hyperliquid. And now all of a sudden you have bigger strategic relationships that change the builder's perspectives on which platform to build. And so Ethereum finds itself where AWS is or was 6 or 7 months ago.

Which is there's been this huge wave of innovation, other people have pushed, and now the developer mindset is shifting. Speaker B: As you think about its comparative advantages and what should be the real advantages of the project, like what do you think that should look like? Like what do you see as the things that the grandfather still has or should lean into that these younger chains maybe can't compete with or will struggle to compete with? Speaker A: The single most important thing in any financial market is liquidity. Ethereum has it now, right?

So the question is, how do you capture that liquidity? Well, stablecoins will be an absolutely essential market and minimizing the transaction costs so that liquidity remains on ETH is priority one. How do they do that? What do they do with the L2s? How do they think about the consensus infrastructure? And then the reward systems for the validators, I think is a really— basically, how do you architect your entire system? Speaker B: Yeah. Speaker A: They'll have to, I think, revisit a lot of the core assumptions because the underlying technology has changed.

And that's okay. But I think a lot of times in the world of Web3, we have attachments to certain ideas and over time they change. Speaker B: Really interesting. As you sort of look at the crypto Web3 landscape, like, are there newer projects that you've, you know, spent a lot of time playing with that you think are really promising or new sort of pieces of infrastructure that you think are, you know, worth discussing that aren't being as discussed right now? Speaker A: Well, I mean, there's a brand new company called Hyperliquid that's worth somewhere between $20 to $40 billion that outside the world of crypto, it's very, not very well known, not venture-backed.

And they did a couple of things that were absolutely brilliant. One, they focused on the perpetuals market, highest margin product. They created a blockchain that has some unique attributes to that market in particular. So they specialized a vertically integrated stack exclusively on it. And so as a result of the vertical integration and then making the blockchain specific and the tokenomics and all that stuff specific to what they were doing, they built a huge, I mean, multi-decacorn business that's now partnering with Fantom, right? So Fantom, now you can trade perps and it's all powered through Hyperliquid.

Speaker A: Well, I mean, there's a brand new company called Hyperliquid that's worth somewhere between $20 to $40 billion that outside the world of crypto, it's very, not very well known, not venture-backed. And they did a couple of things that were absolutely brilliant. One, they focused on the perpetuals market, highest margin product. They created a blockchain that has some unique attributes to that market in particular. So they specialized a vertically integrated stack exclusively on it. And so as a result of the vertical integration and then making the blockchain specific and the tokenomics and all that stuff specific to what they were doing, they built a huge, I mean, multi-decacorn business that's now partnering with Fantom, right?

So Fantom, now you can trade perps and it's all powered through Hyperliquid. Speaker B: And I think turned, as you said, like no venture capital and turned down a lot of venture capital as far as I know, which, uh, is, yeah, pretty cool. Speaker A: Really cool. So, you know, $30 billion bootstrap. Speaker B: Yeah. Yeah. Speaker A: Pretty amazing. Speaker B: Really. Speaker A: And then, you know, they're doing all kinds of other things where they buy their tokens back, right? It's like a stock buyback program, just like Apple.

And so they're actively managing the value of their token. There's a lot of foresight and they deserve a lot of credit for the company that they've built. So I think the vertical integration is one really interesting trend. I think, you know, another big question is, are we moving from proof of work to proof of stake to proof of authority? Speaker B: Can you explain those for folks just very briefly? I know that's not the easiest, but in simple terms. Speaker A: Yeah, so Bitcoin is proof of work. If you want to earn money by minting Bitcoin, you need a really big computer and you basically solve very complex, not complex, but computationally intensive math problems, and then you're rewarded with a fraction of a Bitcoin.

That's proof of work. Proof of stake is someone is vouching for me. If I'm trying to join a, I don't know, whatever, a social club, right? And Mario vouches for me. That's proof of stake. He's staking his reputation. People do that in the financial world. Like, I know Mario's trading. I've got him. Speaker B: Can you explain those for folks just very briefly? I know that's not the easiest, but in simple terms. Speaker A: Yeah, so Bitcoin is proof of work. If you want to earn money by minting Bitcoin, you need a really big computer and you basically solve very complex, not complex, but computationally intensive math problems, and then you're rewarded with a fraction of a Bitcoin.

That's proof of work. Proof of stake is someone is vouching for me. If I'm trying to join a, I don't know, whatever, a social club, right? And Mario vouches for me. That's proof of stake. He's staking his reputation. People do that in the financial world. Like, I know Mario's trading. I've got him. Speaker B: Yeah. Speaker A: That's what it is. Speaker B: Backstopping you in a way. Speaker A: Right. And then proof of authority, and I'm rewarded for that, for backing up Mario. And then proof of authority is, I pick Mario, I think Mario's a great guy, that's enough.

Mario will run his computers, he will validate the transactions, and I trust him. Which is how most databases today work, right? If I send data to Amazon, I trust Amazon will run the database the right way. They're not going to change how I move money, or in my Quicken, however many, they won't change it. I just trust Amazon. So proof of authority with one validator is what we have today. And so we're kind of moving to this world where we have less and less decentralization, but ending up with more efficiency as the sort of benefit of it.

Speaker B: You trade off decentralization for speed, efficiency, et cetera. Speaker A: At some point, you get to the, you get to the place where there's only one validator and you basically have replicated an existing database, right? We were chatting with Patrick O'Grady yesterday, who's one of the key engineers from Coinbase about it. And so there is this, and so the question is, well, why do you have decentralization if you only have delegated authority? Well, you might have delegated authority. We talked about JP Morgan within the world of JP Morgan.

They choose who validates the transactions and says this transaction is valid. They're already running it internally. There are a set of applications where this might work. So Ethereum has to figure out where on that spectrum of consensus they want to be. And I think this is a, it's like another kind of critical moment for the ecosystem to decide what kind of consensus mechanisms. Speaker B: Amazing. Well, I want to talk about AI a little bit. We've talked a little bit about it, but I, you know, in reading your writing, it's so clear to me that you're playing with these technologies so much.

You're building stuff with AI. You're using voice so much. In your own sort of day-to-day life, like what do you find are the most surprising places or the most surprising use cases that you, you do rely on these technologies for? Speaker A: Okay, so I spent the entirety of the July 4th weekend trying to only interact with my computer through AI. So there's a tool called Claude Code. Gemini has another one where you can just type in and say, do this and do this and do this and do this. And I'll give you an example.

So very, you can just ask it a question like you would on com, ai, whatever the domain is. And you can ask it, uh, you know, what is the difference between— this is a query from last night— SEAL Team 6 and Delta Force? I had no idea. And it will answer you. But then you can also tell it, hey, look through all my files and see if you can find this blog post from 2 years ago. Speaker B: Oh, wow. Speaker A: That's another very basic thing you can do. But then you can ask it, create a tool.

So let's create a tool. What, what is my schedule for tomorrow? Okay, so it'll, it will write, I asked it just to write the code itself and then it will go into my Google Calendar and, and then I'll spit it out. And then I created another tool which said, go and get my email and summarize it for me. And then I created another tool which said, tie this to Asana, a task manager. And so before I got on the podcast today, I went through all my email at once and I looked at all the email and I said, do this with this email and this, create a task from this one and add this one to the CRM and Um, you know, check to see if I can meet this person at this time, and if not, send them the slots.

And I can do that all with one voice command. And so what I'm trying to understand is what are the limits, right? If we were going to reimagine a computer that I was only speaking to that had access to all these different tools, where does it break down? Speaker B: It's so interesting because in some ways you've ended up going back to the start of computing in some way, like you're back to almost a command line just with voice. and, and OneTab, uh, that, that's doing all these things for you.

What are the limits that you've discovered a bit? Speaker A: There are a couple of limits. The first is I really want the computer to go off and do something and then come back when it's done. That does— it's not very well handled today. You can use some more technical tools to do that. The second thing is it often makes mistakes. So I've given it a tool to send email. Well, sometimes it just decides to send an email, right? Or sometimes it just decides to archive an email. And so you have to instruct it in its memory to say, never send an email or archive an email without my permission.

And so there's this con— it's like training a brand new person. How do you work? So there is a lot of constant feedback and the bet I'm making, and right now I'm 50-50. Is that within a week or two, all of that training will result in improved productivity. But I couldn't tell you that it's doing that today. Speaker B: Wow. But it's great if you, uh, you know, you only have a couple weeks to figure it out. You think by, by then you'll have a reasonably definitive answer. Speaker A: Because then the tools exist and then I find all of the different cases where the tools fail, right?

So like yesterday I was asking it, what is my calendar for tomorrow? Turns out it was only fetching the first 10 calendar items. And so it was making up nonsense, right? And so anyway, there are all these corner cases. So maybe over time, and then I wonder if companies themselves will have these shared libraries where everyone is accessing the calendar through some, and this is actually what we're building inside of Theory. So, uh, Adam, our great, uh, software engineer, he released the library for the CRM yesterday that's available to AI.

Speaker B: Okay. This is exactly where I wanted to go next, which is how are you using AI within the firm? Uh, because I know that you guys are, Yeah, very technical team and thinking about how to architect the firm in a way that, you know, it involves a decent amount of engineering. Speaker A: The most important thing is that we understand these technologies at a very deep level and feel the same problems that builders feel. Because if we do that, I think we'll be better investors. The second thing is there's a lot of rote work that can be automated.

And large language models are really great at this kind of unstructured automation. So, I mean, think about it just like a sales process. You have a lead, you want to process that lead and qualify that lead as quickly as possible. Go and find as much information about as possible, synthesize. You can, we have an agent that builds market sizing analysis. We have an alpha of an agent that makes investment recommendations that I wouldn't trust, but you can kind of, you can, we're starting to assemble all of those pieces, memo generation, uh, deck extraction, those kinds of things.

And. Reality is none of these technologies are flawless and there are unbelievable improvements in productivity. As much as we look at startups and say, okay, the ARR per engineer should now go from about $150K to $500K. Well, the number of startups covered by a VC investor should go up by a similar percentage. There's no reason it shouldn't. So maybe we can cover 3 times as many spaces or 4 times as many spaces, and we should be able to do it with greater depth. Okay, what is the entire toolkit that's required to be able to do that?

Speaker B: I enjoy creating, you know, Claude projects for investments I'm considering and, you know, forcing it, you know, giving it inputs to say, try and analyze whether I should make a decision or not. I find it's really not very good at at least mapping what my own judgment would be, but it's really interesting and still I find it quite productive almost as a conversation to have with, you know, an intelligence of some kind. What do you find that's like for, for your sort of investment recommendation engine that you have or your, your agent?

Like what does it help you with? Speaker A: Yeah, I think, um, summarization is really good. Um, in a space where we don't know a lot, let's say all of a sudden we decide to invest in chips, it helps you get up to speed very fast. Much faster than you could normally. Sometimes it surfaces questions or insights from history that are important. It's really a summarization that it's quite good at. Ontology generation, it's really good at. So let's say you were looking at MCP servers and you wanted to classify all the different approaches to different model context protocol server, next generation API servers, basically classify them and segment them, or you wanted to look at an even broader universe, AI security companies.

Well, there's data loss prevention companies, firewalls, um, prompt injection company. Anyway, so it's also really good at that. Speaker B: Well, one of the last times that, that we chatted, I remember you saying that one, Theory has like a very concentrated approach where I think you, you know, had, you were sort of aiming for 12 portfolio companies per vintage. And two, that the way you'd sort of come to that number was by running like Monte Carlo simulations to figure out what is the optimal size. What are the other ways you rely on data or this type of simulation to make key strategic decisions around the contours of the firm?

Speaker B: Well, one of the last times that, that we chatted, I remember you saying that one, Theory has like a very concentrated approach where I think you, you know, had, you were sort of aiming for 12 portfolio companies per vintage. And two, that the way you'd sort of come to that number was by running like Monte Carlo simulations to figure out what is the optimal size. What are the other ways you rely on data or this type of simulation to make key strategic decisions around the contours of the firm?

Speaker A: So yes, that's right. We did that. We, I think, and then Carter released an analysis. I'm not sure if you saw of the return distribution of funds as a function of their portfolio construction. It's really interesting where it shows concentrated funds, median is less, but their 75th to 90th percentile or higher. So it's a higher vol, you know, different kind of return profile. I think within the world of early stage, it's really hard because there's not a ton of data. There's not a lot of structured data. There is a lot of unstructured data.

So what people say online about different products or spaces is fascinating. And AI is really good at capturing that, what people say in podcasts or recording our interviews or Conversations that we have with people, that's where the majority of the data is, or the majority of the insight. How do you ask the right questions? What kinds of interviews? Which are the sources that you pick? So I think that's where we use it. And then the other place that we use it is benchmarks and comparables and those kinds of places. But it's not like hedge funds, right?

We're not buying satellite information, satellite images over retailers to predict quarterly earnings. Speaker B: In, in your sort of introductory post about Theory Ventures, you talked about, uh, you know, investing in machine learning as a force multiplier, which feels like the sort of the, the lens for which, through which you look at AI in general. Like, why was that the way that you perceived the AI opportunity and the right way for, for Theory to play it exactly? Speaker A: Let me ask you a question. Okay, so on a, on a manufacturing assembly line for cars, How many humans does a robot replace?

Speaker B: I would guess 2, 1, I don't know. Speaker A: Yeah, 2.5. 2.5? Speaker B: Yeah. Okay. Speaker A: In an Amazon distribution center, so that was my mental model for AI. AI would kind of replace 2.5 people inside of a company. Amazon then released the stat, this is 3 or 4 months ago, where they said in their distribution centers, so their warehouses, One robot replaces 20 humans. Speaker B: Wow. Gosh. Speaker A: Why is a robot in a DC that much more productive than a robot on an assembly line?

Because robot on assembly line, it's the same thing. You're welding two joints or attaching a windshield, whatever it is. And so I don't have the answer to it. The broader point though is, okay, well, if you're a white, if you're a knowledge worker, if you're a white collar worker, what is that ratio? Is it 2? Is it 20? Is it 100? Is it 10? And so, you know, the question behind the question is how many agents should a highly productive worker manage? I think right now I can manage 2 to 4.

And the highest number I've heard is 15. I bet within the next 12 months, we will see workers managing 100 agents working simultaneously. Speaker B: What does the person who manages 15, how do they do that to the extent that you can share? Like, what does that look like? Speaker A: They have a task list. Speaker B: Uh-huh. Speaker A: They create it. It's a software engineer who creates a task, a PR, a pull review, says, go and fix this, go and create this feature, go and fix this 15 times, comes back half an hour and then review's done.

Speaker B: Right. Speaker A: There's no reason that that mental model of working shouldn't apply to a salesperson, right? Or it shouldn't apply to a marketer or customer support rep. And so, um, I don't know. I think this is the question in my mind is, and this is the reason why I'm playing around with AI on the computer is how do I get to a place where I can manage 50 times more agents than I can today? And right now it's, you still need to monitor and watch and you're watching the readouts and making sure that it's working okay.

But there, there has to be a system that has to be one, an inbox, which, which Harrison from LangChain talked about an agentic inbox to manage all these things outside of the world of software engineering. And then the second, there is, there has to be some kind of, of way of putting guardrails around them so they don't send 100 emails to the wrong people on your behalf. Speaker B: Yeah, it feels like you need new systems to manage it on the, on the human level and also just like a different level of reliability such that you can really trust it to, to do what you ask it to do.

Speaker A: Right, exactly right. Speaker B: About a year ago, you wrote that Microsoft was leading the AI race and Google was was lagging. How do you think that has changed in the 18 months since? Speaker A: Well, a lot's changed, right? So the relationship between Microsoft and OpenAI is now quite different. The Gemini models are all state-of-the-art. You look at the efficient frontier of performance as a function of model size, and the Gemma models or the Gemini models are all either number 1 or number 2. So they're meaningfully, I mean, they've made a huge leap.

I think Google is, I mean, Google is clearly subsidizing a lot of their AI products to drive usage. So I, they are, uh, uh, maybe in the developer world a bit behind and they're trying to drive more adoption. And then you, but I think to their strength, you look at the impact on their search business, it must be phenomenal, an improvement in margin because there are two different kinds of ads that Google runs. One is the ones on search and the other ones are on other people's webpages and the traffic to other people's webpages is plummeting, right?

So HubSpot's traffic down 70, 75%, and other sites are seeing more and more declines because of those AI overviews. Well, great, because great for Google, because now all of a sudden there are more and more ads where they're capturing 100% of the ad spend as opposed to some fraction of it. So that's good. So I think on the consumer side, they're in an unbelievable position, an absolutely enviable position. And then the other dynamic that a lot of people don't talk about is access to the GPU, right? Running these models is super expensive.

Chrome, a browser, a dominant browser, can actually use a GPU. So they can put a model in your browser and then have that use your GPU to answer queries, and they don't have to spin up anything in the data center. A lot of the other model companies do not have that advantage. Speaker A: Right, exactly right. Speaker B: About a year ago, you wrote that Microsoft was leading the AI race and Google was was lagging. How do you think that has changed in the 18 months since? Speaker A: Well, a lot's changed, right?

So the relationship between Microsoft and OpenAI is now quite different. The Gemini models are all state-of-the-art. You look at the efficient frontier of performance as a function of model size, and the Gemma models or the Gemini models are all either number 1 or number 2. So they're meaningfully, I mean, they've made a huge leap. I think Google is, I mean, Google is clearly subsidizing a lot of their AI products to drive usage. So I, they are, uh, uh, maybe in the developer world a bit behind and they're trying to drive more adoption.

And then you, but I think to their strength, you look at the impact on their search business, it must be phenomenal, an improvement in margin because there are two different kinds of ads that Google runs. One is the ones on search and the other ones are on other people's webpages and the traffic to other people's webpages is plummeting, right? So HubSpot's traffic down 70, 75%, and other sites are seeing more and more declines because of those AI overviews. Well, great, because great for Google, because now all of a sudden there are more and more ads where they're capturing 100% of the ad spend as opposed to some fraction of it.

So that's good. So I think on the consumer side, they're in an unbelievable position, an absolutely enviable position. And then the other dynamic that a lot of people don't talk about is access to the GPU, right? Running these models is super expensive. Chrome, a browser, a dominant browser, can actually use a GPU. So they can put a model in your browser and then have that use your GPU to answer queries, and they don't have to spin up anything in the data center. A lot of the other model companies do not have that advantage.

Speaker B: Yes. Speaker A: So I think on the consumer side, they're in a great position. On the enterprise side, they're still, they have unbelievable product and they're solving the distribution, which is the classic Google strategic challenge. Speaker B: Which are the models that you end up using most often? Speaker A: I use a lot of open source models. I use a lot of the, the Google models because they're free, but it's for different, for different things. So Gemini is my default model. OpenAI, I think, um, is great at the deep research product.

Speaker B: I really like. Yeah, agreed. Speaker A: Yeah, it's really good. Claude, I used to use a lot for writing. And now when Gemini cannot solve a coding problem, Claude fixes it every time. Speaker B: Oh, wow. Interesting. How do you think about OpenAI's position today? And, you know, I'm especially sort of thinking about it with respect to the, the aggressive moves that, that Zuckerberg's been making and, you know, especially on the hiring front and, and on the, you know, to that end, like where, where do you see Meta in this?

They've obviously taken a very different approach. Speaker A: Yeah, great question. I mean, OpenAI has an unbelievable asset, which is hundreds of millions of daily active users and a brand that is universal, right? And that I think is an asset that is hard to assail. It's hard to attack. So I think they're in a relatively strong position because of that consumer distribution. It's, you look at, I mean, any company that reaches that level of scale is worth at least tens of billions, if not hundreds of billions, if not trillions. right?

So Google got that to that level of scale. Meta got to that level of scale just on consumer distribution. And Snapchat got to some level of scale on that distribution. So all those companies are worth a lot. There's a, you know, long-term business model question there that they're working to solve. And, uh, I think their pricing power is underappreciated. We, you know, talk, look at Cursor increasing their pricing. I think a lot of people, especially in the business world, will pay a lot of money for these tools because of the productivity gains.

And so there's a pricing discovery opportunity that, uh, that, that is there. Um, but I, they used to have a trillion-dollar market cap company in their corner. Now they don't, right? And they have Masa instead. And so they're figuring it out, right? They need to figure out who their allies are and, uh, what the long-term positioning is, but ultimately the brand and the distribution is unbelievably valuable. Speaker B: And, you know, by comparison, how do you think about these aggressive Meta hiring moves and how it positions itself, because it's had to play a bit of catch-up and is doing more of the open-source stuff, but it obviously has so much of that distribution it can rely on.

Speaker A: Okay, so the first thing I'd say is, how awesome is it that you have the CEO of a $2 trillion company being that aggressive? Speaker B: I know, you do. I think it's the most— you do not see people play offense that hard like that I can ever think of. Speaker A: Yeah. I mean, just overt frontal assault, right? And like, I'm coming. Yeah. You have to respect that. That is awesome. Because, you know, and then I was reading the announcement today with Zuckerberg where he invested $3.5 billion into the largest eyeglass maker, Luxottica.

You know, he bought 3 to 5% of the company because that's the new. And so I just love the aggression. I think it makes business fun to see people conduct business that way. Speaker B: Yeah. Go that hard. Speaker A: Yeah. I think he, you know, Llama were the bleeding edge of open source models for a long time until Llama 4. I can't tell what happened there. Speaker B: Yeah. Speaker A: Um, but you know, I used to use a lot of open source Llama models and now I don't anymore.

Uh, it's a Gemma model. And so I think that's, there's. There's some catch-up to do there. Uh, but given the amount of talent influx into Meta, I think give them 6 to 12 months and I think we'll see some pretty spectacular results. Amazing. Speaker B: Well, uh, we always like to sort of wrap up episodes with a few more philosophical questions. So, uh, I think these will be fun. If you had unlimited resources and no operational constraints, what experiment would you like to run? Speaker A: I would build a computer that was entirely AI.

Mm. Speaker B: Like a PC? Speaker A: Yeah. The only thing you could do is talk to it. Speaker B: Mm. Speaker A: I think that'd be a lot of fun. Speaker B: That would be a lot of fun. Would you have a screen at all for output? Speaker A: I think you need a screen sometimes, but not all the time. So I would optimize it for voice. And then when you need to see an image, maybe you have, uh, like Her, right? Like he pulls out a little screen from his pocket to look at a photo, but then he puts it back.

And it's all voice. And I think, I remember meeting this brilliant founder from MIT who was designing a, he was designing a human-computer interaction system for disabled people using their tongue. So he gave you a retainer that would go on the top and then you would manipulate the mouse by using the tip of your tongue against it. And I think we need to reinvent, that was just an inspiration for a completely new form of human-computer interaction. I think there's an opportunity to completely transform human-computer interaction again. And so I think it'd be a lot of fun to embark on that journey and figure out what breaks, where the sharp edge is.

Speaker B: Yeah, I 100% agree. Have you played with any of the sort of, I don't know, new AI hardware, consumer products like, uh, you know, I'm thinking Rabbit or Humane, you know, back in the day and stuff like that. Speaker A: Yeah. I mean, they're all steps forward, right? It's like the Magic Link before the iPad. Speaker B: Yeah, I 100% agree. Have you played with any of the sort of, I don't know, new AI hardware, consumer products like, uh, you know, I'm thinking Rabbit or Humane, you know, back in the day and stuff like that.

Speaker A: Yeah. I mean, they're all steps forward, right? It's like the Magic Link before the iPad. Speaker B: And yeah. Speaker A: Uh, I don't, I think glasses are probably the new form factor. And I'm really excited about the Meta Orion glasses, the little projector and the fact that they listen all the time and the AI is there and it doesn't look like a phone. But the reason I brought up the story about the MIT founder is he says, I want to make sure that people spend just as much time with computers, but less time on screens.

Hmm. Speaker B: Hmm. Yeah. That's also, you know, using the sort of tongue movements. You can even imagine that for people who don't have disabilities, like, you know, there will need to be some sort of subvocal version of these discussions if these become our dominant computers, right? You know, whispering or whatever it is. Speaker A: Another friend of mine said, he had this really great prompt. It was Nikunj, sorry. Nikunj Guattari said, do you believe you're biotic? And I said, no. And he said, well, when was the last time you forgot your phone anywhere?

You already have a computer attached to you. Speaker B: Yeah, totally. Speaker A: So we're close. Speaker B: Yeah. Yeah, absolutely. What's a tradition or practice from either another culture or time period that you think we should more widely adopt? Speaker A: I was listening to a YouTube video of a Shaolin master, and it's been banging around in my head since then. He said, "When you eat, you eat. When you work, you work. When you sleep, you sleep." And so that single focus, I think it's harder and harder, but I think it's really important.

So I would love to see us do more of that, but it's hard. Speaker A: I was listening to a YouTube video of a Shaolin master, and it's been banging around in my head since then. He said, "When you eat, you eat. When you work, you work. When you sleep, you sleep." And so that single focus, I think it's harder and harder, but I think it's really important. So I would love to see us do more of that, but it's hard. Speaker B: Yeah, that level of presence is something to aspire to for sure.

Okay, sort of last question. If you had the power to assign a book to everyone on Earth to read and understand, what book would you choose? Speaker A: One of the most formational books in my life is this book called Narcissus and Goldmund, and it's written by a German guy or Austrian guy named Hermann Hesse, and he was an European who brought Eastern philosophy to the West. And the book Narcissus and Goldmund is a story of two boys who meet at a monastery in the Middle Ages, and they go through very different lives, and then they meet on the deathbed of one of them, and they reflect.

And I don't, I don't, there was an amazing lesson in there. I've read it. My aunt gave it to me when I was 14 or 15. I have never forgotten that book. And there's an amazing lesson in there, which is there are a thousand different ways to live your life. And at the end, it's, you kind of meet the people, you know, you, you meet again and you realize like you kind of get to the same place. And so it's all about choosing that path. Just be very deliberate about the path that you choose.

And so I, that was just because one of them is an ascetic. He, he abstains from everything and the other is a bon vivant. He lives life to the max and they end up in the same place. I mean, for that moment in time, but I would highly recommend that book. Speaker B: I'd never heard of it. I've read some of Herman Hesse's other books, but not that one. So that's an awesome recommendation. Thank you so much for doing this, Tomáš. It's been amazing to chat. And yeah, I'm so grateful for you sharing all of your knowledge with me.

Speaker A: Oh, pleasure was mine, Mario. Thanks for having me on the show. Speaker C: That's it. Thank you for listening to this episode of The Generalist Podcast. Please subscribe on Apple Podcasts, Spotify, or your preferred podcast app. Ratings and reviews help others discover these discussions, so if you enjoyed the conversation, I'd be grateful if you could take a moment to leave one. For all past episodes and more, visit us at com. See you next time as we continue to explore the future.