Martin Casado - Entering Uncharted AI Territory - [Invest Like the Best, EP.381]

23:15And then the more you're competing with a more evolved portion of the human brain. So I think that really is the continuum. How do you think about with your investing hat on, I guess, the horizon to commercialization of a given idea? Are you willing to invest in a robotics company today that's building the foundation model for dexterity? I'm making something up. Do you have to be able to see a customer buying a thing for an economic transaction in order to get excited about it as an investor? Or are you willing to invest in things where the commercialization is like over the visible horizon? So it depends on the domain.

23:47I'm a firm believer that these models train on human data, creating stuff. The model is the app. They're so magic that if you do it, people will find value and they'll use it. And we've seen this and we've seen many of these. We've seen music, we've seen speech, we've seen video, we've seen images. And all of these are these great growing businesses. And it's just so easy to articulate why these are great businesses, because the marginal cost of creating something goes to zero and you're creating something people want, people will buy it. So for those, I think you want to invest in the best team to build the best model.

24:16And then I think you can do this well before. So for example, Ideogram. We invested in Ideogram. This was like the Google Imagine team. One of the pioneers of diffusion models, they went out and they created an image. We were the first money and it was just a very obvious thing to do. As the economics become murkier, then it's harder to make these decisions. The one thing that I've learned looking at robotics companies over the years is that often when you're investing in robotics companies, you're investing in a very verticalized company and you almost need to understand the business, the vertical that it's being applied to as much as the core technology.

24:52In the LLM creativity space, literally some PhD researcher can create a model that consumers love. But if you're building a robot for agriculture, you better be an agriculture company, actually. And if you're building one for mining, you better meet a mining company, actually. I've sat on the boards of these companies. These core technologies that are building drones or robots, especially like it's like shipping or ag or it's forestry or whatever. And the companies end up becoming the specialists of what they are. And so that's the mindset you have to go into those investments. This is an ag company.

25:21And I just learned that I don't know about these verticals. I don't know about construction. I don't get that sales process. I don't get the unit economics. I don't know about mining. I don't know about reforest. That's not me. That's other people. This is one of the reasons we even have an American Dynamism team at Andreessen Horowitz, which is clearly AI is going to change robotics. It's changing robotics. And robotics is our AI, but it's changing how we do things. It's going to change all of it. It's going to change defense. It's going to change safety. It's going to change construction. But to invest in that, you better understand those markets.

25:50And so I do think you need special purpose teams to do that. So for my team, for those ones, we don't do them. But for other teams at Andreessen Horowitz, they do. But they're very much specialists on the markets as opposed to just the tech. How do you think about and analyze the defensibility of these business models where pick a modality, I don't know, music or something? It seems every time some mind bending demo comes out, five other companies come out and there's like rough parity between what they're able to do. And I'm just curious where and how you think these companies will build defensibility and allow them to be huge enterprises.

26:25Yeah, it's a great question. I've got a lot to say about this, but let me say a few maybe unrelated things and then a specific thing. So one unrelated thing is first movers tend to have a massive advantage here. And it's because they're creating a new user behavior and they have a brand and they have a bunch of users and they have the feedback and whatever. So that's a very real thing. And I do think it breaks Pareto where the first movers have 80 % early on and then the 20 % is left for the rest. However, over time, all of that erodes, right? And that's very clear. Now, I've seen many of these model companies launch at this point.

26:53We probably have the largest portfolio in one of them. So I've seen many of them launch. And they all look the following. The company does nothing. And then they launch a model. And it's not very good. So you get a little blip. And then it goes back to zero. And then they go for a while. And then they launch a model. It's not very good. And then blip. And it goes back to zero. And then they launch a model that's good enough that it's over some unknown bar. And then they get this great rope. They're like, amazing. We've made it. However, it's mostly because it's so magical to get a bunch of users.

27:18And then it kind of looks like a TCP saw too. People use it, and then they get bored, and it drops off. They have to launch another model, and it goes up, and it drops off. They have to launch another model. And so then you're in this long march. Let's call it the waves, where you're riding these waves of needing new models, and it's a treadmill, and it sucks. And at that point, that's when the companies have to find a traditional moat. That traditional moat could be a community moat. It could be an integration moat, like an API. It could be a differentiated styling and brand moat. There's a lot of moats, but that's when you have to create the moat and get off the treadmill.

27:49I think if you don't get off the treadmill, this all converges basically to the same model. Let's talk about the moats that actually work. Integration moats are a real thing. OpenAI has a great moat in that many people use the API in order to do their thing. And APIs just tend to be sticky, especially as they evolve over time. Now, interestingly, LLMs have this anti-stickiness property in that they're so unpredictable, you can't rely on them anyways. You have to be ready to swap them out. But APIs are so much sticky. So integration moats are a very real thing. Community moats are a very real thing.

28:18A lot of the, especially creative companies, move to a consumer experience that involves sharing and virality and community. And that has inherent stickiness, of course. Workflow is a very real moat, which is independent of the model. Many of these things still require human input. And so you have my dashboard, you own the access layer, and you're training a user behavior. So that's a real moat. Again, so if we're going to break this up, model companies will launch stuff. A lot of it will be failures. They'll have one that hits. And then they're in this sawtooth pattern of the treadmill of the next big one.

28:48And in that time, they have to figure out a defensibility. It will not just be on the model. I'd love to hear your view on the evolution of the competition between the foundation model providers, both we'll call them frontier or closed source model providers like OpenAI and the open source ones like Mistral. And this will be an excuse to talk about some regulatory stuff and some bigger picture stuff in a few minutes. But first, just on the models themselves, what is your way of understanding these things? How do you watch them? What do you think matters? What interests you? just riff however you want.

29:19So here's something. Everybody asks the defensibility question. And what the mental model people have in their mind when they ask the defensibility question, oh, long-term, there's nothing really defensible, and they end up having the same data and blah, blah, blah. But it's so much worse than that. And the reason it's worse than that, and I think people don't realize to the extent that it's worse than that. Many people don't, which is there's a massive perverse economy of scale because distillation works so well. So what do I mean by distillation? What I mean is, let's say you, Patrick, have your AI company and you released the world's best model and it's an LLM.

29:54It just turns out that I can use the fact that your model is out there to train my model very effectively to catch up to your model. Maybe I won't get it all the way. I can do it a lot cheaper than you did it. Distillation is so effective. We're seeing like a new model will come out and then everybody will catch up to it. This is a massive perverse economy of scale. And I don't think we as an industry understand quite what that means yet. To me, it means if you're releasing a model in text, you better have a good plan to get to defensibility. It probably should be traditional, something that businesses have been built on for quite a while in software.

30:28I don't think being the best model is just enough of an enduring. That said, it's also clear to me when markets expand, they tend to fragment, not consolidate. And so white space is created. Every day, there's more white space. And so I actually think we're seeing so much fragmenting and fracturing. Even if defensibility is a problem, companies are finding independent niches. Let's just take the image category. So one way to think about the image category is there can only be one image company, and that's going to be the winner, or they're all going to look the same. But that's actually not what's happening.

30:57What's happening is they're all finding independent niches. It's almost like when you walk into a bookstore, a bookstore have different sections. Here's the anime section, and here's the sci-fi section, and here's the reference section. Here's the horror section. It's almost like these image models are deciding which sections they are. Pico, which is video, they're very good at anime. And Midrity is like the gritty fantasy realism one. And Ideogram is like the cartoony text one. And Dobie is like the photoreal one. And so I think that when marks expand, they fragment. There's a lot of white space to find niches.

31:27We're seeing that happen. There's multiple viable companies. There will be a consolidation phase, I think, for a long way from it. And so I think we're going to see a lot of great companies being built in the near term. And people are too scared of the consolidation phase where when you have market costs drop this fast, I think we have a lot of runway where we need to worry too much about that. What do you hope to see from the big Anthropic OpenAI, Mistral, Facebook, like the big leaders that have provided the core models? What's the best case scenario in your mind about how that story plays out in the next three years?

31:57Have you run on the thesis that these are a core CS primitive that can solve a very huge class of problems. I suspect that they're going to end up solving different modalities of those problems. I really do. I think it's amazing that Facebook releases Lama. But when you release open source without actually having an enterprise sales force or go to market engine around it, the thing kind of lands when people do whatever they want. But you don't ever push and prod it and pull it in the way that an enterprise buyer would want. Mistral, on the other hand, it's doing open source and it's releasing it, but it also engages with customers.

32:32So it can very directly push and prod and pull it in the way that is actually meaningful. Does that mean these two things are competitive? Not really. They're serving two purposes. One is, here's this model, do whatever you want with it. Another one is, I've got an actual solution for the buyer. And then open an end of topic. Again, I just think they're pushing the state of the art. I hope they continue to push the state of the art. I think that they will likely find somewhat overlapping, but also independent niches in different areas. And I think these things are expanding so quickly, I think they're going to continue to be viable players.

33:01And so, I mean, I applaud all of these companies for basically changing the industry and launching these things. And again, I don't think it's a zero-sum game at all. I think that's actually the worst thinking in this AI wave, zero-sum thinking. And it literally is the most detrimental thing. Somehow it's a zero-sum game. And because you have open source and a roads closed source, and because you have two people competing in the same area, like one has to win, it's not like that. This is like a hundred percent abundance market. It's growing by a hundred times. kind of let's cheer them all on. Can you lay out your position on all the important regulatory considerations that are happening very real time that I think you've been very vocal about that you have strong opinions about?

33:40Can you like lay out your stump speech for what you think is right versus what's going on? I would like to start to say I'm actually not a political person at all. I never get involved in these discussions. And I just feel like now we're just hitting this very dangerous period that I've seen before that I almost feel like it's my obligation to come on before. But it's not my temperament. I just want to be clear. It's something I've thought a lot about. I felt like, okay, I've got to go out and do the right thing here. So here's the thing. I feel that in maybe 2013, 2014, Nick Bosh, he wrote this book.

34:12He's a super smart guy. He's a great philosopher. And he has great things to say about us living in a simulation and how innovation can be the downfall of humanity and how AI can be bad. There are these great intellectual arguments, and they've been going on for a very long time. And I think they impact a lot of people. If you actually read the Walter Isaacson book on Elon Musk, one of the more interesting things is how these kind of early Bostrom arguments impacted people like Demis and Elon and Sam and Ilya, et cetera. But what many people don't realize is that this was all before the systems were actually built.

34:47And this was a platonic armchair thought experiment. And somehow that got conflated with actual systems that were being built. And so you had this, let's prepare for the FUMI recursively self-improvement AI that can end humanity and protect everybody from it before we actually created like these systems that are working. And somehow that thread is being used to, in my opinion, tamper and dampen innovation to what could be one of the most life-changing, industry-changing, value-creating advances we've ever had. And I think that the best analog is during the internet. And I was there and actually very active in these conversations.

35:33So a lot of what my positions draw from that experience. So let me just talk about that, because I think it's a great analog. During the creation of the internet and the web, We had the exact same conversations using a lot of the same rhetoric, which is, oh, my goodness, this stuff, we're running our critical infrastructure on this stuff. Now you can take over people's lives. You can have sovereignty. I actually went to think tanks in D.C. in the early 2000s on sovereignty-ending events for the United States based on the Internet happening. We would talk about digital Pearl Harbor or cyber Pearl Harbor.

36:06There's all of this concern. And all of the same things. Oh, we need to protect the people from it. that we needed to pass all this regulation, all of the same type of stuff. And at the time, it was actually a far more legitimate concern than we have today for two reasons. The first one is it actually changed the security posture of the United States because it introduced a notion of asymmetry. So before that, we were in this kind of Cold War, mutually assured destruction doctrine, which is if we have big weapons and they have big weapons, we have it to talk. And maybe we'll ease tensions, but at least you have a stalemate where...

36:41In the internet, the more advanced society was the more insecure because you were relying on the infrastructure. And so if you were like at a cyber war with a country that doesn't use computers or isn't networked, you're more at risk. So it introduced actually a new doctrine shift, which is this. But the second one is we had actual specific examples of emergent threats that we'd never seen before, like the Morris ones. We had these worms that would take out things, right? So both were you armed with these very specific things that have shown a new threat and yet after all of the discussion, the conclusions were don't hamper innovation.

37:17Open source is good. This is the way to keep the United States ahead. This is the way to have more secure systems. And that's what played out. Many of the top security contributions to the internet came from academia or open source. SC Linux is a very famous example of this. Listen, Door, which became Sourcefire, was a great example. Mandiant is a great example. The entire industry came out of open source. And so I just feel like we've had this discussion. Listen, we're in this AI time where the rhetoric is back, but hey, we don't have any of the proof points that we had on the internet. There is no clear doctrineship.

37:50And a lot of the arguments people are using for this call to regulation, they're on these kind of like non-provable science fiction views of like recursive self-improvement or creating bioweapons, which at this point have been pretty well refuted. And so I'm afraid that this kind of Bostrom view of the world has captured the imagination. We've built these systems and we're using it to hamper innovation in the name of quote unquote security. And we have not learned the lessons from the past. And in the end, it'll hurt the United States. It'll hurt academia. It'll hurt researchers. It'll hurt little tech in the industry.

38:23And so that's why I've been this call to arms. Anybody who's not in a big company who's going to basically benefit from something needs to wake up and get involved and make sure that we don't regulate ourselves out of a leadership position. So if we think about this as the role that small tech will play on all of this, what do you think people need to do? What do you hope happens? What needs to happen? It feels like a very different time than it did 20 years ago because academia has almost been entirely muzzled. I don't know why. So in the past, academia was a very vocal proponent of open source, even for critical infrastructure.

38:55BSD came from Berkeley. This ran the internet. There's nothing more critical. If you take that out, the internet doesn't work. Hospitals don't work. There was nothing more core to the safety of humans than this. And academia was on the forefront of this. The EFF came out of MIT. There was this kind of organic, individual, progressive, almost grassroots support. And now with this AI stuff, if anything, academia is totally complicit. The California bill, this horrible bill in California, which is literally, we need to protect everybody from these models that don't even exist yet. It literally came from a Berkeley professor and her student.

39:34He's coming from academia. So academia is totally complicit. So I think that those in academia that are silent, I think they need to speak up. And I actually think if, for example, they called Scott Wiener's office, he would listen. And if they called the regular, they would listen. And this is a California-specific one. I know this podcast goes to everybody, but I think academia needs to wake up and talk. I think little tech needs to wake up. And I do think that being vocal, I think political lobbying actually is quite important. I think making views known is quite important. I just think the silent masses needs to arise.

40:04If there's one takeaway I've had, having lived in the 2000s, been totally silent, having lived now, is how different the landscape is. It just feels like back then, big tech just wasn't as dominant. They weren't as involved in the government. They weren't as involved in academia. And I just feel like all of these things are not bought and paid for. And there's so few neutral voices left. And so for those that care, I think just coming out and speaking for what's right is very important. Can you teach us conceptually a little bit about the importance and role of open source versus closed source?

40:31Those are words that everyone uses. But in a sort of brass tacks way, describe the role that those two things play and what they both mean to you, just in general and technology. Yeah, for sure. I'm going to be very clear about one thing. I'm in no way an open source zealot. My company had some open source and some closed source. I invest in closed source companies all the time. And so I'm in no way thinking that you should only do open source. But it's played an incredibly important role in the industry. And one way to view it is almost like this democratizer, commoditizer of mature businesses.

41:04Unix was one of the first operating systems. It was a standard. It was used in the enterprise. Then the open source community created Linux, starting from Linux Torvalds. And then it followed the path of Unix. But all of the academics and the researchers and the hobbyists and the companies evolved that they would add security and solve a number of problems. And so because the closed source version had created this market, the open source version could come and create a flourishing ecosystem that everybody can participate. So it democratized operating system. We've seen this in many things. We saw this in databases.

41:39So like Oracle, of course, and Sybase for databases, MySQL comes, Postgres comes, and then it follows along. Of course, the smartphone and Android comes and follows along. So it's always been this or historically been this almost democratizing force that is a checks and balance for the closed source version and allows anybody else who doesn't have access to it to innovate. And if you look at, again, the history of security and safety and innovation, so much of that has been in the open source. So if you remove that, we lose all of that. We lose all of those brains and all of that effort and all of that energy in it.

42:17If you think about the talent in all of this, how does it feel different to you, if at all, from the way you source, interact with the types of people building, where they're coming from, like where you're looking for them versus the pool of SaaS talent or something like that? Does AI feel like a shift in that sense to you as an investor? Yeah, it's actually very interesting, which is you are seeing a rise of the H.D. CEOs, which is actually pretty new. Faculty at Berkeley, PhD guy, George Frazier, five tran. So you're actually starting to see almost these very deep tech focuses. And I don't know if it's because we're early in a super cycle and they're just on the bleeding edge, or it actually is a bit of a different discipline that it's not just engineering and just business, but it's actually more core to science.

43:05I don't know the answer to that. So that's one. There is an open question that we debate often in A16C. And the question is, do you back the core technical team or do you back the commercial team in a different modality? is the tech democratized enough that any good founder can get the right team and use it? Or does the team have to understand the core tech? And I got to say, I personally go back and forth on this a lot. I think because I tend to come from, listen, I did grad school, I did research, I did a company based on that. I tend to err towards the deeply technical teams, but that's clearly not always right.

43:35And so I do think a lot is up in the air. I think commercial versus tech is up in the air. I think researcher versus engineer is up in the air in this realm. And it's an active dialogue. Maybe it's a great excuse, given that a lot of these companies are in this phase of going from zero to 10 plus million of ARR to talk about your concept of company market annealing. It sticks out in my memory as a really interesting way to think about the early stages of companies. And since so many are going through that now, could you describe that idea in that framework? I've read all of these books, and they're all very useful about finding product market fit.

44:06But that's not how it ever seems to work. And now, listen, I've sat on many boards. I've watched this many times. And so the typical story in finding product market fit is you, the company, create some product, and you go ask the market what it wants. And then based on its feedback, you tweak your product and you A-B test your way into finding the right thing. And so you get all these aphorisms that I find so stupid. Solve your own problem, and then you have a company, which is just not true. Or solve a real problem with the customer. And it's not helpful, right? None of these things tell you actually how to find product market fit.

44:39So here's my experience having watched it play out multiple times, which is you both have to know your technology and evolve that as well as make the market pliable and educate the market. This is if you're doing category creation. Marketing millions is for category creation. If you're in a mature market, just go build something better. People already know how to use it. But in new markets, like the customer doesn't know the value that you have. They haven't a clue. They don't know how to use it. They don't have the workflow. So there's a ton of work that you're actually hammering the market and making it so they can accept it.

45:09And so the way that I think about it is almost like this kind of annealing where you work a little bit on your product and then you actually work on the market. By work on the market, I don't mean do interviews. I think this is like the most broken way of building a company. So you go ask, imagine this, you're going to go interview a customer and ask what they want. They're sitting down there and they feel like they need to answer something. So they'll just make something up. Oh, I need this. I want a pony, right? It's just almost like it's too much leading the wind. This is an essential buyer.

45:34And so the most successful companies, they have a true norm. They're like, we know that this technology should involve them this way. And we need to educate them on this. But there's going to be some real organizational constraints and real budget constraints. We're going to find those at the same time. So they go pull and push on the market a little bit. And then they tweak their product to do that. And then they go pull and push on the market. and they treat the product and they do that. And it's just give and take this annealing. And that's how most companies do category creation, in my opinion, not this go ask the market what it wants or go solve your own problem and the market will just understand it.

46:05If that works for you, you're very lucky, but I've never seen it. We've talked a lot about models and some of the apps built on those models. We've talked a little bit less of, first time when you and I talked mostly about the modern data stack and just data in organizations. And we haven't talked much about bare metal, like compute. And I'd love to just spend a minute or two with your impression of what's going on in each of those two worlds, maybe starting with the data side. What are new things that you're thinking about that matter in the world of data and get exaggerated by the rise of AI?

46:37Clearly unstructured data now is very, very important. We come from this cloud data warehouse world where people use SQL. Data is very well behaved and it's in some relational database somewhere. We query it and whatever. That's still incredibly important. It's still growing. but that was the dominant thing. Now more and more, these data pipelines are, give me a bunch of random videos and a bunch of random images, and a bunch of random music and a bunch of random stuff. I'm going to sort of wrangle all of those things and use that to train this model. And oh, by the way, you have to be able to do this in a way that's reproduced and more scriptable.

47:10So it's almost like the unstructured world and the structured world are converging, which I think is great. So clearly data continues to grow in importance like it has been doing since the beginning of compute. But the size for the unstructured has just been growing asymptotically. The world need new things that it doesn't have to handle all of that? New companies, new technologies, new ways of working? I think so. I don't think we yet know how to handle large pools of super unstructured data in a way that's efficient. Imagine how much video there is out there. The ability to query across video, the ability to put it into workflows, the ability to pull stuff out.

47:43I still think there's this kind of a lot of work. And there's a lot of companies working on it. And so I know right now there's a founder that's like, oh, but Bartin, my company does that. Yes, I know that. There's a lot of companies that are working on these problems, but they're not solved in the sense that somebody can't just take a big pool of video and then be able to query it just like SQL right away. And then there's just a lot of edge cases for this. So I do think that we need to mature as an industry, almost like the software industry has matured, just the tooling around managing data and getting ready for this.

48:11The collection of data is absolutely paramount in the labeling of data. And there's one lesson I've come away with in all of this stuff is the existing data that we have is great. But once you exhaust that by throwing a lot more compute at it, you've got to go get more. And nobody really knows how to do that other than maybe just pay a lot of money. I think that we need some real advances there to keep things moving. What about on the hardware side? Anything interesting that you've seen on, obviously, NVIDIA hogs all the attention in the AI world right now, especially in public markets. Anything on just the raw hardware, semis, power side that has you curious or intrigued?

48:48It's great that we're compute bound, right? I mean, the good news is these workloads are very different than cloud workloads. So we're going to see massive innovation all the way up and down the infrastructure stack, right? The communication patterns for training and the requirements on the fabric are very different than cloud workloads, and they're very different than inference. It's not clear that you'd even want to use the same fabric over time. We're back seeing massive innovation networking. The last time we saw that was when we were doing factory work for data centers. So when the data center came out, we never had that amount of density of servers in the same space.

49:19We had to evolve networking to be able to manage that much bandwidth. And then we haven't seen a lot of innovation, but now this AI is pushing us to really innovate on the networking fabric, which just goes to show you how much we have to reimagine the entire stack. And it feels on the silicon, to me, it feels still very early days. Let me just give you just a very interesting anecdote. Let's say you're going to do a billion-dollar training run for a model. So how much does it cost to create a customization? Let's say$200 million to create a customization. So if for$200 million, you could get a 50 % improvement, a 50 % improvement of a billion dollars is$500 million.

49:59$200 million is well spent. Almost like entering this space where it almost makes sense, build a custom ASIC for one training run. The economics are there that it becomes very compelling to do hardcore hardware work. Now, listen, of course, I understand all the complications of that. You have timelines set, et cetera. But I'm just trying to give everybody a mental model that it's been a very long time that you can justify building a custom piece of silicon for a single project like this. But the economics are actually there now, which is why you're seeing so much work in silicon again. So it's very exciting.

50:31I think the entire stack is going to be disrupted. Can you say a word on the relationship between investors and capital markets and pools of capital and whether or not it feels correctly calibrated to you, to the needs of the marketplace to do all this stuff. You've seen relative to other parts of the economy, some pretty lofty valuations relative to fundamentals or whatever, some huge checks being written by very large firms. What do you think? Do we have the right capital market set up for this explosion of innovation and ideas? Let me give you the two bookends views of this and somewhere in the middle.

51:04So one view of this, a common view is this is all a bubble. Investors are stupid. Valuations are crazy. It doesn't make any sense. Markets are not efficient in the sense, this will all go away. That's one view. There's another view, which is actually, we know that the economics work. Actually, there's a bunch of value creation. All of this makes sense. Markets are very efficient. I tend to be more of the latter one, but it's a very nuanced discussion because you can maximize the value in expectation. The expected value is the probability something's going to happen time, the potential payout. The expected value of solving free energy is infinite, but the chances that it happened is almost zero.

51:45And so I think markets are very efficient. I think markets realize that this is disruptive and there's going to be a lot of great companies. They realize that, but they don't know where. And so I actually think the sizing is correct. I think this is the same thing, by the way, with the internet, which is everybody said that go, like the.com was crazy and they overpaid, except for it was literally the growth driver of the world economy for 20 years and way more than made up for the valuations. I think that will happen again. The question is, how do you densely place those bets when you don't know for any given bet what the expected outcome is going to be?

52:21You just don't know these things. I do think this is one of the big ones. I do think this is a super cycle. I do think this is going to be a massive trader. I think once it's all said and done, you look back at these valuations, they're going to look very sensible. But that said, any independent bet may look silly. And I think that's the nature of venture capital. And I think that's the nature of investment. You've talked a lot about places where you feel the story is already being told. Creativity, that the tools that are available and already possible today versus things that are uncertain. What area of uncertainty do you find yourself spending the most personal time reading about, thinking about, talking about?

52:54Do you know what the first video on the internet was? Of course, I'm going to guess like cats or something. It was a coffee pot. I think there's this guy in Cambridge, some researcher. And at the end of the hall was a coffee pot. And he didn't want to walk there if there was no coffee in the coffee pot because that would be a wasted trip. And so he put a camera on that. And of course, it was on the internet. And so it became super, super popular. Because, oh my gosh, if you can have that, then of course you can have like Netflix. Do you remember the big red button? This is a very popular app early on was the big red button on the internet.

53:27It was a web page with a big red button. and if you push it, nothing happened. It just pushed. That was it. That was the entire thing. It was super popular and people were super into it. Then you ask why. You're like, oh, this is just silly. Or you could be like, maybe it's because people saw the big red button and then they imagined like you have one button, you have a bunch of buttons and then you have Salesforce, right? And so I will say, having been through the web transition, I saw all the silly things and they were silly. It was all video games and goofy stuff and culturally marginal stuff.

53:55But those are the things that grew up to be the Googles and the Yahoos and the Amazons. And none of us knew what that looked like on the other side. And there was all of these half steps. Do you remember desktop as a service? The stupid thing where you're like, I've got a desktop here. I'm going to run a desktop in the cloud. It's like, there's all these half steps where you're stuck in the old way of thinking and you can't really do the new way of thinking. So where I am right now is, this is the early days of the web. And a lot of the things that look silly, talking to a virtual girlfriend or making an image of a cat.

54:29Those I know are going to grow up to be the thing. I know it in my bones because I've seen it before, but I don't know what those are going to look like. And so, so much of what I'm asking is, what is the big red button versus not? What is that going to become? What is the future going to look like? Let me just give you one anecdote that I think is interesting on there. So my daughter's 13, COVID kid, right? Months are COVID. And that was very disruptive. It was very tough. Basically, she went from being a very social kid to basically interacting online. and lately she's been a big user of character ai so character ai is from shazir who was one of the transformer authors and but just like these kind of characters that you talk to seem silly but she really likes it she's got these characters and i've noticed recently that not only does she talk to these characters but she'll actually invite them to group chats with her friends and so it's entered the social dynamic so it very well could be that we're entering a phase where the actual social dynamics shift.

55:21And then I ask myself, okay, so for my daughter, in five years, she's 13, in five years, when she opens a bank account, is she going to go to Wells Fargo SaaS app? Or is she going to do it from her character? 100 % from her character, right? It's like almost like the access layer to the internet is changing, right? If she's going to do search, is she going to go to Google? Or is she going to do it through her character? So I just feel like these very kind of almost silly, goofy, fundamental changes that are happening right now may become the thing. And when the access layer to the internet changes or to compute changes, like everything changes behind it.

55:51So that's really what we spend a lot of time thinking about. It brings to mind a question on agents. And right now you've got these chat models where it's like the smartest model you've ever seen that has no memory and can't really affect other systems. And it seems like the next step would be it has memory, it knows about you, or it has context and it can affect other systems that can create new actions elsewhere. How do you think about it? Is that overly simplified way of thinking about what an agent is? What is an agent to you? What do you think about them? Everyone's talking about agents. So one thing that I love working on a team like I do, and I feel so lucky to have the team that I do, like everybody's so smart, so engaged and so knowledgeable is we have a different set of opinions.

56:30And the reason I bring this up this way is the agents is one where we've got very different opinions. We 100 % want to invest in agents and in the agent space. And we look at all of these companies, but I personally have been very bearish on agents as a technical direction. And the reason for this, there's a number of reasons for this, but I just feel like there's some very key technical problems that we've not quite tracked. Like, for example, the memory one that you've mentioned, Erectus is another one that we haven't done. We don't really know how to do planning very well. So it's not clear to me that the advances that we've seen with LLM models carry over to fully automation of agentic stuff.

57:08Now, that said, most of the people on my team disagree with me and think that I'm a boomer. Okay, Boomer, this stuff is amazing. I've seen these things work and they're amazing. I'm very open to be totally wrong on this point, but it just hasn't been super obvious. There's actually another area where I like this, which is this kind of idea that AI is going to code for us. So there's one view. It's like coding is done. We're just going to tell an AI what we want and it'll do everything for us. And that's almost an agentic thing, right? It'll go do the coding. And many people on my team believe that is the future.

57:39And that's great. They should do all of those deals. But my view is coding is thing where correctness is really important. I think people spend like 90 % of their time when they develop maintaining code. So actually understanding and readability is very important too. So it feels to me that the human being still has to know everything that's going on. And so clearly coding will evolve and the UI will evolve and AI will help it. But I don't think that coding goes away. I think we just, we end up coding more. And I actually don't think that developers will have more time in a week, they'll have less time in a week because they're going to be so much more efficient.

58:11So I think just low code, no code has always promised to get rid of programmers and never did it. I think AI will potentially hold the same false problem. But again, in my team, there's massive disagreements on this. And so I'm a huge fan of a company like Cursor. It creates a new IDE that's AI native IDE, and it's going to go ahead and change how people code, but it doesn't replace developers. Where other people are like, listen, why would you ever need an IDE? Because you'll never need a programmer, and I'm going to invest in this company that does code. What is the definition of an agent to you?

58:37How would you define it? An agent to me has a control loop and state. So you give it a high level objective, and then it will do multiple steps along that high level objective, and it maintains states between that. And then it actually has a controller of feedback looper determines the next step. And I don't think we've solved the state problem. I just don't think that's necessarily part of the reasoning fabric right now. It almost feels like it's tacked on. I don't think that we've solved the control loop problem in a way that converges. I feel like we still don't know, really know how to handle the non-determinism in these models.

59:13And so it's not super clear to me that this recent breakthroughs on these large generative models cracks planning practice agentic problem. Again, very open to being wrong, but that's my intuition. In addition to the agent and the automatic coding, are there other areas where you feel like your views are the most divergent, either from your team or from the markets? I think one of the biggest mistakes that we're making as an industry is AI has been around for so long. It's just all these enterprise use cases that we've tried to cram it into before. Customer service chatbots and basic automation and this low-code stuff.

59:51And what we're doing is we're taking this new thing and just cramming it all these old use cases. And we're like, oh, it's like this old version of it. But now with like genitive AI, I think this is just massive lack of creativity and vision. I think that this is a new breakthrough, in my opinion. It's got its own set of properties. And those properties are amazing for certain problems and not amazing for other problems. I personally am very excited and attracted to the problem. I think it's a great fit for the emotion companionship. I think it's a great fit for the creativity problem. I think it's a great fit for the language reasoning problem.

1:00:25It's a great fit for those. I don't think it's as good as a fit for, I'm going to now use an LLM to replace some workflow that requires correctness over long periods of time, where you don't have a human checking, et cetera. I just feel in general, I look towards the areas that are a bit emergent. But then as an industry, we just don't know them as well. They're just like the internet, like things look so silly. Whenever I talk about creativity and companionship, people are like, oh, I thought you were a serious investor. But what they don't know, these are the things that become the new thing.

1:00:55I promise. These are the big red buttons and it's the copy pot on the internet. These are glimmers. These are signs of how apps will look in the future. And that's why I started when you asked me in the very beginning, I think the future is going to be a lot weirder and wilder and more wonderful than it is today. Exactly. If you could interview anyone in the world right now that had been given truth serum, who would you interview and what would you ask them? It's my clever way of asking, what do you not know that you want to know? I would love to note, as many of the people in the AI safety debates, I want to know who's a Baptist and who's a bootlegger.

1:01:30Do you know this kind of dichotomy? Explain it, but sure. I'm going to change your question. I want to be able to ask 10 people, and I want to create these are Baptists and bootleggers. So Baptists and bootleggers is an economic phenomenon where you have regulation, for example, in time of prohibition, and the people that support it, you can often categorize as basically true believers and opportunists. So in the time of prohibition, the true believers of Baptists really thought that alcohol is bad. It costs you do bad things, you're going to go to hell. And they truly believe that the bad stuff prohibition was good and alcohol is terrible.

1:01:58So the true believers actually believe it. It turns out that in any of these situations, you also have opportunists who actually see the regulation as good. They're called bootlakers. So the bootlakers, it turns out, really thought that prohibition was great because then they could actually sell alcohol for a premium and they were put in business. I think that this is a game theoretic theme. I think this is an emergent property of these environments. And it's not a value judgment on any single person. I just think that this will always emerge. And we're definitely in an area where there are regulations that are hampering innovation or will hamper innovation that are being espoused.

1:02:35And you have many people in the room. And I think some of them are true believers. They're true Baptists. They think AI is going to recursively improve itself and kill us all and take jobs. But I don't think there's very many of those. I think the vast majority of those are actual bootleggers. Listen, this is good for my big company. This is good for my agenda. This is good for whatever it is. And they're driving that. And I honestly couldn't tell you who is who. And so what I would do is you're giving me one person to ask and I'd find some way to split it into five people to ask. And I'll try to find out who's a backless bootlegger.

1:03:05I love the answer. To end on a high note, what thing, if you had to go really tactical and specific, everything we talked about, what excites you the most? Can I say two things, actually? I think that excites me the most. I think it's a fait accompli. There's no holding this back. It doesn't matter how much bad regulation. It doesn't matter how much these large companies try to influence things. This shift is going to be so dramatic over time. It'll win. The market will win here for sure. And I feel so good about that. I'm the most frustrated. I just feel very calm. I just know that it's done.

1:03:37We just have to wait. It's just going to be bumpy along the way. And I do think that this, what has been, I do think is going to change how we think about the universe and how we think about the world and how we do it. I really think it's kind of that order of thing and it's going to happen. We just need to let it happen and then we can ease the way up. And then a very specific thing, so that gives me a lot of peace that I just feel that's done. The very specific thing is I do feel that we're very close to being able to type a prompt into a computer and end up with an entire virtual world that you can enter and is fully complete and is incredibly deep and has every aspect that we've ever wanted.

1:04:13We've had these notions in the past of the holotech or the metaverse and all of these is you have to have humans constructing it and designing it etc but i actually think that we've created enough content in the past that we can now auto generate these things and they're going to be as rich as interesting in the real world and so i'll end on this the one takeaway people probably should have taken from bostrom is not that ai is going to take over everything i think the one takeaway is we probably are living in a simulation and we're probably about to create the next one martin so much fun to talk to you your time.

1:04:45Thank you for your time. Yeah, delighted. If you enjoyed this episode, check out joincolossus.com. There you'll find every episode of this podcast complete with transcripts, show notes, and resources to keep learning. You can also sign up for our newsletter, Colossus Weekly, where we condense episodes to the big ideas, quotations, and more, as well as share the best content we find on the internet every week.

1:05:19Thank you.