The Decade of Data (Tomasz Tunguz)

25:36There's no gap. There's no S1, right? But it's coming. How does this change end up accelerating the timeline to IPOs for some of these software companies, do you think? How much does it cost to go public? I'm not talking about the dilution or 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? Gosh, I honestly have no idea. I would put it in those sort of like single digit millions. $15 to$25 million. Okay, wow. I'm off by a good chunk there. Okay, so if you're a$100 million revenue company, is it rational to raise a round of financing that costs you$15 to$25 million?

26:16Yeah, totally. Wow. So to put it into perspective, if you were going to raise a Series X, very late stage round, 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 transaction costs to go public, which is why it used to be in the late 90s, you need like 25 million of trailing revenue to go public. And today, no, no, no, you can't go public unless you have 250, maybe 500 million in revenue because as an expense, you'd be a banana, just raise it in the private markets.

26:57And so that's why you look at the average growth of publicly traded companies, it's asymptoting to 10%, basically, which is software inflation, because it's so expensive to go public. So why is it so expensive? Well, there's just a lot of regulation, right? Sarbanes, Oxley, all that kind of stuff. And so if you look at crypto exchanges, the regulation there is much less. There's no S1. There's no audit. And so some of those components are important. But the point is, we have made it so expensive to go public that if you're a micro-cap software company, if you're somewhere between one and a half to two billion, it's not economic to go public.

27:41Most 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 gets me so excited. And these exist. So you look at Chinex in China or the ASX in the UK or TSX or the rumors of the Texas Stock Exchange, which would be a new venue for trading. All of them, all of those venues are focused on high growth, small cap technology companies where the disclosure requirements are significantly less than the New York Stock Exchange or the NASDAQ. Wow, how interesting. You also mentioned the Robinhood OpenAI example, which is more like secondaries trading, which feels like an exciting other asset class that needs to be opened up.

28:26But there's all these complexities about are these essentially options? Is it owned through an SPV? Who owns it? How do you see that ending up playing out? Well, I've been reading online trying to understand because Robinhood mentioned the OpenAI tokenized stock and OpenAI said we did not authorize this. So in most secondary transactions, the board must authorize those transactions. And so my understanding, which is only based on what I 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.

29:06Anyway, it's not clear. Unclear, yeah. We'll see how it all shakes out, I suppose. 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 and then sells it to a Series E investor.

29:57And 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?

30:31Yes. 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 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. 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.

31:06Generalist Plus delivers a mini-NBA 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. Two, comprehensive guides that distill hundreds of hours of research into actionable insights on investing and company building. 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 who are gaining an edge in understanding markets technology and business fundamentals by visiting the generalist.substack.com that's the generalist.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.

32:33And I thought that was such a good framing and such an interesting way that 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 things head. How have you sort of looked at that? How do you see its place in the ecosystem? I do think there's such a clear story around Bitcoin, as you said, and a clearer one around Solana. But are people too down on Ethereum? How do you think about its potential today? Yeah, you had a brilliant point in there. You said Ethereum soul searching. So just for the audience, Solana's nickname is Sol.

33:08There you go. That's right. Right. And so the major competitor to Ethereum is Solana. So I think Ethereum is 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 stable coin volume is on Ethereum and another big chunk is on Tron. So it's really the vast majority part of the market. And then 60 % plus of the decentralized finance or DeFi market is on Ethereum. So it's the big dog. And Ethereum, it plateaued, right? There was not a lot of innovation. They allowed other companies to innovate on top, which are called layer twos, like arbitrum and optimism.

33:52And they were solving, those L2s were solving performance challenges for the ecosystem. And so they were basically allowing you to move money faster and cheaper than Ethereum and saving you 90%, just 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 SWE, which is lucky enough to be an investor, that also has very, very significant performance performance. That's the ex-meta blockchain team, one of the two.

34:23And 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 an L2 like Arbitrum, it might cost me$2.50. And then if I use a more modern blockchain like Solana or SWE, it might cost me$0.20. Right, so very significant reduction in transaction costs. And so as a result, over time, Well, developers will build on the systems that are more efficient, much better price performance characteristics.

35:02Nobody 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, dissatisfaction. The community decided, okay, we need to react. So they raised$150 million and Vitalik is more involved. And they're at this crux, this critical moment in the blockchain's development where they need to figure out what their competitive advantage is. It's a little bit 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 building on AWS.

35:44And 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 builder's perspectives on which platform to build. 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. As you think about its comparative advantages and what should be the real advantages of the project, what do you think that should look like?

36:28What do you see as the things that the grandfather still has or should lean into that these younger chains maybe can't compete with or will struggle to compete with? The single most important thing in any financial market is liquidity, and Ethereum has it now. The question is, how do you capture that liquidity? Well, stablecoins will be an absolute essential market. Minimizing the transaction costs so that liquidity remains on ETH is priority one. How do they do that? What do they do with the L2s? How do they think about the consensus infrastructure? Then the reward systems for the validators?

37:07Basically, how do architect your entire system, they'll have to, I think, revisit a lot of the core assumptions because the underlying technology has changed. And that's okay. But I think a lot of times in the world of Web3, we have attachments to certain ideas and over time they change. Really interesting. As you sort of look at the crypto Web3 landscape, are there newer projects that you've spent a lot of time playing with that you think are really promising or new sort of pieces of infrastructure that you think are worth discussing that aren't being as discussed right now? Well, I mean, there's a brand new company called Hyperliquid that's worth somewhere between 20 to 40 billion.

37:47That outside the world of crypto, it's not very well known, not venture backed. And they did a couple of things that were absolutely brilliant. One, they focused on the perpetual market, highest margin product. They created a blockchain that has some unique attributes to that market in particular. So they specialized a vertically integrated stack exclusively on it. And so as a result of the vertical integration and then making the blockchain specific and the tokenomics and all that stuff specific to what they were doing, they built a huge, I mean, multi-decacorn business that's now partnering with Fantom, right?

38:22So Fantom now, you can trade perps and it's all powered through Hyperliquid. 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 is pretty cool. Really cool. $30 billion bootstrap. Yeah, pretty amazing. Really amazing. And then 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 tokens. There's a lot of foresight and they deserve a lot of credit for the company that they've built. So I think the vertical integration is one really interesting trend.

38:57And I think, you know, another big question is, are we moving from proof of work to proof of stake to proof of authority? Can you explain those for folks just very briefly? I know that's not the easiest, but in simple terms. Yeah. So Bitcoin is proof of work. If you want to earn money by minting Bitcoin, you need a really big computer and you basically solve very complex, not complex, but competitionally intensive math problems. And then you're rewarded with a fraction of a Bitcoin. That's proof of work. Proof of stake is someone is vouching for me. If I'm trying to join a, I don't know, whatever, social club, right?

39:36And Mario vouches for me, that's proof of stake. He's staking his reputation. People do that in the financial world. It's like, I know Mario's trading. I've got him. Yeah. That's what it is. Backstopping you in a way. 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 is a great guy. That's enough. Mario will run his computers. He will validate the transactions and I trust him, which is how most database today's work, right? If I send data to Amazon, I trust Amazon will run the database the right way.

40:11They're not going to change how I move money or in my Quicken, however many, they won't change it. I just trust Amazon. So proof of authority with one validator is what we have today. And so we're kind of moving to this world where we have less and less decentralization. But ending up with more efficiency as the sort of benefit of it. You trade off decentralization for speed, efficiency, et cetera. At some point, you get to the place where there's only one validator and you basically have replicated an existing database. We were chatting with Patrick O 'Grady yesterday, who's one of the key engineers from Coinbase about it.

40:51And so the question is, well, why do you have decentralization if you only have delegated authority? Well, you might have delegated authority. We talked about JP Morgan. Within the world of JP Morgan, they choose who validates the transactions and says this transaction is valid. So 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. Amazing. Well, I want to talk about AI a little bit.

41:23We've talked a little bit about it, but 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, what do you find are the most surprising places or the most surprising use cases that you do rely on these technologies for? 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.

41:59And 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 6 and Delta Force. I had no idea. And it will answer you. But then you can also tell it, hey, look through all my files and see if you can find this blog post from two years ago. Oh, wow. It's another very basic thing you can do. But then you can ask it, create a tool. So let's create a tool. 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 then it'll spit it out.

42:36And 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 create a task from this one and add this one to the CRM and check to see if I can meet this person at this time. And if not, send them the slots. And I can do that all with one voice command. And so what I'm trying to understand is what are the limits, right? If we were going to reimagine a computer that I was only speaking to that had access to all these different tools, where does it break down?

43:13It'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 one tab that's doing all these things for you. What are the limits that you've discovered a bit? 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.

44:00And so there's this, it's like training a brand new person. How do you work? So there is a lot of constant feedback and the bet I'm making, and right now I'm 50-50, is that within a week or two, all of that training will result in improved productivity. But I couldn't tell you that it's doing that today. Wow. But it's great if you, you know, you only have a couple of weeks to figure it out. You you think by then you'll have a reasonably definitive answer? 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?

44:38It turns out it was only fetching the first 10 calendar items. And so it was making up nonsense, right? And so anyway, there are all these corner cases. So maybe over time, and then I wonder if companies themselves will have these shared libraries where everyone is accessing the calendar through some, and this is actually what we're building inside of theory. So Adam, our great software engineer, he released the library for the CRM yesterday that's available to AI. 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 a very technical team and thinking about how to architect the firm in a way that involves a decent amount of engineering.

45:18The 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. We have an agent that builds market sizing analysis.

45:49We have an alpha of an agent that makes investment recommendations that I wouldn't trust. But we're starting to assemble all of those pieces, memo generation, deck extraction, those kinds of things. And reality is none of these technologies are flawless. And there are unbelievable improvements in productivity. As much as we look at startups and say, okay, the ARR per engineer should now go from about 150K to 500K, well, the number of startups covered by a VC investor should go up by a similar percentage. There's no reason it shouldn't. So maybe we can cover three times as many spaces or four times as many spaces, and we should be able to do it with greater depth.

46:37What is the entire toolkit that's required to be able to do that? I enjoy creating cloud 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 your sort of investment recommendation engine that you have or your agent? Like what does it help you with?

47:11Yeah, 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 quite good. Ontology generation, it's really good. at. So let's say you were looking at MCP servers and you wanted to classify all the different approaches to different model context protocol servers, next generation API servers, basically classify them and segment them.

47:50Or you wanted to look at an even broader universe, AI security companies. Well, there's data loss prevention companies, firewalls, prompt injection company. Anyway, so it's also really good at that. Well, one of the last times 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, 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.

48:30So yes, that's right. We did that. 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 to 90th percentile or higher. So it's a higher value, different kind of return profile. I think within the world of early stage, it's really hard because there's not a ton of data. There's not a lot of structured data. There is a lot of unstructured data. So what people say online about different products or spaces is fascinating.

49:07And AI is really good at capturing that. What people say in podcasts or recording our interviews or conversations that we have with people, that's where the majority of the data is or the majority of the insight. How do you ask the right questions? What kinds of interviews? Which are the sources that you pick? So I think that's where we use it. And then the other place that we use it is benchmarks and comparables. and those kinds of places. But it's not like hedge funds, right? We're not buying satellite information, satellite images over retailers to predict quarterly earnings. In your sort of introductory post about TheoryVentures, you talked about investing in machine learning as a force multiplier, which feels like the lens through which you look at AI in general.

49:54Why was that the way that you perceived the AI opportunity and the right way for theory to play it exactly? Let me ask you a question. Okay, so on a manufacturing assembly line for cars, how many humans does a robot replace? I would guess two, one? I don't know. Two and a half. Two and a half? Yeah, okay. 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 the company. Amazon then released the stat. This is three or four months ago, where they said in their distribution centers, their warehouses, one robot replaces 20 humans.

50:36Wow. Gosh. Why is a robot in a DC that much more productive than a robot on an assembly line? Because a 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 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 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.

51:18I bet within the next 12 months, we will see workers managing 100 agents working simultaneously. 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? They have a task list. It's a software engineer who creates a task, a PR, a pull review. It says, go and fix this. Go and create this feature. Go and fix this. 15 times, comes back half an hour, and then review's done, right? There's no reason that that mental model of working shouldn't apply to a salesperson. or it shouldn't apply to a marketer or a customer support rep. And so I don't know.

51:55I think the question in my mind is, and this is the reason why I'm playing around with AI on the computer, is how do I get to a place where I can manage 50 times more agents than I can today? And right now you still need to monitor and watch and you're watching the readouts and making sure that it's working okay. But there has to be a system. There has to be one in inbox, which Harrison from Langchain talked about in agentic inbox to manage all these things outside the world of software sharing. And then the second is there has to be some kind of way of putting guardrails around them so they don't send 100 emails to the wrong people on your behalf.

52:31Yeah, it feels like you need new systems to manage it on the human level and also just like a different level of reliability such that you can really trust it to do what you ask it to do. Right, exactly right. About a year ago, you wrote that Microsoft was leading the AI race and Google was lagging. How do you think that has changed in the 18 months since? 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 Gemini models are all either number one or number two.

53:10So they're meaningfully, I mean, they've made a huge leap. I think Google is clearly subsidizing a lot of their AI products to drive usage. So they are maybe in the developer world a bit behind, and they're trying to drive more adoption. But I think to their strength, look at the impact on their search business. It must be phenomenal, an improvement in margin, because there are two different kinds of ads that Google runs. one is the ones on search and the other ones are on other people's web pages and the traffic to other people's web pages is plummeting right so hub spots traffic's down 70 75 percent 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.

54:14And then the other dynamic that a lot of people don't talk about is access to the GPU, right? Running these models is super expensive. Chrome, a browser, a dominant browser can actually use a GPU. So they can put a model in your browser and then have that use your GPU to answer queries and they don't have to spin up anything in the data center. A lot of the other model companies do not have that advantage. Yes. So I think on the consumer side, they're in a great position. And the enterprise side, they're still, they have unbelievable products and they're solving the distribution, which is the classic Google strategic challenge.

54:50Which are the models that you end up using most often? 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. Gemini is my default model. OpenAI, I think, is great at the deep research product I really like. Yeah, agreed. Yeah, it's really good. And then Claude, I used to use a lot for writing. And now when Gemini cannot solve a coding problem, Claude fixes it every time. Oh, wow. Interesting. How do you think about OpenAI's position today? And I'm especially sort of thinking about it with respect to the aggressive moves that Zuckerberg's been making, and especially on the hiring front?

55:32And to that end, where do you see meta in this? They've obviously taken a very different approach. 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 are you look at i mean any company that reaches that level of scale is worth at least tens of billions if not hundreds of billions if not trillions right so google got that to that level of scale meta got to that level of scale just on consumer distribution and snapchat got to some level of scale on that distribution so all those companies are worth a lot there's a you know long-term business model question there that they're working to solve and i think their pricing power is underappreciated we should you know talk look at cursor increasing their pricing.

56:28I 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. But they used to have a trillion dollar market cap company in their corner, now they don't, right? And they have Masa instead. And so they're figuring it out, right? They need to figure out who their allies are and what the long-term positioning is. But ultimately, the brand and the distribution is unbelievably valuable. And by comparison, how do you think about these aggressive meta hiring moves and how it sort of positions itself?

57:03Because it's had to play a bit of catch up and is doing more of the open source stuff, but obviously has so much of that distribution it can rely on. 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? I know. I think it's the most, you do not see people play offense that hard that I can ever think of. Yeah. I mean, just overt frontal assault, right? And I'm coming. Yeah. You have to respect that. That is awesome. And then I was reading the announcement today with Zuckerberg, where he invested three and a half billion into the largest eyeglass maker, Luxottica.

57:45He bought three to 5 % of the company because that's new. And so I just love the aggression. I think it makes business fun to see people conduct business that way. Yeah, go that hard. Yeah, I think Lama were the bleeding edge of open source models for a long time until Lama 4. I can't tell what happened there. But I used to use a lot of open source Lama models and now I don't anymore. It's a Gemma model. And so I think 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. Amazing.

58:29Well, 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? I would build a computer that was entirely AI. Like a PC? Yeah. The only thing you do is talk to it. I think that'd be a lot of fun. That would be a lot of fun. Would you have a screen at all for output? 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.

59:11And I think I remember meeting this brilliant founder from MIT who 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. And so I think it'd be a lot of fun to embark on that journey and figure out what breaks where the sharp edge is.

59:45Yeah, I 100 % agree. Have you played with any of the sort of, I don't know, new AI hardware consumer-y products like, you know, I'm thinking Rabbit or Humane, you know, back in the day and stuff like that? Yeah. I mean, they're all steps forward, right? It's like the magic link before the iPad. And 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 your 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.

1:00:24Yeah, that's also, you know, using the sort of tongue movements. You can even imagine that for people who don't have disabilities, like, you know, there will need to be some sort of sub vocal version of these discussions if these become our dominant computers, right? You know, whispering or whatever it is. Another friend of mine said, he had this really great prompt. It was Nikunj, sorry, Nikunj Quatar. He said, do you believe you're biotic? And I said, no. And he said, well, when was the last time you forgot your phone anywhere? You already have a computer attached to you. Yeah, totally. So we're close.

1:00:57Yeah. Yeah, absolutely. What's a tradition or practice from either another culture or time period that you think we should more widely adopt? I was listening to a YouTube video of a Shaolin master. and it's been banging around in my head since then. He said, when you eat, you eat. When you work, you work. When you sleep, you sleep. And so that single focus, I think it's harder and harder. But I think it's really important. So I would love to see us do more of that, but it's hard. 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?

1:01:39One 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.

1:02:24And 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 highly recommend that book. I'd never heard of it. I've read some of Herman Hesse's other books, but not that one. So that's an awesome recommendation. Thank you so much for doing this, Tomas.

1:03:04It's been amazing to chat. And And yeah, I'm so grateful for you sharing all of your knowledge with me. Oh, my pleasure was mine, Mario. Thanks for having me on the show. 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.