Why You Need to Rethink Your Career Now | Richard Socher — Silicon Valley Girl Podcast

Marina Mogilko: Every domain we can verify or simulate AI will get superhuman in the next few years. It's just no doubt.

Marina Mogilko: This is Richard Socher, inventor of Prompt Engineering. Now he's the founder and CEO of Recursive Super Intelligence, a company that just raised $650 million at a $4.65 billion valuation to chase one goal: build Super Intelligence, an AI that improves itself and pushes beyond human capabilities.

Richard Socher: You could actually start to work less and less and you could have much more abundance. We could live much longer.

Marina Mogilko: If you're saying AI is almost here, what is your timeline for super intelligence?

Richard Socher: I think we will actually get to the loops of recursive self-improving super intelligence within 2 years.

Marina Mogilko: So imagine we reach super intelligence today. What would be your first question to that super intelligence?

Marina Mogilko: 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?

Richard Socher: 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.

Marina Mogilko: 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?

Richard Socher: You stand on the shoulders of giants. Somewhat similar to evolution where lots of species like our own species started from other apes and monkeys and other precursors to homo sapiens. 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.

Marina Mogilko: Once you're on the market, can you explain to me as an end consumer how that would change my process? So now I have chatbots 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?

Richard Socher: So there will be different gradations of those goals over time. And when we have true super intelligence, all you will have to do is give it the right rewards, the right goals, and then it will automatically create a lot of the processes to achieve those goals. In the current state of the world, AI has very spiky capabilities. It can be extremely good at this one type of math, but then 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 evolve in this very open-ended search process that is much more akin to 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. Let me give you a concrete example. Let's say you're a company and you told this kind of very intelligent AI that it should improve your customer satisfaction scores, your CSAT scores in your service centers. The AI will say, "Easy, I'll just create a million bots and 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 AI says easy. I'll just give everyone a thousand dollar 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 people—as the AI doesn't have this common sense understanding yet—find, in a weird way, like an autistic person. Like, this is the thing you said you wanted, but you didn't explicitly define all the edge cases that you didn't want. As we're getting closer and closer to that, we have to still think about these rewards and the reward engineering problems that may come.

Marina Mogilko: So what you're saying is that if I give your AI a task to 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?

Richard Socher: As you get closer to super intelligence, 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.

Marina Mogilko: What you're describing right now with all the additional things that it thinks of—I feel like Perplexity or 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? How is it different?

Richard Socher: 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 much more broadly solve your problems and it's going to likely be more useful for companies than for a normal end user. If you're a normal person in a normal life, you may ask like what's a good movie to watch.

Marina Mogilko: But I think we're all becoming companies and entrepreneurs inside what we're doing. So everyone is building some kind of productivity process.

Marina Mogilko: Yes. I think 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 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 super intelligence 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 as 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—universities and the boundaries of knowledge and research are infinite. There's this beautiful book by David Deutsch on The Beginning of Infinity, and so you can actually think of super intelligence 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, super intelligence could benefit humanity to help it flourish.

Marina Mogilko: Let me pause for a second. I'm on calls every single day. We do team syncs. I have partnerships. I have my CPA, my manager, intro chats with guests and founders. And for a long time, I walk out of every call with loads of different notes. Sometimes they will be on a piece of paper, sometimes on my iPad, sometimes on my phone. And then I will try to piece together what we just agreed to. Then I started using Granola and it changed the whole process for me. Here's how it works. Granola is an AI notepad for meetings that transcribes your computer's audio in the background while you stay completely present in the conversation. No bot joins your call and makes everyone uncomfortable. By the time the meeting ends, you have clean, structured notes ready to go. The part I use the most after call, I can chat with my notes. I'll ask it to pull a list of deadlines, draft a follow-up email with everything we agreed on, and prep me for the next one. And because a lot of my calls are with the same people every week, at the start of each one, I have a clean list already of what we agreed on last time, what I still owe them, what they still owe me. And then you can connect it to your cloud. And this is how you're one step closer to building a digital chief of staff. Granola works across Zoom, Google Meet, and Teams. My team's been on it for a few months now for our weekly calls. With AI, the volume of what we're actually executing keeps climbing, and Granola is how we keep up. If you're browsing through transcripts manually, head to granola.ai/marina to get 3 months free with the link in the description or just enter the code marina at the checkout. It is honestly one of the best AI tools I've started using that really has transformed how I work. And now let's get back to Richard. How do you define super intelligence and how is it different from AGI?

Richard Socher: It'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. So for example you can have visual intelligence, communication intelligence, physical intelligence, you can have coordination intelligence with others. That's how humans develop morals and ethics and religions and other things. You can have all kinds of different dimensions and even each of these actually is a space of them, like a space of multiple dimensions. And at the same time you can say this is high visual intelligence. You can be blind and a blind person is still intelligent, so they're not necessary conditions to intelligence. When you multiply along all of these dimensions you get this very large volume and that is what true intelligence is. 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 read all the billions of proteins and then thought, "Oh yeah, I think A is a good amino acid to come next." We can't do that. 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. But one model can. But what we often refer to as super intelligence is when 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 super intelligence as—essentially having superseded humanity across many different dimensions of intelligence that are relevant to our lives.

Marina Mogilko: Which dimension is the main bottleneck now?

Richard Socher: There are some dimensions that no one has even started really working on. I'll give you an example. One space of intelligence I think is metacognition—thinking about thought itself, thinking about what do you want and why do you even want it.

Marina Mogilko: Right? 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. Then the AI gets really spiky and very good in those directions but it never questions whether that's the right objective. There's no subjective function, if you will, as 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.

Richard Socher: And we need it for super intelligence or you think we could build super intelligence on just improving other dimensions?

Marina Mogilko: Yeah, I think reasonable super intelligence is likely focused to a large degree on mathematical and logical reasoning plus language and those actually meet very well in code. Coding for instance is an incredibly powerful thing and coding is also a great example of a domain that you can verify or simulate. Every domain we can verify or simulate, AI will get superhuman in the next few years. This is 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 the AI can try billions and billions of things to get from this to this. Similarly in Go or chess, 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 it will take longer for AI to get super intelligent.

Richard Socher: What are the other dimensions that you mentioned that we haven't touched upon for super intelligence?

Marina Mogilko: Probably one of the more controversial ones outside of cognition and metacognition and just thinking about your own thought is survival. If someone can just end that entire species or that entire existence or intelligence, then probably it wasn't intelligent enough to survive. And so that's also a dimension no one's working on because the truth is that most companies don't want an AI 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 push the knowledge boundary forward on that dimension. So no one's really working on that but that's okay too.

Richard Socher: So would you call that half super intelligence?

Marina Mogilko: That's right. It's going to be a continuum and there are different thresholds people like to define to say, 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. Artificial general intelligence is about having one jointly trained model that gets very, very good at lots of different things and can learn very efficiently. And so clearly AI is not quite good at learning super efficiently with very few training examples—something in research we call few-shot learning or one-shot learning, where I give you one example of something and then humans can very quickly reason 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 maybe not yet there. 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. You ask it to write a poem for your wife. You ask it to think about the tax implications of some stock question. You ask it a medical problem and it will 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 secretly looking at it because no doctor can really read all the latest research results that are coming out every week.

Richard Socher: What is your timeline for super intelligence? If you're saying AI is almost here in like 3 to 5 years, but you could also argue it's partly here. What about super intelligence? What's your timeline?

Marina Mogilko: I think we will actually get to the loops of recursive self-improving super intelligence within like two years. Then it's just a question of how much compute do we give to those self-improving loops. You can have an incredible intelligence, but if you don't have the computational substrate to run it, then it's no good use. And so there's 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 get all the inventions that we want from it.

Richard Socher: So we're basically solving this bottleneck of intelligence and research and everything. Once we kind of solve it, then the next bottleneck is energy.

Marina Mogilko: That's right. In many ways, what a lot of us are thinking about is how much intelligence can we squeeze out of how little energy.

Richard Socher: When you think about this and your timeline is pretty short, like a couple 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 quietly tells you that all your chatbots start telling you to take more breaks and rest more? How do you think about your business?

Marina Mogilko: You could actually start to work less and less and you could have much more abundance. We could live much longer. In terms of businesses, I think more and more you have to have agency, you have to have creativity and some amount of intelligence to guide these AIs. And so I think most businesses will have fewer individual contributors and more people that are managing their AI agent swarms. Every business and every industry will change. We've seen this in the past when you know it used to be that you do manual work in the field and now you have tractors and then eventually you'll have automated tractors. Now we can have much more food with way fewer people. A lot of people thought, oh well, if the tractors take 95% of our jobs then we'll have 95% unemployment.

Richard Socher: But that's the lump of labor fallacy, where really labor isn't this fixed lump where you cut one piece off, give it to AI, and then it just disappears and leaves you with unemployed people. Instead, people will come up with new things that in some cases are hard to predict. Like 150 years ago when 95% of people worked in agriculture, no one predicted a Twitter media manager or a social media marketing manager. Zero people predicted that role to be taken on by people.

Marina Mogilko: 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.

Richard Socher: That even though I'm extremely excited and bullish on AI and the positive impact it will have on humanity, I think we have to acknowledge that short term there will be some industries where 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 diseases and things like that that would have felt impossible to solve before.

Marina Mogilko: 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 are basically out of job. And it's crazy how I talk 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?

Richard Socher: Yeah. So we're already seeing in the knowledge work too at u.com where we provide search results to these LMS so that they're up to date 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 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.

Marina Mogilko: That sounds abstract. So let me give you an example. Illustrations for example, illustrations used to cost a couple hundred bucks and only a few like big newspapers and fancy corporate blog posts could afford getting an illustrator to have a nice illustration for their blog post. Now they cost like a cent and everyone can have an illustration. So we'll 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's only so many illustrations humanity needs. And hence making it super cheap actually put a lot of pressure on the jobs of illustrators.

Richard Socher: Yeah. But software, since you asked about that, has a very different elasticity and demand profile. 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. It could be an app that knows exactly that I want to be a little distracted sometimes, but not too much. I want to know the weather for my paramotor hobby or surfing or whatever you might like predict the waves that day. Then I want to make sure it doesn't distract me too much and brings my work back in. Everyone can have their own super app. So the demand for more software engineering, more ideas to be built in software is much much more elastic and 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.

Marina Mogilko: What are jobs that are similar to software engineering and how they going to grow?

Richard Socher: That is a question 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 healthcare 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 there will be way more demand for generally goods and services that currently only very wealthy people have access to. In fact, that's one of my hacks on how to predict the future: 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 crazy. 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 get. You see this many times in the past. 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 examples are a personal tutor for your kids. They understand exactly which concepts they're still struggling with and they write hyperpersonalized ways to educate and tutor your kids. A personal assistant—there are not enough people and logically not every single person can have a personal assistant because then they would have to have a personal assistant and so on. And so we could all have personal assistants that do all the boring stuff in our lives. Make sure the groceries are stocked, book this flight, find the cheapest version of this and that. All of these things we can delegate to agents when they get cheaper and cheaper. And then the third one, which is one of the most exciting ones, is personal healthcare teams. If you're really wealthy, you have your blood drawn all the time. You have customized measurements. You optimize your diet based on everything that you can. If you have 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 super intelligence, we'll all be able to afford that.

Marina Mogilko: 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 worked 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.

Richard Socher: And I feel like the next frontier is physical because now when you think about billionaires, they have their chef, they have their driver. Do you think 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 Rockwell, one of our co-founders at Recursive, built Genie One, Two, and Three which is the most sophisticated world model ever. It can fully interact with it, prompt complete worlds into existence, interact in those worlds. There's memory. You paint a wall, you turn around, it's still painted. And all of that hasn't really changed the robotics world as much. Most robotics companies will want to have their own AI and not take some off-the-shelf world model. Also, a lot of these world models spend time creating cute dog videos 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. So you might want to think about all the hardware, just tactile feedback when you grab something. We have all these sensors in our fingers so we don't crush something like a glass, 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? Of course you have a car that drives for you. Or nowadays things that used to be a private chauffeur, even that, technology of Uber and so on has done it. But of course once we have AI then the car will self drive then it'll get even cheaper to have a chauffeur.

Marina Mogilko: I really like your approach to finding new business ideas. You have amazing co-founders. What did you tell them that made them leave their companies and DeepMind, OpenAI, Meta? What was that thing that you told me that made them join you?

Richard Socher: I think a lot of it comes down to the vision. It's such an exciting vision to build recursive self-improvement super intelligence 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 Rockel and Jeff Go for instance have worked on open-endedness for a while. One really exciting paper is called the Darwin Girdle Machine. Four of the five authors of that paper including Jenny the first author and Jeff the last author are in the company. That paper basically showed how you can have agents that create their own children agents, child agents, and they're slightly better, they evolve, you evaluate them on benchmarks and then if they're better, then you keep going down this evolutionary process. So they've all thought about various forms of this. I got Alexi Dositzski who pushed computer vision forward with the vision transformer, one of the most cited papers ever. And so 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 scaling laws that have given rise to the LMs that we now see are starting to 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 replace yet another human process with a learned system. The human process here is the scientific method again—the ideation, implementation, and validation of ideas. That's the level that we're now tackling.

Marina Mogilko: 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 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?

Richard Socher: 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 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.

Marina Mogilko: Might be easier than going to meetups all the time trying to find a co-founder.

Richard Socher: Yeah, after five years, you may as well have done a PhD. But that was my path. I think nowadays the barriers of entry are smaller and smaller. You can learn more and more online and then there is also something to be said about living in the right place. You can criticize Silicon Valley for some things but this is the place. If you want to be in AI, you got to be in Silicon Valley. You just cannot go to any party here without meeting a bunch of people who are excited about AI. And the best founding teams are often combinations of strong technical AI expertise with actually interesting industry insights. We have a company called Ayoka that works on AI for architecture, automating getting plans that are really good for real architects to then actually build things. We have companies like SA that do commercial due diligence for large deals and they combine that industry expertise with AI. There are thousands of potential businesses in the world. So you can do this: 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.

Marina Mogilko: You have your PhD, you have your honorary PhD from Technion. Based on that, for someone who wants to get deep into AI, would you still recommend doing a PhD or is it just a title at this point?

Richard Socher: 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. So I totally get the idea of you can just drop out of whatever. Generally, it's nice if you got into Stanford or MIT. Then 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.

Marina Mogilko: Meet all your co-founders, drop out.

Richard Socher: 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 push it forward a little bit in your little field. 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 a 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 AI, 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 to things that aren't working at all yet where I think that will be the expertise. When people and parents ask me what should my kids study, I usually recommend them to know the fundamentals of AI, but find something else that you're really passionate about. Physics, chemistry, biology are great examples, and the various many sub-fields of each. 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 science and linguistics.

Marina Mogilko: 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? 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?

Richard Socher: So yes, just to be clear, this 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 the solar system goodbye. No one wants to spend billions of dollars on that. So that's an example of something where there could be made a lot more progress.

Marina Mogilko: But somebody has to be thinking about this, where AI is directing itself and how it's optimizing for.

Richard Socher: So I think for the foreseeable future people will decide. Even in recursive self-improving super intelligence, you give it some high-level goals and you give it environments and end states that you would like it to get to and then it will find a way to get to those end states. So I think there is a role for government in a lot of different places. 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. You can actually—and we've seen this in previous industrial revolutions—at some point there was enough wealth that you can tax people differently and then distribute that wealth. You bring healthcare systems into countries, you bring public education that's free for everyone into it. 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. So I think we'll see similar things from governments. I think there are labor displacements. It can make sense 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. 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 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 saying let's make the internet slower and not allow big hard drives because then you could store less illegal content on those hard drives. It doesn't make sense. And AI already is and should be regulated when it comes to self-driving cars. You can't just try your startup and drive on the highway without any tests and regulations. You already have the FDA where AI applied to medical procedures should be regulated. I don't want an AI surgeon to just try some reinforcement learning while doing neurosurgery on my brain. So yes, regulate AI as it really impacts people and gets applied in certain industries, but don't try to 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 part doesn't make sense.

Marina Mogilko: So you mentioned no one's working on goals. Is there anything else you think people should be working on and you as an investor would invest in?

Richard Socher: Personally, I love AI for tech bio and applications of it. I think there's still so much more that we can do. I think what calculus was for physics, AI is for biology. And that's 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 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 very interesting end states 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 bio markets are down too 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 and 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 worked but it also destroyed your liver and so they didn't somehow predict that in the drug development process and then the company fails the drug fails and 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 bad for your liver. But you should modify that molecule. There's a company that we invested in called Ignodal Labs. They actually take failed drugs, modify them a little bit and then bring them right back into stage two FDA trials with much faster speed. And those are all examples of things where AI will have a massive impact and that I think will 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 or positive science news and breakthroughs, but you don't really see that in your normal day-to-day news.

Marina Mogilko: 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 I 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 sentiment. What do you think is the next market where people will feel this risk from AI?

Richard Socher: I actually don't think there will be a whole lot of ways to predict this. How much data is there fully digitized with all the labels that you need. Basically illustrations was 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 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 billions of service call interactions to just automate that right away each company has their own but no company wants to share that with any other company and if you're in 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 like program directors of the National Science Foundation rather than individual researchers like pipetting instead of having robots do that for us.

Marina Mogilko: 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

Richard Socher: Yeah I think humans will always want to find new niches and if there's a lot of automation then there will also be a new counter movement to that where it's all about handcrafted art and that. People already don't like, some people say I want to have a handcrafted bowl that you know of ceramic where I see the human touch and the imperfections.

Marina Mogilko: Imperfections exactly. Now when I'm writing my emails and I see my typos, I'm like actually I will leave this in.

Richard Socher: People know that it's real when there's a typo.

Marina Mogilko: Exactly because this is 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 rates because honestly as someone who pays by hours to my editors, for example, I really don't care how much it takes you. You can if you build software that just edits it for you that's perfect. 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?

Richard Socher: We'll see something similar to previous 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. It's like I'm not so good with this whole email. Can you print my emails out? You just can't say those things anymore if you want to have a tech job or 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 will sound as clowny as saying I'm not so good with this computer thing. And so you're going to have to adapt. The people that do adapt will become 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 country level. The countries that embrace this will just run away in intelligence and productivity and outperform the ones that don't. And so even for entry-level jobs, I think there are companies that need to have this new tech forward generation that knows how to use these tools because maybe they already started using them in college. 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 have gotten a lot better also. And so programmers will be more productive and people who embrace this deeply will become more productive and better at their craft if they really consciously use it versus just using it to 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 employee too.

Marina Mogilko: 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?

Richard Socher: For me a lot of things are around 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 all these interesting concepts that I hadn't thought about before and with AI my productivity hack is just that 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 ten or 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 interact with this and learn much more quickly.

Marina Mogilko: What do you use for it for learning? What's your favorite tool?

Richard Socher: You.com. We built the whole thing and we're the first to bring the internet like search engines together with an LLM. 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 LLMs but still works really well.

Marina Mogilko: I have a couple of last questions. So imagine we reach super intelligence today. What would be your first question to that super intelligence?

Richard Socher: How to cure cancer?

Marina Mogilko: Is that the problem that you want to see solved in your lifetime? Is that number one?

Richard Socher: I think it's definitely high up there. I think in some ways obviously cancer is actually lots of different cancers and some cancers are less bad than others and some cases you can cut it out really quickly other cases 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 heart disease, inflammation or cancer like a few things that get all of us at some point and I think as we chop away at more

Marina Mogilko: We've done this with HIV, which used to be a complete death sentence, and now it's just an inconvenience but 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: what's going to get us all? It's aging. And aging is a super complex process. It's different in every one of our tissues and organs. I think that will be another really interesting one and might actually come in just the right time, as almost every really wealthy country doesn't have enough babies anymore to even stay at current levels of population. I think biological and medical questions will be very powerful. The reason we don't work on it directly is that the iterations are very slow, and you only want to experiment on things in the physical world after you got really good at improving your intelligence in the digital world.

Richard Socher: We're still figuring that out for biology.

Marina Mogilko: What gives people meaning in 2035? I think some things will change and some things will not change.

Richard Socher: I think people will continue to get meaning from being really good at something, developing a really deep skill.

Marina Mogilko: Even if AI is better?

Richard Socher: Even if AI is better. Look at chess. AI can play better chess, but there have never been more chess players in the world than there are now. Same with Go, same with programming, same with math. Mathematicians will get better and better now that they have tools. I have this weird idea, I just run it a billion ways to solve these things, and maybe it works. Maybe one, and I'm like, okay, that would have taken me two years to do manually. Now I figured it out in two days and I can think about something else. I think we're all going to improve our crafts. I think social validation from others will continue to be something people care about. When you walk along a promenade or a shopping mall and 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 in Silicon Valley all the time, there are things like luxury handbags—not something I understand, I don't really get it—but a super intelligence will not change the fact that some women like the status symbol of carrying a $10,000 handbag around. Travel: people will still want to see the pyramids and cool ancient streets.

Marina Mogilko: Since we'll have more time.

Richard Socher: Exactly. And I think a big one is entertainment. No one wants to see an AI robot shoot a soccer ball across a field at Mach 5 speed. No one's going to watch that. People will still want to see other people competing against each other. Sports and entertainment will continue to rise. I think the power of brands will still be big. Even for software, there are aspects not immune to AI, but orthogonal vectors like network effects and multi-sided marketplaces. Yes, an AI could build the empty app of Instagram probably now very quickly, but AI will not create the network effect of having millions of people post their stuff on Instagram. So as excited as I am about AI, I think some of the people who think it's just going to be exponential and then no one will catch up and then one company will dominate everything—I think those fears, both positive and negative, are overblown.

Marina Mogilko: Is there a problem that you're thinking about that other people are not thinking about enough?

Richard Socher: 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 happens in the world, and we provide search results for news and other things to AI. I think that's underexplored. The other one 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.

Marina Mogilko: If you're a startup founder and you're ahead of your time, your company is dead and no one cares.

Richard Socher: So you have to be right at the right time.

Marina Mogilko: You have to be both. You pick the right title when you see your idea working or not.

Richard Socher: That's right. So in some sense, maybe it's a good thing. A lot of people are realizing AI is code, AI can code. We should try to make that work. At the same time, we're showing we now have internal results that are better than anyone else in the world across some very important parts of the stack, and we're going to start releasing those in the coming weeks.

Marina Mogilko: 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.

Richard Socher: Thank you so much. That was a very deep, amazing conversation. A lot to think about. Thanks for listening.