$688 Million AUM in 2 Years Investing in Data, AI, Web3 & Blockchain | Tomasz Tunguz

Tomasz Tunguz is a prominent venture capitalist and the founder & GP of Theory Ventures, a $688M AUM fund. Theory focuses on investing $1-25m in early-stage companies that leverage technology discontinuities into go-to-market advantages. Before Theory, he was a managing director at Redpoint Ventures, where he led investments into companies such as Looker, Expensify, Monte Carlo, Dune Analytics, and Kustomer. Throughout his career, Tunguz has been recognized for his data-driven approach to investing and his very popular, personal blog, TomTunguz.com. Tomasz recently raised $450 million in November of 2024 for Theory Ventures' second fund, which marks a significant milestone for the firm. This follows his initial breakout fund, launched in 2023, with $238 million. This is a very fun conversation where we go deep into everything AI, LLM business models, predictions, and more. Molly on X: https://x.com/MollySOShea Tomasz on X: https://x.com/ttunguz Follow Sourcery for the latest updates! https://www.sourcery.vc/ TIMESTAMPS: 0:00 - Welcome Tomasz To Sourcery 1:00 - State of AI Adoption 6:45 - Hyperscaler Power Dymnmic 9:50 - AI Inference 15:00 - Monetizing 20:00 - Raising $600B 28:00 - Decade Of Data 33:30 - Parterning with Former Palantir Executive 35:45 - Tomasz's Childhood 37:40 - What Is Tomasz Looking Forward To The Most This Year #podcast #investing #technology #venturecapital #entrepreneur #startup #siliconvalley

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

• 0:00Molly O'Shea?

  Welcome to Sorcery. I'm your host, Molly O'Shea. Today, we have Tomasz Tunguz, a prominent venture capitalist and the founder and GP of Theory Ventures, a $688 million AUM fund. Theory focuses on investing $1 to $25 million in early-stage companies that leverage technology discontinuities to go-to-market advantages. Before Theory, he was a managing director at Redpoint Ventures, where he led investments into companies such as Looker, Expensify, Monte Carlo, Dune Analytics, and Customer. Throughout his career, Tunguz has been recognized for his data-driven approach to investing and his very popular

• 0:42Molly O'Shea?

  personal blog, tomtunguz.com. Tomasz recently raised a $450 million fund in November of 2024 for Theory Ventures' second fund, which marks a significant milestone for the firm. This follows the initial breakout fund, which launched in 2023 with $238 million. This is a very fun conversation where we go deep into everything AI and LLM business models. I hope you enjoy. Tomasz Tunguz, thank you so much for joining us. This is a conversation we've been wanting to have for quite some time.

• 1:19Speaker 01

  Pleasure's mine. Great to be here with you today, Molly. Thanks.

• 1:21Molly O'Shea?

  To kick things off, where will we see the biggest shift for AI transformation in 2025?

• 1:28Speaker 01

  Well, I think we have to start with Jensen's quote from CES, where he said, "The IT department of the future is the HR department of today." In other words, the IT team will be managing vast numbers of agents. And I don't think we're there yet. The accuracy of the models is not quite there yet. The reasoning, the planning, the reinforcement learning is not quite there. But I do think the year, I mean, 2025, everyone's been talking about agents. I think you'll start to see agents that actually work pretty well.

• 1:56Speaker 01

  And that I think is challenging. I think it's challenging as an investor, it's challenging as an operator, because it means reinventing all of your workflows. And that's means like learning to do your job in a completely different way.

• 2:09Molly O'Shea?

  So where exactly are we in terms of AI adoption? There's been lots of profile funding rounds over the last few years. Are companies actually delivering any value? Where are we at?

• 2:21Speaker 01

  I think one of the biggest barriers to adoptions of AIs that is really expensive, the ROI in most cases is not there. But like you, so let's start with the consumers. Let's organize it. In the consumer world, I think AI adoption is taking off in a really big way. You look at like eighteen to twenty-four year olds, something, The Verge ran a survey, seventy-five percent of those people default to generative search as a place to go. So that's already happening in a really meaningful way. I think you have like OpenAI announced three hundred million, I think, MAU on ChatGPT. So you're kind of approaching the billion user number in consumer AI. I think we'll definitely surpass that this year. And that's awesome, right? It means that Google Search Model is totally up for

• 3:06Speaker 01

  grabs. They add dollars there, complete destabilization of the SEO, SEM market. That's really exciting. A lot of market cap is now becoming loose. Within the enterprise, I'd say it's earlier still, because the use cases within the enterprise are more challenging. Why is that? Well, the range of acceptable outcomes within the enterprise are much narrower, right? I remember I generated this image of a kitty cat on a fire truck, and it was a tonka truck, it wasn't a fire truck. And the kitty cat was not, was a little, you know, five toes or whatever it was. And that's okay for a consumer use case. Maybe I'm making somebody a funny happy birthday card. But it was in the enterprise, I really need to be a cat and I really need to be a fire truck. And it has to have this brand on it. So the

• 3:50Speaker 01

  range of acceptable outcomes is much narrower. And we're starting to understand how to control those outcomes. So you have things like OpenAI structured outputs, which really kind of narrow the output of JSON or the machine code that the computer or the AI system will produce, and that will help. And we have these new architectures that That handle the error, right? So just taking a step back, I mean, these AI systems have two properties that are hard to work with. The first is they're chaotic, which means they're a little bit unpredictable. And they're non deterministic, which means if you ask it the same question twice, you will not get the same answer. And that's very, very different than classic deterministic programming where you write a function,

• 4:32Speaker 01

  add two numbers every single time you put in one and one, it will shoot two. So what we're trying to do around like, okay, let's assume we have an AI brain inside of a program. And we know that sometimes the answer is not quite right. How do we manage that error? There are lots of different techniques. So there's, we can talk about those techniques if you're interested, but there are lots of different techniques. And I think in 2025, going back to your first question, we'll make some pretty material advances there where the predictability is much better. And that will lead into, I think, 2026, where we're starting to see pretty significant ROI coming from these agents.

• 5:04Molly O'Shea?

  Let's talk about the techniques.

• 5:07Speaker 01

  Okay, so if you have, let's say you have a system that's 80% accurate, right? If you have one step, it's 80% accurate. If you have two steps, you have two times, you have two times 20% inaccuracy. And three times through, let's just run the calculation, you know, 0.8 raised to the third. That's the math that I'm doing right now, which I can't do in my head. You have a system that's only accurate 49% of the time. So you have this explosive error through multiple step processes. So how do you manage that error? Well, one technique is you set up an AI system that's then judged by a non-AI system. A non deterministic system is then evaluated by a deterministic system, a bunch of rules.

• 5:50Speaker 01

  Another system is you have two AIs, one that's judging the output of the other AI. A third system is what happens in Mechanical Turk. You ask three different AIs, maybe Claude Gemini and OpenAI, to perform the same task. That's two out of three wins. Like, that's state of the art. That's where we are. And we're trying to figure out, okay, well, for this particular use case, this is the right thing. Sorry, the fourth one is chain of thought, which is you ask an AI model. Please be, gosh, what is the word?

• 6:21Speaker 01

  There's a word that's now like, it'll come back to me. But anyway, if you tell the model to be introspective and explain to you how it's thinking and pause before it thinks, like you might tell a child, it actually performs significantly better. It will write out all the different steps and then it will give you a much better answer, particularly for more complex tasks. So those are four of the different techniques that researchers are playing around with. And we'll see which ones ultimately work in which use cases, but it's probably a combination.

• 6:52Molly O'Shea?

  I know I should have these memorized, but I'm going to run through the hyperscalers names and some more. So we have Google, Microsoft, Meta, Amazon, Apple. We now have XAI, OpenAI, Anthropic. As we move towards a more exhaustive compute environment with inference booting up, advanced agentic ecosystems, where do you see the power dynamics between hyperscalers and these, you know, independent LLMs?

• 7:19Speaker 01

  I don't know if it was a pun or not, but the word power is incredibly important. So electricity is absolutely critical. Somebody, I was talking to a friend of mine who spends a lot of time and energy and he was saying, I was asking him, compared to an electric car, how much does a GPU consume? He says about a hundred X. So if you're building a data center, if you're building a power plant for a bunch of electric cars, typically around like five megawatts. If you're building a power plant for significant data centers, like five to 30 gigawatts. And so the, the major constraint, one of the major constraints today is just energy. So you see the reinvigoration of like Microsoft tried to start Three Mile Island again, the,

• 8:00Speaker 01

  the pressure on the nuclear regulatory agency in the United States to approve modular nuclear reactors. And so I think that that's critical, just truly the energy will be absolutely essential. The next is just the capex. I mean, you have like Microsoft and Google and Meta each deploying something like 70 to 80 billion. In capital expenditure, just buying GPUs and building data centers in just next year, and they were, I think, you know, sixty, fifty, and forty-five this year so far. So amassing those GPUs, Microsoft is still compute constrained, and they project, I think, all the way through 2025, they'll still be needing to buy GPUs aggressively because

• 8:40Speaker 01

  it just cannot service all of the demand. So whoever has the best allocation with Nvidia, at least in the short term, is in a much better place. And then, and then you have the models, right? The research that's happening there, it's that there, I think it's anybody's game, but I would not have expected to be telling you here 2025 that Google has, has really been as aggressive and successful as they have been iterating through all of their different models, like the Gemini Deep Research Model, where I can ask it to summarize. I was working on a project, looking at the next generation ad networks, and I asked it to summarize all the articles with startups on adweek.com over the last 12 months and

• 9:23Speaker 01

  provide a whole bunch of information. And it took 20 minutes, but it did it.

• 9:28Molly O'Shea?

  Love what you're hearing on Sorcery VC? Unlock even more. With Passes, you'll find exclusive bonus content from this podcast, deep dives, behind the scene insights, and extra resources tailored for founders, investors, and startup enthusiasts. Head to passes.com to access everything you need to take your knowledge to the next level. Well, speaking of Jensen, Jensen predicts AI inference will be a billion times larger. This is, you know, setting the stakes, I'd say, low at this point. It's going to get really big, pretty fast. How will this impact opportunities on the early-stage side? How do you inform, or how does this inform your thesis?

• 10:11Speaker 01

  Yeah, great question. I mean, I think you've seen a thousandx reduction in overall inference costs in the last three years. I think we'll probably see another thousandx reduction. There's a paradox called Jevon's Paradox that talks about how, when the cost to do something decreases in technology, the demand actually far outstrips it. And so I think he's right.

• 10:31Speaker 01

  The reasons for this is if it's really cheap to run inference, well, you'll just do it all the time. And you won't feel guilty the way that I do when I spin up a big GPU job. But I think what it means is, and we're trying to figure out, it's like, how do you program computers in the future where you can infer at runtime? And you can decide like, okay, here's an input from a user. Do I use deterministic system to interpret it? Or do I use a non deterministic system to interpret it? Where when I'm a programmer, what part of my application is AI or part of it is not? Today, it's really expensive. We have about 200 buyers that we call theorists around the firm. And we asked them, what is the biggest adoption back blocker to AI?

• 11:15Speaker 01

  And about a year ago, it was security, but today it's cost. The office of the CFO is really pressing on AI leaders within our organization to show ROI. And there's, we talked about it's hard, but it's also expensive. There's a, I was just looking at the analysis between the smallest models, which are about four, two to four billion parameters and the very largest models, which are about 450 billion parameters. You have 100x difference in inference costs.

• 11:44Speaker 01

  There's a big push now to make the smaller models much more accurate, which they are like the 54, 54 model from Microsoft and the Gemma models from Google are incredibly small, incredibly potent. And it's just a year, basically of that research. I think we'll start to see particularly in the enterprise, small language models dominate. So what does that mean? I think overall, there's one debate, which is will enterprises buy off the shelf software or not? And if you need a custom CRM, are you hiring consultant or are you not? That question, nobody knows the answer to. I think the next question is, will every company really become an AI company?

• 12:26Speaker 01

  If a relatively modestly educated engineer can build software using AI, you know, why wouldn't that bakery also deploy it? So those are some of the bigger questions we're trying to answer.

• 12:37Molly O'Shea?

  You mentioned doing some research on the advertising model side. This may be a little tangential, but how do you feel about the signal of perplexities move into the ad monetization space for their business model? And can they and the likes of Anthropic compete on the revenue side against the revenue and share, market share of OpenAI and hyperscalers?

• 13:02Speaker 01

  Yeah, it's a good question. I mean, Google's dominated search, right? I think 90% plus market share in the US. I think it's like 99% in Europe and elsewhere. And the, the search ads, part of the business is by far the most profitable because they don't have to split the revenue with anyone like they would with content ads or AdSense or Publisher Network, I think they call it now. So, but now you have hundreds of millions of people using perplexity and, and OpenAI and Claude for search. And so it completely upends the ad model. I mean, what is search engine optimization, excuse me, in a world of AI? Nobody knows yet because the, the, you know, what is the right answer? Is it consistent? Is it not?

• 13:45Speaker 01

  What is the role of a marketer influencing the answer? How do you surface that to users in a way that maintains the credibility of the overall result? Those are questions that we're really sort of thinking through. There's 250 billion in search engine marketing spend every year. And so all of that is now totally up for grabs. There's another dynamic at play, which is many of the younger generations are now starting with video as the dominant form of search rather than text. And so there's a shift, like, uh, social media advertising for the first time ever surpassed search engine, uh, or SEM in 2024, roughly 250 billion each. So there's, there might be a generational shift there. The ad models are totally unclear.

• 14:29Speaker 01

  I mean, if you're working with AdWords, the way that I would build a, an ad for theory would be to create a list of keywords that might be relevant that people might search for. That doesn't really exist in the world of generated AI. One idea we've been kind of toying around with is, you know, advertisers inject information into the context window, which is the, like the RAM effectively of a machine learning system a bit on those keywords, but it's. It's really exciting and totally unpredictable where it'll end up.

• 15:00Molly O'Shea?

  Yeah, we've, um, we've been researching into that a little bit and it seems like even the feeds going to the feed side, whether it's AdSense, they're constantly changing and they're reorienting them and making it really harder to push through and monetize like it was before. And the arbitrage business of advertising is just constricting. And you're seeing that with some of the public advertising players and where their revenue is going.

• 15:24Speaker 01

  Yeah. So I, you know, there's, we're talking about hundreds of billions of dollars changing hands and change might be changing share.

• 15:29Molly O'Shea?

  Yeah, it's gonna be, it's gonna be wild to watch, but we'll see what happens. So shifting over to AGI, because we all love AGI. I just think this is like a silly question to always ask, but you know, if we are one to three years away from something like AGI, which could plausibly replace human labor for some set of tasks. And these tasks can range from like healthcare to, you know, operating a bakery shop or, you know, maybe some more like computer work. Do you think this is a Jevons paradox situation where efficiency gains lead to more consumption? Where, where are you thinking?

• 16:09Speaker 01

  Yeah, I think it's really exciting. Um, I think, uh, I mean, one of the, one of the interesting questions is like, what is AGI, right? It's like, is it talking to an AI that's just as sophisticated as a high school student? In which case, most models are 80% as smart as a high school student. The OpenAI newest model has PhD level knowledge on many different topics, including like math and programming. And so there's like the knowledge part, you know, many different kinds of intelligence. Knowledge retrieval is one, and then there's the reasoning part. I think, once we get to a place where.

• 16:48Speaker 01

  The AI is no longer like an intern, but is more like an employee. Then we'll start to see some pretty significant productivity gains. And one of the things that's missing there is long-term memory, right? Molly, you and I are talking and I learned something about you. Like, I don't know, your favorite kind of food is Italian and you really like penne. I'm going to remember that because it's memorable and an AI system will forget like your goldfish's memory. And so we're working on that kind of long-term memory, which will be absolutely critical to having sort of longer-term workers. But I don't think, I think the technology will be here much sooner than anyone can take advantage of it. Because all of us have to look at the way that we work, the way that we answer emails

• 17:30Speaker 01

  or make our outbound sales calls or put together presentations and learn again in a completely different way how to put them together. Like I was, I delivered a presentation last week and tried to do it in a completely new way. I went to one of the AI systems. I said, I need to put together a presentation in two days or whatever it was. And it needs to be 15 slides and here's the context. And I want you to write it in the format that my management coach taught me. And like here are a bunch of different bullet points. And then we iterated, iterated and I said, okay, now so just a bunch of images. And then I took all of those and I sent it to Midjourney. And Midjourney is an AI that produces images. And so for each slide, the Midjourney produced the image

• 18:12Speaker 01

  from the content that the AI had produced from my ideas. And then we put it all together. And so the time to make that presentation was less than two hours, where it might've been 20 or 30 before. And it came out great because the script that the AI produced that we collaborated on had a great hook and the transitions between the slides were brilliantly written. And, and then because we delivered virtually, I can read, you know, I can basically read the script. And so that's a completely different way than I would have built a presentation 10 years ago. So there's one thing for the technology itself to reach a point where it works. And then there's another for all of us to learn.

• 18:54Speaker 01

  This is how you, this is how you build a presentation in 2025.

• 18:59Molly O'Shea?

  I know we're going to get to this question in a second, but have you ever done demos? I know we're going to go into your blog, but you are like a master product tester. And so even just explaining that out, have you ever demoed something like that?

• 19:13Speaker 01

  A little bit, a little bit. Yeah, I think a lot of people have asked, I mean, I use dictation all the time. And then we've been playing around with lots of different kinds of software, but I think it's just really important to.

• 19:28Speaker 01

  It's really important to feel the technology and understand like how it works, right? Different kinds of hammers feel differently in your hand and they're good at different things. But you're right, I probably should start like every Friday. This is my cool experiment of this week with AI.

• 19:44Molly O'Shea?

  I'm sure your fans would love that. You have you have many fans.

• 19:49Molly O'Shea?

  Get you on passes and get a subscription going.

• 19:54Molly O'Shea?

  We're going to talk about your fund because this is really exceptional. You were able to raise two very large funds within just a matter of a couple of years. So your first fund you raised in 2023. And then your second fund you announced in November, and this was a $450 million fund too. Because these two funds were raised in such a short period of time. How does that? How does that affect your investing period per fund? And I just, I can't imagine that you've fully deployed Fund One already. So how does this work?

• 20:31Speaker 01

  It, we haven't deployed Fund One fully. It means that we have commitments from investors that when we will start to raise Fund Two, that the money will be there. And I think it's, I'm really grateful for it. I feel to be just very lucky that there are lots of institutional investors who believe in what we're building. Especially during a time, those times when the fundraising market was extremely challenging because there's not a lot of liquidity that was being produced in the exit markets.

• 21:02Molly O'Shea?

  Right. And so as a part of that, how big is your team to date? And what is, what is the actual construction of Theory Ventures look like today?

• 21:13Speaker 01

  We are seven marvelous people, small but mighty team. And we have two great interns who just started a couple of weeks ago. And there are three of us on the investing team. And then the remainder work on what we call the intelligence team, which is a research part of the firm. And we build small, very concentrated portfolios, about 15 companies per fund. And every Monday, we get together and talk about updates on different research ideas. So anyone can come in with an idea. We had an intern last summer who said, I think GPUs are not the architecture for AI. There'll be ASICs or application-specific integrated circuits. And he wrote a 10-page paper on, if you believe that premise,

• 21:56Speaker 01

  here are all the downstream consequences for AI. And so we debated that and it makes for investing to be a lot of fun because people can be creative. They can read something on a Sunday or have a conversation with somebody on a Friday that he starts an idea. Then he staffed that research project and created a market map.

• 22:14Molly O'Shea?

  How do you store all of the research? I know that because the fund is very data-oriented, you might have your own systems internally. But how do you store all of this kinds of research, data, analysis, things you're going after?

• 22:28Speaker 01

  Great question. We store it all in very simple text files. And the whole idea is that AI is really good at processing raw text. So we stored all in text files and we built systems on top. So some of those systems use AI to summarize and categorize and tag. And then we also build deterministic systems on top to do like long-term data analysis. Like, what is the average ARR of a Series A company that we've seen over the last 12 months, for example?

• 22:58Molly O'Shea?

  I mentioned you're the master of product testing. I'd say you're definitely a Swiss Army knife of product testing because I've followed your blog for many years. I feel like every time I read it, you're doing something else. You're trying something else and it's really fun to ride along. I'm just really curious, like internally, like what is the operating system for that? What is the stack? And what are the different programs and tools that you regularly use?

• 23:24Speaker 01

  Yeah, I was, I mean, I was using a command line email client in the terminal. So I'm not using Gmail, but using this really, really old piece of software called Neomatt for a long time. Because actually during COVID, I wanted to learn how to use the command line. So, okay, why? Well, I remember, I remember meeting the Dropbox team at the seed when they came out of Y Combinator and they had commercialized like a Linux command line utility called Rsync that allowed you to synchronize the status of two different folders. And they ended up building, you know, whatever, a $5, $10 billion company on top of that single protocol.

• 24:05Speaker 01

  And I think there are a lot of brilliant programming ideas that exist within the terminal that had been invented 30 or 40 years ago that are constantly surfacing and being exposed to, to different people, like Superhuman Email Client is a great product. And it has a lot of what was built into mut, which is a command line email client or Neomatt, which is a new version of it. And so like, you know, okay, why, why do this? Okay, like, let's say, the first thing I wanted to do with an email was being able to hit a single keyboard shortcut and create a task from it. Like, you email me, you say, okay, here's the agenda. I need you to comment on these three things. Great. I hit F4, it creates a task on the command line and saves it to my task editor.

• 24:49Speaker 01

  Very basic thing, but at least it keeps me in the flow and on to switch windows. The next thing I started to do is I started to play around with dictation. So I had carpal tunnel syndrome and turns out you can speak three times faster than you can type. And so since 2012, I've been speaking to my computer and now we're at a place where the accuracy rates are unbelievable. I mean, you play with like a Whisper model, which is OpenAI's SpeciText model. I put it on my machine and then I, I think it's F7, I can hold it down and the computer will type. Then, then the next thing is, okay, well, take the output of that text, remove the ums, remove the likes, remove the oops, and reconstruct it so it sounds like it's in my voice, but the grammar is better.

• 25:34Speaker 01

  And so you're automatically... Answering those emails and then anyway, so like one thing, just it's like a snowball or take a podcast and transcribe it and then summarize it. That's the project that I've been working on over two weeks ago. And so I think as long as you're playing around with the technology, you're constantly asking the question, okay, what's next? What's next? And then when you meet a founder, there are just like these little comments that you can make about like the friction around the technology that builds trust, which is, I think, the basis of investment and spell and trust of people.

• 26:07Molly O'Shea?

  Tomasz, you're probably the most technical and involved investor I've talked with. So this is like extremely interesting. Theory's thesis is centered around data and you're data-driven. I'm curious how you ended up on that as a differentiation for your fund? And how do you compete against the likes of other data-driven funds like Insight Partners, Signalfire? How do you think about that?

• 26:34Speaker 01

  Yeah, I mean, I saw the power of data firsthand at Google. I just, Google provided us, I mean, internet-scale data and the kinds of products that we could build as a result of having exposure to that data, the kinds of questions we could answer just opened my eyes. And then I was really fortunate to get the job in the venture business and started working with data companies. And the idea there was that, well, maybe we could bring some of that insight, that level of insight from Google to other kinds of companies. And so I've been investing in data companies for a long time. And my graduate coursework was in machine learning. And so I also saw that happen at large scale. And now that the basis of all machine learning is data. And so that's the core idea behind Theory

• 27:19Speaker 01

  is every software company, every product that will be built for a user that's on a computer will have data as its fundamental building blocks. And so we invest in data systems, databases, visualization. We invest in AI, both at the infrastructure and the application layer.

• 27:40Speaker 01

  And then we invest in blockchains because we view them as modern database systems with a very different and compelling architecture for certain use cases.

• 27:48Molly O'Shea?

  Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.vc, where we deliver a one-time-a-week top deals and tech headlines report and go deeper on our conversations. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. The link is in the description to sign up. You more formally bucket this out in terms of the decade of data, AI as the new platform, and decentralized infrastructure as database. Could you break down each of these and maybe provide a portfolio company or two to better emphasize and just expand on that?

• 28:34Speaker 01

  Yeah, for sure. So the decade of data with the modern data stack, post-modern data stack, this is the idea that every company or every product is producing data and they need to make something useful out of it. There's raw material like an ore, and then it needs to be refined to make steel, let's say. And so over the last 10 years, there have been many companies in space. We were lucky, I was lucky to be on the board of a company called Looker, the Google bot for 2.6, 2.7 billion. And we backed many of the original team of that company in a business called Omni. And that business, if you think about like classic BI, there were four companies in the year 2000 that were building BI, reached worth about 2 billion, and they controlled it. They locked it down. It was totally centralized, very, very difficult to get

• 29:19Speaker 01

  access to report. A company called Tableau, that Salesforce bought for about 16 billion, came out of Stanford and said, everybody can have access to BI. And so there was this pendulum swing from centralized control to decentralized control. And then we invested in Looker, which was more centralized control, but on cloud data warehouses. Omni is a combination of the two. We're thrilled to be partners with Jamie and Colin and the rest of the team. And that AI is a new platform.

• 29:47Speaker 01

  We look for three things when we invest in software companies. We look for toil, lots of repetitive, really boring work. The second thing we look for is a labor market that cannot supply enough people to do that work. Either there aren't enough graduates, or people are excited to do it for the pay, or people leave that job fairly often. And then the third is a hiring manager who desperately needs to fill that seat. And so, going back to the point we were talking about, Molly, before, if these systems are only 75 to 80% accurate, well, the confluence of those three parameters, there's somebody who's willing to accept a 75 to 80% accurate solution. And so there, we've invested in a company called Dropzone. Edward was at a security unicorn called Extra Hop.

• 30:28Speaker 01

  He was one of the strongest engineers there, and they build security operations center tools. So every large company in the world has, on average, about 75 different security products. All those security products produce alerts. On average, it produces about 8,000 alerts per day. And humans need to review those alerts, but because it's really boring, fewer than 1% of those alerts are actually reviewed by a human. But if humans were actually reading those alerts and interpreting them, many of the security breaches that happen could be avoided. And in fact, the FTC is now starting to ensure that, particularly in financial services, 100% of those reviews of those alerts are reviewed, otherwise they're fined.

• 31:10Speaker 01

  And so Dropzone actually builds AI agents to review all of these different alerts and look at emails and look at network reports and figure out, is this an intruder or is it not? And then the third one, decentralized infrastructure is databases. In Web 3, it's an incredible, I think it's, I mean, very, very few people appreciate like the scale of Ethereum. Ethereum, yeah. In the first quarter of 24, produced, on a percentage basis, is the most profitable software company in the world. And on an aggregate dollars basis, sixth largest producer of cash. It's worth seven snowflakes put together, it's $350 billion market cap.

• 31:50Speaker 01

  And so there's just, I mean, it's $250 billion worth of meme coins that have been created out of thin air. So there's just a lot of market cap. And we've invested in a company called Alium, and Alium is a team that builds the data infrastructure to power Web3. So if you're building, if you have a wallet that has a lot of coins in it, and you want to know the value of those coins, they will power that. If you are, if you're like PayPal, who pays its auditor in stable coins, and you want to account for that in a way that the street will accept, you will use data that comes from Alium. If you want to understand the launch of Web3 protocol and the successful,

• 32:34Speaker 01

  or whether the marketing campaign was successful or not, you use Alium data. There are even government agencies that are now starting to use it. So those are three examples across those three buckets.

• 32:43Molly O'Shea?

  What have you noticed to be the biggest roadblock in this shift of AI transformation within this early stage of investing?

• 32:52Speaker 01

  I think, I think the biggest roadblock is, I mean, over the last 10 years, people have gotten their data infrastructure to be in a good place. I think their data is in many, many different places. The average enterprise is more than 100 different software applications. And so you might have three or four different Salesforce instances, and your marketing data is in a customer data platform. That's a third thing, a second thing. And maybe you have a bunch of like infrastructure data that tied up a data dog or another visibility tool, and you need to unify all that. I think the movement of the data is really, is really hard. So that's, that's probably the biggest blocker today.

• 33:27Molly O'Shea?

  And, okay, so going back to construction of the fund, you brought in a really interesting individual at the founding moment of the fund. So you brought in Lauren Demus, former Palantir exec, to help build an institutional-grade firm from inception. How did you meet? And what practices did Lauren introduce and build in the firm? And how does this support the data-driven strategy?

• 33:52Speaker 01

  Lauren and I have known each other, we worked together at Google a long time ago. And we just stayed friends and I've watched her career blossom. And as you mentioned, he was a palantir, she worked on the healthcare practice there. And she's no stranger to architecting very sophisticated data systems. And so the, the goal was for someone who really understood technology, really understood data architecture, to join the firm and manage the intelligence team. And she's been phenomenal, we architected, or she architected the system that we're currently using today. And, and I think, you know, couldn't be more excited that, that she's on board and, and all the people who work as part of the E, it's really come together.

• 34:36Molly O'Shea?

  Who else is on the investing team?

• 34:39Speaker 01

  Well, we have two great investors. So the first is Spencer. Spencer is one of five kids, homeschooled, Berkeley, world-class athlete in three sports, won the first Solana hackathon. He and I met through a friend and introduced us to a company called Mistin Labs. We were lucky enough to be the second largest investor at the time, and that's a $50 billion market cap crypto project today. And so we hit it off as like an Uber ride to San Francisco. We joined from very early on. And then the third, last but not least is Andy Tridman. He's a, comes from Boston, family of doctors. I think one of his relatives wrote a book about growing up in a family of doctors.

• 35:20Speaker 01

  And he jazz, piano, saxophone player? Can't remember the instrument, but he loves jazz. And a ski racer when he was young, Brown cognitive neuroscience major. And then he worked at Bain for four years and Eric Schmidt's Fund, Innovation Endeavors for three portfolio company, building data systems, US government, and then joined us.

• 35:40Molly O'Shea?

  Fantastic. Well, shout out to Spencer because Spencer helped us with some of the questions and gave us this really juicy one, which I'm excited to go into. I didn't know this at all, but Spencer mentioned that you have a really interesting childhood through college. Could you share more about that? I'm not going to give away any hints, but could you share more about that journey?

• 36:02Speaker 01

  Yeah, for sure. We moved around a lot as children. So we lived in Europe until we were about 10 and then came to the US and I started a company in South America when I was 17 and used that money to pay for college and grad school. Was lucky enough that

• 36:20Speaker 01

  Guy became a rower on the rowing team and that was a lot of fun. Graduated three degrees in four years and I had a lot of professors who really helped me along the way. There was an elderly woman who gave me a fellowship at school. And so, yeah, I was just really grateful for all the... And then I ended up working at a software company in Washington DC that eventually went public. The CEO actually designs board games. And I remember one night we stayed at his house and played a board game.

• 36:48Molly O'Shea?

  Okay, but we have to emphasize the fact that you built a software company at seventeen and you paid your way through college. That's a huge accomplishment.

• 36:57Speaker 01

  Oh, thanks. I mean, like I said, there was a wonderful guy named Guillermo who really took me under his wing. And I worked on it a lot with my dad, who helped quite a lot, so I can't take a lot of the credit. But it was a lot of fun. It was fun to be in South America, selling software and, you know, having a great adventure that I'll remember for a long time.

• 37:19Molly O'Shea?

  What specifically did the company do?

• 37:21Speaker 01

  We built document management systems for law firms who were international firms, so multi-language, multi-currency document management systems.

• 37:30Molly O'Shea?

  Wow, that's a good market.

• 37:35Speaker 01

  It was a very, very niche market.

• 37:38Molly O'Shea?

  Seemed to work out. As we close out, I always like to end on a positive note and get a grasp of what you're looking forward to for the rest of the year. So, Tomasz, what are you looking forward to most in 2025?

• 37:50Speaker 01

  I am so optimistic about 2025. I think there's a level of ambition within the US as a whole that I'm really excited about. I look at the possibilities for massively improving the electrical grid, the way that AI were changing. I mean, we were driving as a family in, you know, a self-driving car and I have my phone on Gemini Live, all of us are talking to an AI, and it just blew my mind. And so I think we're just at the very precipice of really understanding how we can work together with computers in completely novel ways, and that's thrilling.

• 38:28Molly O'Shea?

  Amazing. Well, thank you so much for the time today. It was a pleasure to have you on and hopefully we'll have you on again.

• 38:34Speaker 01

  It'll be my pleasure. Thank you so much for the questions, Molly, and the time.

• 38:38Molly O'Shea?

  Of course.