The Early Days Of Anthropic And How 21 Of 22 Vcs Rejected It

---TRANSCRIPT--- AI alignment, don’t get me wrong, is hard, but not the hardest problem. Human alignment is really the problem right now. Our guest today is the most prominent AI investor in the ecosystem, an Midhar. Why is he the most prominent? Three reasons. Number one, he’s one of the founding investors of Anthropic. Number two, he led AI investments for Andre Horus where he made investments in Black Forest Labs, Mistral, Sesame among others. And then third and finally today he’s the founder of AMP where he provides compute and invests in the world’s best AI companies. If we don’t secure frontier model inference or what I call state-of-the-art inference behind a coordinated Iron Dome, I don’t think we have a sustainable shot at staying at the frontier over the next decade. There’s no saturation in superconductor discovery at all.

Ready to go. and I am so looking forward to this dude. I have stalked the [ __ ] out of you for the last three or four days. I spoke to Bing Gordon. I had a catch up with Bing before this. Very nice to speak to him. Uh so thank you so much for joining me today, dude. Thanks for having me. It’s too long. It only took us what, eight years, nine years? I forget when it was. I was 12 when we last did it. Yeah. Well, 12 in startup land is 25, right? So, dude, I’m confused. Help me out. I had Damis on the show the other day from DeepMind. He was like, “Yeah, I’m not sure if we’re seeing scaling laws, but we are definitely seeing slightly diminishing like return/performance as we scale.” So, potentially, are we getting to a stage where increased compute is no longer leading to increased performance? Oh, no. Absolutely not. No, that’s that’s not true at all. in in certain domains that are well explored like coding for example yes there’s an increasing amount of compute required to get an incremental gain in some eval that’s super saturated but if you said what about material science you know I’m sitting here at periodic labs office this is my incubate like the my latest in incubation is called periodic labs I spend 3 days a week here in in Mando Park we have a 30,000t facility where we have LLMs that then predict new materials new superc conductors We then have robots synthesize those new materials and then we have we have physical machines like X-ray defraction machines validate whether those materials have the properties that were predicted by the LLMs and then we pipe that we we we pipe that verification data back into our training run. You know how many other times we need and I can tell you throwing more compute at the problem is probably having yeah super exponential gains right now per iteration. So it depends on which domain you’re talking about, which modality. There’s no saturation in superconductor discovery for example at all. The bitter lesson is holding is well and alive. I I totally get that. Can I ask you when we look at the bottlenecks around performance and progression today? What are the bottlenecks that really persist most significantly to you? Is it is it algorithms? Is it data? Is it compute? Can you help me understand which is most lagging? So there’s four or five. It’s um context feedback which I’m happy to talk about. It’s compute. There’s capital which you need to like you know continuously sort of deploy the compute and context feedback loops. And then there’s culture and I think that culture actually might be the most important bottleneck of all time. But those are the four I would say. Now look, algorithmic innovation I think is a function of culture basically because if you have the right culture, you get to attract the best researchers. The best researchers, the best research talent then wants to work on pushing the frontier. And algorithmic innovation just falls out of having a really good team that’s very flexible on what kind of architecture they want to use. If you have the right culture, the algorithmic innovation bottleneck solves itself because then the the researchers are not focused or like tied to one architecture versus another. They’re not going, I’m all in on LLMs or transformers versus diffusion models. The best scientists and researchers just want to solve the problem, the mission. And if you have a very missiondriven culture where they’re like, we want to move the frontier of coding or the frontier of material science, the algorithmic stuff takes care of itself. But so so I’m not that cons that’s actually not the bottleneck anymore in my view. 2 three years ago that was a huge bottleneck where we were trying to figure out which algorithms scale is there are there some limits to the transformer architecture versus diffusion models. And what I’ve come to realize is if you solve the culture problem you can solve the research and the algorithmic problem. Then the bottlenecks of context feedback which is what is the data you need to keep doing frontier research over and over again is is is step number one. because actually I think that is also where you have the most business and commercial advantage. I think there’s lots of alpha and uh value to be gained in pre-training, mid-training and so on. But you know that last mile where you you deploy a model or an agent in some new domain and then you collect feedback on how it’s performing in real time and then you like I was saying here we do physical verification of material science at periodic um where wherever there are some unique context feedback loops that are that are missing today that’s where you probably have the biggest bottlenecks on capabilities. And so what you should be doing if you’re trying to advance the frontiers is going okay you know these models suck. For example about a year ago as an example I realized there was a lot of talk about models being good at physical physics and chemistry AI for science and I was a visiting scientist at the applied physics department at Stanford and we started benchmarking these models you know claude Gemini and so on and surprise they sucked. they were so bad. I was like, there’s there’s there’s this disconnect between the marketing hype of AI for science and the reality where these models are terrible at the time at least. They were starting to get good at code, but they were terrible at scientific analysis. And you know, the conclusion was pretty simple. They were just missing a lot of the the physics and chemistry data you need to reason about the physical world. But to do that, we don’t have enough of that data on the internet because the internet is mostly pre-trained data about things like blogs and blah blah blah and coding. But if you need physics and science, that’s a real bottleneck cuz that data is locked up in national labs and academic labs. It’s locked up in physical uh you know semiconductor manufacturing plants. How do you get that data in? That was the bottleneck I realized was really the the critical part of getting these models to reason about the physics and science frontier which is something I care about deeply. And so the way we solved that at periodic was you know set up a physical lab with robots doing all that. You could you could apply that same recipe to whatever domain where you want to see more and more progress. Then you ask okay how much comput and infrastructure do you need to keep that RL loop or the physical verification loop scaling at bigger and bigger scale. And then you need the capital to fund all this. You need equity, debt, a whole bunch of different structured finance vehicles to get, you know, land, power, shell. So that’s the compute bottleneck. And then lastly is the culture. Cuz if you have all of those three things, but you don’t have the right team and the right missiondriven culture, the whole thing falls apart. And and so those in my mind are the four bottlenecks I wake up, you know, every day trying to figure out how we we unblock for the best teams. If we just go through them, when we look at that context feedback on the data side, will we see then a generation of vertically integrated foundation model companies like periodic for a ton of different things? Yeah. Yeah. You know, when I went to grad school uh for machine learning, I I I went to Stanford for bioinformatics, which was machine learning applied to healthcare. We were the space was not as good as marketing as it is today. So super intelligence, love it. You know, at the end of the day, what are we talking about? We’re talking about very powerful models within some domain and and we are seeing though sort of within distribution very very powerful capabilities that are you could definitely call them superhuman because there’s no way for example I as a an individual scientist could analyze the reams and reams of data coming out of the lab here without AI models there’s just no chance and so the fact that you can take all of the data from you know training from from a a physical lab and just throw it at a bunch of AI models and ask it to analyze things is a superhuman capability. We didn’t have that before. Okay, fine. So, let’s call that super intelligence. Within coding, within material science, within each of these domain distributions, we are seeing capabilities that are super human. We didn’t have them before. And and and in fact, I would say we’re even starting to see automation of those tasks, especially where there’s there’s coding involved to starting to be somewhat recursive, right? where if you have a good coding model then you can say okay let me automate like data analysis let me automate like data cleaning and so on some people would call that recursive self-improvement totally happening but it’s it’s not like I can just say to a coding model please bootstrap a a physical R&D lab for me in Menllo Park get all the permitting go you know go find an to raise money from go set up the physical infrastru structure and just like bootstrap all this data. That’s just an entirely different kind of frontier and execution and sort of problem. My question to you then is like how do I determine what is not going to get claudified in that vertical model company buildout because you could look at a cursor and say well they’ve built their own vertical model end to end and it’s been claified if we’re being blunt. periodic won’t be because of the physical data that’s being produced in the labs. How do I know what will be cladified versus won’t in that model there? Yeah, this is a good question. Okay, if we want to sort of unlock frontier progress generally across a bunch of domains, then where are the bottlenecks and where will the value acrew? Context is is not necessarily the moat. I would not say yet. I I think I think venture capitalists are very quick to analyze modes but I would say context feedback loops where you have you have unique and differentiated access is where progress will be most legible to you and if there are other teams who don’t have access to that context it’ll also be where you have a superior business model and so here’s an example I give in the class right sovereign data are you familiar with the cloud act yeah okay so the you know the the US cloud act says that hey if there’s mission if if there’s any data workloads infrastru cloud workloads running on infrastructure that is managed by an American company then the US government has to be able to access that data now if you happen to be running military defense mission critical workloads in Europe on AI infrastructure that is managed by an American company well that context which is super critical can’t be sent over across the border That’s an example of a unique and sensitive context that needs to be run locally. And so if your ASML, your um CMACGM that’s doing logistics at scale and some of this logistics is with missionritical supplies, you can’t have your supply chain data being processed by an AI bot that’s running on servers that is subject to the cloud act. So what do you do? You look for local infrastructure partners. you start going, hey, who are the providers, AI infrastructure providers in Europe that we trust? Well, it turns out there aren’t that many who can actually handle mission critical infrastructure at scale for AI. So you call up someone called Arthur Mench who is a French scientist from DeepMind turned entrepreneur and starts a lab called Mistral who is running massive workloads and you say Arthur would you actually build infrastructure that can be secure locally and that’s why suddenly in July of 202 at the at Vivate in Paris you have President Mccron and Jensen standing on stage next to Arthur, a 33-year-old scientist unveiling a gigawatt AI infrastructure facility in Paris. Why? Because the context, the mission critical context of those workloads is so important to be run locally that you can’t run them on Amazon AWS, GCP or Azure. And it’s the first time in 15 years that the that the sort of hyperscaler dominance is um up for grabs for startups. With the greatest of respect, is that the core investment thesis of Mistral for you? For me, yeah. Independence at scale of at every part of the AI infrastructure stack like land PowerShell in Europe, that’s sovereign, it’s local, compute infrastructure, that’s local. And models that are trained locally, by the way, fully open, so they can be deployed and customized globally wherever needed. But certainly in Europe, like the full independent stack is is the is the bet. Yeah. Do Anthropic and Open AI just accept that and roll over? I I don’t understand because government is a mega portion of their efforts and workload today and like both of them when I speak to them are like, “Oh, we’re absolutely coming for Europe.” So, so how do they get around that? Well, I can’t speak for OpenAI too much uh cuz I’m not involved there directly, but Anthropic, I will say, you know, the mission and vision has always been very um I think it’s always been very American aligned, right? They’ve always said, “Hey, America is the crown jewel of the world in terms of innovation. This is where we’re located.” Anthropic is located in Silicon Valley. Um, and I think the company really, really wants to do what’s best for the American government and the American way of life, which is democracy and freedom. It turns out the world’s largest enterprise customers are governments and Fortune 500 companies. And many of those that are overseas need these workloads to be running locally. you said about obviously being involved with anthropics since the earliest of days. I’m just fascinated. I think people kind of forget about their early days almost. People talk about like, oh, SPF investing early and what a visionary he was, right? What was what was Anthropic and Dario like in the early days? Well, so I’ve known Tom forever. Uh Tom, you know, was one of the the lead authors on GP3. Um we’ve been friends for many we’d been friends for many years. Tom gave me a call and said, “An you know, we for various reasons, we want to leave and start this new lab called Enthropic. We’re going to need uh a lot of capital. We’re going to need compute.” I I had already sold Ubiquity 6 at that point. So, I’d kind of gone through the founder journey. Um and so Dario, Tom and I started doing these weekly sessions in early 2021 to try to figure out how to turn what was really a research hypothesis, right, which is scale the scaling recipe into a business hypothesis. Um, and look, I would say it it took like really 12 to 24 months. Um, and they did a lot of the hard work on figuring out how how do we really sort of operationalize this the idea of this AI pair programmer, right? where you take the context feedback loop of the local repository, the files, the directories of programming and kind of sort of in a in a very methodical way make predictable progress on the capabilities of um of of software engineering. And I I thought it was a very you know if if anything my biggest flaw is as an investor as a founder is being too early to things. That that was my lesson with ubiquity 6. I was early to the whole computer vision which is now you know obviously blowing up the whole multimodal sort of generative modeling space. Um and since then I have I think updated my strategy on how to get timing right. But at the time you know our our the recipe was pretty simple right? raise some money, buy some compute, get a little bit of context data on programming, put out a basic version of the model, deploy it with with teams that we trust who are doing coding, and then pipe that feedback loop back into the training run over and over again. And when you do that with inference, inference gives you sort of two things, right? It gives you revenue to buy more compute, and it gives you the context feedback to keep improving the capabilities curve. And I was like, great, this makes total sense, guys. let’s go raise money. I invested a bunch of my money uh that was just life savings which was not much given I was a poor founder at the time which where most of my net worth was tied up in Discord stock and it and and it pains me sometimes to to look back at the emails of friends. So I introduced them to 22 you know friends up and down Sand Hill Road and so there’s some investors there and we got 21 nos, right? And I was like what what are you guys thinking? And they said, “Well, an this this recipe sounds good in theory, but like where’s the proof?” And I said, “Proof? The these are the guys who invented GPT3. How much more proof do you want?” And they said, “What’s GPT3?” I was like, “Oh my god.” Like, how do you go about educating somebody who doesn’t even understand the technology and the breakthroughs that are happening in the machine learning community? Now, I was lucky cuz I I had that training from grad school. I’d started a computer vision company. So, something that was super legible to me just was a completely different world. And then for those investors, we were pitching, remember we we originally tried to go out and raise 500 million and then had to reanchor to only raising a hund00 million seed round, which at the time felt like a lot, but of course was tiny compared to how much OpenAI had raised, cuz by then I think Opened a billion dollars. And so the whole idea of compute multipliers where we could for every dollar of venture capital raised produce a unit of of intelligence for six times less was not like the VCs did not understand it which is why you know over the next 24 months the people who got it were either people like you know some of the folks in the ML community who also had an overlap with the effective altruist community like SPF but also Amazon right this was very legible to Amazon on because they were watching what was happening with Azure and OpenAI and they were like, well, this is super aligned. If you guys actually can create a bunch of state-of-the-art models that are hosted on Amazon, that’s super accretive to to the AWS business. And that’s why, you know, it resulted in deep compute and capital for equity partnership with Amazon that was originally $4 billion. You know, a lot of this is public now, but at the time it was it it was a really tough journey. And I would give Daario, Tom, the other co-founders, you know, Daniela, Jack, Sam Mcandlish, um like it Jared, Jared Kaplan, they were it was such a brutal time getting this company going. like people don’t is there anything you would have advised them differently knowing all that you know now I’m not sure I would because the world is a very different place today you know and at the time it really did feel like there was no one they could trust is it not impossible not to be hauled up in front of Congress if you reach a certain scale whether whether you’re Google or whether you’re Facebook or whether you’re anthropic fighting against the pent Pentagon it you get to a scale where it is impossible not to have that conflict. Oh absolutely. No. What are you talking about? Look, I started AMP as a public benefit corporation cuz I I think it’s actually a very aligned model. Have you heard of REI, right? REI is a public benefit corporation. They make billions of dollars in revenue and profit. Have they ever been held up in front of Congress? No. Like Ben and Jerry’s public benefit corporation. Have they been, you know, hauled up in front of Cong? No. It’s because they self-modderated right at a time and they said here’s our mission but we have to make we have to build a business and as long as you hold those two things in sort of those things are not in conflict long term. If your goal in life is long-term to push humanity forward in some stable reliable way, then you all there are always tensions where you have your mission and then you have your profit motive. And you’ve got to be able to to like moderate between those two. And I think public benefit governance allows you to do that. And I think we need more public benefit charters in Silicon Valley and in technology. And I think we will get there. If you look at the arc of infrastructure businesses, for example, right? I I actually I actually had a chat with a mutual friend of ours who asked not to be revealed. Okay. Um and they said, “For [ __ ] sake, all these PBC’s, public benefit corporations, will these startup founders not just [ __ ] win their market first?” I mean, how are they feeling? Are they investors in anthropic? No. Okay. So tell them to give me a call when they’d like to be investors in the world’s fastest growing business of all time. And then they can lecture me about public benefit governance and market share adoptions. Public benefit governance gives the leadership the ability to make decisions that sometimes are not legible to shareholders as best for them. What decision? What decision can you foresee with AMP that is aligned to your mission but does not put the profit motive incentive first? There are many up and down the stack because we see ourselves as a full stack scaling partner to the best frontier technology teams and we also kind of see ourselves a little bit as have our job is to propose independent standards for AI and as an institution try to uh evangelize the adoption of those standards through you know profit generating businesses. We have a venture capital business. We also have an infrastructure business and a good example of this for now is we’re actually giving away most of our compute at cost. Now, if you’re a shareholder, you’d go, “Wait an billions of dollars of compute infrastructure you’re giving away at cost.” Yes, because we think that’s the right thing for humanity. And we think that’s the right way long-term to have a healthy independent ecosystem, which is what our mission is. Our mission says AMP is a public benefit holdings company. Our our vision is is to ensure there’s a healthy independent frontier technology ecosystem. Our mission is to maximize the world’s frontier output. to do that long term. We think the teams that are truly doing innovation like truly doing pushing the frontier of science and engineering need act compute access and many of those teams today can’t afford to pay price gouging the the the in extraordinary prices for comput infrastructure today and so you know what yeah we’re happy to provide them access of that in a way that’s mission aligned an how do you secure the compute supply maybe I should know this but it’s the most starved resource today how do you secure a resource that no one else can seemingly secure. Well, step one is you get there first before people realize how how valuable it is. And uh luck, you know, I’ve been um beating the the drum beat on this for 4 years now, right? I when I got to E16Z as a general partner, the first thing I did is I sat down with Mark and Ben and said, “We need more compute. We need compute access for these incubations I’m going to do.” And they said, “No problem, An. Let’s set up a program. What do you So we used you know our balance sheet to start procuring compute through the oxygen program. That gave me the ability to build pretty deep relationships with the industry and build trust with compute partners who now we have lots and lots of relationships with that we’re scaling um in ways that would be very hard if I didn’t have that time and the uh sort of flexibility to understand that what is required to really get that infrastructure right. You know, we’ve talked a little bit publicly about what we’re building, which is the AMP grid, which is essentially a a a what what the electricity grid did for electricity, we’re trying to do for compute infrastructure. We see ourselves as an independent system operator of the grid. We we’re not a cloud provider. We don’t own our own data centers. Uh we’re not a traditional venture capital firm either. We see ourselves as an independent system operator, which means our job is to coordinate capacity across the ecosystem in a way that allows the best teams, the best independent teams to provision for their base load, not their peak. So they don’t have to overprovision but when they want to be able to spike up and down for training runs for inference needs they they feel secure that the capacity exists. We are roughly in 1885 industrial you know revolution England right now where you have all you know these these frontier labs are like factories that the steam engine has been discovered. You can use steam to produce all kinds of new products and many of them are running their own generators in their backyards at half capacity. And I’m going, this makes no sense. Let’s all pull our generators so that a shoe factory can spike up during the day, a steel factory can spike up during the night, and then you maximize utilization um and ultimately output. When you think about allocating it, are you not using compute and the cost of compute as a loss leader for your venture fund business which then comes in and says okay you name any of your incredible businesses that you own whether it’s your anthropics or your MLS or your Black Forest Labs and say okay you’ll get the compute at cost but for that we need $300 million invested and that’s your way of winning. That’s that’s not at all how we make the th those are not that’s not the deal. The deal is okay we the deal is I incubate new companies like periodic labs one at a time. That’s I can only do this one at a time because I I like to team up with scientists or engineers who at the forefront of their field. It takes a lot of work to create these new companies from scratch. You know it in many ways I had the privilege to to realize that we are entering a back to the future era of venture capital. If if you think about the birth of modern industries, you know, let’s talk about semiconductors, uh, gene editing, you know, the biotech industry or, uh, self-driving cars, Silicon Valley in the early days of the founding of what I call these frontier industries. The way you start the most iconic companies is very different from how fun companies were funded for the last 10 years in the ZER era. Intel for example, right, was a very close partnership between a couple of scientists and a investor called Arthur Rock who was a founding investor and was at the office every day. Arthur literally used to Arthur wrote the stock incentive plan. He used to run all hands at the company every week. If you look at Jenn which was incubated in the basement of Kleiner right Bob um it was co-founders were Herb Boyer professor at UCSF and Bob Swanson who was an associate at Kleiner and I I got to apprentice in that mode of venture capital because when I got to Kleiner you know I was 20 I was wrapping up grad school at at Stanford med school but I was working nights and weekends um at Kleiner on the investing team and Brooke Buyers who was the KPCB&B had an office next to me and he had some free time so I would go to him and be like Brooke you know, teach me your ways. And he regailed me with all the stories of how Genentech was being founded. And I was like, wait. So, you’re saying basically Bob like co-founded Genentech here in the basement at Kleiner. He’s like, yeah, we were that’s what it meant to be a partner. And I said, well, that’s not what happens here anymore. Like we write a bunch of checks to SAS companies and then they go off and do stuff. And he was like different times. And if you look at that, are they mutually exclusive? And what I mean by that is can you have a venture ecosystem where you have a bunch of people writing a bunch of checks as we have done for the last 10 years and a next generation or to your point a back to the future era of venture capital where you co-ound the business side by side. Can they run side by side or are we actually entering an era where we’re back to the future era as you say where value acrruel is in the co-founding and incubation side? Um, I I think it’s very hard for them to coexist inside of one person. And it’s very hard to coexist sometimes inside of even one firm because, you know, there’s a reason I’m sitting here at Periodic Labs. I work here 3 days a week. Every day from 8:00 a.m. to 8:30 a.m. for the last year, Liam Do and I have had a standup every morning where we go through the priorities of the company and then we we make them, we prioritize, we go and execute. I mean the compute team at of AMP is sitting upstairs procuring compute for for the periodic guys. I my role models have always been the Arthur rocks and the Bob Swanson’s and the Mike Mike Mara personal computing effectively the first CEO for the first year of Apple was Mike Markel. He was an angel investor and he was the one doing all the capex, you know, supply chain and capital and all of that stuff that allowed Steve and and jobs and was to focus on the product and the engineering and and that kind of deep partnership is what I get really excited about. Can I go back to something you said before which is like we’re at the industrial revolution stage and I was like, okay, help me understand that. If we’re at the industrial revolution stage, what does that mean for where we’re going and how I should be acting as an investor today? You have to hold two things in conflict that can seem paradoxical. Um, and this is this is the most important thing I learned from Mark and Ben, which is when the future the future is not uh is is not determined. And so anyone who tells you that they can predict the future with certainty should be taken with a healthy dose of uh suspicion and and instead I try to approach things like a scientist and go what are the biggest bottlenecks let’s come up with a hypothesis on how these bottlenecks will be solved and let let’s run multiple experiments in parallel and then whichever one emerges you just have to be very truth seeeking and and be willing to claim like say you’re wrong right and and and I would say as an investor your job is to come up with a hypothesis for where the future is and be willing to to to make multiple different experiments that are aligned with your mission in parallel and be willing to be wrong and be honest with your LPs that some of them may be wrong honest. What do you what do you say to a Brian Singerman of the world who always said that I’m not smart enough to predict the future but I my job is to pick founders that are able to do so. I think that the most the safest way to predict the future is to invent it right. So do the hard work. come up with your point of view on if we’re in industrial revolution England, what happened next and what were the emerging properties of the businesses that became valuable in institutions over the next 50 years after 1885 and then figure out which part of that world which figure from history of that era do you do you look up to the most and what were you know go read about their lives and the businesses they ran and the and the tensions that emerged in the practice of their business later in life cuz then they made mistakes when they were young and try to learn from their mistakes and then and then go and execute. What’s a parallel property direction from 1885 onwards style time frame that you think will play out in the next era? Well, obviously in the world of infrastructure, I think we need something like the grid for in the computer infrastructure. So that’s what I’ve spent most of my days on which is a coordinating mechanism for uh that allowed this the commod not the commoditization necessarily but the transition of uh coal and electricity from being these resources that were being hoarded to being stable reliable uh commodities that that the best engineering teams the best factories had access to. Right? That so that’s that’s what I think about a lot. I think if you’re since since you’re so talented at media and you’re so talented at storytelling um I think I would and and your mission is to push the European continent. I think one of the things if I was you is I would be talk trying to figure out how do we educate the leading capital allocators and infrastructure allocators in Europe about the coming era whether that’s through media whether that’s through educational programs and get them to understand their role in unblocking the bottlenecks for the best scientists and engineers in Europe it’s largely a lack of pension fund reform in a lot of cases to be quite honest okay so spend your time on pension fund reform how much more do we need in Europe for Frontier AI to be what we think it can be? Is it like 2x? Is it 10x? That’s a good question. I I would try to go about it from a top downs approach and bottoms up sizing approach. Um you know for us at AMP when I look at the grid we are building out which is sort of a reasoning by analogy. uh we have started securing about 1.3 gawatts of compute infrastructure that’s roughly $40 billion of cloud spend over the next four years and that is financed roughly you know between with about 20% of equity the remaining is debt so 20% that’s about $10 billion of equity capital the remaining is all debt capital we have a bunch of partners that help us put together these equity and debt packages to secure computer infrastructure for our companies I would say in Europe I would talk to Arthur and figure out how much he thinks is required for the independent ecosystem over there. But in multiples of gigawatt like if if you’re doing sort of your atomic unit of math in gigawatts I would from a from a top down perspective you know I think Google is roughly at 12 to 15 gawatt of that I’m aware aware of of infrastructure for internal and external deployed needs. Now they have a huge land power shell pipeline coming but you know I if Europe does not have access to Google level infrastructure then what are you guys even doing right like that’s roughly what the continent needs for full sovereignty right to have as at least as much infrastructure locally as there is within the alphabet holdings sort of pool over the next four years is the what’s easier the equity raise or the debt raise I would say the biggest challenge has in figuring out the right aligned financial structure across both in a way that’s legible to capital allocators at scale. Took me about a year to really get all the pieces right. But there are very large equity pools. Let me put this. a lot of balance sheets, long-term missional aligned balance sheets in the world who don’t who have um who are missional aligned at wanting to help frontier scientists, re researchers, university labs get access to the comput they want, but they don’t have operex. They don’t have cash to spend on the compute. So, if you can find a way to align equity um debt, balance sheets in a way that’s risk sort of um derisked, the fundraising is not a problem. It’s it’s actually a systems design problem which it took me again a year it probably took me four years to get right but now that we figured it out it’s it it’s not been a problem. Do you think we are underinvested still today in data centers? We are deeply underinvested in security in secure compute. Okay let me put this. We are not in an AI crisis. We are not in an AI bubble for sure. I’ll tell you that which is the the the question I keep getting asked. We are definitely in a GPU wastage bubble where there are stranded pockets of compute like billions of dollars of compute that are sitting unutilized and if we could pull them together on a grid across the independent ecosystem. Why are they unutilized? Sorry. For a couple of different reasons. Um one is they’re comput is not fungeible. So unlike electricity which had to go through a process of standardization you know AC/DC where megawatts or megawatts are megawatts computer is not funible today. So for forget fungeibility of compute across different manufacturers like Nvidia and AMD within a manufacturer Nvidia chips for example the H100s the GB200s the GB300s these are all completely different chip types. So if you have one cluster where you’re doing a training run on H100s and then you want to sort of do continued post training of that or or or have that do a distributed training run of that um training uh workload on GB200’s doesn’t work. So they’re just like stranded pools of compute cuz flops are the atomic unit of computation is flops. I wish flops were fungeible but not all flops are born equal today. And so if you provisioned a cluster 2 3 years ago with H100s and now you want to you actually want to run some of those workloads on for the newer generation models, you’re memory bound by H100 chips, you can’t unlock, you know, the the the benefits of the Blackwell chip without basically just like buying a new cluster. And so now suddenly you have this H100 cluster that you don’t want to do training on anymore because it’s it’s old school. it doesn’t like the chip doesn’t have the right memory memory properties to train your frontier models and so and it’s very hard for any individual company to h like see all of this stuff but when you’re on seven or eight boards like I am and you’ve been doing this you know 15 years and you start to see patterns emerge you’re going wait a minute why is there all this unutilized compute sitting here and there this is lof are frontier models moving faster than the pace of uh chips as you said that with H100s where you you have newer and newer models and then you’re training them on older and older chips because that’s what’s free and it’s not moving in lock step. Is that is that the problem that we’re articulating? No, no, no. The problem we’re articulating is that compute is not funible. There are no standards for fungibility and there are no institutions enforcing standardization of compute enough. So, we are in the pre-standardization era of compute today, which which was the pre-standardization era of electricity in 1885. And the next I I hope we can we can self-regulate, self-standardize and self um enforce standardization so that we can skip the boom and bust cycles that happen with electricity over the next 50 years. And this happens with every infrastructure cycle in the pre-standardization era. It happened with electricity in 1885. It happened with steel. It happened with railroads. And every time you have this boom and bust cycle, what happens is wars are fought. Companies backstab each other. It’s super painful. It’s annoying. And my view is that compute not being funible is what’s resulting in the all this talk about AI, the AI bubble. But what people forget is that we don’t have a AI capabilities bubble. The capabilities are extraordinary in every domain. We have an infra infrastructure wastage crisis right now. And it’s because there are no open standards. There’s no open protocol for how flops from one um data center can flow to somebody else who needs it across chip types across secure boundaries and uh it’s resulting in a lot of pain for the ecosystem. People are just if we have compute standardization in the way that you said will we remove the boom and bust cycle or is that just one part of it? I think that will go a long way in in preventing this and instead just allowing this. I’m sorry for asking. So, you’re like, “Jesus Christ, Harry, I’m a professor at Stanford and you waste my time with this.” Which is a fair statement. Um, British accent goes a long way though. Um, what is the biggest bottleneck or barrier to compute standardization that you want to achieve? Uh, it all goes back to alignment, man. Misaligned incentives up and down the stack. How is Silicon Valley and DC not on the same alignment? For one, I don’t think we have standardized on whether AI should be regulated, treated, procured as just as good old-fashioned software or like a new kind of system, you know, like I again I went to grad school for machine learning and what you learn in machine learning 101 is models are statistical. They’re not deterministic, right? So when you have a statistical system, it’s different from there are some properties of a statistical system that are different from a spreadsheet. A spreadsheet is deterministic software and a statistical model today is not. And so should the procurement of a spreadsheet be the same from an IT perspective as a statistical model? Open debate. That is the core debate. That’s the problem. Like AI alignment, don’t get me wrong, is hard but not the hardest problem. Human misalignment, human alignment is really is really the problem right now we have in in the world. We need technologists who are who understand the difference between deterministic software and statistical systems to propose a set of standards for how procurement for this should work. And then we need standards people in DC. We have this thing called NIST. We have various bodies in the government that should get together and say, “Thank you guys for proposing this standard. This is where it makes sense. This is where it doesn’t.” This is called an RFC process. And we’re going to standardize on this definition of procurement. This is what happened with TCP IP with the internet. It happened with ACDC and electricity. We have not done that yet for the model era. And unfortunately, the difference between st like these are called open standards. The standardization process is being confused with marketing. Now, President Trump is actually, I think, trying to do his best from what I can tell in at least giving America enough freedom to innovate that these standards can even be discovered in our labs here cuz first you need somebody to actually pioneer and figure out what the standards even should look like. I think that there’s just a lot of noise. Do you worry that basically the CCP is subsidizing a generation of Chinese models that are now being used by American companies whereby they have frontier models to essentially set where model capabilities can be and then have a real effort to make the open- source Chinese models as close to those benchmarks as possible much much cheaper. I mean the engineering execution right now up and down the stack in China is extreme. Here’s what’s happening right? What they realized is that the AI scaling race is not a chip race. It’s a full stack systems code race where if you if you can’t compete head-to-head on chips for now, what do you do? You compete on systems design. You say, “Okay, we can’t we don’t have leading edge chips here, right, yet. So, let’s try to compete on systems.” the you co-design the chip that you have might be Huawei with the computer infrastructure with the training run and then you design that okay to to have a bunch of performance improvements at every layer of the stack and then what you do is you do adversarial distillation at scale where you take western models and then you from various different endpoints you distill the the state-of-the-art and then you try to get as many performance gains as possible on that data and then you release that back out to the world as open models and then you see what people react to and then you get feedback and then you do the next run and the next run and then you catch up and at the point you catch up you say wait a minute we’re starting to be at the frontier. Why do we need to open source anymore? This is good enough for our local domestic needs. It’s beautiful. It’s actually and and and that has actually by the way resulted in innovation. They’re they’re innovating at every step part of the cycle. And that’s why Huawei chips are able to produce capabilities, improvements today in China that rival some of the best chips here when when integrated up and down the stack. In a sense, it’s the Google strategy, right? Google is integrated land power shell, TPUs, Borg, Xborg, GQM, Gemini. Then the deployment I mean the systems code design there up and down results in efficiency that that gives you huge performance gains at the end of the day. China’s replicated that strategy using open source as sort of a bootstrapping mechanism to catch up. It’s it’s extraordinary. Does that concern you? Are you kidding? Absolutely. That’s why I think what we need is a western grid that is where all inference frontier inference is served through an iron dome, right? where where if there’s any adversarial distillation attacks on any one of our teams, we coordinate together. So, because I’m on seven boards, I I’m in group chats where I get texted by one founder saying, “An is anyone else noticing today that there’s a huge spike in distillation on from this region and then I put them in a group chat, we coordinate.” It’s very informal right now, but what we need is you said before that state sponsored attacks on Frontier AI labs are getting worse. What do we not know that we should know? Um, we should know that there are insider threats. We should know that there’s distillation happening across the US and Europe that is taking advantage of our dist of of us all not being united. They’re that that distillation is is taking advantage of our political systems that our mission critical infrastructure is is quite vulnerable especially data centers that are serving uh workloads that are being used by enterprises and I think that from a business standpoint if we don’t secure frontier model inference or what I call state-of-the-art inference behind a coordinated Iron Dome we I don’t think we have a sustainable shot at at staying at the frontier over the next decade. I’m sorry. What does that mean? An iron dome for inference in terms of sustaining it. It means that all inference is served, no matter which company is serving it, is served through a shared proxy that can tell each other when there’s an attack happening on one part of the frontier. Think of it as an iron dome across the entire Western Front, right? And just because you’re here, you’re in one company, you you you can’t see that your model being served through this other company is being distilled. So it’s it’s a deployment coordination protocol. It it’s it’s basically my group chat that I’ve got with like you know a bunch of different founders but scaled where people go we’re seeing this attack today and others go we are too. Let’s coordinate on defensive response. I’m sorry for my lack of cohesion on question. really I feel guilty and I don’t blame you for leaving this interview thinking God he’s got worse over the 8 years not better but I was watching this interview was speaking of inference with someone I think from base 10 and they were saying that the demand for inference has grown not linearly but combinatorally and that is how we would see it progress over the next 3 to 5 years do you agree with that if we keep scaling capabilities that will definitely happen the problem is there are a couple bottlenecks on scaling capabilities that are quite existential. One of them we’ve talked about is I mean the four core bottlenecks on the capabilities progress we’ve talked about right it’s context compute capital and culture and I think capital allocation huge problem we got to educate people on why this is why these capabilities are extraordinary like this this is like the biggest financial bonanza of all time if you know where to allocate I mean there’s a reason why I invest in anthropic in the seed round and now as you’ve pointed out like the returns of all the the body of work I’ve done the last four years are attracting LPS at the highest levels But we’re just getting started. And so that that I I think some of these projections you see are correct. If we unblock the bottlenecks along the way in computer infrastructure, secure compute infrastructure that’s funible, that’s standardized, that’s the biggest bottleneck. I think if there’s any reason why OpenAI, Enthropic, Gemini, and so on don’t hit their revenue targets over the next few years, it’s because they won’t have access to enough compute. I will say there’s there’s like a related bottleneck. When I was at Stanford many years ago as a kid, I I took this class that Peter taught called uh I think it was turned into this book called 0ero to1. This is Peter Teal. I used to be I was an editor for the Stanford Review and he had this um quote right which is competition is for losers and um you know having done this now for 15 years I’ve kind of updated my theory of business and I think he was he was not wrong but he was insufficiently precise which is that I think perfect competition is for losers. I also think monopolistic what does that what does that mean perfect competitions for these it means that if you have 10 different like 50 companies all doing LLM training or doing coding models that’s that’s a losing proposition it’s it’s like you know perfect competition is like restaurants there’s no defensibility that’s why restaurants go out of business all the time it’s very hard for them to differentiate on the other hand in monopolistic comp monopolies are mafias if once you have a monopoly at one part of the stack they stop innovating and instead they try to go up or down by using the balance sheet to acquire. They start hoarding resources. They start saying, “You give me this and I will force you to basically subsume yourself to me.” And I’m seeing that kind of behavior up and down the stack. And mafias are not good for innovation. I I think we’re in an era of op what we need is optimal competition. The optimal competition setup is you have three or four teams in every frontier that are making extraordinary progress and so if you invest in them you get extraordinary returns but they’re not so comfortable as to be a monopoly such that they can stop innovating and that’s important because when they stop innovating as humanity we’re [ __ ] And so I believe that optimal competition we are living we we need to transition to the optimal competition in frontier technology and I think we need leaders stewards venture capitalists politicians educators to remind the world that we have already lived through this era of boom and bust and so on and so these these companies like what’s going to happen right like you said an banan and inference all these companies inference is an extraordinary growth curve ahead But it’s not going to be an extraordinary growth curve if there are 50 inference companies all competing with each other on a race to the bottom, which is kind of what’s happening right now. Like it is not clear to me that we need 50 inference companies. And it’s not clear to me that VCs are smart enough to realize that they’re just lighting hundreds of millions of dollars on fire in a category where having four or five really good inference trusted providers is net good. But will the VC subsidization of 50 20 50 60 70 whatever companies it is not make it impossible for the good companies the four five to progress through that cycle. It it’s a bit of a selfdestructive mechanism because if you have 50 different companies all competing for scarce compute resources then the the folks who are actually innovating don’t have can’t get it and so they can’t do their next round of product innovation and so on. And that’s the problem when you have like this Is that where we are now though? That’s where we are right now is the best inference teams are calling me up. Actually, all inference teams are calling me up and saying, “And do you have compute for us?” Cuz that’s their product is reselling compute. But it’s been hoarded. It’s been hoarded by the hyperscalers. It’s been hoarded by people who are not innovating but are sitting on compute. And it’s so obvious to me now that I’ve left A6Z, I’m an independent ecosystem public benefit corporation that the that the existential threat to innovation in this category is lack of compute. Now that’s why AMP started procuring compute for the independent ecosystem a while ago. And so we are trying to find a way to get these teams enough compute that they need to keep innovating. But we’ll determine the four or five inference companies that win versus the others that don’t. Supply access to supply. It’s that simple. Yep. Comput supply. If you don’t have compute, how do you do inference, man? What are you selling? You need a product to sell. So, if you’re if you’re making a steam engine, you need coal. One of your former partners tweeted last night that we’re going to enter a time where only model I’m trying to remember it and I wrote down parts of it, but only model creators access the most powerful models and that will power obviously the services and the application layer or the apps that they provide. Do you believe that will be a world in which we exist where model providers inherently kind of safeguard the best models for their provisioning of apps? Allah Claude potentially or not? What Martine is suggesting is that in competing cases they will offer a worse model which gives them an advantage. As an example, 11 Labs, which serves a huge amount of application layer companies, will reserve their latest models so they can offer the best customer support and then sell their older models to Sierra and Decagon so they have a worse quality model retaining the best for themselves. The embedded assumption there right what we have learned over the year like empirically over the history of technology is that you want if you have a general purpose product like the iPhone right that works for everybody then the natural the natural incentive is to amortize the cost of product development of this over the largest number of users. So if you have a general model that’s good for everybody it will be available to everyone. If you have specialized models that are good for some people, there will be price there will be product segmentation. And I think what this is telling us is that if there are many custom models, they will some of them will be accessible, others will not be. And so if anything, I I think we should see the fact that like there are Frontier Model Labs saying, “Hey, here’s a new model we have. It only makes sense for some large enterprises to access this as vindication of the of the like ecosystem truth that they’re going to there’s going to be an ecosystem of different models of different types. There’s no one large god model and uh if because if there was I think there would be the market desire to have you know prime ministers, presidents and I and students all use the same iPhone cuz inherently you can raise the most money and invest the most product budget dollars to for a general product and amortize the cost of that across everybody. But if you have specialized models, yeah, I don’t think they’re going to be accessible to everybody and they don’t need to be. I I I I think this open and closed access thing is somewhat overblown. I think just empirically from a systems perspective, if you look at the history of technology, if you have general products, they’re they’re they’re distributed to the masses. If you have custom products, they have enterprise segmentation. Some are accessible to the enterprise, others are not. Are there foundation model layer companies that are yet to be built that will be worth over hundred billion dollars? Oh, so many. I’m periodic is one I’m sitting in one right here, right? But they’re not foundation model companies. I would call them frontier systems companies. This is the problem. Every time I kept calling trying to educate people, you know, four years ago where they’d be like an but you know, Anthropic is a foundation model company and Mistral is a foundation model company. No guys, that’s just one part of what they do. Maybe they’re starting there because that’s very that’s a core competence but there’s a reason why you know anthropic also has a thing called cloud code and there’s al there’s a reason why mistral has something called mistral compute and there’s something called there’s a there’s a reason why you know Microsoft who’s a cloud also has a co-pilot business you know these labels or categories of foundation model when need to be viewed I think with more suspicion than they are like what matters is the full the systems code design the systems the the full stack like like frontier research loop that you need to run with customers and then later when that happens when you say oh my god anthropic is now they have they have they were a model company and now they’re launching a product called cloud code I was like what do you mean that was part of the plan all along of course you need to have a a pair programmer interface for a model like why why would you assume otherwise oh cuz you just weren’t paying attention and you had your neat market maps that your associates were giving you and you thought that was That was truth. The these the commercial community has forgotten how to build businesses and they’ve forgotten the difference between first principles and marketing. That’s the problem. That’s one of the other misalignment problems. The ground truth of these businesses, machine learning systems businesses, they’ve always been frontier systems businesses. They were never just foundation model businesses. Now, okay, if you had to package that up and tell your LPs that because that was legible to them, then I I can’t blame you, I guess. But the LPs I work with, I’m very upfront with them. I say, “Look, these categories are going through huge reinventions and and and if you want when you partner with me, what you get is a full stack sort of partner.” And I will tell you the first principles of what’s going on and these first principles insights will change over time. But you got to be comfortable with huge capex outlays in businesses that end up winning the entire category. That’s what Frontier Technology is. So I don’t know I think foundation models have been a deeply mis and and this is part of why I started the class four years ago. I just thought security at scale was going through a bunch of reinvention and then we reinvented the class to be infrastructure at scale last year and this year it’s frontier systems because not enough people realize that to keep the the tech the capabilities frontier moving you need to think about these projects these companies as frontier systems projects not foundation model projects. Does that make sense? It does. But when I hear about the capex required, I I respectfully ask, do you have enough money? I think the $1.3 billion was Yeah. like how much money Yeah. How much money do you need? An well for the gigawatt 1.3 gawatt which was kind of our our proof of concept that that capital is not a problem. I think the question is if we want to scale beyond that, yeah, we need way more capital to be deployed in across the western front in the United States and US allied countries. How much money do you think you need? As long as the capabilities frontier keep moving and we want a healthy independent ecosystem, we’ll just keep raising more capital. There’s no end to that. I I don’t I don’t really The day machine learning stops working as a systematic way to give humanity more capabilities, that’s when I’ll say we have enough, Harry, but that’s so far out I don’t even know how to reason about that. I could talk to you all day, but before we do a quick fire, how will Vans be fundamentally different in 5 years time than it is today? Well, again, go back to history, right? I think there will be a few people like Arthur Rock and um you know Bob Swanson and and Mike Mara who turn their their practice into institutions then there’ll be others who don’t and I think if they don’t evolve themselves for what entrepreneurs of this era need then I think they should get out of the venture capital business because we don’t need more bankers like you know one of the beautiful things I I my friend Vlad who runs Robin Hood floated did recently this like venture fund thing on on Robin Hood. Yeah. Venture Robin Hood Ventures I think it is. Yeah. Yeah. But like when you have software that can play many of the coordinating roles of venture capital firms, why do you need somebody who’s just a pure to borrow a Marcism, a rapper on LPs, right? The the look here’s here’s what I’m most concerned about with the capital ecosystem. Not enough of the wealth creation opportunity that’s happening in Frontier is being shared with the public and and that’s not good for anybody because if you don’t share this wealth creation opportunity with the people who are supposed to be welcoming this technology into their lives which is ultimately the public what are they going to do say I don’t want these data with the with the greatest of respect a lot of the money in venture capital funds are from endowments pension funds teachers funds and so that wealth distribution should ultimately trickle down if we believe in that. But how many venture capital firms were in the seed round of entropic? Oh, none. That’s the answer for you. And that’s happening again and again and again. There’s a huge misallocation of public capital into venture managers who did are not capturing enough value in Frontier AI. Instead, they’re investing a bunch of stuff that’s not going to exist and the public’s going to be mad. Did you put 300 million bucks into Anthropic in one go? I’ve had the privilege to invest many hundreds of millions of dollars into Anthropic across several rounds from the first to the most recent one. So, I consider that uh lucky. I I intend to give most that away to public benefit um causes, public benefit education programs. And I I I I think we’re at the very beginning part of anthropics uh journey on commercial progress. Dude, I’m going to do a quick fire around with you because otherwise I’m going to take all day. You can advise, you can advise an LP investing in venture funds. One thing, what do you advise them? Educate yourself. Take the class. Do all the readings. Do the readings. Do don’t skip the hard work. too too many LPs are outsourcing their hard work, the the work they’re supposed to be doing as capital allocators, which is like understanding what’s actually going on and then decide which venture managers and allocators you think have a unique defensible advantage of the bottlenecks. I I would be investing in the bottlenecks basically. Dude, too many too many GPS are not doing the work. The amount of GPS who’ve never built anything with AI is astonishing. I agree. Completely agreed. And I don’t think you can be like I don’t you’ll laugh at me like I’ve built with every different like vibe code provider. I’m trying to turn my media company into an AI first media company. It’s pathetic compared to the [ __ ] that you do. But at least I’m trying. I’m seeing the bottlenecks of superbase integrations and everything that comes with it. And you learn by building. I think if you’re not doing that in the be beginning, you shouldn’t be investing period. I completely agree. I mean I was there there’s a sovereign country that came to me at the end of last year and said we want to bring 26 of our ministers to your house and do a one-year program where we educate it’s a frontier program where we learn what’s going on in AI from from lectures and so on and then we want to do a deployment project where each of our ministers actually build AI agents and I said you know what that that like if you take take Stanford CS153 that it’s a microcosm of this course I’m doing with this country, the sovereign fund that we partnered with. Um and that’s the way you you have to work like do the work to read the literature understand what’s going on in research and then deploy yourself like build tools uh you know the class project the Stanford CS153 class project is the oneperson frontier lab because I do believe genuinely that what would have taken 50 people to do four years ago now with the right AI tools you can do with one person and as a leader if you haven’t played with these tools and deployed yourself and built your own agent I don’t think you understand what’s going on. I’m not letting the the ministers who are taking this class with me, I’m not letting them graduate until they build and deploy agents. I’ve told them they’re not getting they’re not getting their graduate certification. Have you told your wife that you’ve got 26 ministers coming to your house? She let me co-host date night. An she let me co-host them at our house in SF, you know, few weeks ago. And I’m very lucky to Viv. I don’t deserve Viv, I’ll tell you that. But she’s very very she she’s missional aligned and we both believe that the best thing we could be doing with our time is is educating at scale. What makes Dario so good that other people don’t see from the outside? One sheer scientific brilliance truly like world-class technical ability in his domain. an obsessive um desire for truth seeeking to admit like to to to keep reasoning reasoning reasoning doing to keep doing experiments until he’s he’s a physicist at heart right like I I think Dario is a physicist at the end of the day he’s not actually a computer scientist um and so a physicist a world-class physicist tries to derive and and and he’s an applied physicist um derive laws, general laws of reality by looking at data and running empirical experiments. He’s an empiricist and he has an obsessive desire to be a good empiricist. And the third is mission alignment culture. He says this is our focus. This is our mission. No drift. We won’t take shortcuts. We we are willing to make huge tradeoffs to hit this mission. And that attracts the best talent, incredible talent. In the face of criticism of people saying you’re a mercenary, you’re blah blah blah. You’re just doing this for profit. No, actually, it turns out there’s a ruthless desire to to stay focused on the mission. And that results in hard trade-offs and priorities. And if you don’t if you’re not aligned on that mission, then you’ll just think he’s crazy or, you know, he’s evil or whatever. It’s crazy how much ad hominemum attacks people I’ve seen against him. But he’s that got that clarity of mission. What have you changed your mind on in the last 12 months? You know, the biggest one is um health. Um I I’ve had some health experiences between both my family members and myself have had health experiences that made me realize we all just don’t know how much time we have on Earth. And that makes you stop taking for granted how much time we have. And so I started taking time much more seriously. But I would say and this was my my kind of and you know every lecture I do at Stanford um we talk a lot about scaling laws and technical stuff but I also give the kids like an Andre’s life scaling laws lesson you know at every I’m very inspired by Richard Fineman uh Fineman’s lectures you know always kind of combine technical education with a little bit of life coaching for them and and my f my my like number one scaling law for them for the students was take life seriously but don’t take it so seriously that you forget what makes it worth living, which is have fun with friends, work on interesting projects with people you love. Don’t take relationships for granted. It’s humans that make the world go around. And if you’re so focused on your next fund or your next raise or whatever, you just take for granted the one thing we all don’t know how much we have, which is time with each other. And so I just start valuing my time more, my relationships with people. You know, there’s so many people. I mean my parents, you know, I left my parents behind in India to move to college um at Stamford and I have gone weeks of my life not calling them or texting them and now they’re, you know, in their 60s and I’ve I would give you a hug if we were in person. [ __ ] I’m so sorry, man. I Don’t worry. It’s okay. Oh Jesus. You know, the first money we ever made from the show, we made it because my mom has MS and we couldn’t afford treatment for her. And the only way that I could pay for it was by putting adverts in the show. And that was how we did it. And they still pay for it. Thank you to Vant for paying for Mom’s MS. Thank you, Christina. Yeah. Thank you for the corporate sponsors. Um, yeah, man. The trade-offs, you know, the sacrifices are parents are amazing. Parents are insane. How do you escape the money treadmill? I I didn’t have money when I grew up and I was like, I’ll be happy when I get like, you know, x amount of money. Any advice on escaping that money treadmill? I was very lucky that you know I went to Singapore on a government scholarship and um Lie Kuwan Yu who is the you know was the founding father of Singapore I’m a big lieuanist realized that you know the the best like they’re they didn’t have many resources they had they didn’t have they didn’t have money as a founding nation they didn’t have they didn’t had nothing basically other than themselves and their location their strategic location and he realized we need to build a talent program. We need to run this country like a company and I we would recruit um the best talent from across Asia and because I I think I was the top 10 or something in some public exam when in the 10th grade in India I was tapped to be a scholar in Singapore and I took I was a government scholar. Now I didn’t have to actually I was lucky enough that my parents could have paid for it. had a family business in telecom but it was very important to me to be independent from my parents because in Indian culture and a lot of cultures where like if you don’t have financial independence you are always kind of beholden to somebody else and in in the case of community cultures like India like there’s a lot of pressure to adhere to their values and so on um and I I think I did subconsciously I’m very lucky I have a sister actually who lives in London and who fought my battle for me. She was she’s 7 years older and I got to see that she was a rebel and she wanted to do all kinds of, you know, things including she wanted to go to fashion school and they didn’t want her. So, but she had to go to law school because they were paying for it. And she actually, I think, fought some of my battles and made me realize like the more independent I was, the more I more freedom I had. And freedom matters to me a lot. And so I have always found I’m willing to I I I just define my goal, my financial goals by through independence is what m has always mattered to me. And so I’m willing to make big tradeoffs in money to retain my independence. And anytime I find my independence feeling threatened, I go, you know what? I need a change. So I I that that’s what matters to me. I I I think you need to figure out what is your mission. what what matters to you more than anything else. So, you’re willing to just turn down all kinds of money and job opportunities and so on because that clarifies a lot where you spend your time basically. My mission Yeah. My mission is really to enrich the already very rich family offices of Europe. That’s it. Okay. Well, then you’ve got a ways to go on the treadmill, brother. Was that not a bug? I didn’t read the memo. [ __ ] Um, you know, I also think that people are just not very funny anymore. Like we lack a little bit of humor in a lot of society. It’s so sad. Yeah. I I was with my partner the other day. I’m like, “You speak like AI.” And he’s like, “I know.” And you know what? I talk to my wife like I talk to Claude and she [ __ ] me. I’m like, “Yeah, that’s not a good thing.” Dude, a final one. Um, it’s a bit morbid, but like what do you want to be remembered for? Like what do you want Ana’s legacy to be? You know, Viv asks me asked me this like three years ago at a party. We were like I think it was at at our like anniversary or something. We were with like 10 of our friends, our closest friends, including some of the co-founders of Entropic. And uh so there were all these you know it it it was one of these classic San Francisco kind of like dinner parties and she puts me on the spot and I just blurted out I want she I think what she had asked was like what do you wanted to say on your tombstone? H and I and I blurted out he was right. And and the room just went dead quiet and they were like, “Yep.” And it’s because I have this obsessive desire and need to try to learn where the future is going and then tell everybody about it. And then everybody thinks I’m like some snake oil salesman or whatever. And then like now that’s changed because now it turns out I was so right. I made LP so much money that they’re all now asking me an can you you know people like I was invited back to teach this class at Stanford, right? So people are are f I guess have realized okay an might know a thing or two about the future. Let’s go get his take. I went to this boarding school in India called Rishi Valley and it was founded by seven years of no tech. No tech. Seven years. Yeah. Rural India. No wonder. No wonder you’re a happy and adjusted person. It’s taken me a while to get here. But yeah. Would you let your children have social media? Yes. I I I I I think it’d be crazy to not let them have it in social have access to social media, but I I think it has to be done in moderation and most parents have a really hard time moderating it with their with their kids and then it’s really hard to moderate. You know, I with Rishi Valley I had access to a computer once a week and so you need to enforce something like that where you you don’t take it for granted. it’s within a structured sort of environment and then you develop good habits and protocols and practices to not be dependent on it but you like I would plan my Wikipedia sessions like I had to plan my like you got one hour a week in the computer room in Rishi Valley so you really got to plan like the highest use of that time and so you’re not dependent on it but you just use it as a high lever strategic asset I that’s how I think technology should be viewed you shouldn’t take it for granted like the problem is you know people keep saying we’re we’re we’re going to have the singularity. Like, have you realized we’re we’ve been at this for like 10 years. Half of you outsourced your brain and thinking to this device. Anyway, oh dude, uh I I so enjoyed doing this. Thank you so much for putting up with me. You’ve been utterly fantastic. No problem, man. Thank you for doing the consistency with which you’ve kept up this. I mean, you’re an institution now, right? Like the hard thing is I mean, I can’t believe this. You you’ve been at this we’ve gotten old together, right? When you were doing this, like you said, you were a kid. I was much younger, dude. Me and Pat Grady, Pat Grady was like one of the first people I met in Venture and he was an associate and I was like a 17year-old and like I I laughed with Pat. I said to him the other day, “Dude, you’ve gone from associate to like a head of Sequoia and I’ve gone from podcaster to to podcaster.