BIG IDEAS BY NEW ECONOMIES
00:00:33 · Ollie Forsyth, Richard Socher
[00:00:33] S1: This is Richard Socher, founder of You.com and Recursive. He's scaled You.com into a $1.5 billion valuation and has just raised over $600 million to build self-improving superintelligence. And now he's breaking down how AI is cracking the biggest bottlenecks in biology and what superintelligence unlocks next. [00:00:50] S0: A lot of people think the future is kind of this prediction exercise, but it's often a construction exercise. We need to actually build these things. And so we need to collect a lot more data to be able to build, for instance, a complete virtual cell, then a virtual tissue, and then a fully sort of virtual twin of multi -organ systems and ultimately like full people. And that is in a state where we've developed enough things that now it's just about scaling them, making them faster better cheaper and that is something that China is very good at. It's very obvious that the US is better at coming up with truly novel ideas and really pushing different here. China is better taking ideas from the West and making them [music] [00:01:42] S1: Who do you think is going to win in this AI race, China or the US? Yeah. So welcome to NEW ECONOMIES. [music] Richard, welcome to NEW ECONOMIES. Thank you so much for joining us. Thanks for having me. Excited to chat. So you have just published your first book. What is the biggest opportunity we are having right now when so much is happening in AI? You talk a lot about the biology side of things and what that can do for the future of humanity. What is the book all about and why did you decide to write it now?
00:02:29 · Richard Socher
[00:02:29] S0: Yeah, the book is called The Eureka Machine, and it's about how AI will usher in this new era of scientific discoveries. And in many ways, people think of AI as this potential disruptive force that could automate or replace or enhance different kinds of jobs. But I think the most interesting thing humanity has done with our intellect is understanding our past and modifying our future through inventions and better theories, and better explanations. And so I think when we invent new technologies it usually creates a lot of jobs. We can of course lament all the people like carrying and selling candles, but we can also acknowledge that electricity has brought in more jobs than candle industry and fire and coal industry and so on has had. And so I think there is just a ton of potential. And so in the book, I'm going through Basically from the micro and physics, chemistry, biology, to neuroscience, understanding our brain better finally, to economics and to astrophysics. And so I think that is one of the biggest opportunities. And the reason I wrote it was partially personal. I just had a lot of pent up energy thinking about super intelligence that I wanted to channel and spend a lot of my nights and weekends on. And then part of it was also that It felt to me like the field of AI was a little bit stuck in just saying, oh, we're just going to take this and use more internet data and scale up these existing models. When I think the time was ripe to actually expand the use of AI into the sciences. And first I was worried that the book was going to be too slow, but I actually feel like it came out right at the right time. So I did have to rewrite several chapters from someone should build a virtual cell to The Chan Zuckerberg Foundation started a virtual cell. And so some of the ideas that would have been very novel when I started writing a book two and a half years ago, and even more novel when I finished it a year ago and submitted it to the publisher, are not as novel anymore.
00:04:45 · Ollie Forsyth, Richard Socher
[00:04:45] S1: But still, I think, very timely. When you were writing The The Eureka Machine and she had the idea, first of all, why did it take you so long? I mean, I know AI is changing so quickly, but a couple of years to write the book is pretty long. [00:04:59] S0: Yeah, I mean, I'm not an author. You know, I mostly run companies and a venture fund. And so it was just like the occasional nights and many weekends. But sometimes I would have to work on weekends, too. So it's also not every weekend and so on. So it's just a passion project in many ways. [00:05:16] S1: And that's why it took so long. When you're describing, you know, AI is potentially the key unlock for this whole, you know, abundance of opportunities we have, especially, you know, creating and solving problems for diseases. When you were writing the book, was anything that you learned that became very, your thesis was very clear around, actually, this is like a potential huge opportunity?
00:05:41 · Richard Socher
[00:05:41] S0: In many ways, there are abundant opportunities in every field right now. I actually love the fact that right now so many people are charging hard after who gets to solve hard, meaty problems for humanity first. There's always this joke that very successful people say, the only thing worse than me saving the world is other people saving the world faster or better than I do or something silly like that. But it is a bit of a race. Who gets to solve more hard math problems before anyone else does, which I think there will be a race to curing all the meaty and interesting and important and hard and debilitating diseases out there. And so that I think is a major opportunity. But then, of course, if you really improve fusion, if you really build much, much cheaper batteries to store energy, if you can harness energy way more efficiently from way more sources, fission or fusion, And then solar panel chemistry and all of these things like, I think there's also a lot there, but biology just has this sort of built in mechanism where if you develop an interesting molecule, you can also make a lot of money. In the past I used to struggle with this, like why should medicine make people that wealthy? But then in many ways you want more smart people to be attracted to medicine no matter why and how, ideally just to help humanity, but in practice if you make a lot of money in it, And you have a lot of smart kids from Princeton, Harvard, and so on. They need to pay off their student debts. They're just going to go in high paying jobs. That used to be consulting and now it's really AI and startup tech and so on, big tech. But I'd rather have more people go into medicine because they make more money. Why should people selling ads make more money than people curing diseases? In many ways, I'm excited for AI really making incredible progress. This isn't just hypothetical. Sure, cancer is a particularly tricky set of diseases. But there are certain diseases, for instance, single genome kinds of diseases that will be curable in the next 12 to 24 months. And those have fewer people. But in aggregate, there are actually a lot of people who have individually different rare diseases that are often based on one single genome having been wrong in their DNA. And that can become fixable. So I think this isn't just sci-fi anymore.
00:08:11 · Ollie Forsyth, Richard Socher
[00:08:11] S1: This will happen. And this is what's awesome about AI, right? We were just chatting before we came online, like, you know, AI is moving so fast. People are concerned, like, it's going to change jobs forever. That's true. But people who have, you know, some form of disease, I think they're probably super excited about the opportunity that's unlocked right because they want some form of cure for these diseases that are happening in the world. Have there been any diseases that you've been surprised about that have started to be cured and where do you think we are on that timeline for that to become fully unlocked? [00:08:44] S0: Yeah. So rare genetic diseases, I know several people are working on and have already cures that they're being submitted and so on. So those are the most common ones that I think have a fix and a way to deliver the fix and so on to people. Some first versions where babies were born with a genetic disease that would have not helped them or would have prevented them from leading long, healthy lives were cured. And I think we'll see more of that. I think that would be the first set. And then HIV made a lot of progress already without AI. It used to be a death sentence. And for some countries, it was like 50% of all their deaths were from HIV. Already, there are a lot of ways to... Is [00:09:36] S1: there a disease that you think is going to be just very hard to solve with the current technology advancements that are currently available?
00:09:50 · Richard Socher
[00:09:50] S0: So the final enemy in medicine will be aging. And I think in the next few decades, as we cure more and more diseases, which will require us to collect more and more data so that biology can learn the bitter lesson that we've learned in natural language processing, where A lot of times people are like, you have no idea about how complex biology is. And I fully acknowledge how complex biology is, but language is pretty complicated too. And if you asked all the experts in natural language processing 10, 20 years ago, if we have one model, you can just have And I'll just always give you a reasonable answer and help you work through any problem and do all your homework and so on. I would have been like, no, that language is too complex. You have no idea how language works and blah, blah, blah. And I happened to study a lot of linguistics and I didn't know a But I also knew that indeed I could never manually write out all the rules as an expert of doing that. And so that is what we're seeing now. But that bitter lesson of large models with a lot of data being able to learn incredibly complex things. We'll take a while to arrive in biology because unlike the internet, we have not collected enough biology data just sort of naturally and billions of people kind of collaborating on that. But once we have enough data, then we will be able to cure more and more diseases. And then I think the ultimate hardest one will arrive at at the end that will get us all is aging. But then aging is also incredibly hard because every organ in our body ages differently at different speeds and different people have varying aspects of it. There's a lot of lifestyle factors that come in and so on. So, you know, that will probably be the hardest one.