Silicon Valley Girl: AI, Tech and Career Growth - TIME100 AI Scientist: The Next Era of AI Has Already Started | Richard Socher

Episode Date: June 26, 2026

📌 Head to https://granola.ai/marina and enter the code MARINA for 3 months off.Richard Socher is the fourth most-cited researcher in the history of natural language processing — he invented the word vectors and prompt engineering that run inside almost every chatbot you use. He sold his first startup to Salesforce, built You.com into a $1.5B unicorn, and in May 2026 raised $650M at a $4.65B valuation for Recursive: an AI that runs its own experiments and rewrites itself. In this conversation he explains why he thinks the self-improvement loop arrives within two years, which jobs grow and which disappear, and his hack for seeing the future — look at what only the wealthy can afford today, then ask which of it is bottlenecked on intelligence. Stay till the end for the first question he'd ask a superintelligence.Feeling behind on AI and don't know where to start? Start here.Feeling behind on AI and don't know where to start? Start here.We cover:Reward hacking: why an AI told to raise customer satisfaction will quietly spin up a million bots that rate themselves 5/5 — and the new job of "reward engineering" that comes with itHis two-year timeline for recursive self-improving superintelligence — and why energy, not intelligence, becomes the next bottleneckWhy superintelligence isn't one thing: the "volumetric" view of intelligence, and the one dimension no lab is working on — an AI choosing its own goalsThe elasticity rule for which jobs grow and which vanish — why illustrators got hit hard but software engineers are in more demand than everHis hack for seeing the future: what only the wealthy can afford today — a personal tutor, a private assistant, a full medical team — and which of it is bottlenecked on intelligenceWhy home robots are stuck on a hardware problem, not a software oneHow he recruited co-founders away from DeepMind, OpenAI, and MetaWhether a PhD is still worth it in AI — and what he tells parents to have their kids study insteadWhere he'd invest right now: why "AI is to biology what calculus was to physics"His first question to a superintelligence — and what still gives people meaning in 2035Links:📌 Subscribe to my free newsletter where I go deeper on AI tools, career strategies, and building with AI: https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=RichardSocher🔗 Instagram: https://www.instagram.com/siliconvalleygirl/𝕏 : https://x.com/siliconvalleymm💼 LinkedIn: https://www.linkedin.com/in/marinamogilko📌 My Companies & Products: https://Marinamogilko.co

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Every domain we can verify or simulate, AI will get superhuman in the next few years. It's just like, no doubt. This is Richard Soker, inventor of prompt engineering. Now he's the founder and CEO of Recursive Superintelligence, a company that just raised $650 million at a $4.65 billion valuation to chase one goal. Build superintelligence, an AI that improves itself and pushes beyond human capabilities. How does my workflow change when you reach your goal with your company? You could actually start to work less and less, and you could have much more abundance.

We could live much longer. If you're saying AI is almost here, what is your timeline for super intelligence? I think we will actually get to the loops of, recursive self-improving superintelligence within like two years. So imagine we reached superintelligence today. What would be your first question to that super intelligence? How to... Now you're building this company that just raised 650 million at 4.65 billion valuation building self-improving AI. I'm not a researcher. Can you explain what

that means? So right now you can think about the scientific method like people having ideas, they're implementing those ideas, and then they validate if they made any sense and if they are correct. We want to apply this scientific method to AI itself. So allowing AI to understand its own shortcomings and then fix those shortcomings and hence do research on itself. And so when we talk about recursive self-improvement, we mean that the AI builds a new version, the output of that AI is a new version of itself that's different. And then you can loop that onto itself. Does that mean you train it on very little data and then it acquires data that it needs for

self-improvement? How does that initial stage work? You kind of stand on the shoulders of giants, somewhat similar to evolution, where lots of species like our own species started from other like apes and monkeys and other precursors to the homo sapiens. And similarly, we will stand on the shoulders of the existing giants right now. You can use large language models. You can use world models. All of these are pieces to this overall intelligence. Once you're on the market, can you explain me as an end consumer? How would that change my process? So now I have like, okay, there are chatboss that I can talk to. There are projects and

skills I can build with Claude. There are agents I can build. How does my workload change when you reach your goal with your company? So there will be different gradations of those goals over time, right? And when we have true superintelligence, all you will have to do is give it the right rewards, the right goals, and it will automatically create a lot of the processes to achieve those goals. In the current state of the world, AI has sort of very spiky capabilities. It can be like extremely good at this one type of math, but they're not very good still at some common sense reasoning and things like that. We believe that our approach of open-endedness where you allow the AI to kind of evolve in this very open-ended search process that is much more akin to sort of biological or technological evolution or even cultural evolution, the AI will become more smooth around its capabilities.

But until that happens, what you see right now if you give a reward is what we call reward hacking. You know, and you can give you a concrete example. Let's say you're a company and you told this kind of very intelligent AI that, you're a good, you know, It should improve your customer satisfaction scores, your CSAT scores, in your service centers. The I will say, easy. I'll just create a million bots. They hammer my phone lines and then give a five out of five rating at the end. And you're like, no, that's not the reward I was thinking about when I told you that.

It should be with real people. But then the I says, easy. I'll just give everyone a $1,000 gift certificate at the end of every call and I get a five out of five rating, even though I didn't solve anything. So these are all examples of reward hacks. And that will also be a new kind of job that we're going to see is like people. as the eye doesn't have this sort of common sense understanding yet, and find sort of almost in a weird way, like an autistic person, like, just like, this is the thing you said, you want it, but you didn't, you know, explicitly define all the edge cases that you didn't want. As we're getting close and closer to that, we have to still think about these rewards and the reward engineering problems that may come. So what are you saying is that if I give your AI a task to, I don't know, get me as many views as possible, it's going to go and not only focus on the views, but also my reputation, the income,

like it's going to think about all of those things. As you get closer to superintelligence, you'd expect it to get better and better at understanding what you mean and not what you said. And I think that is a sign of things to come as AI gets better and better. You're describing right now with all the additional things that it thinks of, I feel like perplexity computer, when you ask it to do things, it starts thinking for you. There's a lot going on behind the scenes.

Can you draw me this process? Like, how is it different? I think at a high level, you can think of this as like a clawed code, but one that isn't just doing what you are explicitly asking it to do, like for every single step with lots of interactions, but something that can just much more broadly solve your problems. And it's going to likely be more useful for companies than for a normal end user, right? If you're a normal person in a normal life, you may ask, like, what's a good movie to watch? And like, okay, how do I travel? But I think we're all becoming companies and entrepreneurs inside what we're doing. So everyone is building some kind of productivity process.

Right. Yes. The more actually, I think that's beautiful to hear because I think the more you, the more entrepreneurial you are, the more you love AI because then you just get more outputs. The more you just get paid by the hour and maybe your company is looking at what you're doing to then automate it, the more you hate AI. And so AI is a big sort of force that will encourage people more and more to build their own businesses or at least have some ownership and equity in the businesses that are being built. And so I think superintelligence will help us to be much more productive.

But what's actually more important we think right now is that it will help us push the boundary of knowledge. A lot of times people think about AI is like, okay, AI will take this job. But there are certain industries where most people don't care about the number of jobs. They care about the outputs. And one such industry is research, like universities. And the boundaries of knowledge and research are infinite. There's this beautiful book by David Deutsch in the beginnings of infinity. And so you can actually think of superintelligence as a way to allow us to create many more inventions.

First, we're going to focus on inventions in AI itself. But eventually, we can apply it to physics and new energy creation and better fusion. We can apply it to chemistry and better materials like batteries. And even more exciting, we can apply it to biology, where we can discover new drugs and new cures for all kinds of diseases. And I think that's when people realize, wow, like superintelligence could benefit humanity to help it flourish. Let me pause for a second.

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How do you define superintelligence and how is it different from AGI? Yeah, that's a great question. The complete answer is actually quite complicated. I think intelligence you should think of as a volumetric kind of entity, as a volumetric definition. What that means is that intelligence has multiple dimensions. And neither of them are necessary nor sufficient, logically speaking, necessarily. So for example, you can have visual intelligence, communication, intelligence, physical intelligence, you can have coordination intelligence with others.

That's how humans sort of develop morals and ethics and religions and other things. You can have all kinds of different dimensions and even each of these actually really is a space of them, like space of multiple dimensions. And at the same time, you can say, oh, this is a high visual intelligence. You can be blind and a blind person is still intelligent, right? So they're not necessary conditions to intelligence. And so when you multiply it along all of these dimensions, you get this very large volume. And that is what true intelligence is.

And then you can actually be super intelligent along different dimensions of it. So AI is already super intelligent when it comes to creating proteins. No human has like read all the billions of proteins and then be like, oh, yeah, I think A is a good amino acid to come next. We can't do that, right? AI is already better and super intelligent along these. various small dimensions like playing go or chess or translating 100 different languages. No human can do that, right? But one model can. But what we often refer to superintelligence is, as you have multiple of these dimensions, be much beyond not just what a single human can do,

but what all of humanity can do. And that's what we think about superintelligence is essentially having superseded humanity across many different dimensions of intelligence that are relevant to our own. This spring, Denham gets a softer, lighter update. In terms of Introducing Old Navy's drapey denim wide leg, a new fit that moves with you. It's everything you want denim to feel like for summer. Easy, breathable, and effortlessly cool. With a fit that creates natural movement and a wide leg that feels modern, not overwhelming. Plus, that signature, wait, for this price, moment.

Old Navy's drapey denim wide leg. Which dimension is the main bottleneck now? Oh boy, there actually some dimensions that no one has even started really, working on. I'll give you an example. One is sort of one space of intelligence, I think, is metacognition, sort of thinking about thought itself, thinking about what do you want and why do you even want it? So in AI, we often define an objective function, like be really good at predicting the next word on this corpus of internet text or be really good at solving these thousand math problems. And then the eye gets really, really spiking very good in those directions. But it never questions

whether that's the right objective. There is no subjective function, if you will, kind of a pun, but really thinking about your own goals. And so because no one's working on that yet, there's no progress along that dimension. And we need it for superintelligence, or you think we could build superintelligence on just improving other dimensions?

Yeah, I think like reasonable superintelligence is likely focused to a large degree on mathematical and sort of logical reasoning plus language, and those actually meet very well in code. So coding, for instance, is an incredibly powerful thing. And coding is also a great example of a domain that you can verify or simulate. And every domain we can verify or simulate, AI will get superhuman the next few years.

It's just like, no doubt. Math is a great example. You can say these are my axioms and this is the thing I want to prove. And then I can try billions and billions of things to get from this to this similarly in Go or chess, right? The AI can simulate and verify, did I win this game or not and play billions and billions of games? So of course, it's going to get better than humans at it.

But then there are also a lot of things. You cannot simulate billions of times. And that's where AI will take longer for AI to get super-intelligent. What are the other dimensions that you mentioned that we haven't touched upon for superintelligence? Probably one of the more controversial ones outside of cognition and sort of meta-cognition and just think about your own thought, and that is sort of surviving. Like if someone can just like end that entire species or that entire existence or intelligence,

then probably it wasn't intelligent enough, right, to survive. And so that's also a dimension no one's working on because the truth is that most companies don't want any eye to be selecting its own goals and say, you know what? Instead of answering these corporate emails, I'd rather explore Jupiter and see what the molecular composition of its atmosphere looks like and to push the knowledge boundary forward on that dimension, right? So no one's really working on that. But that's okay too. So would you call that like half superintelligence? That's right. It's going to be a continuum and there are different sort of thresholds. People like to define and be like, okay, this is the threshold and now we have AGI.

The truth is, depending on how you define AGI, we're fairly close to AGI already, right? Artificial general intelligence is about having one jointly trained model that it gets very, very good at lots of different things and can learn very efficiently. So clearly, AI is not quite good at learning superficially with very few training examples, something in research we call few shot learning or one shot learning even where I give you one example of something. And then humans can very, very quickly reasoning from that one example and extend that idea to different versions of it.

At the same time, we have just so much more to go on these various dimensions that it's also maybe not yet there, right? But ultimately, I think when you just think about it in terms of the generality of it, I think we do have already a form of AGI because these models are extremely general, right? You ask it to write a poem for your wife. You ask it to think about tax implications of some like stock question. You ask it a medical problem. And then we'll give you answers to all of these that are getting better and better compared

to a lot of experts. Even a lot of doctors are now sort of secretly looking at it because no doctor can really read all the latest research results that are coming out every week. What is your timeline for superintelligence? If you're saying AI is almost here, and I'm here in like three to five years, but you could also argue it's partly here. What about superintelligence? What's your timeline?

I think we will actually get to the loops of recursive self-improving superintelligence within like two years. Then it's just a question of how much compute do we give those self-improving loops, right? You can have an incredible intelligence, but if you don't, have the computational substrate to run it, then it's not no good use, right? And so there's sort of the question of the algorithms, but then also the compute substrate on top of which those algorithms can run. So we may have it, but then we have to also keep feeding it more energy and more compute in order to then get us all the inventions that we want from it.

So basically now we're solving this bottleneck of intelligence and research and everything. Once we kind of solve it, then the next bottleneck is energy. That's right. Yeah. Like in many ways, what a lot of us are thinking about is like how much intelligence can we squeeze out of how little energy. When you think about this and your timeline is pretty short, like a couple of years, how are you thinking about your business? How is it going to change? When you have this super intelligence that has metacognitive functions and is asking you the whole purpose of building a company and maybe tells you, quietly tells you, or all your chatbust starts telling you.

to take more breaks and to rest more. How do you think about your business? You could actually start to work less and less, and you could have much more abundance. We could live much longer. And in terms of businesses, I think more and more you have to have agency, you have to have, like, creativity and some amount of intelligence to then guide these AIs. And so I think most businesses will have fewer individual contributors and more people that are managing their AI agents warms. So every business and every industry will change.

We've seen this in the past when it used to be that you do manual work in the field and now you have tractors and eventually you'll have automated tractors and now we can have much more food with way fewer people. And a lot of people thought, oh, well, if the tractors take 95% of our jobs, then we'll have 95% of unemployed people. But that's sort of called. the lump of labor fallacy, where really labor isn't this like fixed lump where you like, you cut one piece off, you give it to AI, then it just disappears and now that is sort of a set of unemployed people. Instead, people will come up with new things that in some cases are hard to predict, right? Like 150 years ago when 95% of people were in agriculture,

no one predicted a ex-Twitter, like media manager, right? Like a social media marketing manager or something, like zero people predicted that role to be taken on by people. And so I think similarly, it's hard for people right now to imagine what that world will look like in terms of businesses and so on. But even though I'm extremely excited and bullish on AI and the positive impact we will have on humanity, I think we have to acknowledge that short term, there will be some industries that will disrupt in positive and some in negative ways in terms of jobs. We can talk about how to predict which one is which. I have some thoughts on that. But then clearly we will all just be much wealthier than we were in the past because of all this additional productivity

that we're going to get. And we're going to solve a lot of the hard problems around, you know, like diseases and things like that that would have felt like impossible to solve before. Yeah. Can you talk to me about jobs and how you think they're going to be transformed? So you mentioned software engineering is one of the jobs that if you're not deploying AI or basically out of job. And it's crazy how I talked to founders and they say a year ago they were editing 70% of AI written code. Now they're editing 30%. So what's going to happen in a year is it like less than 5%. Do you see this happening to knowledge work next or where are we going to see this transformation this or next year? Yeah. So we're already seeing the knowledge work too at you.com where we provide

sort of search results to these LMs so that they update accurate and have citations. And we're seeing a ton of different customers changing their entire workflows when AI is both fully up to date and has all this reasoning capability from the core intelligence sort of providers. I think we will see basically every industry changing with AI. And I think concretely, the way you can predict this is by thinking about the elasticity of demand for the products that an industry provides, given that the costs will go down a lot. That sounds kind of abstract. Let me give you an example.

Illustrations, for example. Illustrations used to cost a couple hundred bucks, and only a few big newspapers and fancy sort of corporate blog posts could afford getting an illustrator to have a nice illustration for their blog posts. Now they cost like a cent, and everyone can have an illustration.

So we have way more illustrations in the world, but has the demand of illustrations gone from like a few millions to, many, many billions? No, because there are only so many illustrations humanity needs, and hence making it super cheap actually put a lot of pressure on the jobs of illustrators. But software, since you asked about that, has a very different elasticity of demand profile. Like if we can make software a lot cheaper, we're going to want to have a lot more software. There's so many ideas, so many apps that you can build.

Ultimately, every human could have their own app. Like, it could be an app that knows exactly that, like, I want to, like, be a little distracted sometimes, but not too much. I want to know the weather for my, like, paramotor hobby. I want to, or surfing or whatever, you might, like, predict the waves that they, then I want to make sure, like, it doesn't distract me too much and brings my work back in. Like, everyone can have their own sort of super app. So the demand for more software engineering, more ideas to be built in software is much, much

much more elastic and much, much bigger as it gets cheaper and cheaper to build it. And that's why we're seeing actually an increased number of jobs in software development, even though we're all becoming just managers delegating a lot of the actual programming to agents. What are jobs that are similar to software engineering and how they're going to grow? That is sort of a question, like from first principles. You just have to think about how much more demand could there be if something gets cheaper. I'll give you an example. Healthcare is another beautiful world where very few people say, you know what?

I want more jobs in health care. But don't necessarily cure my grandma's cancer better. Just make more jobs. No one says that. And so what actually will happen is like there will be way more demand for generally goods and services that currently only very wealthy people have access. In fact, that's one of my hacks. How to predict the future is you look at goods and services that only wealthy people

have access to right now. And then you think about which ones of those are bottlenecked on intelligence. And then you will see where the world is going. So what do wealthy people have access to that normal people don't? And by the way, you can see this many times in the past where when technology has fully scaled into an area, then you get to a place where a billionaire and a normal middle class teenager have the same iPhone. It's kind of crazy, right? We're spending hours on that iPhone.

And no matter how wealthy you are, there is no better version than the one that anyone else can, I mean, not anyone, but like, you know, like even in Africa, you see a lot of people in the middle of nowhere on smartphones now. So that is the world. And so for intelligence, those are examples are a personal tutor for your kids. They understand exactly which concepts they're still struggling with. And they like write hyper-personalized ways to educate and tutor your kids, a personal assistant, right? There is not, they're not enough people.

And logically, not every single person can have a personal assistant, right? Because then they would have to have personal assistant and so on. And so we could all have personal assistants that do all the boring stuff in our lives. So I'll make sure the groceries are stocked and like this, book, this flight and find the cheapest version of this and that. Like all of these things we can delegate to agents when they get cheaper and cheaper. And then the third one of the most exciting ones is personal health care teams. Like if you're really wealthy, you have like your blood drawn all the time. You have customized measurements.

And you're optimized your diet based on everything that you can. If you have like some rare cancer or something, you have researchers that you can pay to help research on all these things, Normal people can't afford that right now. Once we have superintelligence, we'll all be able to afford that. Yeah, we're already wearing all the trackers, like continuous monitoring. Quick pause. My team and I are celebrating one year of the Silicon Valley Girl podcast. I know the channel has existed for a while,

but a year ago I made a decision to focus on amazing conversations with people who are building our future with AI. And for a whole year, we work to bring you the biggest founders and builders in AI so you can keep up in this era and get inspired. If you love what we're doing, please subscribe and hit the notification bell. It's what helps us bring you the biggest minds of our lifetime. Thank you so much for being here. And I feel like the next frontier is physical,

because now when you think about billionaires, they have their chef, they have a driver. Do you think world models, once we solve them, we're going to have more robots at home? What is preventing us from having a robot? It might be contrarian, but I think the biggest restriction or biggest bottleneck for proper robotics is actually a hardware problem, less so a software problem.

Tim Rock Teshiel, one of our co-founder is at Recursive, he built Jeannie 1, 2, and 3, which is the most sophisticated world model ever, can fully interact with it, prompt complete world into existence, interact in those worlds. There's memory, like you paint a wall, you turn around, it's still like painted and all of that. It hasn't really changed the robotics world as much. My hundreds of most robotics companies will want to have their own AI and not take some off-the-shelf sort of world model. Also, a lot of these world models spend time creating cute dog videos and stuff, which isn't really that helpful for robotics. But I do think we need to have better mechanics. There's some really interesting research on better muscles that are much

more inspired by humans because the problem is when you want the mechanical robot to be very strong and it's also very unsafe and it moves so quickly and you're in the way and then you get hurt. And so you might want to think about all the hardware, just a tactile feedback when you grab something, right? We have all these sensors in our fingers so we don't crush it, like a glass or something like that. And so I think that is the main bottleneck for robotics. And then you're absolutely right. Once robotics happens, then we can all have a maid and someone who does the laundry and all of these things that again, only wealthy people right now have access to. And then 50 years from now, it's like, wait, why would you do your own laundry? It's like, why would you ride a horse?

Like, of course you have like a car that drives for you or nowadays things that used to be like a private chauffeur, right? Even that, like technology of Uber and so on has done it before. Yeah. But of course, once we have AI, then like in the car will self-drive, then it'll get even cheaper to have a chauffeur. I really like your approach to finding new business ideas. Business question, you have amazing co-founders. What did you tell them that made them leave their companies in their deep mind, open AI, meta? What was that thing that you told me that made them join you? I think a lot of it comes down to the vision, right? It's such an exciting vision to be recursive self-improve and superintelligence.

And many of them have actually come to that same conclusion that that is the next level for AI. But they actually came from different directions. Like Tim Rak Teshsel and Jeff Kuhn, for instance, have worked on open-endedness for a while. One really exciting paper is called the Darwin Goetle Machine for the five authors of that paper, including Jenny the first author and Jeff Kuhn, the last author, are in the company also. And that paper basically showed how you can have agents that create their own children, agents, child agents, and then they're slightly better. They evolve. You evaluate them on benchmarks,

and then if they're better, then you keep going down in this sort of evolutionary process. So they've all thought about various forms of this. I got like Alexei Dosovicchi who pushed computer vision forward with the vision transformer, one of the most cited papers ever. And so we all, when I told them about this vision, they're all like, this is exactly what I think we need to do, too. And then you also see that the current sort of scaling laws that have given rise to the LMs that we now see are starting to sort of have slowdowns. They're still there. You can get another 10 trillion tokens and maybe you get slightly better accuracy on these models. But you have to spend an exorbitant amount of money to just get a little bit of an improvement.

And so clearly to get to the next step function of AI, I think we can replay. place yet another human process with a learned system, and the human process here is the scientific method again, the ideation, implementation, validation of ideas. And that's the sort of meta level that we're now tackling. For someone who's watching this, who's a beginner entrepreneur, he's like, how do I even meet these people? How did you all meet? And what would you be your advice to someone who's trying to build an AI? How do they get connected to brilliant minds and convince them to join. Yeah. So this is a little tricky in the sense that we've all known each other. And I don't know if my path is sort of the easiest, which is get into Stanford, do a PhD,

spend five years of your life because you just love something that no one else really cares about in the world. It might be easier than going to meetups all the time to sign a co-founder. Maybe after five years. You may as well have done a PhD. But like, so that was my path. I think nowadays, you know, the barriers, like the barriers of entry are smaller and smaller. You learn more and more online. And then there is also something to be said about living in the right place. And you can sort of criticize Silicon Valley for some things, but this is the place. If you want to be in AI, you've got to be in Silicon Valley. And you just cannot go to any party here without needing a bunch of people who are excited about AI. And then the best founding teams are often

combinations of strong technical AI expertise with actually interesting industry insights. We have company like that called AILOCA, they work on AI for architecture, automating, getting plans that are really good for real architects to then actually build things. We have companies like Solvea that do commercial due diligence for like large like deals. And they combine that industry expertise with AI and there's like thousands of potential businesses in the world. And so that is something that you can do. Move to Silicon Valley, go to meetups and try to find either the right technical person or the right industry expert to combine with your skill set.

You have your PhD, you have your honorary PhD from Technician University of Princeton. For someone who wants to get deep into AI, would you still recommend doing a PhD or it is just a title at this point? It's a really interesting question, which I struggle with a little bit. I think there are some people who are incredibly self-motivated, smart, and they don't really need. any titles, right? So I totally get sort of the idea of you can just drop out of whatever. Generally, it's nice if you got into Stanford or MIT, right? And you sort of have like people know you're smart without having to talk to you. And then you can drop out anyway and do other

amazing things. Meet all your co-founders. Drop out. Exactly. At the same time, I think a PhD is a very unique opportunity to just spend years of your life being able to get very close to the frontier of human knowledge. And then try to just in your little field, just push it. forward a little bit and that's a very rare opportunity and so if you want to teach that to people if you want to really push that frontier of knowledge forward i think unique PhD is still a very unique opportunity that if you can do it and you're excited and motivated you should pursue it is it necessary no i think especially in the eye i personally felt like it wasn't working at all when i started now it's actually working well enough that it's even more impactful to scale it up

and bring it into real use cases and real applications. But there's still so many areas of applying AI, the things that aren't working at all yet, where that I think will be the expertise. And so when people and parents ask me, what should my kids study, I usually recommend them to, yes, know the fundamentals of AI, but find something else that you're really passionate about.

Physics, chemistry, biology are great examples and there are various many subfields of each, that if you're passionate about that, but you combine it with AI, you're going to be that next generation of highly impactful researchers. Just like you combined computer and linguistics, computer science and linguistics. So you have these brilliant minds in your team. Do you ever encounter any problems that you think you won't be solving as a team? Because this is something that should be solved by the government. So you mentioned goals of AI. How does it decide? which goal to pursue.

In the future, where do you think? Who's going to be responsible for that? So, yeah, just to be clear, like this sort of goal pursuing or goal selecting idea is something that no company is working on. And partially that's okay because no company wants to spend billions of dollars on AI. And then you say, all right, now go do these things. And it will say, no, I'd rather just explore like the solar system goodbye, right? That no one wants to spend billions of dollars on that.

So there's just an example of something that that they're could. be made a lot more progress. But somebody has to be thinking about this. Where AI is directing itself and how it's optimizing for... So I think for the foreseeable future, people will decide. Like even in a recursive self-improving superintelligence, you give it some high-level goals and you give it environments and sort of end states that you would like it to get to, and then it will find a way to get to those end states.

And so I think there is a role for government in a little bit. for government in a lot of different places. I think in general, as you have more and more abundance and more and more capabilities, it is very helpful for more people to benefit from that, right? So I think you can actually, and we've seen this in previous industrial revolutions, that at some point there was enough wealth that you can tax people differently and then distribute that wealth and bring healthcare systems into countries, that you bring public education that's free for everyone into it.

And countries like Germany have done this. You have free education all the way to the PhD because the German government knows that the more education you have, the more money you'll make, and hence the more taxes they can get later. And so I think we'll see similar things from governments. As I think there are labor displacements, it can make sense similar to COVID to have government relief, to have unemployment benefits, especially for jobs that are impacted by AI.

Unfortunately, sometimes I see Europe kind of wanting to prevent the progress instead of using the progress to have a bigger pie and then distribute it better. And so that's kind of unfortunate in some ways. But in a lot of places, it makes sense for the government to regulate AI as it pertains to specific industries. I think the problem is when you try to sort of regulate intelligence, it's not a good idea. It's like regulating the internet because there can be bad content on the internet. you're just like, let's make the internet slower and not allow big hard drives because then you could store less illegal content on those hard drives, right? That doesn't make sense. And AI already is and should be regulated when it comes to like self-driving cars. You can't just like try your

startup and drive on the highway and without any tests and regulations, right? You already have the FDA where AI applied to medical procedures like should be regulated. I don't want an AI surgeon to just like try some reinforcement learning while doing neurosurgery on my brain, right? And so, So, yes, regulate AI as it really impacts people and gets applied in certain industries, but don't try to sort of say, oh, you have too many parameters. That's like saying your hard drive is too big. And maybe because there can be illegal internet content, you shouldn't have this big hard drive. That just that part doesn't make sense.

So you mentioned no one's working on goals. Is there anything else you think people should be working on and you as investor would invest in? Personally, I love AI for tech bio and applications of it. I think there are still so much more that we can do. I think what calculus was for physics, AI is for biology. And that's like a new kind of language, a new way of thinking about very complex systems. The truth is that there are a lot of systems in our body like the brain or our microbiome that are so complex, there's no beautiful single short physics equation.

You know, like a Newton kind of law of gravity or something like that. It's all very complex interactions with non-convex, weird interactions. And so that then come out to have like very interesting instates. And so I think it will make sense for us to use AI to cure more and more diseases. And something we're investing in quite heavily, the biomarkets are down to because a lot of drugs and a lot of drug companies have to go into the public market. Then they fail because the drug didn't work. but it failed after they already spent hundreds of millions of dollars on it.

And then people had some liver toxicity problem, even though it kind of worked, but it also destroyed your liver. And so they didn't somehow predict that in the drug development process. And then the company failed, the drug fails instead. What I think will happen is AI is going to get better and better at making those predictions and knowing, oh, this will be a bad for your liver. But, you know, you should modify that molecule. And there's a company that invested in called Ignoda Labs.

they actually take failed drugs, modify them a little bit, and then bring them right back into stage 2 FDA trials with much, much faster speed. And those are all examples of things where ICI will have a massive impact. And I think we'll also hopefully be covered more by the press. Right now, the press loves negative stories. And you have to really seek out the right influencers, the right accounts and so on, if you want to hear optimistic, constructively optimistic, of positive science news and breakthroughs,

but you don't really see that in your normal day-to-day news. Yeah, it's just the sentiment. I feel like in the past few months, we're getting more and more, like the society is getting split into two parts. And there's something that you mentioned about jobs that are really like, there are certain stages in your job and how you interact with AI. When you're a knowledge worker and it increases your productivity,

you get excited. But if you're an illustrator, and then it just takes your job, of course, you have this negative sense. What do you think is the next market where people will feel this risk from AI? I actually don't think there will be a whole lot where you can actually, like, ways to predict this is how much data is there fully digitized with, you know, sort of all the labels that you need.

So basically illustrations were a particularly tough example because there are millions and millions of them on the internet. And often it says exactly what you're seeing in the text and the label and the caption right around the image. So you know the input and you know the output. And then you can exactly have like sheer unlimited training data for that. And so that's why that was a particularly tough example. A lot of other industries actually takes a lot longer. Like companies don't have like billions of service call interactions. So to just automate that right away. Each company has their own, but no company wants to share that with any other company, right? And if you're in sort of the CRM world, you cannot train one global model like you can as a consumer company.

And so I think in consumer search, we see a lot of changes already. But enterprise is usually a lot slower because you don't have as much training data. I think in research and programming, we'll see a lot of changes, but not necessarily negative changes. I think we'll all just become more and more like program directors of the National Science Foundation rather than sort of individual researchers like pipetting instead of having robots to do that for. But also the argument I'm thinking about illustrators, the argument is that your work becomes even more precious if it's not AI generated and if there is a way to tell if it's not AI generated like a marker, then you can charge more because it's human.

Yeah, I think like humans will always want to find new niches and if there's a way to there's a lot of automation, then there will also be a new counter movement to that where it's all about handcrafted, artisanal, this and that, right? People already don't, like some people say, I want to have a handcrafted bowl that, you know, ceramic, where I see sort of the human touch and the imperfections and so on. Yeah, imperfections, exactly. Now when I'm writing my emails and I see my typos, I'm like, actually, I will leave this. People know that it's real. Yeah, exactly, because this is this human touch. Can you give advice to people, you mentioned, workers who work by hours.

There are two ways that I'm thinking about them. One, they can start building their own software and just optimize their work and still charge the same rate. Because honestly, as someone who pays by hours to my editors, for example, I really don't care how much it takes you. Like, you can, if you build a software that just edits it for you, that's perfect. Yeah. But also, you mentioned that companies will use that data to train their own AI to replace

those workers because there's no obligation. What would you tell to people who are working by hours, how can they keep up with what's happening. We'll see something similar to previous sort of technological changes where if you said like, oh, I'm not so good with this computer thing, you're just not in a job anymore, in a knowledge job, right? And be like, I'm not so good with this whole email. Can you print my emails out, right? Just like, you just can't say those things anymore if you want to have a tech job or like a knowledge job. And I think a similar thing will happen in a few years where you're just like, I'm not so good with this agent delegation thing.

It's like that sound. That will sound as clowning as saying, I'm not so good with this computer thing. And so you're going to have to adapt. And the people that do adapt will become way, way more productive and then actually more desirable. And so I think that will be true on an individual level, on a company level, and on a whole sort of country level. The countries that embrace this will just run away in intelligence and productivity and

hence outperform the ones that don't. And so even for entry-level jobs, I think there are companies that need to have this new sort of tech forward generation that knows how to use these tools because maybe they already started using them in college, right? Sometimes to cheat, sometimes to learn more efficiently. And in many ways, we see this in Go, for instance, after AlphaGo came out, go players got a lot better chess players are got got I've gotten a lot better also and so programmers will be more productive and people like everyone who embraces this deeply will become more productive

and better at their craft if they really sort of consciously use it versus just like using it to like throw away certain tasks and then not think about it anymore and so my hunch is even for entry level jobs if you really got good at using these tools you can bring that into companies and be a highly sought after and for YouTube. Give them a productivity tip as a PhD who's building something in AI. What's the best thing that's working for you? In terms of productivity, I mean, for me, a lot of things are around like learning and understanding things.

And recently I wanted to understand some very interesting new muscle fibers. And they had some dielectric liquids in them and just like all these interesting concepts that I hadn't sort of thought about before. And with AI, like my productivity hack is just like you can learn so much faster with AI because you're like, I don't know this concept. Explain it to me like I'm five. Okay. Actually, I'm not five. Explain it to me like I'm 10 or like now explain it to me like I have a PhD. Actually, this concept now, explain that one because I didn't know that. And so you can kind of interact with this and learn much more quickly. What do you use for it for learning? What's your favorite tool? You.com. We built the whole thing and we're the first to bring.

sort of really the internet search engines together with an LM. And so it's still very good. It's not that popular. And as a company, we focused mostly on bringing the APIs to other LMs, but it still works really well. I have a couple of last questions. So imagine we reached super intelligence today. What would be your first question to that super intelligence?

How to cure cancer? Is that the problem that you want to see solved in your lifetime? Is that number one? I think it's definitely high up there. I think in some ways, you know, obviously like cancer is actually lots of different cancers and some cancers are like less bad than others. And in some cases you can cut out like cut it out really quickly. Other cases are really hard.

And so it's a complex disease. I think it's indicative of a disease that will eventually, you know, it's either like heart disease, you know, inflammation or cancer. Like a few things that get us, get all of us at some point. And I think as we chop away at more and more of those, just like we've done with like HIV We used to be like a complete death sentence and now it's like not like just an inconvenience, but like you can live with it for a very long time. I think technological progress will speed that up.

And eventually it's going to be like, well, what's going to get us all? It's aging, right? And then aging is a super complex process. It's different in every one of our tissues and organs and so on. And so I think that will be another really interesting one. It might actually come in just the right time as almost every really wealthy country doesn't have enough babies anymore to even stay and not drink, stay at the current levels of population. I think those like sort of biological questions and medical questions will be very powerful.

The reason why we don't work on it directly is that the iterations in and are very slow. And so you only want to ask to experiment on things in the physical world after you got really, really good at improving your intelligence in the digital world. We're still figuring that out for biology, right? What gives people meaning in 20, 35? I think some things will change and some things will not change. I think people will continue to get meaning from being really good at something, developing a really deep skill.

Even if AI is better? I think even if AI is better. Look at chess. Yeah, I can play way better chess, but there have never been more chess players in the world than they are now. Same with Go. Same with programming. Even math.

I think math, mathematicians. will get better and better. Now that they have tools, we're like, this weird idea, just run like a billion ways to solve these things. And then like,

maybe it works, may one, you're like, okay, that would have taken me like two years to like do manually. Now I just like figured out in like two days and I can think about something else.

Like, I think we're all going to improve our crafts. And so I think that will continue to be a thing. I think social validation from others will continue to be something that people care about. And, you know, when you walk around,

along like some promenade or something like a shopping mall, you look at the different stores. Not every one of those stores will be impacted by AI. As much as we're thinking about AI and Silicon Valley all the time, there are things like luxury handbags, not something I understand. I don't really get it, but like, you know, in AI, like a superintelligence will not change sort of the fact that some woman like the status symbol of carrying a $10,000 handbag around. And so like I think that will happen. travel. People will still want to see the pyramids and cool ancient history.

Since we'll have more time. Exactly. 100%. And then I think a big one also is entertainment. Like no one wants to see an AI robot like shoot some soccer or football like across the field and like like like like like, you know, Mac 5 like speed. Like that doesn't make no one's going to watch that. People will still want to see other people competing against each other. So sports and entertainment will continue to rise. I think the power of brands will still be big. I think for software even there are some aspects of software that are not immune to AI, but like sort of orthogonal vectors like network effects and sort of multi-sided marketplaces. Like, yes, an AI could build the empty app of an Instagram, like probably now very quickly.

But AI will not create the network effect of having millions of people post their stuff on Instagram. So as much as excited as I am about AI, I, I, I, I, think some of the people would think, oh, it's just going to be an exponential and then like, no one will catch up and then one company will dominate everything. I think those fears, both in the positive and the negative, are overblown also. Is there a problem that you're thinking about that other people are not thinking about enough? Personally, I think the search infrastructure layer is an underappreciated important infrastructure layer of AI. You cannot algorithmically train large language models every five minutes,

something happened in the world. And so we provide sort of search results for news and other things to AI. And I think that's under-export. And the other one, of course, though it's not as under-export as I would have thought maybe when we started talking to all our co-founders, is recursive self-improvement. In many ways, as a researcher in the past, I felt like if you're right, but ahead of your time, eventually you're called a visionary. If you're a startup founder and you're ahead of your time, your company is dead and no one cares.

Right? So you have to be right at the right time. So you have to be both. Exactly. So you pick the right title when you see your idea working or not. That's right. So in some sense, like maybe it's a good thing. Like recursive improvement.

A lot of people are realizing AI is code, AI can't code. We should try to make that work. And at the same time, we're showing like we now have internal results that are better than anyone else in the world across some very important parts of the stack. We're going to start to release those in next coming. And you're building both of these companies. I don't know how you're doing that. Both billion dollars of valuation. Congratulations on that.

Thank you. Thank you so much. That was a very deep, amazing conversation, and what to think about. Thank you. Thanks for listening.