The Misty AI Frontier and How to Spot Billion-Dollar Companies Before Everyone Else — Elad Gil

There are moments in time where it's There are moments in time where it's very smart to be contrarian. Mhm. And very smart to be contrarian. Mhm. And very smart to be contrarian. Mhm. And there are moments in time where being there are moments in time where being there are moments in time where being consensus is the smartest possible thing consensus is the smartest possible thing consensus is the smartest possible thing you can do. And I think right now we're you can do. And I think right now we're you can do. And I think right now we're in a moment in time where being in a moment in time where being in a moment in time where being consensus is very right. consensus is very right. consensus is very right. You know, and you can really overthink You know, and you can really overthink You know, and you can really overthink it and what's the contrarian thing? We it and what's the contrarian thing? We it and what's the contrarian thing? We should go do a bunch of hardware stuff should go do a bunch of hardware stuff should go do a bunch of hardware stuff cuz blah blah blah. cuz blah blah blah. cuz blah blah blah. And like maybe I should buy more AI. You And like maybe I should buy more AI. You And like maybe I should buy more AI. You know what I mean? It's I think people know what I mean? It's I think people know what I mean? It's I think people make these things way too complicated. make these things way too complicated. make these things way too complicated. Oh, yeah. True in every aspect of life, Oh, yeah. True in every aspect of life, Oh, yeah. True in every aspect of life, probably. probably. probably. A lot, nice to see you. Thanks for A lot, nice to see you. Thanks for A lot, nice to see you. Thanks for making the time. Appreciate it. Good to making the time. Appreciate it. Good to making the time. Appreciate it. Good to see you as always. And I thought we see you as always. And I thought we see you as always. And I thought we could begin with something we were could begin with something we were could begin with something we were chatting about or you were explaining chatting about or you were explaining chatting about or you were explaining before we started recording, which is before we started recording, which is before we started recording, which is a new phenomenon of sorts. Could you a new phenomenon of sorts. Could you a new phenomenon of sorts. Could you explain what we were just talking about? explain what we were just talking about? explain what we were just talking about? Oh, yeah. We were we were just talking Oh, yeah. We were we were just talking Oh, yeah. We were we were just talking about some of the acquisitions that are about some of the acquisitions that are about some of the acquisitions that are happening in the AI world. We saw that happening in the AI world. We saw that happening in the AI world. We saw that XAI just got an option to effectively XAI just got an option to effectively XAI just got an option to effectively purchase Cursor, it looks like. purchase Cursor, it looks like. purchase Cursor, it looks like. Obviously, Scale was sort of partially Obviously, Scale was sort of partially Obviously, Scale was sort of partially taken by Meta. There've been a variety taken by Meta. There've been a variety taken by Meta. There've been a variety of these sort of deals that have been of these sort of deals that have been of these sort of deals that have been happening over the last year or two. happening over the last year or two. happening over the last year or two. And separate from that, we were just And separate from that, we were just And separate from that, we were just talking about what does that mean for talking about what does that mean for talking about what does that mean for the AI research community and the AI the AI research community and the AI the AI research community and the AI community in general? And I think the community in general? And I think the community in general? And I think the most interesting or one of the most interesting or one of the most interesting or one of the interesting things that's happened over interesting things that's happened over interesting things that's happened over the last year or so is Meta really the last year or so is Meta really the last year or so is Meta really started aggressively bidding on AI started aggressively bidding on AI started aggressively bidding on AI talent, which is a very rational talent, which is a very rational talent, which is a very rational strategy, right? They're going to spend strategy, right? They're going to spend strategy, right? They're going to spend tens of billions of dollars on compute. tens of billions of dollars on compute. tens of billions of dollars on compute. So, it made sense to have a real budget So, it made sense to have a real budget So, it made sense to have a real budget to go after people. to go after people. to go after people. And normally what happens in tech is a And normally what happens in tech is a And normally what happens in tech is a single company will go public. single company will go public. single company will go public. And a bunch of people from that company And a bunch of people from that company And a bunch of people from that company will be enriched and then a subset of will be enriched and then a subset of will be enriched and then a subset of them will continue to be heads down and them will continue to be heads down and them will continue to be heads down and working really hard and focus on their working really hard and focus on their working really hard and focus on their original mission. And a subset of people original mission. And a subset of people original mission. And a subset of people start to get distracted. They may go and start to get distracted. They may go and start to get distracted. They may go and work on passion projects for society. work on passion projects for society. work on passion projects for society. They may get involved with politics. They may get involved with politics. They may get involved with politics. They may go start a company. They may They may go start a company. They may They may go start a company. They may just kind of check out and hang out or just kind of check out and hang out or just kind of check out and hang out or go to the beach uh hum kind of thing. go to the beach uh hum kind of thing. go to the beach uh hum kind of thing. And what happened recently is because of And what happened recently is because of And what happened recently is because of the Meta offers and then all the other the Meta offers and then all the other the Meta offers and then all the other major tech companies having to match major tech companies having to match major tech companies having to match offers for their best researchers, offers for their best researchers, offers for their best researchers, somewhere between 50 and a few hundred somewhere between 50 and a few hundred somewhere between 50 and a few hundred people effectively had an IPO but as a people effectively had an IPO but as a people effectively had an IPO but as a class of people. it wasn't like they class of people. it wasn't like they class of people. it wasn't like they were a one company, they were spread were a one company, they were spread were a one company, they were spread across Silicon Valley, but all of their across Silicon Valley, but all of their across Silicon Valley, but all of their pay packages suddenly went up pay packages suddenly went up pay packages suddenly went up dramatically and they experienced the dramatically and they experienced the dramatically and they experienced the equivalent of an IPO. And that's really equivalent of an IPO. And that's really equivalent of an IPO. And that's really unusual. It's kind of the personal IPO. unusual. It's kind of the personal IPO. unusual. It's kind of the personal IPO. And the only time in history I can think And the only time in history I can think And the only time in history I can think of where I've seen it happen before is of where I've seen it happen before is of where I've seen it happen before is in crypto, in crypto, in crypto, where a bunch of the really early crypto where a bunch of the really early crypto where a bunch of the really early crypto holders or founders suddenly as a class holders or founders suddenly as a class holders or founders suddenly as a class all went effectively public in 20, I all went effectively public in 20, I all went effectively public in 20, I guess 17-ish. Mhm. And then again more guess 17-ish. Mhm. And then again more guess 17-ish. Mhm. And then again more recently, but this is really recently, but this is really recently, but this is really interesting. It's kind of under interesting. It's kind of under interesting. It's kind of under discussed. It It may not have huge discussed. It It may not have huge discussed. It It may not have huge long-term implications, but it does mean long-term implications, but it does mean long-term implications, but it does mean a subset of people will a subset of people will a subset of people will change what they're focused on, try and change what they're focused on, try and change what they're focused on, try and do big science projects to help do big science projects to help do big science projects to help humanity, work on humanity, work on humanity, work on AI for science maybe, maybe some people AI for science maybe, maybe some people AI for science maybe, maybe some people will go off and do personal quests or, will go off and do personal quests or, will go off and do personal quests or, you know, things like that. Or just you know, things like that. Or just you know, things like that. Or just quiet quit and do lots of drugs and quiet quit and do lots of drugs and quiet quit and do lots of drugs and chase vices. I mean, there's that, too. chase vices. I mean, there's that, too. chase vices. I mean, there's that, too. Definitely not. In that case, you look Definitely not. In that case, you look Definitely not. In that case, you look around say Austin, you've got the around say Austin, you've got the around say Austin, you've got the Dellionaires, which refers to Dell Dellionaires, which refers to Dell Dellionaires, which refers to Dell post-IPO early employees and so on. But post-IPO early employees and so on. But post-IPO early employees and so on. But as a class of people when that happens, as a class of people when that happens, as a class of people when that happens, I suppose we don't know how I suppose we don't know how I suppose we don't know how how large or how long-term the how large or how long-term the how large or how long-term the implications are, but there seem to be implications are, but there seem to be implications are, but there seem to be implications. And I don't know anyone, implications. And I don't know anyone, implications. And I don't know anyone, well, I know only a few people who I well, I know only a few people who I well, I know only a few people who I would go to as would go to as would go to as technical enough and also kind of broad technical enough and also kind of broad technical enough and also kind of broad enough in their awareness and networks enough in their awareness and networks enough in their awareness and networks to watch AI and to the extent that to watch AI and to the extent that to watch AI and to the extent that someone can watch it comprehensively, I someone can watch it comprehensively, I someone can watch it comprehensively, I would put you in that bucket. And you would put you in that bucket. And you would put you in that bucket. And you wrote this week just to talk about some wrote this week just to talk about some wrote this week just to talk about some of the other of the other of the other kind of elements at play here. kind of elements at play here. kind of elements at play here. The compute constraints that AI labs are The compute constraints that AI labs are The compute constraints that AI labs are facing and the implications and maybe facing and the implications and maybe facing and the implications and maybe for the next for the next for the next one to five years. This is in a piece one to five years. This is in a piece one to five years. This is in a piece people should check out. It's random people should check out. It's random people should check out. It's random thoughts while gazing at the misty AI thoughts while gazing at the misty AI thoughts while gazing at the misty AI frontier. Good headline, by the way. frontier. Good headline, by the way. frontier. Good headline, by the way. Very dramatic. Yeah, very dramatic. I Very dramatic. Yeah, very dramatic. I Very dramatic. Yeah, very dramatic. I love it. It's very evocative. Would you love it. It's very evocative. Would you love it. It's very evocative. Would you mind explain Actually, before we move to mind explain Actually, before we move to mind explain Actually, before we move to the compute constraints, cuz I do want the compute constraints, cuz I do want the compute constraints, cuz I do want you to you to you to talk to that next, but for people who talk to that next, but for people who talk to that next, but for people who don't have any real context on the don't have any real context on the don't have any real context on the talent wars and what you were just talent wars and what you were just talent wars and what you were just mentioning earlier with meta like on the mentioning earlier with meta like on the mentioning earlier with meta like on the high end, what do some of these pay high end, what do some of these pay high end, what do some of these pay {slash} equity packages, compensation {slash} equity packages, compensation {slash} equity packages, compensation packages look like that are getting packages look like that are getting packages look like that are getting offered? I don't have exact knowledge of offered? I don't have exact knowledge of offered? I don't have exact knowledge of the full range and everything else. The the full range and everything else. The the full range and everything else. The rumors and the things that have kind of rumors and the things that have kind of rumors and the things that have kind of made it into the press, the claims are made it into the press, the claims are made it into the press, the claims are that that that these things are between tens of these things are between tens of these things are between tens of millions and hundreds of millions of millions and hundreds of millions of millions and hundreds of millions of dollars per person. dollars per person. dollars per person. And again, it's a very small number of And again, it's a very small number of And again, it's a very small number of people who get anything that's quite people who get anything that's quite people who get anything that's quite that outsized. that outsized. that outsized. But I think the basic idea is we're in But I think the basic idea is we're in But I think the basic idea is we're in one of the most important technology one of the most important technology one of the most important technology races of all times. races of all times. races of all times. And the faster that we get to sort of And the faster that we get to sort of And the faster that we get to sort of better and better AI, the more economic better and better AI, the more economic better and better AI, the more economic value will effectively show up. And value will effectively show up. And value will effectively show up. And therefore people are really willing to therefore people are really willing to therefore people are really willing to pay in an outsized way for the handful pay in an outsized way for the handful pay in an outsized way for the handful of people who are the world's best at of people who are the world's best at of people who are the world's best at this thing. this thing. this thing. And 5, 10 years ago, these people were And 5, 10 years ago, these people were And 5, 10 years ago, these people were like well compensated, but it was a like well compensated, but it was a like well compensated, but it was a completely different ballgame. They completely different ballgame. They completely different ballgame. They never just wasn't the core of everything never just wasn't the core of everything never just wasn't the core of everything that's happening in technology, but also that's happening in technology, but also that's happening in technology, but also honestly societally and politically and honestly societally and politically and honestly societally and politically and you know, for education and health. Like you know, for education and health. Like you know, for education and health. Like it's going to have all these really it's going to have all these really it's going to have all these really broad and I think largely positive broad and I think largely positive broad and I think largely positive implications for the world. implications for the world. implications for the world. But it is the moment of transformation But it is the moment of transformation But it is the moment of transformation and so some of these key packages are and so some of these key packages are and so some of these key packages are going way up. What are the compute going way up. What are the compute going way up. What are the compute constraints that you discuss in your constraints that you discuss in your constraints that you discuss in your recent piece? So basically all the recent piece? So basically all the recent piece? So basically all the different people call them labs now. different people call them labs now. different people call them labs now. There's OpenAI, there's Anthropic, There's OpenAI, there's Anthropic, There's OpenAI, there's Anthropic, there's Google, there's xAI, etc. All there's Google, there's xAI, etc. All there's Google, there's xAI, etc. All the labs are basically training these the labs are basically training these the labs are basically training these giant models and effectively what you do giant models and effectively what you do giant models and effectively what you do is you buy a bunch of chips from Nvidia is you buy a bunch of chips from Nvidia is you buy a bunch of chips from Nvidia and you're actually building out a and you're actually building out a and you're actually building out a system. So you have chips from Nvidia, system. So you have chips from Nvidia, system. So you have chips from Nvidia, you have memory from Hynix and Samsung you have memory from Hynix and Samsung you have memory from Hynix and Samsung and other places and you're building out and other places and you're building out and other places and you're building out data center. There's all these things data center. There's all these things data center. There's all these things that go into building these big systems that go into building these big systems that go into building these big systems and data centers and everything else and and data centers and everything else and and data centers and everything else and you basically have clusters of hundreds you basically have clusters of hundreds you basically have clusters of hundreds of thousands or millions or of thousands or millions or of thousands or millions or the scale keeps going up the scale keeps going up the scale keeps going up of systems that you're buying from of systems that you're buying from of systems that you're buying from Nvidia and from others. Google has their Nvidia and from others. Google has their Nvidia and from others. Google has their TPU, there's other systems as well. And TPU, there's other systems as well. And TPU, there's other systems as well. And you're using that to basically train an you're using that to basically train an you're using that to basically train an AI model. And what that means is you're AI model. And what that means is you're AI model. And what that means is you're running huge amounts of data against running huge amounts of data against running huge amounts of data against these these big clouds, and eventually these these big clouds, and eventually these these big clouds, and eventually the crazy thing is your output or your the crazy thing is your output or your the crazy thing is your output or your model is literally like a flat file. model is literally like a flat file. model is literally like a flat file. It's like almost like outputting a text It's like almost like outputting a text It's like almost like outputting a text doc or something. And that text doc is doc or something. And that text doc is doc or something. And that text doc is what you then load to run AI. what you then load to run AI. what you then load to run AI. Which is insane if you think about it. Which is insane if you think about it. Which is insane if you think about it. You use a giant cloud for months and You use a giant cloud for months and You use a giant cloud for months and months and months and your output is months and months and your output is months and months and your output is like a small file. like a small file. like a small file. >> [snorts] >> [snorts] >> [snorts] >> And [laughter] that small file is a mix >> And [laughter] that small file is a mix >> And [laughter] that small file is a mix of representing all of humanity's of representing all of humanity's of representing all of humanity's knowledge that's available on the knowledge that's available on the knowledge that's available on the internet plus logic and reasoning and internet plus logic and reasoning and internet plus logic and reasoning and other things built into it. And you can other things built into it. And you can other things built into it. And you can kind of think about that in the context kind of think about that in the context kind of think about that in the context of your brain, right? You have three or of your brain, right? You have three or of your brain, right? You have three or four billion base pairs of DNA, and four billion base pairs of DNA, and four billion base pairs of DNA, and that's more than enough to specify that's more than enough to specify that's more than enough to specify everything about your physical being, everything about your physical being, everything about your physical being, but also your brain and your mind and but also your brain and your mind and but also your brain and your mind and how it works and how you can see things how it works and how you can see things how it works and how you can see things and talk and taste things and all your and talk and taste things and all your and talk and taste things and all your senses and everything's just senses and everything's just senses and everything's just encapsulated in these very small number encapsulated in these very small number encapsulated in these very small number of genes, actually. And so similarly, of genes, actually. And so similarly, of genes, actually. And so similarly, you can encapsulate all of human you can encapsulate all of human you can encapsulate all of human knowledge into like this flat file, knowledge into like this flat file, knowledge into like this flat file, effectively. How do you think about the effectively. How do you think about the effectively. How do you think about the constraints, then? What are the constraints, then? What are the constraints, then? What are the constraints? Every year the constraint constraints? Every year the constraint constraints? Every year the constraint building out these big clouds to train building out these big clouds to train building out these big clouds to train AI. And then also what's known as AI. And then also what's known as AI. And then also what's known as inference, where you're actually using inference, where you're actually using inference, where you're actually using these chips to understand to run the AI these chips to understand to run the AI these chips to understand to run the AI system itself. You need lots and lots of system itself. You need lots and lots of system itself. You need lots and lots of chips from Nvidia to do this or TPUs or chips from Nvidia to do this or TPUs or chips from Nvidia to do this or TPUs or others, but then you also need other others, but then you also need other others, but then you also need other things. You need packaging to actually things. You need packaging to actually things. You need packaging to actually be able to package the chips. And so be able to package the chips. And so be able to package the chips. And so there's a whole supply chain around there's a whole supply chain around there's a whole supply chain around building out these systems. And building out these systems. And building out these systems. And different parts of that supply chain different parts of that supply chain different parts of that supply chain have constraints on them at different have constraints on them at different have constraints on them at different times. And so right now the major times. And so right now the major times. And so right now the major constraint is memory, or a specific type constraint is memory, or a specific type constraint is memory, or a specific type of memory that's largely made by Korean of memory that's largely made by Korean of memory that's largely made by Korean companies, although there's some broader companies, although there's some broader companies, although there's some broader providers of it. providers of it. providers of it. And people think that that memory And people think that that memory And people think that that memory constraint will exist for about two constraint will exist for about two constraint will exist for about two years, maybe plus or minus, years, maybe plus or minus, years, maybe plus or minus, because ultimately the capacity of those because ultimately the capacity of those because ultimately the capacity of those companies has been lower than the companies has been lower than the companies has been lower than the capacity for everything else in the capacity for everything else in the capacity for everything else in the system. People think other constraints system. People think other constraints system. People think other constraints in the future may literally be building in the future may literally be building in the future may literally be building out the data centers or power and energy out the data centers or power and energy out the data centers or power and energy to run these things, right? But for to run these things, right? But for to run these things, right? But for today it's this memory. today it's this memory. today it's this memory. And so everybody in the industry is And so everybody in the industry is And so everybody in the industry is constrained in terms of how much compute constrained in terms of how much compute constrained in terms of how much compute they can buy to throw out these things. they can buy to throw out these things. they can buy to throw out these things. And so what that does is it creates a And so what that does is it creates a And so what that does is it creates a ceiling on top of how big you can scale ceiling on top of how big you can scale ceiling on top of how big you can scale these models up in the short run. these models up in the short run. these models up in the short run. Cuz every lab is buying as much as it Cuz every lab is buying as much as it Cuz every lab is buying as much as it can, a bunch of startups are buying as can, a bunch of startups are buying as can, a bunch of startups are buying as much of these computers as they can. much of these computers as they can. much of these computers as they can. And everybody's constrained. And everybody's constrained. And everybody's constrained. What that means though is you have an What that means though is you have an What that means though is you have an artificial ceiling on how big artificial ceiling on how big artificial ceiling on how big a model can get in the short run and how a model can get in the short run and how a model can get in the short run and how much inference can run or how many much inference can run or how many much inference can run or how many things you can actually do with the AI things you can actually do with the AI things you can actually do with the AI right now. And that also means that right now. And that also means that right now. And that also means that you're effectively enforcing a situation you're effectively enforcing a situation you're effectively enforcing a situation where no one lab can pull so far ahead where no one lab can pull so far ahead where no one lab can pull so far ahead of everybody else because they can't buy of everybody else because they can't buy of everybody else because they can't buy 10 times as much computer as everybody 10 times as much computer as everybody 10 times as much computer as everybody else. else. else. And there are these scale laws that the And there are these scale laws that the And there are these scale laws that the more compute you have the bigger the AI more compute you have the bigger the AI more compute you have the bigger the AI model you can build and in many cases model you can build and in many cases model you can build and in many cases the more performing it can be the more performing it can be the more performing it can be eventually. eventually. eventually. And so that may mean that over the next And so that may mean that over the next And so that may mean that over the next two years-ish all these labs should be two years-ish all these labs should be two years-ish all these labs should be roughly close to each other because roughly close to each other because roughly close to each other because nobody has the capacity to pull ahead. nobody has the capacity to pull ahead. nobody has the capacity to pull ahead. And when the constraint comes off there And when the constraint comes off there And when the constraint comes off there is some world where you could make an is some world where you could make an is some world where you could make an argument that suddenly somebody can pull argument that suddenly somebody can pull argument that suddenly somebody can pull far ahead of everybody else. far ahead of everybody else. far ahead of everybody else. So right now OpenAI, Anthropic, Google, So right now OpenAI, Anthropic, Google, So right now OpenAI, Anthropic, Google, you know, they're they're reasonably you know, they're they're reasonably you know, they're they're reasonably close in terms of capabilities although close in terms of capabilities although close in terms of capabilities although some will pull ahead on one thing versus some will pull ahead on one thing versus some will pull ahead on one thing versus another. another. another. That should roughly continue everybody That should roughly continue everybody That should roughly continue everybody thinks for the next at least two years thinks for the next at least two years thinks for the next at least two years because of this. Google is also because of this. Google is also because of this. Google is also constrained by constrained by constrained by the memory from Samsung, Micron, etc. the memory from Samsung, Micron, etc. the memory from Samsung, Micron, etc. They're they're similarly constrained as They're they're similarly constrained as They're they're similarly constrained as the other players. Right now everybody the other players. Right now everybody the other players. Right now everybody is similarly constrained. is similarly constrained. is similarly constrained. And you know, a subset of these labs And you know, a subset of these labs And you know, a subset of these labs either are already making their own either are already making their own either are already making their own chips or systems like Google has TPUs chips or systems like Google has TPUs chips or systems like Google has TPUs and other things. Amazon has actually and other things. Amazon has actually and other things. Amazon has actually built its own chips called Traniums. And built its own chips called Traniums. And built its own chips called Traniums. And so there's basically like different so there's basically like different so there's basically like different systems for different companies but systems for different companies but systems for different companies but fundamentally all of them are are fundamentally all of them are are fundamentally all of them are are limited in terms of how much they can limited in terms of how much they can limited in terms of how much they can either manufacture themselves, purchase either manufacture themselves, purchase either manufacture themselves, purchase themselves. themselves. themselves. And a year or two ago the main And a year or two ago the main And a year or two ago the main constraint was packaging, now it's it's constraint was packaging, now it's it's constraint was packaging, now it's it's memory. memory. memory. Two years from now who knows, maybe Two years from now who knows, maybe Two years from now who knows, maybe something else. We constantly are something else. We constantly are something else. We constantly are hitting bottlenecks as we're trying to hitting bottlenecks as we're trying to hitting bottlenecks as we're trying to do this build out. This is probably do this build out. This is probably do this build out. This is probably going to be a naive question because I'm going to be a naive question because I'm going to be a naive question because I'm a muggle and not able to write technical a muggle and not able to write technical a muggle and not able to write technical white papers or anything approaching white papers or anything approaching white papers or anything approaching that, that, that, but it seems to me that I'm not the but it seems to me that I'm not the but it seems to me that I'm not the first person to say this. We're first person to say this. We're first person to say this. We're better at forecasting problems than better at forecasting problems than better at forecasting problems than solutions potentially. And so for solutions potentially. And so for solutions potentially. And so for instance, way back in the day, the price instance, way back in the day, the price instance, way back in the day, the price per gallon of gasoline or petrol goes per gallon of gasoline or petrol goes per gallon of gasoline or petrol goes above a certain point. Okay, people are above a certain point. Okay, people are above a certain point. Okay, people are forecasting forecasting forecasting doom and destruction. But doom and destruction. But doom and destruction. But past a certain price per barrel, past a certain price per barrel, past a certain price per barrel, suddenly new means of extraction became suddenly new means of extraction became suddenly new means of extraction became feasible. And there were investments feasible. And there were investments feasible. And there were investments made in things like fracking and so on. made in things like fracking and so on. made in things like fracking and so on. Is there sort of a plausible scenario in Is there sort of a plausible scenario in Is there sort of a plausible scenario in which there is some type of workaround? which there is some type of workaround? which there is some type of workaround? If you're along those lines, if that If you're along those lines, if that If you're along those lines, if that makes any sense. I don't know. Maybe makes any sense. I don't know. Maybe makes any sense. I don't know. Maybe there isn't. As far as I know, there so there isn't. As far as I know, there so there isn't. As far as I know, there so far at least is not. Mhm. Part of that far at least is not. Mhm. Part of that far at least is not. Mhm. Part of that is because of the way that some of these is because of the way that some of these is because of the way that some of these things are built and it's basically things are built and it's basically things are built and it's basically the capacity that you need, for example, the capacity that you need, for example, the capacity that you need, for example, for memory is basically a type of fab. for memory is basically a type of fab. for memory is basically a type of fab. Mhm. And so you need time to build out Mhm. And so you need time to build out Mhm. And so you need time to build out the fab and to get the equipment and put the fab and to get the equipment and put the fab and to get the equipment and put the lines in place. Right. the lines in place. Right. the lines in place. Right. >> So it's a traditional sort of cap X into >> So it's a traditional sort of cap X into >> So it's a traditional sort of cap X into infrastructure cycle. Mhm. Yeah. These infrastructure cycle. Mhm. Yeah. These infrastructure cycle. Mhm. Yeah. These companies basically under invested in companies basically under invested in companies basically under invested in that. Because they they didn't quite that. Because they they didn't quite that. Because they they didn't quite believe the demand forecasts that other believe the demand forecasts that other believe the demand forecasts that other people had around this stuff. Mhm. And people had around this stuff. Mhm. And people had around this stuff. Mhm. And so now they're trying to catch up. And so now they're trying to catch up. And so now they're trying to catch up. And so it's it's one of these things where so it's it's one of these things where so it's it's one of these things where everybody keeps saying, "Well, AI is everybody keeps saying, "Well, AI is everybody keeps saying, "Well, AI is growing so fast. How can it possibly growing so fast. How can it possibly growing so fast. How can it possibly keep growing at this rate?" But it keeps keep growing at this rate?" But it keeps keep growing at this rate?" But it keeps growing at this rate. It just keeps growing at this rate. It just keeps growing at this rate. It just keeps going. And that's because its going. And that's because its going. And that's because its capabilities are so impactful and so capabilities are so impactful and so capabilities are so impactful and so important. And so you look at the important. And so you look at the important. And so you look at the revenue of these companies revenue of these companies revenue of these companies and it's interesting. I can I can send and it's interesting. I can I can send and it's interesting. I can I can send you the chart later, but you the chart later, but you the chart later, but Jared on my team put together a graph of Jared on my team put together a graph of Jared on my team put together a graph of how long did it take for companies to how long did it take for companies to how long did it take for companies to get to a billion dollars in revenue and get to a billion dollars in revenue and get to a billion dollars in revenue and then from a billion to 10 billion and then from a billion to 10 billion and then from a billion to 10 billion and then from 10 to like 100. And there's then from 10 to like 100. And there's then from 10 to like 100. And there's only a small number of companies that only a small number of companies that only a small number of companies that have ever done that. And you can have ever done that. And you can have ever done that. And you can literally look by generation of company literally look by generation of company literally look by generation of company how long it took. And so for example, I how long it took. And so for example, I how long it took. And so for example, I can't remember ADP or somebody it took can't remember ADP or somebody it took can't remember ADP or somebody it took them 30 years to get to a billion them 30 years to get to a billion them 30 years to get to a billion revenue or whatever it is. And Anthropic revenue or whatever it is. And Anthropic revenue or whatever it is. And Anthropic and OpenAI did that in like a year. And and OpenAI did that in like a year. And and OpenAI did that in like a year. And for Google it took four years or for Google it took four years or for Google it took four years or whatever. I don't remember exactly what whatever. I don't remember exactly what whatever. I don't remember exactly what the numbers are. But it was kind of like the numbers are. But it was kind of like the numbers are. But it was kind of like as you go through these subsequent as you go through these subsequent as you go through these subsequent generations it gets faster and faster to generations it gets faster and faster to generations it gets faster and faster to get to scale. get to scale. get to scale. Right now, OpenAI and Anthropic are each Right now, OpenAI and Anthropic are each Right now, OpenAI and Anthropic are each rumored to be roughly around $30 billion rumored to be roughly around $30 billion rumored to be roughly around $30 billion run rate. run rate. run rate. Which is insane. That's crazy. That's Which is insane. That's crazy. That's Which is insane. That's crazy. That's 0.1% [snorts] 0.1% [snorts] 0.1% [snorts] of US GDP. of US GDP. of US GDP. So, AI probably went from zero to half a So, AI probably went from zero to half a So, AI probably went from zero to half a percent of GDP, at least as a as a percent of GDP, at least as a as a percent of GDP, at least as a as a revenue contributor. revenue contributor. revenue contributor. And you extrapolate out and if they hit And you extrapolate out and if they hit And you extrapolate out and if they hit 100 billion in revenue in the next year 100 billion in revenue in the next year 100 billion in revenue in the next year or 2 years, whatever it is, then we're or 2 years, whatever it is, then we're or 2 years, whatever it is, then we're getting close to a place where each of getting close to a place where each of getting close to a place where each of these companies is a percent or two of these companies is a percent or two of these companies is a percent or two of GDP. That's insane if you think about GDP. That's insane if you think about GDP. That's insane if you think about that. It's bananas. Yeah, it's bananas. that. It's bananas. Yeah, it's bananas. that. It's bananas. Yeah, it's bananas. That stuff is really actually important That stuff is really actually important That stuff is really actually important and useful a lot. and useful a lot. and useful a lot. That doesn't include like the cloud That doesn't include like the cloud That doesn't include like the cloud revenue for Azure for revenue for Azure for revenue for Azure for doing AI stuff or, you know, Google GCP doing AI stuff or, you know, Google GCP doing AI stuff or, you know, Google GCP or Amazon like it's just those two or Amazon like it's just those two or Amazon like it's just those two companies. It's insane. Mhm. I would companies. It's insane. Mhm. I would companies. It's insane. Mhm. I would love to dig into your thinking cuz love to dig into your thinking cuz love to dig into your thinking cuz you're you're one of the best you're you're one of the best you're you're one of the best kind of first principles and also kind of first principles and also kind of first principles and also systems thinkers I've met. And systems thinkers I've met. And systems thinkers I've met. And I love having conversations with you I love having conversations with you I love having conversations with you because I always learn something new and because I always learn something new and because I always learn something new and it's not necessarily a data point, but it's not necessarily a data point, but it's not necessarily a data point, but often it might be a lens often it might be a lens often it might be a lens or a framework for thinking about or a framework for thinking about or a framework for thinking about different things. And that framework different things. And that framework different things. And that framework evolves for you as well. But for evolves for you as well. But for evolves for you as well. But for instance, if I was looking at this instance, if I was looking at this instance, if I was looking at this interview you did, this is a while back, interview you did, this is a while back, interview you did, this is a while back, with First Round Capital and you're with First Round Capital and you're with First Round Capital and you're talking about sort of market first and talking about sort of market first and talking about sort of market first and then strength of team second. But you then strength of team second. But you then strength of team second. But you talked about passing on investing in talked about passing on investing in talked about passing on investing in Lyft Series C. This was at the time. And Lyft Series C. This was at the time. And Lyft Series C. This was at the time. And ultimately part of it seemed to hinge on ultimately part of it seemed to hinge on ultimately part of it seemed to hinge on winner-take-all versus oligopoly versus winner-take-all versus oligopoly versus winner-take-all versus oligopoly versus other. And I'm curious how you are other. And I'm curious how you are other. And I'm curious how you are thinking about that within the AI space thinking about that within the AI space thinking about that within the AI space because I mean, you started skating for because I mean, you started skating for because I mean, you started skating for that puck that puck that puck before almost anyone I know, before almost anyone I know, before almost anyone I know, if not everyone I know. And this how are if not everyone I know. And this how are if not everyone I know. And this how are you thinking about that? And this ties you thinking about that? And this ties you thinking about that? And this ties into something that you mentioned in into something that you mentioned in into something that you mentioned in your piece that I haven't heard your piece that I haven't heard your piece that I haven't heard anyone else talking about, but I'll give anyone else talking about, but I'll give anyone else talking about, but I'll give the sentence as a cue. I don't think the sentence as a cue. I don't think the sentence as a cue. I don't think you'll need it, but founders running you'll need it, but founders running you'll need it, but founders running successful AI companies should all take successful AI companies should all take successful AI companies should all take a cold, hard look at exiting in the next a cold, hard look at exiting in the next a cold, hard look at exiting in the next 12 to 18 months, which might be a value 12 to 18 months, which might be a value 12 to 18 months, which might be a value maximizing moment for outcomes. And you maximizing moment for outcomes. And you maximizing moment for outcomes. And you sort of went back to sort of went back to sort of went back to the dot com bust and the sort of the dot com bust and the sort of the dot com bust and the sort of survival rates and then breakout rates. survival rates and then breakout rates. survival rates and then breakout rates. Could you just explain that sentence Could you just explain that sentence Could you just explain that sentence sure and then also explain how you're sure and then also explain how you're sure and then also explain how you're thinking about whether you think this thinking about whether you think this thinking about whether you think this will be winner take all oligopoly like will be winner take all oligopoly like will be winner take all oligopoly like what type of dynamic you think emerges? what type of dynamic you think emerges? what type of dynamic you think emerges? In terms of the precedent and that In terms of the precedent and that In terms of the precedent and that doesn't mean it's going to happen here, doesn't mean it's going to happen here, doesn't mean it's going to happen here, but if you look at every technology but if you look at every technology but if you look at every technology cycle cycle cycle 90 95 99% of the companies in that cycle 90 95 99% of the companies in that cycle 90 95 99% of the companies in that cycle go bust. Mhm. And that dates way back go bust. Mhm. And that dates way back go bust. Mhm. And that dates way back even to what was high-tech 100 years even to what was high-tech 100 years even to what was high-tech 100 years ago, which was the automotive industry ago, which was the automotive industry ago, which was the automotive industry in Detroit dozens of car companies and in Detroit dozens of car companies and in Detroit dozens of car companies and hundreds of suppliers and it collapsed hundreds of suppliers and it collapsed hundreds of suppliers and it collapsed into a small number of auto companies into a small number of auto companies into a small number of auto companies eventually. And so this is not a new eventually. And so this is not a new eventually. And so this is not a new story during the internet cycle or story during the internet cycle or story during the internet cycle or bubble of the 90s bubble of the 90s bubble of the 90s 450 companies went public in 99, 450 or 450 companies went public in 99, 450 or 450 companies went public in 99, 450 or so companies went public in the first so companies went public in the first so companies went public in the first few months of of 2000. few months of of 2000. few months of of 2000. And so that was 900 companies and say And so that was 900 companies and say And so that was 900 companies and say another another another 500 or 1000 went public in the couple 500 or 1000 went public in the couple 500 or 1000 went public in the couple years before that. So you had somewhere years before that. So you had somewhere years before that. So you had somewhere between 1500 and 2000 companies go between 1500 and 2000 companies go between 1500 and 2000 companies go public. public. public. Go public. So that means they kind of Go public. So that means they kind of Go public. So that means they kind of made it. Mhm. And of those how many have made it. Mhm. And of those how many have made it. Mhm. And of those how many have survived? A dozen? Maybe two dozen? survived? A dozen? Maybe two dozen? survived? A dozen? Maybe two dozen? Yeah. Yeah. Yeah. And so out of 2000 companies 1980 And so out of 2000 companies 1980 And so out of 2000 companies 1980 or so went under. Mhm. What form or or so went under. Mhm. What form or or so went under. Mhm. What form or another. Maybe they got bought for a another. Maybe they got bought for a another. Maybe they got bought for a little bit. little bit. little bit. And so there's no reason to think that And so there's no reason to think that And so there's no reason to think that AI cycle will be any different. And AI cycle will be any different. And AI cycle will be any different. And every cycle was like that. SaaS was like every cycle was like that. SaaS was like every cycle was like that. SaaS was like that, automobile was like that and that, automobile was like that and that, automobile was like that and crypto was like that. crypto was like that. crypto was like that. So most companies are not going to make So most companies are not going to make So most companies are not going to make it. A handful will and we can talk about it. A handful will and we can talk about it. A handful will and we can talk about those. And so if you're running an AI those. And so if you're running an AI those. And so if you're running an AI company right now, you should ask company right now, you should ask company right now, you should ask yourself what is the nature of the yourself what is the nature of the yourself what is the nature of the durability of your company and are you durability of your company and are you durability of your company and are you one of that dozen or two that are going one of that dozen or two that are going one of that dozen or two that are going to be really important 10 years from now to be really important 10 years from now to be really important 10 years from now or is now a good moment for you to sell or is now a good moment for you to sell or is now a good moment for you to sell because what you're doing will start to because what you're doing will start to because what you're doing will start to get commoditized or will be competed by get commoditized or will be competed by get commoditized or will be competed by a lab, or will be something that the a lab, or will be something that the a lab, or will be something that the market will shift or the technology will market will shift or the technology will market will shift or the technology will shift and you'll become obsolete. shift and you'll become obsolete. shift and you'll become obsolete. And there's a handful of companies that And there's a handful of companies that And there's a handful of companies that will continue to be great. They should will continue to be great. They should will continue to be great. They should never sell, they should never exit, they never sell, they should never exit, they never sell, they should never exit, they should keep going. should keep going. should keep going. But there's probably a lot of companies But there's probably a lot of companies But there's probably a lot of companies that now or the next 12 to 18 months that now or the next 12 to 18 months that now or the next 12 to 18 months is the best moment for them possible in is the best moment for them possible in is the best moment for them possible in terms of value that they'll get for what terms of value that they'll get for what terms of value that they'll get for what they're doing. And for every company they're doing. And for every company they're doing. And for every company there's a value maximizing moment where there's a value maximizing moment where there's a value maximizing moment where they hit their peak. And it's usually a they hit their peak. And it's usually a they hit their peak. And it's usually a window. There's usually a you know 6 to window. There's usually a you know 6 to window. There's usually a you know 6 to 12 months where what you're doing is 12 months where what you're doing is 12 months where what you're doing is important enough, you're scaling enough, important enough, you're scaling enough, important enough, you're scaling enough, everything's working before some everything's working before some everything's working before some headwind hits you. Mhm. And sometimes headwind hits you. Mhm. And sometimes headwind hits you. Mhm. And sometimes it's very predictable that that headwind it's very predictable that that headwind it's very predictable that that headwind is coming and you can see it. And often is coming and you can see it. And often is coming and you can see it. And often you see it in the second derivative of you see it in the second derivative of you see it in the second derivative of growth. Like how fast are you growing growth. Like how fast are you growing growth. Like how fast are you growing starts to plateau a little bit and starts to plateau a little bit and starts to plateau a little bit and you're either going to keep going up or you're either going to keep going up or you're either going to keep going up or you should sell. you should sell. you should sell. >> Mhm. And so that's really what that's >> Mhm. And so that's really what that's >> Mhm. And so that's really what that's meant to be. I'm incredibly bullish meant to be. I'm incredibly bullish meant to be. I'm incredibly bullish around AI as you can tell from the rest around AI as you can tell from the rest around AI as you can tell from the rest of the conversation. of the conversation. of the conversation. And so it's lots about the And so it's lots about the And so it's lots about the transformation that's happening overall transformation that's happening overall transformation that's happening overall because of the technology. because of the technology. because of the technology. And more that only a handful are going And more that only a handful are going And more that only a handful are going to continue to be really important. And to continue to be really important. And to continue to be really important. And so are you one of them or not? If you're so are you one of them or not? If you're so are you one of them or not? If you're one of them you should never ever ever one of them you should never ever ever one of them you should never ever ever sell. So what are the characteristics of sell. So what are the characteristics of sell. So what are the characteristics of that handful? The handful that have that handful? The handful that have that handful? The handful that have durable advantage? Because durable advantage? Because durable advantage? Because you look back at 2000 it's like man, you look back at 2000 it's like man, you look back at 2000 it's like man, what would you have used to try to pick what would you have used to try to pick what would you have used to try to pick out Google and Amazon? Yeah. out Google and Amazon? Yeah. out Google and Amazon? Yeah. >> [laughter] >> [laughter] >> [laughter] >> And I'm not saying that's the best >> And I'm not saying that's the best >> And I'm not saying that's the best comparator, but with in the comparator, but with in the comparator, but with in the many avalanche of AI companies Mhm. many avalanche of AI companies Mhm. many avalanche of AI companies Mhm. which of those that you think have which of those that you think have which of those that you think have durable advantage? I mean of course some durable advantage? I mean of course some durable advantage? I mean of course some of the name brand labs come to mind. of the name brand labs come to mind. of the name brand labs come to mind. Maybe they become the interface for Maybe they become the interface for Maybe they become the interface for everything else, who knows. But how everything else, who knows. But how everything else, who knows. But how would you answer that in terms of either would you answer that in terms of either would you answer that in terms of either shared characteristics or actual names? shared characteristics or actual names? shared characteristics or actual names? What sets apart the handful that you What sets apart the handful that you What sets apart the handful that you think think think will make it? will make it? will make it? I think the core labs will be around for I think the core labs will be around for I think the core labs will be around for a while. So that's OpenAI, Anthropic, a while. So that's OpenAI, Anthropic, a while. So that's OpenAI, Anthropic, Google barring some accident or disaster Google barring some accident or disaster Google barring some accident or disaster or some blow up, but or some blow up, but or some blow up, but it seems like they're in a really it seems like they're in a really it seems like they're in a really durable spot. And to your point on like durable spot. And to your point on like durable spot. And to your point on like market structure, I wrote a Substack market structure, I wrote a Substack market structure, I wrote a Substack post I don't know 3 years ago or post I don't know 3 years ago or post I don't know 3 years ago or something predicting that that would something predicting that that would something predicting that that would probably be an oligopoly market and probably be an oligopoly market and probably be an oligopoly market and they'll be a handful and they'll be they'll be a handful and they'll be they'll be a handful and they'll be aligned with the clouds. And that's aligned with the clouds. And that's aligned with the clouds. And that's roughly kind of what happened. I mean, roughly kind of what happened. I mean, roughly kind of what happened. I mean, there's Meta, there's XAI, and there's there's Meta, there's XAI, and there's there's Meta, there's XAI, and there's other players that may change this that other players that may change this that other players that may change this that didn't exist when I wrote that post. didn't exist when I wrote that post. didn't exist when I wrote that post. But it feels to me like in the short run But it feels to me like in the short run But it feels to me like in the short run that's an oligopoly. Like there's no that's an oligopoly. Like there's no that's an oligopoly. Like there's no reason for that to be a monopoly market reason for that to be a monopoly market reason for that to be a monopoly market unless one of them pulls ahead so much unless one of them pulls ahead so much unless one of them pulls ahead so much in capabilities that it just becomes a in capabilities that it just becomes a in capabilities that it just becomes a default for everyone. And that could default for everyone. And that could default for everyone. And that could happen. But so far it hasn't. And again, happen. But so far it hasn't. And again, happen. But so far it hasn't. And again, this compute constraint may prevent that this compute constraint may prevent that this compute constraint may prevent that in the short run or at least provide an in the short run or at least provide an in the short run or at least provide an asymptote on it. As you move up the asymptote on it. As you move up the asymptote on it. As you move up the stack and you see well there's different stack and you see well there's different stack and you see well there's different application companies, you know, there's application companies, you know, there's application companies, you know, there's Harvey for legal, there's a Bridge for Harvey for legal, there's a Bridge for Harvey for legal, there's a Bridge for health, there's Decagon and Sierra for health, there's Decagon and Sierra for health, there's Decagon and Sierra for customer success. You know, there's customer success. You know, there's customer success. You know, there's these different companies per these different companies per these different companies per application. There's three or four application. There's three or four application. There's three or four lenses that you can look at. lenses that you can look at. lenses that you can look at. One is if the underlying model gets One is if the underlying model gets One is if the underlying model gets better, does your product or service get better, does your product or service get better, does your product or service get dramatically better for your customers dramatically better for your customers dramatically better for your customers in a way that they still want to keep in a way that they still want to keep in a way that they still want to keep using you? using you? using you? Second, how deep and broad are you going Second, how deep and broad are you going Second, how deep and broad are you going from a product perspective? from a product perspective? from a product perspective? Are you building out multiple products? Are you building out multiple products? Are you building out multiple products? Are they all integrated into a cohesive Are they all integrated into a cohesive Are they all integrated into a cohesive whole? Is it really being built directly whole? Is it really being built directly whole? Is it really being built directly into the processes in a company in a way into the processes in a company in a way into the processes in a company in a way that it's hard to pull out? that it's hard to pull out? that it's hard to pull out? Often the issue for companies Often the issue for companies Often the issue for companies in adoption of AI isn't how good is the in adoption of AI isn't how good is the in adoption of AI isn't how good is the AI, it's how much do you have to change AI, it's how much do you have to change AI, it's how much do you have to change the workflows and the ways that that my the workflows and the ways that that my the workflows and the ways that that my people do things in order to adopt it. people do things in order to adopt it. people do things in order to adopt it. It's about change management usually, It's about change management usually, It's about change management usually, it's not about technology. And so if it's not about technology. And so if it's not about technology. And so if you've been able to embed yourself you've been able to embed yourself you've been able to embed yourself enough into workflows and how people do enough into workflows and how people do enough into workflows and how people do business and how they work and how business and how they work and how business and how they work and how everything else kind of ties together, everything else kind of ties together, everything else kind of ties together, that tends to be quite durable. Mhm. Are that tends to be quite durable. Mhm. Are that tends to be quite durable. Mhm. Are you capturing and storing and using you capturing and storing and using you capturing and storing and using proprietary data? Sometimes that's proprietary data? Sometimes that's proprietary data? Sometimes that's useful. I think data moats in general useful. I think data moats in general useful. I think data moats in general are overstated. are overstated. are overstated. But I think sometimes it can be actually But I think sometimes it can be actually But I think sometimes it can be actually quite useful. And that's usually the quite useful. And that's usually the quite useful. And that's usually the system of record view of the world. system of record view of the world. system of record view of the world. So, you know, there's a handful of So, you know, there's a handful of So, you know, there's a handful of criteria around like will this thing be criteria around like will this thing be criteria around like will this thing be long-term long-term long-term defensible or not? And the application defensible or not? And the application defensible or not? And the application level that's often one potential lens on level that's often one potential lens on level that's often one potential lens on it. Mhm. So, question, if if people are it. Mhm. So, question, if if people are it. Mhm. So, question, if if people are listening to this and they are in the listening to this and they are in the listening to this and they are in the position of perhaps a founder who should position of perhaps a founder who should position of perhaps a founder who should consider identifying their consider identifying their consider identifying their kind of short period of maximum kind of short period of maximum kind of short period of maximum valuation and perhaps hitting the valuation and perhaps hitting the valuation and perhaps hitting the parachute in some way, what are the parachute in some way, what are the parachute in some way, what are the options? Because I think of some of options? Because I think of some of options? Because I think of some of these companies, I'm not going to name these companies, I'm not going to name these companies, I'm not going to name them, but there are multiple companies them, but there are multiple companies them, but there are multiple companies that have multi-billion dollar that have multi-billion dollar that have multi-billion dollar valuations. As you know. There seems to valuations. As you know. There seems to valuations. As you know. There seems to be, again, from a mostly layperson be, again, from a mostly layperson be, again, from a mostly layperson perspective, I mean perspective, I mean perspective, I mean >> [laughter] >> [laughter] >> [laughter] >> that the labs >> that the labs >> that the labs probably can build what they are probably can build what they are probably can build what they are currently selling without too much currently selling without too much currently selling without too much trouble. Do they aim to be acquired by a trouble. Do they aim to be acquired by a trouble. Do they aim to be acquired by a lab, in which case there's sort of a lab, in which case there's sort of a lab, in which case there's sort of a build versus buy decision for the lab build versus buy decision for the lab build versus buy decision for the lab itself? Are they aiming for one of not itself? Are they aiming for one of not itself? Are they aiming for one of not the OpenAIs or Anthropic's, but maybe the OpenAIs or Anthropic's, but maybe the OpenAIs or Anthropic's, but maybe somebody who's trying to get more skin somebody who's trying to get more skin somebody who's trying to get more skin in the game, like Amazon or fill in the in the game, like Amazon or fill in the in the game, like Amazon or fill in the blank? blank? blank? What are the exit options? I think What are the exit options? I think What are the exit options? I think there's a lot of exit options, and the there's a lot of exit options, and the there's a lot of exit options, and the thing that's crazy right now is if you thing that's crazy right now is if you thing that's crazy right now is if you go back 10 or 15 years go back 10 or 15 years go back 10 or 15 years the biggest market cap in the world was the biggest market cap in the world was the biggest market cap in the world was like 300 billion. Mhm. The biggest tech like 300 billion. Mhm. The biggest tech like 300 billion. Mhm. The biggest tech market cap was, I don't know, 200-ish or market cap was, I don't know, 200-ish or market cap was, I don't know, 200-ish or something. I think the biggest one at something. I think the biggest one at something. I think the biggest one at the time was the time was the time was Exxon or somebody, like 15 years ago. Exxon or somebody, like 15 years ago. Exxon or somebody, like 15 years ago. Mhm. Mhm. Mhm. And over the last 10 or 15 years, what And over the last 10 or 15 years, what And over the last 10 or 15 years, what happens is we suddenly ended up with happens is we suddenly ended up with happens is we suddenly ended up with these multi-trillion dollar market caps, these multi-trillion dollar market caps, these multi-trillion dollar market caps, which everybody thought was nuts at the which everybody thought was nuts at the which everybody thought was nuts at the time, but things will probably only get time, but things will probably only get time, but things will probably only get bigger. There will probably be more bigger. There will probably be more bigger. There will probably be more aggregation versus less in the biggest aggregation versus less in the biggest aggregation versus less in the biggest winners. winners. winners. And there's more and more companies who And there's more and more companies who And there's more and more companies who have these market caps between say 100 have these market caps between say 100 have these market caps between say 100 billion and a few trillion. Mhm. In a billion and a few trillion. Mhm. In a billion and a few trillion. Mhm. In a way this is unprecedented. And that way this is unprecedented. And that way this is unprecedented. And that means there's enormous buying power, means there's enormous buying power, means there's enormous buying power, because 1% of 3 trillion is 30 billion. because 1% of 3 trillion is 30 billion. because 1% of 3 trillion is 30 billion. Right? You can take 1% and pay 30 Right? You can take 1% and pay 30 Right? You can take 1% and pay 30 billion dollars for something, which is billion dollars for something, which is billion dollars for something, which is insane, right? That's that's really insane, right? That's that's really insane, right? That's that's really unprecedented. unprecedented. unprecedented. And that means that these really big And that means that these really big And that means that these really big acquisitions can happen. For the acquisitions can happen. For the acquisitions can happen. For the companies that I'm imagining, again, I companies that I'm imagining, again, I companies that I'm imagining, again, I don't want to name names don't want to name names don't want to name names that may have seem to have a limited that may have seem to have a limited that may have seem to have a limited lifespan, right? When I'm in these these lifespan, right? When I'm in these these lifespan, right? When I'm in these these small group threads with friends of mine small group threads with friends of mine small group threads with friends of mine who are often time, not always, but I'm who are often time, not always, but I'm who are often time, not always, but I'm in a bunch of them and when they're in a bunch of them and when they're in a bunch of them and when they're tech investors, very successful tech tech investors, very successful tech tech investors, very successful tech investors, and I'm like, "Okay, these investors, and I'm like, "Okay, these investors, and I'm like, "Okay, these five companies, you've got 10 chips. How five companies, you've got 10 chips. How five companies, you've got 10 chips. How would you allocate your 10 chips?" would you allocate your 10 chips?" would you allocate your 10 chips?" There's certain companies that There's certain companies that There's certain companies that consistently get zero, even though consistently get zero, even though consistently get zero, even though they're reasonably well known. Why would they're reasonably well known. Why would they're reasonably well known. Why would one of the labs buy one of those? one of the labs buy one of those? one of the labs buy one of those? Depends on what it is, and it may be a Depends on what it is, and it may be a Depends on what it is, and it may be a lab, it may be one of the big tech lab, it may be one of the big tech lab, it may be one of the big tech incumbents, an Apple, Amazon, Right. incumbents, an Apple, Amazon, Right. incumbents, an Apple, Amazon, Right. Google's kind of both things. Google's kind of both things. Google's kind of both things. There's Oracle, Mhm. there's Samsung, There's Oracle, Mhm. there's Samsung, There's Oracle, Mhm. there's Samsung, there's there's there's Tesla, there's SpaceX now in the market Tesla, there's SpaceX now in the market Tesla, there's SpaceX now in the market doing things. There's a bunch of doing things. There's a bunch of doing things. There's a bunch of different buyers of different types. different buyers of different types. different buyers of different types. There's Snowflake and Databricks, There's Snowflake and Databricks, There's Snowflake and Databricks, there's Stripe, there's Stripe, there's Stripe, Coinbase if you're doing financial Coinbase if you're doing financial Coinbase if you're doing financial service. There's just a ton of companies service. There's just a ton of companies service. There's just a ton of companies that actually are quite large. That's that actually are quite large. That's that actually are quite large. That's kind of the point. And so often you end kind of the point. And so often you end kind of the point. And so often you end up selling to one of four things. You up selling to one of four things. You up selling to one of four things. You can sell to one of the big labs or can sell to one of the big labs or can sell to one of the big labs or hyperscalers or giant tech companies. hyperscalers or giant tech companies. hyperscalers or giant tech companies. You can sell to somebody who cares a lot You can sell to somebody who cares a lot You can sell to somebody who cares a lot about your vertical. So for example, a about your vertical. So for example, a about your vertical. So for example, a Thomson Reuters if you're doing legal or Thomson Reuters if you're doing legal or Thomson Reuters if you're doing legal or accounting or things that are kind of accounting or things that are kind of accounting or things that are kind of related to that. Mhm. I mean, I think related to that. Mhm. I mean, I think related to that. Mhm. I mean, I think actually one thing that doesn't happen actually one thing that doesn't happen actually one thing that doesn't happen enough is merger of competitors, enough is merger of competitors, enough is merger of competitors, particularly private companies where you particularly private companies where you particularly private companies where you can do that. Because ultimately, if your can do that. Because ultimately, if your can do that. Because ultimately, if your primary vector is winning and you're primary vector is winning and you're primary vector is winning and you're neck and neck with somebody and you're neck and neck with somebody and you're neck and neck with somebody and you're competing on every deal and you're competing on every deal and you're competing on every deal and you're destroying pricing for each other, like destroying pricing for each other, like destroying pricing for each other, like maybe it's better to just merge. It maybe it's better to just merge. It maybe it's better to just merge. It actually was actually was actually was X.com and PayPal in the '90s, right? X.com and PayPal in the '90s, right? X.com and PayPal in the '90s, right? >> Yeah. You and Elon Musk here you know, >> Yeah. You and Elon Musk here you know, >> Yeah. You and Elon Musk here you know, were running different companies and were running different companies and were running different companies and they merged because they said we're two they merged because they said we're two they merged because they said we're two people doing this, why fight? Yeah, or people doing this, why fight? Yeah, or people doing this, why fight? Yeah, or Uber Lyft way back in the day, right? Uber Lyft way back in the day, right? Uber Lyft way back in the day, right? That might not have been a merger, it That might not have been a merger, it That might not have been a merger, it might have been an acquisition, but it's might have been an acquisition, but it's might have been an acquisition, but it's like Yeah, and the rumor is that that like Yeah, and the rumor is that that like Yeah, and the rumor is that that almost happened and then, you know, that almost happened and then, you know, that almost happened and then, you know, that Uber side walked away from it. Mhm. But Uber side walked away from it. Mhm. But Uber side walked away from it. Mhm. But all the money that Uber spent on all the money that Uber spent on all the money that Uber spent on fighting Lyft for all those years maybe fighting Lyft for all those years maybe fighting Lyft for all those years maybe would have been better spent just buying would have been better spent just buying would have been better spent just buying them. Maybe not. I don't know the exact them. Maybe not. I don't know the exact them. Maybe not. I don't know the exact math. Mhm. math. Mhm. math. Mhm. But often it actually does make sense to But often it actually does make sense to But often it actually does make sense to say, you know what? Like, we'll just say, you know what? Like, we'll just say, you know what? Like, we'll just stop fighting it out and we'll just stop fighting it out and we'll just stop fighting it out and we'll just combine combine combine and just go win. Cuz if the primary and just go win. Cuz if the primary and just go win. Cuz if the primary purpose is to win the market, you're purpose is to win the market, you're purpose is to win the market, you're already fighting all these big already fighting all these big already fighting all these big incumbents that already exist anyhow. incumbents that already exist anyhow. incumbents that already exist anyhow. So, why why make it even harder? As you So, why why make it even harder? As you So, why why make it even harder? As you know, and we talked about this a lot, know, and we talked about this a lot, know, and we talked about this a lot, but we'll talk about but we'll talk about but we'll talk about you with your investing hat on. But, you with your investing hat on. But, you with your investing hat on. But, before you even put that, let's call it before you even put that, let's call it before you even put that, let's call it full-time investing hat on, full-time investing hat on, full-time investing hat on, you had a lot in your background that you had a lot in your background that you had a lot in your background that may or may not have helped you, and I'm may or may not have helped you, and I'm may or may not have helped you, and I'm curious if you look at your biology curious if you look at your biology curious if you look at your biology background, the math background, do you background, the math background, do you background, the math background, do you think any of those things or other think any of those things or other think any of those things or other elements materially contributed to how elements materially contributed to how elements materially contributed to how you think about investing that has given you think about investing that has given you think about investing that has given you an advantage you an advantage you an advantage in in in >> [clears throat] >> [clears throat] >> [clears throat] >> I suppose there are different stages to >> I suppose there are different stages to >> I suppose there are different stages to kind of winning deals, but sometimes kind of winning deals, but sometimes kind of winning deals, but sometimes they're not crowded, but let's just talk they're not crowded, but let's just talk they're not crowded, but let's just talk about about about the selection process. The math stuff the selection process. The math stuff the selection process. The math stuff helped me, I think, in two ways. One is helped me, I think, in two ways. One is helped me, I think, in two ways. One is it's helped me it's helped me it's helped me with certain aspects of like with certain aspects of like with certain aspects of like technical or algorithmic CS and technical or algorithmic CS and technical or algorithmic CS and understanding it, and sometimes that's understanding it, and sometimes that's understanding it, and sometimes that's useful in the context of how certain useful in the context of how certain useful in the context of how certain things work in AI or things like that, things work in AI or things like that, things work in AI or things like that, or just fluency of or just fluency of or just fluency of numbers and data, and numbers and data, and numbers and data, and I don't know to call it nerd language or I don't know to call it nerd language or I don't know to call it nerd language or something. something. something. And I did the math degree, honestly, And I did the math degree, honestly, And I did the math degree, honestly, just for fun, and I think that's just for fun, and I think that's just for fun, and I think that's actually the thing that was helpful. actually the thing that was helpful. actually the thing that was helpful. We did an undergrad degree in math, so I We did an undergrad degree in math, so I We did an undergrad degree in math, so I didn't go that far with it, but didn't go that far with it, but didn't go that far with it, but I did the very sort of abstract pure I did the very sort of abstract pure I did the very sort of abstract pure math stuff, and I think that was a good math stuff, and I think that was a good math stuff, and I think that was a good forcing function of how to really think forcing function of how to really think forcing function of how to really think logically step by step about things, cuz logically step by step about things, cuz logically step by step about things, cuz roughly the way that at least I learned roughly the way that at least I learned roughly the way that at least I learned how to do proofs was you do the the how to do proofs was you do the the how to do proofs was you do the the logical sequence, but then sometimes you logical sequence, but then sometimes you logical sequence, but then sometimes you do these intuitive leaps, and then go do these intuitive leaps, and then go do these intuitive leaps, and then go back and try and prove it to yourself. back and try and prove it to yourself. back and try and prove it to yourself. Mhm. Or flesh out the Mhm. Or flesh out the Mhm. Or flesh out the the reasoning behind that intuitive the reasoning behind that intuitive the reasoning behind that intuitive leap. And I think sometimes investing is leap. And I think sometimes investing is leap. And I think sometimes investing is a little bit like that. When did you a little bit like that. When did you a little bit like that. When did you first have the inkling that you could be first have the inkling that you could be first have the inkling that you could be good at investing? good at investing? good at investing? And that could be investing large could And that could be investing large could And that could be investing large could be maybe within the context of our be maybe within the context of our be maybe within the context of our conversations startups and angel conversations startups and angel conversations startups and angel investing. When did you first kind of go investing. When did you first kind of go investing. When did you first kind of go aha? aha? aha? Yeah, maybe I could be good at this. Was Yeah, maybe I could be good at this. Was Yeah, maybe I could be good at this. Was there a moment or a deal or anything there a moment or a deal or anything there a moment or a deal or anything like that that comes to mind? Not like that that comes to mind? Not like that that comes to mind? Not really. I'm really hard on myself. So really. I'm really hard on myself. So really. I'm really hard on myself. So even now I second guess myself a lot. even now I second guess myself a lot. even now I second guess myself a lot. Mhm. Somebody was telling me that the Mhm. Somebody was telling me that the Mhm. Somebody was telling me that the two people that always beat themselves two people that always beat themselves two people that always beat themselves up the most in hindsight is me and this up the most in hindsight is me and this up the most in hindsight is me and this one other person who's another one other person who's another one other person who's another well-known founder / investor. And so I well-known founder / investor. And so I well-known founder / investor. And so I don't think there's a single moment don't think there's a single moment don't think there's a single moment where I'm like, "Wow, this makes sense where I'm like, "Wow, this makes sense where I'm like, "Wow, this makes sense for me to do." I think it just kind of for me to do." I think it just kind of for me to do." I think it just kind of organically kept going because I was organically kept going because I was organically kept going because I was getting into some very strong companies getting into some very strong companies getting into some very strong companies and then and then and then you know, that allowed me to sort of you know, that allowed me to sort of you know, that allowed me to sort of continue what I'm doing. But Okay. Yeah, continue what I'm doing. But Okay. Yeah, continue what I'm doing. But Okay. Yeah, I wish I had a moment like that. I wish I had a moment like that. I wish I had a moment like that. >> [laughter] >> [laughter] >> [laughter] >> God dammit, you need to revise your >> God dammit, you need to revise your >> God dammit, you need to revise your Genesis story like every every good Genesis story like every every good Genesis story like every every good founder. Yeah, [laughter] founder. Yeah, [laughter] founder. Yeah, [laughter] ever since I was seven I've been ever since I was seven I've been ever since I was seven I've been thinking about investing in technology. thinking about investing in technology. thinking about investing in technology. [laughter] All right, now we're talking. [laughter] All right, now we're talking. [laughter] All right, now we're talking. This is This is This is So getting into those deals, right? So getting into those deals, right? So getting into those deals, right? What allowed you to get into those What allowed you to get into those What allowed you to get into those deals, right? Because some people have deals, right? Because some people have deals, right? Because some people have an informational advantage and they put an informational advantage and they put an informational advantage and they put themselves in a position to have an themselves in a position to have an themselves in a position to have an informational advantage, right? And I informational advantage, right? And I informational advantage, right? And I think that had I not I don't want this think that had I not I don't want this think that had I not I don't want this to be a leading question. But it's like to be a leading question. But it's like to be a leading question. But it's like had I not move to Silicon Valley Yeah. had I not move to Silicon Valley Yeah. had I not move to Silicon Valley Yeah. when I did like 2000 when I did like 2000 when I did like 2000 and then subsequently, you know, stayed and then subsequently, you know, stayed and then subsequently, you know, stayed there, moved to San Francisco there, moved to San Francisco there, moved to San Francisco specifically like nothing that I was specifically like nothing that I was specifically like nothing that I was able to do an angel investing would have able to do an angel investing would have able to do an angel investing would have been possible. So but there's more to been possible. So but there's more to been possible. So but there's more to your story cuz a lot of people move your story cuz a lot of people move your story cuz a lot of people move there with hopes of startup riches in there with hopes of startup riches in there with hopes of startup riches in whatever capacity. Not saying that whatever capacity. Not saying that whatever capacity. Not saying that that's why you moved there. But that's why you moved there. But that's why you moved there. But what was it that allowed you to get into what was it that allowed you to get into what was it that allowed you to get into those deals? Maybe there are certain those deals? Maybe there are certain those deals? Maybe there are certain things that come to mind things that come to mind things that come to mind based on our prior conversations. But based on our prior conversations. But based on our prior conversations. But I'll just leave it at that. Like why I'll just leave it at that. Like why I'll just leave it at that. Like why were you able to were you able to were you able to get into or select those deals. I think get into or select those deals. I think get into or select those deals. I think there's what happened early and what there's what happened early and what there's what happened early and what happens now and I think those two things happens now and I think those two things happens now and I think those two things are different. I think to your point, are different. I think to your point, are different. I think to your point, the single most important thing for the single most important thing for the single most important thing for anybody wanting to break into any anybody wanting to break into any anybody wanting to break into any industry is go to the headquarters or industry is go to the headquarters or industry is go to the headquarters or cluster of that industry. Like move to cluster of that industry. Like move to cluster of that industry. Like move to wherever that thing is. wherever that thing is. wherever that thing is. And all the advice that you can do And all the advice that you can do And all the advice that you can do anything from anywhere and everything's anything from anywhere and everything's anything from anywhere and everything's remote is all BS. And you see that for remote is all BS. And you see that for remote is all BS. And you see that for every industry, not just tech. You know, every industry, not just tech. You know, every industry, not just tech. You know, if you wanted to get into the movie if you wanted to get into the movie if you wanted to get into the movie business, people wouldn't say business, people wouldn't say business, people wouldn't say "Hey, you can write a film script from "Hey, you can write a film script from "Hey, you can write a film script from anywhere. You can digitally score from anywhere. You can digitally score from anywhere. You can digitally score from anywhere. You can edit it from anywhere. anywhere. You can edit it from anywhere. anywhere. You can edit it from anywhere. You can film it anywhere." Like go to You can film it anywhere." Like go to You can film it anywhere." Like go to Dallas and join their burgeoning Dallas and join their burgeoning Dallas and join their burgeoning you know, film scene. They'd say, "Go to you know, film scene. They'd say, "Go to you know, film scene. They'd say, "Go to Hollywood." And if you want to do Hollywood." And if you want to do Hollywood." And if you want to do something in finance and you're like, something in finance and you're like, something in finance and you're like, "Well, you could raise money from "Well, you could raise money from "Well, you could raise money from anywhere and come up with trading anywhere and come up with trading anywhere and come up with trading strategies and a hedge fund strategy strategies and a hedge fund strategy strategies and a hedge fund strategy from anywhere and you could do it from from anywhere and you could do it from from anywhere and you could do it from anywhere." anywhere." anywhere." People wouldn't say, "Hey, go to you People wouldn't say, "Hey, go to you People wouldn't say, "Hey, go to you know, whatever, Seattle." They'd be know, whatever, Seattle." They'd be know, whatever, Seattle." They'd be like, "Go to New York." Or go to XYZ like, "Go to New York." Or go to XYZ like, "Go to New York." Or go to XYZ financial center. So, the same is true financial center. So, the same is true financial center. So, the same is true for tech and for tech and for tech and Shrayan on my team Shrayan on my team Shrayan on my team has been performing this sort of unicorn has been performing this sort of unicorn has been performing this sort of unicorn analysis of where is all the private analysis of where is all the private analysis of where is all the private market cap aggregating for technology. market cap aggregating for technology. market cap aggregating for technology. And traditionally about half of it's And traditionally about half of it's And traditionally about half of it's been the US and then half of that has been the US and then half of that has been the US and then half of that has been the Bay Area. But with AI, 91% of been the Bay Area. But with AI, 91% of been the Bay Area. But with AI, 91% of private technology market cap is the Bay private technology market cap is the Bay private technology market cap is the Bay Area. 91% of the entire global set of AI Area. 91% of the entire global set of AI Area. 91% of the entire global set of AI market cap is all in one 10 by 10 area, market cap is all in one 10 by 10 area, market cap is all in one 10 by 10 area, right? right? right? So, if you want to do stuff in AI, you So, if you want to do stuff in AI, you So, if you want to do stuff in AI, you should probably be in the Bay Area. Mhm. should probably be in the Bay Area. Mhm. should probably be in the Bay Area. Mhm. Probably the secondary place is New York Probably the secondary place is New York Probably the secondary place is New York and then after that it drops off a and then after that it drops off a and then after that it drops off a cliff. And really it's the Bay Area. cliff. And really it's the Bay Area. cliff. And really it's the Bay Area. If you want to do defense tech, you If you want to do defense tech, you If you want to do defense tech, you probably should be in probably should be in probably should be in Southern California close to where Southern California close to where Southern California close to where SpaceX and Anduril are in sort of SpaceX and Anduril are in sort of SpaceX and Anduril are in sort of Irvine, Orange County, etc. Or El Irvine, Orange County, etc. Or El Irvine, Orange County, etc. Or El Segundo, there's a lot of startups Segundo, there's a lot of startups Segundo, there's a lot of startups there. there. there. If you want to do fintech and crypto, If you want to do fintech and crypto, If you want to do fintech and crypto, maybe it's New York. But the reality is maybe it's New York. But the reality is maybe it's New York. But the reality is these are very strong clusters. So, to these are very strong clusters. So, to these are very strong clusters. So, to your point, number one is I was just in your point, number one is I was just in your point, number one is I was just in the right location. the right location. the right location. I was in the right networks and I I was in the right networks and I I was in the right networks and I default was I was running a startup default was I was running a startup default was I was running a startup myself. I was at Google for many years myself. I was at Google for many years myself. I was at Google for many years and then I left to start a company. and then I left to start a company. and then I left to start a company. And people just start coming to me for And people just start coming to me for And people just start coming to me for advice. And the way I ended up investing advice. And the way I ended up investing advice. And the way I ended up investing in Airbnb is as helping them when they in Airbnb is as helping them when they in Airbnb is as helping them when they were eight people or something raise were eight people or something raise were eight people or something raise their Series A. their Series A. their Series A. And I introduced them to a bunch of And I introduced them to a bunch of And I introduced them to a bunch of people and helped with some of the people and helped with some of the people and helped with some of the strategy there in very light ways, strategy there in very light ways, strategy there in very light ways, right? They would have done it without right? They would have done it without right? They would have done it without me, but and they said, "Hey, at the end me, but and they said, "Hey, at the end me, but and they said, "Hey, at the end of it, do you want to invest a little of it, do you want to invest a little of it, do you want to invest a little bit?" I said, "Great. That sounds bit?" I said, "Great. That sounds bit?" I said, "Great. That sounds wonderful." So, it was very organic. Or wonderful." So, it was very organic. Or wonderful." So, it was very organic. Or the way I invested in Stripe is the way I invested in Stripe is the way I invested in Stripe is I'd sold sort of infrastructure early I'd sold sort of infrastructure early I'd sold sort of infrastructure early API company to Twitter. And when Twitter API company to Twitter. And when Twitter API company to Twitter. And when Twitter was, say, 90 people or so. And I sent an was, say, 90 people or so. And I sent an was, say, 90 people or so. And I sent an email to Patrick, the CEO of Stripe, email to Patrick, the CEO of Stripe, email to Patrick, the CEO of Stripe, just saying, "Hey, I've heard great just saying, "Hey, I've heard great just saying, "Hey, I've heard great things about you, and I really like what things about you, and I really like what things about you, and I really like what Stripe is doing, and I would use it for Stripe is doing, and I would use it for Stripe is doing, and I would use it for my own startup, and I I sold this API my own startup, and I I sold this API my own startup, and I I sold this API company myself. Do you want to just talk company myself. Do you want to just talk company myself. Do you want to just talk about this stuff?" about this stuff?" about this stuff?" And so, we went on a couple walks, and And so, we went on a couple walks, and And so, we went on a couple walks, and then then then a week or two later, he texts me, and a week or two later, he texts me, and a week or two later, he texts me, and he's like, "Hey, we're doing a round. Do he's like, "Hey, we're doing a round. Do he's like, "Hey, we're doing a round. Do you want to invest?" you want to invest?" you want to invest?" So, the first few things that I did were So, the first few things that I did were So, the first few things that I did were very organic, where the founders were very organic, where the founders were very organic, where the founders were like, "We want you on board." Mhm. I like, "We want you on board." Mhm. I like, "We want you on board." Mhm. I didn't think, "Oh, I should be an didn't think, "Oh, I should be an didn't think, "Oh, I should be an investor, and I'm going to chase things. investor, and I'm going to chase things. investor, and I'm going to chase things. I'm going to" I just really like talking I'm going to" I just really like talking I'm going to" I just really like talking to smart people, and I liked working on to smart people, and I liked working on to smart people, and I liked working on certain business problems, and I love certain business problems, and I love certain business problems, and I love technology, and its translation into the technology, and its translation into the technology, and its translation into the world. And so, it's very like world. And so, it's very like world. And so, it's very like you know, I was just a nerd, and I I met you know, I was just a nerd, and I I met you know, I was just a nerd, and I I met other nerds, and we hit it off. That's other nerds, and we hit it off. That's other nerds, and we hit it off. That's kind of the early like story for me. It kind of the early like story for me. It kind of the early like story for me. It just struck me that just struck me that just struck me that I'm sure people have heard or I'm sure I'm sure people have heard or I'm sure I'm sure people have heard or I'm sure you've heard this before, but you know, you've heard this before, but you know, you've heard this before, but you know, if you want money, ask for advice, and if you want money, ask for advice, and if you want money, ask for advice, and if you want advice, ask for money. It if you want advice, ask for money. It if you want advice, ask for money. It just struck me that it kind of goes the just struck me that it kind of goes the just struck me that it kind of goes the other way around, too. It's like other way around, too. It's like other way around, too. It's like if you offer a bunch of advice, if you offer a bunch of advice, if you offer a bunch of advice, oftentimes you get to give money. And if oftentimes you get to give money. And if oftentimes you get to give money. And if you try to give money, you try to give money, you try to give money, >> Yeah. you might get solicited for >> Yeah. you might get solicited for >> Yeah. you might get solicited for advice. Yeah, yeah, that's a good point. advice. Yeah, yeah, that's a good point. advice. Yeah, yeah, that's a good point. When did you write The High Growth When did you write The High Growth When did you write The High Growth Handbook? When [clears throat] was that Handbook? When [clears throat] was that Handbook? When [clears throat] was that published? It's a while ago now. It's published? It's a while ago now. It's published? It's a while ago now. It's probably like seven-ish years ago, probably like seven-ish years ago, probably like seven-ish years ago, something like that. Seven years ago. something like that. Seven years ago. something like that. Seven years ago. All right. We're going to come back to All right. We're going to come back to All right. We're going to come back to that in a minute. that in a minute. that in a minute. Because you you were in the right place, Because you you were in the right place, Because you you were in the right place, geographically speaking, right? You were geographically speaking, right? You were geographically speaking, right? You were in the center of the switchboard. in the center of the switchboard. in the center of the switchboard. And like you said, these some of these And like you said, these some of these And like you said, these some of these initial kind of standout investments initial kind of standout investments initial kind of standout investments came about very organically. came about very organically. came about very organically. And And And what I'd be curious to hear because you what I'd be curious to hear because you what I'd be curious to hear because you also said yourself not too long ago that also said yourself not too long ago that also said yourself not too long ago that there's there's what I did then, there's there's there's what I did then, there's there's there's what I did then, there's what I did now. There's also what you what I did now. There's also what you what I did now. There's also what you did in between, right along the way. did in between, right along the way. did in between, right along the way. And I'm wondering for instance if you And I'm wondering for instance if you And I'm wondering for instance if you would still stand by this. This is from would still stand by this. This is from would still stand by this. This is from that first round interview I was that first round interview I was that first round interview I was mentioning. As a general rule when I mentioning. As a general rule when I mentioning. As a general rule when I make investments, it's market first and make investments, it's market first and make investments, it's market first and the strength of the team second. And the strength of the team second. And the strength of the team second. And there's more to it. But would you still there's more to it. But would you still there's more to it. But would you still agree with that? 90% yes. Everyone's agree with that? 90% yes. Everyone's agree with that? 90% yes. Everyone's going to meet somebody exceptional and going to meet somebody exceptional and going to meet somebody exceptional and you just back them. you just back them. you just back them. Or something may be so early. Like when Or something may be so early. Like when Or something may be so early. Like when I led the first round of Perplexity. I led the first round of Perplexity. I led the first round of Perplexity. Mhm. Like the very very first round. Mhm. Like the very very first round. Mhm. Like the very very first round. And the way that came about was Arvin, And the way that came about was Arvin, And the way that came about was Arvin, the CEO, just I think he like pinged me the CEO, just I think he like pinged me the CEO, just I think he like pinged me on LinkedIn. Literally. And this is why on LinkedIn. Literally. And this is why on LinkedIn. Literally. And this is why nobody was doing anything in AI and he nobody was doing anything in AI and he nobody was doing anything in AI and he was like an OpenAI engineer or was like an OpenAI engineer or was like an OpenAI engineer or researcher and he's like, "Hey, I'm at researcher and he's like, "Hey, I'm at researcher and he's like, "Hey, I'm at OpenAI." which nobody cares about at the OpenAI." which nobody cares about at the OpenAI." which nobody cares about at the time. time. time. And I'm thinking of doing something in And I'm thinking of doing something in And I'm thinking of doing something in AI and I heard that you're talking about AI and I heard that you're talking about AI and I heard that you're talking about this stuff and nobody else is talking this stuff and nobody else is talking this stuff and nobody else is talking about it and can we meet up? about it and can we meet up? about it and can we meet up? And so we just started meeting every 2 And so we just started meeting every 2 And so we just started meeting every 2 weeks and brainstorming, right? And then weeks and brainstorming, right? And then weeks and brainstorming, right? And then that led to like investing in that. And that led to like investing in that. And that led to like investing in that. And that was kind of a a people first thing that was kind of a a people first thing that was kind of a a people first thing where he was just so good. And every where he was just so good. And every where he was just so good. And every time we talk, he'd show up a week later time we talk, he'd show up a week later time we talk, he'd show up a week later with a thing that we discussed built. with a thing that we discussed built. with a thing that we discussed built. Like who does that? Like who does that? Like who does that? >> Yeah, that's a good sign. So good. Mhm. >> Yeah, that's a good sign. So good. Mhm. >> Yeah, that's a good sign. So good. Mhm. Or [clears throat] the way I ended up Or [clears throat] the way I ended up Or [clears throat] the way I ended up investing in Anduril was Google shuts investing in Anduril was Google shuts investing in Anduril was Google shuts down Maven, which was their sort of down Maven, which was their sort of down Maven, which was their sort of defense project. defense project. defense project. And so I think, well, if if the And so I think, well, if if the And so I think, well, if if the incumbents aren't going to do it, what a incumbents aren't going to do it, what a incumbents aren't going to do it, what a great place for startups to play. great place for startups to play. great place for startups to play. Cuz there's been a long history of Cuz there's been a long history of Cuz there's been a long history of Silicon Valley in the defense industry. Silicon Valley in the defense industry. Silicon Valley in the defense industry. That's HP and that's a lot of the, you That's HP and that's a lot of the, you That's HP and that's a lot of the, you know, early brands. know, early brands. know, early brands. And so I was just looking for something, And so I was just looking for something, And so I was just looking for something, or somebody to work on this area and it or somebody to work on this area and it or somebody to work on this area and it was very unpopular at the time. was very unpopular at the time. was very unpopular at the time. And I ran into, I think it was Trae And I ran into, I think it was Trae And I ran into, I think it was Trae Stephens, who's one of the co-founders Stephens, who's one of the co-founders Stephens, who's one of the co-founders of Anduril, who's also at Founders Fund. of Anduril, who's also at Founders Fund. of Anduril, who's also at Founders Fund. It's launch or something else again, It's launch or something else again, It's launch or something else again, right city to be in. And he said, "Oh, right city to be in. And he said, "Oh, right city to be in. And he said, "Oh, I'm working on this new defense thing." I'm working on this new defense thing." I'm working on this new defense thing." And I said, "Amazing. Let's talk about And I said, "Amazing. Let's talk about And I said, "Amazing. Let's talk about it." Sometimes it's just looking for it." Sometimes it's just looking for it." Sometimes it's just looking for these things too in a market and these things too in a market and these things too in a market and sometimes it's people. So Anduril was sometimes it's people. So Anduril was sometimes it's people. So Anduril was looking for a market and then finding looking for a market and then finding looking for a market and then finding amazing people. Perplexity was kind of amazing people. Perplexity was kind of amazing people. Perplexity was kind of in between where it was like I was in between where it was like I was in between where it was like I was looking at everything in AI looking at everything in AI looking at everything in AI cuz I thought it was going to be cuz I thought it was going to be cuz I thought it was going to be incredibly important, but not very many incredibly important, but not very many incredibly important, but not very many people were. And then I just ran across people were. And then I just ran across people were. And then I just ran across an exceptional individual. an exceptional individual. an exceptional individual. And that's when I funded OpenAI, that's And that's when I funded OpenAI, that's And that's when I funded OpenAI, that's when I funded Harvey, which is the early when I funded Harvey, which is the early when I funded Harvey, which is the early legal tech. I funded a lot of really legal tech. I funded a lot of really legal tech. I funded a lot of really early stuff cuz they were the only early stuff cuz they were the only early stuff cuz they were the only people doing anything in this market people doing anything in this market people doing anything in this market that I thought would be really that I thought would be really that I thought would be really important. Let me come back to a few important. Let me come back to a few important. Let me come back to a few things you said. So, you mentioned the things you said. So, you mentioned the things you said. So, you mentioned the Perplexity founder or later the founder Perplexity founder or later the founder Perplexity founder or later the founder who said you're you're talking about who said you're you're talking about who said you're you're talking about this stuff, right? Or he heard or read this stuff, right? Or he heard or read this stuff, right? Or he heard or read or found you talking about this stuff. or found you talking about this stuff. or found you talking about this stuff. Yeah. Where was that? Was that post on Yeah. Where was that? Was that post on Yeah. Where was that? Was that post on your blog? Was it somewhere else? How your blog? Was it somewhere else? How your blog? Was it somewhere else? How did he actually find you talking about did he actually find you talking about did he actually find you talking about anything? I mean, I think he pinged me anything? I mean, I think he pinged me anything? I mean, I think he pinged me in part because I was involved with a in part because I was involved with a in part because I was involved with a bunch of the prior wave of technology bunch of the prior wave of technology bunch of the prior wave of technology companies, Airbnb, Stripe, Coinbase, companies, Airbnb, Stripe, Coinbase, companies, Airbnb, Stripe, Coinbase, Instacart, Square, a bunch of stuff like Instacart, Square, a bunch of stuff like Instacart, Square, a bunch of stuff like that. And so, I think at that point I that. And so, I think at that point I that. And so, I think at that point I was already known as was already known as was already known as founder and investor. founder and investor. founder and investor. But then in top of that, I was just I But then in top of that, I was just I But then in top of that, I was just I was trolling AI researchers and just was trolling AI researchers and just was trolling AI researchers and just asking them about what's going on asking them about what's going on asking them about what's going on because it was so interesting. There was because it was so interesting. There was because it was so interesting. There was a bunch of art that was being done with a bunch of art that was being done with a bunch of art that was being done with these things called GANs at the time, these things called GANs at the time, these things called GANs at the time, these generative adversarial networks. these generative adversarial networks. these generative adversarial networks. And so, I was playing around with that. And so, I was playing around with that. And so, I was playing around with that. I tried to hire engineers to build me I tried to hire engineers to build me I tried to hire engineers to build me effectively what was Midjourney cuz I effectively what was Midjourney cuz I effectively what was Midjourney cuz I just thought it'd be really cool to be just thought it'd be really cool to be just thought it'd be really cool to be make it easy to make AI art. Okay, so make it easy to make AI art. Okay, so make it easy to make AI art. Okay, so let let me pause for a second because let let me pause for a second because let let me pause for a second because this is my second question and it's a this is my second question and it's a this is my second question and it's a good time. good time. good time. When you mentioned, you know, AI, I When you mentioned, you know, AI, I When you mentioned, you know, AI, I thought it would be incredibly thought it would be incredibly thought it would be incredibly important. important. important. >> Yeah. What were the >> Yeah. What were the >> Yeah. What were the indicators of that, right? What was the indicators of that, right? What was the indicators of that, right? What was the smoke in the distance where you're like, smoke in the distance where you're like, smoke in the distance where you're like, "Oh, that's an interesting direction." I "Oh, that's an interesting direction." I "Oh, that's an interesting direction." I think there was two or three things. AI think there was two or three things. AI think there was two or three things. AI was one of those things that people have was one of those things that people have was one of those things that people have always talked about. So, when I was always talked about. So, when I was always talked about. So, when I was doing my math degree, I took a lot of doing my math degree, I took a lot of doing my math degree, I took a lot of kind of theoretical CS classes and there kind of theoretical CS classes and there kind of theoretical CS classes and there were the early neural network classes were the early neural network classes were the early neural network classes and things like that. And the math and things like that. And the math and things like that. And the math behind it. And and so, there's always behind it. And and so, there's always behind it. And and so, there's always this promise of building these this promise of building these this promise of building these artificial intelligences of different artificial intelligences of different artificial intelligences of different forms. forms. forms. And one could argue Google was a AI And one could argue Google was a AI And one could argue Google was a AI company. And back then it was called company. And back then it was called company. And back then it was called machine learning, and it was different machine learning, and it was different machine learning, and it was different technology bases in some sense. And I technology bases in some sense. And I technology bases in some sense. And I think 2012 was when AlexNet came out, think 2012 was when AlexNet came out, think 2012 was when AlexNet came out, and there was this proof that you can and there was this proof that you can and there was this proof that you can start scaling things and have really start scaling things and have really start scaling things and have really interesting characteristics in terms of interesting characteristics in terms of interesting characteristics in terms of how AI systems work. And then 2017 is how AI systems work. And then 2017 is how AI systems work. And then 2017 is when the team at Google ran to the when the team at Google ran to the when the team at Google ran to the transformer architecture, which transformer architecture, which transformer architecture, which everything is based on now, or roughly everything is based on now, or roughly everything is based on now, or roughly everything. And so, for example, if you everything. And so, for example, if you everything. And so, for example, if you look at G T for chat GPT, the T stands look at G T for chat GPT, the T stands look at G T for chat GPT, the T stands for transformer. And around 2020-ish, I for transformer. And around 2020-ish, I for transformer. And around 2020-ish, I think was when GPT-3 came out. And that think was when GPT-3 came out. And that think was when GPT-3 came out. And that was such a big step from GPT-2, and it was such a big step from GPT-2, and it was such a big step from GPT-2, and it still wasn't good enough to really do still wasn't good enough to really do still wasn't good enough to really do stuff with. stuff with. stuff with. But you you're like, "Oh, [ __ ] the But you you're like, "Oh, [ __ ] the But you you're like, "Oh, [ __ ] the scaling law papers are out." scaling law papers are out." scaling law papers are out." The step function and capabilities was The step function and capabilities was The step function and capabilities was huge. You suddenly have a generalizable huge. You suddenly have a generalizable huge. You suddenly have a generalizable model available via an API that anybody model available via an API that anybody model available via an API that anybody can ping. can ping. can ping. And so, just extrapolate that out to the And so, just extrapolate that out to the And so, just extrapolate that out to the next step, and this is going to be next step, and this is going to be next step, and this is going to be really important. really important. really important. >> Mhm. So, it's basically looking at that >> Mhm. So, it's basically looking at that >> Mhm. So, it's basically looking at that capability step and playing around with capability step and playing around with capability step and playing around with the technology, and then reading the the technology, and then reading the the technology, and then reading the scaling law papers. Or just in general, scaling law papers. Or just in general, scaling law papers. Or just in general, the the scaling laws seem to work for the the scaling laws seem to work for the the scaling laws seem to work for everything. And you're like, "Wow, this everything. And you're like, "Wow, this everything. And you're like, "Wow, this is going to be really, really important. is going to be really, really important. is going to be really, really important. So, let me start getting involved with So, let me start getting involved with So, let me start getting involved with it." Do you think you would have or it." Do you think you would have or it." Do you think you would have or could have done that without a could have done that without a could have done that without a mathematics background? I'm I'm guessing mathematics background? I'm I'm guessing mathematics background? I'm I'm guessing there were probably some other folks, there were probably some other folks, there were probably some other folks, but that leads me to the question of but that leads me to the question of but that leads me to the question of like, how are you finding and ingesting like, how are you finding and ingesting like, how are you finding and ingesting that, right? Was it the talk of the that, right? Was it the talk of the that, right? Was it the talk of the town? So, it was in a sense like within town? So, it was in a sense like within town? So, it was in a sense like within your social circles and the networks your social circles and the networks your social circles and the networks that you're a part of, that you're a part of, that you're a part of, it was a open discussion, so you were it was a open discussion, so you were it was a open discussion, so you were engaged with it, or are you ingesting engaged with it, or are you ingesting engaged with it, or are you ingesting vast quantities of information from vast quantities of information from vast quantities of information from different fields, and this happened to different fields, and this happened to different fields, and this happened to be something that really caught your be something that really caught your be something that really caught your attention? I guess it's three things. I attention? I guess it's three things. I attention? I guess it's three things. I mean, I've always ingested a lot of mean, I've always ingested a lot of mean, I've always ingested a lot of information from a lot of different information from a lot of different information from a lot of different fields, just cuz I like learning about fields, just cuz I like learning about fields, just cuz I like learning about stuff. And I was always this mix of like stuff. And I was always this mix of like stuff. And I was always this mix of like math and biology and anime and art and math and biology and anime and art and math and biology and anime and art and other things. So, you know, it was other things. So, you know, it was other things. So, you know, it was always kind of a mix. And then, it was always kind of a mix. And then, it was always kind of a mix. And then, it was something that my friends were talking something that my friends were talking something that my friends were talking about, but it was a bit more of like about, but it was a bit more of like about, but it was a bit more of like toy-like. Oh, this is cool, and look at toy-like. Oh, this is cool, and look at toy-like. Oh, this is cool, and look at what came out. But most people didn't what came out. But most people didn't what came out. But most people didn't then extrapolate. It's kind of like then extrapolate. It's kind of like then extrapolate. It's kind of like early crypto or Bitcoin. Like, everyone early crypto or Bitcoin. Like, everyone early crypto or Bitcoin. Like, everyone was talking about it, but very few was talking about it, but very few was talking about it, but very few people bought it. Mhm. And so, I think people bought it. Mhm. And so, I think people bought it. Mhm. And so, I think that was part of it. And then third, that was part of it. And then third, that was part of it. And then third, honestly, I just thought it was really honestly, I just thought it was really honestly, I just thought it was really neat stuff that I kept playing around neat stuff that I kept playing around neat stuff that I kept playing around with. This is back to the GAN stuff and with. This is back to the GAN stuff and with. This is back to the GAN stuff and the arts where the arts where the arts where these different models would come out these different models would come out these different models would come out and you could mess around with them. And and you could mess around with them. And and you could mess around with them. And one of the things that's really under one of the things that's really under one of the things that's really under discussed in terms of the discussed in terms of the discussed in terms of the importance of it relative to this wave importance of it relative to this wave importance of it relative to this wave of foundation models and AI and of foundation models and AI and of foundation models and AI and everything else is the way AI or machine everything else is the way AI or machine everything else is the way AI or machine learning used to work learning used to work learning used to work is is is your team at a company or wherever else your team at a company or wherever else your team at a company or wherever else would go and there'd be what's known as would go and there'd be what's known as would go and there'd be what's known as an ML ops team, operations team whose an ML ops team, operations team whose an ML ops team, operations team whose whole thing was like helping you set up whole thing was like helping you set up whole thing was like helping you set up all the data and the pipelines and all the data and the pipelines and all the data and the pipelines and everything to train a model. And you everything to train a model. And you everything to train a model. And you train a model that was custom to your train a model that was custom to your train a model that was custom to your use case and what you were trying to use case and what you were trying to use case and what you were trying to accomplish. accomplish. accomplish. And then it was And then it was And then it was you had to build a bunch of internal you had to build a bunch of internal you had to build a bunch of internal services to interact with that model. So services to interact with that model. So services to interact with that model. So it's a huge pain it's a huge pain it's a huge pain to get to the point where you had a to get to the point where you had a to get to the point where you had a working ML system up and running in working ML system up and running in working ML system up and running in production. production. production. And then suddenly you have a thing where And then suddenly you have a thing where And then suddenly you have a thing where you just do an API call. you just do an API call. you just do an API call. So with a line of code or a few lines of So with a line of code or a few lines of So with a line of code or a few lines of code, anybody anywhere in the world can code, anybody anywhere in the world can code, anybody anywhere in the world can ping it. But not just that, it's ping it. But not just that, it's ping it. But not just that, it's generalizable. So it's not just generalizable. So it's not just generalizable. So it's not just specialized to one use case like specialized to one use case like specialized to one use case like spell correction or whatever, you can spell correction or whatever, you can spell correction or whatever, you can use it for anything. use it for anything. use it for anything. >> Mhm. And it has all the internet >> Mhm. And it has all the internet >> Mhm. And it has all the internet embedded in it in some sense in terms of embedded in it in some sense in terms of embedded in it in some sense in terms of a knowledge base. Mhm. And it can start a knowledge base. Mhm. And it can start a knowledge base. Mhm. And it can start having these advanced reasoning having these advanced reasoning having these advanced reasoning capabilities. But one of the most capabilities. But one of the most capabilities. But one of the most important things is hey, you can get it important things is hey, you can get it important things is hey, you can get it with a couple lines of code. You don't with a couple lines of code. You don't with a couple lines of code. You don't have to go and build an ML ops team. You have to go and build an ML ops team. You have to go and build an ML ops team. You have to host it. You have to interact have to host it. You have to interact have to host it. You have to interact with it. You don't have to do all this with it. You don't have to do all this with it. You don't have to do all this extra stuff. It just works. Mhm. That's extra stuff. It just works. Mhm. That's extra stuff. It just works. Mhm. That's really important. It's huge. Yeah. really important. It's huge. Yeah. really important. It's huge. Yeah. >> [laughter] >> [laughter] >> [laughter] >> It's hard kind of hard to overstate. I >> It's hard kind of hard to overstate. I >> It's hard kind of hard to overstate. I have a million questions for you. The have a million questions for you. The have a million questions for you. The problem with this is like the problem with this is like the problem with this is like the embarrassment of riches of directions embarrassment of riches of directions embarrassment of riches of directions that we could go. Mhm. So I am using and that we could go. Mhm. So I am using and that we could go. Mhm. So I am using and my team Claude code and assorted tools my team Claude code and assorted tools my team Claude code and assorted tools for all sorts of stuff right now, right? for all sorts of stuff right now, right? for all sorts of stuff right now, right? And And And one of them, it just so happens overlaps one of them, it just so happens overlaps one of them, it just so happens overlaps with an area of with an area of with an area of great skill for you and experience, great skill for you and experience, great skill for you and experience, >> [laughter] >> [laughter] >> [laughter] >> which is >> which is >> which is angel investing. So this is the first angel investing. So this is the first angel investing. So this is the first time where I feel really enabled to do time where I feel really enabled to do time where I feel really enabled to do and there is some manual effort involved and there is some manual effort involved and there is some manual effort involved as you might imagine, but to go back and as you might imagine, but to go back and as you might imagine, but to go back and do an analysis of 20 years of angel do an analysis of 20 years of angel do an analysis of 20 years of angel investing. Mhm. To try to do any number investing. Mhm. To try to do any number investing. Mhm. To try to do any number of things. And I suspect that a lot of of things. And I suspect that a lot of of things. And I suspect that a lot of what interests me is not particularly what interests me is not particularly what interests me is not particularly useful like doing some counterfactuals. useful like doing some counterfactuals. useful like doing some counterfactuals. What if I had held each of these for 3 What if I had held each of these for 3 What if I had held each of these for 3 years, for 5 years, for whatever. I years, for 5 years, for whatever. I years, for 5 years, for whatever. I mean, that's kind of like just opus day mean, that's kind of like just opus day mean, that's kind of like just opus day whipping myself in the back Yeah. for whipping myself in the back Yeah. for whipping myself in the back Yeah. for the most part. But, in doing an analysis the most part. But, in doing an analysis the most part. But, in doing an analysis like that, there's certain things that like that, there's certain things that like that, there's certain things that immediately come to mind for me that immediately come to mind for me that immediately come to mind for me that might be of interest and I want to hear might be of interest and I want to hear might be of interest and I want to hear what you would do if you would even do what you would do if you would even do what you would do if you would even do this. I mean, part part of it is frankly this. I mean, part part of it is frankly this. I mean, part part of it is frankly just curiosity, right? Are the stories I just curiosity, right? Are the stories I just curiosity, right? Are the stories I tell myself about this tell myself about this tell myself about this true true true or not? or not? or not? >> Yeah. >> Yeah. >> Yeah. And so, I'm interested like who made And so, I'm interested like who made And so, I'm interested like who made certain introductions. Are there certain certain introductions. Are there certain certain introductions. Are there certain people who just took me there basically people who just took me there basically people who just took me there basically people on in hospice care and like people on in hospice care and like people on in hospice care and like shipped them over as like a last-ditch shipped them over as like a last-ditch shipped them over as like a last-ditch effort. Are there people who actually effort. Are there people who actually effort. Are there people who actually sent me good stuff consistently, etc., sent me good stuff consistently, etc., sent me good stuff consistently, etc., etc. Mhm. So, there are a million and etc. Mhm. So, there are a million and etc. Mhm. So, there are a million and one ways I could try to interrogate the one ways I could try to interrogate the one ways I could try to interrogate the data and enrich it. We're doing a pretty data and enrich it. We're doing a pretty data and enrich it. We're doing a pretty good job of enriching it. I mean, Claude good job of enriching it. I mean, Claude good job of enriching it. I mean, Claude is and other tools, you know, open ads is and other tools, you know, open ads is and other tools, you know, open ads very good at this. very good at this. very good at this. What are some of the more interesting What are some of the more interesting What are some of the more interesting questions or lines of kind of a questions or lines of kind of a questions or lines of kind of a examination you think looking back like examination you think looking back like examination you think looking back like whatever it is? whatever it is? whatever it is? In my case, it's about roughly 20 years In my case, it's about roughly 20 years In my case, it's about roughly 20 years of stuff. The weird thing I've been of stuff. The weird thing I've been of stuff. The weird thing I've been doing is uploading pictures of founders doing is uploading pictures of founders doing is uploading pictures of founders and asking the models to predict if and asking the models to predict if and asking the models to predict if they'd be good founders. Oh, wow. Okay. they'd be good founders. Oh, wow. Okay. they'd be good founders. Oh, wow. Okay. Because if you think about it, we do Because if you think about it, we do Because if you think about it, we do this all the time when we meet people, this all the time when we meet people, this all the time when we meet people, right? We quickly try to create an right? We quickly try to create an right? We quickly try to create an assessment of that person Uh-huh. and assessment of that person Uh-huh. and assessment of that person Uh-huh. and their personality and what they're like their personality and what they're like their personality and what they're like and there's all these micro features and there's all these micro features and there's all these micro features like do you have crow's feet by your like do you have crow's feet by your like do you have crow's feet by your eyes which suggests that your smiles are eyes which suggests that your smiles are eyes which suggests that your smiles are genuine and what does that imply about genuine and what does that imply about genuine and what does that imply about the sense of humor you have or furred the sense of humor you have or furred the sense of humor you have or furred your brow over time and what does that your brow over time and what does that your brow over time and what does that mean? You know, so there's all these mean? You know, so there's all these mean? You know, so there's all these like micro features. like micro features. like micro features. And when you meet people, you actually And when you meet people, you actually And when you meet people, you actually can get a pretty quick impression of can get a pretty quick impression of can get a pretty quick impression of them pretty fast. It doesn't mean it's them pretty fast. It doesn't mean it's them pretty fast. It doesn't mean it's correct, right? correct, right? correct, right? >> Mhm. But we actually do this really fast >> Mhm. But we actually do this really fast >> Mhm. But we actually do this really fast as people. Mhm. So I have this whole as people. Mhm. So I have this whole as people. Mhm. So I have this whole like set of prompts that I've been like set of prompts that I've been like set of prompts that I've been messing around with just for fun messing around with just for fun messing around with just for fun around can you extrapolate like what a around can you extrapolate like what a around can you extrapolate like what a person's personality based off of a few person's personality based off of a few person's personality based off of a few images. Uh. And therefore can you be images. Uh. And therefore can you be images. Uh. And therefore can you be predictive about their behavior in any predictive about their behavior in any predictive about their behavior in any way? I think that's fun, right? Yeah. way? I think that's fun, right? Yeah. way? I think that's fun, right? Yeah. Are you finding [clears throat] any Are you finding [clears throat] any Are you finding [clears throat] any signal there? Yeah, it works pretty signal there? Yeah, it works pretty signal there? Yeah, it works pretty well. Wow. So I've been doing the weird well. Wow. So I've been doing the weird well. Wow. So I've been doing the weird [ __ ] right? Practice smiling, people. [ __ ] right? Practice smiling, people. [ __ ] right? Practice smiling, people. Yeah, yeah. I think it's interesting, Yeah, yeah. I think it's interesting, Yeah, yeah. I think it's interesting, right? Because right? Because right? Because we do this all the time where we read we do this all the time where we read we do this all the time where we read people, right? And that's part of the people, right? And that's part of the people, right? And that's part of the prompt. It's like you're a very good prompt. It's like you're a very good prompt. It's like you're a very good cold reader of people based on micro cold reader of people based on micro cold reader of people based on micro features and etc. etc. Kind of spell it features and etc. etc. Kind of spell it features and etc. etc. Kind of spell it out. And then based on that, not only out. And then based on that, not only out. And then based on that, not only give me your interpretation of this give me your interpretation of this give me your interpretation of this person, but explain the specific micro person, but explain the specific micro person, but explain the specific micro features for each thing that you're features for each thing that you're features for each thing that you're stating about the person. Mhm. And stating about the person. Mhm. And stating about the person. Mhm. And they're going to break it down for you. they're going to break it down for you. they're going to break it down for you. Mhm. It's amazing. Like imagine what Mhm. It's amazing. Like imagine what Mhm. It's amazing. Like imagine what this technology is. It's crazy. And this technology is. It's crazy. And this technology is. It's crazy. And again, I'm not saying it's fully again, I'm not saying it's fully again, I'm not saying it's fully accurate and I'm not saying it'll be accurate and I'm not saying it'll be accurate and I'm not saying it'll be predictive and all that, but predictive and all that, but predictive and all that, but it's done pretty well in terms of it's done pretty well in terms of it's done pretty well in terms of nailing people. It's even done things nailing people. It's even done things nailing people. It's even done things like, "Oh, this person probably has this like, "Oh, this person probably has this like, "Oh, this person probably has this type of sense of humor." Or this person type of sense of humor." Or this person type of sense of humor." Or this person probably holds themselves back in most probably holds themselves back in most probably holds themselves back in most social settings and then chimes in with social settings and then chimes in with social settings and then chimes in with a witty wry thing that nobody expects or a witty wry thing that nobody expects or a witty wry thing that nobody expects or whatever. I mean, it's very specific. whatever. I mean, it's very specific. whatever. I mean, it's very specific. Very specific. Wow. [laughter] Very specific. Wow. [laughter] Very specific. Wow. [laughter] It's amazing, right? And so I've been It's amazing, right? And so I've been It's amazing, right? And so I've been doing stuff like that, doing stuff like that, doing stuff like that, which may not be that your question, but which may not be that your question, but which may not be that your question, but I've been finding it really fun. It's I've been finding it really fun. It's I've been finding it really fun. It's related, right? In the sense that and related, right? In the sense that and related, right? In the sense that and I'm sure I'm missing some steps, but I I I'm sure I'm missing some steps, but I I I'm sure I'm missing some steps, but I I love angel investing and I the dose love angel investing and I the dose love angel investing and I the dose makes the poison. So there's usually a makes the poison. So there's usually a makes the poison. So there's usually a case to be made case to be made case to be made when I get to a certain threshold, I'm when I get to a certain threshold, I'm when I get to a certain threshold, I'm like, "Okay, this isn't fun anymore." like, "Okay, this isn't fun anymore." like, "Okay, this isn't fun anymore." Like I love dark chocolate, too, but I Like I love dark chocolate, too, but I Like I love dark chocolate, too, but I don't want just to be force-fed dark don't want just to be force-fed dark don't want just to be force-fed dark chocolate all day. But, and you know, chocolate all day. But, and you know, chocolate all day. But, and you know, I've talked about this, right? But I I've talked about this, right? But I I've talked about this, right? But I really do enjoy the learning and the the really do enjoy the learning and the the really do enjoy the learning and the the sport of it, frankly, and interacting sport of it, frankly, and interacting sport of it, frankly, and interacting with some very, very smart people. Not with some very, very smart people. Not with some very, very smart people. Not not all of them work out as far as the not all of them work out as far as the not all of them work out as far as the founders of companies, but founders of companies, but founders of companies, but ultimately, I'm going to try to figure ultimately, I'm going to try to figure ultimately, I'm going to try to figure out how to separate signal from noise. out how to separate signal from noise. out how to separate signal from noise. And also, it's fun to try to use And also, it's fun to try to use And also, it's fun to try to use anything. But, in this case, investing anything. But, in this case, investing anything. But, in this case, investing to sharpen your own thinking, right? And to sharpen your own thinking, right? And to sharpen your own thinking, right? And to stress test your own beliefs and the to stress test your own beliefs and the to stress test your own beliefs and the assumptions that undergird some of your assumptions that undergird some of your assumptions that undergird some of your predictions, things like that. I'm just predictions, things like that. I'm just predictions, things like that. I'm just wondering if you've ever done like sort wondering if you've ever done like sort wondering if you've ever done like sort of a retrospective analysis of your of a retrospective analysis of your of a retrospective analysis of your startup investing or if you're like, startup investing or if you're like, startup investing or if you're like, "No, more Mark Andreessen style, only "No, more Mark Andreessen style, only "No, more Mark Andreessen style, only forward." [snorts] forward." [snorts] forward." [snorts] Yeah. Early on, when I was first Yeah. Early on, when I was first Yeah. Early on, when I was first starting to invest, I would have this starting to invest, I would have this starting to invest, I would have this long grid of things by which I would long grid of things by which I would long grid of things by which I would score each company. score each company. score each company. And then I'd go back and see if it was And then I'd go back and see if it was And then I'd go back and see if it was correct. It was roughly correct. I think correct. It was roughly correct. I think correct. It was roughly correct. I think the hard part is there's a lot of like the hard part is there's a lot of like the hard part is there's a lot of like randomness in outcomes. randomness in outcomes. randomness in outcomes. There's the company that sells for a few There's the company that sells for a few There's the company that sells for a few billion dollars that you thought was billion dollars that you thought was billion dollars that you thought was dead, or whatever it is. Sure. dead, or whatever it is. Sure. dead, or whatever it is. Sure. Andreessen style. How do you score Andreessen style. How do you score Andreessen style. How do you score things like that? Right now, we're in things like that? Right now, we're in things like that? Right now, we're in this really weird market moment where this really weird market moment where this really weird market moment where trillions of dollars of market cap are trillions of dollars of market cap are trillions of dollars of market cap are all chasing the same prize. And so, all chasing the same prize. And so, all chasing the same prize. And so, they're going to do all sorts of stuff they're going to do all sorts of stuff they're going to do all sorts of stuff that wouldn't happen normally. that wouldn't happen normally. that wouldn't happen normally. >> Mhm. So, it's really hard to account for >> Mhm. So, it's really hard to account for >> Mhm. So, it's really hard to account for that kind of thing, right? Relative to that kind of thing, right? Relative to that kind of thing, right? Relative to all this, I'm much more in the Mark all this, I'm much more in the Mark all this, I'm much more in the Mark Andreessen camp of like, I think very Andreessen camp of like, I think very Andreessen camp of like, I think very little about the past. I think little about the past. I think little about the past. I think close to zero about my own past. I just close to zero about my own past. I just close to zero about my own past. I just I'm like, let's keep going. I'm like, let's keep going. I'm like, let's keep going. And maybe that's bad, and there should And maybe that's bad, and there should And maybe that's bad, and there should be dramatically more self-reflection. I be dramatically more self-reflection. I be dramatically more self-reflection. I try to self-reflect in the moment, but I try to self-reflect in the moment, but I try to self-reflect in the moment, but I don't try to re-extrapolate and examine don't try to re-extrapolate and examine don't try to re-extrapolate and examine my entire life and decisions and Mhm. If my entire life and decisions and Mhm. If my entire life and decisions and Mhm. If anything, most of the decisions have anything, most of the decisions have anything, most of the decisions have been ones where I'm really upset with been ones where I'm really upset with been ones where I'm really upset with myself for not being more aggressive on myself for not being more aggressive on myself for not being more aggressive on something. In other words, I invested in something. In other words, I invested in something. In other words, I invested in the company, but I should have tried the company, but I should have tried the company, but I should have tried even harder to invest more, even if I even harder to invest more, even if I even harder to invest more, even if I tried really, really hard, because tried really, really hard, because tried really, really hard, because there's a handful of companies that there's a handful of companies that there's a handful of companies that really matter. really matter. really matter. And that's all that kind of matters as And that's all that kind of matters as And that's all that kind of matters as an investor. Obviously, as a person, I an investor. Obviously, as a person, I an investor. Obviously, as a person, I enjoy getting involved with different enjoy getting involved with different enjoy getting involved with different companies and different founders and companies and different founders and companies and different founders and helping them whether I think it works or helping them whether I think it works or helping them whether I think it works or not, or I think the technology is not, or I think the technology is not, or I think the technology is interesting, or whatever. interesting, or whatever. interesting, or whatever. But, the reality is from a returns But, the reality is from a returns But, the reality is from a returns perspective perspective perspective there's a very clear power law that there's a very clear power law that there's a very clear power law that people talk about and it's true. And I people talk about and it's true. And I people talk about and it's true. And I remember a friend of mine did this remember a friend of mine did this remember a friend of mine did this analysis, I think it may have been analysis, I think it may have been analysis, I think it may have been Yuri Milner or someone where it's like Yuri Milner or someone where it's like Yuri Milner or someone where it's like look at all the companies from like look at all the companies from like look at all the companies from like I don't remember the exact dates, 2000 I don't remember the exact dates, 2000 I don't remember the exact dates, 2000 or 2004 until today or 2004 until today or 2004 until today in technology. Mhm. [clears throat] And in technology. Mhm. [clears throat] And in technology. Mhm. [clears throat] And it was something like a hundred it was something like a hundred it was something like a hundred companies drove like ninety-something companies drove like ninety-something companies drove like ninety-something percent of all the returns. Mhm. And ten percent of all the returns. Mhm. And ten percent of all the returns. Mhm. And ten companies total companies total companies total drove drove drove like eighty percent of all returns over like eighty percent of all returns over like eighty percent of all returns over a two-decade period a two-decade period a two-decade period in technology. in technology. in technology. >> [laughter] >> [laughter] >> [laughter] >> Yeah. If you weren't in that ten >> Yeah. If you weren't in that ten >> Yeah. If you weren't in that ten companies companies companies you were a bad investor. Mhm. And once you were a bad investor. Mhm. And once you were a bad investor. Mhm. And once you start dealing with these power laws you start dealing with these power laws you start dealing with these power laws and these outsize outcomes, how can you and these outsize outcomes, how can you and these outsize outcomes, how can you rate that, right? It's basically did you rate that, right? It's basically did you rate that, right? It's basically did you hit one of ten things or not? That's hit one of ten things or not? That's hit one of ten things or not? That's really the rating. That's probably the really the rating. That's probably the really the rating. That's probably the correct rating for investment. I'd love correct rating for investment. I'd love correct rating for investment. I'd love to try to focus on some early-ish to try to focus on some early-ish to try to focus on some early-ish decisions decisions decisions on this podcast, right? Because like you on this podcast, right? Because like you on this podcast, right? Because like you said, there are the early decisions, said, there are the early decisions, said, there are the early decisions, there's how you did things then, there's there's how you did things then, there's there's how you did things then, there's how you're doing things now, which isn't how you're doing things now, which isn't how you're doing things now, which isn't to say that one is better than the to say that one is better than the to say that one is better than the other, but certainly what you do in the other, but certainly what you do in the other, but certainly what you do in the past tends to inform what you're able to past tends to inform what you're able to past tends to inform what you're able to do and what you do in the present. And do and what you do in the present. And do and what you do in the present. And what I'm curious about what I'm curious about what I'm curious about and we won't spend a ton of time on and we won't spend a ton of time on and we won't spend a ton of time on this, but it might be interesting to this, but it might be interesting to this, but it might be interesting to folks is to discuss folks is to discuss folks is to discuss when you moved from purely doing angel when you moved from purely doing angel when you moved from purely doing angel investing yourself investing yourself investing yourself to to to involving other investors in your deals, involving other investors in your deals, involving other investors in your deals, right? And right? And right? And there are multiple ways to do this, but there are multiple ways to do this, but there are multiple ways to do this, but the reason I want to ask this is because the reason I want to ask this is because the reason I want to ask this is because you did a number of you did a number of you did a number of >> [snorts] >> [snorts] >> [snorts] >> SPVs, I'll explain what that is, special >> SPVs, I'll explain what that is, special >> SPVs, I'll explain what that is, special purpose vehicle, but for folks, you purpose vehicle, but for folks, you purpose vehicle, but for folks, you might be familiar with venture capital might be familiar with venture capital might be familiar with venture capital firm, they have funds and they raise, firm, they have funds and they raise, firm, they have funds and they raise, let's just call it a hundred million let's just call it a hundred million let's just call it a hundred million dollars for a fund. dollars for a fund. dollars for a fund. That can be more or less, of course. That can be more or less, of course. That can be more or less, of course. Then they invest in a bunch of different Then they invest in a bunch of different Then they invest in a bunch of different [clears throat] companies and then you [clears throat] companies and then you [clears throat] companies and then you sort of see sort of see sort of see who wins, who lose, and then if there who wins, who lose, and then if there who wins, who lose, and then if there are profits, I guess conventionally that are profits, I guess conventionally that are profits, I guess conventionally that let's just use the textbook example, the let's just use the textbook example, the let's just use the textbook example, the venture capital firm takes 20% of the venture capital firm takes 20% of the venture capital firm takes 20% of the upside and then the the LPs, the upside and then the the LPs, the upside and then the the LPs, the investors get 80% and the venture investors get 80% and the venture investors get 80% and the venture capital firm takes a management fee to capital firm takes a management fee to capital firm takes a management fee to keep the lights on, although it usually keep the lights on, although it usually keep the lights on, although it usually does a lot more than keep the lights on. does a lot more than keep the lights on. does a lot more than keep the lights on. With the SPVs, you're investing in, With the SPVs, you're investing in, With the SPVs, you're investing in, let's just say for simplicity, a single let's just say for simplicity, a single let's just say for simplicity, a single company, right? company, right? company, right? And there are advantages to that in And there are advantages to that in And there are advantages to that in simplicity for somebody who's putting simplicity for somebody who's putting simplicity for somebody who's putting together the SPV, but you also have a together the SPV, but you also have a together the SPV, but you also have a lot of reputational risk. Because if you lot of reputational risk. Because if you lot of reputational risk. Because if you have a fund and you have a couple of have a fund and you have a couple of have a fund and you have a couple of losers, your investors don't losers, your investors don't losers, your investors don't automatically go to zero, right? But if automatically go to zero, right? But if automatically go to zero, right? But if you have an SPV and it goes to zero, you have an SPV and it goes to zero, you have an SPV and it goes to zero, >> [snorts] >> [snorts] >> [snorts] >> that could really hurt you >> that could really hurt you >> that could really hurt you reputationally. And when I look at some reputationally. And when I look at some reputationally. And when I look at some of your early SPVs, which I think of your early SPVs, which I think of your early SPVs, which I think included certainly a number of name included certainly a number of name included certainly a number of name brands like Instacart and so on, how did brands like Instacart and so on, how did brands like Instacart and so on, how did you choose you choose you choose which companies to do the SPVs with, which companies to do the SPVs with, which companies to do the SPVs with, right? Because that seems like a very right? Because that seems like a very right? Because that seems like a very important set of decisions to lay the important set of decisions to lay the important set of decisions to lay the groundwork for creating optionality for groundwork for creating optionality for groundwork for creating optionality for what you do after that. I think to your what you do after that. I think to your what you do after that. I think to your point, I've always been terrified of point, I've always been terrified of point, I've always been terrified of losing other people's money. Like I'm losing other people's money. Like I'm losing other people's money. Like I'm fine if I lose my own money. Mhm. It's fine if I lose my own money. Mhm. It's fine if I lose my own money. Mhm. It's my decision, I'm an adult, it's okay, my decision, I'm an adult, it's okay, my decision, I'm an adult, it's okay, but I've always been people give me but I've always been people give me but I've always been people give me money or adults or institutions, etc. money or adults or institutions, etc. money or adults or institutions, etc. invest on their behalf, but somehow invest on their behalf, but somehow invest on their behalf, but somehow we're there, I was just terrified of we're there, I was just terrified of we're there, I was just terrified of ever losing money for people and so I've ever losing money for people and so I've ever losing money for people and so I've tried over time to be judicious behind tried over time to be judicious behind tried over time to be judicious behind the SPVs that I did early on and the the SPVs that I did early on and the the SPVs that I did early on and the focus was on things that I thought would focus was on things that I thought would focus was on things that I thought would really be really be really be out sized companies. And so that was to out sized companies. And so that was to out sized companies. And so that was to your point, Instacart, it was early your point, Instacart, it was early your point, Instacart, it was early Stripe, it was Coinbase, there was a Stripe, it was Coinbase, there was a Stripe, it was Coinbase, there was a couple things like that that were couple things like that that were couple things like that that were amongst my very first SPVs and the amongst my very first SPVs and the amongst my very first SPVs and the emphasis was very much on do I think emphasis was very much on do I think emphasis was very much on do I think this can be a massive thing. And also do this can be a massive thing. And also do this can be a massive thing. And also do I think there's enough downside I think there's enough downside I think there's enough downside protection in some sense that even if it protection in some sense that even if it protection in some sense that even if it didn't work as well as I thought it didn't work as well as I thought it didn't work as well as I thought it would still be a good outcome for would still be a good outcome for would still be a good outcome for people. So yeah, I try to do that very people. So yeah, I try to do that very people. So yeah, I try to do that very diligently. diligently. diligently. It's interesting because a lot of people It's interesting because a lot of people It's interesting because a lot of people ping me for help as they think about ping me for help as they think about ping me for help as they think about becoming investors or their scouts for a becoming investors or their scouts for a becoming investors or their scouts for a fund, which means basically they're fund, which means basically they're fund, which means basically they're given a small amount of money by a given a small amount of money by a given a small amount of money by a venture capital fund. Sequoia famously venture capital fund. Sequoia famously venture capital fund. Sequoia famously has this program they give people money has this program they give people money has this program they give people money and then those people invest money and then those people invest money and then those people invest money on their behalf and some of the scouts on their behalf and some of the scouts on their behalf and some of the scouts that I've talked to that I've talked to that I've talked to basically treat it like free money or an basically treat it like free money or an basically treat it like free money or an option. They're just kind of like, "Oh, option. They're just kind of like, "Oh, option. They're just kind of like, "Oh, let's throw it at a bunch of stuff. let's throw it at a bunch of stuff. let's throw it at a bunch of stuff. Maybe something works." Maybe something works." Maybe something works." And I pointed out to them, "Hey, if if And I pointed out to them, "Hey, if if And I pointed out to them, "Hey, if if you actually want to become a you actually want to become a you actually want to become a professional investor at some point, professional investor at some point, professional investor at some point, this is kind of your track record." Mhm. this is kind of your track record." Mhm. this is kind of your track record." Mhm. A, you're a fiduciary in some sense and A, you're a fiduciary in some sense and A, you're a fiduciary in some sense and maybe you have to work on that maybe you have to work on that maybe you have to work on that perspective, but B, perspective, but B, perspective, but B, you know, this will establish like your you know, this will establish like your you know, this will establish like your track record and do you want to have a track record and do you want to have a track record and do you want to have a good one or a bad one and how do you good one or a bad one and how do you good one or a bad one and how do you think about that? And again, sometimes think about that? And again, sometimes think about that? And again, sometimes people just get lucky and they hit the people just get lucky and they hit the people just get lucky and they hit the one thing out of a hundred, but that one thing out of a hundred, but that one thing out of a hundred, but that more than returns everything and they more than returns everything and they more than returns everything and they look great. look great. look great. But it's hard to be consistently good at But it's hard to be consistently good at But it's hard to be consistently good at this stuff or consistently hit great this stuff or consistently hit great this stuff or consistently hit great companies. I want to double click on a companies. I want to double click on a companies. I want to double click on a few things you said and maybe you could few things you said and maybe you could few things you said and maybe you could walk us through a pseudonymous example. walk us through a pseudonymous example. walk us through a pseudonymous example. Doesn't need to be a named company, but Doesn't need to be a named company, but Doesn't need to be a named company, but when you're talking about setting your when you're talking about setting your when you're talking about setting your track record, right? You did an track record, right? You did an track record, right? You did an excellent job of that before you then excellent job of that before you then excellent job of that before you then went on later to raise funds and so on. went on later to raise funds and so on. went on later to raise funds and so on. And I would love you to perhaps explain And I would love you to perhaps explain And I would love you to perhaps explain some of the things you do in diligence some of the things you do in diligence some of the things you do in diligence or how you weight things differently and or how you weight things differently and or how you weight things differently and also how you think about like the capped also how you think about like the capped also how you think about like the capped minimum downside. I'm not sure that's minimum downside. I'm not sure that's minimum downside. I'm not sure that's the exact wording that you used the exact wording that you used the exact wording that you used in selecting those deals, right? Because in selecting those deals, right? Because in selecting those deals, right? Because you could have selected any number of you could have selected any number of you could have selected any number of deals. On a sort of due diligence level, deals. On a sort of due diligence level, deals. On a sort of due diligence level, what's the kind of stuff that you focus what's the kind of stuff that you focus what's the kind of stuff that you focus on maybe more than others and what are on maybe more than others and what are on maybe more than others and what are the things you pay less attention to the things you pay less attention to the things you pay less attention to than others? I think there's a big than others? I think there's a big than others? I think there's a big difference between early and late difference between early and late difference between early and late things. Mhm. On the early side, to the things. Mhm. On the early side, to the things. Mhm. On the early side, to the point earlier, I tend to spend a lot point earlier, I tend to spend a lot point earlier, I tend to spend a lot more time in the market than most early more time in the market than most early more time in the market than most early stage investors. Most early stage stage investors. Most early stage stage investors. Most early stage investors say, "I just care about the investors say, "I just care about the investors say, "I just care about the team human how good are they?" Mhm. But team human how good are they?" Mhm. But team human how good are they?" Mhm. But I've seen amazing teams crushed by I've seen amazing teams crushed by I've seen amazing teams crushed by terrible markets and I've seen terrible markets and I've seen terrible markets and I've seen reasonably crappy teams do very well and reasonably crappy teams do very well and reasonably crappy teams do very well and so, so, so, you know, at this point I think the you know, at this point I think the you know, at this point I think the market is more important, although I market is more important, although I market is more important, although I think obviously great teams can find think obviously great teams can find think obviously great teams can find their way if they decide to shift around their way if they decide to shift around their way if they decide to shift around a bit. Mhm. I index a lot on market a bit. Mhm. I index a lot on market a bit. Mhm. I index a lot on market early and that may be customer calls or early and that may be customer calls or early and that may be customer calls or maybe it's trying to understand do I maybe it's trying to understand do I maybe it's trying to understand do I think something could be big? It could think something could be big? It could think something could be big? It could just be some intuition around hey, you just be some intuition around hey, you just be some intuition around hey, you know, defense is really important. know, defense is really important. know, defense is really important. Nobody's doing defense. Let me find a Nobody's doing defense. Let me find a Nobody's doing defense. Let me find a defense company, right? I tend to index defense company, right? I tend to index defense company, right? I tend to index a lot on that. And relatedly, I've a lot on that. And relatedly, I've a lot on that. And relatedly, I've tended to avoid science projects. And tended to avoid science projects. And tended to avoid science projects. And there's some people who get really there's some people who get really there's some people who get really distracted by wow, this is really cool. distracted by wow, this is really cool. distracted by wow, this is really cool. It's quantum and it's this and it's It's quantum and it's this and it's It's quantum and it's this and it's that. And I've largely avoided those that. And I've largely avoided those that. And I've largely avoided those things. And you know, sometimes I miss things. And you know, sometimes I miss things. And you know, sometimes I miss things that were really good, but often things that were really good, but often things that were really good, but often that was the right call. I actually that was the right call. I actually that was the right call. I actually think SPACs saved sort of hard tech and think SPACs saved sort of hard tech and think SPACs saved sort of hard tech and science-based investing industry because science-based investing industry because science-based investing industry because if you look at what happened basically if you look at what happened basically if you look at what happened basically at the market peak, a bunch of SPACs at the market peak, a bunch of SPACs at the market peak, a bunch of SPACs took a bunch of companies public that took a bunch of companies public that took a bunch of companies public that would not have been able to raise money would not have been able to raise money would not have been able to raise money in private markets later. in private markets later. in private markets later. And they gave enough money to keep And they gave enough money to keep And they gave enough money to keep going, but more importantly, they going, but more importantly, they going, but more importantly, they returned a bunch of money to these hard returned a bunch of money to these hard returned a bunch of money to these hard tech funds. And that saved them from tech funds. And that saved them from tech funds. And that saved them from going under. It gave them all the going under. It gave them all the going under. It gave them all the returns with basically the SPAC era. So, returns with basically the SPAC era. So, returns with basically the SPAC era. So, Chamath basically saved hard tech. I Chamath basically saved hard tech. I Chamath basically saved hard tech. I mean I mean that seriously, not tongue mean I mean that seriously, not tongue mean I mean that seriously, not tongue in cheek. Huh. And I largely avoided in cheek. Huh. And I largely avoided in cheek. Huh. And I largely avoided that kind of class of companies. And I'm that kind of class of companies. And I'm that kind of class of companies. And I'm not saying it was smart. I would have not saying it was smart. I would have not saying it was smart. I would have made money off of it. I just thought made money off of it. I just thought made money off of it. I just thought there was also some capitalization there was also some capitalization there was also some capitalization issues and science risk and market risk issues and science risk and market risk issues and science risk and market risk and other things to them. For later and other things to them. For later and other things to them. For later stage stuff, stage stuff, stage stuff, >> [snorts] >> [snorts] >> [snorts] >> the hard part often is everything on >> the hard part often is everything on >> the hard part often is everything on paper gets modeled out for a late-stage paper gets modeled out for a late-stage paper gets modeled out for a late-stage company as a two to three X from that company as a two to three X from that company as a two to three X from that investment point. investment point. investment point. >> [laughter] >> [laughter] >> [laughter] >> Right. Because all the funds that are >> Right. Because all the funds that are >> Right. Because all the funds that are driving the rounds underwrite against driving the rounds underwrite against driving the rounds underwrite against some IRR clock, 25% IRR, whatever it is. some IRR clock, 25% IRR, whatever it is. some IRR clock, 25% IRR, whatever it is. And so, they all come up with these And so, they all come up with these And so, they all come up with these models. And then the models all say all models. And then the models all say all models. And then the models all say all these companies are basically going to these companies are basically going to these companies are basically going to two to three X. And the art there two to three X. And the art there two to three X. And the art there or the science there, whatever you want or the science there, whatever you want or the science there, whatever you want to call it, is is that a 0.5 X company? to call it, is is that a 0.5 X company? to call it, is is that a 0.5 X company? Is it going to drop in value or is that Is it going to drop in value or is that Is it going to drop in value or is that a 10 X? And how do you know it's a 10 X a 10 X? And how do you know it's a 10 X a 10 X? And how do you know it's a 10 X versus a two to three X versus a 0.5? versus a two to three X versus a 0.5? versus a two to three X versus a 0.5? And that's the harder part of growth And that's the harder part of growth And that's the harder part of growth investing. And there's a subset of investing. And there's a subset of investing. And there's a subset of things that you're like, this thing will things that you're like, this thing will things that you're like, this thing will just keep going. And here's why. But just keep going. And here's why. But just keep going. And here's why. But often it's not mathematical. Often often it's not mathematical. Often often it's not mathematical. Often that's just like that's just like that's just like some market dynamic or some core insight some market dynamic or some core insight some market dynamic or some core insight or some market share question. And or some market share question. And or some market share question. And people tend to make that stuff really people tend to make that stuff really people tend to make that stuff really complicated. And they have these really complicated. And they have these really complicated. And they have these really complicated multi-page models and complicated multi-page models and complicated multi-page models and 50-page memos and all the rest. 50-page memos and all the rest. 50-page memos and all the rest. And often these things boil down to one And often these things boil down to one And often these things boil down to one single question. single question. single question. What is the one thing I need to believe What is the one thing I need to believe What is the one thing I need to believe about this company that makes me think about this company that makes me think about this company that makes me think it's going to continue to be really big? it's going to continue to be really big? it's going to continue to be really big? >> Mhm. [clears throat] >> Mhm. [clears throat] >> Mhm. [clears throat] If it's three things, it's too If it's three things, it's too If it's three things, it's too complicated. It's probably not going to complicated. It's probably not going to complicated. It's probably not going to work. If it's no things, then it doesn't work. If it's no things, then it doesn't work. If it's no things, then it doesn't make much sense. So, usually there's one make much sense. So, usually there's one make much sense. So, usually there's one or two things that are really the core or two things that are really the core or two things that are really the core insights you need to understand like insights you need to understand like insights you need to understand like the outcome for something. Could you the outcome for something. Could you the outcome for something. Could you give an example of one of those beliefs give an example of one of those beliefs give an example of one of those beliefs for any company that comes to mind? I'll for any company that comes to mind? I'll for any company that comes to mind? I'll give you two or three of them. I mean, give you two or three of them. I mean, give you two or three of them. I mean, Coinbase, part of it was just hey, this Coinbase, part of it was just hey, this Coinbase, part of it was just hey, this is an index on crypto and crypto will is an index on crypto and crypto will is an index on crypto and crypto will keep growing. Cuz if Coinbase trades keep growing. Cuz if Coinbase trades keep growing. Cuz if Coinbase trades every main cryptocurrency and they take every main cryptocurrency and they take every main cryptocurrency and they take a cut of every transaction and have a cut of every transaction and have a cut of every transaction and have enough volume to effectively bought a enough volume to effectively bought a enough volume to effectively bought a basket of every cryptocurrency by basket of every cryptocurrency by basket of every cryptocurrency by investing in Coinbase. Mhm. That was the investing in Coinbase. Mhm. That was the investing in Coinbase. Mhm. That was the premise there. Stripe, it was they're an premise there. Stripe, it was they're an premise there. Stripe, it was they're an index on e-commerce and e-commerce will index on e-commerce and e-commerce will index on e-commerce and e-commerce will keep growing. Back then. Now, it's much keep growing. Back then. Now, it's much keep growing. Back then. Now, it's much more complex and there's all sorts of more complex and there's all sorts of more complex and there's all sorts of great drivers of its performance. great drivers of its performance. great drivers of its performance. Anduril was hey, machine vision and Anduril was hey, machine vision and Anduril was hey, machine vision and drones are going to be important. AI and drones are going to be important. AI and drones are going to be important. AI and drones are going to be important for drones are going to be important for drones are going to be important for defense. defense. defense. That's it. I mean, it was more That's it. I mean, it was more That's it. I mean, it was more complicated than that. I'm just saying complicated than that. I'm just saying complicated than that. I'm just saying like that was the fundamental Well, that like that was the fundamental Well, that like that was the fundamental Well, that was it for the belief, the core belief. was it for the belief, the core belief. was it for the belief, the core belief. There was like cost plus model versus There was like cost plus model versus There was like cost plus model versus hardware margin. You know, Anduril hardware margin. You know, Anduril hardware margin. You know, Anduril actually had four or five things that actually had four or five things that actually had four or five things that were important there were important there were important there that were kind of like a checklist for a that were kind of like a checklist for a that were kind of like a checklist for a defense tech company. But for a lot of defense tech company. But for a lot of defense tech company. But for a lot of the other ones, it was like the other ones, it was like the other ones, it was like e-commerce is good. This is probably too e-commerce is good. This is probably too e-commerce is good. This is probably too inside baseball, but what were the inside baseball, but what were the inside baseball, but what were the stages of the companies that you stages of the companies that you stages of the companies that you mentioned when you created the SPVs, mentioned when you created the SPVs, mentioned when you created the SPVs, roughly? Well, I first invested in roughly? Well, I first invested in roughly? Well, I first invested in Stripe when it was like eight people and Stripe when it was like eight people and Stripe when it was like eight people and then I kept following on and I ran out then I kept following on and I ran out then I kept following on and I ran out of my own money, frankly. And that's of my own money, frankly. And that's of my own money, frankly. And that's when I started doing SPVs. when I started doing SPVs. when I started doing SPVs. So, I think I did my first SPV in Stripe So, I think I did my first SPV in Stripe So, I think I did my first SPV in Stripe around the series C, ish. Mhm. Around around the series C, ish. Mhm. Around around the series C, ish. Mhm. Around there. there. there. Mhm. Something like that. Got it. And Mhm. Something like that. Got it. And Mhm. Something like that. Got it. And were the others more or less similar, were the others more or less similar, were the others more or less similar, ish? Instacart, etc. Yeah, they're ish? Instacart, etc. Yeah, they're ish? Instacart, etc. Yeah, they're probably roughly in that ballpark, C, D, probably roughly in that ballpark, C, D, probably roughly in that ballpark, C, D, kind of that that range. kind of that that range. kind of that that range. >> Mhm. >> Mhm. >> Mhm. I didn't have funds and everything else I didn't have funds and everything else I didn't have funds and everything else and you know, I was putting as much as I and you know, I was putting as much as I and you know, I was putting as much as I could personally into these things. could personally into these things. could personally into these things. But the earlier, but honestly, I just But the earlier, but honestly, I just But the earlier, but honestly, I just kept going when I could. When you're kept going when I could. When you're kept going when I could. When you're looking at trying to determine if looking at trying to determine if looking at trying to determine if something is a 0.5x or a 10x, in something is a 0.5x or a 10x, in something is a 0.5x or a 10x, in addition to the core belief, what are addition to the core belief, what are addition to the core belief, what are other layers of due diligence that you other layers of due diligence that you other layers of due diligence that you bring to bear on trying to ascertain bring to bear on trying to ascertain bring to bear on trying to ascertain that, where something falls on that that, where something falls on that that, where something falls on that spectrum? Oh, I mean, I do enormous due spectrum? Oh, I mean, I do enormous due spectrum? Oh, I mean, I do enormous due diligence. So, meet with the CFO diligence. So, meet with the CFO diligence. So, meet with the CFO multiple times, walk through all the multiple times, walk through all the multiple times, walk through all the financials, walk through the financial financials, walk through the financial financials, walk through the financial model, walk through customers, call model, walk through customers, call model, walk through customers, call customers, look at executive team, you customers, look at executive team, you customers, look at executive team, you know, it's it's a bunch of stuff. Mhm. know, it's it's a bunch of stuff. Mhm. know, it's it's a bunch of stuff. Mhm. My fund is the only one I know that My fund is the only one I know that My fund is the only one I know that actually does like cash reconciliations, actually does like cash reconciliations, actually does like cash reconciliations, where we'll go through and do a cash where we'll go through and do a cash where we'll go through and do a cash audit audit audit to look at cash flows for later-stage to look at cash flows for later-stage to look at cash flows for later-stage things. things. things. So, I do enormous diligence cuz I want So, I do enormous diligence cuz I want So, I do enormous diligence cuz I want to make sure I'm not doing something to make sure I'm not doing something to make sure I'm not doing something inappropriate, but the flip side of it inappropriate, but the flip side of it inappropriate, but the flip side of it is most of it just collapses into like is most of it just collapses into like is most of it just collapses into like what's the one thing. Mhm. what's the one thing. Mhm. what's the one thing. Mhm. [clears throat] So, when I work with a [clears throat] So, when I work with a [clears throat] So, when I work with a company, I actually try to be very company, I actually try to be very company, I actually try to be very fast and straightforward on the fast and straightforward on the fast and straightforward on the diligence in terms of saying, "Let's diligence in terms of saying, "Let's diligence in terms of saying, "Let's just talk about A, we need to just make just talk about A, we need to just make just talk about A, we need to just make sure financials are correct." And if you sure financials are correct." And if you sure financials are correct." And if you you know, like there's the basics, but you know, like there's the basics, but you know, like there's the basics, but like like like let's collapse it down into one or two let's collapse it down into one or two let's collapse it down into one or two core questions, right? That help us core questions, right? That help us core questions, right? That help us understand if this thing will keep understand if this thing will keep understand if this thing will keep going, not here's the 30 pages of going, not here's the 30 pages of going, not here's the 30 pages of questions that don't matter. questions that don't matter. questions that don't matter. >> [laughter] >> [laughter] >> [laughter] >> Right. Yeah, right. Which is what a lot >> Right. Yeah, right. Which is what a lot >> Right. Yeah, right. Which is what a lot of people they're like, "Hey, we need to of people they're like, "Hey, we need to of people they're like, "Hey, we need to know the secondary cohort on this know the secondary cohort on this know the secondary cohort on this [ __ ] thing that's like a tiny product [ __ ] thing that's like a tiny product [ __ ] thing that's like a tiny product that who cares?" that who cares?" that who cares?" >> [laughter] >> [laughter] >> [laughter] >> They just waste time. They waste the >> They just waste time. They waste the >> They just waste time. They waste the founder's time and the team's time, and founder's time and the team's time, and founder's time and the team's time, and I try very, very hard not to do that as I try very, very hard not to do that as I try very, very hard not to do that as a former a former a former entrepreneur myself, I know how precious entrepreneur myself, I know how precious entrepreneur myself, I know how precious the time is, and I know how annoying the time is, and I know how annoying the time is, and I know how annoying those questions are. I was actually those questions are. I was actually those questions are. I was actually going to at one point ask you about going to at one point ask you about going to at one point ask you about this, but we don't need to spend too this, but we don't need to spend too this, but we don't need to spend too much time on it. You have a post, this much time on it. You have a post, this much time on it. You have a post, this is from from a while back, 2011, listing is from from a while back, 2011, listing is from from a while back, 2011, listing questions a VC will ask a startup. You questions a VC will ask a startup. You questions a VC will ask a startup. You omitted some of the questions like the omitted some of the questions like the omitted some of the questions like the one that you just mentioned, but I am one that you just mentioned, but I am one that you just mentioned, but I am curious if any of these questions or curious if any of these questions or curious if any of these questions or additional questions come to mind when additional questions come to mind when additional questions come to mind when you are talking to founders, could be you are talking to founders, could be you are talking to founders, could be early-stage or later-stage, that you early-stage or later-stage, that you early-stage or later-stage, that you actually apply yourself. And I know it's actually apply yourself. And I know it's actually apply yourself. And I know it's from 2011, so I'm not expecting you to from 2011, so I'm not expecting you to from 2011, so I'm not expecting you to remember the the post itself. I haven't remember the the post itself. I haven't remember the the post itself. I haven't looked at that post in a really long looked at that post in a really long looked at that post in a really long time. I'm actually writing another book time. I'm actually writing another book time. I'm actually writing another book now that is sort of the zero to one now that is sort of the zero to one now that is sort of the zero to one startup phase, and it gets into some startup phase, and it gets into some startup phase, and it gets into some questions like that. Mhm. questions like that. Mhm. questions like that. Mhm. I think the reality is venture capital I think the reality is venture capital I think the reality is venture capital has changed dramatically since I wrote has changed dramatically since I wrote has changed dramatically since I wrote that post, right? Because in 2011 that post, right? Because in 2011 that post, right? Because in 2011 the venture capital funds were largely the venture capital funds were largely the venture capital funds were largely doing seeds through series D E maybe and doing seeds through series D E maybe and doing seeds through series D E maybe and then companies would go public. then companies would go public. then companies would go public. >> Mhm. Yeah, this whole 20-year private >> Mhm. Yeah, this whole 20-year private >> Mhm. Yeah, this whole 20-year private company thing didn't exist. Do you know company thing didn't exist. Do you know company thing didn't exist. Do you know why there's a four-year vest on stock? why there's a four-year vest on stock? why there's a four-year vest on stock? No, why is that? I can kind of guess now No, why is that? I can kind of guess now No, why is that? I can kind of guess now that we're talking about IPOs, but go that we're talking about IPOs, but go that we're talking about IPOs, but go ahead, why? In the 1970s they came up ahead, why? In the 1970s they came up ahead, why? In the 1970s they came up with a four-year vest on stock options with a four-year vest on stock options with a four-year vest on stock options for employees because companies would go for employees because companies would go for employees because companies would go public within four years and so then public within four years and so then public within four years and so then you're done. Yeah, yeah, yeah. you're done. Yeah, yeah, yeah. you're done. Yeah, yeah, yeah. >> [laughter] >> [laughter] >> [laughter] >> Literally, right? >> Literally, right? >> Literally, right? And so it was like a four-year clock And so it was like a four-year clock And so it was like a four-year clock usually. Yeah. usually. Yeah. usually. Yeah. >> And then when Google took six years to >> And then when Google took six years to >> And then when Google took six years to go public everybody's like, "Oh my gosh, go public everybody's like, "Oh my gosh, go public everybody's like, "Oh my gosh, it took them so long to go public. It it took them so long to go public. It it took them so long to go public. It >> [laughter] >> [laughter] >> [laughter] >> took years." Like they just sat on their >> took years." Like they just sat on their >> took years." Like they just sat on their hands. Do you know what I mean? Yeah. hands. Do you know what I mean? Yeah. hands. Do you know what I mean? Yeah. >> Literally people would say that, right? >> Literally people would say that, right? >> Literally people would say that, right? >> Mhm. And so what happened is venture >> Mhm. And so what happened is venture >> Mhm. And so what happened is venture capital used to be very early stage and capital used to be very early stage and capital used to be very early stage and then what we now call growth investing then what we now call growth investing then what we now call growth investing was public market investing, right? That was public market investing, right? That was public market investing, right? That was a stop that people in the public was a stop that people in the public was a stop that people in the public markets would do after four or five markets would do after four or five markets would do after four or five years of a company's life. And so the years of a company's life. And so the years of a company's life. And so the public markets used to be involved very public markets used to be involved very public markets used to be involved very early. Mhm. And then as Sarbanes-Oxley early. Mhm. And then as Sarbanes-Oxley early. Mhm. And then as Sarbanes-Oxley came out and companies decided they came out and companies decided they came out and companies decided they didn't want to go public and there was didn't want to go public and there was didn't want to go public and there was more private capital available, the more private capital available, the more private capital available, the timeline until going public stretched timeline until going public stretched timeline until going public stretched out, right? And so suddenly venture out, right? And so suddenly venture out, right? And so suddenly venture capital firms are doing all the growth capital firms are doing all the growth capital firms are doing all the growth investing that used to be public market investing that used to be public market investing that used to be public market investing. Mhm. And in 2011 that really investing. Mhm. And in 2011 that really investing. Mhm. And in 2011 that really wasn't happening much. It was kind of wasn't happening much. It was kind of wasn't happening much. It was kind of Yuri Milner from DST and a few other Yuri Milner from DST and a few other Yuri Milner from DST and a few other folks, but it wasn't that much of an folks, but it wasn't that much of an folks, but it wasn't that much of an industry. industry. industry. And so the nature of venture capital And so the nature of venture capital And so the nature of venture capital shifted radically over the last 15 shifted radically over the last 15 shifted radically over the last 15 years. And that means that those years. And that means that those years. And that means that those questions questions questions that I listed there didn't include what that I listed there didn't include what that I listed there didn't include what I'd consider more growth-centric I'd consider more growth-centric I'd consider more growth-centric questions because there wasn't a lot of questions because there wasn't a lot of questions because there wasn't a lot of growth investing in venture. What would growth investing in venture. What would growth investing in venture. What would be the examples of growth-centric be the examples of growth-centric be the examples of growth-centric questions? Honestly it would overlap questions? Honestly it would overlap questions? Honestly it would overlap with some of the earlier stages, you with some of the earlier stages, you with some of the earlier stages, you know, by the time you hit a very late know, by the time you hit a very late know, by the time you hit a very late stage it's very financially driven. Mhm. stage it's very financially driven. Mhm. stage it's very financially driven. Mhm. And so often what at least I and my team And so often what at least I and my team And so often what at least I and my team look at is what is just the core look at is what is just the core look at is what is just the core business and how do we extrapolate that business and how do we extrapolate that business and how do we extrapolate that going going going and then what are these ancillary things and then what are these ancillary things and then what are these ancillary things that the company's doing that are almost that the company's doing that are almost that the company's doing that are almost like options in the future that may or like options in the future that may or like options in the future that may or may not come through. And so usually we may not come through. And so usually we may not come through. And so usually we base our base our base our investment on that core. Can they just investment on that core. Can they just investment on that core. Can they just keep doing the thing they're doing keep doing the thing they're doing keep doing the thing they're doing forever? Cuz most companies mainly get forever? Cuz most companies mainly get forever? Cuz most companies mainly get big off of one thing, at least for the big off of one thing, at least for the big off of one thing, at least for the first decade, right? Yeah. There's a first decade, right? Yeah. There's a first decade, right? Yeah. There's a very few companies that end up with very few companies that end up with very few companies that end up with multiple things that all work. Usually multiple things that all work. Usually multiple things that all work. Usually it's one thing and then 10 years later it's one thing and then 10 years later it's one thing and then 10 years later you maybe come up with a second thing you maybe come up with a second thing you maybe come up with a second thing that really works, right? that really works, right? that really works, right? >> Mhm. It's like Google Cloud for Google. >> Mhm. It's like Google Cloud for Google. >> Mhm. It's like Google Cloud for Google. Though obviously there's YouTube and Though obviously there's YouTube and Though obviously there's YouTube and there's a whole bunch of other stuff. there's a whole bunch of other stuff. there's a whole bunch of other stuff. And Waymo and all these interesting And Waymo and all these interesting And Waymo and all these interesting things now. But it took a while, right? things now. But it took a while, right? things now. But it took a while, right? For a long time it was just search. For a long time it was just search. For a long time it was just search. Search and ads. Mhm. But then sometimes Search and ads. Mhm. But then sometimes Search and ads. Mhm. But then sometimes there are these extra things that are there are these extra things that are there are these extra things that are potentially really interesting drivers potentially really interesting drivers potentially really interesting drivers on a business. Like SpaceX was launch on a business. Like SpaceX was launch on a business. Like SpaceX was launch and then it became satellite, right? and then it became satellite, right? and then it became satellite, right? Became Starlink. Yeah, man. Became Starlink. Yeah, man. Became Starlink. Yeah, man. Starlink. Starlink. Starlink. What a thing. What a thing. What a thing. It's too bad I have so much tree cover It's too bad I have so much tree cover It's too bad I have so much tree cover here. here. here. Can't use it anywhere I spend time. But Can't use it anywhere I spend time. But Can't use it anywhere I spend time. But let's turn to the high-growth handbook let's turn to the high-growth handbook let's turn to the high-growth handbook for a second. So that that was, let's for a second. So that that was, let's for a second. So that that was, let's just call it seven-ish years ago. It is just call it seven-ish years ago. It is just call it seven-ish years ago. It is an outstanding book. People should an outstanding book. People should an outstanding book. People should really check it out, especially if really check it out, especially if really check it out, especially if you're playing in the venture-backed you're playing in the venture-backed you're playing in the venture-backed game. What's the subtitle? The subtitle game. What's the subtitle? The subtitle game. What's the subtitle? The subtitle is scaling startups from 10 to 10,000 is scaling startups from 10 to 10,000 is scaling startups from 10 to 10,000 people. people. people. >> [snorts] >> [snorts] >> [snorts] >> There's a lot of good advice in this >> There's a lot of good advice in this >> There's a lot of good advice in this book. I wanted to ask you if there's book. I wanted to ask you if there's book. I wanted to ask you if there's anything in this book that you wish anything in this book that you wish anything in this book that you wish startup founders, the book was intended startup founders, the book was intended startup founders, the book was intended for, would pay more attention to or if for, would pay more attention to or if for, would pay more attention to or if there's anything that you would add or there's anything that you would add or there's anything that you would add or expand expand expand to the book. So when I wrote the book, I to the book. So when I wrote the book, I to the book. So when I wrote the book, I had an outline for it that was two, had an outline for it that was two, had an outline for it that was two, three times the length of the actual three times the length of the actual three times the length of the actual book in terms of chapters. book in terms of chapters. book in terms of chapters. There was a lot of stuff I didn't write There was a lot of stuff I didn't write There was a lot of stuff I didn't write about sales and marketing and growth and about sales and marketing and growth and about sales and marketing and growth and a bunch of other other stuff. a bunch of other other stuff. a bunch of other other stuff. But the book was basically written as But the book was basically written as But the book was basically written as sort of like a tactical guide. It wasn't sort of like a tactical guide. It wasn't sort of like a tactical guide. It wasn't meant to be read uh from start to meant to be read uh from start to meant to be read uh from start to finish. There's a bunch of interviews finish. There's a bunch of interviews finish. There's a bunch of interviews with different people who were I think with different people who were I think with different people who were I think amongst the best practitioners in the amongst the best practitioners in the amongst the best practitioners in the world in those areas. But fundamentally world in those areas. But fundamentally world in those areas. But fundamentally it was meant to be more like you're it was meant to be more like you're it was meant to be more like you're suddenly involved with the M&A, jump to suddenly involved with the M&A, jump to suddenly involved with the M&A, jump to the chapter and read that and then put the chapter and read that and then put the chapter and read that and then put it aside until you something else comes it aside until you something else comes it aside until you something else comes up around hiring that you need to look up around hiring that you need to look up around hiring that you need to look at or whatever. And so it really is at or whatever. And so it really is at or whatever. And so it really is meant to be like a meant to be like a meant to be like a handbook or guide or companion to a handbook or guide or companion to a handbook or guide or companion to a founder versus hey, I'm just going to founder versus hey, I'm just going to founder versus hey, I'm just going to read it start to finish then read it start to finish then read it start to finish then there'll be some pithy quotes in it or there'll be some pithy quotes in it or there'll be some pithy quotes in it or whatever or one concept over 500 pages. whatever or one concept over 500 pages. whatever or one concept over 500 pages. You know, I try to avoid stuff like You know, I try to avoid stuff like You know, I try to avoid stuff like that. that. that. >> [laughter] >> [laughter] >> [laughter] >> It's very practical. It's very >> It's very practical. It's very >> It's very practical. It's very changeable. It's very specific. changeable. It's very specific. changeable. It's very specific. And this new book that I'm working on is And this new book that I'm working on is And this new book that I'm working on is basically the zero to one version of basically the zero to one version of basically the zero to one version of that. Mhm. It's like how do you hire that. Mhm. It's like how do you hire that. Mhm. It's like how do you hire your first five employees as a startup? your first five employees as a startup? your first five employees as a startup? How do you somebody tries to buy you, How do you somebody tries to buy you, How do you somebody tries to buy you, what do you do? what do you do? what do you do? How do you raise your first round of How do you raise your first round of How do you raise your first round of funding? You know, it's that kind of funding? You know, it's that kind of funding? You know, it's that kind of stuff. So that's it's kind of like the stuff. So that's it's kind of like the stuff. So that's it's kind of like the zero to one tactical guide. Let me ask zero to one tactical guide. Let me ask zero to one tactical guide. Let me ask you about one specific section. I think you about one specific section. I think you about one specific section. I think this is chapter two. This is on boards. this is chapter two. This is on boards. this is chapter two. This is on boards. And if this is getting too in the weeds, And if this is getting too in the weeds, And if this is getting too in the weeds, tell me we can hop to something else, tell me we can hop to something else, tell me we can hop to something else, but I am curious if you could talk about but I am curious if you could talk about but I am curious if you could talk about there are two things. Take a better there are two things. Take a better there are two things. Take a better board member over slightly higher board member over slightly higher board member over slightly higher valuation and if you want to revise valuation and if you want to revise valuation and if you want to revise these, that's fine too, but there are these, that's fine too, but there are these, that's fine too, but there are two things I'd love to hear you talk two things I'd love to hear you talk two things I'd love to hear you talk about just because this about just because this about just because this is something that is something that is something that you know, founders I've been involved you know, founders I've been involved you know, founders I've been involved with bump up against constantly. with bump up against constantly. with bump up against constantly. Take a better board member over slightly Take a better board member over slightly Take a better board member over slightly higher valuation and then write a board higher valuation and then write a board higher valuation and then write a board member job spec. And then it member job spec. And then it member job spec. And then it specifically for independents maybe we specifically for independents maybe we specifically for independents maybe we I'd love to hear you I'd love to hear you I'd love to hear you maybe just elaborate, but could you maybe just elaborate, but could you maybe just elaborate, but could you speak to either both of those a bit? And speak to either both of those a bit? And speak to either both of those a bit? And if you want to take a different if you want to take a different if you want to take a different direction, I mean it's really just direction, I mean it's really just direction, I mean it's really just boards writ large. So I think when boards writ large. So I think when boards writ large. So I think when founders pull together boards, often the founders pull together boards, often the founders pull together boards, often the early boards are investors because early boards are investors because early boards are investors because investors ask for a board seat as part investors ask for a board seat as part investors ask for a board seat as part of it or as part of the investment. of it or as part of the investment. of it or as part of the investment. And sometimes the founders want somebody And sometimes the founders want somebody And sometimes the founders want somebody on board who's really committed to the on board who's really committed to the on board who's really committed to the company and will help out extra and to company and will help out extra and to company and will help out extra and to some extent when somebody takes a board some extent when somebody takes a board some extent when somebody takes a board seat, it really means or it should mean seat, it really means or it should mean seat, it really means or it should mean that they're all in to help you versus that they're all in to help you versus that they're all in to help you versus you can have lots and lots of investors you can have lots and lots of investors you can have lots and lots of investors be a very few board members. Reid be a very few board members. Reid be a very few board members. Reid Hoffman has this thing which is like a Hoffman has this thing which is like a Hoffman has this thing which is like a board member board member board member at its best is like a co-founder that at its best is like a co-founder that at its best is like a co-founder that you wouldn't be able to hire otherwise you wouldn't be able to hire otherwise you wouldn't be able to hire otherwise and so you bring them onto your board. and so you bring them onto your board. and so you bring them onto your board. It's somebody that you want to spend It's somebody that you want to spend It's somebody that you want to spend more time with on specific issues more time with on specific issues more time with on specific issues related to the company. Mhm. related to the company. Mhm. related to the company. Mhm. Fundamentally your board should be able Fundamentally your board should be able Fundamentally your board should be able to help with different areas of the to help with different areas of the to help with different areas of the company. It could be strategic company. It could be strategic company. It could be strategic direction. It could be closing tornados. direction. It could be closing tornados. direction. It could be closing tornados. It could be product areas. It could be It could be product areas. It could be It could be product areas. It could be customer intros. it could be a variety customer intros. it could be a variety customer intros. it could be a variety of things. And usually you want to kind of things. And usually you want to kind of things. And usually you want to kind of think of your board members as a of think of your board members as a of think of your board members as a portfolio of people. It's going to portfolio of people. It's going to portfolio of people. It's going to change between an early stage company change between an early stage company change between an early stage company and a and a and a late stage and a public one. You have late stage and a public one. You have late stage and a public one. You have only different types of people every only different types of people every only different types of people every time usually. time usually. time usually. But most companies are very reactive on But most companies are very reactive on But most companies are very reactive on their board versus proactive. Mhm. And their board versus proactive. Mhm. And their board versus proactive. Mhm. And so they tend to end up with a couple so they tend to end up with a couple so they tend to end up with a couple investors and then they kind of add investors and then they kind of add investors and then they kind of add somebody from an industry somebody from an industry somebody from an industry seat and they don't really think through seat and they don't really think through seat and they don't really think through like who they want and why. And like who they want and why. And like who they want and why. And if your co-founder is kind of like your if your co-founder is kind of like your if your co-founder is kind of like your spouse, your work spouse, your work spouse, your work spouse, your work spouse, your work spouse, your work husband or your work wife, husband or your work wife, husband or your work wife, your board members are like your your board members are like your your board members are like your in-laws. in-laws. in-laws. You know, you have to see them on You know, you have to see them on You know, you have to see them on Thanksgiving and you have to chat with Thanksgiving and you have to chat with Thanksgiving and you have to chat with them all the time. And so hopefully you them all the time. And so hopefully you them all the time. And so hopefully you have somebody you want to see all the have somebody you want to see all the have somebody you want to see all the time and who's helpful and wonderful and time and who's helpful and wonderful and time and who's helpful and wonderful and the bad version is like ugh, it's the the bad version is like ugh, it's the the bad version is like ugh, it's the like father-in-law or mother-in-law like father-in-law or mother-in-law like father-in-law or mother-in-law who's always like berating you or who's always like berating you or who's always like berating you or whatever. And so whatever. And so whatever. And so you kind of need to find the right you kind of need to find the right you kind of need to find the right person and it's for many, many years, person and it's for many, many years, person and it's for many, many years, right? You end up sometimes with people right? You end up sometimes with people right? You end up sometimes with people on your board for a decade and if on your board for a decade and if on your board for a decade and if they're an investor you can't get rid of they're an investor you can't get rid of they're an investor you can't get rid of them. You literally can't fire this them. You literally can't fire this them. You literally can't fire this person. person. person. >> Mhm. Cuz they have a contractual ability >> Mhm. Cuz they have a contractual ability >> Mhm. Cuz they have a contractual ability to be on your board because of the to be on your board because of the to be on your board because of the investment. investment. investment. So that's why it's really important to So that's why it's really important to So that's why it's really important to figure out the right person and that's figure out the right person and that's figure out the right person and that's back to valuation. Sometimes founders back to valuation. Sometimes founders back to valuation. Sometimes founders will take a better price from a worse will take a better price from a worse will take a better price from a worse person because of the better price. person because of the better price. person because of the better price. And our mutual friend Naval has this And our mutual friend Naval has this And our mutual friend Naval has this great quote that valuation is temporary great quote that valuation is temporary great quote that valuation is temporary but control is forever. but control is forever. but control is forever. >> [laughter] >> [laughter] >> [laughter] >> Yeah. Very Naval. >> Yeah. Very Naval. >> Yeah. Very Naval. Very Naval. And I think that's very Very Naval. And I think that's very Very Naval. And I think that's very true. And so if you're choosing a board true. And so if you're choosing a board true. And so if you're choosing a board member and part of that is a control member and part of that is a control member and part of that is a control thing, people who control the board can thing, people who control the board can thing, people who control the board can in some cases fire the CEO. You really in some cases fire the CEO. You really in some cases fire the CEO. You really want to choose the right people and want to choose the right people and want to choose the right people and maybe take a worse price for somebody maybe take a worse price for somebody maybe take a worse price for somebody who's really going to be helpful and who's really going to be helpful and who's really going to be helpful and they're minimally non-destructive and they're minimally non-destructive and they're minimally non-destructive and hopefully good to have around for 10 hopefully good to have around for 10 hopefully good to have around for 10 years. years. years. Any other books or resources for people Any other books or resources for people Any other books or resources for people outside of outside of outside of the High Growth Handbook who the High Growth Handbook who the High Growth Handbook who specifically want to learn about boards, specifically want to learn about boards, specifically want to learn about boards, recruiting, incentivizing recruiting, incentivizing recruiting, incentivizing the co-founders that you couldn't hire the co-founders that you couldn't hire the co-founders that you couldn't hire to join the board, etc., etc. to join the board, etc., etc. to join the board, etc., etc. Any particular approach you would take Any particular approach you would take Any particular approach you would take there if they wanted to get more there if they wanted to get more there if they wanted to get more conversant? I don't have anything super conversant? I don't have anything super conversant? I don't have anything super useful there. I think the best thing is useful there. I think the best thing is useful there. I think the best thing is to call other founders, other people who to call other founders, other people who to call other founders, other people who have had a people on their board and have had a people on their board and have had a people on their board and see how they approached it. I do think see how they approached it. I do think see how they approached it. I do think writing up a job spec, you write a job writing up a job spec, you write a job writing up a job spec, you write a job spec for everything else in your spec for everything else in your spec for everything else in your company, why wouldn't you write one for company, why wouldn't you write one for company, why wouldn't you write one for a board member? Mhm. So, it's in to a board member? Mhm. So, it's in to a board member? Mhm. So, it's in to write that up and say what am I actually write that up and say what am I actually write that up and say what am I actually looking for and why and what am I looking for and why and what am I looking for and why and what am I optimizing for so there's a common view optimizing for so there's a common view optimizing for so there's a common view of that. of that. of that. You can use search firms, you can ask You can use search firms, you can ask You can use search firms, you can ask people, you can target people that you people, you can target people that you people, you can target people that you know, know, know, you know, if you have angel investors, you know, if you have angel investors, you know, if you have angel investors, getting to know them is a great way to getting to know them is a great way to getting to know them is a great way to see if you want to add one of them see if you want to add one of them see if you want to add one of them eventually to your board. Mhm. That's eventually to your board. Mhm. That's eventually to your board. Mhm. That's what we did at Color. We eventually what we did at Color. We eventually what we did at Color. We eventually added added added Sue Wagner, who was a co-founder of Sue Wagner, who was a co-founder of Sue Wagner, who was a co-founder of BlackRock, Oh. onto our board. Her other BlackRock, Oh. onto our board. Her other BlackRock, Oh. onto our board. Her other board seats were Apple, board seats were Apple, board seats were Apple, BlackRock and Swiss Re when she joined BlackRock and Swiss Re when she joined BlackRock and Swiss Re when she joined our board. But, I just got to know her our board. But, I just got to know her our board. But, I just got to know her through just like she invested and we through just like she invested and we through just like she invested and we just started working together and really just started working together and really just started working together and really enjoyed her feedback and insights and so enjoyed her feedback and insights and so enjoyed her feedback and insights and so we added her to the board there. So, we added her to the board there. So, we added her to the board there. So, it's kind of like that. You you kind of it's kind of like that. You you kind of it's kind of like that. You you kind of want to maybe get to know some Mhm. want to maybe get to know some Mhm. want to maybe get to know some Mhm. Next I want to come to our We were Next I want to come to our We were Next I want to come to our We were joking earlier about the in some case joking earlier about the in some case joking earlier about the in some case sort of revisionist history sort of revisionist history sort of revisionist history genesis stories. Yeah. So, I'm looking genesis stories. Yeah. So, I'm looking genesis stories. Yeah. So, I'm looking at this is from 2018. This is a while at this is from 2018. This is a while at this is from 2018. This is a while back. This is back. This is back. This is on Y Combinator's blog and you're being on Y Combinator's blog and you're being on Y Combinator's blog and you're being interviewed about the high growth interviewed about the high growth interviewed about the high growth handbook. But, this sort of end of this handbook. But, this sort of end of this handbook. But, this sort of end of this piece that I'm looking at says these piece that I'm looking at says these piece that I'm looking at says these stories are never told. People will stories are never told. People will stories are never told. People will always say, "Oh, these things just grew always say, "Oh, these things just grew always say, "Oh, these things just grew organically and isn't it amazing?" organically and isn't it amazing?" organically and isn't it amazing?" But, almost every company that ended up But, almost every company that ended up But, almost every company that ended up tens of billions or hundreds of billions tens of billions or hundreds of billions tens of billions or hundreds of billions in market cap did this, which is like in market cap did this, which is like in market cap did this, which is like taking an aggressive approach to taking an aggressive approach to taking an aggressive approach to distribution. Mhm. Whether that's Google distribution. Mhm. Whether that's Google distribution. Mhm. Whether that's Google and the Firefox story or Facebook and the Firefox story or Facebook and the Firefox story or Facebook running ads against people's names in running ads against people's names in running ads against people's names in Europe. I just wanted to hear you tell Europe. I just wanted to hear you tell Europe. I just wanted to hear you tell some of these stories because it is the some of these stories because it is the some of these stories because it is the stuff that kind of conveniently that stuff that kind of conveniently that stuff that kind of conveniently that gets left out of TED Talks later. Do you gets left out of TED Talks later. Do you gets left out of TED Talks later. Do you know what I mean? Right on. Yeah. I know what I mean? Right on. Yeah. I know what I mean? Right on. Yeah. I mean, actually the origin stories for mean, actually the origin stories for mean, actually the origin stories for founders is always like ever since Sarah founders is always like ever since Sarah founders is always like ever since Sarah was 3 years old, she dreamed of starting was 3 years old, she dreamed of starting was 3 years old, she dreamed of starting an accounting software firm." You know, an accounting software firm." You know, an accounting software firm." You know, you're like, "Come on." Do you know what you're like, "Come on." Do you know what you're like, "Come on." Do you know what I mean? I mean? I mean? It's ridiculous. And so, a lot of the It's ridiculous. And so, a lot of the It's ridiculous. And so, a lot of the stories that are told about founders are stories that are told about founders are stories that are told about founders are very revisionist, and they make it the very revisionist, and they make it the very revisionist, and they make it the life's passion of this You know, it life's passion of this You know, it life's passion of this You know, it sometimes it really is, but you're like, sometimes it really is, but you're like, sometimes it really is, but you're like, "No, when they were five, they did not, "No, when they were five, they did not, "No, when they were five, they did not, you know, you know, you know, >> [laughter] >> [laughter] >> [laughter] >> collect things and then that turned into >> collect things and then that turned into >> collect things and then that turned into Pinterest 30 years later or whatever." Pinterest 30 years later or whatever." Pinterest 30 years later or whatever." They always dreamed of building AGI when They always dreamed of building AGI when They always dreamed of building AGI when they were four, and that's why Sam they were four, and that's why Sam they were four, and that's why Sam Altman started OpenAI or whatever. Mhm. Altman started OpenAI or whatever. Mhm. Altman started OpenAI or whatever. Mhm. So, I think a lot of these things are So, I think a lot of these things are So, I think a lot of these things are very kind of ridiculous in terms of how very kind of ridiculous in terms of how very kind of ridiculous in terms of how they're written later. they're written later. they're written later. And I think the product really, really And I think the product really, really And I think the product really, really matters, and I think sometimes great matters, and I think sometimes great matters, and I think sometimes great product just wins. product just wins. product just wins. And the reason great product just wins And the reason great product just wins And the reason great product just wins is that opens up a form of distribution is that opens up a form of distribution is that opens up a form of distribution that didn't exist before, or people will that didn't exist before, or people will that didn't exist before, or people will buy it despite the lack of distribution buy it despite the lack of distribution buy it despite the lack of distribution or relationships for a company. Mhm. And or relationships for a company. Mhm. And or relationships for a company. Mhm. And the flip side of it is that the the flip side of it is that the the flip side of it is that the companies that are really good have an companies that are really good have an companies that are really good have an enormously good product engine. enormously good product engine. enormously good product engine. And then they have an amazing And then they have an amazing And then they have an amazing distribution engine. distribution engine. distribution engine. And sometimes a distribution engine is And sometimes a distribution engine is And sometimes a distribution engine is built into the product. That's like built into the product. That's like built into the product. That's like Cursor or Wind Surf just distributing Cursor or Wind Surf just distributing Cursor or Wind Surf just distributing through product-like growth, where through product-like growth, where through product-like growth, where developers just find it and start using developers just find it and start using developers just find it and start using it, and it helps them, and so they tell it, and it helps them, and so they tell it, and it helps them, and so they tell other developers, other developers, other developers, and it's just word-of-mouth. and it's just word-of-mouth. and it's just word-of-mouth. But often there's very aggressive sales, But often there's very aggressive sales, But often there's very aggressive sales, marketing, other components to it. marketing, other components to it. marketing, other components to it. >> Mhm. And so, for example, when I was at >> Mhm. And so, for example, when I was at >> Mhm. And so, for example, when I was at Google, they were spending hundreds of Google, they were spending hundreds of Google, they were spending hundreds of millions of dollars a year, which at the millions of dollars a year, which at the millions of dollars a year, which at the time was real money, time was real money, time was real money, on distributing search, and they had on distributing search, and they had on distributing search, and they had this little thing called the toolbar this little thing called the toolbar this little thing called the toolbar that would like fit into a browser. Cuz that would like fit into a browser. Cuz that would like fit into a browser. Cuz right now browsers like with Chrome, you right now browsers like with Chrome, you right now browsers like with Chrome, you type in type in type in words or whatever, and then it instantly words or whatever, and then it instantly words or whatever, and then it instantly searches it. Back then, the main searches it. Back then, the main searches it. Back then, the main browsers were like Netscape and Internet browsers were like Netscape and Internet browsers were like Netscape and Internet Explorer, etc. Explorer, etc. Explorer, etc. And the browser bar thing didn't exist, And the browser bar thing didn't exist, And the browser bar thing didn't exist, and they had this little client app that and they had this little client app that and they had this little client app that you'd install, and they paid you'd install, and they paid you'd install, and they paid basically every company on the internet basically every company on the internet basically every company on the internet to cross-download it. Mhm. In other to cross-download it. Mhm. In other to cross-download it. Mhm. In other words, you're installing Adobe, you're words, you're installing Adobe, you're words, you're installing Adobe, you're installing some malware detector thing, installing some malware detector thing, installing some malware detector thing, you'd install and it would always you'd install and it would always you'd install and it would always download the toolbar, cuz it they got download the toolbar, cuz it they got download the toolbar, cuz it they got paid to distribute it, right? It's a paid to distribute it, right? It's a paid to distribute it, right? It's a aggressive distribution tactics. And to aggressive distribution tactics. And to aggressive distribution tactics. And to your point, that was Facebook and your point, that was Facebook and your point, that was Facebook and Facebook buying ads against people's Facebook buying ads against people's Facebook buying ads against people's names in Europe. Can you explain that? names in Europe. Can you explain that? names in Europe. Can you explain that? What are they doing? What was their end What are they doing? What was their end What are they doing? What was their end game? They were basically trying to game? They were basically trying to game? They were basically trying to create network liquidity in markets create network liquidity in markets create network liquidity in markets where they were where they were where they were earlier behind. And so they would earlier behind. And so they would earlier behind. And so they would basically buy ads of literally a basically buy ads of literally a basically buy ads of literally a person's name, and one of the most person's name, and one of the most person's name, and one of the most common queries is people searching common queries is people searching common queries is people searching themselves. themselves. themselves. And so you'd be like, "Oh, let me look And so you'd be like, "Oh, let me look And so you'd be like, "Oh, let me look up Tim Ferriss on Google or whatever." up Tim Ferriss on Google or whatever." up Tim Ferriss on Google or whatever." And there'd be a Facebook ad saying, And there'd be a Facebook ad saying, And there'd be a Facebook ad saying, "Hey, Tim Ferriss on Facebook." [ __ ] "Hey, Tim Ferriss on Facebook." [ __ ] "Hey, Tim Ferriss on Facebook." [ __ ] you'd click and you'd land on the you'd click and you'd land on the you'd click and you'd land on the sign-up flow for Facebook, right? This sign-up flow for Facebook, right? This sign-up flow for Facebook, right? This was years ago. was years ago. was years ago. This was TikTok and ByteDance, right? It This was TikTok and ByteDance, right? It This was TikTok and ByteDance, right? It was basically they spent billions of was basically they spent billions of was basically they spent billions of dollars dollars dollars distributing TikTok so they could build distributing TikTok so they could build distributing TikTok so they could build enough of a network to train AI enough of a network to train AI enough of a network to train AI algorithms to start telling people what algorithms to start telling people what algorithms to start telling people what to do, and also to get content creators to do, and also to get content creators to do, and also to get content creators on. Where did they spend that money? On on. Where did they spend that money? On on. Where did they spend that money? On distribution in this case of, say, distribution in this case of, say, distribution in this case of, say, TikTok? My sense is it's ads again. TikTok? My sense is it's ads again. TikTok? My sense is it's ads again. Yeah. You kind of see this over and over Yeah. You kind of see this over and over Yeah. You kind of see this over and over again. I mean, for enterprise, Snowflake again. I mean, for enterprise, Snowflake again. I mean, for enterprise, Snowflake spent billions of dollars on sales spent billions of dollars on sales spent billions of dollars on sales people and compensation and channel people and compensation and channel people and compensation and channel partnerships. partnerships. partnerships. So again, like distribution is really So again, like distribution is really So again, like distribution is really important. Mhm. Every once in a while important. Mhm. Every once in a while important. Mhm. Every once in a while you see a company that actually wins not you see a company that actually wins not you see a company that actually wins not because of product, but because they're because of product, but because they're because of product, but because they're just better at sales and marketing and just better at sales and marketing and just better at sales and marketing and distribution. distribution. distribution. And often that's a bummer for And often that's a bummer for And often that's a bummer for technologists such as myself, cuz you're technologists such as myself, cuz you're technologists such as myself, cuz you're like, you know, the best product should like, you know, the best product should like, you know, the best product should always win. Mhm. Sometimes it does, but always win. Mhm. Sometimes it does, but always win. Mhm. Sometimes it does, but sometimes it's just who was early and sometimes it's just who was early and sometimes it's just who was early and developed a brand, or who got ahead on developed a brand, or who got ahead on developed a brand, or who got ahead on distribution, you know. I'm looking at a distribution, you know. I'm looking at a distribution, you know. I'm looking at a piece in front of me. This is from a piece in front of me. This is from a piece in front of me. This is from a while ago, but while ago, but while ago, but it's you discussing long-held dogma that it's you discussing long-held dogma that it's you discussing long-held dogma that ends up being unviable. So, for ends up being unviable. So, for ends up being unviable. So, for instance, the common-held belief after instance, the common-held belief after instance, the common-held belief after PayPal's sale to eBay that fraud will PayPal's sale to eBay that fraud will PayPal's sale to eBay that fraud will kill you in the payment space, right? kill you in the payment space, right? kill you in the payment space, right? And I'm wondering how you orient And I'm wondering how you orient And I'm wondering how you orient yourself as an investor to yourself as an investor to yourself as an investor to stress test stress test stress test those types of dogma. It's really hard those types of dogma. It's really hard those types of dogma. It's really hard because you often end up You start out because you often end up You start out because you often end up You start out with some set of beliefs. You think with some set of beliefs. You think with some set of beliefs. You think something's interesting. something's interesting. something's interesting. Well, maybe you invest in it. Maybe you Well, maybe you invest in it. Maybe you Well, maybe you invest in it. Maybe you start a company in it. start a company in it. start a company in it. And then it turns out the thing you And then it turns out the thing you And then it turns out the thing you think is really interesting turns out to think is really interesting turns out to think is really interesting turns out to be really hard and you get killed. be really hard and you get killed. be really hard and you get killed. And then 5 years later a company comes And then 5 years later a company comes And then 5 years later a company comes up that actually does it and wins. Mhm. up that actually does it and wins. Mhm. up that actually does it and wins. Mhm. And And And the question is why? Why did the thing the question is why? Why did the thing the question is why? Why did the thing suddenly work when it didn't before? Or suddenly work when it didn't before? Or suddenly work when it didn't before? Or there's 10 attempts to do X and then there's 10 attempts to do X and then there's 10 attempts to do X and then suddenly is it the technology got good suddenly is it the technology got good suddenly is it the technology got good enough? It could be a regulatory change. enough? It could be a regulatory change. enough? It could be a regulatory change. It could be a market shift. It could be It could be a market shift. It could be It could be a market shift. It could be whatever. An example that may be Harvey whatever. An example that may be Harvey whatever. An example that may be Harvey and Legal where selling the law firm and Legal where selling the law firm and Legal where selling the law firm traditionally has been awful. traditionally has been awful. traditionally has been awful. And Harvey's not much broader than that, And Harvey's not much broader than that, And Harvey's not much broader than that, right? They also have strong enterprise right? They also have strong enterprise right? They also have strong enterprise adoption and adoption and adoption and lots of different people using them in lots of different people using them in lots of different people using them in different ways, but the dogma was always different ways, but the dogma was always different ways, but the dogma was always like building stuff for law firms is like building stuff for law firms is like building stuff for law firms is crappy as a business and you should crappy as a business and you should crappy as a business and you should never do it. But what AI did is it never do it. But what AI did is it never do it. But what AI did is it shifted things from selling tools to shifted things from selling tools to shifted things from selling tools to selling work product or selling units of selling work product or selling units of selling work product or selling units of labor. That's really the labor. That's really the labor. That's really the shift in generative AI. shift in generative AI. shift in generative AI. We're going from seats and we're going We're going from seats and we're going We're going from seats and we're going from software from software from software and SaaS and we're moving into a world and SaaS and we're moving into a world and SaaS and we're moving into a world where we're selling human labor where we're selling human labor where we're selling human labor equivalents. We're selling work hours or equivalents. We're selling work hours or equivalents. We're selling work hours or labor hours or whatever you want to call labor hours or whatever you want to call labor hours or whatever you want to call it. Mhm. cognition. And so Harvey is it. Mhm. cognition. And so Harvey is it. Mhm. cognition. And so Harvey is effectively helping really augment effectively helping really augment effectively helping really augment lawyers in different ways. And part of lawyers in different ways. And part of lawyers in different ways. And part of that's a knowledge corpus, but a lot of that's a knowledge corpus, but a lot of that's a knowledge corpus, but a lot of it is this tooling that really helps it is this tooling that really helps it is this tooling that really helps lawyers achieve the goals that they have lawyers achieve the goals that they have lawyers achieve the goals that they have in different ways in a collaborative in different ways in a collaborative in different ways in a collaborative manner in some cases. And so this is a manner in some cases. And so this is a manner in some cases. And so this is a fundamentally different type of product fundamentally different type of product fundamentally different type of product from what people were selling before. from what people were selling before. from what people were selling before. And so it opened up the market in a way And so it opened up the market in a way And so it opened up the market in a way that the market wasn't open before. that the market wasn't open before. that the market wasn't open before. There's actually a broader conversation There's actually a broader conversation There's actually a broader conversation around is the world market limited or around is the world market limited or around is the world market limited or founder limited in terms of founder limited in terms of founder limited in terms of entrepreneurial success. The Y entrepreneurial success. The Y entrepreneurial success. The Y Combinator school of thought is that we Combinator school of thought is that we Combinator school of thought is that we just don't have enough founders and if just don't have enough founders and if just don't have enough founders and if we had 10 times as many founders, we'd we had 10 times as many founders, we'd we had 10 times as many founders, we'd have 10 times as many big companies. have 10 times as many big companies. have 10 times as many big companies. And there's an alternate school of And there's an alternate school of And there's an alternate school of thought which is how many markets are thought which is how many markets are thought which is how many markets are actually open in any given moment in actually open in any given moment in actually open in any given moment in time. And those are the ones where you time. And those are the ones where you time. And those are the ones where you can build big companies cuz if the can build big companies cuz if the can build big companies cuz if the market isn't open to innovation or market isn't open to innovation or market isn't open to innovation or change or whatever or hasn't is a change or whatever or hasn't is a change or whatever or hasn't is a undergoing a shift, you can't really undergoing a shift, you can't really undergoing a shift, you can't really build anything there anyhow, so why do build anything there anyhow, so why do build anything there anyhow, so why do it? And the striking thing about AI is it? And the striking thing about AI is it? And the striking thing about AI is it's opened up tons and tons of markets it's opened up tons and tons of markets it's opened up tons and tons of markets that were closed for a long time. that were closed for a long time. that were closed for a long time. And it's opened it up because of And it's opened it up because of And it's opened it up because of capabilities, but it's also opened it up capabilities, but it's also opened it up capabilities, but it's also opened it up because every CEO is asking themselves, because every CEO is asking themselves, because every CEO is asking themselves, "What's my AI story?" "What's my AI story?" "What's my AI story?" And there's way more openness to try And there's way more openness to try And there's way more openness to try things than I've ever seen in my life. things than I've ever seen in my life. things than I've ever seen in my life. And so, And so, And so, we have this odd moment in time where we have this odd moment in time where we have this odd moment in time where things are massively available for things are massively available for things are massively available for founders to do new things. founders to do new things. founders to do new things. And if you're an AI company and you're And if you're an AI company and you're And if you're an AI company and you're not seeing explosive growth quickly, not seeing explosive growth quickly, not seeing explosive growth quickly, something's fundamentally broken. something's fundamentally broken. something's fundamentally broken. Because the markets are so open Because the markets are so open Because the markets are so open that you can suddenly grow at a rate that you can suddenly grow at a rate that you can suddenly grow at a rate that you've never grown before. There's that you've never grown before. There's that you've never grown before. There's always been cases of companies that just always been cases of companies that just always been cases of companies that just go like this. go like this. go like this. But again, you look at the ramps of But again, you look at the ramps of But again, you look at the ramps of OpenAI and Anthropic, and it's the OpenAI and Anthropic, and it's the OpenAI and Anthropic, and it's the fastest ramps to tens of billions ever. fastest ramps to tens of billions ever. fastest ramps to tens of billions ever. Percentages of GDP, it's like crazy. If Percentages of GDP, it's like crazy. If Percentages of GDP, it's like crazy. If we come back to your comment of we come back to your comment of we come back to your comment of not necessarily market first and not necessarily market first and not necessarily market first and strength of team second all the time, strength of team second all the time, strength of team second all the time, but like you said, you 90% agree with but like you said, you 90% agree with but like you said, you 90% agree with that, right? And that, right? And that, right? And if you have an excellent team in a if you have an excellent team in a if you have an excellent team in a terrible market, like that's going to be terrible market, like that's going to be terrible market, like that's going to be that's going to be a difficult one to that's going to be a difficult one to that's going to be a difficult one to execute. How do you determine what is a execute. How do you determine what is a execute. How do you determine what is a good versus great market or just what is good versus great market or just what is good versus great market or just what is a great market? What do you look for? a great market? What do you look for? a great market? What do you look for? And the example you gave, I might be And the example you gave, I might be And the example you gave, I might be over reading this, but when you said over reading this, but when you said over reading this, but when you said that when Google shut down, I think it that when Google shut down, I think it that when Google shut down, I think it was Maven, right? That's an interesting was Maven, right? That's an interesting was Maven, right? That's an interesting kind of event-based approach as an input kind of event-based approach as an input kind of event-based approach as an input to investing, right? Cuz you're like, to investing, right? Cuz you're like, to investing, right? Cuz you're like, "Okay, if they're not going to build it, "Okay, if they're not going to build it, "Okay, if they're not going to build it, We're really that suddenly creates We're really that suddenly creates We're really that suddenly creates a playing field for a playing field for a playing field for startups. Yeah. To play in that space. startups. Yeah. To play in that space. startups. Yeah. To play in that space. So, could you speak to more So, could you speak to more So, could you speak to more of how you determine or look for great of how you determine or look for great of how you determine or look for great markets? I mean, there's a few different markets? I mean, there's a few different markets? I mean, there's a few different ways to think about it. One is like, ways to think about it. One is like, ways to think about it. One is like, some people take the framework of why some people take the framework of why some people take the framework of why now? What's shifted now that makes it now? What's shifted now that makes it now? What's shifted now that makes it suddenly an interesting market because suddenly an interesting market because suddenly an interesting market because people have been trying to do things for people have been trying to do things for people have been trying to do things for a long time in every market. And so, a long time in every market. And so, a long time in every market. And so, that may be a regulatory shift, right? that may be a regulatory shift, right? that may be a regulatory shift, right? Samsara, the fleet management company, Samsara, the fleet management company, Samsara, the fleet management company, benefited from the fact that suddenly benefited from the fact that suddenly benefited from the fact that suddenly there's regulation around needing in-cab there's regulation around needing in-cab there's regulation around needing in-cab monitoring of drivers. So, you had monitoring of drivers. So, you had monitoring of drivers. So, you had suddenly cameras watching people so they suddenly cameras watching people so they suddenly cameras watching people so they don't fall asleep while they're driving don't fall asleep while they're driving don't fall asleep while they're driving trucks on the road, right? And so, that trucks on the road, right? And so, that trucks on the road, right? And so, that was their entry point to then start was their entry point to then start was their entry point to then start building out a the software. building out a the software. building out a the software. But it was a regulatory shift. Sometimes But it was a regulatory shift. Sometimes But it was a regulatory shift. Sometimes there's technology shifts like what's there's technology shifts like what's there's technology shifts like what's happening in AI. And the crazy thing happening in AI. And the crazy thing happening in AI. And the crazy thing about that AI shift is about that AI shift is about that AI shift is the foundation models instantly plugged the foundation models instantly plugged the foundation models instantly plugged into a massive set of markets which is into a massive set of markets which is into a massive set of markets which is basically all enterprise data and basically all enterprise data and basically all enterprise data and information and email and just information and email and just information and email and just all white collar work was suddenly all white collar work was suddenly all white collar work was suddenly available to AI cuz it was the perfect available to AI cuz it was the perfect available to AI cuz it was the perfect technology for that. It also plugged technology for that. It also plugged technology for that. It also plugged into code which is a type of white into code which is a type of white into code which is a type of white collar work. So it's just suddenly it collar work. So it's just suddenly it collar work. So it's just suddenly it just inserts into language and language just inserts into language and language just inserts into language and language is used everywhere in in enterprises as is used everywhere in in enterprises as is used everywhere in in enterprises as well as in consumer. So there's just a well as in consumer. So there's just a well as in consumer. So there's just a massive market to tap into and transform massive market to tap into and transform massive market to tap into and transform or set of markets. Robotics is a little or set of markets. Robotics is a little or set of markets. Robotics is a little bit different from that because even if bit different from that because even if bit different from that because even if you had the world's best robotic model, you had the world's best robotic model, you had the world's best robotic model, the submarkets that already have robotic the submarkets that already have robotic the submarkets that already have robotic hardware are quite small on a relative hardware are quite small on a relative hardware are quite small on a relative basis. basis. basis. And so you don't have that instant And so you don't have that instant And so you don't have that instant runway that you would with runway that you would with runway that you would with language unless you come up with language unless you come up with language unless you come up with something new there. That's kind of an something new there. That's kind of an something new there. That's kind of an aside, but I think robotics is really aside, but I think robotics is really aside, but I think robotics is really interesting and will be important. It's interesting and will be important. It's interesting and will be important. It's more just that nuance of like what's more just that nuance of like what's more just that nuance of like what's that instant thing you plug into that instant thing you plug into that instant thing you plug into commercially? And then commercially? And then commercially? And then there's regulatory shifts, there's there's regulatory shifts, there's there's regulatory shifts, there's technology shifts, there's technology shifts, there's technology shifts, there's incumbency or company shifts, incumbency or company shifts, incumbency or company shifts, competitive shifts. competitive shifts. competitive shifts. A company may blow itself up, it may get A company may blow itself up, it may get A company may blow itself up, it may get bought by a competitor. One company I'm bought by a competitor. One company I'm bought by a competitor. One company I'm I'm excited about on the security side I'm excited about on the security side I'm excited about on the security side is called Infracycle and they're is called Infracycle and they're is called Infracycle and they're basically competing in part with Hashi. basically competing in part with Hashi. basically competing in part with Hashi. Hashi got bought by IBM. Anytime you get Hashi got bought by IBM. Anytime you get Hashi got bought by IBM. Anytime you get bought by IBM you slow you slow down a bought by IBM you slow you slow down a bought by IBM you slow you slow down a lot usually. Mhm. Suddenly it creates lot usually. Mhm. Suddenly it creates lot usually. Mhm. Suddenly it creates more opportunity for a startup. So I more opportunity for a startup. So I more opportunity for a startup. So I just feel like there are these different just feel like there are these different just feel like there are these different things that can change in a given moment things that can change in a given moment things that can change in a given moment in time. Mhm. It could be the market in time. Mhm. It could be the market in time. Mhm. It could be the market growing really fast. That's Coinbase in growing really fast. That's Coinbase in growing really fast. That's Coinbase in crypto, right? You just have suddenly crypto, right? You just have suddenly crypto, right? You just have suddenly this adoption and proliferation of token this adoption and proliferation of token this adoption and proliferation of token types. There's lots and lots and lots of types. There's lots and lots and lots of types. There's lots and lots and lots of different markets that are interesting. different markets that are interesting. different markets that are interesting. The commonality is usually like is it The commonality is usually like is it The commonality is usually like is it also big? Is there a big enough TAM? And also big? Is there a big enough TAM? And also big? Is there a big enough TAM? And there's two types of TAMs. There's fake there's two types of TAMs. There's fake there's two types of TAMs. There's fake TAM. Just for people listening who might TAM. Just for people listening who might TAM. Just for people listening who might not have it. Yeah, total addressable not have it. Yeah, total addressable not have it. Yeah, total addressable market. market. market. >> Total addressable market. So what's the >> Total addressable market. So what's the >> Total addressable market. So what's the market you're in? market you're in? market you're in? And sometimes people come up with these And sometimes people come up with these And sometimes people come up with these fake markets. They're like, "Oh, well, fake markets. They're like, "Oh, well, fake markets. They're like, "Oh, well, we are facilitating we are facilitating we are facilitating global e-commerce and global e-commerce global e-commerce and global e-commerce global e-commerce and global e-commerce I'm making up the number is $30 a year. I'm making up the number is $30 a year. I'm making up the number is $30 a year. And so we're in a $30 a year market. And And so we're in a $30 a year market. And And so we're in a $30 a year market. And if we get just a 10th of a percent of if we get just a 10th of a percent of if we get just a 10th of a percent of that, it's $300 billion of revenue, and that, it's $300 billion of revenue, and that, it's $300 billion of revenue, and you're like, that's not you're like, that's not you're like, that's not that's not your market. Your market is that's not your market. Your market is that's not your market. Your market is like, you built this little optimization like, you built this little optimization like, you built this little optimization engine for SMB websites or whatever. engine for SMB websites or whatever. engine for SMB websites or whatever. That's not a $30 trillion That's not a $30 trillion That's not a $30 trillion market. And so, really it's kind of market. And so, really it's kind of market. And so, really it's kind of defining the market. There's a really defining the market. There's a really defining the market. There's a really famous example of this where defining famous example of this where defining famous example of this where defining your market changes how you think about your market changes how you think about your market changes how you think about it. it. it. And so, that was Coca-Cola, right? So, And so, that was Coca-Cola, right? So, And so, that was Coca-Cola, right? So, Coke and Pepsi were roughly neck and Coke and Pepsi were roughly neck and Coke and Pepsi were roughly neck and neck in terms of market share neck in terms of market share neck in terms of market share for decades. for decades. for decades. And then, one of the Coke CEOs said, And then, one of the Coke CEOs said, And then, one of the Coke CEOs said, "Hey, "Hey, "Hey, maybe we should be thinking about our maybe we should be thinking about our maybe we should be thinking about our share as share of share as share of share as share of liquid sold. liquid sold. liquid sold. Like drinks, not share of soda. Like drinks, not share of soda. Like drinks, not share of soda. And so, we just went from 50% market And so, we just went from 50% market And so, we just went from 50% market share to 0.5%. share to 0.5%. share to 0.5%. And that's why they bought Dasani, and And that's why they bought Dasani, and And that's why they bought Dasani, and that's why they entered all these other that's why they entered all these other that's why they entered all these other markets, right? Because they said, markets, right? Because they said, markets, right? Because they said, "Our definition of our market is wrong. "Our definition of our market is wrong. "Our definition of our market is wrong. We're not in the soda pop business, We're not in the soda pop business, We're not in the soda pop business, we're in the drinks business." And so, I we're in the drinks business." And so, I we're in the drinks business." And so, I think also sometimes reconceptualizing think also sometimes reconceptualizing think also sometimes reconceptualizing what you're doing can really help change what you're doing can really help change what you're doing can really help change your scope of ambition or how you think your scope of ambition or how you think your scope of ambition or how you think about what you're doing. If you're about what you're doing. If you're about what you're doing. If you're trying to spot trying to spot trying to spot along the lines of the fraud will kill along the lines of the fraud will kill along the lines of the fraud will kill you in the payment space, right? Any you in the payment space, right? Any you in the payment space, right? Any dogma in the AI world, the sphere of AI, dogma in the AI world, the sphere of AI, dogma in the AI world, the sphere of AI, right? right? right? Anything Anything come to mind where you Anything Anything come to mind where you Anything Anything come to mind where you think, "Uh, maybe that's not true now, think, "Uh, maybe that's not true now, think, "Uh, maybe that's not true now, or maybe in like 2 years it'll be or maybe in like 2 years it'll be or maybe in like 2 years it'll be completely untrue, but people will have completely untrue, but people will have completely untrue, but people will have latched onto this belief as latched onto this belief as latched onto this belief as one of the thou shalt not or thou thou one of the thou shalt not or thou thou one of the thou shalt not or thou thou shalt shalt shalt commandments? I don't know. I mean, commandments? I don't know. I mean, commandments? I don't know. I mean, there's some things that have circulated there's some things that have circulated there's some things that have circulated in the past around what's the ROI on the in the past around what's the ROI on the in the past around what's the ROI on the capex spend of the and will it ever be capex spend of the and will it ever be capex spend of the and will it ever be paid back? And I just like, paid back? And I just like, paid back? And I just like, I think that stuff is probably off. But I think that stuff is probably off. But I think that stuff is probably off. But yeah, I think fundamentally, there are yeah, I think fundamentally, there are yeah, I think fundamentally, there are moments in time where it's very smart to moments in time where it's very smart to moments in time where it's very smart to be contrarian. And there are moments in be contrarian. And there are moments in be contrarian. And there are moments in time where being consensus is the time where being consensus is the time where being consensus is the smartest possible thing you can do. And smartest possible thing you can do. And smartest possible thing you can do. And I think right now we're in a moment in I think right now we're in a moment in I think right now we're in a moment in time where being consensus is very time where being consensus is very time where being consensus is very right. right. right. You know, and you can really overthink You know, and you can really overthink You know, and you can really overthink it, and what's the contrarian thing? We it, and what's the contrarian thing? We it, and what's the contrarian thing? We should go do a bunch of hardware stuff should go do a bunch of hardware stuff should go do a bunch of hardware stuff cuz blah blah blah. cuz blah blah blah. cuz blah blah blah. And you're like, maybe I should just buy And you're like, maybe I should just buy And you're like, maybe I should just buy more AI. You know what I mean? I think more AI. You know what I mean? I think more AI. You know what I mean? I think people make these things way too people make these things way too people make these things way too complicated. Oh, yeah, true in every complicated. Oh, yeah, true in every complicated. Oh, yeah, true in every aspect of life, probably. Let's just say aspect of life, probably. Let's just say aspect of life, probably. Let's just say you were you were you were mentoring. This is somebody you really mentoring. This is somebody you really mentoring. This is somebody you really care about, right? care about, right? care about, right? We can make up an avatar, whatever. Like We can make up an avatar, whatever. Like We can make up an avatar, whatever. Like >> [laughter] >> [laughter] >> [laughter] >> nephew of one of your best friends, or >> nephew of one of your best friends, or >> nephew of one of your best friends, or son of one of your best friends, or son of one of your best friends, or son of one of your best friends, or daughter, who's really smart, got an daughter, who's really smart, got an daughter, who's really smart, got an engineering degree, came out of MIT, has engineering degree, came out of MIT, has engineering degree, came out of MIT, has a couple of a couple of a couple of hits in angel investing, and they're hits in angel investing, and they're hits in angel investing, and they're like, all right, I think I'm going to like, all right, I think I'm going to like, all right, I think I'm going to raise a fund. Mhm. They don't have the raise a fund. Mhm. They don't have the raise a fund. Mhm. They don't have the access necessarily that you do to AI, access necessarily that you do to AI, access necessarily that you do to AI, let's just say. let's just say. let's just say. Are there any things categorically you Are there any things categorically you Are there any things categorically you would say would be on the do not invest would say would be on the do not invest would say would be on the do not invest list because they're likely to be list because they're likely to be list because they're likely to be annihilated annihilated annihilated or consumed or replicated by AI? or consumed or replicated by AI? or consumed or replicated by AI? >> [clears throat] >> [clears throat] >> [clears throat] >> I think the reality is that when people >> I think the reality is that when people >> I think the reality is that when people start off as investors, start off as investors, start off as investors, a lot of the times the reason they have a lot of the times the reason they have a lot of the times the reason they have early-stage funds is because you can early-stage funds is because you can early-stage funds is because you can always get access to the early stages of always get access to the early stages of always get access to the early stages of companies if you just start helping companies if you just start helping companies if you just start helping people. I mean, that's kind of what I people. I mean, that's kind of what I people. I mean, that's kind of what I did accidentally, but the reality is did accidentally, but the reality is did accidentally, but the reality is I've seen it over and over. I've seen it over and over. I've seen it over and over. You fall in with the right group of You fall in with the right group of You fall in with the right group of people cuz the smartest people also people cuz the smartest people also people cuz the smartest people also aggregate together, and you start aggregate together, and you start aggregate together, and you start helping people out, and they just ask if helping people out, and they just ask if helping people out, and they just ask if you want to invest, and you start you want to invest, and you start you want to invest, and you start investing, and suddenly you have a great investing, and suddenly you have a great investing, and suddenly you have a great track record, and you raise bigger track record, and you raise bigger track record, and you raise bigger funds, and then you go later stage cuz funds, and then you go later stage cuz funds, and then you go later stage cuz that same cohort has grown up, and that same cohort has grown up, and that same cohort has grown up, and they've started doing later stuff, and they've started doing later stuff, and they've started doing later stuff, and Mhm. when somebody can get access to Mhm. when somebody can get access to Mhm. when somebody can get access to everything else. That's kind of the everything else. That's kind of the everything else. That's kind of the traditional venture story, and it has traditional venture story, and it has traditional venture story, and it has been, I think, for decades in some been, I think, for decades in some been, I think, for decades in some sense. So, I think that's still very sense. So, I think that's still very sense. So, I think that's still very tenable, and you can still do it for AI. tenable, and you can still do it for AI. tenable, and you can still do it for AI. You can do it for anything. I don't You can do it for anything. I don't You can do it for anything. I don't think you have to go off and do like think you have to go off and do like think you have to go off and do like energy investing or something. energy investing or something. energy investing or something. You have mentioned in the past a key You have mentioned in the past a key You have mentioned in the past a key learning, [snorts] learning, [snorts] learning, [snorts] maybe that's an overstatement, but you maybe that's an overstatement, but you maybe that's an overstatement, but you can correct me, from Vinod Khosla, can correct me, from Vinod Khosla, can correct me, from Vinod Khosla, and [snorts] and [snorts] and [snorts] I think the wording is along the lines I think the wording is along the lines I think the wording is along the lines of your market entry strategy is off and of your market entry strategy is off and of your market entry strategy is off and different from your market disruption different from your market disruption different from your market disruption strategy? Yeah. Could you speak to that? strategy? Yeah. Could you speak to that? strategy? Yeah. Could you speak to that? There's sort of two or three versions of There's sort of two or three versions of There's sort of two or three versions of this. Version one is you do something this. Version one is you do something this. Version one is you do something that's really weird and it starts off that's really weird and it starts off that's really weird and it starts off looking like a toy and then it turns out looking like a toy and then it turns out looking like a toy and then it turns out to be really important. And that would to be really important. And that would to be really important. And that would be Instagram or Twitter or some of these be Instagram or Twitter or some of these be Instagram or Twitter or some of these more social products, right? Where more social products, right? Where more social products, right? Where the initial use case is very different the initial use case is very different the initial use case is very different from how it's used today and it kind of from how it's used today and it kind of from how it's used today and it kind of evolved as a product and how people evolved as a product and how people evolved as a product and how people perceive it and use it. And that's one perceive it and use it. And that's one perceive it and use it. And that's one version of it and that's usually more version of it and that's usually more version of it and that's usually more consumer-centric. Another version of consumer-centric. Another version of consumer-centric. Another version of that would be SpaceX and Starlink where that would be SpaceX and Starlink where that would be SpaceX and Starlink where they started off with launch and getting they started off with launch and getting they started off with launch and getting things up into space and they realized, things up into space and they realized, things up into space and they realized, "Hey, they have a cost advantage for "Hey, they have a cost advantage for "Hey, they have a cost advantage for satellites." And then they built out the satellites." And then they built out the satellites." And then they built out the Starlink network, which is now like a Starlink network, which is now like a Starlink network, which is now like a major driver of their business, right? major driver of their business, right? major driver of their business, right? And so, what they did expanded a lot and And so, what they did expanded a lot and And so, what they did expanded a lot and kind of shifted in terms of their market kind of shifted in terms of their market kind of shifted in terms of their market entry with space launch, their entry with space launch, their entry with space launch, their disruption is Starlink in some sense. disruption is Starlink in some sense. disruption is Starlink in some sense. So, I do think there's lots of examples So, I do think there's lots of examples So, I do think there's lots of examples like that over time. Coming back to like that over time. Coming back to like that over time. Coming back to information and just consumption, information and just consumption, information and just consumption, >> [laughter] >> [laughter] >> [laughter] >> how do you consume most of your >> how do you consume most of your >> how do you consume most of your information? Like, what would the pie information? Like, what would the pie information? Like, what would the pie chart break down to in terms of chart break down to in terms of chart break down to in terms of if you listens to podcasts versus books if you listens to podcasts versus books if you listens to podcasts versus books versus X versus white papers versus versus X versus white papers versus versus X versus white papers versus something else? I think a lot of what something else? I think a lot of what something else? I think a lot of what I've done is collapsed into three I've done is collapsed into three I've done is collapsed into three things. It's X, things. It's X, things. It's X, it's reading some technical paper / it's reading some technical paper / it's reading some technical paper / journals in some cases if it's more of journals in some cases if it's more of journals in some cases if it's more of the biology side. A lot of it I do the biology side. A lot of it I do the biology side. A lot of it I do biology investing. It's like a biology investing. It's like a biology investing. It's like a But, you know, papers. Although, the But, you know, papers. Although, the But, you know, papers. Although, the papers in the AI industry have really papers in the AI industry have really papers in the AI industry have really dropped off given the competitive nature dropped off given the competitive nature dropped off given the competitive nature of everything now. Mhm. And then talking of everything now. Mhm. And then talking of everything now. Mhm. And then talking to people. I found that like 20 minutes to people. I found that like 20 minutes to people. I found that like 20 minutes with somebody really smart on a topic with somebody really smart on a topic with somebody really smart on a topic gives me more information and insights gives me more information and insights gives me more information and insights and leads on what to go read about than and leads on what to go read about than and leads on what to go read about than doing some exhaustive research. doing some exhaustive research. doing some exhaustive research. Actually, the fourth thing is now using Actually, the fourth thing is now using Actually, the fourth thing is now using models to do research for me. Mhm. models to do research for me. Mhm. models to do research for me. Mhm. [clears throat] That could be OpenAI, [clears throat] That could be OpenAI, [clears throat] That could be OpenAI, that could be Claude, that could be that could be Claude, that could be that could be Claude, that could be Perplexity, that could be Gemini. But, Perplexity, that could be Gemini. But, Perplexity, that could be Gemini. But, and for each of them I actually use and for each of them I actually use and for each of them I actually use different things or I do different different things or I do different different things or I do different things with each of them. What do you do things with each of them. What do you do things with each of them. What do you do with the different models? I'll just with the different models? I'll just with the different models? I'll just give you one example versus go through give you one example versus go through give you one example versus go through every single one of them, but every single one of them, but every single one of them, but >> Sure. Gemini I actually feel like if I'm >> Sure. Gemini I actually feel like if I'm >> Sure. Gemini I actually feel like if I'm looking up more like activities, like looking up more like activities, like looking up more like activities, like hey, I'm planning a trip somewhere. hey, I'm planning a trip somewhere. hey, I'm planning a trip somewhere. I actually feel like the Google corpus I actually feel like the Google corpus I actually feel like the Google corpus and all the stuff they built over time and all the stuff they built over time and all the stuff they built over time is quite useful for like travel tips of is quite useful for like travel tips of is quite useful for like travel tips of certain types. Mhm. And so that'd be a certain types. Mhm. And so that'd be a certain types. Mhm. And so that'd be a Gemini specific thing. That doesn't mean Gemini specific thing. That doesn't mean Gemini specific thing. That doesn't mean the other models can't do it well. It's the other models can't do it well. It's the other models can't do it well. It's more just like I've I've tended to get more just like I've I've tended to get more just like I've I've tended to get more accurate like rankings of things more accurate like rankings of things more accurate like rankings of things that way. Mhm. And I'll ask for like that way. Mhm. And I'll ask for like that way. Mhm. And I'll ask for like breakdowns and breakdowns and breakdowns and rankings across multiple dimensions and rankings across multiple dimensions and rankings across multiple dimensions and all the stuff all the stuff all the stuff for scoring of things. I did like a deep for scoring of things. I did like a deep for scoring of things. I did like a deep dive in a few different areas of ADHD dive in a few different areas of ADHD dive in a few different areas of ADHD and ASD. What's ASD? Oh, I'm sorry. It's and ASD. What's ASD? Oh, I'm sorry. It's and ASD. What's ASD? Oh, I'm sorry. It's autism spectrum. I see. I got it. So autism spectrum. I see. I got it. So autism spectrum. I see. I got it. So basically like if you look at autism, basically like if you look at autism, basically like if you look at autism, it went from I'm going to misquote the it went from I'm going to misquote the it went from I'm going to misquote the numbers, so you know, I should look numbers, so you know, I should look numbers, so you know, I should look these up better. But I think it's these up better. But I think it's these up better. But I think it's something like one in a few thousand of something like one in a few thousand of something like one in a few thousand of the population was diagnosed with autism the population was diagnosed with autism the population was diagnosed with autism like 30 years ago, 40 years ago. And now like 30 years ago, 40 years ago. And now like 30 years ago, 40 years ago. And now it's like 3%. Mhm. So you're like, well, it's like 3%. Mhm. So you're like, well, it's like 3%. Mhm. So you're like, well, what is that? Is that change in older what is that? Is that change in older what is that? Is that change in older parents having more kids, which it turns parents having more kids, which it turns parents having more kids, which it turns out that that's not the driver. out that that's not the driver. out that that's not the driver. Is it Is it Is it some shift in the environment? Is it It some shift in the environment? Is it It some shift in the environment? Is it It turns out that just diagnostic criteria turns out that just diagnostic criteria turns out that just diagnostic criteria has shifted. Uh-huh. And [clears throat] has shifted. Uh-huh. And [clears throat] has shifted. Uh-huh. And [clears throat] there's a lot of incentives to actually there's a lot of incentives to actually there's a lot of incentives to actually diagnose people in the schools. That's diagnose people in the schools. That's diagnose people in the schools. That's roughly the summary of why we have so roughly the summary of why we have so roughly the summary of why we have so many kids that are classified as either many kids that are classified as either many kids that are classified as either having attention deficit having attention deficit having attention deficit where there's also like a financial where there's also like a financial where there's also like a financial incentive for doctors to do it cuz they incentive for doctors to do it cuz they incentive for doctors to do it cuz they can prescribe drugs Mhm. versus autism, can prescribe drugs Mhm. versus autism, can prescribe drugs Mhm. versus autism, but both have gone up dramatically in but both have gone up dramatically in but both have gone up dramatically in terms of diagnoses, right? And terms of diagnoses, right? And terms of diagnoses, right? And it's unclear to me that more people it's unclear to me that more people it's unclear to me that more people actually have it. actually have it. actually have it. >> Mhm. It's just diagnosed dramatically >> Mhm. It's just diagnosed dramatically >> Mhm. It's just diagnosed dramatically more broadly. Which model were you more broadly. Which model were you more broadly. Which model were you investigating that with? Usually when I investigating that with? Usually when I investigating that with? Usually when I do things like that, I use two or three do things like that, I use two or three do things like that, I use two or three models at once and then I ask for models at once and then I ask for models at once and then I ask for primary literature and then ask for primary literature and then ask for primary literature and then ask for summary charts. I actually have this summary charts. I actually have this summary charts. I actually have this whole breakdown of like stuff that I ask whole breakdown of like stuff that I ask whole breakdown of like stuff that I ask for each output so that I can go back for each output so that I can go back for each output so that I can go back and double-check the data. Mhm. And then and double-check the data. Mhm. And then and double-check the data. Mhm. And then reread through the literature and reread through the literature and reread through the literature and everything else. And there's really everything else. And there's really everything else. And there's really interesting things that came out of the interesting things that came out of the interesting things that came out of the autism one in particular because it autism one in particular because it autism one in particular because it turned out maternal age actually has a turned out maternal age actually has a turned out maternal age actually has a bigger impact than paternal age Mhm. in bigger impact than paternal age Mhm. in bigger impact than paternal age Mhm. in some of the studies. And people always some of the studies. And people always some of the studies. And people always talk about paternal age. Mhm. And then talk about paternal age. Mhm. And then talk about paternal age. Mhm. And then you're like, well, why are people only you're like, well, why are people only you're like, well, why are people only talking about paternal age? Is there a talking about paternal age? Is there a talking about paternal age? Is there a societal incentive for that? Is it a societal incentive for that? Is it a societal incentive for that? Is it a political belief system? Like why is political belief system? Like why is political belief system? Like why is that the point of emphasis? Mhm. Which I that the point of emphasis? Mhm. Which I that the point of emphasis? Mhm. Which I thought was really interesting, right? thought was really interesting, right? thought was really interesting, right? >> Mhm. So there's other things that kind >> Mhm. So there's other things that kind >> Mhm. So there's other things that kind of come out of that in terms of of come out of that in terms of of come out of that in terms of questions, in terms of the why of questions, in terms of the why of questions, in terms of the why of things. But why were you looking into things. But why were you looking into things. But why were you looking into that specifically? I just thought it was that specifically? I just thought it was that specifically? I just thought it was interesting. Yeah. Okay. Seems like it's interesting. Yeah. Okay. Seems like it's interesting. Yeah. Okay. Seems like it's gone up a lot. Let me try and understand gone up a lot. Let me try and understand gone up a lot. Let me try and understand why. Mhm. And so I started looking into why. Mhm. And so I started looking into why. Mhm. And so I started looking into it. Mhm. I was also talking to a friend it. Mhm. I was also talking to a friend it. Mhm. I was also talking to a friend of mine who is in her sort of mid to of mine who is in her sort of mid to of mine who is in her sort of mid to late 30s late 30s late 30s and she was dating a guy who was in his and she was dating a guy who was in his and she was dating a guy who was in his late 40s, early 50s and she brought up late 40s, early 50s and she brought up late 40s, early 50s and she brought up oh she was worried about oh she was worried about oh she was worried about autism and what would happen with them autism and what would happen with them autism and what would happen with them if they had kids and all the stuff. if they had kids and all the stuff. if they had kids and all the stuff. And so then I did this deep dive as part And so then I did this deep dive as part And so then I did this deep dive as part of that, too. Mhm. And the takeaway was of that, too. Mhm. And the takeaway was of that, too. Mhm. And the takeaway was I can't remember exactly what it was. I can't remember exactly what it was. I can't remember exactly what it was. I'm making it up, so please don't quote I'm making it up, so please don't quote I'm making it up, so please don't quote me on this. I can look it up later, but me on this. I can look it up later, but me on this. I can look it up later, but it was like there's a 10% increase for it was like there's a 10% increase for it was like there's a 10% increase for every 5 to 10 years incremental paternal every 5 to 10 years incremental paternal every 5 to 10 years incremental paternal and maternal age and again maternal was and maternal age and again maternal was and maternal age and again maternal was actually a little bit stronger in some actually a little bit stronger in some actually a little bit stronger in some of the data sets. And the thing is of the data sets. And the thing is of the data sets. And the thing is though if you believe that it's one in though if you believe that it's one in though if you believe that it's one in 5,000 or one in whatever in the 5,000 or one in whatever in the 5,000 or one in whatever in the population population population that 10%, 20% difference doesn't matter. that 10%, 20% difference doesn't matter. that 10%, 20% difference doesn't matter. Mhm. Right? From a population frequency Mhm. Right? From a population frequency Mhm. Right? From a population frequency perspective, this is diagnostic criteria perspective, this is diagnostic criteria perspective, this is diagnostic criteria went way up. That's that's true for a went way up. That's that's true for a went way up. That's that's true for a lot a lot of diagnoses. A lot of stuff, lot a lot of diagnoses. A lot of stuff, lot a lot of diagnoses. A lot of stuff, but like societally we're told oh it's but like societally we're told oh it's but like societally we're told oh it's like the age of the parents is driving like the age of the parents is driving like the age of the parents is driving all these autism rates up and you're all these autism rates up and you're all these autism rates up and you're like no, it's like all these incentives like no, it's like all these incentives like no, it's like all these incentives and then you look at some of the school and then you look at some of the school and then you look at some of the school systems is like 60% of all the autism systems is like 60% of all the autism systems is like 60% of all the autism diagnoses and I think it was a state of diagnoses and I think it was a state of diagnoses and I think it was a state of New Jersey or something New Jersey or something New Jersey or something were were were not actually based on any clinical not actually based on any clinical not actually based on any clinical criteria. Is this a teacher randomly criteria. Is this a teacher randomly criteria. Is this a teacher randomly saying this person has autism? Oh god, saying this person has autism? Oh god, saying this person has autism? Oh god, terrible. terrible. terrible. >> Right? And [snorts] so you start digging >> Right? And [snorts] so you start digging >> Right? And [snorts] so you start digging into these things and you're like wow, into these things and you're like wow, into these things and you're like wow, this is super interesting and these this is super interesting and these this is super interesting and these models are really valuable and helpful models are really valuable and helpful models are really valuable and helpful for that. So I've been doing a lot of for that. So I've been doing a lot of for that. So I've been doing a lot of back to your question of where do I get back to your question of where do I get back to your question of where do I get information, part of it has been these information, part of it has been these information, part of it has been these deep dives with models into like deep dives with models into like deep dives with models into like questions that I just find interesting questions that I just find interesting questions that I just find interesting where where where I ask them to aggregate clinical trial I ask them to aggregate clinical trial I ask them to aggregate clinical trial data or aggregate different types of data or aggregate different types of data or aggregate different types of information and then give me the primary information and then give me the primary information and then give me the primary sources and then give me summaries and sources and then give me summaries and sources and then give me summaries and double check things and so I have like a double check things and so I have like a double check things and so I have like a whole series of prompts around that to whole series of prompts around that to whole series of prompts around that to kind of also clean data and check it and kind of also clean data and check it and kind of also clean data and check it and that's really fun. And then I always set that's really fun. And then I always set that's really fun. And then I always set it up in multiple models and just see it up in multiple models and just see it up in multiple models and just see like what they each come up with. When like what they each come up with. When like what they each come up with. When you talk to people and this may be you talk to people and this may be you talk to people and this may be too much of a kind of amorphous topic for us to dive into in a amorphous topic for us to dive into in a meaningful way, but let's just say meaningful way, but let's just say meaningful way, but let's just say you find somebody you want to talk to you find somebody you want to talk to you find somebody you want to talk to for 20 minutes. How do you typically for 20 minutes. How do you typically for 20 minutes. How do you typically find those people? I suspect there are a find those people? I suspect there are a find those people? I suspect there are a lot of ways, but are you finding them on lot of ways, but are you finding them on lot of ways, but are you finding them on X versus finding them in a technical X versus finding them in a technical X versus finding them in a technical paper versus finding them somewhere paper versus finding them somewhere paper versus finding them somewhere else? Just to get an idea. And then when else? Just to get an idea. And then when else? Just to get an idea. And then when you get on the phone with such a person you get on the phone with such a person you get on the phone with such a person are there repeating trains of are there repeating trains of are there repeating trains of questioning or certain ways that you questioning or certain ways that you questioning or certain ways that you like to approach it? I think there's like to approach it? I think there's like to approach it? I think there's three different types of things. One is three different types of things. One is three different types of things. One is hey, I'm doing a deep dive in an area hey, I'm doing a deep dive in an area hey, I'm doing a deep dive in an area just cuz I think it's interesting or just cuz I think it's interesting or just cuz I think it's interesting or maybe it's relevant to like an area I maybe it's relevant to like an area I maybe it's relevant to like an area I want to invest in. Often honestly just want to invest in. Often honestly just want to invest in. Often honestly just is it interesting? is it interesting? is it interesting? And then I'll try to quickly triangulate And then I'll try to quickly triangulate And then I'll try to quickly triangulate for the smartest people on the thing and for the smartest people on the thing and for the smartest people on the thing and that may technical papers, that may just that may technical papers, that may just that may technical papers, that may just be asking each person I talk to who's be asking each person I talk to who's be asking each person I talk to who's really smart. There's one form of that really smart. There's one form of that really smart. There's one form of that which is hey, it's very informational which is hey, it's very informational which is hey, it's very informational and I'm trying to do a deep dive on and I'm trying to do a deep dive on and I'm trying to do a deep dive on something. I mean, I work with some of something. I mean, I work with some of something. I mean, I work with some of the early AI researchers at Google. the early AI researchers at Google. the early AI researchers at Google. That's how I knew like Noam Shazeer who That's how I knew like Noam Shazeer who That's how I knew like Noam Shazeer who started Character and then went back to started Character and then went back to started Character and then went back to Google and that's how I met a bunch of Google and that's how I met a bunch of Google and that's how I met a bunch of other folks, but some of the people I other folks, but some of the people I other folks, but some of the people I just met, you know, just interesting just met, you know, just interesting just met, you know, just interesting paper, let me look them up or hey, paper, let me look them up or hey, paper, let me look them up or hey, everybody says this person's really everybody says this person's really everybody says this person's really smart, let me talk to them. That's one smart, let me talk to them. That's one smart, let me talk to them. That's one form. A second form is I do think like form. A second form is I do think like form. A second form is I do think like really smart people tend to aggregate really smart people tend to aggregate really smart people tend to aggregate and so if you're just hanging out with and so if you're just hanging out with and so if you're just hanging out with smart people, you keep meeting other smart people, you keep meeting other smart people, you keep meeting other smart people. Mhm. And people who are smart people. Mhm. And people who are smart people. Mhm. And people who are polymathic tend to hang out with people polymathic tend to hang out with people polymathic tend to hang out with people who are polymathic and it's kind of like who are polymathic and it's kind of like who are polymathic and it's kind of like like attracts like for all sorts of like attracts like for all sorts of like attracts like for all sorts of things. So that's sort of a second set. things. So that's sort of a second set. things. So that's sort of a second set. These are probably the two main things. These are probably the two main things. These are probably the two main things. I mean, sometimes people also just refer I mean, sometimes people also just refer I mean, sometimes people also just refer people over to me. They'll say, "Hey, I people over to me. They'll say, "Hey, I people over to me. They'll say, "Hey, I think you two would like chatting." Mhm. think you two would like chatting." Mhm. think you two would like chatting." Mhm. There's a separate thing which is There's a separate thing which is There's a separate thing which is there's people that I go back to there's people that I go back to there's people that I go back to recurrently, right? Which is more like I recurrently, right? Which is more like I recurrently, right? Which is more like I think this is one of the smartest people think this is one of the smartest people think this is one of the smartest people about where AI is heading. Let me talk about where AI is heading. Let me talk about where AI is heading. Let me talk to them all the time or to them all the time or to them all the time or this is one of the smartest people about this is one of the smartest people about this is one of the smartest people about longevity. Like Kristen, the CEO of I longevity. Like Kristen, the CEO of I longevity. Like Kristen, the CEO of I call sometimes about random call sometimes about random call sometimes about random longevity-related things because she longevity-related things because she longevity-related things because she knows so much about every topic in it. knows so much about every topic in it. knows so much about every topic in it. She's very thoughtful. She's very She's very thoughtful. She's very She's very thoughtful. She's very willing to question her own assumptions. willing to question her own assumptions. willing to question her own assumptions. It's very just like It's very just like It's very just like truth-seeking in a way that most people truth-seeking in a way that most people truth-seeking in a way that most people aren't. And people always use that term, aren't. And people always use that term, aren't. And people always use that term, but she really is just like, "What's but she really is just like, "What's but she really is just like, "What's correct? Let me just figure it out." correct? Let me just figure it out." correct? Let me just figure it out." She's like a PhD and postdoc in like She's like a PhD and postdoc in like She's like a PhD and postdoc in like bioinformatics and aging. She's super bioinformatics and aging. She's super bioinformatics and aging. She's super legit. And so, that's an example of legit. And so, that's an example of legit. And so, that's an example of somebody that I'll call for like somebody that I'll call for like somebody that I'll call for like longevity stuff. longevity stuff. longevity stuff. So, I just have certain people I'll call So, I just have certain people I'll call So, I just have certain people I'll call for certain topics. So, you have for certain topics. So, you have for certain topics. So, you have literacies. literacies. literacies. It's kind of quaint how, you know, I It's kind of quaint how, you know, I It's kind of quaint how, you know, I went to the first Quantified Self meet went to the first Quantified Self meet went to the first Quantified Self meet up in up in up in whatever it was, 2008 or something, with whatever it was, 2008 or something, with whatever it was, 2008 or something, with 12 people sitting around in Kevin 12 people sitting around in Kevin 12 people sitting around in Kevin Kelly's house talking about Kelly's house talking about Kelly's house talking about measuring things with Excel measuring things with Excel measuring things with Excel spreadsheets. The world has changed. So, spreadsheets. The world has changed. So, spreadsheets. The world has changed. So, there are armies of tens of thousands of there are armies of tens of thousands of there are armies of tens of thousands of self-described biohackers and so on self-described biohackers and so on self-described biohackers and so on talking about longevity. There's a lot talking about longevity. There's a lot talking about longevity. There's a lot of nonsense. For yourself, personally, of nonsense. For yourself, personally, of nonsense. For yourself, personally, where have you landed in terms of where have you landed in terms of where have you landed in terms of interventions or thinking about interventions or thinking about interventions or thinking about interventions interventions interventions for yourself? I haven't done a ton. You for yourself? I haven't done a ton. You for yourself? I haven't done a ton. You know, it feels like a lot collapses into know, it feels like a lot collapses into know, it feels like a lot collapses into like sleep well, exercise a lot, you like sleep well, exercise a lot, you like sleep well, exercise a lot, you know, etc. Like there's a handful of know, etc. Like there's a handful of know, etc. Like there's a handful of things that kind of matter. Eat well. things that kind of matter. Eat well. things that kind of matter. Eat well. And so, I've kind of collapsed on all of And so, I've kind of collapsed on all of And so, I've kind of collapsed on all of that stuff. I think there's one or two that stuff. I think there's one or two that stuff. I think there's one or two things that maybe you can take that are things that maybe you can take that are things that maybe you can take that are helpful. And then, there's some things I helpful. And then, there's some things I helpful. And then, there's some things I always thought it'd be fun to experiment always thought it'd be fun to experiment always thought it'd be fun to experiment with that I haven't done yet. Like what? with that I haven't done yet. Like what? with that I haven't done yet. Like what? I thought it'd be cool to try like a I thought it'd be cool to try like a I thought it'd be cool to try like a rapamycin pulse or something. rapamycin pulse or something. rapamycin pulse or something. So, stuff like that. But, the reality is So, stuff like that. But, the reality is So, stuff like that. But, the reality is that I'm kind of waiting for the real that I'm kind of waiting for the real that I'm kind of waiting for the real drugs to come out. And then, maybe I'd drugs to come out. And then, maybe I'd drugs to come out. And then, maybe I'd use those. Some of the ones that use those. Some of the ones that use those. Some of the ones that actually think will really impinge on actually think will really impinge on actually think will really impinge on longevity or certain systems. Like we longevity or certain systems. Like we longevity or certain systems. Like we were talking earlier about, as you age, were talking earlier about, as you age, were talking earlier about, as you age, the muscle that holds the lens of your the muscle that holds the lens of your the muscle that holds the lens of your eye weakens. And that's part of the eye weakens. And that's part of the eye weakens. And that's part of the reason that your ability to focus kind reason that your ability to focus kind reason that your ability to focus kind of gets screwed up. And so, of gets screwed up. And so, of gets screwed up. And so, there should be eye drops for that. Like there should be eye drops for that. Like there should be eye drops for that. Like there's a bunch of stuff around there's a bunch of stuff around there's a bunch of stuff around neurosensory aging that I'd love to fund neurosensory aging that I'd love to fund neurosensory aging that I'd love to fund a startup. a startup. a startup. >> Mhm. There's a bunch of stuff around the >> Mhm. There's a bunch of stuff around the >> Mhm. There's a bunch of stuff around the cosmetics of aging that I've long been cosmetics of aging that I've long been cosmetics of aging that I've long been talking about trying to find I actually talking about trying to find I actually talking about trying to find I actually funded a clinical trial at Stanford to funded a clinical trial at Stanford to funded a clinical trial at Stanford to to work on that, for example. to work on that, for example. to work on that, for example. >> Mhm. >> Mhm. >> Mhm. Cuz I think it's very interesting and Cuz I think it's very interesting and Cuz I think it's very interesting and peptides to me is basically that. peptides to me is basically that. peptides to me is basically that. I think a lot of those people are taking I think a lot of those people are taking I think a lot of those people are taking peptides as like certain forms of peptides as like certain forms of peptides as like certain forms of health, but also certain forms of health, but also certain forms of health, but also certain forms of cosmetic applications like 5-HKCU and cosmetic applications like 5-HKCU and cosmetic applications like 5-HKCU and melanotan and all these things are melanotan and all these things are melanotan and all these things are basically cosmetic in nature. You basically cosmetic in nature. You basically cosmetic in nature. You mentioned a handful of things that seem mentioned a handful of things that seem mentioned a handful of things that seem helpful to take. Are those just the helpful to take. Are those just the helpful to take. Are those just the basic [laughter] basic [laughter] basic [laughter] you know, vitamin D or we talking about you know, vitamin D or we talking about you know, vitamin D or we talking about other things? What are what are more on other things? What are what are more on other things? What are what are more on that short list? Vitamin D and creatine. that short list? Vitamin D and creatine. that short list? Vitamin D and creatine. Yeah. Got it. Yeah. Got it. Yeah. Got it. >> If you want to lift. I don't know what's >> If you want to lift. I don't know what's >> If you want to lift. I don't know what's on your list. I mean, you've thought on your list. I mean, you've thought on your list. I mean, you've thought about this so much more than I have. about this so much more than I have. about this so much more than I have. What are you taking or what are you What are you taking or what are you What are you taking or what are you thinking about or I'm much more thinking about or I'm much more thinking about or I'm much more conservative than I think people would conservative than I think people would conservative than I think people would expect. You know, I've expect. You know, I've expect. You know, I've played around with a lot of things in my played around with a lot of things in my played around with a lot of things in my earlier days and a lot of it is very, I earlier days and a lot of it is very, I earlier days and a lot of it is very, I would say, capped risk if you're would say, capped risk if you're would say, capped risk if you're experimenting as I was with first experimenting as I was with first experimenting as I was with first generation Dexcom continuous glucose generation Dexcom continuous glucose generation Dexcom continuous glucose monitors in 2008, right? They were were monitors in 2008, right? They were were monitors in 2008, right? They were were 2009, very unpleasant to wear. Yeah. And 2009, very unpleasant to wear. Yeah. And 2009, very unpleasant to wear. Yeah. And I wasn't aware of any non-type 1 I wasn't aware of any non-type 1 I wasn't aware of any non-type 1 diabetics using them at the time. But I diabetics using them at the time. But I diabetics using them at the time. But I wasn't using much in terms of wasn't using much in terms of wasn't using much in terms of let's [snorts] just say questionable let's [snorts] just say questionable let's [snorts] just say questionable gene therapy, flying to other countries gene therapy, flying to other countries gene therapy, flying to other countries to to to use something like a follistatin. Not to use something like a follistatin. Not to use something like a follistatin. Not to throw it under the bus, but I feel like throw it under the bus, but I feel like throw it under the bus, but I feel like the general heuristic of no biological the general heuristic of no biological the general heuristic of no biological free lunch, I recognize it's very free lunch, I recognize it's very free lunch, I recognize it's very simplistic, but it's pretty helpful. At simplistic, but it's pretty helpful. At simplistic, but it's pretty helpful. At least it will aid you in avoiding a lot least it will aid you in avoiding a lot least it will aid you in avoiding a lot of pitfalls, right? So, I mean, there of pitfalls, right? So, I mean, there of pitfalls, right? So, I mean, there are things I'm experimenting with. are things I'm experimenting with. are things I'm experimenting with. Different forms of ketone esters and Different forms of ketone esters and Different forms of ketone esters and salts, for instance. I think some could salts, for instance. I think some could salts, for instance. I think some could be very, very interesting for be very, very interesting for be very, very interesting for cerebral vasculature. And since I have cerebral vasculature. And since I have cerebral vasculature. And since I have Alzheimer's disease, Parkinson's, etc. Alzheimer's disease, Parkinson's, etc. Alzheimer's disease, Parkinson's, etc. in my family, including for people who in my family, including for people who in my family, including for people who are APOE3/3, so there are certainly many are APOE3/3, so there are certainly many are APOE3/3, so there are certainly many other risk factors. I'm paying a lot of other risk factors. I'm paying a lot of other risk factors. I'm paying a lot of attention to that side of things, you attention to that side of things, you attention to that side of things, you know, obeticholic acid, I think is one know, obeticholic acid, I think is one know, obeticholic acid, I think is one to keep an eye on. That's not yet ready to keep an eye on. That's not yet ready to keep an eye on. That's not yet ready for prime time, but rapamycin is for prime time, but rapamycin is for prime time, but rapamycin is interesting. I do think rapamycin is interesting. I do think rapamycin is interesting. I do think rapamycin is interesting with a lot of asterisks interesting with a lot of asterisks interesting with a lot of asterisks because you can screw yourself up if you because you can screw yourself up if you because you can screw yourself up if you don't know what you're doing. don't know what you're doing. don't know what you're doing. And And And if you're playing with any if you're playing with any if you're playing with any immunosuppressant, I mean, you just have immunosuppressant, I mean, you just have immunosuppressant, I mean, you just have to be very careful, to be very careful, to be very careful, but but but looking at combining that, for instance, looking at combining that, for instance, looking at combining that, for instance, one of the experiments that I might do one of the experiments that I might do one of the experiments that I might do is and I would have a cleaner read of is and I would have a cleaner read of is and I would have a cleaner read of signal if I only did one intervention, signal if I only did one intervention, signal if I only did one intervention, but real life is different from but real life is different from but real life is different from Yeah. Waiting for Yeah. Waiting for Yeah. Waiting for >> [laughter] >> [laughter] >> [laughter] >> science sometimes. Revert So, possibly >> science sometimes. Revert So, possibly >> science sometimes. Revert So, possibly combining Norwegian 4 by 4 interval combining Norwegian 4 by 4 interval combining Norwegian 4 by 4 interval training with rapamycin pulsing to look training with rapamycin pulsing to look training with rapamycin pulsing to look at volumetric changes, if any, in the at volumetric changes, if any, in the at volumetric changes, if any, in the hippocampus and other areas. Like, I hippocampus and other areas. Like, I hippocampus and other areas. Like, I think that's a pretty interesting think that's a pretty interesting think that's a pretty interesting hypothesis we're testing. hypothesis we're testing. hypothesis we're testing. But otherwise, it's basic basic, right? But otherwise, it's basic basic, right? But otherwise, it's basic basic, right? It's creatine, it's the vitamin D's. It's creatine, it's the vitamin D's. It's creatine, it's the vitamin D's. Look, if you have methylation issues or Look, if you have methylation issues or Look, if you have methylation issues or you're taking medication as I am, like you're taking medication as I am, like you're taking medication as I am, like omeprazole, which can inhibit magnesium omeprazole, which can inhibit magnesium omeprazole, which can inhibit magnesium absorption and other things. Like, you absorption and other things. Like, you absorption and other things. Like, you want to keep an eye on that, but not too want to keep an eye on that, but not too want to keep an eye on that, but not too fancy, you know, I think urolithin A is fancy, you know, I think urolithin A is fancy, you know, I think urolithin A is pretty interesting. pretty interesting. pretty interesting. >> [clears throat] >> [clears throat] >> [clears throat] >> The data keeps mounting on that. I do >> The data keeps mounting on that. I do >> The data keeps mounting on that. I do have a keen interest in mitochondrial have a keen interest in mitochondrial have a keen interest in mitochondrial health. So, if there are things, which health. So, if there are things, which health. So, if there are things, which could also include could also include could also include regular intermittent fasting and regular intermittent fasting and regular intermittent fasting and occasional 3 to 7-day fasting, which occasional 3 to 7-day fasting, which occasional 3 to 7-day fasting, which could be a fast mimicking diet, most could be a fast mimicking diet, most could be a fast mimicking diet, most recently for me, based on the input from recently for me, based on the input from recently for me, based on the input from Dr. Dominic D'Agostino, trying to foster Dr. Dominic D'Agostino, trying to foster Dr. Dominic D'Agostino, trying to foster autophagy and mitophagy with some autophagy and mitophagy with some autophagy and mitophagy with some regularity, not all the time. Sure. I'm regularity, not all the time. Sure. I'm regularity, not all the time. Sure. I'm not trying to optimize for that all the not trying to optimize for that all the not trying to optimize for that all the time. One thing I've been wondering, so time. One thing I've been wondering, so time. One thing I've been wondering, so if you look at like a computer if you look at like a computer if you look at like a computer and often the key to fixing your laptop and often the key to fixing your laptop and often the key to fixing your laptop or the key to fixing any system is you or the key to fixing any system is you or the key to fixing any system is you just [ __ ] reboot it, right? Yeah. just [ __ ] reboot it, right? Yeah. just [ __ ] reboot it, right? Yeah. >> You reload the system and it just works >> You reload the system and it just works >> You reload the system and it just works magically. Is there like a equivalent of magically. Is there like a equivalent of magically. Is there like a equivalent of that? Is it like going under for that? Is it like going under for that? Is it like going under for anesthesia? anesthesia? anesthesia? Is it Is it Is it >> Is it some nerve freezing thing that >> Is it some nerve freezing thing that >> Is it some nerve freezing thing that some people have been doing recently? some people have been doing recently? some people have been doing recently? Oof. Yeah, I don't know. Oof. Yeah, I don't know. Oof. Yeah, I don't know. Sounds scary. Oh, maybe stellate Sounds scary. Oh, maybe stellate Sounds scary. Oh, maybe stellate ganglion block? ganglion block? ganglion block? >> Yeah, that's it. The stellate ganglion >> Yeah, that's it. The stellate ganglion >> Yeah, that's it. The stellate ganglion block. block. block. >> Yeah. Yeah, I mean the rebooting Ah, man, I'm like letting out an exhale Ah, man, I'm like letting out an exhale because because because I there are some interesting options for I there are some interesting options for I there are some interesting options for very specific use cases. It makes sense very specific use cases. It makes sense very specific use cases. It makes sense conceptually. I mean, you're more conceptually. I mean, you're more conceptually. I mean, you're more qualified to speak to this, but I would qualified to speak to this, but I would qualified to speak to this, but I would say just spending a lot of time around say just spending a lot of time around say just spending a lot of time around neuroscientists and I I spend a lot of neuroscientists and I I spend a lot of neuroscientists and I I spend a lot of my time in terms of information intake my time in terms of information intake my time in terms of information intake reading reading reading or doing my best. Fortunately with AI or doing my best. Fortunately with AI or doing my best. Fortunately with AI tools, it's become a lot easier. Not tools, it's become a lot easier. Not tools, it's become a lot easier. Not just getting a synopsis, but actually just getting a synopsis, but actually just getting a synopsis, but actually using it to help you using it to help you using it to help you learn concepts that you can kind of learn concepts that you can kind of learn concepts that you can kind of layer in some layer in some layer in some rational sequence. rational sequence. rational sequence. >> Sure. But I read a lot of neuroscience >> Sure. But I read a lot of neuroscience >> Sure. But I read a lot of neuroscience stuff and a lot of optical stuff. stuff and a lot of optical stuff. stuff and a lot of optical stuff. There's actually a surprising amount of There's actually a surprising amount of There's actually a surprising amount of I mean, there's maybe not so surprising I mean, there's maybe not so surprising I mean, there's maybe not so surprising like like like very strong intersection there. So if very strong intersection there. So if very strong intersection there. So if you're looking at like PBM and like you're looking at like PBM and like you're looking at like PBM and like photobiomodulation through the eyes, I photobiomodulation through the eyes, I photobiomodulation through the eyes, I mean, you can do it transcranially as mean, you can do it transcranially as mean, you can do it transcranially as well. I would give a note of caution for well. I would give a note of caution for well. I would give a note of caution for that for folks, but the reboot side, I that for folks, but the reboot side, I that for folks, but the reboot side, I would say for instance, and people have would say for instance, and people have would say for instance, and people have experienced this to a lesser extent with experienced this to a lesser extent with experienced this to a lesser extent with GLP-1 agonists. If they take it for GLP-1 agonists. If they take it for GLP-1 agonists. If they take it for weight loss, maybe they stop smoking or weight loss, maybe they stop smoking or weight loss, maybe they stop smoking or they cut back on drinking or they cut back on drinking or they cut back on drinking or they have these they have these they have these kind of system-wide decreases or kind of system-wide decreases or kind of system-wide decreases or increases in it in impulse control. increases in it in impulse control. increases in it in impulse control. Yeah. For someone who's saying opiate Yeah. For someone who's saying opiate Yeah. For someone who's saying opiate addict, I think that addict, I think that addict, I think that ibogaine, which ibogaine, which ibogaine, which in the future may take the form of an in the future may take the form of an in the future may take the form of an active metabolite or something like active metabolite or something like active metabolite or something like that, that, that, in flood dosing, in flood dosing, in flood dosing, at least that's seems pretty necessary at least that's seems pretty necessary at least that's seems pretty necessary at this point, relatively high doses, at this point, relatively high doses, at this point, relatively high doses, under medical supervision cuz you can under medical supervision cuz you can under medical supervision cuz you can have fatal cardiac events. have fatal cardiac events. have fatal cardiac events. Co-administration of magnesium seems to Co-administration of magnesium seems to Co-administration of magnesium seems to help, but it's dangerous stuff. People help, but it's dangerous stuff. People help, but it's dangerous stuff. People should be careful. should be careful. should be careful. You can and there are lots of people You can and there are lots of people You can and there are lots of people historically who deserve a lot of credit historically who deserve a lot of credit historically who deserve a lot of credit for this, like Howard Lotsof for this, like Howard Lotsof for this, like Howard Lotsof and his wife, but and his wife, but and his wife, but opiate addicts can go through opiate addicts can go through opiate addicts can go through flood dosing of Ibogaine and come out flood dosing of Ibogaine and come out flood dosing of Ibogaine and come out and they're basically given a window and they're basically given a window and they're basically given a window with which they with which they with which they won't experience withdrawal symptoms, won't experience withdrawal symptoms, won't experience withdrawal symptoms, physical withdrawal symptoms. physical withdrawal symptoms. physical withdrawal symptoms. And I think there probably applications And I think there probably applications And I think there probably applications to other things with Ibogaine or to other things with Ibogaine or to other things with Ibogaine or pharmacological interventions like pharmacological interventions like pharmacological interventions like Ibogaine. And some of the craziest Ibogaine. And some of the craziest Ibogaine. And some of the craziest stuff, honestly, related to that stuff, honestly, related to that stuff, honestly, related to that molecule is molecule is molecule is the the the and I'm skeptical of this simple and I'm skeptical of this simple and I'm skeptical of this simple description, but it's a reversal in description, but it's a reversal in description, but it's a reversal in brain age. It's a changes in the brain brain age. It's a changes in the brain brain age. It's a changes in the brain based on MRIs. Nolan Williams, rest in based on MRIs. Nolan Williams, rest in based on MRIs. Nolan Williams, rest in peace, and his lab looked at this pretty peace, and his lab looked at this pretty peace, and his lab looked at this pretty closely, pre- and post-dosing of closely, pre- and post-dosing of closely, pre- and post-dosing of Ibogaine for veterans with traumatic Ibogaine for veterans with traumatic Ibogaine for veterans with traumatic brain injury. And some of that might be brain injury. And some of that might be brain injury. And some of that might be due to something called glial derived due to something called glial derived due to something called glial derived neurotrophic factor, right? People might neurotrophic factor, right? People might neurotrophic factor, right? People might be familiar with like BDNF. be familiar with like BDNF. be familiar with like BDNF. So, Ibogaine is one interesting option. So, Ibogaine is one interesting option. So, Ibogaine is one interesting option. Anesthesia, I've become a lot more Anesthesia, I've become a lot more Anesthesia, I've become a lot more cautious with general anesthesia. Yeah. cautious with general anesthesia. Yeah. cautious with general anesthesia. Yeah. Like I just had surgery yesterday and I Like I just had surgery yesterday and I Like I just had surgery yesterday and I opted for local anesthesia, which in opted for local anesthesia, which in opted for local anesthesia, which in this case was not a big deal cuz it was this case was not a big deal cuz it was this case was not a big deal cuz it was just you can see it, like had something just you can see it, like had something just you can see it, like had something cut out of my head. cut out of my head. cut out of my head. But But But coming back to the coming back to the coming back to the and I'm going to riff for a second here, and I'm going to riff for a second here, and I'm going to riff for a second here, but the but the but the autism spectrum disorder and ADHD autism spectrum disorder and ADHD autism spectrum disorder and ADHD example you were unpacking where you example you were unpacking where you example you were unpacking where you talked about the incentives, they might talked about the incentives, they might talked about the incentives, they might be perverse incentives to diagnose. be perverse incentives to diagnose. be perverse incentives to diagnose. Well, Well, Well, I mean, I mean, I mean, not to quote Munger, right? But it's not to quote Munger, right? But it's not to quote Munger, right? But it's like follow the money, right? like follow the money, right? like follow the money, right? >> [laughter] >> [laughter] >> [laughter] >> And a lot of people are put under >> And a lot of people are put under >> And a lot of people are put under general who really don't need to be put general who really don't need to be put general who really don't need to be put under general, but it adds a very, very, under general, but it adds a very, very, under general, but it adds a very, very, very huge very huge very huge line item to the tab. line item to the tab. line item to the tab. And there are people And there are people And there are people who go under anesthesia and wake up and who go under anesthesia and wake up and who go under anesthesia and wake up and do not retain do not retain do not retain the same ability to recall memories and the same ability to recall memories and the same ability to recall memories and so on. Like their personalities become so on. Like their personalities become so on. Like their personalities become in some way destabilized. And the fact in some way destabilized. And the fact in some way destabilized. And the fact of the matter is of the matter is of the matter is that a lot of anesthesia is very poorly that a lot of anesthesia is very poorly that a lot of anesthesia is very poorly understood. We know it works, but it's understood. We know it works, but it's understood. We know it works, but it's very poorly understood. And I don't very poorly understood. And I don't very poorly understood. And I don't think think think a lot of people a lot of people a lot of people realize because why would they unless realize because why would they unless realize because why would they unless they've they've they've you know, just spending a lot of time you know, just spending a lot of time you know, just spending a lot of time looking into this. There are lots of looking into this. There are lots of looking into this. There are lots of medications that are incredibly medications that are incredibly medications that are incredibly well-known, commonly prescribed for well-known, commonly prescribed for well-known, commonly prescribed for which the mechanisms of action are which the mechanisms of action are which the mechanisms of action are really poorly understood, if they're really poorly understood, if they're really poorly understood, if they're understood at all. You know, like we understood at all. You know, like we understood at all. You know, like we know based on studies they appear to be know based on studies they appear to be know based on studies they appear to be well tolerated. Like side effects well tolerated. Like side effects well tolerated. Like side effects profiles include A through Z. profiles include A through Z. profiles include A through Z. And it certainly seems to exert this And it certainly seems to exert this And it certainly seems to exert this effect or have an impact on biomarker X, effect or have an impact on biomarker X, effect or have an impact on biomarker X, but we don't actually [ __ ] know how but we don't actually [ __ ] know how but we don't actually [ __ ] know how it works, you know? And there's just a it works, you know? And there's just a it works, you know? And there's just a lot of stuff that falls into that lot of stuff that falls into that lot of stuff that falls into that bucket. And so I am cautious with a lot bucket. And so I am cautious with a lot bucket. And so I am cautious with a lot of it. But to come back to your of it. But to come back to your of it. But to come back to your question, I went off on a bit of a TED question, I went off on a bit of a TED question, I went off on a bit of a TED Talk. The most interesting reboot that Talk. The most interesting reboot that Talk. The most interesting reboot that I've seen and I don't I don't want to I've seen and I don't I don't want to I've seen and I don't I don't want to really water it down to like the really water it down to like the really water it down to like the dopaminergic system cuz there's a lot dopaminergic system cuz there's a lot dopaminergic system cuz there's a lot more to it, but I begin I think more so more to it, but I begin I think more so more to it, but I begin I think more so than I began itself shows what is than I began itself shows what is than I began itself shows what is possible. And I don't know if that's possible. And I don't know if that's possible. And I don't know if that's limited to drugs. I am very bullish and limited to drugs. I am very bullish and limited to drugs. I am very bullish and there're going to be fuck-ups, there're there're going to be fuck-ups, there're there're going to be fuck-ups, there're going to be some sidebars that don't going to be some sidebars that don't going to be some sidebars that don't look so good, but brain stimulation and look so good, but brain stimulation and look so good, but brain stimulation and bioelectric medicine, bioelectric medicine, bioelectric medicine, broadly speaking, broadly speaking, broadly speaking, is one of the great next frontiers, is one of the great next frontiers, is one of the great next frontiers, certainly in treating what we might certainly in treating what we might certainly in treating what we might consider psychiatric disorders, consider psychiatric disorders, consider psychiatric disorders, but also for performance enhancement. but also for performance enhancement. but also for performance enhancement. And And And we're at a point kind of looking for we're at a point kind of looking for we're at a point kind of looking for those external why now answers, right? those external why now answers, right? those external why now answers, right? There are actually some really good There are actually some really good There are actually some really good answers to why now for this as a field. answers to why now for this as a field. answers to why now for this as a field. And I think people will be experimenting And I think people will be experimenting And I think people will be experimenting a lot with this, but without the use of a lot with this, but without the use of a lot with this, but without the use of pills and potions and IVs and actually pills and potions and IVs and actually pills and potions and IVs and actually non-invasive brain stimulation, maybe non-invasive brain stimulation, maybe non-invasive brain stimulation, maybe some invasive in the case of implants. some invasive in the case of implants. some invasive in the case of implants. So that's a long answer, but yeah, So that's a long answer, but yeah, So that's a long answer, but yeah, that's on what I'm thinking about and that's on what I'm thinking about and that's on what I'm thinking about and tracking. I mean, some of this stuff, tracking. I mean, some of this stuff, tracking. I mean, some of this stuff, we'll see, but I think a lot of this we'll see, but I think a lot of this we'll see, but I think a lot of this stuff could be outpatient procedure. You stuff could be outpatient procedure. You stuff could be outpatient procedure. You walk in, you're in there for an hour or walk in, you're in there for an hour or walk in, you're in there for an hour or two, and then you're out. two, and then you're out. two, and then you're out. So, we'll see. Let me ask just a couple So, we'll see. Let me ask just a couple So, we'll see. Let me ask just a couple of last questions and then if there's of last questions and then if there's of last questions and then if there's anything else we want to bat around, we anything else we want to bat around, we anything else we want to bat around, we can bat it around, but I appreciate the can bat it around, but I appreciate the can bat it around, but I appreciate the time. time. time. A lot of 5 years from now is looking A lot of 5 years from now is looking A lot of 5 years from now is looking back at a a lot of today. Yeah. Are back at a a lot of today. Yeah. Are back at a a lot of today. Yeah. Are there any beliefs, positions, could be there any beliefs, positions, could be there any beliefs, positions, could be related to AI or otherwise, that you related to AI or otherwise, that you related to AI or otherwise, that you think are more likely than others to be think are more likely than others to be think are more likely than others to be wrong? Hmm. wrong? Hmm. wrong? Hmm. That's a good question. I think there's That's a good question. I think there's That's a good question. I think there's all sorts of things I'm going to get all sorts of things I'm going to get all sorts of things I'm going to get wrong. I mean, I think we're living wrong. I mean, I think we're living wrong. I mean, I think we're living through a period of big changes means through a period of big changes means through a period of big changes means big uncertainty, and so big uncertainty, and so big uncertainty, and so I wouldn't be surprised if half the I wouldn't be surprised if half the I wouldn't be surprised if half the things I think are going to happen don't things I think are going to happen don't things I think are going to happen don't or happen even more so or whatever may or happen even more so or whatever may or happen even more so or whatever may be. And that's part of the fun of it in be. And that's part of the fun of it in be. And that's part of the fun of it in terms of if we had a perfectly terms of if we had a perfectly terms of if we had a perfectly predictive future, it'd be very boring, predictive future, it'd be very boring, predictive future, it'd be very boring, right? Cuz we we'd know exactly what's right? Cuz we we'd know exactly what's right? Cuz we we'd know exactly what's going to happen. It'd be awful. going to happen. It'd be awful. going to happen. It'd be awful. Ties into notions of free will and all Ties into notions of free will and all Ties into notions of free will and all sorts of other things, right? I'm sure sorts of other things, right? I'm sure sorts of other things, right? I'm sure there's a lot. I think there's a there's a lot. I think there's a there's a lot. I think there's a separate question of just one exercise separate question of just one exercise separate question of just one exercise I've been going through recently is, and I've been going through recently is, and I've been going through recently is, and I've never done this before, you know, a I've never done this before, you know, a I've never done this before, you know, a lot of what you do in life, it's back lot of what you do in life, it's back lot of what you do in life, it's back the John Lennon quote, life is what the John Lennon quote, life is what the John Lennon quote, life is what happens while you're making other plans. happens while you're making other plans. happens while you're making other plans. For the first time I'm actually thinking For the first time I'm actually thinking For the first time I'm actually thinking like, what's my 10-year plan, right? like, what's my 10-year plan, right? like, what's my 10-year plan, right? Across a few different dimensions of Across a few different dimensions of Across a few different dimensions of life. life. life. And the basic question is And the basic question is And the basic question is I won't get it right, right? I can try I won't get it right, right? I can try I won't get it right, right? I can try and have a plan for 10 years. Of course, and have a plan for 10 years. Of course, and have a plan for 10 years. Of course, it's not going to be what I think. But it's not going to be what I think. But it's not going to be what I think. But it's more does it change the scope of it's more does it change the scope of it's more does it change the scope of ambition ambition ambition that you have? Does it change how you that you have? Does it change how you that you have? Does it change how you think about life? And so, I've been think about life? And so, I've been think about life? And so, I've been trying to think in those terms, like trying to think in those terms, like trying to think in those terms, like what do I want to do over the next what do I want to do over the next what do I want to do over the next decade? decade? decade? And that What does that mean in terms of And that What does that mean in terms of And that What does that mean in terms of the near term what I do in order to get the near term what I do in order to get the near term what I do in order to get there in 10 years? And so, I think there in 10 years? And so, I think there in 10 years? And so, I think that's been very that's been very that's been very eye-opening for me in terms of shifting eye-opening for me in terms of shifting eye-opening for me in terms of shifting some of my mindset around what I should some of my mindset around what I should some of my mindset around what I should be trying or not trying to do. Now, be trying or not trying to do. Now, be trying or not trying to do. Now, the AGI cult people will say, well, in the AGI cult people will say, well, in the AGI cult people will say, well, in two years we have AGI, so it doesn't two years we have AGI, so it doesn't two years we have AGI, so it doesn't matter what your plans are. matter what your plans are. matter what your plans are. But I find that to be a very kind of But I find that to be a very kind of But I find that to be a very kind of defeatist view of the world, you know, defeatist view of the world, you know, defeatist view of the world, you know, it's like I'm going to give up because I it's like I'm going to give up because I it's like I'm going to give up because I was was was versus saying, great, I'm going to have versus saying, great, I'm going to have versus saying, great, I'm going to have this plan and I can adjust it as needed, this plan and I can adjust it as needed, this plan and I can adjust it as needed, but through this time of change there'll but through this time of change there'll but through this time of change there'll be some really interesting things for me be some really interesting things for me be some really interesting things for me to do in the world. Elad, do you have to do in the world. Elad, do you have to do in the world. Elad, do you have anything else you'd like to say, anything else you'd like to say, anything else you'd like to say, comments, requests for the audience, comments, requests for the audience, comments, requests for the audience, things to point people to, anything at things to point people to, anything at things to point people to, anything at all before we all before we all before we wind to a close? People can find you on wind to a close? People can find you on wind to a close? People can find you on X at Elad Gil, eladgil.com, certainly X at Elad Gil, eladgil.com, certainly X at Elad Gil, eladgil.com, certainly the Substack blog, blog.eladgil.com, the Substack blog, blog.eladgil.com, the Substack blog, blog.eladgil.com, and elsewhere. We'll link everything in and elsewhere. We'll link everything in and elsewhere. We'll link everything in the show notes, but anything else that the show notes, but anything else that the show notes, but anything else that you'd like to add? Yeah, it was nice to you'd like to add? Yeah, it was nice to you'd like to add? Yeah, it was nice to chat with you as always. I really enjoy chat with you as always. I really enjoy chat with you as always. I really enjoy it, so thanks for having me on. Yeah, it, so thanks for having me on. Yeah, it, so thanks for having me on. Yeah, thanks, man. Always a pleasure. And to thanks, man. Always a pleasure. And to thanks, man. Always a pleasure. And to everybody listening or watching, we will everybody listening or watching, we will everybody listening or watching, we will link everything in the show notes, link everything in the show notes, link everything in the show notes, tim.blog/podcast. tim.blog/podcast. tim.blog/podcast. And until next time, as always, be a bit And until next time, as always, be a bit And until next time, as always, be a bit kinder than is necessary to others, but kinder than is necessary to others, but kinder than is necessary to others, but also to yourself. Thanks for tuning in.