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

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

Marina Mogilko: This is Richard Soker, inventor of Prompt Engineering. 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?

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: I. Is almost here, what is your timeline for superintelligence?

Richard Socher: I think we will actually get to the loops of recursive self-improving superintelligence within like two years.

Marina Mogilko: So imagine we reach superintelligence today, what would be your first question to that superintelligent? How to. 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 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 kind of stand on the shoulders of giants, somewhat similar to evolution where, you know, lots of species like our own species started from, you know, other like apes and monkeys and other, yeah, like precursors to the homo sapiens. And similarly, we will stand on the shoulders off the existing giants right now. You can use large language models. You can't use world models. All of these are pieces to this overall intelligence.

Marina Mogilko: 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 chatbots that I can talk to. There are projects and skills I can build with Claude. There are agents I can built. 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, 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 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 kind of evolve in this very open-handed 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.

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 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. They hammer my phone lines and then give a 5 out of 5 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 eye says, easy, I'll just give everyone a $1,000 gift certificate at the end of every call and I get a 5 out of 5 rating even though I didn't solve anything, right?

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 like an autistic person like just 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, I don't know, get me as many views as possible, it's gonna 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 superintelligence, you would 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: You're describing right now with like 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?

Richard Socher: I think at a high level, you can think of this as like a Claude 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. If you're a normal person in a normal life, you may ask, what's a good movie to watch? And like.

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.

Richard Socher: Yes, the more, actually, I think that's beautiful to hear, because I think the more entrepreneurial you are, the more you love AI, because then you just get more outputs. The more you just got paid by the hour, and maybe your company is looking at what you're doing to then automate it, the more 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 as like, OK, I 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 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 to chemistry and better materials like batteries. And even more exciting, we can applied 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 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 a team syncs, I have partnerships, I have my CPA, my manager, intro chats with guests and founders, and for a long time, I'd 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 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 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. ai slash marina to get three 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: 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 volume metric 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 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 a ready better and super intelligent along these various small dimensions like playing Go or chess or translating a hundred different languages. No human can do that, right. But one model can. But what we often refer to super intelligence 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

Marina Mogilko: which dimension is the main bottleneck now.

Richard Socher: Oh, boy, there are 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? 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. And then the AI gets really, really spiky and 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 like in really thinking about your own goals. And so because no one's working on that yet, it's not even, there's no progress along that dimension.

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

Richard Socher: Yeah, I think 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 simulate. AI will get superhuman in the next few years. There'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 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 millions 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.

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

Richard Socher: Probably one of the more controversial ones outside of cognition and sort of metacognition and just thinking about your own thought, and that is sort of survival. 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 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 to push the knowledge boundary forward on that dimension, right? So no one's really working on that. But that's OK, too.

Marina Mogilko: So would you call that like half super intelligence?

Richard Socher: That's right, it's going to be a continuum. And there are different sort of thresholds. People like to define and be like, OK, this is the threshold, and now we have AGI. 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 gets very, very good at lots of different things and can learn very efficiently. And 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 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're asked to write a poem for your wife, you're asked it to think about your 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 doctors are now secretly looking at it because no doctor can really read all the latest research results that are coming out every week.

Marina Mogilko: What is your timeline for superintelligence? If you're saying HAI 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?

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

Marina Mogilko: 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.

Richard Socher: 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?

Marina Mogilko: 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 gonna 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 all your chatbots start telling you to take more breaks and to rest more, how do you think of your business.

Richard Socher: 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 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 agent swarms. 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 then eventually you'll have automated tractors, and now we can have much more food.

Um with way fewer people right 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 worked in agriculture. No one predicted a ex-Twitter 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 it 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 gonna 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: 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, you're 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: com, where we provide sort of 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. Uh, basically every industry changing with AI. Um, and I think, uh, 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, so let me give you an example. Illustrations, for example, um, illustrations used to cost a couple hundred bucks and only a few like. 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 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. 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. Like be a little distracted sometimes but not too much. I want to know the weather for my like paramotor hobby.

I wanted or surfing or whatever you might like predict the waves that day. Then I want 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 more lasting, 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.

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

Richard Socher: That is sort of 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. Health care 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 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 of 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, like 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 write hyper-personalized ways to educate and tutor your kids. A personal assistant, right? There is not, there are not enough people and logically, not every single person can have a personal assistant, right?

Cause 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 that groceries are like 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, which is one of the most exciting ones, is personal health care teams. If you're really wealthy, you have your blood drawn all the time. You have customized measurements. And you optimize your diet based on everything that you can. If you have some rare cancer or something, you have researchers that you could pay to help research on all of these things. Normal people can't afford that right now. Once we have superintelligence, we'll all be able to afford that.

Marina Mogilko: Yeah, we're already, you know, 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 gonna have more robots at home?

What is preventing us from having a robot?

Richard Socher: 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 Rocktaschel, one of our co-founders at Recursive, he built Genie 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 painted, and all of that. It hasn't really changed the robotics world as much. 100S 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. 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 to 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 in 50 years from now, it's like, wait, why would you do your own laundry? It's like why would your 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 AI. 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. 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 him join you?

Richard Socher: I think a lot of it comes down to the vision, right? It's such an exciting vision to build recursive self-influence 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 Rocktaschel and Jeff Boone, 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 also. 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 evolutionary process. They've all thought about various forms of this.

I got like Alexey Dostoyevsky who pushed computer vision forward with 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 replace 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.

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 you be your advice to someone who is 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. , spend five years of your life because you just love something that no one else really cares about in the world.

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

Richard Socher: Maybe after five years. You may as well have done a PhD. So 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. And you can sort of criticize Silicon Valley for some things, but this is the place. If you want to be an AI, you've 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 then the best founding teams are often combinations of. Strong technical AI expertise with actually interesting industry insights. We have a company like that called Ailoka. They work on AI for architecture, automating getting plans that are really good for real architects and then actually build things.

We have companies like Sothea that do commercial due diligence for like large 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.

Marina Mogilko: , you have your honorary Ph. D. From Technician University at Tristan. For someone who wants to get deep into AI, would you still recommend doing a Ph. D. Or it is 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, 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.

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

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 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 wanna really push that frontier of knowledge forward, I thing a PhD still a very opportunity that if you can do it and you're excited and 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 it 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.

Marina Mogilko: Just like you combined computer and linguistics, computer science and linguists. So you have these brilliant minds and you're a 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 gonna be responsible for that?

Richard Socher: So yeah, just to be clear, this goal-pursuing or goal-selecting idea is something that no company is working on. And partially, that's OK, 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, that no one wants to spent billions of on that. So that's just an example of something that there could be made a lot more progress in.

Marina Mogilko: But somebody has to be thinking about this. Sorry, yes.

Richard Socher: Sorry, yes. So, sorry.

Marina Mogilko: 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-improvement superintelligence, you give it some high-level goals, and you give 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. And so I think there is a role for government in a lot of different places.

I think in general, as you have more and more abundance uh and more and more capabilities it is very helpful for more people to benefit from that right so i think um 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 health care systems into countries that you bring public education that's free for everyone into it and you know countries like Germany have done this like 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 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 wanting to prevent the progress instead of using the progress to have a bigger pie and then distribute it better. And so that's 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. That 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 and without any tests and regulations.

You already have the FDA, where AI applied to medical procedures should be regulations. I don't want an AI surgeon to just like try some reinforcement learning while doing neurosurgery on my brain, right? And 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 like, maybe because there can be illegal internet content, you shouldn't have this big hard drive. That just, 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 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 like a new kind of language, a new way of thinking about very complex systems. The truth is that like 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 of non-convex weird interactions, and so that didn't come out to have like 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 biomarkets 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, 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 fails, 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 should modify that molecule. And there's a company that invested in public Nodal labs, they actually take failed drugs, modify them a little bit, and bring them right back into stage two FDA trials with much, faster speed.

And those are all examples of things where ICI will have a massive impact and that I think will also hopefully be covered more by the press, right? 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: 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 are a knowledge worker and it increases your productivity, you get, 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'll 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 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 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 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 a 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 lots 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 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 is not AI generated like a marker, then you can charge more.

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 artisanal this and that, right? People already don't, like some people say, I want to have a handcraft bowl of ceramic where I see sort of the human touch and the imperfections and so on.

Marina Mogilko: 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 when there's a typo. 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 so 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 if you build a 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, oh, I'm not so good with this computer thing, you're just not in a job anymore, in a knowledge job. And be like, I am not so 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 like, oh I'm so good with this agent delegation thing. That will sound as clowny 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 the 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 have 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 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?

Richard Socher: In terms of productivity, 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. With AI like my productivity hack is to say 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 be like I've 10 or like now explain it me like i have a PhD actually this concept now explain that one because I didn't know that like and so you can kind of 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: com. We built the whole thing and we're the first to bring sort of really the internet like 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 LM's, but it 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: Like 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, 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 it out really quickly. Other cases like really hard. And so it's a complex disease. I think, it's indicative of a disease that will eventually, you now, it's either like heart disease, inflammation or cancer. Like there are few things that 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 HIV, it used to be a complete death sentence. And now it's not 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 like, well, what's going 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 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 it are very slow. And so you only want to ask to experiment on things in the physical world after you've got really, really good at improving your intelligence in the digital world.

Marina Mogilko: We're still figuring that out for biology, right? What gives people meaning in 2035? I think...

Richard Socher: 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.

Marina Mogilko: Even if AI is better.

Richard Socher: I think that's better. Look at chess. Yeah, I can play way better chess, but there have never been more chess players in the world than there are now. Same with Go, same with programming, even math. I think math mathematicians will get better and better now that they have tools like this weird idea. Just run like a billion ways to solve these things. And then maybe it works, maybe it won't, you're like, OK. That would have taken me like two years to do manually. Now I just figure it 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 it to be something that people care about.

And when you walk along some promenade or some 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 in 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 super intelligence will not change sort of the fact that some women like the status symbol of carrying a $10,000 handbag around. And so like, I think that will happen. Travel, people still want to see the pyramids and cool ancient history.

Marina Mogilko: Since we'll have more time.

Richard Socher: 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 in like light, you know, Mac five, 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, right? So as excited as I am about AI.

I think some of the people who 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.

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 so we provide search results for news and other things to AI. And I think that's under-explored. And the other one, of course, though it's not as under-sport 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 time, your company is dead and no one cares.

Marina Mogilko: Yeah.

Richard Socher: Right? So you have to be right at the right time. So you have to do both. Exactly.

Marina Mogilko: So 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. Like, recursive self-improvement, a lot of people are realizing AI is code, AI can 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. And we're going to start to release those in the next coming week.

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. And thank you so much. That was a very deep, amazing conversation. I want to think about. Thank you.

Richard Socher: Thanks for listening.