Pod Insights How I Built This with Guy Raz AI is smarter than you think with Shane Legg of Google DeepMind About this episode For decades, Shane Legg has anticipated the arrival of “artificial general intelligence” or AGI.
In other words: an artificial agent that can do all the kinds of cognitive tasks that people can typically do, and possibly more...
Now as the Chief AGI Scientist and a co-founder of Google DeepMind, he stands by that prediction and is calling on the world to prepare.
This week on How I Built This Lab, Shane’s path to becoming an early AI expert and the work he and his team are doing to prepare for the technological revolution ahead.
This episode was produced by Sam Paulson with music composed by Ramtin Arablouei. It was edited by John Isabella with research help from Carla Esteves. Our audio engineer was Cena Loffredo.
You can follow HIBT on X https://twitter.com/HowIBuiltThis & Instagram https://www.instagram.com/howibuiltthis, and email us at [email protected] mailto:[email protected] .
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Guy Raz? Our presenting sponsor today is Anthropic, the team behind Claude. Every founder on this show started with a hard question. Not the marketing plan, the 2 a.m. kind. Is this idea any good? Am I the one to build it? Should I quit my job? Anthropic got built the same way. A public benefit corporation founded on one hard question. How do we make sure AI goes well for people? So they asked over 100,000 people their hopes and their fears and are publishing what they find, even the uncomfortable parts. Me? I've got my own hard question about AI. Here it is. If everyone has access to something that can help them write the business plan,
0:46 Guy Raz? build the prototype, analyze the market, maybe even come up with the idea, what separates the people who actually build something from everyone else? I don't have a clean answer yet, so I've been sitting with it with Claude. There's hope in hard questions. Ask yours at claude.ai/hibt. We get support from U.S. Bank. A question I love asking founders. What's the one decision you almost didn't make? The one that changed everything? Because it's never the obvious stuff. It's never, I decided to start a company. It's the small, boring, Tuesday afternoon decision you barely remember making until you realize it's the reason everything after it either worked or didn't.
1:32 Guy Raz? Say you're 18 months in. Business is growing. You've got expenses coming from six directions. Gas for the van, office supplies, your phone bill, team lunches that are actually client meetings, and you're putting it all on whatever card you grabbed in year one. You're not thinking about it. Why would you? You've got bigger problems. But here's the thing. Path A, you keep going like that. End of the year, you're sorting through statements, trying to figure out what went where. You've left hundreds, maybe thousands, on the table in rewards you never earned, money that could have gone back into the business. Path B, you pause and get the US Bank Triple Cash Rewards Visa Business Card. Now, you're earning 3% cash back
2:16 Guy Raz? on gas and EV charging, office supply stores, cellphone service, and restaurants. You pick up an extra $750 cash back as a bonus. And you've got a low APR on purchases and balance transfers for 12 months while you're scaling. Same business, same expenses, two completely different outcomes based on one small decision. Visit usbank.com/business to learn more. The creditor and issuer of this card is US Bank National Association, pursuant to a license from Visa USA, Inc. Some restrictions apply. If you travel often, you'll want to hear about this unprecedented offer. For the first time ever,
3:00 Guy Raz? new Delta SkyMiles Reserve American Express Card members can earn two Delta Comfort Round Trip flights for travel within the 48 contiguous US states, Puerto Rico, or the US Virgin Islands. Plus, you can earn 50,000 bonus miles towards your next getaway. And who doesn't appreciate an elevated airport experience? Delta SkyMiles Reserve Card members get access to the Delta Sky Club, your pre-flight oasis to recharge, with 15 visits and four one-time guest passes each medallion year when flying Delta together. That's up to $950 in annual value. Value estimate based on $50 per visit rate to purchase additional visits
3:45 Guy Raz? or bring a guest to the Delta Sky Club. Make every trip feel more rewarding with the car that upgrades your travel. Apply today at delta.com/cardsocial. Offer ends November 4, 2026. Minimum spending requirements and terms apply. Flights are fulfilled as flight certificates valid for Delta Comfort or Delta Maine to select domestic destinations. Book 21-plus days in advance. Up to $80 per person and taxes and fees apply. Other restrictions apply. When I pull up to a gas pump to fill my car, I'm not thinking about chemistry or engineering. I'm thinking about getting in, filling up, and getting on with my day. And it reminds me a little bit of the founders I talked to on this show, because the best
4:27 Guy Raz? ones obsess over things their customers will never see. Tiny details, invisible improvements, relentless iteration. And not because anyone's watching, but because it makes the product better. That's what the mobile brand does. It engineers fuel the same way great founders engineer their products. Mobile Supreme Plus Premium Gas helps keep engines three times cleaner, which can mean better performance and improved gas mileage. It's for people who care about what's under the hood, even if they never see it. Three times cleaner compared to mobile regular gas in port fuel-injected engines. Actual results may vary. Visit mobilefuels.com for details.
5:12 Guy Raz? Hey, it's Guy here. And before we start the show, I want to tell you about a super exciting thing we are launching on How I Built This. So if you own your own business or trying to get one off the ground, we might put you on the show. Yes, on the show! And when you come on, you won't just be joining me, but you'll be speaking with some of our favorite former guests, who also happen to be some of the greatest entrepreneurs on Earth. And together, we'll answer your most pressing questions about launching and growing your business. Imagine getting real-time branding advice from Sun Bums Tom Rinks, or marketing tips from Von Weaver of Uncle Nearest Whiskey. If you'd like to be considered, send us a one-minute message
5:56 Guy Raz? that tells us about your business and the issues or questions that you'd like help with. And make sure to tell us how to reach you. Each week, we'll pick a few callers to join us on this show. You can send us a voice memo at [email protected] , or you can call 1-800-433-1298 and leave a message there. That's 1-800-433-1298. And that's it. Hope to hear from you soon, and we are so excited to have you come on the show. And now, on to the show.
6:42 Guy Raz? Hello and welcome to How I Built This Lab. I'm Guy Raz. So, artificial intelligence is already changing the way we live and work. Believe it or not, even if you don't realize it. And so far, most of the AI applications we've seen have been focused on specific tasks, like generating images or video from a text prompt, or identifying chemical compounds for new drug treatments, or using satellite imagery to spot forest fires before they spread, or even producing a podcast like this one. But what if there was a single AI program that could do just about anything a human could do, but even better? Well, my guest today, Shane Legg, has devoted his entire career to creating
7:27 Guy Raz? a general-purpose AI that can be effective for, well, just about anything.
7:33 Guy Raz? In 2010, Shane co-founded DeepMind with the mission of bringing this artificial general intelligence to life. Today, the company is part of Google's AI division, and Shane is on the front lines of a technological revolution. But his journey started back in the 1980s, when he was just a kid in New Zealand with a new birthday present.
7:55 Speaker 01 So for my 10th birthday, my parents bought a small computer. It was called a VZ200, and it had an 8-bit microprocessor and had whole 8 kilobytes of memory, and had built-in basic programming language. And as a 10-year-old boy, I'd heard about computers, you know, in movies, or read about them in books, or things like that. But I had a real computer of my own now. And this was prior to the internet and all these sorts of things. So there was really only one thing to do on the computer, and that was the program. It was a place where you could make things. You could write your own programs, you could you could start learning the language and so on, and you could just dream. You could invent
8:40 Speaker 01 things. You can invent little spaces with little pixels or little graphics that would chase each other around the screen, or do all sorts of things like this. And it was just sort of a playground for my imagination. I could bring these little worlds to life. I could bring these little characters to life. And that just absolutely captivated me when I was 10, 11, and so on.
9:01 Guy Raz? You would go on to study mathematics and statistics in New Zealand.
9:06 Speaker 01 Yep.
9:06 Guy Raz? This is in the late 90s, when you kind of start your career. The notion of artificial intelligence had been around. It had been written about in science fiction novels, and it'd already been depicted in films. Was that on your radar as a young man when you were starting out your career?
9:23 Speaker 01 When I first started, it wasn't. I started doing mathematics. I liked the subject because it was very challenging. I did some computer science as well. I found computer science very easy, because I'd spent my adolescence programming. But it was only at the end of my second year, when in my spare time, I wrote some software that did calculus. And I showed that to some of the professors, and then they said, "Well, actually, we've got a summer job." And on a machine learning project, would you be interested in coming to work for us for the summer?
9:58 Guy Raz? What did machine learning mean in 1993? It's like the equivalent of like a, I don't know, like a mainframe computer to a laptop today, right?
10:08 Speaker 01 Yeah, it was early days then. It was a relatively new field then that had sort of split off from artificial intelligence not that many years earlier. And the difference in flavor was the emphasis on instead of engineering like reasoning systems or something like that. The emphasis was on using data and learning a model of something. And so that's where the machine learning comes from.
10:33 Guy Raz? And presumably at that time, by the way, you had to feed the data into the machine. Because it's really, I mean, there really wasn't, I mean, the internet was around, but it wasn't populated with endless reams of information.
10:45 Speaker 01 Yeah, it wasn't. And also, even if you had endless reams of information, the algorithms and the computers couldn't handle it.
10:51 Guy Raz? Yeah.
10:51 Speaker 01 So what you would typically do is you would have specific small data sets. I mean, by small, you could like also print them out on a few sheets of paper kind of size. And you would have algorithms, particularly classification algorithms. So what they would do is you'd have a list of, I don't know, let's say people. And you'd have different measurements about their blood results and then whether or not they have a disease. And you would learn a function which is, given this person's blood results, do they or do they not have this disease? And then in a new instance, you'd get the blood results and you wouldn't know if they have the disease. But you'd learn to predict it, to classify that individual as, say, having their disease or not. So that's a classification problem.
11:30 Guy Raz? So that was your first contact with what sort of kind of AI, machine learning?
11:35 Speaker 01 Yeah.
11:35 Guy Raz? But in early 2000, right? Most people who were going into the field of computing were really focused on the internet. But you went right into artificial intelligence at a time when it was like really nascent. It wasn't really much of an industry yet.
11:54 Speaker 01 Yeah, there wasn't much of an industry. It was certainly an active area in academia. And so, yeah, I, after doing my undergraduate and then master's degree, I ended up working to do things like document classification for newspapers. Like determining this document here that we use the word bank, is it really about rivers or is it about finance, right?
12:14 Guy Raz? Yeah. So what you're talking about is Basically creating a specific task for a machine that can get, you know, more or less can get a bit more sophisticated doing that single task.
12:29 Speaker 01 Yeah.
12:29 Guy Raz? And and this was sort of a kind of a crude, you know, sort of a compared to what what these machines can do today, relatively crude, but but this is what you pursued. I mean, you pursued a PhD in in artificial intelligence or, you know, already starting in 2003 and really began to think about machine superintelligence. This was the the the subject of your thesis, machine superintelligence. Was the, I mean, when you talk about machine superintelligence, at that time, were you already using the term artificial general intelligence?
13:05 Speaker 01 Yeah, so maybe it's worth going back a little bit to about 1999. So I ended up working for a company called Intelligenis, and they were doing various things, including this sort of document classification and so on. But one of the things they were interested in is building artificial intelligence systems that were very general and capable, building a thinking machine or something, if you like. And they they didn't, I'd say they didn't really get very far in that, but it did get me thinking about that subject. And then when that company collapsed, I spent a lot more time thinking about the subject. And I actually became convinced, particularly after reading The Age of Spiritual Machines by Ray Kurzweil, that artificial, that is very powerful and general AI systems were
13:48 Speaker 01 going to come, but some a few decades into the future. So I actually came up with an estimate then that it was about a 50% chance of what I call AGI now by about 2028. And that's also about the time that I proposed the term artificial general intelligence. And and to be clear, the way I define artificial general intelligence is it's an artificial agent that can do all the kinds of cognitive tasks that people can typically do and possibly more. And I like that definition because I think it's very intuitive. We have a good understanding the sorts of cognitive things that people can typically do. And we can look around at existing systems and see that they can do some of the sorts of cognitive tasks people can do, but they can't do others. And so we have some sort
14:32 Speaker 01 of intuitive sense of the kind of breadth and capability that you would need to be classified as an artificial general intelligence.
14:40 Guy Raz? Okay, so we're about four years away from seeing whether your prediction is right. We're going to come back to that later in this conversation, but I want to talk about what you would go on to do because while you were in graduate school, you met Demis Hassabis, still at Google today, and Mustafa Suleyman in the news now because he was just brought on by Microsoft to head up their AI program.
15:02 Speaker 01 Yep.
15:03 Guy Raz? And together you founded a company called DeepMind in the UK.
15:07 Speaker 01 Yeah.
15:07 Guy Raz? What was the goal? What was the presumably was to develop an AGI?
15:12 Speaker 01 Yes, our business plan from September 2010 had our logo on the front and DeepMind, and it had one sentence, which is build the world's first artificial general intelligence.
15:26 Guy Raz? And eventually you would develop a program, you'd develop a system that could defeat the greatest Go players in the world. And for people who don't know this game, and I don't know it very well, this is a much more complex game than chess. This is a this was a huge challenge to try and figure out how to actually build that. Can you explain why you guys were focused on Go?
15:50 Speaker 01 Yeah, to be clear, we did work on many projects.
15:53 Guy Raz? Yeah.
15:53 Speaker 01 We had a broad range of things, but AlphaGo ended up being, of course, one of the big famous ones. Yeah, it was a longstanding problem in artificial intelligence. In a game like chess, the number of possible moves is not so great that you can actually use brute force computation, more or less, to actually search through all the space of possibilities of which moves, of which counter moves, and so on, and find very good combinations. And the game of Go, that's much more difficult because the board is much, much larger, and there's a much larger number of possibilities at each point. So when you start looking through the trees of different possible moves, it exponentially grows at a much, much faster
16:35 Speaker 01 rate. And so this basically tripped up all the more brute force approaches to search and planning that would try to play this game well. So we had to come up with something new. And what we did is we actually blended together um, search techniques, something called MCTS,
16:56 Guy Raz? um.
16:56 Speaker 01 With deep learning. And the deep learning would actually learn uh two things. It would learn which moves were likely to be good moves just by looking at the patterns on the board using a deep neural network. And it would also learn, given a certain state of the game, who is more likely to win or lose. And then what we would do is we'd get our our AI system to play against itself. And as it would win or lose games, it would take that signal. And then it would apply these learning algorithms to improve these deep learning networks. So they've become better and better at anticipating which are the likely good moves. And at any given point in time, who which of the two players was most likely to win the game?
17:41 Speaker 01 Now, if you combine that with a search, then you start having a very, very powerful algorithm. It both has this sort of classical search, but this sort of slightly more intuitive, deep learning aspect, where it's kind of picking up subtle patterns in the game and trying to figure out, you know, which way it's going based on sort of these more subtle structures and so on.
17:59 Guy Raz? Hmm.
17:59 Speaker 01 And it was that beautiful combination of the two that would lead through to the breakthrough in performance.
18:05 Guy Raz? We're going to take a quick break. But when we come back, how DeepMind went from mastering Go to working on some of the biggest challenges of our time. Stay with us. I'm Guy Raz and you're listening to How I Built This Lab.
18:22 Guy Raz? Does your team want a website that looks and feels handcrafted, but is still fast to ship? Well, Framer is built exactly for that. With Framer, you design on a visual canvas with responsive layouts, hosting and a CMS built in. So the work is production ready from day one. And you can get it done with help from Framer's AI agents. Framer is the AI website builder that helps creators, teams and businesses ship production ready sites faster than ever while getting every detail right. Prompt, inspect, edit and publish in one place at a whole new pace. AI agents and humans work together.
19:04 Guy Raz? The agents bring speed and scale and people bring taste, judgment and control. Learn how you can get more out of your site from a Framer specialist or get started building for free today at framer.com/built for 30% off a Framer Pro Annual Plan. That's framer.com/built. Rules and restrictions may apply. AI isn't just opening new business opportunities. It's changing the way businesses get work done. The questions they ask, the decisions they make, the routine tasks they can hand off. That's the idea behind NetSuite Next, the next generation of NetSuite with AI woven into the tools your business already relies on.
19:49 Guy Raz? NetSuite Next surfaces insights exactly when you need them, puts agents to work on routine tasks and problems, and lets you ask complex questions in plain language, like talking to a knowledgeable colleague. But technology is only part of the story. AI is also reshaping how people and teams work together, and that shift is already underway. That's why NetSuite wants to put a new, expert-created business guide in your hands today. It's called Aligning for the Agentic Era: How AI is Changing Everyday Work. And right now, it's yours, absolutely free. Inside, you'll discover how AI could reshape work across your entire organization and how to position your business to prosper from it.
20:34 Guy Raz? If your revenues are at least in the seven figures, get NetSuite's free business guide, Aligning for the Agentic Era: How AI is Changing Everyday Work, at netsuite.com/built. The guide is free to you at netsuite.com/built. netsuite.com/built. Stripe is built for whatever comes next because the pace of change has never been faster than it is right now. New business models, new currencies, and new expectations for speed, reliability, and scale. Stripe builds financial infrastructure that helps businesses start, grow, and operate globally. From payments and billing to fraud protection and cross-border payouts,
21:18 Guy Raz? Stripe is built to support the new pace of commerce. They're trusted by the companies building at the frontier, from 86% of the Forbes AI 50 to global leaders like Salesforce and OpenAI.
21:33 Guy Raz? In fact, more than 1.6% of global GDP flows through Stripe, with Stripe's users processing a combined $1.9 trillion last year. So if you're already thinking about what's next for your business, see what's possible at stripe.com.
21:59 Guy Raz? Welcome back to How I Built This Lab. I'm Guy Raz. So in 2014, only a few years after its founding, DeepMind was acquired by Google. This is, from a business perspective, an amazing story. You had only around 75 employees, I think, at DeepMind. And Google acquired the company for between reportedly $500 million and $650 million. And then you became, for about nine years, sort of an independent part of Google. And we'll talk about why that changed recently. But essentially, working on different projects, including projects around drug development and working with, trying to model proteins
22:44 Guy Raz? that could potentially offer lifesaving cures.
22:47 Speaker 01 Yeah, so that was another big success, which is protein folding. So your body is largely built out of proteins. These are the molecules that are the building blocks of most of biology. And it's not very difficult to know what the atoms are that make up the molecule. It's a certain chain of atoms. But what happens is the different atoms in that chain attract or repel each other in different ways. And the result of that is that chain actually folds up into a three-dimensional shape. And that three-dimensional shape can be all sorts of things. It can be an axle, it can be a spiral, it can be a sheet, it can be a sphere that contains something. You can build all sorts of interesting structures out of these shapes,
23:32 Speaker 01 sort of like 3D Lego blocks, if you like. And these shapes are very important if you want to understand what that protein does. Now, the problem is that while it's easy to find out the molecules that make up the protein, it's very difficult to find out the shape. And the techniques that people used to use uh would often require several years of research to find out what the shape was and cost maybe two hundred thousand dollars. Um it's maybe something that somebody would do as a PhD thesis is find out the shape of one protein. And there are hundreds of millions of proteins.
24:09 Speaker 01 So there was this computational challenge that had been around for many decades, which is, is it possible to take the molecule, just the the knowledge of the atoms and compute what the shape is directly?
24:21 Guy Raz? Hmm.
24:22 Speaker 01 Um rather than go through this laborious process of several years of experiments and all these sorts of things. And so that's what's called the protein folding problem. How does a protein fold into the three dimensional shape and can you predict this computationally?
24:34 Guy Raz? Hmm.
24:35 Speaker 01 And so people have been trying to do that for a long time. Um and we thought using uh advanced machine learning, deep learning techniques and so on, that we had a shot at solving this problem. And yeah, long story short, we we spent a few years working on it and we we basically solved the problem, yeah.
24:53 Guy Raz? And and just just for some clarification around it, I mean, what does it mean in in practical terms? I mean, is it is it ready to go in terms of enabling treatments and drug development?
25:05 Speaker 01 Uh yeah, it's not quite that direct. Um what we did is we folded all the proteins known
25:10 Guy Raz? Yeah.
25:10 Speaker 01 uh to science and we released them all to the public for free. So you can find them all online on the internet and you can look at the shapes and so on. And we've had about one point seven million researchers um use that resource. Um it doesn't mean you suddenly know how to build a drug or something rather.
25:25 Guy Raz? Hmm.
25:26 Speaker 01 But it does mean that you can now see what the different shapes of the proteins are. The proteins that you might have in the drugs, the proteins that might be, I don't know, part of your liver or some other part of your your biology. And you can see how they maybe interact with each other and all sorts of things like this. So it's it's not like it you suddenly can solve the problem. But but before, when you're operating the dark and you didn't even have any idea what any of these things looked like and what need to learn how they might connect together and other things like that. Now you can see a whole lot of this information. And so that's incredibly useful when you want to go and then develop drugs and so on. So you might see, I don't know, there's a particular problem taking place and it's to do with a particular protein. You might be able to then go, okay, what proteins are going to connect into this other protein and act on it in certain ways?
26:09 Speaker 01 And you can then target specific things that that look like they're going to be, you know, very interesting and so on. So it's a it's not a solution to drug discovery and everything, but it's a great enabler and accelerator of this kind of process.
26:24 Guy Raz? Can you explain what happened around 2017 in the field of of AI research? My understanding is that large language models were essentially introduced. And I guess that was like a turning point in the acceleration of the development towards artificial general intelligence?
26:43 Speaker 01 I think it was. We, Google had invented an extremely powerful algorithm called Transformer. And we'd been experimenting with it for things like translation between different multiple language targets in and out and all sorts of things like that. And we'd found it to be very, very scalable. And then what happened was that another company, OpenAI, latched on to the idea that this is in fact extremely scalable, more scalable than anybody had appreciated. And they just basically scaled it up and scaled it up and scaled it up. And, you know, it just kept on scaling, basically. And so there was a there was a it was a bit of a surprise in just seeing how far these language models could go when you made them extremely big.
27:28 Speaker 01 And you started feeding in, you know, a significant chunk of text from from the internet. And so that that came as a surprise to many people. They didn't think it would go it would go quite so far, yeah.
27:41 Guy Raz? So last year in 2023, DeepMind, which had been essentially an independent part of Google for a decade, was merged with Google's AI division. And I think that was in response to what was happening with OpenAI and and maybe even some other. Competitors in the space to to really kind of ramp up what Google could do around artificial intelligence. Did part of that feel like you were joining an arms race that that, you know, did it feel like, OK, all hands on deck, we've got to compete against these other companies?
28:16 Speaker 01 Yeah, so so what happened basically was that, you know, it was becoming clear that extremely scaled up models were going to become a really important thing in the future. And, you know, for Google, it was important that we had the biggest and best models. And it doesn't make sense to have, you know, two different groups both developing big models. We needed to, you know, come together. We were both had a lot of expertise in this sort of thing, come together and use all the, you know, resources in terms of all the people together and then all the compute and everything to make the best models we possibly could.
28:54 Guy Raz? All right. Around that time, Shane, you signed a letter that was signed by many other people in in the AI industry, warning of extinction risk. That it's this is the quote, one of the quotes is mitigating the risk of extinction from AI should be a global priority. Everyone signed this letter. I mean, Sam Altman signed this letter, Jeffrey Hinton, the sort of the godfather of AI, the heads of AI at Microsoft and Anthropic and everyone signed it. And and and I again, this technology is is going to happen, right? But part of me is like, OK, all these people who are creating this this technology and warning that there's a risk of extinction from the technology they're creating are
29:39 Guy Raz? signing this letter to sort of say, we should be figuring this out. But at the same time, like, we're just going to keep moving forward and marching forward. And so to me, there's sort of a dis again, I'm not and I'm not criticizing or attacking you. I'm just trying to figure, understand your thinking around this because on the one hand, you're signing a letter that's that's sort of ringing the alarm bells and then, you know, and then a couple hours later, you're going back to your desk and doing your job.
30:04 Speaker 01 Yeah, well, doing my job is often working on safety, to be clear. Hmm. Okay. I mean, why am I doing what I do? Well, one, I think that something deeply transformational is about to happen in the coming years. Yeah. The, the world that we live in is being shaped by human intelligence. The clothes I wear, the headphones I have on, the internet we're talking to each other, the words we're using, the concepts we're using. Uh, even the atmosphere we're breathing at the moment has been affected by human intelligence and combustion engines that we've invented and all these sorts of things, right? So, human intelligence is profoundly powerful thing. It's affected the world very, very deeply in many, many ways.
30:44 Speaker 01 Now, what's about to happen is that machine intelligence is going to arrive. And it is going to be potentially very, very powerful. So this is going to be a deeply transformative event. Now, this could be amazing. This could be unbelievable. This could be a new golden age for humanity, opening up all sorts of possibilities, solving all kinds of problems and just being really a, a, a, a mind-bendingly fantastical thing. But like any very, very powerful technology, you know, there are things that we don't understand going into this. There could be unintended consequences. We could possibly get some things wrong, right? And so we need to take it very, very seriously.
31:27 Speaker 01 We need to take seriously the possibility that things could possibly go wrong when we're going into such a, such a transition. And then the other point is that I don't see any way to stop this. Can't put the genie back in the bottle. You can't put the genie back in the bottle. Intelligence is profoundly valuable for many, many reasons. And I don't know of any way to globally get everybody to stop using and developing this very, very valuable technology. And so as far as I can see, this will be developed. And so the important thing is that we understand that this is indeed extremely transformative
32:12 Speaker 01 and valuable. And we approach it with an appropriate level of care so that we can understand where the risks lie, understand where the challenges are, and we can navigate that wisely. So we end up in a future where this powerful intelligence is giving humanity many wonderful blessings and gifts, and we avoid all kinds of people misusing it or different types of problems where it's been misfiring in some way and causing some sort of problem or something like that.
32:45 Guy Raz? I get nervous when I hear a future of maximum human flourishing. It really feels in some ways like a false promise. Not to say that parts of that won't happen, but a version of that quote is almost exactly what Eric Schmidt said when your model beat the go in 2016. You know, this is going to usher in an era where humanity is the winner. And I'm not trying to be cynical here. And by the way, I really appreciate that you acknowledge you don't have the answers. I mean, you're not a policy guy. You're a scientist.
33:15 Speaker 01 I don't, I don't believe anybody does. We're going into something which is at least as profound as the Industrial Revolution.
33:21 Guy Raz? Yeah.
33:22 Speaker 01 Now, could you have anticipated all the consequences of the Industrial Revolution before it happened? There's no way you could. It affected everything. It affected how cities are built. It affected international trade. It affected health. It affected diet. It affected the structure of families. It affected culture.
33:38 Guy Raz? It enabled mass industrialized murder. It enabled massive warfare too. Right, of course, there were hugely profoundly negative consequences, but massive benefits too.
33:48 Speaker 01 Yes. All around us, we see the benefits of it. So these deep transformations are subtle and complex, and you can't see all the different things coming up. So that's why I signed the letter. I signed the letter to say to people, hey, wait a minute, there is enormous potential here, an enormous promise here, but there can be some bad things here too. And we need to be really serious about this. We need to understand how big a transition this is, and we need to treat it with the appropriate care so that we can get the benefits and try to avoid the downsides.
34:21 Guy Raz? We're going to take another quick break, but when we come back, AI with a million times the power of a human brain, and why we should all be paying attention. Stay with us. I'm Guy Raz, and you're listening to How I Built This Lab.
34:42 Speaker 02 Football is back, and PrizePix is bringing out their best offer yet. New users who sign up will get $150 in lineups if you win your first $5 lineup. That's right, if you win your first $5 lineup, you'll get $150 in lineups. On PrizePix, you simply pick two to six players, pick more or less on their stat projections, and submit your lineup. Then sweat it out as you watch your picks play out live. It's the best way to get more locked in on game day. Make all the picks you want this football season, all in the same app. And now, PrizePix is available nationwide. So what are you waiting for? Lock in for kickoff, because everyone and everything is on PrizePix. What you on?
35:27 Speaker 03 Download the PrizePix app today and use code WONDERY, that's code WONDERY, to get $150 instantly in lineups if you win your first $5 lineup. PrizePix, run your game. Must be present in certain states. Visit PrizePix.com for restrictions and details.
35:42 Speaker 04 College students, get 12 months free of Uber One for Students membership program and get deals on things you're already doing. You're already ordering pizza, why not do it with 10% off Uber Eats? You're already ordering a ride to karaoke. Now you can save on those Ubers, even at peak times. And you're ordering caffeine the next morning. Order it with a $0 delivery fee. Save on what you're already doing with 12 free months of Uber One for Students and save on Uber and Uber Eats. 12-month free membership offer for eligible students who aren't members. Available while supplies last after 12/31/26, whichever comes first. Subject to change or cancellation, revocable due to fraud if not an eligible student.
36:19 Guy Raz? Welcome back to How I Built This Lab. I'm Guy Raz. Here's more from my conversation with Shane Ley, co-founder of Google DeepMind. I think sometimes we humans take an ahistorical perspective on things because we're looking at what is going on at this point in time, and we're not sort of fully thinking about the sweep and scale of human history. But let me actually try and take a historical perspective for a second, because you could argue that our brains, our human brains, are not more intelligent than a human brain 30,000 years ago. Maybe marginally, right? But could a human 30,000 years ago in the right environment be a member of the Manhattan Project team? I think it's possible. I don't know how much has changed in the brain in
37:03 Guy Raz? 30,000 years. But what we're talking about, especially if your prediction is right, and it's four years away, is a machine that could go from early Homo sapien to Manhattan Project physicist in a matter of weeks, eventually days, and then minutes, and then seconds. I mean, that's kind of what could happen. A machine that just gets infinitely smarter faster.
37:28 Speaker 01 Yeah. I mean, it's not clear how infinitely smarter, faster it can get. There may be limits to how quickly certain things can happen. We don't know what those limits are yet, so I don't want to make too many promises. But there may be certain limitations in terms of certain scaling, certain exponential costs and so on as certain things develop. But at a high level, I agree. The one way I think about it is this: the human brain consumes something like 20 watts. It weighs a few pounds, and it sends signals via axons inside the brain. They're electrochemical wave propagations. They travel at about 30 meters per second,
38:11 Speaker 01 and the cycling, the frequency of the signals is on the order of 100 hertz. Now, if you compare that to just a present-day supercomputer... Instead of 20 watts, you can have easily 20 megawatts. So you've got a million times the energy consumption. Instead of the size of the brain, it can be a million times that size. Instead of sending signals through an axon at 30 meters per second, you can send signals at the speed of light, 300,000 kilometers per second. Instead of a frequency of transmission in the signal of 100 hertz, you can be, you know, a billion hertz, 10 billion hertz. So if you look at energy consumption, physical space, speed of signal transmission and the frequency on the signal,
38:55 Speaker 01 you're looking at six to seven orders of magnitude in every direction, just with present day technology. Now, how intelligent a system will it be possible to construct, given these sorts of parameters? And I think the answer is probably an extremely intelligent system.
39:14 Guy Raz? Extremely intelligent.
39:15 Speaker 01 Extremely intelligent.
39:16 Guy Raz? So I think the answer is we don't know, but I also think that it's not implausible to imagine a near future, not so distant, where machines are becoming exponentially more intelligent to the point where the definition of an artificial general intelligence system isn't can it perform all the cognitive functions of a human, but rather what can't it do?
39:40 Speaker 01 Yeah.
39:40 Guy Raz? Right? What can't it think?
39:42 Speaker 01 So that definition of AGI to me is the is the bar. That's the entry level into AGI. But I do not think that's where it stops. I think you start going into what they call ASI or artificial superintelligence. And it may be the case in certain dimensions is the scaling of capability tops out at a certain rate and you don't actually go that much beyond humans. But in other dimensions, it may go far beyond humans. And so in some shape or form that's not very well understood, I think we will end up with systems that are, in general, are far more intelligent than humans. And so this is why it is so important that people think about what's coming.
40:23 Speaker 01 They think about how to navigate the possibilities that are opening up so that these very capable systems can solve cancer, that can create clean energy systems like solving fusion, can do all sorts of amazing things to benefit humans. That is, that is why this is so important. That is why I talk about this. This is why I've been talking about this since back in, you know, two thousand and five, when people would listen to me, you know, AGI is a thing. It is coming and when it comes, it's going to be so important that this is handled very, very carefully
41:06 Speaker 01 so that humanity can get the benefits of this.
41:11 Guy Raz? I know the Biden administration put together an executive order last year, which is sort of a, one of the most robust kind of attempts to build guardrails. Still, a lot of people don't think that that's enough. You are, you work at one of the most valuable companies in the world, one of the most powerful companies in the world, right? So what can you do? I mean, I mean, is there an answer? Like what could, you're talking to me about this. You're trying to shake the public and hopefully our listeners are like, okay, what do we do? But you're one of the people working on this at a company that has access to, you know, the White House and, and 10 Downing Street
41:53 Guy Raz? and, you know, the halls of power around the world. How do you build in a protective system?
42:00 Speaker 01 Yeah, it's a very complex thing because it's not about a specific thing. It's like the internet is a good example. It's not like there's a very particular technological solution to get the value out of the internet and avoid the problems. It actually affects all kinds of aspects of society, of markets, of social relations, all kinds of things. So it's really something that a very broad range of people from society have to engage with. That's really the only way to do it. It's not going to be solved by some people in a company somewhere. That's not how it's going to work. It's something that society as a whole needs to engage with because that's the nature of the transformation which is at work here. And it would be inappropriate, I think, if it, it was like, you know, society was relying on, I know, a few people in a company to do the right thing.
42:45 Speaker 01 That's, that's not really a robust way to go about deep transformations. So we need to, as a society, and as I said, it's not just the machine learning people, but all sorts of aspects of society, all sorts of people from different disciplines, need to think very carefully about what's becoming possible and take seriously, because for a long time, a lot of people didn't take this seriously. Take seriously the idea that general intelligence in machines is actually coming. What does that mean? What are some of the implications of that? What do we need to start doing to prepare for that? Are there certain things that just shouldn't be allowed? Are there certain rules or policies or regulations or other stuff like that? So we can try to navigate this as wisely as possible.
43:27 Guy Raz? All right. This might be a weird question, Shane, but when you think about you, who you are, your family, are there, I'm gonna sound like I'm a survivalist or crazy conspiracy theorist, but are there things that you ever think about, like, I better do this? I better think about the way I'm doing this in my life to prepare for the potential downside consequences of what this other thing could mean?
43:55 Speaker 01 No, not really. The way I think about it is that the maximum leverage I have as an individual in all of this is to support things like AGI safety research. I've been a vocal advocate for this for many years, including when it was widely ridiculed and there's a lot of eye rolling going on. I led the AGI safety group here at DeepMind for many years and was always advocating publicly for people to take this topic seriously. I personally think about the subject quite a lot and different approaches and the pros and cons to them and discuss this with various people.
44:39 Speaker 01 And I engage with other companies and with government. I've talked to the UK government here about these subjects and so on, and consulting them about AGI safety and the challenges around that and the different approaches and so on. So as an individual, rather than, you know, prepping for some sort of scenario, I don't know what you have in mind there going.
44:58 Guy Raz? Move to, you can move to New Zealand. You're a New Zealand citizen.
45:00 Speaker 01 I'm a New Zealand citizen. I can move to New Zealand and have a bunker there, but you know, that's, that's, that's, that's not okay. There's, there's something that's happening and it's big in the world and it's gonna affect, it's gonna affect a lot of people. It's gonna affect everyone. The, the point of leverage that I have in this whole process has been to stick my neck out and say, look, you know, this is serious. We have to take this seriously. We have to think very hard about questions like AGI safety and socio-technical risks and all kinds of things like this, and policy and governance questions and all this sort of stuff, so that we have the best chance possible of navigating this wisely in a way
45:41 Speaker 01 which is broadly beneficial to the world. That's, that's the point of leverage I have. There's no, no use going to try to build a bunker somewhere. That's, that's, that's not gonna, that's not gonna get, get me very far.
45:52 Guy Raz? We're, I, we're all gonna, either we're all gonna, it's all gonna, it's gonna work out or we're all gonna die. So I hear you. I, I think that's my.
45:58 Speaker 01 I wouldn't, I wouldn't put it such a, such a extreme spectrum. I, I think with these technologies, there's actually a wide range of possibilities. There are some, some very, very good ones, and there's, there's probably many mixed scenarios. So if you look at, again, something like the internet, you know, the internet is not all blessings, right? It's not all good, but it has its wonderful aspects as well, right? It has wonderful aspects. And so I think realistically, there will be positive and negative aspects of artificial intelligence. And if we can do this wisely, we can get a lot of the positive benefits. And the positive benefits can be amazing, but we need to take it seriously if we're gonna navigate this well.
46:36 Guy Raz? That's Shane Legg, co-founder and chief AGI scientist at Google DeepMind. Shane Legg, thanks so much.
46:44 Speaker 01 Thank you.
46:46 Guy Raz? Hey, thanks so much for listening to the show this week. Please make sure to click the follow button on your podcast app so you never miss a new episode of the show. And as always, it's free. This episode was produced by Sam Paulson with music composed by Ramtin Arablouei. It was edited by John Isabella with research help from Carla Esteves. Our audio engineer was Sina LaFredo. Our production staff also includes Alex Chung, Casey Herman, Chris Messini, Carrie Thompson, Jacey Howard, Malia Agudelo, Niva Grant, and Catherine Seifer. I'm Guy Raz, and you've been listening to How I Built This Lab.
Previous Mythical: Rhett and Link Next CrunchLabs: Mark Rober Page updated October 26, 2026 · transcript and summary are machine-generated
