{"id":12889,"date":"2026-04-30T22:43:18","date_gmt":"2026-05-01T02:43:18","guid":{"rendered":"https:\/\/parmaks.com\/Resources\/the-tim-ferriss-show-transcripts-elad-gil-consigliere-to-empire-builders-how-to-spot-billion-dollar-companies-before-everyone-else-the-misty-ai-frontier-how-coke-beat-pepsi-when-consens\/"},"modified":"2026-04-30T22:43:18","modified_gmt":"2026-05-01T02:43:18","slug":"the-tim-ferriss-show-transcripts-elad-gil-consigliere-to-empire-builders-how-to-spot-billion-dollar-companies-before-everyone-else-the-misty-ai-frontier-how-coke-beat-pepsi-when-consens","status":"publish","type":"post","link":"http:\/\/parmaks.com\/Resources\/the-tim-ferriss-show-transcripts-elad-gil-consigliere-to-empire-builders-how-to-spot-billion-dollar-companies-before-everyone-else-the-misty-ai-frontier-how-coke-beat-pepsi-when-consens\/","title":{"rendered":"The Tim Ferriss Show Transcripts: Elad Gil, Consigliere to Empire Builders \u2014 How to Spot Billion-Dollar Companies Before Everyone Else, The Misty AI Frontier, How Coke Beat Pepsi, When Consensus Pays, and Much More\u00a0(#863)"},"content":{"rendered":"<p> <a href=\"https:\/\/hop.clickbank.net\/?affiliate=infohatch&amp;vendor=J1R2C\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-10614 aligncenter\" src=\"http:\/\/parmaks.com\/Resources\/wp-content\/uploads\/2025\/05\/profit-gen400px.png\" alt=\"Profit Gen\" width=\"400\" height=\"217\" srcset=\"http:\/\/parmaks.com\/Resources\/wp-content\/uploads\/2025\/05\/profit-gen400px.png 400w, http:\/\/parmaks.com\/Resources\/wp-content\/uploads\/2025\/05\/profit-gen400px-300x163.png 300w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/><\/a><br \/>\n<\/p>\n<div>\n<p>Please enjoy this transcript of <a href=\"https:\/\/tim.blog\/2026\/04\/29\/elad-gil\/\">my interview with Elad Gil<\/a> (<a target=\"_blank\" href=\"https:\/\/x.com\/eladgil\">@eladgil<\/a>), CEO of Gil &amp; Co, a multi-stage investment firm, holding company, and operating company working on the world\u2019s most advanced technologies. Elad is a serial entrepreneur, operating executive, and investor or advisor to private companies, including AirBnB, Anduril, Coinbase, Figma, Instacart, OpenAI, SpaceX, and Stripe. He was previously VP of Corporate Strategy at Twitter and started mobile at Google. He was the founder and CEO of Mixerlabs and Color. Elad is the author of the bestseller <a target=\"_blank\" href=\"https:\/\/www.amazon.com\/High-Growth-Handbook-Elad-Gil\/dp\/1732265100\/?tag=offsitoftimfe-20\"><strong><em>High Growth Handbook: Scaling Startups from 10 to 10,000 People<\/em><\/strong><\/a>.<\/p>\n<p><a href=\"https:\/\/tim.blog\/2026\/04\/29\/elad-gil\/#:~:text=SELECTED%20LINKS%20FROM%20THE%20EPISODE\" target=\"_blank\" rel=\"noreferrer noopener\">Books, people, tools, and resources mentioned in the interview<\/a><\/p>\n<p><a href=\"https:\/\/tim.blog\/2026\/04\/30\/elad-gil-transcript\/#Elad-Gil-legal-conditions-transcript\">Legal conditions\/copyright information<\/a><\/p>\n<div class=\"podcast-player\">\n<div class=\"podcast-player-inner-wrap\">\n<p>Elad Gil, Consigliere to Empire Builders \u2014 How to Spot Billion-Dollar Companies Before Everyone Else, The Misty AI Frontier, How Coke Beat Pepsi, When Consensus Pays, and Much More<\/p>\n<p><noscript><iframe src=\"https:\/\/www.art19.com\/shows\/58dacbdc-646e-4585-9914-19c3de11d1ba\/episodes\/f62b6d9e-fa23-40a1-9c3f-f4bb6bec89fe\/embed?type=micro\" style=\"width: 100%; height: 30px; border: 0 none;\" scrolling=\"no\"><\/iframe><\/noscript><\/div>\n<\/div>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<h3 class=\"wp-block-heading\">Additional podcast platforms<\/h3>\n<p><strong>Listen to this episode on\u00a0<a href=\"https:\/\/podcasts.apple.com\/us\/podcast\/863-elad-gil-consigliere-to-empire-builders-how-to\/id863897795?i=1000764580528\" target=\"_blank\" rel=\"noreferrer noopener\">Apple Podcasts<\/a>,\u00a0<a href=\"https:\/\/open.spotify.com\/episode\/4TMAVjvGRqpTstHCfYFeY5?si=Zamk2SV3QkirB_DmnUEqsg\" target=\"_blank\" rel=\"noreferrer noopener\">Spotify<\/a>,\u00a0<a href=\"https:\/\/overcast.fm\/+AAKebu1yzoU\" target=\"_blank\" rel=\"noreferrer noopener\">Overcast<\/a>,\u00a0<a href=\"https:\/\/podcastaddict.com\/podcast\/2031148#\" target=\"_blank\" rel=\"noreferrer noopener\">Podcast Addict<\/a>,\u00a0<a href=\"https:\/\/pca.st\/timferriss\" target=\"_blank\" rel=\"noreferrer noopener\">Pocket Casts<\/a>,\u00a0<a href=\"https:\/\/castbox.fm\/channel\/id1059468?country=us\" target=\"_blank\" rel=\"noreferrer noopener\">Castbox<\/a>,\u00a0<a target=\"_blank\" href=\"https:\/\/music.youtube.com\/playlist?list=PLuu6fDad2eJyWPm9dQfuorm2uuYHBZDCB\">YouTube Music<\/a>,\u00a0<a href=\"https:\/\/music.amazon.com\/podcasts\/9814f3cc-1dc5-4003-b816-44a8eb6bf666\/the-tim-ferriss-show\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Music<\/a>,\u00a0<a href=\"https:\/\/www.audible.com\/podcast\/The-Tim-Ferriss-Show\/B08K58QX5W\" target=\"_blank\" rel=\"noreferrer noopener\">Audible<\/a>, or on your favorite podcast platform.<\/strong><\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<p>Transcripts may contain a few typos. With many episodes lasting 2+ hours, it can be difficult to catch minor errors. Enjoy!<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<p><strong>Tim Ferriss:<\/strong> Elad, nice to see you. Thanks for making the time. Appreciate it.<\/p>\n<p><strong>Elad Gil: <\/strong>Great to see you, as always.<\/p>\n<p><strong>Tim Ferriss: <\/strong>I thought we could begin with something we were chatting about, or you were explaining before we started recording, which is a new phenomenon of sorts. Could you explain what we were just talking about?<\/p>\n<p><strong>Elad Gil: <\/strong>Oh yeah, we were just talking about some of the acquisitions that are happening in the AI world. We saw that xAI just got an option to effectively purchase Cursor, it looks like. Obviously, Scale was partially taken by Meta. There\u2019s been a variety of these deals that have been happening over the last year or two.<\/p>\n<p>And separate from that, we were just talking about, well, what does that mean for the AI research community and the AI community in general? And I think the most interesting, or one of the interesting things that\u2019s happened over the last year or so is Meta really started aggressively bidding on AI talent, which was a very rational strategy. They\u2019re going to spend tens of billions of dollars on compute, so it made sense to have a real budget to go after people. And normally, what happens in tech is a single company will go public, and a bunch of people from that company will be enriched and then a subset of them will continue to be heads down and working really hard and focused on their original mission. And a subset of people will start to get distracted. They may go and work on passion projects for society. They may get involved with politics. They may go start a company. They may just check out and hang out or go to the beach kind of thing.<\/p>\n<p>And what happened recently is because of the Meta offers and then all the other major tech companies having to match offers for their best researchers, somewhere between 50 and a few hundred people effectively had an IPO, but is a class of people. It wasn\u2019t like they were at one company. They were spread across Silicon Valley, but all of their pay packages suddenly went up dramatically and they experienced the equivalent of an IPO, and that\u2019s really unusual. It\u2019s kind of the personal IPO. And the only time in history I can think of where I\u2019ve seen it happen before is in crypto where a bunch of the really early crypto holders or founders suddenly as a class all went effectively public in \u201920, I guess \u201917-ish, and then again, more recently.<\/p>\n<p>But this is really interesting, it\u2019s under discussed. It may not have huge long-term implications, but it does mean a subset of people will change what they\u2019re focused on, try and do big science projects to help humanity work on AI for science maybe. Maybe some people will go off and do personal quests or things like that.\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. Or just \u201cquiet quit\u201d and do lots of drugs and chase vices. I mean, there\u2019s that too.\u00a0<\/p>\n<p><strong>Elad Gil: <\/strong>Definitely that.<\/p>\n<p><strong>Tim Ferriss:<\/strong> In that case, you look around, say Austin, you\u2019ve got the Dellionaires, which refers to Dell post-IPO or early employees and so on. But as a class of people, when that happens, I suppose we don\u2019t know how large or how long term the implications are, but there seem to be implications. And I know only a few people who I would go to as technical enough and also broad enough in their awareness and networks to watch AI. To the extent that someone can watch it comprehensively, I would put you in that bucket. And you wrote this week just to talk about some of the other elements at play here, the compute constraints that AI labs are facing and the implications maybe for the next one to five years. This is in a piece people should check out: \u201cRandom Thoughts While Gazing at the Misty AI Frontier.\u201d Good headline, by the way.<\/p>\n<p><strong>Elad Gil: <\/strong>Very<strong> <\/strong>dramatic.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah, very dramatic, I love it. It\u2019s very evocative. So would you mind explaining, actually, before we move to the compute constraints, because I do want you to talk to that next, but for people who don\u2019t have any real context on the talent wars and what you were just mentioning earlier with Meta, on the high end, what does some of these pay\/equity packages, compensation packages look like that are getting offered?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. I don\u2019t have exact knowledge of the full range and everything else. The rumors and the things that have made it into the press, the claims are that these things are between tens of millions and hundreds of millions of dollars per person. And again, it\u2019s a very small number of people who would get anything that\u2019s outsized. But I think the basic idea is we\u2019re in one of the most important technology races of all times. The faster that we get to better and better AI, the more economic value will effectively show up. And therefore, people are really willing to pay in an outsized way for the handful of people who are the world\u2019s best at this thing.<\/p>\n<p>And five, 10 years ago, these people were well compensated, but it was a completely different ballgame because it just wasn\u2019t the core of everything that\u2019s happening in technology. But also honestly, societally and politically and for education and health, it\u2019s going to have all these really broad, and I think largely positive implications for the world, but it is the moment of transformation, and so suddenly these pay packages were going way up.<\/p>\n<p><strong>Tim Ferriss: <\/strong>What are the compute constraints that you discussed in your recent piece?<\/p>\n<p><strong>Elad Gil: <\/strong>All the different \u2014 people call them labs now \u2014 that\u2019s OpenAI, that\u2019s Anthropic, that\u2019s Google, that\u2019s xAI, et cetera. All the labs are basically training these giant models. And effectively, what you do is you buy a bunch of chips from NVIDIA. You\u2019re actually building out a system so you have chips from NVIDIA, you have memory from Hynix and Samsung and other places and you\u2019re building out data center. There\u2019s all these things that go into building these big systems and data centers and everything else. And you basically have clusters of hundreds of thousands or millions, or the scale keeps going up of systems that you\u2019re buying from NVIDIA and from others. Google has your TPU, there\u2019s other systems as well, and you\u2019re using that to basically train an AI model.<\/p>\n<p>What that means is you\u2019re running huge amounts of data against these big clouds, and eventually the crazy thing is your output or your model is literally like a flat file. It\u2019s almost like outputting a text doc or something. And that text doc is what you then load to run AI, which is insane if you think about it. You use a giant cloud for months and months and months, and your output is like a small file.<\/p>\n<p>And that small file is a mix of representing all of humanity\u2019s knowledge that\u2019s available on the internet, plus logic and reasoning and other things built into it. And you can think about that in the context of your brain. You have three or four billion base pairs of DNA, and that\u2019s more than enough to specify everything about your physical being, but also your brain and your mind and how it works and how you can see things and talk and taste things and all your senses and everything\u2019s just encapsulated in these very small number of genes, actually. And so similarly, you can encapsulate all of human knowledge into this slot file effectively.<\/p>\n<p><strong>Tim Ferriss: <\/strong>How do you think about the constraints then? What are the constraints?<\/p>\n<p><strong>Elad Gil: <\/strong>Every year, the constraint on building out these big clouds to train AI, and then also what\u2019s known as inference, where you\u2019re actually using these chips to understand, to run the AI system itself, you need lots and lots of chips from NVIDIA to do this or TPUs or others, but then you also need other things. You need packaging to actually be able to package the chips, and so there\u2019s a whole supply chain around building out these systems. Different parts of that supply chain have constraints of them at different times, and so right now the major constraint is memory or a specific type of memory that\u2019s largely made by Korean companies, although there\u2019s some broader providers of it. People think that that memory constraint will exist for about two years, maybe, plus or minus, because ultimately the capacity of those companies has been lower than the capacity for everything else in the system.<\/p>\n<p>People think other constraints in the future may literally be building out the data centers or power and energy to run these things, but for today it\u2019s this memory. Everybody in the industry is constrained in terms of how much compute they can buy to throw out these things. What that does is it creates a ceiling on top of how big you can scale these models up in the short run because every lab is buying as much as it can. A bunch of startups are buying as much of this compute as they can, and everybody\u2019s constrained. What that means though is you have an artificial ceiling on how big a model can get in the short run, and how much inference can run or how many things you can actually do with AI right now.<\/p>\n<p>That also means that you\u2019re effectively enforcing a situation where no one lab can pull so far ahead of everybody else because they can\u2019t buy 10 times as much compute as everybody else. And there are these scale laws that the more compute you have, the bigger the AI model you can build, in many cases, the more performant it can be eventually. That may mean that over the next two years-ish, all these labs should be roughly close to each other because nobody has the capacity to pull ahead. And when the constraint comes off, there is some world where you could make an argument that suddenly somebody can pull far ahead of everybody else. So right now, OpenAI, Anthropic, Google, they\u2019re reasonably close in terms of capabilities, although some will pull ahead on one thing versus another. That should roughly continue everybody thinks for the next at least two years because of this.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Google is also constrained by the memory from Samsung, Micron, et cetera. They\u2019re similarly constrained as the other players?<\/p>\n<p><strong>Elad Gil: <\/strong>Right now, everybody is similarly constrained and a subset of these labs either are already making their own chips or systems like Google has TPUs and other things. Amazon has actually built its own chips called Trainiums. There\u2019s basically different systems for different companies, but fundamentally all of them are limited in terms of how much they can either manufacture themselves, purchase themselves. And a year or two ago, the main constraint was packaging, now it\u2019s memory. Two years from now, who knows, maybe it\u2019s something else. We constantly are hitting the bottlenecks as we\u2019re trying to do this build out.<\/p>\n<p><strong>Tim Ferriss: <\/strong>This is probably going to be a naive question because I\u2019m a muggle and not able to write technical white papers or anything approaching that, but it seems to me that, I\u2019m not the first person to say this, we\u2019re better at forecasting problems than solutions, potentially. And so for instance, way back in the day, the price per gallon of gasoline or petrol goes above a certain point. Okay, people are forecasting doom and destruction, but past a certain price per barrel, suddenly new means of extraction became feasible and there were investments made in things like fracking and so on. Is there a plausible scenario in which there is some type of workaround? Along those lines, if that makes any sense. I don\u2019t know. Maybe there isn\u2019t.<\/p>\n<p><strong>Elad Gil: <\/strong>As far as I know, there, so far at least, is not. And part of that is because of the way that some of these things are built and it\u2019s basically the capacity that you need, for example, for memory is basically a type of fab, and so you just need time to build out the fab and to get the equipment and put the lines in place. It\u2019s a traditional CapEx into infrastructure cycle and these companies basically underinvested in that because they didn\u2019t quite believe the demand forecast that other people had around this stuff, and so now they\u2019re trying to catch up.<\/p>\n<p>And so it\u2019s one of these things where everybody keeps saying, \u201cWell, AI is growing so fast, how can it possibly keep growing at this rate?\u201d But it keeps growing at this rate, it just keeps going, and that\u2019s because these capabilities are so impactful and so important. And so you look at the revenue of these companies and it\u2019s interesting, I can send you the chart later, but Jared on my team pulled together a graph of how long did it take for companies to get to a billion dollars in revenue, and then from a billion to 10 billion, and then from 10 to 100. And there\u2019s only a small number of companies that have ever done that. And you can literally look by generation of company how long it took. And so for example, I can\u2019t remember, it was ADP or somebody, it took them 30 years to get to a billion in revenue or whatever it is, and Anthropic and OpenAI did that in a year.<\/p>\n<p>So for Google, it took four years or whatever. I don\u2019t remember exactly what the numbers are, but it was like as you go through these subsequent generations, it gets faster and faster to get to scale. Right now, OpenAI and Anthropic are each rumored to be roughly around $30 billion run rate.<\/p>\n<p><strong>Tim Ferriss: <\/strong>That\u2019s crazy.<\/p>\n<p><strong>Elad Gil:<\/strong> Because four years ago they didn\u2019t have any revenue. And that\u2019s 0.1 percent of US GDP. So AI probably went from zero to half a percent of GDP, at least as a revenue contributor. And you extrapolate out, and if they hit 100 billion in revenue in the next year, two years, whatever it is, then we\u2019re getting close to a place where each of these companies is a percent or two of GDP. That\u2019s insane if you think about that.<\/p>\n<p><strong>Tim Ferriss: <\/strong>It\u2019s bananas, yeah. It\u2019s bananas.<\/p>\n<p><strong>Elad Gil: <\/strong>This stuff is really actually important and useful. That doesn\u2019t include the cloud revenue for Azure for doing AI stuff or Google GCP or Amazon. It\u2019s just those two companies. It\u2019s insane.<\/p>\n<p><strong>Tim Ferriss: <\/strong>I would love to dig into your thinking because you\u2019re one of the best first principles and also systems thinkers I\u2019ve met, and I love having conversations with you because I always learn something new, and it\u2019s not necessarily a data point, but often it might be a lens or a framework for thinking about different things. And that framework evolves for you as well. But for instance, if I was looking at this interview you did, this is a while back with first round capital and you were talking about market first and then strength of team second, but you talked about passing on investing in Lyft Series C. This was at the time, right? And ultimately, part of it seemed to hinge on winner-take-all versus oligopoly versus other.<\/p>\n<p>I\u2019m curious how you are thinking about that within the AI space, because I mean, you started skating for that puck before almost anyone I know, if not everyone I know. And so how are you thinking about that? And this ties into something that you mentioned in your piece that I haven\u2019t heard anyone else talking about, but I\u2019ll give the sentence as a cue. I don\u2019t think you\u2019ll need it, but founders running successful AI companies should all take a cold, hard look at exiting in the next 12 to 18 months, which might be a value maximizing moment for outcomes. And you went back to the dot-com bust and the survival rates and then breakout rates. How are you thinking about, could you just explain that sentence?<\/p>\n<p><strong>Elad Gil: <\/strong>Sure.<\/p>\n<p><strong>Tim Ferriss: <\/strong>And then also explain how you\u2019re thinking about whether you think this will be winners-take-all, oligopoly, like what type of dynamic you think emerges?<\/p>\n<p><strong>Elad Gil: <\/strong>So in terms of the precedent, and that doesn\u2019t mean it\u2019s going to happen here, but if you look at every technology cycle, 90 percent, 95 percent, 99 percent of the companies in that cycle go bust. And that dates way back even to what was high-tech a hundred years ago, which was the automotive industry. Detroit had dozens of car companies and hundreds of suppliers, and it collapsed into a small number of auto companies essentially, and so this is not a new story. During the internet cycle or bubble of the \u201990s, 450 companies went public in \u201999, 450 or so companies went public in the first few months of 2000, and so that was 900 companies. And, say, another 500 to 1,000 went public in the couple years before that. So you had somewhere between 1,500 and 2,000 companies go public.<\/p>\n<p>Go public, so that means they kind of made it. And of those, how many have survived? A dozen, maybe two dozen. And so that\u2019s out of 2,000 companies, 1,980 or so went under, one form or another, or maybe they got bought for a little bit. And so there\u2019s no reason to think that AI cycle will be any different. And every cycle\u2019s like that. SaaS was like that and mobile was like that and crypto was like that. Most companies are not going to make it. A handful will, and we can talk about those. And so if you\u2019re running an AI company right now, you should ask yourself, what is the nature of the durability of your company? And are you one of that dozen or two that are going to be really important 10 years from now? Or is now a good moment for you to sell because what you\u2019re doing will start to get commoditized, or will be competed by a lab, or will be something that the market will shift or the technology will shift and you\u2019ll become obsolete?<\/p>\n<p>There\u2019s a handful of companies that will continue to be great. They should never sell, they should never exit, they should keep going. But there\u2019s probably a lot of companies that now, or the next 12 to 18 months is the best moment for them possible in terms of the value that they\u2019ll get for what they\u2019re doing. For every company, there\u2019s a value maximizing moment where they hit their peak, and it\u2019s usually a window. Usually six, 12 months where what you\u2019re doing is important enough, you\u2019re scaling enough, everything\u2019s working before some headwind hits you.<\/p>\n<p>And sometimes it\u2019s very predictable that that headwind is coming and you can see it. And often, you see it in the second derivative of growth. How fast are you growing starts to plateau a little bit and you\u2019re either going to keep going up or you should sell. That\u2019s really what that\u2019s meant to be. I\u2019m incredibly bullish around AI, as you can tell from the rest of the conversation. And so it\u2019s less about the transformation that\u2019s happening overall because of this technology, and more that only a handful of companies are going to continue to be really important, and so are you one of them or not? If you\u2019re one of them, you should never, ever, ever sell.<\/p>\n<p><strong>Tim Ferriss: <\/strong>So what are the characteristics of that handful? The handful that have durable advantage, right? Because you look back at 2000 and it\u2019s like, man, what would you have used to try to pick out Google and Amazon, right?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah.\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>And I\u2019m not saying that\u2019s the best comparator, but within the many just avalanche of AI companies, which are those that you think have durable advantage? I mean, of course, some of the name brand labs come to mind. Maybe they become the interface for everything else, who knows? But how would you answer that in terms of either shared characteristics or actual names? What sets apart the handful that you think will make it?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. I mean, I think the core labs will be around for a while, so that\u2019s OpenAI, Anthropic, Google, barring some accident or disaster or some blowup, but it seems like they\u2019re in a relatable spot. And to your point on market structure, I wrote a Substack post, I don\u2019t know, three years ago or something predicting that that would probably be an oligopoly market and there\u2019d be a handful and then be aligned with the clouds, and that\u2019s roughly what happened. I mean, there\u2019s Meta and there\u2019s xAI and there\u2019s other players that may change this. It didn\u2019t exist when I wrote that post. But it feels to me like in the short run, that\u2019s an oligopoly. There\u2019s no reason for that to be a monopoly market, unless one of them pulls ahead so much on capabilities that it just becomes the default for everyone. And that could happen, but so far it hasn\u2019t. And again, this compute constraint may prevent that in the short run or at least provide an asset to it on it.<\/p>\n<p>As you move up the stack and you say, \u201cWell, there\u2019s different application companies, there\u2019s Harvey for legal, there\u2019s Abridge for health, there\u2019s Decagon and Sierra for customer success.\u201d There\u2019s these different companies per application. There\u2019s three or four lenses that you can look at. One is if the underlying model gets better, does your product or service get dramatically better for your customers in a way that they still want to keep using you?<\/p>\n<p>Second, how deep and broad are you going from a product perspective? Are you building out multiple products? Are they all integrated in a cohesive whole? Is it really being built directly into the processes in a company and a way that it\u2019s hard to pull out? Often the issue for companies and adoption of AI isn\u2019t how good is the AI, it\u2019s how much do I have to change the workflows and the ways that my people do things in order to adopt it? It\u2019s about change management, usually. It\u2019s not about technology.<\/p>\n<p>And so if you\u2019ve been able to embed yourself enough into workflows and how people do business and how they work and how everything else ties together, that tends to be quite durable. Are you capturing and storing and using proprietary data? Sometimes it\u2019s useful. I think data modes in general are overstated, but I think sometimes it can be actually quite useful and that\u2019s usually the system of record view of the world. There\u2019s a handful of criteria around, will this thing be long-term defensible or not? And at the application level, that\u2019s often one potential lens on it.<\/p>\n<p><strong>Tim Ferriss: <\/strong>So, question, if people are listening to this and they are in the position of perhaps a founder who should consider identifying their short period of maximum valuation and perhaps hitting the parachute in some way, what are the options? Because I think of some of these companies, I\u2019m not going to name them, but there are multiple companies that have multi-billion dollar valuations. There seems to be, again, from a mostly layperson perspective, i.e. me, that the labs probably can build what they are currently selling without too much trouble. Do they aim to be acquired by a lab, in which case there\u2019s a build versus buy decision for the lab itself? Are they aiming for one of, not the OpenAIs or Anthropics, but maybe somebody who\u2019s trying to get more skin in the game like Amazon or fill in the blank. What are the exit options?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, I think there\u2019s a lot of exit options. And the thing that\u2019s crazy right now is if you go back 10 or 15 years, the biggest market cap in the world was 300 billion. And the biggest tech market cap was, I don\u2019t know, 200-ish or something. I think the biggest one at the time was Exxon or somebody 15 years ago. And over the last 10 or 15 years, what happens is we suddenly ended up with these multi-trillion dollar market caps, which everybody thought was nuts at the time, but things will probably only get bigger. There\u2019ll probably be more aggregation versus less into the biggest winners.<\/p>\n<p>There\u2019s more and more companies who have these market caps between say a hundred billion and a few trillion in a way that is unprecedented. And that means there\u2019s enormous buying power because one percent of three trillion is 30 billion. We can dilute one percent and pay $30 billion for something, which is insane. That\u2019s truly unprecedented. And that means that these really big acquisitions can happen.<\/p>\n<p><strong>Tim Ferriss: <\/strong>For the companies that I\u2019m imagining, again, I don\u2019t want to name names, that may have, seem to have a limited lifespan. When I\u2019m in these small group threads with friends of mine who are oftentimes, not always, but I\u2019m in a bunch of them. And when they\u2019re tech investors, very successful tech investors, and I\u2019m like, \u201cOkay, these five companies, you\u2019ve got 10 chips. How would you allocate your 10 chips?\u201d There\u2019s certain companies that can consistently get zero, even though they\u2019re reasonably well known. Why would one of the labs buy one of those?<\/p>\n<p><strong>Elad Gil: <\/strong>Depends on what it is. And it may be a lab, it may be one of the big tech incumbents and Apple, Amazon, Google\u2019s kind of both things. There\u2019s Oracle, there\u2019s Samsung, there\u2019s Tesla, there\u2019s SpaceX now in the market doing things that there\u2019s a bunch of different buyers of different types. There\u2019s Snowflake and Databricks. There\u2019s Stripe, Coinbase if you\u2019re doing financial service. There\u2019s just a ton of companies that actually are quite large, that\u2019s kind of the point. And so often you end up selling to one of four things, right? You can sell to one of the big labs or hyperscalers or giant tech companies.<\/p>\n<p>You can sell to somebody who cares a lot about your vertical. For example, a Thomson Reuters if you\u2019re doing legal or accounting or things that are kind of related to that. I mean, I think actually one thing that doesn\u2019t happen enough is merger of competitors, particularly private companies where you can do that because ultimately if your primary vector is winning and you\u2019re neck and neck with somebody and you\u2019re competing on every deal and you\u2019re destroying pricing for each other, maybe it\u2019s better to just merge. That actually was X.com and PayPal in the \u201990s. Elon Musk, Peter Thiel were running different companies and they merged because they said, \u201cWere there two people doing this? Why fight?\u201d<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. Or Uber, Lyft way back in the day. That might not have been a merger. It might have been an acquisition.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. And the rumor is that that almost happened and then the Uber side walked away from it, but all the money that Uber spent on fighting Lyft for all those years maybe would\u2019ve been better spent just buying them. Maybe not, I don\u2019t know the exact math on it. But often, it actually does make sense to say, \u201cYou know what? We\u2019ll just stop fighting it out and we\u2019ll just combine and just go win.\u201d Because if the primary purpose is to win the market, you\u2019re already fighting all these big incumbents that already exist anyhow, so why make it even harder?<\/p>\n<p><strong>Tim Ferriss: <\/strong>As you know, and we talk about this a lot, but we\u2019ll talk about you with your investing hat on. But before you even put that, let\u2019s call it full-time investing hat on, you had a lot in your background that may or may not have helped you. And I\u2019m curious, if you look at your biology background, the math background, do you think any of those things or other elements materially contributed to how you think about investing that has given you an advantage in, I suppose there are different stages to winning deals, but sometimes they\u2019re not crowded, but let\u2019s just talk about the selection, the selection process.<\/p>\n<p><strong>Elad Gil: <\/strong>The math stuff helped me, I think, in two ways. One is it\u2019s helped me with certain aspects of technical or algorithmic CS and understanding it, and sometimes it\u2019s useful in the context of how certain things work in AI or things like that or just fluency of numbers and data and I don\u2019t have to call it nerd language or something. And I did the math degree, honestly, just for fun. I think that\u2019s actually the thing that was helpful. I only did an undergrad degree in math, so I didn\u2019t go that far with it, but I did the very abstract pure math stuff. And I think that was a good forcing function of how to really think logically step by step about things because roughly the way that at least I learned how to do proofs was you do the logical sequence, but then sometimes you do these intuitive leaps and then go back and try and prove it to yourself, or flesh out the reasoning behind that intuitively. And I think sometimes investing is a little bit like that.<\/p>\n<p><strong>Tim Ferriss: <\/strong>When did you first have the inkling that you could be good at investing? And that could be investing writ large, it could be maybe within the context of our conversations, startups and angel investing. When did you first go, \u201cHuh, yeah, maybe I could be good at this\u201d? Was there a moment or a deal or anything like that that comes to mind?<\/p>\n<p><strong>Elad Gil: <\/strong>Not really. I\u2019m really hard on myself so even now I second guess myself a lot. Somebody was telling me that the two people that always beat themselves up the most in hindsight is me and this one other person who\u2019s another well-known founder\/investor. And so I don\u2019t think there\u2019s a single moment where I\u2019m like, \u201cWow, this makes sense for me to do.\u201d I think it just organically kept going because I was getting into some very strong companies, and then that allowed me to continue what I\u2019m doing.<strong> <\/strong>Yeah, I wish I had a moment like that.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Goddammit, you need to revise your genesis story like every good founder.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, ever since I was seven, I\u2019ve been thinking about investing in technology.<\/p>\n<p><strong>Tim Ferriss: <\/strong>So getting into those deals, what allowed you to get into those deals? Because some people have an informational advantage and they put themselves in a position to have an informational advantage. And I think that had I not \u2014 I don\u2019t want this to be a leading question, but it\u2019s like had I not moved to Silicon Valley when I did, 2000, and then subsequently stayed there, moved to San Francisco specifically, nothing that I was able to do in angel investing would\u2019ve been possible. But there\u2019s more to your story because a lot of people moved there with hopes of startup riches in whatever capacity. Not saying that that\u2019s why you moved there, but what was it that allowed you to get into those deals? There are certain things that come to mind based on our prior conversations, but I\u2019ll just leave it at that. Why were you able to get into or select those deals?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. I think there\u2019s what happened early and what happens now, and I think those two things are different. I think to your point, the single most important thing for anybody wanting to break into any industry is go to the headquarters or cluster of that industry. Move to wherever that thing is, and all the advice that you can do anything from anywhere and everything\u2019s remote is all BS. And you see that for every industry, not just tech.<\/p>\n<p>If you wanted to get into the movie business, people wouldn\u2019t say, \u201cHey, you can write a film script from anywhere, you can digitally score it from anywhere, you can edit it from anywhere, you can film it anywhere, go to Dallas and join their burgeoning film scene.\u201d<\/p>\n<p>They\u2019d say, \u201cGo to Hollywood.\u201d<\/p>\n<p>And if you want to do something in finance, and you\u2019re like, \u201cWell, you could raise money from anywhere and come up with trading strategies and a hedge fund strategy from anywhere and you could do it from anywhere.\u201d<\/p>\n<p>People would say, \u201cHey, go to whatever.\u201d Seattle, they\u2019d be like, \u201cGo to New York or go to X, Y, Z financial center.\u201d<\/p>\n<p>So the same is true for tech. And Shreyan on my team has been performing this sort of unicorn analysis of where is all the private market cap aggregating for technology. And traditionally, about half of it\u2019s been the US and then half of that has been the Bay Area. But with AI, 91 percent of private technology market cap is the Bay Area, 91 percent of the entire global set of AI market cap is all in one 10 by 10 area. If you want to do stuff in AI, you should probably be in the Bay Area. Probably the secondary place is New York, and then after that, it just drops off a cliff, and really it\u2019s the Bay Area. If you want to do defense tech, you probably should be in Southern California, close to where SpaceX and Anduril are, and Irvine, Orange County, et cetera, or El Segundo. There\u2019s a lot of startups there. If you want to do FinTech and crypto, maybe it\u2019s New York.<\/p>\n<p>But the reality is these are very strong clusters. To your point, number one, is I was just in the right location. I was in the right networks and a default was, I was running a startup myself. I was at Google for many years, and then I left to start a company. People just started coming to me for advice. The way I ended up investing in Airbnb is I was helping them when there were eight people or something, raise their Series A, and introduced them to a bunch of people and help with some of the strategy there in very light ways. They would\u2019ve done it without me. They said, \u201cHey, at the end of it, do you want to invest a little bit?\u201d I said, \u201cGreat, that sounds wonderful.\u201d This was very organic.<\/p>\n<p>Or the way I invested in Stripe is I had sold an infrastructure, early API company to Twitter. When Twitter was, say, 90 people or so, and I sent an email to Patrick, the CEO of Stripe, just saying, \u201cHey, I\u2019ve heard great things about you and I really like what Stripe is doing and I would use it for my own startup. I sold this API company myself. Do you want to just talk about this stuff?\u201d I went on a couple walks and then a week or two later he texted me and he\u2019s like, \u201cHey, we\u2019re doing a round. Do you want to invest?\u201d The first few things that I did were very organic where the founders were like, \u201cI want you on board.\u201d<\/p>\n<p>I didn\u2019t think, \u201cOh, I should be an investor and I\u2019m going to chase things.\u201d I just really liked talking to smart people and I liked working on certain business problems, and I love technology and it\u2019s translation to the world. I was just a nerd and I met other nerds and we hit it off. It\u2019s the early story for me.<\/p>\n<p><strong>Tim Ferriss: <\/strong>But it just struck me that I\u2019m sure people have heard, or I\u2019m sure you\u2019ve heard this before, but if you want money, ask for advice. If you want advice, ask for money. It just struck me that it goes the other way around too. It\u2019s like, if you offer a bunch of advice, oftentimes you get to give money. If you try to give money, you might get solicited for advice.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, good point.<\/p>\n<p><strong>Tim Ferriss: <\/strong>When did you write the <em>High Growth Handbook<\/em>? When was that published?<\/p>\n<p><strong>Elad Gil: <\/strong>It\u2019s a while ago now. It\u2019s probably seven-ish years ago, something like that.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Seven years ago. All right. Yeah, we\u2019re going to come back to that in a minute because you were in the right place geographically speaking. You were in the center of the switchboard. Like you said, some of these initial standout investments came about very organically. What I\u2019d be curious to hear, because you also said yourself not too long ago that there\u2019s what I did then, there\u2019s what I did now. There\u2019s also what you did in between along the way. I\u2019m wondering, for instance, if you would still stand by this, this is from that first round interview I was mentioning.<\/p>\n<p>\u201cAs a general rule, when I make investments, it\u2019s market first and the strength of the team second,\u201d and there\u2019s more to it. But would you still agree with that?<\/p>\n<p><strong>Elad Gil: <\/strong>90 percent, yes. Every once in a while you meet somebody exceptional and you just back them or something maybe so early. I led the first round of Perplexity, the very, very first round. The way that came about was Aravind, the CEO, I think he pinged me on LinkedIn, literally. This was when nobody was doing anything in AI and he was an OpenAI engineer or a researcher. He\u2019s like, \u201cHey, I\u2019m at OpenAI,\u201d which nobody cares about at the time, \u201cI\u2019m thinking of doing something in AI. I heard that you\u2019re talking about this stuff and nobody else is talking about it. Can we meet up?\u201d<\/p>\n<p>We just started meeting every two weeks and brainstorming, and then that led to investing in that. That was a people first thing where he was just so good. Every time we talk, he\u2019d show up a week later with a thing that we discussed built. Who does that?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah, that\u2019s a good sign.<\/p>\n<p><strong>Elad Gil: <\/strong>It\u2019s so good. Or, the way I ended up investing in Anduril. Google shuts down Maven, which was their defense project. I think, \u201cWell, if the incumbents are going to do it, what a great place for startups to play.\u201d Because there\u2019s been a long history of the Silicon Valley and the defense industry. That\u2019s HP and that\u2019s a lot of the early brands. I was just looking for something or somebody to work on this area and it was very unpopular at the time. I ran into, I think it was Trae Stephens, who\u2019s one of the co-founders of Anduril, who\u2019s also at Founders Fund, at some blunch or something else. Again, right city to be in.<\/p>\n<p>He said, \u201cOh, I\u2019m working on this new defense thing.\u201d I said, \u201cAmazing. Let\u2019s talk about it.\u201d It was very just looking, sometimes just looking for these things too in a market and sometimes it\u2019s people. Anduril was looking for a market and then finding amazing people. Perplexity was in between where it was like I was looking at everything in AI, because I thought it was going to be incredibly important, but not very many people were. Then I just ran across an exceptional individual, and that\u2019s when I funded OpenAI. That\u2019s when I funded Harvey, which is the early legal \u2014 I funded a lot of really early stuff because they were the only people doing anything in this market that I thought would be really important.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Let me come back to a few things you said. You mentioned the Perplexity founder, or later the founder who said you\u2019re talking about this stuff or heard or read or found you talking about this stuff, where was that? Was that post on your blog? Was it somewhere else? How did he actually find you talking about anything?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, I think he pinged me in part because I was involved with a bunch of the prior wave of technology companies, Airbnb, Stripe, Coinbase, Instacart, Square, a bunch of stuff like that. I think at that point I was already known as a founder and investor. But then on top of that, I was trolling AI researchers and just asking them about what\u2019s going on because it was so interesting. There was a bunch of art that was being done with these things called GANs at the time, these generative adversarial networks.<\/p>\n<p>I was playing around with that. I tried to hire engineers to build me effectively what\u2019s Midj ourney, because I just thought it\u2019d be really cool to make it easy to make AI art.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Let me pause for a second because this is my second question and it\u2019s a good time. When you mentioned AI, I thought it would be incredibly important. What were the indicators of that? What was the smoke in the distance where you\u2019re like, \u201cOh, that\u2019s an interesting direction.\u201d<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, I think there was two or three things. AI was one of those things that people have always talked about. When I was doing my math degree, I took a lot of theoretical CS classes and there were the early neural network classes and things like that and the math behind it. There\u2019s always this promise of building these artificial intelligences of different forms. One could argue Google was a first AI first company. Back then it was called machine learning and it was different technology basis in some sense. I think 2012 was when AlexNet came out and there\u2019s this proof that you can start scaling things and have really interesting characteristics in terms of how AI systems work.<\/p>\n<p>Then, 2017 is when Google, a team at Google invented the transformer architecture, which everything is based on now, or roughly everything. For example, if you look at GPT for ChatGPT, the T stands for transformer. Around 2020-ish I think was when GPT-3 came out, and that was such a big step from GPT-2. It still wasn\u2019t good enough to really do stuff with, but you\u2019re like, oh, shit, the scaling law papers are out. The step function and capabilities was huge. You suddenly have a generalizable model available via an API that anybody can ping. Just extrapolate that out to the next step and this is going to be really important.<\/p>\n<p>It\u2019s basically looking at that capability step and playing around with the technology, and then reading the scaling law papers, or just in general, the scaling law seem to work for everything. You\u2019re like, wow, this is going to be really, really important, so let me start getting involved with it.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Do you think you would have or could have done that without a mathematics background? I\u2019m guessing there were probably some other folks, but that leads me to the question of, how are you finding and ingesting that? Was it the talk of the town? It was in a sense within your social circles and the networks that you\u2019re a part of, it was a open discussion, so you were engaged with it. Or, are you ingesting vast quantities of information from different fields and this happened to be something that really caught your attention?<\/p>\n<p><strong>Elad Gil: <\/strong>I guess it\u2019s three things. I\u2019ve always ingested a lot of information from a lot of different fields just because I like learning about stuff. I was always this mix of math and biology and anime and art and other things. It was always a mix. Then it was something that my friends were talking about, but it was a bit more toy-like, \u201cOh, this is cool and look at what came out.\u201d But most people didn\u2019t then extrapolate. It\u2019s like early crypto or Bitcoin, everybody was talking about it, but very few people bought it. I think that was part of it. Then third, honestly, I just thought it was really neat stuff that I kept playing around with.<\/p>\n<p>This is back to the GAN stuff and the art where these different models would come out and you could mess around with them. One of the things that\u2019s really underdiscussed in terms of the importance of it relative to this wave of foundation models and AI and everything else is, the way AI or machine learning used to work is your team at a company or wherever else would go and there\u2019d be what\u2019s known as an MLOps team. An operations team whose whole thing was helping you set up all the data and the pipelines and everything to train a model. You train a model that was accustomed to your use case and what you were trying to accomplish.<\/p>\n<p>Then you had to build a bunch of internal services to interact with that model. It was a huge pain to get to the point where you had a working ML system up and running in production. Then suddenly, you have a thing where you just do an API call. With a line of code or a few lines of code, anybody anywhere in the world can ping it, but not just that, it\u2019s generalizable. It\u2019s not just specialized to one use case, like spell correction or whatever. You can use it for anything and it has all of the internet embedded in it in some sense, in terms of the knowledge base. It can start having these advanced reasoning capabilities. But one of the most important things is, hey, you can get it with a couple lines of code.<\/p>\n<p>You don\u2019t have to go and build an MLOps team. You don\u2019t have to host it. You don\u2019t have to interact with it. You don\u2019t have to do all this extra stuff. It just works. That\u2019s really important.<\/p>\n<p><strong>Tim Ferriss:<\/strong> It\u2019s huge. Yeah, it\u2019s hard to overstate.<\/p>\n<p>I have a million questions for you. The problem with this is the embarrassment of riches of directions that we could go. I am using, in my team, Claude Code and assorted tools for all sorts of stuff right now. One of them, it just so happens, overlaps with an area of great skill for you and experience, which is angel investing. This is the first time where I feel really enabled to do, and there is some manual effort involved, as you might imagine, but to go back and do an analysis of 20 years of angel investing to try to do any number of things. I suspect that a lot of what interests me is not particularly useful, like doing some counter-factuals.<\/p>\n<p>What if I had held each of these for three years, for five years, for whatever? That\u2019s like just Opus Dei, whipping myself in the back for the most part. But in doing an analysis like that, there are certain things that immediately come to mind for me that might be of interest. I want to hear what you would do if you would even do this. Part of it is, frankly, just curiosity. Are the stories I tell myself about this true or not? I\u2019m interested, like who made certain introductions? Are there certain people who just took me there, basically people in hospice care, and shipped them over as a last ditch effort? Are there people who actually sent me good stuff consistently, et cetera?<\/p>\n<p>There are a million and one ways I could try to interrogate the data and enrich it. We\u2019re doing a pretty good job of enriching it, Claude and other tools. OpenAI is very good at this. What are some of the more interesting questions or lines of examination you think looking back, like whatever it is, in my case, it\u2019s about roughly 20 years of stuff.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. The weird thing I\u2019ve been doing is uploading pictures of founders and asking the models to predict if they\u2019d be good founders.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Oh, wow.<\/p>\n<p><strong>Elad Gil: <\/strong>Because if you think about it, we do this all the time when we meet people. We quickly try to create an assessment of that person, their personality and what they\u2019re like. There\u2019s all these micro features. Do you have crow\u2019s feet by your eyes which suggests that your smiles are genuine? What does that imply about the sense of humor you have? Or, have you furrowed your brow over time and what does that mean? There\u2019s all these micro features. When you meet people, you actually can get a pretty quick impression of them pretty fast. It doesn\u2019t mean it\u2019s correct, right? But we actually do this really fast as people.<\/p>\n<p>I have this whole set of prompts that I\u2019ve been messing around with just for fun around, can you extrapolate a person\u2019s personality based off of a few images? Therefore, can you be predictive about their behavior in any way? I think that\u2019s fun, right?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. You\u2019re finding any signal there or not? TBD?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, it actually works pretty well. I\u2019ve been doing weird shirt, right?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Right, practice smiling people.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, yeah. No, but I think it\u2019s interesting because we do this all the time where we read people and that\u2019s part of the prompt. It\u2019s like you\u2019re a very good cold reader of people based on micro features and et cetera, spell it out. Then based on that, not only you give me your interpretation of this person, but explain the specific micro features for each thing that you\u2019re stating about the person, and it will break it down for you. It\u2019s amazing. Imagine what this technology is. It\u2019s crazy. Again, I\u2019m not saying it\u2019s fully accurate and I\u2019m not saying it\u2019ll be predictive, but it\u2019s done pretty well in terms of nailing people.<\/p>\n<p>It\u2019s even done things like, \u201cOh, this person probably has this type of sense of humor.\u201d Or, \u201cThis person probably holds themselves back in most social settings and then chimes in with a witty wry thing that nobody expects or whatever.\u201d It\u2019s very specific.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Very specific.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. It\u2019s amazing. I\u2019ve been doing stuff like that, which may not be your question, but I\u2019ve been finding it really fun.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Well, it\u2019s related in the sense that, and I\u2019m sure I\u2019m missing some steps, but I love angel investing and the dose makes the poison, so there\u2019s usually a case to be made when I get to a certain threshold. I\u2019m like, \u201cOkay, this isn\u2019t fun anymore.\u201d I love dark chocolate too, but I don\u2019t want just to be force-fed dark chocolate all day. I\u2019ve talked about this, but I really do enjoy the learning and the sport of it, frankly, and interacting with some very, very smart people. Not all of them work out as far as founders of companies, but ultimately, I\u2019m trying to figure out how to separate signal from noise.<\/p>\n<p>Also, it\u2019s fun to try to use anything, but in this case investing, to sharpen your own thinking and to stress test your own beliefs and the assumptions that undergird some of your predictions, things like that. I\u2019m just wondering if you\u2019ve ever done a retrospective analysis of your startup investing or if you\u2019re like, \u201cNo, more Marc Andreessen style, only forward.\u201d<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. Early on when I was first starting to invest, I would have this long grid of things by which I would score each company, and then I\u2019d go back and see if it was correct. It was roughly correct. I think the hard part is there\u2019s a lot of randomness in outcomes. There\u2019s the company that sells for a few billion dollars that you thought was dead or whatever it is, right?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Sure.<\/p>\n<p><strong>Elad Gil: <\/strong>How do you score things like that? It\u2019s like, well, right now we\u2019re in this really weird market moment where trillions of dollars of market cap are all chasing the same prize. They\u2019re going to do all sorts of stuff that wouldn\u2019t happen normally. It\u2019s really hard to account for that kind of thing relative to all this. I\u2019m much more in the Marc Andreessen camp of, I think very little about the past. I think close to zero about my own past, I just am like, \u201cLet\u2019s keep going.\u201d Maybe that\u2019s bad and there should be dramatically more self-reflection.<\/p>\n<p>I try to self-reflect in the moment, but I don\u2019t try to re-extrapolate and examine my entire life and decisions. If anything, most of the decisions have been ones where I\u2019m really upset with myself for not being more aggressive on something. In other words, I\u2019ve invested in the company, but I should have tried even harder to invest more even if I tried really, really hard because there\u2019s a handful of companies that really matter, and that\u2019s all that matters as an investor. Obviously, as a person, I enjoy getting involved with different companies and different founders and helping them whether the thing works or not, or I think the technology\u2019s interesting or whatever.<\/p>\n<p>But the realities from a returns perspective, there\u2019s a very clear power law that people talk about and it\u2019s true. I remember a friend of mine did this analysis, I think it may have been Yuri Milner or someone where it\u2019s like, look at all the companies from, I don\u2019t remember the exact dates, 2000 or 2004 until today in technology. It was something like a hundred companies drove like 90 something percent of all the returns and 10 companies total drove 80 percent of all returns over a two decade period in technology. If you weren\u2019t in that 10 companies, you were a bad investor. Once you start dealing with these power laws and these outsize outcomes and all of that, how can you rate that?<\/p>\n<p>It\u2019s basically, did you hit one of 10 things or not? That\u2019s really the rating. That\u2019s probably the correct rating for investment.<\/p>\n<p><strong>Tim Ferriss: <\/strong>I\u2019d love to try to focus on some early-ish decisions on this podcast, right? Because like you said, they\u2019re the earlier decisions. There\u2019s how you did things then, there are how you\u2019re doing things now, which isn\u2019t to say that one is better than the other, but certainly what you do in the past tends to inform what you\u2019re able to do and what you do in the present. What I\u2019m curious about, and we won\u2019t spend a ton of time on this, but it might be interesting to folks, is to discuss when you moved from purely doing angel investing yourself to involving other investors in your deals, right?<\/p>\n<p>There are multiple ways to do this, but the reason I want to ask this is because you did a number of SPVs, I\u2019ll explain what that is, special purpose vehicle, but for folks, you might be familiar with venture capital firm. They have funds and they raise, let\u2019s just call it $100 million for a fund. It can be more or less, of course. Then they invest in a bunch of different companies and then you see who wins, who lose, and then if there are profits, like I guess conventionally, let\u2019s just use the textbook example, the venture capital firm takes 20 percent of the upside and then the LPs, the investors get 80 percent.<\/p>\n<p>The venture capital firm takes a management fee to keep the lights on, although it usually does a lot more than keep the lights on. With the SPVs, you\u2019re investing in, let\u2019s just say, for simplicity, a single company, right? There are advantages to that in simplicity, for somebody who\u2019s putting together, the SPV, but you also have a lot of reputational risk, because if you have a fund, you have a couple of losers. Your investors don\u2019t automatically go to zero, but if you have an SPV and it goes to zero, that could really hurt you reputationally.<\/p>\n<p>When I look at some of your early SPVs, which I think included, certainly, a number of name brands like Instacart and so on, how did you choose which companies to do the SPVs with? Because that seems like a very important set of decisions to lay the groundwork for creating optionality for what you do after that.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. I think to your point, I\u2019ve always been terrified of losing other people\u2019s money. I\u2019m fine if I lose my own money. It\u2019s my decision. I\u2019m an adult. It\u2019s okay, but I\u2019ve always been \u2014 people giving money are adults or institutions, et cetera, to invest on their behalf. But similarly there, I was just terrified of ever losing money for people. I\u2019ve tried over time to be judicious behind the SPVs that I did early on and the focus was on things that I thought would really be outsized companies. That was, to your point, Instacart, it was early Stripe, it was Coinbase, it was a couple things like that that were amongst my very first SPVs.<\/p>\n<p>The emphasis was very much on, do I think this can be a massive thing? Also, do I think there\u2019s enough downside protection in some sense that even if it didn\u2019t work as well as I thought it would still be a good outcome for people. Yeah, I try to do that very diligently. It\u2019s interesting because a lot of people ping me for help as they think about becoming investors or they\u2019re scouts for a fund, which means basically they\u2019re given a small amount of money by a venture capital fund. Sequoia famously has this program, they give people money and then those people invest money on their behalf. Some of the scouts that I\u2019ve talked to basically treat it like free money or an option.<\/p>\n<p>They\u2019re just like, \u201cOh, just throw out a bunch of stuff, maybe something works.\u201d I\u2019ve pointed out to them, \u201cHey, if you actually want to become a professional investor at some point, this is your track record.\u201d A, you\u2019re a fiduciary in some sense, and maybe I\u2019ll be more careful from that perspective, but B, this will establish your track record, and do you want to have a good one or a bad one? How do you think about that? Again, sometimes people just get lucky and they hit the one thing out of a hundred, but that more than returns everything and they look great. But it\u2019s hard to be consistently good at this stuff or consistently hit great companies.<\/p>\n<p><strong>Tim Ferriss: <\/strong>All right. I want to double click on a few things you said and maybe you could walk us through a pseudonymous example. It doesn\u2019t need to be a named company, but when you\u2019re talking about setting your track record, you did an excellent job of that before you then went on later to raise funds and so on. I would love you to perhaps explain some of the things you do in diligence or how you weight things differently and also how you think about the capped minimum downside. I\u2019m not sure that\u2019s the exact wording that you used in selecting those deals, because you could have selected any number of deals on a due diligence level. What\u2019s the kind of stuff that you focus on maybe more than others and what are the things you pay less attention to than others?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, I think there\u2019s a big difference between early and late things. On the early side, to the point earlier, I tend to spend a lot more time on the market than most early stage investors. Most early stage investors say, \u201cI just care about the team and how good are they?\u201d But I\u2019ve seen great teams crushed by terrible markets and I\u2019ve seen reasonably crappy teams do very well. At this point, I think the market is more important, although I think obviously great teams can find their way if they decide to shift around a bit. I index a lot on market early and that may be customer calls, that maybe is trying to understand, do I think something could be big?<\/p>\n<p>It could just be some intuition around, \u201cHey, defense is really important. Nobody\u2019s doing defense. Let me find a defense company.\u201d I tend to index a lot on that. Relatedly, I\u2019ve tended to avoid science projects. There are some people who get really distracted by, \u201cWow, this is really cool. It\u2019s quantum and it\u2019s this and it\u2019s that.\u201d I\u2019ve largely avoided those things. Sometimes I miss things that were really good, but often that was the right call. I actually think SPAC saved the hard tech and science-based investing industry because if you look at what happened basically at the market peak, a bunch of SPACs took a bunch of companies public that would not have been able to raise money in private markets later.<\/p>\n<p>They gave them enough money to keep going, but more importantly, they returned a bunch of money to these hard tech funds and that saved them from going under. It gave them all the returns. It was basically the SPAC era. Chamath basically saved hard tech. I mean that seriously, not tongue-in-cheek. I largely avoided that kind of class of companies. I\u2019m not saying I was smart. I would\u2019ve made money off of it. I just thought there was all capitalization issues and science risk and market risk and other things to them. For later stage stuff, the hard part often is everything on paper gets modeled out for a late stage company as a two to three X from that investment point.<\/p>\n<p>Because all the funds that are driving the rounds underwrite against some IRR clock, 25 percent IRR, whatever it is. They all come up with these models and then the models all say all these companies are basically going to two to three X. The art there or the science there, whatever you want to call it is, is that a 0.5 X company? Is it going to drop in value or is that a 10X? How do you know it\u2019s a 10X versus a two to three X versus a 0.5? That\u2019s the harder part of growth investing. There\u2019s a subset of things that you\u2019re like, this thing will just keep going and here\u2019s why.<\/p>\n<p>But often it\u2019s not mathematical. Often that\u2019s just like some market dynamic or some key core insight or some market share question. People tend to make that stuff really complicated and they have these really complicated multi-page models and 50-page memos and all the rest. Often these things boil down to one single question. What is the one thing I need to believe about this company that makes me think it\u2019s going to continue to be really big? If it\u2019s three things, it\u2019s too complicated, it\u2019s probably not going to work. If it\u2019s no things, then it doesn\u2019t make much sense. Usually, there\u2019s one or two things that are really the core insights you need to understand the outcome for something.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Can you give an example of one of those beliefs for any company that comes to mind?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, I\u2019ll give you two or three of them. I mean, Coinbase, part of it was just, hey, this is an index on crypto and crypto will keep growing. Because if Coinbase trades every main cryptocurrency and they take a cut of every transaction and they have enough volume, they\u2019ve effectively bought a basket of every cryptocurrency by investing in Coinbase. That was the premise there. Stripe, they\u2019re an index on e-commerce and e-commerce will keep growing. Back then, now it\u2019s much more complex and there\u2019s all sorts of great drivers of its performance. Anduril was, hey, machine vision and drones are going to be important, AI and drones are going to be important for defense.<\/p>\n<p><strong>Tim Ferriss: <\/strong>That\u2019s it.<\/p>\n<p><strong>Elad Gil: <\/strong>I mean, it was more complicated than that. I\u2019m just saying.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Right, right. Well, that was it for the belief, the core belief.<\/p>\n<p><strong>Elad Gil: <\/strong>There was cost-plus model versus hardware margin. Anduril actually had four or five things that were important there, that were kind of like a checklist for a defense tech company. But for a lot of the other ones, it was like e-commerce is good.<\/p>\n<p><strong>Tim Ferriss: <\/strong>This is probably too inside baseball, but what were the stages of the companies that you mentioned when you created the SPVs? Roughly. Yeah.<\/p>\n<p><strong>Elad Gil: <\/strong>Stripe. Well, I first invested in Stripe when it was eight people, and then I kept following on. And I ran out of my own money, frankly, and that\u2019s when I started doing SPVs. So I think I did my first SPV in Stripe around the Series C-ish, somewhere around there, something like that.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Got it. And were the others more or less similar-ish, Instacart, et cetera?<\/p>\n<p><strong>Elad Gil: <\/strong>It was probably roughly in that ballpark, C, D, that range. I didn\u2019t have funds and everything else, and I was putting as much as I could personally into these things, both earlier, but honestly, I just kept going when I could.<\/p>\n<p><strong>Tim Ferriss: <\/strong>When you\u2019re looking at trying to determine if something is a 0.5X or a 10X, in addition to the core belief, what are other layers of due diligence that you bring to bear on trying to ascertain that, where something falls on that spectrum?<\/p>\n<p><strong>Elad Gil: <\/strong>Oh, I mean, I do enormous due diligence. So meet with the CFO multiple times, walk through all the financials, walk through the financial model, walk through customers, call customers, look at executive team. It\u2019s a bunch of stuff. My fund is the only one I know that actually does cash reconciliations, where we\u2019ll go through and do a cash audit to look at cash flows for later stage things. So I do enormous diligence, because I want to make sure I\u2019m not doing something inappropriate. But the flip side of it is, most of it just collapses into what\u2019s the one thing. So when I work with a company, I actually try to be very fast and straightforward on the diligence in terms of saying, let\u2019s just talk about, A, we need to just make sure financials are correct and there\u2019s the basics, but let\u2019s collapse it down into one or two core questions that help us understand if this thing will keep going. Not, here\u2019s 30 pages of questions that don\u2019t matter, right?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Right.<\/p>\n<p><strong>Elad Gil: <\/strong>Which is what a lot of people, they\u2019re like, hey, we need to know the secondary cohort on this fucking thing that\u2019s like a tiny product, that who cares. They just waste time. They waste the finder\u2019s time, the team\u2019s time. And I try very, very hard not to do that. As a former entrepreneur myself, I know how precious the time is and I know how annoying those questions are.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Well, I was actually going to, at one point, ask you about this, but we don\u2019t need to spend too much time on it. You have a post, this is from a while back, 2011, listing questions a VC will ask a startup. You omitted some of the questions, like the one that you just mentioned, but I am curious if any of these questions or additional questions come to mind when you are talking to founders, could be early stage or later stage, that you actually apply yourself. And I know it\u2019s from 2011, so I\u2019m not expecting you to remember the post itself.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. I haven\u2019t looked at that post in a really long time. I\u2019m actually writing another book now that is sort of the zero to one startup phase, and it gets into some questions like that. I think the reality is venture capital has changed dramatically since I wrote that post, because in 2011, the venture capital funds were largely doing seeds through Series D, E maybe, and then companies would go public. And this whole 20-year private company thing didn\u2019t exist. And so a lot of \u2014 do you know why there\u2019s a four-year vest on stock?<\/p>\n<p><strong>Tim Ferriss: <\/strong>No. Why is that? I can kind of guess now that we\u2019re talking about IPOs, but go ahead. Why?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. In the 1970s, they came up with a four-year vest on stock options for employees because companies would go public within four years. And so then you\u2019re done. Literally, right? And so it was like a four-year clock usually. And then when Google took six years to go public, everybody\u2019s like, oh my gosh, it took them so long to go public, six years. They just sat on their hands. Do you know what I mean?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah.<\/p>\n<p><strong>Elad Gil: <\/strong>Literally, people would say that. And so what happened is venture capital used to be very early stage, and then what we now call growth investing was public market investing. That was the stuff that people in the public markets would do after four or five years of a company\u2019s life. And so public markets used to be involved very early. And then as Sarbanes-Oxley came out and companies decided they didn\u2019t want to go public and there was more private capital available, the timeline until going public stretched out. And so suddenly venture capital firms were doing all the growth investing that used to be public market investing. And in 2011, that really wasn\u2019t happening much. It was kind of Yuri Milner from DST and a few other folks, but it wasn\u2019t that much of an industry. And so the nature of venture capital shifted radically over the last 15 years. And that means that those questions that I listed there didn\u2019t include what I\u2019d consider more growth-centric questions because there wasn\u2019t a lot of growth investing in venture. Venture \u2014\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>What would be examples of growth-centric questions?<\/p>\n<p><strong>Elad Gil: <\/strong>Honestly, it would overlap with some of the earlier stages, but it would be much more \u2014 by the time you hit a very late stage, it\u2019s very financially driven. And so often what, at least I and my team look at is, what is just the core business and how do we extrapolate that going? And then what are these ancillary things that the company\u2019s doing that are almost like options in the future that may or may not come through? And so usually we base our investment on that core. Can they just keep doing the thing they\u2019re doing forever? Because most companies mainly get big off of one thing. At least for the first decade. There\u2019s very few companies that have multiple things that all work.<\/p>\n<p>Usually it\u2019s one thing, and then 10 years later you maybe come up with a second thing that really works, like Google Cloud for Google, although obviously there\u2019s YouTube and there\u2019s a bunch of other stuff, and Waymo and all these interesting things now. But it took a while. For a long time it was just search, search and ads. But then sometimes there are these extra things that are potential really interesting drivers on a business. That SpaceX was launch and then it became satellite, right, it became Starlink.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah, man. Starlink, what a thing. It\u2019s too bad I have so much tree cover here, I can\u2019t use it anywhere I spend time. But let\u2019s turn to the <em>High Growth Handbook<\/em> for a second. So that was, let\u2019s just call it seven-ish years ago. It is an outstanding book, people should really check it out. I mean, especially if you\u2019re playing in the venture back game. What\u2019s the subtitle? The subtitle is, <em>Scaling Startups from 10 to 10,000 People<\/em>. There\u2019s a lot of good advice in this book. I wanted to ask you if there\u2019s anything in this book that you wish startup founders, the book was intended for, would pay more attention to? Or if there\u2019s anything that you would add or expand to the book?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. So when I wrote the book, I had an outline for it that was two, three times the length of the actual book in terms of chapter. So there\u2019s a lot of stuff I didn\u2019t write about, sales and marketing and growth and a bunch of other stuff. But the book was basically written as a tactical guide, it wasn\u2019t meant to be read it from start to finish. There\u2019s a bunch of interviews with different people who are, I think, amongst the best practitioners in the world at those areas. But fundamentally, it was meant to be more like, you\u2019re suddenly involved with the M&amp;A, jump to the chapter and read that, and then put it aside until something else comes up around hiring that you need to look at or whatever. And so it really is meant to be a handbook or guide or companion to a founder versus, hey, I\u2019m just going to read it start to finish. And there\u2019ll be some pithy quotes in it or whatever.<\/p>\n<p>Or one concept over 500 pages. I try to avoid stuff like that. So it\u2019s very tactical, it\u2019s very tangible, it\u2019s very specific. And this new book that I\u2019m working on is basically the zero to one version of that. It\u2019s like, how do you hire your first five employees as a startup? Somebody tries to buy you, what do you do? How do you raise your first round of funding? It\u2019s that kind of stuff. So it\u2019s kind of like the zero to one tactical guide.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Let me ask you about one specific section. I think this is chapter two, this is on boards. And if this is getting too in the weeds, tell me, we can hop to something else. But I am curious if you could talk about \u2014 there are two things. Take a better board member over a slightly higher valuation, and if you want to revise these, that\u2019s fine too. But there are two things \u2014\u00a0<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah.<\/p>\n<p><strong>Tim Ferriss: <\/strong>\u2014 I\u2019d love to hear you talk about, just because this is something that founders I\u2019ve been involved with bump up against constantly. Take a better board member over a slightly higher valuation and then write a board member job spec. And then specifically for independence maybe, I\u2019d love to hear you maybe just elaborate. But could you speak to either or both of those a bit? And if you want to take it a different direction, I mean, it\u2019s really just boards writ large.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. So I think when founders pull together boards, often the early boards are investors because the investors ask for a board seat as part of it or as part of the investment. And sometimes the founders want somebody on board who\u2019s really committed to the company and will help out extra. And to some extent when somebody takes a board seat, it really means, or it should mean that they\u2019re all in to help you, right, versus you can have lots and lots of investors but have very few board members. Reid Hoffman has this thing, which is a board member at its best is a co-founder that you wouldn\u2019t be able to hire otherwise. And so you bring them onto your board and it\u2019s somebody that you want to spend more time with on specific issues related to the company. Fundamentally, your board should be able to help with different areas of the company.<\/p>\n<p>It could be strategic direction, it could be closing candidates, it could be product areas, it could be customer intros, it could be a variety of things. And usually, you want to think of your board members as a portfolio of people. It\u2019s going to change between an early stage company and a late stage in a public one, you\u2019ll need different types of people over time usually. But most companies are very reactive on their board versus proactive. And so, they tend to end up with a couple investors and then they add somebody from an industry seat and they don\u2019t really think through who they want and why. And if your co-founder is kind of like your spouse, your work spouse, your work husband or your work wife, your board members are like your in laws. You have to see them at Thanksgiving and you have to chat with them all the time.<\/p>\n<p>And so, hopefully you have somebody you want to see all the time and who\u2019s helpful and wonderful. And the bad version is like, ugh, it\u2019s the father-in-law or mother-in-law who\u2019s always berating you or whatever. And so you kind of need to find the right person. And it\u2019s for many, many years, right, you end up sometimes with people on your board for a decade, and if they\u2019re an investor, you can\u2019t get rid of them. You literally can\u2019t fire this person, because they have a contractual ability to be on your board because of the investment. So that\u2019s why it\u2019s really important to figure out the right person, and that\u2019s back to valuation.\u00a0<\/p>\n<p>Sometimes founders will take a better price from a worse person because it\u2019s a better price. And our mutual friend, Naval, has this great quote that \u201cvaluation is temporary, but control is forever.\u201d<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah.<\/p>\n<p><strong>Elad Gil: <\/strong>Very Naval, right?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Very Naval.<\/p>\n<p><strong>Elad Gil: <\/strong>And I think that\u2019s very true. And so if you\u2019re choosing a board member and part of that is a control thing, right, people who control the board can in some cases fire the CEO, you really want to choose the right people. And maybe take a worse price for somebody who\u2019s really going to be helpful and they\u2019re minimally non-destructive, and hope you get to have around for 10 years.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Any other books or resources for people who, outside of the <em>High Growth Handbook<\/em>, who specifically want to learn about boards, recruiting, incentivizing the co-founders that you couldn\u2019t hire to join the board, et cetera, et cetera? Any particular approach you would take there if they wanted to get more conversant?<\/p>\n<p><strong>Elad Gil: <\/strong>I don\u2019t have anything super useful there. I think the best thing is to call other founders, other people who\u2019ve added people to their board, and see how they approached it. I do think writing up a job spec, you write a job spec for everything else in your company, why wouldn\u2019t you write one for a board member? So it\u2019s good to write that up and say, what am I actually looking for and why? And what am I optimizing for? So there\u2019s a common view of that. You can use search firms, you can ask people, you can target people that you know. If you have angel investors, getting to know them is a great way to see if you want to add one of them eventually to your board. That\u2019s what we did at Color, we eventually added Sue Wagner, who was a co-founder of BlackRock onto our board.<\/p>\n<p>Her other board seats were Apple, BlackRock, and Swiss Re when she joined our board. But I just got to know her through just, she invested and we just started working together, and really enjoyed her feedback and insights. And so we added her to the board there. So it\u2019s kind of like that, you kind of want to maybe get to know some people.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Next, I want to come to our \u2014 we were joking earlier about the, in some case, sort of revisionist history genesis stories.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah.<\/p>\n<p><strong>Tim Ferriss: <\/strong>So I\u2019m looking at, this is from 2018, this is a while back, this is on Y Combinator\u2019s blog and you\u2019re being interviewed about the <em>High Growth Handbook<\/em>. But the sort of end of this piece that I\u2019m looking at says, \u201cThese stories are never told. People always say, \u2018Oh, these things just grow organically and isn\u2019t it amazing.\u2019\u201d But almost every company that ended up tens of billions or hundreds of billions in markcap did this, which is taking an aggressive approach to distribution, whether that\u2019s sort of Google and the Firefox story, or Facebook running ads against people\u2019s names in Europe. I just wanted to hear you tell some of these stories, because it is the stuff that kind of conveniently that gets left out of TED Talks later. Do you know what I mean?<\/p>\n<p><strong>Elad Gil: <\/strong>Oh yeah, yeah. I mean, actually the origin stories for founders is always like, \u201cEver since Sarah was three years old, she dreamed of starting an accounting software firm.\u201d Come on. You know what I mean?<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. Yeah.<\/p>\n<p><strong>Elad Gil: <\/strong>It\u2019s so ridiculous. And so a lot of the stories that are told about founders are very revisionist and they make it the life\u2019s passion of this \u2014 sometimes it really is. But you\u2019re like, no, when they were five they did not collect things and then that turned into Pinterest 30 years later or whatever, they always dreamed of building AGI when they were four, and that\u2019s why Sam started OpenAI or whatever. So I think a lot of these things are very kind of ridiculous in terms of how they\u2019re written later.nd I think the product really, really matters and I think sometimes great product just wins. And the reason great product just wins is it opens up a form of distribution that didn\u2019t exist before or people will buy it despite the lack of distribution or relationships for a company.<\/p>\n<p>And the flip side of it is that the companies that are really good have an enormously good product engine, and then they have an amazing distribution engine. And sometimes that distribution engine is built into the product, that\u2019s like Cursor or Windsurf just distributing through product like growth where developers just find it and start using it and it helps them. And so they tell other developers and it spreads word of mouth. But often there\u2019s very aggressive sales, marketing, other components to it. And so for example, when I was at Google, they were spending hundreds of millions of dollars a year, which at the time was real money, on distributing search. And they had this little thing called the toolbar that would fit into a browser, because right now browsers, with Chrome, you type in words or whatever, and then it instantly searches it. Back then, the main browsers were Netscape and Internet Explorer, et cetera, and the browser bar thing didn\u2019t exist.<\/p>\n<p>And they had this little client app that you\u2019d install and they paid basically every company on the internet to cross download it. In other words, you\u2019re installing Adobe, you\u2019d be installing some malware detector thing, and it would always download the toolbar because they got paid to distribute it. So very aggressive distribution tactics. And to your point, Facebook buying ads against people\u2019s names in Europe \u2014\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>Can you explain that? What are they doing? Yeah. What was their end game?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. They were basically trying to create network liquidity in markets where they were earlier behind. And so they would basically buy ads of literally a person\u2019s name. And one of the most common queries is people searching themselves, and so you\u2019d be like, oh, let me look up Tim Ferriss on Google, or whatever. And there\u2019d be a Facebook ad saying, \u201cHey, Tim Ferriss on Facebook.\u201d And you\u2019d click and you\u2019d land on a signup flow for Facebook. This was years ago. This was TikTok and ByteDance, right, it was basically, they spent billions of dollars distributing TikTok so they could build enough of a network to train AI algorithms to start telling people what to do and also to get content creators on it.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Where did they spend that money on distribution in this case of, say, TikTok?<\/p>\n<p><strong>Elad Gil: <\/strong>My sense is, it\u2019s ads again.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah.<\/p>\n<p><strong>Elad Gil: <\/strong>But you kind of see this over and over again. I mean, for enterprise, Snowflake spent billions of dollars on salespeople and compensation and channel partnerships. So again, distribution is really important. And every once in a while you see a company that actually wins, not because of product, but because they\u2019re just better at sales and marketing and distribution. And often that\u2019s a bummer for technologists such as myself, because you\u2019re like, the best product should always win. And sometimes it does, but sometimes it\u2019s just who was early and developed a brand, or who got ahead on distribution.<\/p>\n<p><strong>Tim Ferriss: <\/strong>I\u2019m looking at a piece in front of me, this is from a while ago, but it\u2019s you discussing long-held dogma that ends up being unviable. So for instance, the common held belief after PayPal\u2019s sale to eBay that fraud will kill you in the payment space. And I\u2019m wondering how you orient yourself as an investor to stress test those types of dogma.<\/p>\n<p><strong>Elad Gil: <\/strong>It\u2019s really hard because you often end up, you start off with some set of beliefs, you think something\u2019s interesting and maybe you invest in it, maybe you start a company in it, and then it turns out the thing you think is really interesting turns out to be really hard and you get killed. And then five years later, a company comes up that actually does it and wins. And the question is, why? Why did the things suddenly work when it didn\u2019t before? Or there\u2019s 10 attempts to do X, and then suddenly is it the technology got good enough? It could be a regulatory change, it could be a market shift, it could be whatever. An example of that may be Harvey and legal, where selling to law firms traditionally has been awful. And Harvey\u2019s not much broader than that, they also have very strong enterprise adoption and lots of different people using them in different ways. But the dogma was always like, building stuff for law firms is crappy as a business and you should never do it.<\/p>\n<p>But what AI did is it shifted things from selling tools to selling work product, or selling units of labor. That\u2019s really the shift in generative AI. We\u2019re going from seats and we\u2019re going from software and SaaS and we\u2019re moving into a world where we\u2019re selling human labor equivalents. We\u2019re selling work hours or labor hours, or whatever you want to call it. It\u2019s of cognition. And so, Harvey is effectively helping really augment lawyers in different ways. And part of that\u2019s a knowledge corpus, but a lot of it is this tooling that really helps lawyers achieve the goals that they have in different ways, in a collaborative manner in some cases. And so it\u2019s just a fundamentally different type of product from what people were selling before. And so it opened up the market in a way that the market wasn\u2019t open before. There\u2019s actually a broader conversation around, is the world market limited or founder limited in terms of entrepreneurial success?<\/p>\n<p>The Y Combinator school of thought is that we just don\u2019t have enough founders. And if we had 10 times as many founders, we\u2019d have 10 times as many big companies. And there\u2019s an alternate school of thought, which is how many markets are actually open in any given moment in time. And those are the ones where you can build big companies. Because if the market isn\u2019t open to innovation or change or whatever or is undergoing a shift, you can\u2019t really build anything there anyhow, so why do it? And the striking thing about AI is it\u2019s opened up tons and tons of markets that were closed for a long time, and it\u2019s opened it up because of capabilities, but it\u2019s also opened it up because every CEO is asking themselves, what\u2019s my AI story? And so there\u2019s way more openness to try things than I\u2019ve ever seen in my life. And so we have this odd moment in time where things are massively available for founders to do new things.<\/p>\n<p>And if you\u2019re an AI company and you\u2019re not seeing explosive growth quickly, something\u2019s fundamentally broken, because the markets are so open that you can suddenly grow at a rate that you\u2019ve never grown before. There\u2019s always been cases of companies that just go like this. But again, you look at the ramps of OpenAI and Anthropic and it\u2019s the fastest ramps to tens of billions ever. It\u2019s like percentages of GDP, it\u2019s like crazy.<\/p>\n<p><strong>Tim Ferriss: <\/strong>So if we come back to your comment of not necessarily market first and strength of team second all the time, but like you said, you 90 percent agree with that. And if you have an excellent team in a terrible market, that\u2019s going to be a difficult one to execute. How do you determine what is a good versus great market? Or just, what is a great market? What do you look for? And the example you gave, I might be overreading this, but when you said, that when Google shut down, I think it was Maven, and then that\u2019s an interesting kind of event-based approach as an input to investing. Because you\u2019re like, okay, if they\u2019re not going to build it, that suddenly creates a playing field for startups to play in that space. So could you speak to more of how you determine or look for great markets?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah. I mean, there\u2019s a few different ways to think about it. One is, some people take the framework of, why now? What\u2019s shifted now that makes this suddenly an interesting market? Because people have been trying to do things for a long time in every market. And so that may be a regulatory shift. Samsara, the fleet management company benefited from the fact that suddenly there\u2019s regulation around needing end cap monitoring of drivers. So you had suddenly cameras watching people so they don\u2019t fall asleep while they\u2019re driving trucks on the road. So that was their entry point to then start building out a suite of software, but it was a regulatory shift. Sometimes there\u2019s technology shifts, like what\u2019s happening in AI.<\/p>\n<p>And the crazy thing about the AI shift is, the foundation models instantly plugged into a massive set of markets, which is basically all enterprise data and information and email and just all white collar work was suddenly available to AI, because it was the perfect for that. It also plugged into code, which is a type of white collar work. So it\u2019s just suddenly it just inserts into language and language is used everywhere in enterprises as well as in consumer, and so there\u2019s just a massive market to tap into and transform, or set of markets. Robotics is a little bit different from that because even if you had the world\u2019s best robotic model, the sub-markets that already have robotic hardware are quite small, on a relative basis. And so you don\u2019t have that instant runway that you would with language, unless you come up with something new there. That\u2019s kind of an aside. But I think robotics is really interesting and it\u2019ll be important, it\u2019s more just that nuance of what\u2019s the instant thing you plug into commercially.<\/p>\n<p>And then there\u2019s regulatory shifts, there\u2019s technology shifts, there\u2019s incumbency or company shifts, competitive shifts. A company may blow itself up, it may get bought by a competitor. One company I\u2019m excited about on the security side is called Infisical, and they\u2019re basically competing in part with Hashi. Hashi got bought by IBM. Anytime you get bought by IBM, you slow down a lot usually. So suddenly it creates more opportunity for a startup. So I just feel like there are these different things that can change in a given moment in time. It could be the market\u2019s growing really fast, it\u2019s Coinbase and crypto, you just have suddenly this adoption and proliferation of token types. So there\u2019s lots and lots and lots of different markets that are interesting. The commonality is usually like, is it also big?\u00a0<\/p>\n<p>Is there a big enough TAM? And there\u2019s two types of TAMs. There\u2019s fake TAM.<\/p>\n<p><strong>Tim Ferriss: <\/strong>So just for people listening, who might not have it, total addressable market.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, total addressable market. So what\u2019s a market you\u2019re in? And sometimes people come up with these fake markets. They\u2019re like, oh, well, we are facilitating global e-commerce and global e-commerce, I\u2019m making up the number, is $30 billion a year, and so it\u2019s, I mean $30 trillion a year, and so we\u2019re in a $30 trillion a year market. And if we get just a 10th of a percent of that, it\u2019s 300 billion of revenue. And you\u2019re like, that\u2019s not your market. Your market is like, you built this little optimization engine for SMB websites or whatever, that\u2019s not a $30 trillion market. And so really it\u2019s kind of defining the market. There\u2019s a really famous example of this, where defining your market changes how you think about it. And so that was Coca-Cola. So Coke and Pepsi were roughly neck and neck in terms of market share for decades.<\/p>\n<p>And then one of the Coke CEOs said, \u201cHey, maybe we should be thinking about our share as share of liquids sold, like drinks, not share of soda.\u201d And so we just went from 50 percent market share to 0.5 percent, and that\u2019s why they bought Dasani and that\u2019s why they entered all these other markets. Because they said our definition of our market is wrong, we\u2019re not in the soda pop business, we\u2019re in the drinks business. And so I think also sometimes reconceptualizing what you\u2019re doing can really help change your scope of ambition or how you think about what you\u2019re doing or \u2014\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. If you were trying to spot along the lines of, the fraud will kill you in the payment space, any dogma in the AI world, the sphere of AI, anything hop to mind where you think, eh, maybe that\u2019s not true now? Or maybe in two years it\u2019ll be completely untrue, but people will have latched onto this belief as one of the, thou shalt not or thou shall commandment.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, I don\u2019t know. I mean, there\u2019s some things that have circulated in the past around what\u2019s the ROI on the CapEx spend of the, and will it ever be paid back? I think that stuff is probably off. But yeah, I think fundamentally there are moments in time where it\u2019s very smart to be contrarian, and there\u2019s moments in time where being consensus is the smartest possible thing you can do. And I think right now we\u2019re in a moment in time where being consensus is very right. And you can really overthink it, and what\u2019s a contrarian thing? We should go do a bunch of hardware stuff because blah, blah, blah. And you\u2019re like, maybe just buy more AI. You know what I mean? I think people make these things way too complicated.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah, yeah, true. In every aspect of life, probably. What for you then has gone on the \u2014 let\u2019s just say you were mentoring, this is somebody you really care about, right? We can make up an avatar, whatever, like nephew of one of your best friends or son of one of your best friends or daughter who\u2019s really smart, got an engineering degree, came out of MIT, has a couple of hits in angel investing and they\u2019re like, all right, I think I\u2019m going to raise a fund. But they don\u2019t have the access necessarily that you do to AI, let\u2019s just say. Are there any things categorically you would say would be on the do not invest list because they\u2019re likely to be annihilated or consumed or replicated by AI?<\/p>\n<p><strong>Elad Gil: <\/strong>I think the reality is that when people start off as investors, a lot of the times the reason they have early stage funds is because you can always get access at the earliest stages of companies if you just start helping people. I mean, that\u2019s what I did accidentally, but the reality is I\u2019ve seen it over and over. You follow in with the right group of people, because the smartest people all self-aggregate together, and you just start helping people out and they just ask if you want to invest and you start investing and suddenly you have a great track record and you raise bigger funds, and then you go later stage because that same cohort has grown up and they\u2019ve started doing later stuff and then suddenly you can get access to everything else. That\u2019s kind of the traditional venture story and it has been, I think, for decades in some sense. So I think that\u2019s still very tenable and you can still do it for AI and you can do it for anything. I don\u2019t think you have to go off and do energy investing or something.<\/p>\n<p><strong>Tim Ferriss: <\/strong>You have mentioned in the past a key learning, maybe that\u2019s an overstatement, but you can correct me, from Vinod Khosla. And I think the wording is along the lines of, your market entry strategy is often different from your market disruption strategy.<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Could you speak to that?<\/p>\n<p><strong>Elad Gil: <\/strong>There\u2019s sort of two or three versions of this. Version one is, you do something that\u2019s really weird and it starts off looking like a toy and then it turns out to be really important. And that would be Instagram or Twitter or some of these more social products, where the initial use case is very different from how it\u2019s used today and it kind of evolved as a product and how people perceive it and use it. And so that\u2019s one version of it, and that\u2019s usually more consumer centric. Another version of that would be SpaceX and Starlink, where they started off with launch and getting things up into space and they realized, hey, they have a cost advantage for satellites. And then they built out the Starlink network, which is now a major driver of their business. And so what they did expanded a lot and kind of shifted in terms of, their market entry was space launch, their disruption is Starlink, in some sense. So I do think there\u2019s lots of examples like that over time.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Coming back to information and just consumption, how do you consume most of your information? What would the pie chart break down to, in terms of if he listens to podcasts versus books versus X versus white papers versus something else?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, I think a lot of what I\u2019ve done is collapsed into three things. It\u2019s X, it\u2019s reading some technical papers\/journals. In some cases, if it\u2019s more of the biology side, although I don\u2019t do biology investing, I just like it. But papers, although the papers in the AI industry have really dropped off given the competitive nature of everything now. And then talking to people. And so I found that 20 minutes with somebody really smart on a topic gives me more information and insights and leads on what to go read about than doing some exhaustive search. Actually, the fourth thing is now using models to do research for me. So that could be OpenAI, that could be Claude, that could be Perplexity, that could be Gemini. And for each of them, I actually use different things or I do different things with each of them.<\/p>\n<p><strong>Tim Ferriss: <\/strong>What do you do with the different models?<\/p>\n<p><strong>Elad Gil: <\/strong>I\u2019ll just give you one example versus go through every single one of them. But \u2014\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>Sure.<\/p>\n<p><strong>Elad Gil: <\/strong>\u2014 Gemini, I actually feel like if I\u2019m looking up more activities, like, \u201cHey, I\u2019m planning a trip somewhere.\u201d I actually feel like the Google Corpus and all the stuff they built over time is quite useful for travel tips of certain types. And so that\u2019d be a Gemini-specific thing. That doesn\u2019t mean the other models can\u2019t do it well. It\u2019s more just like I\u2019ve tended to get more accurate rankings of things that way. And I\u2019ll ask for breakdowns and rankings across multiple dimensions and all this stuff for scoring of things. I did a deep dive on a few different areas of ADHD and ASD.\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>What\u2019s ASD?<\/p>\n<p><strong>Elad Gil: <\/strong>Oh, I\u2019m sorry. It\u2019s autism spectrum.<\/p>\n<p><strong>Tim Ferriss: <\/strong>I see. I got it.<\/p>\n<p><strong>Elad Gil: <\/strong>So basically, if you look at autism, it went from \u2014 I\u2019m going to misquote the numbers, so I should look this up later. But I think it\u2019s something like one in a few thousand of the population was diagnosed with autism 30 years ago, 40 years ago, and now it\u2019s like 3 percent. So you\u2019re like, well, what is that? Is that a change in older parents having more kids? Which it turns out that\u2019s not the driver. Is it some shift in the environment? It turns out it\u2019s just diagnostic criteria shifted. And then there\u2019s a lot of incentives to actually diagnose people in the schools. That\u2019s roughly the summary of why we have so many kids that are classified as either having attention deficit where there\u2019s also a financial incentive for doctors to do it because they can prescribe drugs versus autism, but both have gone up dramatically in terms of diagnoses. And it\u2019s unclear to me that more people actually have it. It\u2019s just diagnosed dramatically more broadly.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Which model were you investigating that with?<\/p>\n<p><strong>Elad Gil:<\/strong> Usually when I do things like that, I use two or three models at once and then I ask for primary literature and then ask for summary charts. And I actually have this whole breakdown of stuff that I asked for it to output so that I can go back and double check the data and then read through the literature and everything else. And there\u2019s really interesting things that came out of the autism one in particular, because it turned out maternal age actually has a bigger impact than paternal age in some of the studies. And people always talk about paternal age. And then you\u2019re like, \u201cWhy are people only talking about paternal age? Is there a societal incentive for that? Is it a political belief system? Why is that the point of emphasis?\u201d Which I thought was really [inaudible], right? So there\u2019s other things that kind of come out of that in terms of questions, in terms of the why of things.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Why were you looking into that specifically?<\/p>\n<p><strong>Elad Gil: <\/strong>I thought it was interesting.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. Okay.<\/p>\n<p><strong>Elad Gil: \u201c<\/strong>Seems like it\u2019s gone up a lot. Let me try and understand why.\u201d And so I started looking into it. I was also talking to a friend of mine who is in her sort of mid to late 30s, and she was dating a guy who was in his late 40s, early 50s, and she brought up, oh, she was worried about autism and what would happen with them if they had kids and all this stuff. And so then I did this deep dive as part of that too. And the takeaway was, I can\u2019t remember exactly what it was. It was like, I\u2019m making it up, so please don\u2019t quote me on this. I can look it up later, but it was like there\u2019s a 10 percent increase for every 5 to 10 years incremental paternal and maternal age.<\/p>\n<p>And again, maternal was actually a little bit stronger in some of the data sets. And the thing is though, if you believe that it\u2019s 1 in 5,000 or one in whatever in the population, that 10 percent, 20 percent difference doesn\u2019t matter from a population frequency perspective. This diagnostic criteria went way up.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah, it\u2019s true for a lot of diagnoses.<\/p>\n<p><strong>Elad Gil: <\/strong>A lot of stuff, but societally we\u2019re told, \u201cOh, it\u2019s the age of the parents that\u2019s driving all these autism rates up.\u201d And you\u2019re like, \u201cNo, it\u2019s all these incentives.\u201d And then you look at some of the school systems, it was like 60 percent of all the autism diagnoses, and I think it was the state of New Jersey or something, were not actually based on any clinical criteria. It was a teacher randomly saying this person has autism.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Oh, God. Terrible.<\/p>\n<p><strong>Elad Gil: <\/strong>And so you start digging into these things and you\u2019re like, wow, this is super interesting. And these models are really valuable and helpful for that. So I\u2019ve been doing a lot of back to your question of where do I get information? Part of it has been these deep dives with models into questions that I just find interesting where I ask them to aggregate clinical trial data or aggregate different types of information and then give me the primary sources and then give me summaries and double check things. And so I have a whole series of prompts around that to also clean data and check it. And so it\u2019s really fun. And then I always set it up in multiple models and just see what they each come up with.<\/p>\n<p><strong>Tim Ferriss: <\/strong>When you talk to people, and this may be too much of a kind of amorphous topic for us to dive into in a meaningful way, but let\u2019s just say you find somebody you want to talk to for 20 minutes, right? How do you typically find those people? I suspect there are a lot of ways, but are you finding them on X versus finding them in a technical paper versus finding them somewhere else just to get an idea? And then when you get on the phone with such a person, are there repeating trains of questioning or certain ways that you like to approach it?<\/p>\n<p><strong>Elad Gil: <\/strong>Oh, I think there\u2019s three different types of things. One is, \u201cHey, I\u2019m doing a deep dive in an area just because I think it\u2019s interesting or maybe it\u2019s relevant to an area I want to invest in.\u201d Often, honestly, just is interesting. And then I\u2019ll try to quickly triangulate who are the smartest people on the thing and that may be technical papers that may just be asking each person I talk to who\u2019s really smart. There\u2019s one form of that which is, hey, it\u2019s very informational and I\u2019m trying to do a deep dive on something. I mean, I worked with some of the early AI researchers at Google. That\u2019s how I knew Noam Shazeer who started Character and then went back to Google and that\u2019s how I\u2019ve met a bunch of other folks. But some of the people I just met, just interesting paper, let me look them up, or, \u201cHey, everybody says this person\u2019s really smart. Let me talk to them.\u201d That\u2019s one form.<\/p>\n<p>A second form is I do think really smart people tend to aggregate. And so if you\u2019re just hanging out with smart people who keep meeting other smart people, and people who are polymathic tend to hang out with people who are polymathic and people who are \u2014 it\u2019s kind of like attracts like for all sorts of things. So that\u2019s sort of a second set. Those are probably the two main things. I mean, sometimes people also just refer people over to me. They\u2019ll say, \u201cHey, I think you two would like chatting.\u201d There\u2019s a separate thing, which is there\u2019s people that I go back to recurrently, which is more like, I think this is one of the smartest people about where AI is heading and let me talk to them all the time.<\/p>\n<p>Or there\u2019s one of the smartest people about longevity. Like Kristen, the CEO of BioAge, I call sometimes about random longevity-related things because she knows so much about every topic in it. She\u2019s very thoughtful. She\u2019s very willing to question her own assumptions. It\u2019s very just truth seeking in a way that people aren\u2019t. And people always use that term and say, but she really is just like, \u201cWhat\u2019s correct? Let me just figure it out.\u201d She\u2019s like a PhD and postdoc in bioinformatics and aging and all. She\u2019s super legit. And so that\u2019s an example of somebody that I\u2019ll call for longevity stuff. And so I just have certain people I\u2019ll call for certain topics.<\/p>\n<p><strong>Tim Ferriss: <\/strong>So you have literacy in biologies. It\u2019s kind of quaint how I went to the first Quantified Self meetup in whenever it was, 2008 or something with 12 people sitting around in Kevin Kelly\u2019s house talking about measuring things with Excel spreadsheets. The world has changed, right? So there are armies of tens of thousands of self-described biohackers and so on, talking about longevity. There\u2019s a lot of nonsense. For yourself personally, where have you landed in terms of interventions or thinking about interventions for yourself?<\/p>\n<p><strong>Elad Gil: <\/strong>Oh, I haven\u2019t done a ton. It feels like a lot collapses into sleep well, exercise a lot, et cetera. There\u2019s a handful of things that kind of matter, eat well. And so I\u2019ve kind of collapsed some of that stuff. I think there\u2019s one or two things that maybe you can take that are helpful. And then there\u2019s some things I always thought it\u2019d be fun to experiment with that I haven\u2019t done yet.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Like what?<\/p>\n<p><strong>Elad Gil: <\/strong>I thought it\u2019d be cool to try a rapamycin pulse or something. So stuff like that. But the reality is that I\u2019m kind of waiting for the real drugs to come out and then maybe I\u2019d use those. Some of the ones that I actually think will really impinge on longevity or certain systems like we were talking earlier about how as you age, the muscle that holds the lens of your eye weakens, and that\u2019s part of the reason that your ability to focus gets screwed up. And so there should be eyedrops for that. There\u2019s a bunch of stuff around neurosensory aging that I\u2019d love to fund a startup. There\u2019s a bunch of stuff around the cosmetics of aging that I\u2019ve long been talking about trying to fund. I actually funded a clinical trial at Stanford to work on that, for example, because I think it\u2019s very underinvested in. And peptides to me is basically that. I think a lot of the people are taking peptides as certain forms of health, but also certain forms of cosmetic applications like 5-HKCU and melatonin and all these things are basically cosmetic in nature.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Well, you mentioned a handful of things that seem helpful to take. Are those just the vitamin D or are we talking about other things? What are more on that shortlist?<\/p>\n<p><strong>Elad Gil: <\/strong>Vitamin D and creatine.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. Got it.<\/p>\n<p><strong>Elad Gil: <\/strong>If you want to a lift.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah.<\/p>\n<p><strong>Elad Gil: <\/strong>I don\u2019t know. What\u2019s on your list? I mean, you\u2019ve thought about this so much more than I have. What are you taking or what are you thinking about or \u2014\u00a0<\/p>\n<p><strong>Tim Ferriss: <\/strong>I\u2019m much more conservative than I think people would expect. I played around with a lot of things in my earlier days and a lot of it is very, I would say, capped risk. If you\u2019re experimenting as I was with first generation Dexcom continuous glucose monitors in 2008 or 2009, very unpleasant to wear. And I wasn\u2019t aware of any non-type one diabetics using them at the time, but I wasn\u2019t using much in terms of, let\u2019s just say, questionable gene therapy flying to other countries to use something like a follistatin, not to throw it under the bus, but I feel like the general heuristic of no biological free lunch, I recognize it\u2019s very simplistic, but it\u2019s pretty helpful. At least it will aid you in avoiding a lot of pitfalls. So I mean, there are things I\u2019m experimenting with. Different forms of ketone esters and salts, for instance, I think some could be very, very interesting for cerebral vasculature.<\/p>\n<p>And since I have Alzheimer\u2019s disease, Parkinson\u2019s, et cetera, in my family, including for people who are APOE3, so there are certainly many other risk factors. I\u2019m paying a lot of attention to that side of things. Obicetrapib, I think, is one to keep an eye on that\u2019s not yet ready for prime time, but rapamycin\u2019s interesting. I do think rapamycin is interesting with a lot of asterisks because you can screw yourself up if you don\u2019t know what you\u2019re doing. And if you\u2019re playing with any immunosuppressant, I mean, you just have to be very careful. But looking at combining that, for instance, one of the experiments that I might do is \u2014 and I would have a cleaner read of signal if I only did one intervention, but real life is different from waiting for science sometimes.<\/p>\n<p>So possibly combining a Norwegian four by four interval training with rapamycin pulsing to look at volumetric changes, if any, in the hippocampus and other areas. I think that\u2019s a pretty interesting hypothesis worth testing, but otherwise it\u2019s basic-basic, right? It\u2019s creatine, it\u2019s the vitamin D is look, if you have methylation issues or you\u2019re taking medication as I am like omeprazole, which can inhibit magnesium absorption and other things, you want to keep an eye on that, but not too fancy. I think urolithin A is pretty interesting. The data keeps mounting on that. So I do have a key and interest in mitochondrial health. So if there are things, which could also include regular intermittent fasting and occasional three to seven day fasting, which could be a fast mimicking diet most recently for me based on the input from Dr. Dominic D\u2019Agostino. Trying to foster autophagy and mitophagy with some regularity, not all the time.<\/p>\n<p><strong>Elad Gil: <\/strong>Sure.<\/p>\n<p><strong>Tim Ferriss: <\/strong>I\u2019m not trying to optimize for that all the time.<\/p>\n<p><strong>Elad Gil:<\/strong> One thing I\u2019ve been wondering, so if you look at a computer and often the key to fixing your laptop or the key to fixing any system is you just fucking reboot it, right? You reload the system and it just works magically and there\u2019s a bunch of crap that kind of can \u2014 is there a equivalent of that? Is it like going under for anesthesia? There\u2019s some nerve freezing thing that some people have been doing recently.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah, I don\u2019t know. It sounds scary. Oh, maybe stellate ganglion block?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, that\u2019s it. The stellate ganglion block.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Yeah. I mean, the rebooting \u2014 oh, man, I\u2019m letting out an exhale because there are some interesting options for very specific use cases, right?<\/p>\n<p>It makes sense conceptually. I mean, you\u2019re more qualified to speak to this, but I would say just spending a lot of time around neuroscientists, and I spend a lot of my time in terms of information intake, reading or doing my best. Fortunately with AI tools, it\u2019s become a lot easier, not just getting a synopsis, but actually using it to help you learn concepts that you can kind of layer in some rational sequence. But I read a lot of neuroscience stuff and a lot of optical stuff, there\u2019s actually a surprising amount of \u2014 I mean, there\u2019s maybe not so surprising, like very strong intersection there. So if you\u2019re looking at PBM and photobiomodulation through the eyes, I mean, you can do it transcranially as well. I would give a note of caution for that for folks.<\/p>\n<p>But the reboot side, I would say, for instance, and people who have experienced this to a lesser extent with GLP-1 agonists, if they take it for weight loss, maybe they stop smoking or they cut back on drinking, or they have these kind of system-wide decreases or increases in impulse control, right?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah.<\/p>\n<p><strong>Tim Ferriss: <\/strong>For someone who\u2019s, say, an opiate addict, I think that ibogaine, which, in the future may take the form of an active metabolite or something like that, in flood dosing, at least that seems pretty necessary at this point, relatively high doses. Under medical supervision, because you can have fatal cardiac events, co-administration of magnesium seems to help, but it\u2019s dangerous stuff. People should be careful. You can, and there are lots of people historically who deserve a lot of credit for this, like Howard Lotsof and his wife, but opioid addicts can go through flood dosing of ibogaine and come out and they\u2019re basically given a window with which they won\u2019t experience withdrawal symptoms, physical withdrawal symptoms.<\/p>\n<p>And I think there\u2019re probably applications to other things with ibogaine or pharmacological interventions like ibogaine. I mean, some of the craziest stuff, honestly, related to that molecule is the \u2014 and I\u2019m skeptical of this simple description, but sort of reversal and brain age. So it changes in the brain based on MRIs, Nolan Williams, rest in peace, and his lab looked at this pretty closely pre and post dosing of ibogaine for veterans with traumatic brain injury. And some of that might be due to something called glial derived neurotrophic factor, right? People might be familiar with like BDNF.<\/p>\n<p>So ibogaine is one interesting option. Anesthesia, I\u2019ve become a lot more cautious with general anesthesia. I just had surgery yesterday and I opted for local anesthesia, which in this case was not a big deal because it was just, you can see it, like had something cut out of my head, but coming back to the \u2014 and I\u2019m going to riff for a second here, but the autism spectrum disorder and ADHD example you were unpacking where you talked about the incentives, they might be in perverse incentives to diagnose, well, I mean, not to quote Munger, but it\u2019s like, follow the money, right?<\/p>\n<p>And a lot of people are put under general who really don\u2019t need to be put under general, but it adds a very, very, very huge line item to the tab. And there are people who go under anesthesia and wake up and do not retain the same ability to recall memories and so on. Their personalities become in some way destabilized. And the fact of the matter is that a lot of anesthesia is very poorly understood, really poorly. We know it works, but it\u2019s very poorly understood. And I don\u2019t think a lot of people realize, because why would they, unless they\u2019ve just spending a lot of time looking into this. There are lots of medications that are incredibly well-known, commonly prescribed, for which the mechanisms of action are really poorly understood if they\u2019re understood at all.<\/p>\n<p>We know based on studies, they appear to be well tolerated, like side effects profiles include A through Z, and it certainly seems to exert this effect or have an impact on biomarker X, but we don\u2019t actually fucking know how it works. And there\u2019s just a lot of stuff that falls into that bucket. And so I am cautious with a lot of it, but to come back to your question, I went off on a bit of a TED Talk. The most interesting reboot that I\u2019ve seen, and I don\u2019t want to really water it down to the dopaminergic system because there\u2019s a lot more to it, but ibogaine, I think more so than ibogaine itself shows what is possible. And I don\u2019t know if that\u2019s limited to drugs, right? I am very bullish and they\u2019re going to be fuck-ups. There are going to be some sidebars that don\u2019t look so good, but brain stimulation, I think is, and bioelectric medicine, broadly speaking, is one of the great next frontiers, certainly in treating what we might consider psychiatric disorders, but also for performance enhancement.<\/p>\n<p>And we\u2019re at a point kind of looking for those external why now answers, right? There are actually some really good answers to why now for this as a field. And I think people will be experimenting a lot with this, but without the use of pills and potions and IVs and actually non-invasive brain stimulation, maybe some invasive in the case of implants. So that\u2019s a long answer, but yeah, that\u2019s somewhat I\u2019m thinking about and tracking.<\/p>\n<p>And I mean, some of this stuff we\u2019ll see, but I think a lot of this stuff could be outpatient procedure. You walk in, you\u2019re in there for an hour or two, and then you\u2019re out. So we\u2019ll see. Let me ask just a couple of last questions, and then if there\u2019s anything else we want to bat around, we can bat it around, but I appreciate the time.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Elad of five years from now is looking back at Elad of today. Are there any beliefs, positions, could be related to AI or otherwise, that you think are more likely than others to be wrong?<\/p>\n<p><strong>Elad Gil: <\/strong>That\u2019s a good question. I think there\u2019s all sorts of things I\u2019m going to get wrong, and I think we\u2019re living through a period of big change, which means big uncertainty. And so I wouldn\u2019t be surprised if half the things I think are going to happen don\u2019t or happen even more so or whatever it may be. And that\u2019s part of the fun of it in terms of if we had a perfectly predictive future, it\u2019d be very boring because we\u2019d know exactly what\u2019s coming and it\u2019d be awful. And this ties into notions of free will and all sorts of other things. So I\u2019m sure there\u2019s a lot. I think there\u2019s a separate question of just one exercise I\u2019ve been going through recently is, and I\u2019ve never done this before, a lot of what you do in life, it\u2019s back to the John Lennon quote life is what happens when you\u2019re making other plans.<\/p>\n<p>For the first time, I\u2019m actually thinking, what\u2019s my 10-year plan across a few different dimensions of life? And the basic question is, I won\u2019t get it right. I can try and have a plan for 10 years. Of course, it\u2019s not going to be what I think, but it\u2019s more, does it change the scope of ambition that you have? Does it change how you think about life? And so I\u2019ve been trying to think in those terms, what do I want to do over the next decade? And then what does that mean in terms of the near term what I do in order to get there in 10 years?<\/p>\n<p>And so I think that\u2019s been very eye-opening for me in terms of shifting some of my mindset around what I should be trying or not trying to do. Now, the AGI people will say, \u201cWell, in two years we have AGI, so it doesn\u2019t matter where your plans are.\u201d But I find that to be a very defeatist view of the world. It\u2019s like, I\u2019m going to give up because of those versus saying, \u201cGreat, I\u2019m going to have this plan and I can adjust it as needed.\u201d But through this time of change, there\u2019ll be some really interesting things maybe to do in the world.<\/p>\n<p><strong>Tim Ferriss: <\/strong>Elad, do you have anything else you\u2019d like to say, comments, requests for the audience, things to point people to anything at all before we wind to a close? People can find you on X @eladgil, eladgil.com, certainly the Substack blog, blog.eladgil.com, and elsewhere. We\u2019ll link to everything in the show notes, but anything else that you\u2019d like to add?<\/p>\n<p><strong>Elad Gil: <\/strong>Yeah, wonderful to chat with you as always. I really enjoy it, so thanks for having me on.<strong>Tim Ferriss: <\/strong>Yeah, thanks, man. Always a pleasure. And to everybody listening or watching, we will link to everything in the show notes, tim.blog\/podcast. And until next time, as always, be a bit kinder than is necessary to others, but also to yourself, thanks for tuning in.<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<h3 class=\"wp-block-heading\" id=\"Elad-Gil-legal-conditions-transcript\">DUE TO SOME HEADACHES IN THE PAST, PLEASE NOTE LEGAL CONDITIONS:<\/h3>\n<p><em>Tim Ferriss owns the copyright in and to all content in and transcripts of The Tim Ferriss Show podcast, with all rights reserved, as well as his right of publicity.<\/em><\/p>\n<p><em>WHAT YOU\u2019RE WELCOME TO DO:<\/em>\u00a0<em>You are welcome to share the below transcript (up to 500 words but not more) in media articles (e.g.,\u00a0<\/em>The New York Times<em>,\u00a0<\/em>LA Times<em>,\u00a0<\/em>The Guardian<em>), on your personal website, in a non-commercial article or blog post (e.g., Medium), and\/or on a personal social media account for non-commercial purposes, provided that you include attribution to \u201cThe Tim Ferriss Show\u201d and link back to the tim.blog\/podcast URL. For the sake of clarity, media outlets with advertising models are permitted to use excerpts from the transcript per the above.<\/em><\/p>\n<p><em>WHAT IS NOT ALLOWED:<\/em>\u00a0<em>No one is authorized to copy any portion of the podcast content or use Tim Ferriss\u2019 name, image or likeness for any commercial purpose or use, including without limitation inclusion in any books, e-books, book summaries or synopses, or on a commercial website or social media site (e.g., Facebook, Twitter, Instagram, etc.) that offers or promotes your or another\u2019s products or services. For the sake of clarity, media outlets are permitted to use photos of Tim Ferriss from\u00a0<\/em><a href=\"https:\/\/tim.blog\/media\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>the media room on tim.blog<\/em><\/a><em>\u00a0or (obviously) license photos of Tim Ferriss from Getty Images, etc.<\/em><\/p>\n<\/div>\n<p><a href=\"https:\/\/hop.clickbank.net\/?affiliate=infohatch&amp;vendor=J1R2C\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-10614 aligncenter\" src=\"http:\/\/parmaks.com\/Resources\/wp-content\/uploads\/2025\/05\/profit-gen400px.png\" alt=\"Profit Gen\" width=\"400\" height=\"217\" srcset=\"http:\/\/parmaks.com\/Resources\/wp-content\/uploads\/2025\/05\/profit-gen400px.png 400w, http:\/\/parmaks.com\/Resources\/wp-content\/uploads\/2025\/05\/profit-gen400px-300x163.png 300w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/><\/a><br \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Please enjoy this transcript of my interview with Elad Gil (@eladgil), CEO of Gil &amp; Co, a multi-stage investment firm, holding company, and operating company [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":12882,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-12889","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-growth"],"_links":{"self":[{"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/posts\/12889","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/comments?post=12889"}],"version-history":[{"count":0,"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/posts\/12889\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/media\/12882"}],"wp:attachment":[{"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/media?parent=12889"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/categories?post=12889"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/parmaks.com\/Resources\/wp-json\/wp\/v2\/tags?post=12889"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}