The Decade of Data (Tomasz Tunguz)

Tomasz Tunguz has spent almost two decades turning data into investment insights. After an impressive run at Redpoint Ventures, where he backed Looker, Expensify, Monte Carlo, and more, Tomasz launched Theory Ventures in 2022. His debut fund, which closed at $238 million, was followed 19  months later by a $450 million second fund. Theory’s goal is simple but striking: to build an “investing corporation” where researchers, engineers, and operators sit alongside investors, arming the partnership with real‐time market maps, in‑house AI tooling, and domain expertise. Centered on data, AI, and crypto infrastructure, the firm operates at the very heart of many of today’s most consequential technological shifts. In our conversation, we explore: • How Theory’s “investing corporation” model works • Why crypto exchanges could create a viable path to public markets for small-cap software companies • The looming power crunch—why data centers could consume 15% of U. S. electricity within five years • Stablecoins’ rapid ascent as major banks route 5‑10% of U. S. dollars through them • Why Ethereum faces an existential challenge similar to AWS losing ground to Azure in the AI era • Why Tomasz believes today’s handful of agents will become 100+ digital co‑workers by year‑end • Why Meta is betting billions on AR glasses to change how we interact with machines • How Theory Ventures uses AI to accelerate market research, deal analysis, and investment decisions • Much more — Thank you to the partners who make this possible Brex https://www.brex.com/mario: The banking solution for startups. Generalist+ https://thegeneralist.substack.com/: Essential intelligence for modern investors and technologists. — Transcript: https://www.generalist.com/p/the-decade-of-data-with-tomasz-tunguzh-h-tomasz-tunguz — Timestamps (00:00) Intro (03:37) Tomasz's background and journey to founding Theory Ventures (04:57) Theory's data-driven approach to venture (09:25) The importance of functional experts on investment teams (11:45) Theory's focus on data systems and why they matter (15:02) The decade of data (18:40) The challenges of chip-level investment (20:11) The state of crypto (23:30) How crypto could accelerate IPO timelines (28:12) Tokenized secondaries and venture capital’s evolution (32:18) Ethereum's challenges against competitors like Solana (37:25) Hyperliquid (38:38) The shift from proof of work to proof of stake to proof of authority (41:20) How Tomasz uses AI for personal productivity (45:03) How Theory Ventures uses AI internally (49:41) The future of work with AI agents (52:42) The current state of the AI race between Microsoft, Google, and Meta (58:28) Final meditations — Follow Tomasz Tunguz Website: https://tomtunguz.com/ LinkedIn: https://www.linkedin.com/in/tomasztunguz/ X: https://x.com/ttunguz — Resources and episode mentions —Book— • Narcissus and Goldmund: https://www.amazon.com/Narcissus-Goldmund-Hermann-Hesse/dp/0553275860 —People— • Patrick O’Grady on X: https://x.com/_patrickogrady —Other resources— • Theory Ventures: https://theory.ventures/ • Insight Partners: https://www.insightpartners.com/ • Palantir: https://www.palantir.com/ • Databricks: https://www.databricks.com/ • Snowflake: https://www.snowflake.com/ • Ethereum: https://ethereum.org https://ethereum.org/ • Solana: https://solana.com/ • Jamie Dimon blasts crypto, tells Senate he would ‘close it down’—even as JPMorgan pushes forward with blockchain payments: https://fortune.com/crypto/2023/12/06/jamie-dimon-crypto-senate-hearing-jpmorgan-blockchain-payments/ • Robinhood: https://robinhood.com/ • Coinbase: https://www.coinbase.com/ • Uniswap: https://app.uniswap.org/ • Sarbanes-Oxley Act: https://en.wikipedia.org/wiki/Sarbanes%E2%80%93Oxley_Act • ASX: https://www.asx.com.au/ ... Episode resources continued at: https://www.generalist.com/p/the-decade-of-data-with-tomasz-tunguzh-h-tomasz-tunguz — Production and marketing by penname.co https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  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.

• 0:16Mario Gabriele?

  How does this change end up accelerating the timeline to IPOs for some of these software companies, do you think?

• 0:21Speaker 00

  We have made it so expensive to go public that if you're a micro-cap 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?

• 0:45Mario Gabriele?

  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. 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 Tomasz Tunguz, the founder and managing partner of Theory Ventures. In just two years, Theory has raised nearly $700 million in capital and backed promising startups across AI, crypto, data analytics, and data infrastructure. As Tomasz 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

• 1:30Mario Gabriele?

  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.

• 1:45Mario Gabriele?

  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 Tomasz 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 Tomasz. 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

• 2:25Mario Gabriele?

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• 3:08Mario Gabriele?

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• 3:38Mario Gabriele?

  All right. Tomasz, it's so good to have you here. I've been an admirer of your writing and your public thinking, basically since I first learned about what venture capital was, 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 running Theory Ventures?

• 4:02Speaker 00

  Yeah, I, when I was 17 and started a company with my dad, it got me into startups. I was fascinated that you could start a company. And then after college, I studied mechanical engineering and computer science. And then I went to work for a startup that was founded by three 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 an angel investor talking to a founder outside at a restaurant. I was like, oh, my gosh, I can't believe that's a career. I hope I get to do that one day.

• 4:45Speaker 00

  And I was very lucky that I had the opportunity to start and now have a firm of our own called Theory.

• 4:53Mario Gabriele?

  And Theory, I think, is two, two and a half years old. Is that about right?

• 4:56Speaker 00

  Exactly right. Yeah.

• 4:58Mario Gabriele?

  Okay. 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 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 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?

• 5:23Speaker 00

  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, 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. 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.

• 6:07Speaker 00

  And that was a big inspiration. And, you know, I look at Some of the hedge funds that have done really well, they build information asymmetry with compounds over time. I think the same thing is true in venture. The data sets are obviously quite different, but if we build the internal systems of knowledge the right way, then I think over long periods of time it compounds.

• 6:25Mario Gabriele?

  That's really interesting. I would say that one of the maybe more common ways of thinking about venture would be that these asymmetries get sanded away super fast because things just get competed away really, really quickly. What are the types of asymmetries that you see as actually being compounding or having the ability to compound for you guys?

• 6:48Speaker 00

  The real asymmetry is just is getting there earlier.

• 6:54Speaker 00

  I mean, you have the venture capital asset class has grown from eight 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. 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

• 7:39Speaker 00

  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.

• 8:06Speaker 00

  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, three 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. 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 requirements? And that that kind of investing is fun because out of that Monday, there's a lot of energy, right?

• 8:51Speaker 00

  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 are increasingly sophisticated on many different spaces.

• 9:26Mario Gabriele?

  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. 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?

• 9:51Speaker 00

  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 six or seven 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, two or three 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?

• 10:36Speaker 00

  But we have kept this notion of a partnership. And there are many different reasons for that. But I think ultimately, we are off. If you look at hedge funds, they're structured as investing corporations. If you look at private equity firms, they're, they have investing teams and ops, 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. 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.

• 11:21Speaker 00

  And we need those conversations and that understanding to happen very quickly. 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, 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 a market.

• 11:45Mario Gabriele?

  Super interesting. I'm also interested to, to dig in a little bit deeper onto this, this thesis development piece. It sounds like, you know, on the one hand, You are constantly generating these theses as a, 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 data, AI, crypto, there you have, you know, much sharper lenses around those different things. But 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?

• 12:26Mario Gabriele?

  So you don't get stuck, you know, chasing a dead end or chasing something that's so, so broad that you, you know, would really actually end up having no edge?

• 12:36Speaker 00

  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 and they are sold to different buyers.

• 12:56Mario Gabriele?

  That's really interesting. So then what is the deep, the deep interest in data systems there? Like, why, why is that the thing that you're like, this, this is the big opportunity for us?

• 13:06Speaker 00

  Well, I love data.

• 13:07Mario Gabriele?

  Yes. Part of it is personal, right?

• 13:09Speaker 00

  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 Deca coins. Maybe we'll see them becoming sent to coins within the world of AI, right? You have Anthropic and OpenAI, many others that will be crowned Deca coins. 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 space models?

• 13:51Speaker 00

  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's requires a particular set of expertise and a desire to understand it. And so the combination of those two things, really, really large markets, the sort of 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.

• 14:35Speaker 00

  So in venture capital, we might invest in a company and, and work with that company for, for five, 10, maybe 15 years. But ideally the, the cycles, 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 in one's investment career.

• 15:02Mario Gabriele?

  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, as something to spend your time on? What is the elegance of these business models that like really animates something in you?

• 15:26Speaker 00

  Well, it's all understanding. I remember being in, so I had this amazing professor in grad school. His name was Ming Fang and he, I was, this is in a class, 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 you can, and that six actuators move the receiver. And you have to move that receiver in real time to maximize the signal, given earthquakes and the earth, and the earth moving.

• 16:09Speaker 00

  And so, you know, whatever, 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. The same thing, you know, he was doing something in the stock market. And so it's kind of like fracking, 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.

• 16:51Speaker 00

  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 frac data. We've been taking this raw, unstructured data, the Word documents, the conversations that you and I have, the podcasts, blog posts, and we're able to run it through an extremely

• 17:34Speaker 00

  inefficient system, but actually get something useful out of it with AI.

• 17:37Mario Gabriele?

  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 are sort of some of the other things that like make you feel like we're really at a potential inflection point for, for this magical material?

• 17:52Speaker 00

  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, their capacity constraint, 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.

• 18:15Mario Gabriele?

  Wow.

• 18:15Speaker 00

  And so

• 18:16Mario Gabriele?

  Wow.

• 18:17Speaker 00

  When you get a sense of this scale of the, the computers, the data centers, but the really computers that we're building, I was reading a research report, we think in five years, 15% of American electricity, which is growing, the demand, 15% will be used to feed data centers. That gets me, that gives 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.

• 18:42Mario Gabriele?

  Given, you know, what you said about your intern Alex, who was talking about, uh, you know, Things down to the chip level, do you look 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 afield?

• 18:57Speaker 00

  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 two or three publicly traded companies,

• 19:40Speaker 00

  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.

• 20:08Mario Gabriele?

  Yeah, hard to break in 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, you know, a lot of the mania that surrounds it. You know, a decade, decade and a half in, depending on how you want to, you know, 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?

• 20:37Speaker 00

  I think it's broadly underappreciated, and it's been extraordinarily massive.

• 20:43Speaker 00

  I mean, you look at Bitcoin for a new generation is gold. It's the equivalent of gold. People trust it more. And that I think would have 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 two or three. 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 U.S. of stable coins.

• 21:17Mario Gabriele?

  Yeah, yeah.

• 21:19Speaker 00

  Absolutely. I mean, by the end of the year, I bet most major large banks in the U.S. have their own stable coin project, a pretty significant fraction. Five to 10% of U.S. dollars moving through systems will likely be through stables. And I think two years ago, that would have been unfathomable.

• 21:38Mario Gabriele?

  Yeah, that seems like it's happened insanely fast. Like, I remember writing about it just, yeah, probably about two or three 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.

• 21:59Speaker 00

  Right. Jamie Dimon said very famously, crypto is rat poison. And now JP Morgan has an internal blockchain where they move one to $10 billion a day on it for international settlement. So we've come a long way. You know, and then I think 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 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.

• 22:40Speaker 00

  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, 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.

• 23:12Mario Gabriele?

  Yes.

• 23:13Speaker 00

  But 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.

• 23:42Mario Gabriele?

  Oh, wow. Interesting. I want to unpack both of those quickly. But the first part, like you say, they should fuse. Can you explain to folks like why that almost is sort of an inevitability that, you know, these things are going to come together from, from like an architectural advantages standpoint?

• 24:00Speaker 00

  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, a 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.

• 24:44Speaker 00

  And the equities market is coming. The other thing that's happening is publicly traded software companies trade on multiples, right? We're talking about forward multiples, EBIT, EBIT to NTM. And for a long time, crypto tokens did not. I mean, you look at some of these multiples, they were in the tens of thousands of times of revenue.

• 25:01Mario Gabriele?

  Yes.

• 25:02Speaker 00

  And over the last six months. Certain categories of crypto tokens are now trading at 40 to 60 times revs.

• 25:10Speaker 00

  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 S1, right? But it's coming.

• 25:40Mario Gabriele?

  How does this change end up accelerating the timeline to IPOs for some of these software companies, do you think?

• 25:45Speaker 00

  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?

• 26:00Mario Gabriele?

  Gosh, I honestly have no idea. I would put it in those sort of like single-digit millions.

• 26:04Speaker 00

  15 to 25 million.

• 26:06Mario Gabriele?

  Okay, wow, I'm off by a, by a good chunk there.

• 26:09Speaker 00

  Okay, so if you're a hundred million dollar revenue company, is it rational to raise around a financing that costs you 15 to 25 million?

• 26:16Mario Gabriele?

  Yeah, totally. Wow.

• 26:18Speaker 00

  So to put it in perspective, if you were going to raise a Series X, very late-stage round,

• 26:24Mario Gabriele?

  Yeah.

• 26:24Speaker 00

  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 this step up of like three or four 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 needed 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 banana, just raise it in the private markets.

• 26:58Speaker 00

  And so that's why you look at the average growth of publicly traded companies. It's asymptoting to just maybe to 10% basically, which is software inflation. Because it's so, it's so expensive to go public.

• 27:13Speaker 00

  So why is it so expensive? Well, there's just a lot of regulation, right? Sarbanes-Oxley, all that kind of stuff.

• 27:19Speaker 00

  And so if you look at crypto exchanges, the regulation there is much less. There's no S1, 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 micro cap software company. If you were somewhere between one and a half to two 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 Chianext in China, or the ASX in the UK, or TSX, or the rumors of the Texas Stock Exchange,

• 28:01Speaker 00

  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 nice, than the New York Stock Exchange or the NASDAQ.

• 28:13Mario Gabriele?

  Wow, how interesting. You also mentioned, you know, the Robinhood OpenAI example, which is more like, you know, secondary's trading, which feels like an exciting, you know, other set of, another asset class that sort of 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, like who owns it? How do you see that ending, like ending up playing out?

• 28:37Speaker 00

  Well, I, I've been reading online, trying to understand because Robinhood mentioned that OpenAI took an I 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.

• 29:08Speaker 00

  It's not clear.

• 29:09Mario Gabriele?

  Unclear. Yeah, we'll see how it all shakes out, I suppose.

• 29:11Speaker 00

  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 four 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,

• 29:55Speaker 00

  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 pay it in is much faster, which makes subsequent fundraising much faster. There's specialization on individual stages. And so the cycle of money basically just moves a whole lot faster. We're nowhere close to that, right?

• 30:31Mario Gabriele?

  Yes.

• 30:31Speaker 00

  We talked about venture capital is a $250 billion asset class. In the last three 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. 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 three or four years. Series A funds are liquidating, maybe not the entirety, but some fraction thereof.

• 30:55Mario Gabriele?

  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 tiering box at just a teeny fraction of the cost, just $22 a month or $220 annually. So what's included? One, tactical interviews where elite founders and investors reveal their actual strategies and decision frameworks.

• 31:26Mario Gabriele?

  Two, comprehensive guides that distill hundreds of hours of research into actionable insights on investing and company building. Three, an exclusive database of emerging startups poised 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 Plus 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

• 32:06Mario Gabriele?

  who are gaining an edge in understanding markets, technology, and business fundamentals by visiting thegeneralist.substack.com. That's thegeneralist.substack.com. 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. 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. How have you sort of looked at that? How do you see its place in the ecosystem? You know, I do

• 32:51Mario Gabriele?

  think there's such a clear story around Bitcoin, as you said, and, and a clearer one around Solana. But, you know, are people too down on Ethereum? Like, how, how do you think about its potential today?

• 33:01Speaker 00

  Yeah, I love, you had a brilliant point in there. You said Ethereum soul searching. So just for the audience, Solana's nickname is Soul.

• 33:07Mario Gabriele?

  There you go. That's right.

• 33:09Speaker 00

  Right. And so the major competitor to Ethereum is Solana. So I think Ethereum is

• 33:17Speaker 00

  Kind of 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 D5 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 twos, 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

• 34:02Speaker 00

  and saving you 90%. Just to 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 as the ex-Men of blockchain team, one of the two. And so within the span of three years, you had two 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 used, so if I used an L2 like Arbitrum, it might cost me $2.50.

• 34:45Speaker 00

  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. Yeah. And so as a result, over time, while developers will build on the systems that are more efficient, much better price performance characteristics. Nobody wants to pick those fees. And so that's where we found ourselves with ETH, maybe three to four months ago. And then people started, all of a sudden. Waking up. Yeah, the satisfaction, the community decided, okay, we need to, we need to react. So they raised $150 million and Vitalik is more involved. And

• 35:23Speaker 00

  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? Robinhood has a special relationship with Hyperliquid. And now, all of a sudden, you have bigger strategic relationships that change the builders' perspectives on which platform to build.

• 36:07Speaker 00

  And so Ethereum finds itself where AWS is or was six or seven months ago. Which is, there's been this huge wave of innovation. Other people have pushed, and now the developer mindset is shifting.

• 36:18Mario Gabriele?

  As you think about its comparative advantages and what should be the real advantages of the project, like, what, what do you think that should look like? Like, what do you see as the, the, 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?

• 36:40Speaker 00

  The single most important thing in any financial market is liquidity. And Ethereum has it now, right? So the question is, how do you capture that liquidity? Well, stable coins 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 did they do with the L2s? How did they think about the consensus infrastructure? And then the reward systems for the validators? Dick is a really, basically, how do you architect your entire system?

• 37:10Mario Gabriele?

  Yeah.

• 37:11Speaker 00

  They'll have to, I think, revisit a lot of the core assumptions because underlying technology has changed. And that's okay. But I think a lot of times in In the world of Web 3, we have attachments to certain ideas and over time they change.

• 37:25Mario Gabriele?

  Really interesting. As you, as you sort of look at the, the crypto Web 3 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 uh new sort of pieces of infrastructure that you think are, you know, worth discussing that aren't being as discussed right now?

• 37:43Speaker 00

  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-deck of coin business that's now partnering with Phantom, right? So Phantom now you can trade perps and it's all powered through Hyperliquid.

• 38:25Mario Gabriele?

  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.

• 38:32Speaker 00

  Really cool. To, you know, $30 billion bootstrap.

• 38:36Mario Gabriele?

  Yeah, yeah. Pretty amazing.

• 38:38Speaker 00

  Brilliant. 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, a lot of foresight, and they deserve a lot of credit for the, the company that they've built. So I think the vertical integration is one really interesting trend. I think, you know, one, one, another big question is, are we moving from proof of work to proof of stake to proof of authority?

• 39:04Mario Gabriele?

  Can you explain those for folks just very briefly? I know that's not, not the easiest, but uh in simple terms.

• 39:10Speaker 00

  Yeah. So Bitcoin is proof of work. If you want to earn money by um minting Bitcoin, you need a really big computer. And you, 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.

• 39:29Speaker 00

  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.

• 39:45Mario Gabriele?

  Yeah.

• 39:45Speaker 00

  That's what it is.

• 39:46Mario Gabriele?

  Backstopping you in a way.

• 39:48Speaker 00

  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's 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 don'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.

• 40:30Mario Gabriele?

  But ending up with more efficiency as the sort of benefit of it. You know, you trade off decentralization for for speed, efficiency, et cetera.

• 40:39Speaker 00

  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, 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, if 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.

• 41:21Mario Gabriele?

  Amazing. Well, I want to talk about AI a little bit. We've talked a little bit about it, but I, 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?

• 41:43Speaker 00

  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 you can just ask it a question like you would on Claude.com, Claude.ai, whatever the domain is. And you can ask it, you know, what is the difference between, this is a query from last night, SEAL Team Six and Delta Force? I had no idea. And it will answer you. But then you can also tell it, hey, look through all of my files and see if you can find this blog post from two years ago.

• 42:19Mario Gabriele?

  Oh, wow.

• 42:20Speaker 00

  It's another very basic thing you can do.

• 42:23Speaker 00

  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 will write, I asked it just to write the code itself and then it will go into my Google Calendar and, and then it'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, 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?

• 43:07Speaker 00

  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?

• 43:13Mario Gabriele?

  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 one tab that, that's doing all these things for you. What are the limits that you've discovered a bit?

• 43:27Speaker 00

  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, it's like training a brand new person. How do you work? So there is a lot of constant feedback.

• 44:10Speaker 00

  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.

• 44:21Mario Gabriele?

  Wow, but it's great if you, you know, you only have a couple of weeks to figure it out. You think by then you'll have a reasonably definitive answer.

• 44:29Speaker 00

  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 I 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 Adam, our great software engineer, he released a library for the CRM yesterday that's available to AI.

• 45:04Mario Gabriele?

  Okay, this is exactly where I wanted to go next, which is how are you using AI within the firm? Because I know that you guys are, yeah, a very technical team and thinking about how to architect the firm in a way that, you know, it involves a decent amount of engineering.

• 45:18Speaker 00

  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 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 would trust. But you can kind of, you can, we're starting to assemble all of those pieces, memo generation, deck extraction, those kinds of things.

• 46:02Speaker 00

  And reality is, none of these technologies are flawless. And they're unbelievable.

• 46:10Speaker 00

  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 three, three times as many spaces or four 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?

• 46:41Mario Gabriele?

  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?

• 47:11Speaker 00

  Yeah, I think summarization is really good. 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, it's quite good. Ontology generation, it's, 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 protocols, server, next generation API servers, basically, classify them and segment them. Or you wanted to look at an even broader universe, AI security companies.

• 47:53Speaker 00

  Well, there's data loss prevention companies, firewalls, prompt injection company. Anyway, so it's also really good at that.

• 48:01Mario Gabriele?

  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, you know, to figure out what is the optimal size. You know, what are the other ways you rely on data or this type of simulation to make sort of like? Key strategic decisions around the contours of the firm.

• 48:30Speaker 00

  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 they're 75th and 90th percentile are higher. So it's a higher vault, you know, different kind of return profile.

• 48:54Speaker 00

  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 then, yeah, it's 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, 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?

• 49:33Speaker 00

  We're not buying, we're not buying satellite informations, satellite images over retailers to predict quarterly earnings.

• 49:41Mario Gabriele?

  In your sort of introductory post about Theory Ventures, you talked about, you know, investing in machine learning as a force multiplier, which feels like the sort of 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?

• 50:01Speaker 00

  Let me ask you a question. Okay, so on a, on a manufacturing assembly line for cars, how many humans does a robot replace?

• 50:11Mario Gabriele?

  I would guess two, one? I don't know.

• 50:15Speaker 00

  Two and a half.

• 50:16Mario Gabriele?

  Two and a half? Yeah, okay.

• 50:18Speaker 00

  In, in an Amazon distribution center, so that was my mental model for AI. AI would kind of replace two and a half people inside of a company. Amazon then released this stat, this is three or four months ago, where they said in their distribution centers, their warehouses.

• 50:34Speaker 00

  One robot replaces 20 humans.

• 50:36Mario Gabriele?

  Wow, gosh.

• 50:37Speaker 00

  Why is a robot in a DC that much more productive than a robot on an assembly line? Because robot on an 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 two? 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 two to four. And the highest number I've heard is 15.

• 51:18Speaker 00

  I bet within the next 12 months, we will see workers managing 100 agents working simultaneously.

• 51:25Mario Gabriele?

  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?

• 51:31Speaker 00

  They have a task list.

• 51:32Mario Gabriele?

  Uh huh.

• 51:33Speaker 00

  They create, it's a software engineer who creates a task, a PR 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 reviews done, right? 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 a customer support rep. And so, I don't know, I think the question in my mind is, and this is the reason why I'm playing around with AI on the computers. How do I get to a place where I can manage 50 times more agents than I can today? And right now, you still need to monitor and watch and you're watching the readouts and making sure that it's working okay.

• 52:12Mario Gabriele?

  Yeah.

• 52:13Speaker 00

  But there, there has to be a system, there has to be one, an inbox, 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 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.

• 52:31Mario Gabriele?

  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.

• 52:41Speaker 00

  Right, exactly right.

• 52:43Mario Gabriele?

  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? Yeah, in the 18 months since?

• 52:54Speaker 00

  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, the Gemini models are all either number one or number two. So they're meaningfully, I mean, a huge leap. I think the Google is, I mean, Google is clearly subsidizing a lot of their AI products to drive usage. So they're maybe 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

• 53:39Speaker 00

  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's down 70, 75%. And other 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.

• 54:21Speaker 00

  Chrome, a browser, 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.

• 54:38Mario Gabriele?

  Yes.

• 54:39Speaker 00

  So I think on the consumer side, they're in a great position. On the enterprise side, they're still, they have unbelievable products and they're solving the distribution, which is the classic Google strategic challenge.

• 54:50Mario Gabriele?

  Which are the models that you end up using most often?

• 54:53Speaker 00

  I use a lot of open source models. I use a lot of the Google models because they're free, but it's different for different things. So Gemini is my default model. OpenAI, I think,

• 55:04Speaker 00

  is great at the deep research product. I really like.

• 55:07Mario Gabriele?

  Yeah, agreed. Yeah, it's really good.

• 55:09Speaker 00

  Claude, I used to use a lot for writing and now when Gemini cannot solve a coding problem, Claude fixes it every time.

• 55:19Mario Gabriele?

  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 aggressive moves that Zuckerberg's been making and, you know, especially on the hiring front. And then on the, you know, to that end, like where do you see Meta in this? They've obviously taken a very different approach.

• 55:38Speaker 00

  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 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 I

• 56:23Speaker 00

  think their pricing power is underappreciated. We, you know, 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 is there.

• 56:41Speaker 00

  But I they used to have a trillion dollar market cap company in their corner and 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 what the long-term positioning is. But ultimately, the brand and the distribution is unbelievably valuable.

• 56:57Mario Gabriele?

  And, you know, by comparison, how do you think about these, you know, aggressive meta hiring moves and how it sort of positions itself? Because it's had to play a bit of catch-up and is, you know, doing more of the open source stuff, but it obviously has so much of that distribution it can rely on.

• 57:11Speaker 00

  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?

• 57:22Mario Gabriele?

  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.

• 57:29Speaker 00

  Yeah, I mean, just overt frontal assault, right? And I'm coming.

• 57:36Mario Gabriele?

  Yeah, you have to respect that.

• 57:37Speaker 00

  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 three to 5% of the company because that's the new. And so I just love the aggression. I think it's it makes business fun to see people conduct business that way.

• 57:57Mario Gabriele?

  Yeah, go that hard.

• 57:59Speaker 00

  Yeah, I think he, you know, the Llama were the lead bleeding edge of open source models for a long time until Llama 4. I can't tell what happened there.

• 58:10Mario Gabriele?

  Yeah.

• 58:11Speaker 00

  But, you know, I used to use a lot of open source Llama models and now I don't anymore. It's a Gemma model. And so I think that's there's there's some catch-up to do there. But given the amount of talent influx into meta, I think give them six to 12 months and I think we'll see some pretty spectacular results.

• 58:28Mario Gabriele?

  Amazing. Well, we always like to sort of wrap up episodes with a few more philosophical questions. So I think these will be fun. If you had unlimited resources and no operational constraints, what experiment would you like to run?

• 58:42Speaker 00

  I would build a computer that was entirely AI.

• 58:45Mario Gabriele?

  Hmm, like a PC?

• 58:47Speaker 00

  Yeah, the only thing you could do is talk to it.

• 58:49Mario Gabriele?

  Hmm.

• 58:50Speaker 00

  I think that'd be a lot of fun.

• 58:52Mario Gabriele?

  That would be a lot of fun. Would you have a screen at all for output?

• 58:55Speaker 00

  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 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 us. He was designing a human computer interaction system for disabled people using their tongue. So he gave you a retainer that would go in 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.

• 59:40Speaker 00

  And so I think it'd be a lot of fun to embark on that journey and figure out what breaks where the sharp edges.

• 59:44Mario Gabriele?

  Yeah, 100% agree. Have you played with any of the sort of, I don't know, new AI hardware consumery products like, you know, I'm thinking Rabbit or Humane, you know, back in the day and stuff like that?

• 59:56Speaker 00

  Yeah, I mean, they're all steps forward, right? It's like the Magic Link before the iPad and

• 1:00:01Mario Gabriele?

  Yeah.

• 1:00:02Speaker 00

  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."

• 1:00:23Mario Gabriele?

  Hmm, 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 be need to be some sort of sub vocal version of these discussions if these become our dominant computers, right? You know, whispering or whatever it is.

• 1:00:41Speaker 00

  Another friend of mine said, he had this really great prompt. It was Nikunj, sorry, Nikunj Khatari said, "Do you believe you're bionic?" 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."

• 1:00:55Mario Gabriele?

  Yeah, totally.

• 1:00:56Speaker 00

  So we're close.

• 1:00:57Mario Gabriele?

  Yeah, yeah, absolutely. What's a tradition or practice from either another culture or time period that you think we should more widely adopt?

• 1:01:04Speaker 00

  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.

• 1:01:20Mario Gabriele?

  Yeah.

• 1:01:20Speaker 00

  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.

• 1:01:29Mario Gabriele?

  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?

• 1:01:39Speaker 00

  One of the most formational books in my life is this book called Narcissus and Goldman. And it's written by a German guy or Austrian guy named Hermann Hess. And he was an European who brought Eastern philosophy to the West. And the book Narcissus and Goldman 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 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.

• 1:02:24Speaker 00

  And at the end, it's, you kind of meet the people, you know, you meet again and you realize like, you kind of get to the same place. So it's all about choosing that path. Just be very deliberate about the path that you choose. And so that was just because one of them is an ascetic. 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, I would highly recommend that book.

• 1:02:54Mario Gabriele?

  I'd never heard of it. I've read some of Hermann Hess's other books, but, but not that one. So that's, that's an awesome recommendation. Thank you so much for doing this, Tomasz. It's been amazing to chat. And yeah, I'm so grateful for you sharing all of your knowledge with me.

• 1:03:09Speaker 00

  A pleasure with mine, Mario. Thanks for having me on the show.

• 1:03:12Mario Gabriele?

  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 thegeneralist.substack.com. See you next time as we continue to explore the future.