# Foundation Models: Who Wins & Who Loses How Economies and Labour Markets Need to Change in a World of AI · China vs the US in an AI Race: What You Need to Know · Rich Socher, Founder @ You.com 20VC · Apr 18, 2025 · 64 min · 12,697 words Speakers: Harry Stebbings, Richard Socher Source: https://www.996.fm/episodes/20vc--ep-e5d047ec/ ## Cold open **Harry Stebbings** [0:00]: You are listening to 20 VC with me, Harry Stebbings. Now what on earth is going on in the foundational model layer? It seems like there are new releases, new updates, new winners and losers every week. I wanted to sit down with one of the best in the space to understand what really is going on and how we should think about it. So joining me in the hot seat today, we have Rich Socher, founder and CEO of you.com. Before founding You, Rich served as chief scientist and EVP at Salesforce. Before that, he was the CEO, CTO of the AI startup, MetaMind, which Salesforce acquired. 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And so when you said you're in London, was like, we have to make this happen. So thank you for joining me. Thanks for having me. It's a beautiful day. It is a beautiful day. It's it's never like this. Wanna start with a little bit on you for those that are listening for the first time. High level on why you're a rockstar and what you is. **Richard Socher** [4:09]: High level, did my PhD at Stanford. I'm credited for having brought neural networks into the field of natural language processing. It was a very controversial idea at the time. It's very obvious in retrospect. It's just sort of the story of my life. So, you know, brought like word vectors, like improved those massively and sort of build one of the two most popular word vectors. Then we pushed contextual vectors, so you can pre train not just a single word vector, but a whole sentence embedding. That then became LLM, which became BERT, which is one of the most cited papers still in the world. And then we invented prompt engineering, which was majorly rejected publicly on open review, an idea that made no sense to the reviewers. And now in retrospect, it's so obvious, like, no one could have even invented it. It's just like, of course, you can ask questions to one model, and no matter the question, you'll get an answer. So done a lot of research. After PhD, did a startup called MetaMind. It was acquired by Salesforce where I became chief scientist. After four and a half happy years at Salesforce, I started you.com. You.com basically came from this idea that we have a single model now, a single neural net, that can answer all the different kinds of questions. So clearly, people on the Internet should get better answers than lists of blue links that we get from Google. And so we started with that premise, but eventually realized a lot of people ask fairly simple questions to Google, like, what's the weather tomorrow? What's the score of the soccer game? Who's the president of The US? What's the price of the stock? And like, on a lot of those questions, you don't really have the opportunity to be 10x better than a Google. You get that answer within one second, and that's it. And so we realized eventually the killer app for large language models and complex answers is an enterprise. And so we're now helping companies with Answers, agents, and a path towards AGI at you.com. **Harry Stebbings** [5:57]: Would love to start with top level because it is very noisy and it is very confusing to understand what's going on. When you evaluate where we are in the LLM landscape today, how do you evaluate the current status of where we are? Boy, **Richard Socher** [6:11]: I think AI is kind of this tide that's rising, but on top of that tide, you have a lot of little hype bubbles that come up and down. And, you know, in some ways, you can think about when Sam, for instance, says the next generation of models will be as good as a PhD student. The corollary there is that most jobs don't even require a PhD. If you do service for DoorDash or something, like, you don't need a PhD to answer service questions. And so LMs are already good enough. They just need to be brought into companies to be actually made useful. And so I think that's sort of one state. And then the future state is, of course, we will get even better at reasoning. And at some point, there are very, very narrow niches where these models can be as good or better than an expert human. So there's still a lot of room to grow. And so, in fact, it's such a confusing state that part of this book I'm writing includes a chapter on what I call the upper bounds of intelligence, where I essentially group intelligence into 10 different dimensions or groups of dimensions, and then we can kind of say, in this dimension, there is, like, a fairly low upper bound, and we're fairly close to it. For example, object detection and computer vision. It's actually kind of solved. We can classify most objects on the planet now, and the upper bound that that type of intelligence can ever get to is all objects on the planet. And so I think we're already 90% there. But in the other bounds like knowledge, well, if you include the molecular composition of every planet in the universe as part of knowledge, we are astronomically, quite literally and figuratively speaking, away from ever having AI reach that upper bound that is basically in the world of physics. And so the state of LMs is very perplexing right now. **Harry Stebbings** [7:59]: We were talking about the speed with which the landscape moves. We are seeing this seemingly intense commoditization of LLMs. Do you agree with the commoditization of LLMs? 100%, yeah. If they are being commoditised, is there value in them? **Richard Socher** [8:13]: It's very interesting, but on value, you have to also differentiate between value creation and value capture. I think LLM companies, especially just the pure thin infrastructure layer of LMs, are going to look, I think, more and more like telcos, in the sense that it's high CapEx, huge expenditure to build it, especially if you want to build it from scratch. It creates a lot of value in the world. You can't build an Uber app if people don't have internet everywhere, but you don't necessarily capture that value just like Vodafone and T Mobile and whoever don't get a cut of Uber working now. Right? And so I think that's kind of the mental model I built for LMs. **Harry Stebbings** [8:59]: Can I interject there and say the the core of a telco business model is actually sustainability and retention? It's why they often tie you into very long term contracts. If you look at the LTVs of a Vodafone or a T Mobile customer, these are incredibly long, ten year plus. **Richard Socher** [9:13]: You're right. That's where it breaks. It's even worse because the one thing where it breaks is software, you don't have you have even less of a moat, and with open source, it's even more. **Harry Stebbings** [9:21]: Now I said this to Kevin Scott at Microsoft, and he said, what's the moat with search? Go from Google to Bing just the same, but you don't. You stay with Google. Well, he didn't obviously say that because he's obviously with Bing. But my question to you then is, like, how do you see the distribution of value across the LLM space with the recognition of that? **Richard Socher** [9:40]: That's why I said if you're in that thin infrastructure layer, and that was an important qualification, because OpenAI is a consumer app company. They make their revenue, the vast majority of their revenue, from a consumer app called ChatGPT. If you're now just in that API infrastructure layer, it's very different. You have a lot more pressure. Anthropic has a lot more pressure to keep building the best models because Claude is so much smaller in terms of market share for the consumer app. And so that's why that analogy doesn't work. And you're right. Like, consumers, once you're really famous and you cross that threshold of just, like, well known, being the default for a lot of people, all the other LM apps companies are almost rounding errors to ChatGPT. **Harry Stebbings** [10:21]: Why do Anthropic still keep the consumer product of Claude? They are clearly very, very good at engineering. They're clearly used by the best engineering companies in the infrastructure layer. Why bother keeping the consumer facing products? **Richard Socher** [10:35]: I **Harry Stebbings** [10:35]: think in **Richard Socher** [10:35]: AI, if you say I have the best model, but people can play around with it very quickly, people call BS on you. **Harry Stebbings** [10:41]: So there's not a data network effect, there's not a data acquisition play really there on user inputs that make models better? It could be. It could be that they use random conversations and maybe train and guide it. Do you think we're seeing the specialisation of these different providers? Like you said there, you have Anthropic obviously very much focusing on engineering, so to speak, the consumer product of ChatGPT. You said about you really focusing on enterprise and enterprise workflows. Are we seeing this realisation of shit? There's no inherent value in horizontal products. We need to be specialised. **Richard Socher** [11:10]: Maybe just because ChatGPT owns the consumer market that hard, you have to find a different part of the niche, and and we're saying we're seeing that play out in a variety of different ways. Like, we're also focusing more on enterprise. My hunch is other people will follow us into that, because that is just like normal consumers. Again, either they have a majority of very simple questions or they don't wanna pay. Ads and chat are really hard. We actually evaluated that. They work about 10 to a 100 x worse than search ads, and you have twice the cost, about. **Harry Stebbings** [11:39]: Talk to me about that, sorry. So ads in LLM do not work? **Richard Socher** [11:43]: They work 10 to a 100 x worse than chat search ads. Just in search, right, you, like, Google at some point found that no matter how bad they make the product, people don't know how well to Google. Right? So they they default. Also, the scary scary and sad statistic is, like, 80% of all iPhone users never change a single setting of any kind. Whatever is the default gets used, and that's why, like, Google pays Apple $20,000,000,000 a year to be that default. Right? And so it's very hard to to move away, from that kind of powerful of a lock in. And then if you realize that, you can say, well, if we need to make more revenue this quarter compared to last quarter, let's just do six ads instead of five ads. And when the organic link results get worse and worse because they're SEO, then everyone knows they're kind of getting terrible, turns out you make even more money because the product gets worse, the ads become more and more relevant. And so after doing that for ten years as an untouchable monopoly, the experience suffered. Right? And so there is like some potential here, but the default is still very, very strong. **Harry Stebbings** [12:42]: What happens from this point on? We had like Guillermo Rauch from Vercel tweet the other day that actually they've seen conversions to site trans for 1.6% to now 4.5%. It used to be that that's, like, direct from ChatGPT. Mhmm. My question to you is, like, do we just see this slow migration away from Google? Do we see Gemini integrated into Google Search? How do we see that conversion happen? There's sort of **Richard Socher** [13:08]: big waves in consumer applications of bundling and unbundling. For a while, I was hoping we would be in a bundling kind of wave still, but I think it's fairly clear that we're still in a very large wave of unbundling. Consumers are okay going to Yelp for a restaurant review, and then going to, if they really care about the weather because they are into flying sports or something, going to a specific weather app like Windy, then they go to a specific app like Uber Eats to get food delivery or DoorDash or whatever to get their food. So if they want to make a very small purchase that's like $20 and they don't care about it, they search directly on Amazon. Young people now look directly on TikTok because they want the food to look good in their Instagram or TikTok or whatever videos. And so I think there's a huge unbundling wave, and so LMs, as part of that unbundling wave of Google, LMs will capture whenever you have a more complex question. And then you just look at, like, how many people have complex questions in their lives, and the more you are a knowledge worker, the more complex questions you have in your life. And very often you have them at work, that's where, like, efficiency matters too. And those are some of the many reasons why you.com moved into enterprise. Does one need to be the best and innovative today when you can just distills very effectively? I think that answer depends on the dimension that you want to be best at. I do think it's good to it's obviously always helpful to be the best. We pride ourselves to be the most accurate, and that is a never ending game. So whenever you say you're the best, you're the best in that moment, and you have to keep working on it. And and it does help. It does help with marketing and branding and sales for the most part. I feel like we have still not maxed out our abilities on the marketing side and branding side of things, but at least sales works well enough now that we're really increasing revenue, and that's ultimately what matters. **Harry Stebbings** [14:57]: Do you think we overestimate adoption in the short term for large enterprises and underestimate it in the long term? **Richard Socher** [15:03]: We have seen actually, of our largest enterprise customers now had failed OpenAI projects or had failed sales **Harry Stebbings** [15:14]: projects? **Richard Socher** [15:15]: I'll tell you that. Or built their own with some APIs. Those are some of the largest customers we've had, and they fail usually for two reasons. One is adoption. They had to pay a thousand seat licenses for OpenAI, and then six months later, they realize only 6% are actually using them every week. So they've been sitting and paying for 90% licenses. Why is that? With AI, every person will become a manager. But most people are not used to managing other people or processes. They're used to being individual doing a specific type of work and doing it well. Now, when you become a manager, you have to learn to distill all your knowledge in a very succinct and unambiguous way to another entity, in this case, an agent. Right? And so we're helping people essentially train up their own agents. So whatever process they have in their company takes them enough hours. Repeat it every couple of weeks or every couple of days. Like, we teach them on how to actually tell that to an AI, and then they just have their own agent, and it'll **Harry Stebbings** [16:15]: just automate that for them. Do you think we will operate in a world of horizontal agents that do many different tasks and that's kind of like your personal assistant, or do you think we operate in a world of many verticalized agents which are specialized for very specific tasks? **Richard Socher** [16:28]: Yeah. I think a lot of companies right now would love to immediately own the end user and then do all of that below, but my hunch is, we've gone through this at you.com too, when we're still doing more consumer, we're looking into, basically, we wanted to make it easier for users to get things done. Right? This is the same idea that now we see with agents. And when you look into it, like, I I remember this this demo where where a startup founder was, had his little device, and he's like, I wanna have a trip to London with my four kids and, you know, on this weekend, and then one, two, three, and it's done. And I was like, that was definitely BS. Like, there's no way that was true. Because when you've if you've ever booked a trip, right, there's so many little nuances, and you realize, like, as much as I love natural language, natural language is not the single best interface for a lot of different types of answers. Sometimes you want to see a map, like, sometimes you want to see a table, sometimes you want to see a map with a bunch of specific overlays, and sometimes it's okay to just do it over voice. But even flight booking becomes complicated and changes over time. Let's take something as simple as booking a flight when you're a student and then six months later after you've had a job. Well, once you have a job and you have more money but less time, you'd rather pay extra for a direct flight versus a one stop flight. The AI needs to know all these subtle details about you to get really good, and we're sort of in this valley of disillusionment on a lot of these, what I call action agents, that go on the web and actually do something for you and take actions that you can't undo, and let's say you buy a ticket that's not refundable or something. There's this this valley of disillusionment that we're in right now because the agents just don't know enough yet about the **Harry Stebbings** [18:06]: user. Do we have the data to allow these agents to operate effectively? A lot of internal processes and actions within companies aren't actually codified in data. Do we actually have that? In some consumer use **Richard Socher** [18:18]: cases, you would assume if Google really wanted to have it, Apple really wanted to have it, in theory, they could. **Harry Stebbings** [18:25]: You've been quite vocal about Google before. Are you impressed by their latest enhancements with Gemini and where they've really positioned themselves? **Richard Socher** [18:33]: I think it's never been a question of technical strength for Google. It's just a question of classic innovator's dilemma. You make money by showing ads in lists of blue links, so it's hard to give people just a straightforward, useful answer. And they have to play around with that now because there's too much pressure. But in a perfect world for them, not perfect for the end user, they wouldn't want to change that that experience. It just prints $500,000,000 a day. Google made this so much money, they didn't know what to do with it. They don't want to pay dividends because then you kind of admit defeat that you can't grow anymore and you don't know, you ran out of ideas. So you have to keep doing something. And they built internet balloons and self driving cars and just infrastructure, fiber infrastructure. They have so much money, they know **Harry Stebbings** [19:19]: what to It's also very difficult to acquire because you have the regulatory provisions, which mean it's super, super hard to get anything through that's non core, which is why you get something like Wizz, I think, which is like enterprise is not core enough for it to be blockable by any regulators. So I totally agree with you there. But I do think Google are actually best positioned. When you think about one of the most important things being touch points to end consumers, I don't think anyone other than them Microsoft are actually better positioned. I **Richard Socher** [19:43]: mean, in theory, Apple would be so well positioned, **Harry Stebbings** [19:46]: but in practice, they've been not doing much. What is the thing that's holding back the progression today of LLMs and specifically how we use them? **Richard Socher** [19:55]: I think one is that personalization, and that's why it's so easy for people to switch around LLMs too. Know, like, DeepSeek overtook almost every other thing other than ChatGPT within a week. Like, with almost no proper, like, sort of marketing, like, Other companies in our space spend millions of dollars every week on marketing, right? And DeepSeek comes in and- What do you think they did to do that? I mean, obviously, one, the fact that it was the first open source model that wasn't just almost up to par, but in some cases was actually above the closed source models was just a shock. You could almost hear billions of dollars of VC investment evaporate sort of into the ether when that happened. Everyone said that should have been impossible. And there's a narrative of you gotta have billions and billions of dollars to be able to even compete, so don't even try. That was the big shock. The timing was great too. Like, it was actually for several weeks. It was the absolute best model, which is harder and harder. You know? Like, this weekend, we have a new LLM A four model. Why was it launched on a weekend? Maybe they know something we don't. Maybe in, like, a week or two, there's an even better model that's gonna come out. Right? There's some luck involved too. And then just like that the fact that it came out of China gave it even more press, controversial that that could have happened in China. Of course, like, why are you surprised? China has been incredibly good at taking technology and making it scalable and cheaper. **Harry Stebbings** [21:18]: One of the big elements you mentioned there was the cost effectiveness with which they said they train the models. Do you buy how efficient they were in when they stated how much money it cost? Of course **Richard Socher** [21:27]: not. Like, I think they had and that was part of their marketing narrative and part of the other marketing narrative for a closed source company because it's super expensive, and they all have sort of underlying reasons of why they pushed the number to be super high or why they pushed the number to be super low. It's also clear that it probably cost them a 100 to $100,000,000, but it's still incredibly cheaper than billions of dollars that we're told it would take to train these kinds of models. So the fact that, you know, I think they floated like $56,000,000, that was maybe the very last training run at best if you just count electricity costs or something. But, know, electricity costs can be higher and they fluctuate. Like, buying the GPUs is not included in that. Generally, in model development, you train one finally, like, really good, final good model. On the path to that, you had to run many what we call ablations or hyperparameter runs, where you tune a little bit, like, should this joint be, you know, moving this much or that much? Right? Like, these large models have thousands of these hyperparameters, and you need to tune them. And so on the path towards the best model, you usually train hundreds, if not thousands, of smaller models with increasing sizes. So all of these will have cost, like, several millions of dollars too, or maybe hundreds of thousands, and they're smaller. And so long story short, you put all that together, it was probably closer to a 100 to a 100. Is **Harry Stebbings** [22:43]: distillation wrong? **Richard Socher** [22:44]: No. It's a **Harry Stebbings** [22:45]: useful thing to make models smaller and yeah. Do you think models of venture investments, when you look at the dilution that happens to venture investors over the successive rounds, costs so much money to do. And then you also have stock based comp, which makes it just even more dilutive. I don't think any venture investor is going to make money from LLMs. **Richard Socher** [23:04]: Yeah, again, if you're just in that narrow niche of infrastructure rather than you own the end user in some capacity, you own some vertical, I have personally stayed away from investing in any pure companies. **Harry Stebbings** [23:19]: If you were an investor, say, full time, where would you be spending **Richard Socher** [23:21]: the most time? Early stage, strong technical teams that also have some market insights into a specific vertical, into a specific app. And one of the big verticals that I love so much and think from first principles is the right time to buy right now is in biology too. And so biotech is essentially a perfect storm. Right now the markets are really down, public bio, valuations are much lower for early stage startups, even if they already make good revenue. And the technology is just the perfect tool to really push biology to the next level. **Harry Stebbings** [23:53]: You said there about kind of reasonable pricing with bio. Do you think we're in a bubble in terms of AI early stage? Again, **Richard Socher** [24:00]: this is sort of where I think the analogy is the tide is rising, but it's also hot, so from time to time there are bubbles on top of it. I don't think we're in an AI bubble period. I think intelligence, the fact that the marginal cost of intelligence goes down is the same as the marginal cost of electricity or coal or something going down, but we will just use it more and more. A couple of years ago, I tweeted and read about this thing called Jevan's Paradox. A couple of weeks or months ago, some other people have found it also and talk about it a lot, but it is, for those who haven't yet seen it, it's a very useful analogy here. So, in the first industrial revolution, like 1860s or so, Jevons was an economist, and he looked into the price of coal. And a lot of the smartest engineers and minds at the time made more and more efficient coal, like steam engines. And so he eventually thought, and a lot of people thought, well, if steam engines get more and more efficient, then we'll need less and less coal, so the price of coal will go down. But what actually happened is you just used steam engines in more and more places. Eventually, they could create electricity, they can move steamboats, and so on, and you just used it more and more, and so the price of coal actually went up instead. And so I think the analogy here is that, yes, AI, the marginal cost of intelligence keeps going down, but that just means we're going to use it more and more places. Where do you think we don't use it today, or don't even think to use it today, where we will be using it? Two areas where I think almost every person also agrees on the planet that it's not about the jobs, but about the outcomes, and that's research and medicine. Most people don't say, I want more PhD students, like, in the world. They just want the cool things that PhD students develop. And most people don't say, I want more jobs in healthcare. They just want cheaper, faster, better healthcare. And so I think those are two beautiful areas that still were They haven't had their ChatGPT moment. And I'll maybe mention one more, is AI and economics. I'm writing this book right now. We've had this paper in 2018, I think, called The AI Economist. And the field of economics hasn't had their ChatGPT moment yet because it's such an old, slow moving field. They don't have archive papers, they don't have conferences where you publish every couple months, and so we actually built this two level reinforcement learning model where you had an AI economist and you had a bunch of intelligent agents that were just maximizing their own utilities, and they're adapting to different taxation schemes and tariffs and subsidies and so on. And you basically could now ask this model, this system, what's the most efficient taxation and subsidization scheme for maximizing this objective that I'm giving you? And people can disagree on what the objectives are. One reasonable one was productivity multiplied with equality. And then that system could simulate billions and billions of years of taxation and subsidization schemes until you basically would find the optimal brackets and whatnot. And just imagine how many millions of lives were were lost to humanity trying to figure those things out. Instead, we could ask an AI to give us some advice on, like, what would be the actual best setup if you have a certain set of goals. You wouldn't have to, like, try out, like, a massive tariff change in the world. You could just ask an AI model first to simulate it for you. That is an underrated area of AI. **Harry Stebbings** [27:19]: That's fascinating. I've never heard about that before. **Richard Socher** [27:21]: That paper, we did that work at Salesforce and the science marketing. You realize, like, even science, you think, oh, it's just like someone has a eureka moment, and then because of that, they, like, become super famous and everyone loves that idea. Science is also a human system, and you have to do brand and marketing, like something that DeepMind in London here does incredibly well. Their science marketing is probably the best in the world, and so that paper never quite had its moment in the sun yet. **Harry Stebbings** [27:48]: One thing I worry about is the excitement around robotics. I find that robotics have not had their ChatGPT moment on the foundation model side. How do you think about robotics as having their ChatGPT moment or lack of yet, and maybe excitement or lack of excitement that you have towards it moving forward? **Richard Socher** [28:04]: That is a great question. I think the tricky bit in robotics is that the part of why ChatGPT had this amazing moment is that it's so general, right? You can just ask it anything. So the equivalent to the generality of a ChatGPT is a humanoid. But the humanoids don't work really well yet. The problem is when robotics, like the cost, right, you only want to build specific types of robots when there's a highly scalable process. And now once there's a highly scalable process, the humanoid form factor is not the most optimal factor. Right? Like, if you have, like, a highly scalable process is in the fields, in agriculture, you don't want a bunch of humanoid robots hunching over and, like, weeding. No. You just get a freaking, like, massive tractor with a bunch of lasers and, like, thousands of little arms and spray, like, cannons, and then you just, like, either zap the the weeds away. You don't have them hand plucked by, like, a humanoid robot. Right? And so for almost every process that is highly repeatable, there's a better, more quickly evolved new hardware form than five fingers on two arms. And so humanoid only works in cases of ambiguity? In ambiguity, and where you have a massive scale of many different tasks in environments that are custom built for people, where maybe the speed and efficiency doesn't matter as much, and so on. Just take, like, for consumer, right, you have a dishwasher. Imagine you could have a humanoid doing the dishes, but it would be so much less efficient than a dishwasher would be. And so maybe humanoids, I love them. Unfortunately, excitement in the future isn't a zero sum game. I can love custom made robots. I can still be excited about humanoids as well. Then **Harry Stebbings** [29:45]: Why would you what's the bull case to excited about humanoids then? Because I just listened to you there, I'm like, it's all completely rational, right, and fair. Paint the bull case. **Richard Socher** [29:53]: I think the bull case is at home, like, when it actually the speed doesn't matter, but there's just a whole host of many different tasks. So you can have a Roomba, and the Roomba will do one thing better than a humanoid. You can have a dishwasher, and it will do that thing better than a humanoid. But if you now also have, like, 50 other things, sort my socks and do this and open the door and get a package from outside, put it inside, and like 50 or a 100 other little tasks, none of which you actually have to at scale massively every day, all the time, so the custom robot form factor doesn't make sense, then I think humanoids can be helpful. And if they're quiet enough and the task execution is quiet enough, you can have them work slowly all night. You have a party and you wake up and the whole place is clean again. Now, the problem is that that is and it's like these cluttered environments that are all different, there's no standardization, that's really, really hard for AI. **Harry Stebbings** [30:44]: Also, the dexterity that's required within hands to know about picking up a glass and how much tension to put on it versus not breaking it to plump a pillow, as stupid as it sounds. Yeah. I was speaking to one founder the other day and he said, we're so far away from that. **Richard Socher** [30:58]: It's similar to what we see and what we have seen with like radiology, for instance. It's very easy to build one radiology classifier for one thing and make that better than a human, but then there's this very long tail of things. And so you need a lot of money and a lot of resources and a lot of data to see the long tail of all things in radiology before you could actually automate a radiology process fully from end to end and get it fully FDA certified and so on. And in household robotics, you have the same very long tail. **Harry Stebbings** [31:28]: It's so interesting. It's almost like the opposite of LLM's where, like, the technology is ready and it's actually human adoption that's the thing that's stopping it versus, like, robotics, which is, actually the adoption I think would be there to have a cleaner in every household. But actually, it's the technology that's blocking the **Richard Socher** [31:42]: adoption. Still like a lot of hard work and very interesting research and more and more development. Think like Facebook Meta had open sourced, like, some new fingertips also that had some pressure sensors and stuff, and you'd have to incorporate that. I think it's doable now. It's just, like, it's a lot **Harry Stebbings** [31:59]: a lot of work. I've had guests on the show before talk about different medical discoveries. You know, my mother's got MS. You mentioned about biology. Do you think that we will find the solution to some of the biggest medical problems in the next ten years through some of the discussion that we've had already? Yes. That is that is one **Richard Socher** [32:18]: of, like, several, like, several chapters in my book are about that. Think that's one of the most exciting part of AI, that AI will change. In in many ways, science, I think, has been stuck in understanding the micro really, really well, but has not yet found a tool to understand complex systems really well. Like, we know every neuron, and how every single neuron in your brain works, just from first principles. You can take one out, it has all the synapses, you can compare, and then you see, okay, there's inputs, outputs, and this is how it fires. But then when you put a bunch of them together, and suddenly you have intelligence, no one understands these thresholds, these complex systems emerging and having these emergent properties. And why is it when the brain is slightly bigger and has enough neurons and the right setup, now all of a sudden you have true human intelligence versus much less strong animal intelligence. But they can have better visual intelligence, but not language intelligence, and all these different things, these emerging properties. Same with the microbiome in our gut. We understand how one bacterium what one bacterium does, but then you put them all together, no one can really tell you, like, why, like, certain foods will have a skin create a skin issue or change your mood and and things like that and how that all connects to your microbiome. People literally do poop transplants because we don't understand. And sometimes they work and work magically well and cure actual diseases for people because now they have better gut microbiome. And so I think there's so much complexity and AI is the perfect tool to tackle that kind of complexity because we can now have similar things. We understand how one neuron works, but when you have enough of them, you scale it up enough, all of a sudden you can have a conversation and people think it's human being on the other side. Right? And so AI is the perfect tool. Medicine is the perfect application for that. **Harry Stebbings** [34:04]: Where are you most concerned about AI? **Richard Socher** [34:06]: Job changes are brutal in the moment. They will add a lot of pressure on social systems. I think long term, I'm an optimist, but short term, you've got to have good social systems and help people with a path in this new AGI future as their jobs change and fall away. The modern day ludites of our illustrators, right? Because it used to be that you can charge $500 for illustration. And if you cared about just seeing illustrations as art and you want to see as much art as possible in the world, you're excited about having AI now create Ghibli and all other kinds of beautiful illustrations. But if that was your life's income and you spend years honing that skill and now that skill is just not worth it anymore, then it costs 5¢ or less to make an illustration, you hate the technology, right? And this is understandable. Now, of course, just like the past day Luddites, we all appreciate having a T shirt and everyone, even in Africa, can have as many T shirts as they want and it's cheap. And we like most humanity likes the outcomes, but most people don't like it when they get paid by the hour for those outcomes. And so that is a negative downside that I think it's onto governments to help their people not to block the technology and enforce not using it, but actually use it and use the proceeds and help people to learn new kinds of skills. Everyone **Harry Stebbings** [35:18]: always says, well, you know, we've always had this before. You've got the agricultural revolution. You've got, like, bluntly PCs into entering workforces in the nineties. That took actually multi decades. It took long, long time periods to actually move physical machinery into large farms. In still, in a lot of cases, there isn't in some parts of the world. Same with PCs. It took a decade actually for PCs to fully be. This is a software update, so the speed of transition is completely different. **Richard Socher** [35:41]: Yes and no. It is a software update, but you'd be surprised, and we talked about this a little bit earlier, like the adoption is not as fast as you think. It takes people to change their process. It takes them some time. I don't think it'll happen quickly as people think. There's still, I think 60% of all adults in The US have never talked to a chat model, like at all. And that's The US. I think just go to Europe, I'm sure that is even higher. **Harry Stebbings** [36:04]: Do you think in the future we will choose which model we use? I find this archaic that we are choosing the model when we're putting in a prompt. It's like it's like you're taking your car to the, you know, car mechanic and being like, want that span and not that span. It's like, no, you'll just get given the best model for that specific prompt and request. **Richard Socher** [36:21]: No? Yeah. Yeah. So it's actually something that we we have shipped internally, and I think it'll come out in in a week or two on you.com, like, to just automate that orchestration completely away, and only the power users who really want to can double click into the interface and then choose which model to use. **Harry Stebbings** [36:36]: Find that absolutely bizarre. 100%. Staying on that, they're like, is the right solution for government? Everyone says UBI. This will lead to mass unemployment in in, probably, the short term. And retraining is hard. **Richard Socher** [36:48]: Retraining is hard. I used to be a big fan of UBI, and I thought that seems like a fair thing to do. And I think you have to, one, acknowledge that everything falls in some normal distribution. I think that we have to teach kids a drive to want to create something, like to actually have desires to make improvements to the world. In fact, I think one of the biggest civilizationary unlocks would be to all agree that entropy or darkness in the universe is the enemy and that we should, like, spread consciousness into the universe. If we can all agree that's the goal, then we can always be motivated to do more of that, to to spread intelligence and intelligent entities into the universe. That would be beautiful. But zooming back in, I think the problem is that while most people complain about their jobs, it does give them meaning. It does give them meaning to be a valuable part of society and to have earned something that they can then give to their family, to their kids and so on. And so I think UBI will take that meaning away. And we're already in a meaning crisis with the technology and society that we have set up for ourselves in many places, and I think that will just exacerbate it. **Harry Stebbings** [37:56]: Do you think speaking of, like, meaning, do you think we will have AI friends in a more significant way than we have human friends? **Richard Socher** [38:03]: I think we will have more significant AI friends, but I hope it's not more significant than our friendships with people. And you believe **Harry Stebbings** [38:10]: that we'll have, like, the AI companion? **Richard Socher** [38:12]: You know, I've been an investor in, like, Replica, like, many, many years ago. To me, you can find beautiful examples where people just say, look, I used to journal. Now I write into this thing, and sometimes it asks me questions back, and it makes me feel like some someone cares. Think about your best friend. If they kept coming into you every single day with all their little problems, you know, like, that problem didn't cross my threshold of being relevant enough for you to tell me about it. You know, like but some people have, like, that desire to just tell other people about their problems all the time, And very few people have the just time and capacity and emotional capacity to hear that all the time every day. So I think it's a perfectly fine use case for people to work through their problems just like they used to write a diary. **Harry Stebbings** [38:57]: So if you were advising a 19 year old or a 21 year old coming out of university or school about where they should focus to prevent themselves bluntly being in a position where they have to retrain, where would that be? **Richard Socher** [39:10]: It's similar but different, like, because in high school, after you finish high school, you can still study for a couple of years. So that's a big difference. Once you're already out of college, it's much harder. But knowing computer science is incredibly important. Knowing how to program is incredibly important. Why **Harry Stebbings** [39:26]: do you say that with the commoditization and a lot of low level programming? **Richard Socher** [39:30]: For the same reason why we're speaking English. And like, I could have stayed in Germany and never learned English. And then every time you say something, you know, for a while I type it in and then hear it, and then every two minutes would have some useful bit of information going back and forth. Or eventually, I'm gonna have just like a full Babelfish in my ear, and it will translate it hopefully fairly well and get the connotations and maybe capture my voice. And maybe that technology is like fully there in five years or so, like, so good, so cheap, so prevalent that everyone will just travel with it and everything. I think even then, it'll be useful for some people to know some languages. But do we need to translate as much? No. Actually, in my master's, I was studying Chinese back in Germany. I just fell in love at that time with statistical machine learning, pattern recognition, statistics, and what we now call AI, and realize, like, that is a more useful way to spend my time than learning five more languages, which is beautiful for me, but not useful for humanity. And so I do think that will fall away, but programming isn't just about programming itself and being, like, an IT or being a programmer or a developer or so on. It's also about a different way of thinking, and it's a different way of understanding the world that you're in. And so if you have an understanding of that, then it feels less like magic, and that you are more empowered to actually contribute to that world. So I think that's important. Even if you want to study law, medicine, chemistry or whatever, you should combine it with computer science because all of these fields are going to change over the next couple of decades. **Harry Stebbings** [41:06]: How do engineering teams and their structures change over the next couple of decades? **Richard Socher** [41:10]: I think the biggest problem and biggest worry I have is that the entry level jobs right now are more and more automatable. And so you hopefully have companies that have a long enough time horizon such that they're willing to train people even though an AI could do the job because they're either cheap enough or, like, they just have a long term view on the world. And then by the time they are senior enough, hopefully AI hasn't automated that senior job also, and then they can be in that senior role and then manage all the AI agents because they understand the processes. Turns out, if you've never done something, it's much harder to manage someone to do it for you. Same with, like, vibe coding. It's super fun. It's awesome. I just posted this really funny video of this comedian about vibe coding. But if you don't know how to program at all, you're also not going to be as good of a vibe coder. Like, there's just certain things like complexity theory. You just say, Oh, do this sorting for me. If you don't know how sorting algorithms work and how fast they could be, make it faster, make it faster. In some ways, there are certain complexity theoretic upper bounds of how fast a sorting algorithm can be, and you just telling it that it'll go to jail if it doesn't do it faster is not gonna make it any faster. And so it's useful to understand that you also have seen that even LMs benefit in their general reasoning skills when they know and have seen programming as training data, and I think that's true for people too. **Harry Stebbings** [42:32]: So will engineering teams be smaller? **Richard Socher** [42:34]: I think so. I think a lot of teams will be more efficient, be managers of AI. I think all the boring bits of all work. I think one of the ways to think about the future of work is one, we'll all become managers of AI. Number two, managing is hard, and so we have to train people to it. What that means is like, in the future, every time you do a job like a dozen, two dozen times, you're gonna be like, wait, why hasn't the AI now taken that over from me? Why do I still have to do this repeatable, boring thing? I personally hate repeatable, boring things, so I'm all for it. But that is a mindset shift that schools don't teach yet, and that will take a generation of kids to have to grow up in this mindset. **Harry Stebbings** [43:10]: We see this kind of continuous battle between Windsurf and Cursor. And a lot of people say that there's actually very little switching cost between moving between the two. How do you think about that and how that market plays out? **Richard Socher** [43:23]: Yeah. And Codium, I wish I could have invested in Cursor. One of the founders was actually an intern at you.com, and I would have loved to invest. But yeah. So and then Codium, I fortunately was able to invest in. Was **Harry Stebbings** [43:35]: he brilliant? **Richard Socher** [43:35]: Yeah. He was very, very smart. I really tried to keep him at you.com and stuff. He went from intern to, I think, CEO. Yeah. And of course and so, yeah, brilliant. Why did you **Harry Stebbings** [43:43]: not? Why did you not follow his money while, like, bunny try and invest when he **Richard Socher** [43:47]: started? I tried. Some other investors were like, oh, well, they had an even closer relationship with him. Bastards. Hate ambassadors. **Harry Stebbings** [43:55]: Okay. I'm happy for him. **Richard Socher** [43:57]: So, yeah, there is very little switching costs. Same is true for LMs in the consumer world, right? None of them are that amazing yet. None of them do enough personalization yet. Now, where there is a lot of switching costs is if you have company internal data or you have unique data assets at funds. Concerned are **Harry Stebbings** [44:13]: they for you in terms of giving very, very private data to you, where traditionally it might be held on prem? They're nervous about security, they're nervous about privacy. How much of an actual barrier is that? **Richard Socher** [44:23]: It's a huge barrier. LMs are garbage in, garbage out to a large degree. So if you have a search model or an index and you ask like, what's new with Trump? And that search index brings back pages from, like, six years ago. The LM will tell you wrong and outdated things about that query. So the search is kind of the often forgotten infrastructure layer for LMs. And so companies are rightfully concerned about privacy and all of that. And that's why we have zero data retention. We have agreements that we don't train any model on company internal data. That's how you get into enterprise. **Harry Stebbings** [45:01]: Do you think we are seeing the biggest corporate misbehavior from this generation taking ChatGPT and putting company data in it that they shouldn't? They should stop doing that if they **Richard Socher** [45:12]: have done it in the past. Do not think they are? I think a lot of people are doing it, but we also know now we have customers who are like, okay, like, we want to know exactly which models are accessible and have these security requirements and so on. And that's where you really can't be a great consumer company and a great enterprise company. At the same time, it's very, very hard, and we're focused on that enterprise and make sure that the security is there, the trust is there, the accuracy of the answers there, the ability to say I don't know is there. All of these things are important aspects. **Harry Stebbings** [45:43]: To what extent is cash and the weight of cash a weapon in this market? In terms of like startups? Like, is cash a moat for LLMs and for companies in this market? **Richard Socher** [45:54]: It can be, but we have seen some companies now that had raised hundreds of millions of dollars and still died. We're going to see a correction when companies are trading 100 ADX their ARR and they don't have a real moat and the switching cost is close to zero to go to DeepSeek or something else. I think there are these some retention numbers on deep seek. I have not. **Harry Stebbings** [46:18]: They're pretty shit. **Richard Socher** [46:19]: I'm not **Harry Stebbings** [46:19]: surprised. No one's staying with deep seek. I'm not surprised. Yeah. And so it's like, actually, is that very valuable? Does that not prove that the ultimate value in this market is consumer brand? **Richard Socher** [46:28]: In some ways, I think AI has gotten so exciting for so many people that the startup world is going back to the basics. Like, you know, it's just like when there were a 100 photo sharing apps and only one Instagram and maybe a Flickr and so on, no pun intended, Flickr, I think we'll see something similar in AI. It just goes back to, is your branding good, your marketing, your sales, your distribution, and then, of course, a lot of the technology things. But those get commoditized more and more, just like sharing photos was a fairly commodity capability, but a lot of little subtle details were better for Instagram. Now, that's consumer. In consumer, we usually end up in monopoly or duopoly situations. Enterprise is a very different world. **Harry Stebbings** [47:09]: To what extent do you think we see ChatGPT and OpenAI move and kill a tonne of different more consumer facing apps? So we looked at a company recently and they were in the basically picture creation space for fashion. So you take a picture of a model in a T shirt and it'll do it in all the T shirts in the SKU line. Yeah. OpenAI just released that the other day. There's 10 of them that just died. Background remover, 10 just died. To what extent will we see OpenAI kill a generation of companies of this material? **Richard Socher** [47:39]: I actually don't think they're all gonna just die overnight. Like, think about speech recognition. It's very commodity. There's tons of open source speech recognition, like algorithms you can download and so on. There's still several companies that make millions and, like, tens or even hundreds of millions of revenue doing speech recognition just perfectly, that last little bit. Like, you don't just throw a full feature length movie and make it in this new style with ChatGPT. Companies are going to want to have custom solutions for them. Even that fashion thing, I think there's a space where you take the picture and it's just integrated in your camera, in your workflow. You can change the skew, you can change the model's hair color, and every little pixel is just perfect. So you can put it on a big billboard. I think there will still be like specialized companies that go and do deeper things than you could do if you knew how to use it all yourself and like you're really clever and like you are an early adopter. **Harry Stebbings** [48:39]: What do you think is the biggest misconception misconception that that people people have have today today around around AI and LLM's? **Richard Socher** [48:44]: I think the biggest misconception is that we have sort of this bimodal, like black and white when things are often in some gray in between. Like, some people think it's gonna take off over all the jobs. And then because it's so brilliant, like, overnight, like, everything will be gone, and then, like, the next night, it'll kill us all. Right? Like, it's just, like, this extreme optimism that then can sometimes also switch into extreme, like, pessimistic optimism of, like, oh, the technology is so good and becomes fully self aware and conscious that it will then obviously want to kill us all. So there's like the misconception on that side. Then there's the misconception to still like, and I see this in Germany still, there's still people like, you know, two years ago it was crypto, this year it AI, like, maybe it'll just go away. And, like, that's also a huge misconception that still exists in the world. It's hard for you and I to imagine as we live sort of in a bubble, I see it when I travel. **Harry Stebbings** [49:30]: Do think with this advancement, we have escaped Moore's Law in terms of our speed of progression? Is Moore's Law ever escapable? **Richard Socher** [49:37]: Of course. And I think parallelism has helped a ton. And NVIDIA shows us that, yeah, we might not double the number of transistors or whatever every eighteen months, but we may have more parallel ones. And then there's still the black horse of quantum, which may, you know, a lot of announcements, unclear if they're, like, how real they all are, but, like, that will also be a major shift when it finally does happen, but it might still take five, ten, fifteen minutes. **Harry Stebbings** [50:01]: What would be the most significant changes that result from quantum development in a way that we would like to see? **Richard Socher** [50:07]: I mean, there's the obvious one that we don't wanna see, which is just like all the passwords need to be re encrypted and changed, and all the data that has been leaked but is encrypted might get decrypted, and hence, like, people know what's in some things of the past that they had hacked but couldn't really decipher yet or or decrypt. I think on the positive side, what I'm really excited about is getting quantum computers to a scale where we can simulate a cell. We can right now, really, really accurately only simulate a few 100 atoms at best and how they really, truly interact with one another. And neural nets can approximate that. There's a lot of cool things where you can hack simulations, but from real first principles, really deeply model physics and chemistry that is complex and eventually biology, that will be such a massive unlock because in AI, anything you can simulate, AI can solve every problem in that domain. You can simulate Go or chess. You can simulate a computer game because it's in the computer and you can play around with it. Obviously, AI is going to solve that at some point. None of those results were that surprising to me, though they, again, amazing marketing and grazing, actually doing it, it's really hard, but it's not that surprising. But we can't simulate most of the interesting things in the world, such as a cell. But once you can simulate a cell and then multiple cells and organs and organisms, all of sudden AI can try billions of different things on how to cure that cancer, how to cure MS, how to cure all the bacteria and viruses, and all these different things. Like, it will be such a massive unlock to be able to simulate those things with quantum computers and maybe also more and more with normal computers. **Harry Stebbings** [51:46]: Dude, I would love to do a quick fart. I've peppered you with different questions literally from every different spectrum. And **Richard Socher** [51:52]: I'm still I'm still thinking about the negative consequences and what kids need to do, but, yeah, **Harry Stebbings** [51:55]: well, **Richard Socher** [51:55]: let let's do a quick What **Harry Stebbings** [51:57]: what did **Richard Socher** [51:57]: you believe that you now no longer believe? Like I said, I think a big one for me was this sort of skepticism about the future. I'll tell you a story. Like, one of the co founders of OpenAI and I started Bet, I think, seven years ago or so, where he said, We'll have AGI in nine or ten years. And I was like, I mean, I'll work hard on research to make my prediction be wrong, but I really don't think we will. This was at an AI conference, and we're both still more junior than we are now. He had this really strong belief, and they worked on robotic hands, they're like, Another step towards AGI. I'm like, It was a cool robotics project. They worked on DOTA game playing and OpenAI, and I'm like, That's a cool RL project. And they're like, Another step towards AGI. And then they saw our prompt engineering paper, which they cited, and that NLP route was a more legit step towards AGI than the previous things. But long story short, we did this bet. And in the bet, he has to win. I think it ends in 2027. So three things have to be true. We have to have a personal robot that cleans the whole house the way my cleaning team does, and it needs to be purchasable, like, for reasonable amounts of money. It needs to solve a millennium math problem, and it needs to, like, translate a book perfectly so that the actual author would be like, that should be my official translation. All three have to be true for him to win the bet. And I will probably still win my thousand dollar bet, but he became a billionaire in the process of proving me wrong. And so that kind of shows you you have to just have the constructive optimism, and that was probably something that a belief I changed. It's just like, even if you don't think it can be quite possible, you should try to make the most audacious goals and set the most audacious goals for yourself, and then basically work on pragmatic milestones towards those goals. **Harry Stebbings** [53:46]: OpenAI at 300, Anthropic at 60 or Grok at 50, which would you most invest in? Can I choose none? I mean, open source puts a lot of pressure on How do you think about the distribution of value between closed source and open source? And do you not think it follows the latest of Linux? **Richard Socher** [54:03]: If you're really sophisticated, you can use the open source more and more. And as the open source models have caught up, it's just very hard to say, this is like super unique technology. It just becomes like, you know, for a while in technology, like, having a really good database was really powerful. And indeed, was like, there is an Oracle now. And so if you're really, really crazy large scale, then you might still use an Oracle. But for a lot of other folks, they can just use simpler, other smaller databases, and you have that wild horse of **Harry Stebbings** [54:33]: Do you think they will, though? It's like you said they're the final 10% for, you know, speech recognition, which is, yeah, they can do, but they don't. **Richard Socher** [54:40]: Yeah. I think, like, again, it's very different for consumer. Like, OpenAI actually has a massive number of consumers, and that's super duper powerful. And so, yeah, all of them have advantages. Like, Sonnet 3.7 is the best model for a lot of things, especially in coding and engineering and so on, like you said. It's a little bit unclear to me. I also just love investing in early stage where you can have 1000x's and so on. I just don't see 1000x's for those companies. **Harry Stebbings** [55:06]: Do you care about money? I don't. Has that always been the case? **Richard Socher** [55:09]: It has been always the case. Yeah, I was an academic for a long time. I want to be a professor. I want to do research. I miss the research also now still. I now have to I realized at some point you have to care about money because money is one of the best indicators of impact and allows you to do epic, cool things. But I don't intrinsically care about it. **Harry Stebbings** [55:31]: How do you think about defining or measuring success for yourself? **Richard Socher** [55:35]: I think a lot of it boils down to sort of how much positive impact you've had in the world that matters enough so that people in the future will still remember it. **Harry Stebbings** [55:44]: Is there an element of ego to that, about being remembered as the creator of? **Richard Socher** [55:48]: It's undeniable. Like, in some ways, it's beautiful if you do it, but the difference here for me at least is that I'm okay just being remembered by the people that know. Like, most people have no idea who invented penicillin. And most people can't name the people who invented the cure for their cancer, because they're just like, some doctor gave it to me and I got this medication now. Right? But the people in the know know, and that would be good enough. **Harry Stebbings** [56:15]: What trait are you slightly ashamed of but has contributed to your success? **Richard Socher** [56:20]: I do get quite extreme too if I get into into a zone, and then, like, everything is kind of a nuisance, and and then you just get, like, just really intense about No. I'm also happily married, so that helps not being lonely. But, yeah, I don't I don't need people all the time around me. In fact, I'm an introvert. I enjoy I have, like, sometimes events at my my my place. I have, like, hundreds of people, and, like, I enjoy that a lot. I get sort of there's an activation energy, and then you're like, I'm now in this mode. But afterwards, I'm like, I don't have to see anyone for another month now. **Harry Stebbings** [56:51]: Final one. When you look forward to the next ten years, what are you most excited for that you think is realistic and we will see happen? **Richard Socher** [56:57]: Improving longevity is one of those AI plus bio corollaries or or follows. That is just really, really exciting. And I think longevity is massively underrated, and there are a lot of people who are snarky and say, oh, yeah, whatever. I don't care. Want it. Like, they all people say that until it's a few days before they're dead and they're in pain and they're like, fuck. I wish I, like, was a little bit healthier, you know, beforehand. And was that really worth it to, like, drink all the time, not sleep enough and so on? So I am trying to be better about it personally. **Harry Stebbings** [57:30]: What do you do that you'd most like to stop doing? **Richard Socher** [57:33]: Most like to, like, not sleep enough. **Harry Stebbings** [57:35]: What's your sleep schedule? **Richard Socher** [57:37]: I'm traveling right now, so it's all screwed up, and that's like, that's actually one of the worst parts of of traveling now is like, it just messes with your sleep. Actually, I stopped drinking last year, also for longevity reasons. And life is like 20% less fun, especially in some evenings and so on, it is better. But I realized actually after not drinking anymore that the majority of times when I felt bad in the morning traveling actually from alcohol, just from like sleep deprivation and like shitty sleep schedules. So I feel just as bad. **Harry Stebbings** [58:07]: Totally get you. Final one, final one, I promise. The magic you want is in the actions you are avoiding. What actions are you avoiding? **Richard Socher** [58:15]: Whenever I see something that I should be doing, I try to write it down and then I try to work towards it. I like to think I don't avoid many actions, but I guess when it comes to longevity, like every night I wake up in the morning and I was like, I should have gone to bed earlier last night, and I would probably feel better right now. So maybe that's **Harry Stebbings** [58:34]: one Is the EU fucking itself for AI regulation? **Richard Socher** [58:37]: Unfortunately, the EU has shot itself in the foot. A lot of different kinds of regulation and in particular, AI regulation destroying a fledgling ecosystem that could never get off the ground. **Harry Stebbings** [58:47]: What should we be doing that we're not doing? What would you do if you were in charge? **Richard Socher** [58:51]: A lot of things. I would make computer science a mandatory subject in all schools, a very sought after minor for almost every major in college. I would help people and excite people more. It's also a marketing thing to some degree and just mindset shift for people to start their own companies. Because every person that cares about outcomes and outputs loves AI. It's only the people who think of how to make money in terms of hours spent that might not like AI. And so trying to do some marketing for the populace to have that mindset shift of like, I can make outputs, can be an owner and creator, and then possibly start a sovereign wealth fund so that you can just participate in all the upside of amazing AI technology worldwide, not just like sovereign wealth fund that only invests internally inside the country, but also externally, and make it easier for large enterprises to buy small startups also, maybe get some tax benefits or something for it. Because there's a lot of not invented here mentality in large European companies. And so you just don't have as much of that ecosystem. I would reduce the barriers to go public so that you have even more benefits for startups and have, you know, they're basically two exit routes, right? You either get acquired or you go public for VCs in the ecosystem. And so you get more VC if both routes work out better. I'll stop there, but been asked that That's question a fantastic. Yeah. I try to be helpful when people ask me that question. **Harry Stebbings** [60:22]: Dude, thank you so much for putting up with me. Completely just peppering around. Like, as I said, I I I have these quite honed schedules, and then I'm like, fuck it. Let's just go for it. You've been fantastic. So thank you so much. It's always a pleasure talking to you, man. That was so much fun to have Rich in the studio there. And if you wanna see the full episode, you can find it on Spotify by searching for 20 v c. 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