Google’s James Manyika: Who Actually Gets Paid in the AI Economy

James Manyika runs research and labs at Google, and his team recently studied millions of real Gemini conversations to see how people use AI at work. In a typical job, it's only about a fifth of the tasks. I asked him what that means for your job and your salary, why he bet against the people who predicted half of white-collar jobs would disappear in two years, which three signals would change his forecast, and which AI breakthroughs are already in your life without you noticing.📩 Follow my Newsletter: https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=description&utm_campaign=futureproof-sub&utm_content=30-days-plan&utm_term=james-manyika https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=description&utm_campaign=futureproof-sub&utm_content=30-days-plan&utm_term=james-manyika ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/𝕏 : ⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://x.com/siliconvalleymm⁠https://x.com/siliconvalleymm⁠⁠⁠⁠⁠⁠⁠⁠ https://x.com/siliconvalleymm 🔗 Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/⁠https://www.instagram.com/siliconvalleygirl/⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/ 💼 LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko⁠https://www.linkedin.com/in/marinamogilko⁠⁠⁠⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko 📌 My Companies & Products: ⁠⁠⁠⁠⁠⁠⁠⁠https://Marinamogilko.co⁠ https://Marinamogilko.co

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• 1:00Speaker 04

  It's such an exciting time. The possibilities are extraordinary, but the challenges and the rest of complexities are also as important to make sure we're working on both things.

• 1:08Speaker 05

  This is James Manjika, Google's Senior Vice President of Research, Labs, Technology, and Society. He's spent years studying how technology changes work and how workers can benefit from those changes.

• 1:20Marina Mogilko?

  What do you think will happen in the next five years?

• 1:22Speaker 04

  The one thing that will happen is that there will be some jobs that will decline.

• 1:26Marina Mogilko?

  What are the biggest jobs at risk?

• 1:28Speaker 04

  People are doing a lot of kind of routine tasks, testing code, for example. But I think it's important to complete the picture. The other parts you'll see are jobs gained, new occupations that didn't exist before.

• 1:39Marina Mogilko?

  Is there a warning sign you would be looking for that could accelerate the job loss?

• 1:45Speaker 04

  There's several things I've been looking at that could.

• 1:49Marina Mogilko?

  James, thank you so much for doing this.

• 1:51Speaker 04

  Thank you so much for having me.

• 1:52Marina Mogilko?

  I like how you're plugged into everything that Google does when it comes to technology and AI. So you're the best person to discuss so many topics. But I wanted to start with something that my audience is particularly worried and interested in is what's happening to our jobs. And you were willing to take a bet. I watched on your podcast that we won't see 10% of jobs automated away within a year. That was your bet.

• 2:15Speaker 04

  This is a topic I've been spending quite some time over many years. The question of what happens when AI intersects and impacts jobs. And I've always had the view, and a lot of the research that I've done, and actually many economists have done, is basically describe that three things will happen, and all of them will happen. We can debate the proportions of them. The one thing that will happen is that there will be some jobs that will decline. And that's because some of the constituent tasks involved become easy to automate. So we'll see some jobs lost over time.

• 2:47Marina Mogilko?

  What are the biggest jobs at risk?

• 2:50Speaker 04

  Well, I think you'll see that, so for example, anytime people are doing a lot of kind of routine tasks, whether it's, you know, testing code, for example, or, you know, looking for bugs or those kinds of things. So there's a whole range of tasks. In fact, by the original estimate that I did and many others did back maybe five, several years ago, we keep updating these. The proportion of jobs that could be lost that way, added up to single digits number as a percentage of jobs, say, in the U.S. economy. So, but you will see some jobs lost in that sense. But I think it's important to complete the picture. The other parts you'll see are jobs gained. And the jobs gained will be of two kinds. On the one hand, there'll be some jobs whose demand will just go up,

• 3:35Speaker 04

  existing jobs. And we're seeing some of that today with electricians and a whole range of jobs where the demand will actually go up for those jobs, even though they exist. And this is the story of what happened with radiologists, what happened with even bank tellers, the advent of cash machines. So there'll be some jobs gained of that kind. The other jobs that'll be gained will be new occupations that didn't exist before. There's a long history of this. David Alt at MIT has have done some research that basically shows if you looked at the U.S. economy back in the 50s compared to now, something like 68% of the jobs we have today, occupations just didn't exist before. It's kind of like if you had looked in 1995 and said, is there a job called web designer?

• 4:18Marina Mogilko?

  Content creator.

• 4:19Speaker 04

  Content creator, content creator, podcast. You'd say, no. But now in hindsight, it goes, oh, yeah, those are real jobs. So jobs gained will be of two kinds. But the biggest effect that almost most economists have looked at this will point to is the jobs changed, which is the third effect, where many jobs, the content of them will change, even though we may still keep the same name for them. So the reason I was taking the bet was because, you know, starting maybe about four years ago, there were some AI folks who were arguing that in two years' time, we're going to wipe out 50% of white collar jobs. And that's the bet I was willing to take. In fact, I took a bet with a couple of the people who were making those pronouncements.

• 5:00Marina Mogilko?

  And you were right.

• 5:01Speaker 04

  And I asked them two years later and say, time's up. What's happened?

• 5:04Marina Mogilko?

  What was their response?

• 5:06Speaker 04

  And they said, no, no, we'll reset the timeline. So they'll say, okay, no, it's the next two years. But I think this expectation that we're going to see this massive job declines, I just don't think will happen, at least in the kind of 10, 20-year timeframes. Because part of what happens, I think the effect of technol, I mean, while technology is moving very quickly and the capabilities are extraordinary. I mean, we're right in the midst of it ourselves and many of the other frontier labs. The rate at which that translates into economic activity and into the economy is much slower. Look at the pronouncers people were making last year. Look at what the job numbers actually say today. Jobs are actually up.

• 5:44Marina Mogilko?

  What do you think will happen in the next five years? Will the supply of new jobs exceed jobs lost?

• 5:50Speaker 04

  My expectation, the growth in jobs coming from both new jobs and existing ones expanding will be bigger than the declines we see. I mean, this is, you may have seen the recent, I think the economists did a good roundup of this, which basically said at most, the most jobs we can attribute to have been lost from AI is what? I think their number was 200,000. And the jobs that had grown as a result of the AI effects were actually more than a million. So that's, that's the data so far. And even in areas that we all are very close to, like software development, those jobs are actually up.

• 6:25Marina Mogilko?

  Yeah, yeah.

• 6:26Speaker 04

  I should look at the actual data. So I think, you know, this is not to say, Marina, that we should, this will be the forever picture. We should take seriously the possibilities of the impacts of this technology on jobs and work. It's just that it hasn't happened yet. The worst things that people expect have not happened. They may not happen, in my view.

• 6:45Marina Mogilko?

  You touched upon the economic effect. And this is very interesting because we have such powerful models and they're capable of so many things, escaping, breaking rules, et cetera. But at the same time, when it comes to practical tasks, I think your Atlas study suggests it's about 21% that could be automated with AI. Why is that so? What is going on with the other 79% tasks? Is it the model is not capable? Or we're still adopting the technology?

• 7:12Speaker 04

  I think a lot of it, because the 21% you're referring to, that was the median percentage, the percentage of a median job where the tasks were being used, where AI is being used in those tasks. And even then, it wasn't even to complete the task. So what we found in the Atlas study, maybe I should explain that. What that study looked at was we looked at all the activities through the Gemini app and Gemini APIs, and also in AI search, in AI mode in search. And that's, that's a total of close to 2 billion people are using because Gemini app is now about a billion people using it monthly. And AI mode is also about a billion. And then people accessing the Gemini API.

• 7:54Speaker 04

  So we saw a good, a huge chunk of activity directly. And what we found was that the adoption is very wide, but very shallow. And the 21% points to the shallowness of it. And we also found that in most of those tasks, these were assistive tasks as opposed to total automation of the tasks. So the human is still working with the technology. So I think that's what will take much, much, much longer when, you know, before we get to whole tasks being fully automated. Keep in mind that there's lots of bottlenecks along the way to complete a full task. And many occupations consist of many, many tasks. And I think that's part of what you're seeing.

• 8:34Marina Mogilko?

  So what is the bottleneck? Is that the model? Because sometimes you use AI and you start feeding it data and then the answer is completely different. You're like, oh, that was how I was supposed to use the app, right?

• 8:46Speaker 04

  Right. I think it's both things. I think on the one hand, the technology is still what many people refer to as jagged. So you often need to check the output. You need to evaluate the output. And then sometimes the task you want just takes many, many, many more steps. And the technology is not quite ready to do that yet. So that's, that's a big part of it. And now, of course, the technology will improve. And we already know that the time horizon of what these agentic systems can do is actually extending all the time. So that will get better. But the other part of it is that people interacting with technology, we haven't involved yet the workflows enough to make the technology fully assistive or to fully automate them. And in any case, most people seem to find greater value

• 9:29Speaker 04

  when the technology is being used to complement human activity. The combination, the car is seems to be much better when it's a complement as opposed to a full automation. The few, the tasks where full automation is the optimal output is still very small.

• 9:44Marina Mogilko?

  That's a great point. And for someone who's worried about their job, what should they be doing to identify how risky it is for them to not adopt AI?

• 9:53Speaker 04

  Well, I think part of it is just as humans and as workers and as people, just continue to evolve the value add of what we do versus what the technology is able to do. So in any of our tasks and activities that we do, there are parts where we still bring the judgment, the expertise, the ability to check, and the ability to frame the right questions. And the technology will be good at some things. And you as a human will be better at something. So I think this idea is people to constantly evolve the value add of what we do, working alongside these technologies. I think that's the ongoing continual learning work we're going to need to do. I'll say one thing, though, which I often worry about,

• 10:33Speaker 04

  which I think people throw around this idea that if you're exposed to AI, I think somebody says if you expose, you're hosed. I think that's just the wrong way to look at it because we've seen the exposure to this technology go both ways. So some people are exposed. Yes, some of the tasks that they do become easy to automate. But in other cases, people who are exposed are actually more able to capitalize on the technology where the combination becomes more powerful. So I think the idea of using exposure as a predictor of the worst outcomes, I just think is plain wrong. Because I think both exposure can lead in both directions.

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• 12:10Marina Mogilko?

  Because then if you're exposed, 50% of your job is now done faster. But that means your strategic thinking, whatever your taste, now matters more.

• 12:18Speaker 04

  It matters more. It allows you to be more productive. Because I mean, this is the case of the wonderful case of the radiologists. Radiologists were clearly exposed. But what actually happened, because there's so much more demand for what they do, they became actually more productive. And actually, growth in radiologists actually went up. Right? It's the same thing that happened with historically, the example of the bank teller. People had predicted back in the 1970s before ATMs were prevalent that we're going to see bank tells are the most exposed, obviously, because of the activity of taking money in and out of a cash machine or ATM. But what actually happened? The job of the bank teller actually grew. So the demand elasticity was actually up.

• 13:01Speaker 04

  So I think often we forget the demand elasticities that happen when the technology complements what people do. There's some activities where the demand is actually outstrips the complementary effects of the technology. But there will be somewhere it actually goes the other way. So just exposure on its own doesn't predetermine necessarily what the outcomes will be.

• 13:23Marina Mogilko?

  What happens to your salary? If you've become more productive, what are you seeing now? Maybe inside your organization?

• 13:30Speaker 04

  I think this is one of the most important questions, the wage effects. Because all too often, we focus primarily just on, you know, what's happening to jobs and displacement. I think the wage effects are important because what happens is, so imagine if you are somebody who you're complemented by the technology and your output is actually even more productive and more valuable. Your wages should go up with that because you're doing more valuable work, complemented by the technology, and it expands. I think the risk comes when, in fact, you're complemented by the technology, but the truly value-added portions of your work are being done by AI. And what's left over is less value-added. I think then you have, you could have adverse wage effects.

• 14:14Speaker 04

  So the wage effect question, again, can also go both ways. I think this is one of the things we're going to need to think a lot about. Economists typically think about this idea of the marginal contribution of labor to any particular task. And that can go both ways. But this is one of the things we're going to need to worry about and pay attention to. Because I think we'll see some occupations, particularly when the demand for what they do, complemented by technology, goes up. And there's greater demand. I think we'll see those wages go up, or we should. And when people are becoming more productive and creating more value-added output, I think the wages should go up. So let's make sure that happens. But there will also be some other adverse effects where the opposite could happen.

• 14:55Marina Mogilko?

  Yeah, I think people who are seeing the increase in productivity should be also advocating for themselves.

• 15:00Speaker 04

  Oh, I think, I think so. I think so.

• 15:02Marina Mogilko?

  Because I don't think businesses will be able to catch up that fast, especially small businesses. You're like, you're just not accounting for that.

• 15:09Speaker 04

  No, I think you're exactly right, Marina. Because one of the things, and this has been a long-running trend, even before this air moment. If you look at the history of the last sort of 30, 40 years, the idea of productivity is that as productivity goes up and GDP and output goes up, wages should go up with it. But we've seen for the last 40 years, the growth in wages go up, but not as fast as the output has gone up. I think that's a structural challenge in our economy. I don't think AI introduced that. But AI could actually make that worse. So we should think about how do we make sure that as people become more productive, that is reflected in their incomes and their wages

• 15:51Speaker 04

  as they make all these extraordinary outputs and value-added contributions?

• 15:55Marina Mogilko?

  What would be your advice to workers who are struggling with this?

• 15:58Speaker 04

  They should advocate for themselves. And I think, and I think, you know, the more they make their particular contributions unique and distinctive, particularly in a way that complements what the technology does, I think they should, that that should lead to higher output and wages. You could argue we're seeing some of that already in some occupations where wages are going up. The question is, are they going up commensurate with the productivity contributions that are coming out of their work? I think that's the question.

• 16:25Marina Mogilko?

  Yeah, and I think because of that, we're also seeing transition to more independent workers, like contractors, who do a particular function, but for multiple companies. And AI allows them to do that at an increased speed. And then you're charging for the output, so.

• 16:37Speaker 04

  That is actually one of the things I'm most excited about. In fact, one of the things we're looking for, and you mentioned our Atlas study. That's an ongoing study. So hopefully, you'll see, we're going to keep kind of producing output of that. So but one of the things we're watching for is the emergence of new activities and new kinds of firms and new kinds of work emerging. Because that's what's happened historically, where anytime a new technology comes up, you start to have new kinds of firms, new kinds of businesses, and new kinds of work start to enter the picture. So we're constantly trying to watch, what is that? Have we seen that before? What's that activity? And is it growing? What do we call it? What do we name it? I think that's going to be one of the most exciting things to watch.

• 17:18Marina Mogilko?

  What is your advice to entrepreneurs? What markets should they be looking at?

• 17:22Speaker 04

  I think we're going to see a range of new services emerge. I think the difficult thing, by the way, is to predict what those are. I think one of the things that's been interesting for me as somebody who's interested in the history of these things is that most new occupations and new firm activities, we've only been able to name them in hindsight. At the time, there's a lot going on. There's a lot going on. And it's hard to actually name it and say, make those predictions. So I'd be very wary about making predictions. But I have no doubt we're going to see new kinds of firms and new kinds of activities and new kinds of work emerge. I think the great opportunity for entrepreneurs is to experiment, build new kinds of firms, go after the market opportunities. What are new work being emerging?

• 18:06Speaker 04

  So are there people now overseeing agents? Are there people now watching agent orchestration? Are there people now doing new kinds of evaluations? Are there new services that larger and smaller firms need? So those are the things I'd be trying to experiment if I was an entrepreneur.

• 18:21Marina Mogilko?

  Yeah, and also, I think I see as someone who started as an employee and then became an entrepreneur, you're seeing the process inside your company. And that's your idea. That's your business idea.

• 18:31Speaker 04

  Exactly, exactly.

• 18:33Marina Mogilko?

  So much more possible to execute on. Okay, to kind of wrap up this AI job loss section, is there a warning sign you would be looking for that could accelerate the job loss?

• 18:46Speaker 04

  There's several things I've been looking at that could change the dynamics I just described. I think on the one hand, I'd be looking to see, are we extending the capabilities of the technology to do much longer time horizon tasks? Have we now solved and addressed much of the jaggedness that we see in the AI capabilities themselves?

• 19:04Marina Mogilko?

  What's the time horizon? Is it now, what is it now, 16 hours?

• 19:07Speaker 04

  For some tasks, it's up to 16 hours. But it's very specific. I mean, when you now think that sometimes the evals and the benchmarks don't tell the full story of what actually happens in the real world. Yeah, so in some simulations, we've seen these things run for much, much, much longer. There's actually a wonderful study that economists at MIT have done and looking at the time horizons. And that is a predictor of, at least an early warning sign, or at least an early sign of extending capabilities. So I think we should watch that in the real world, not just in the evals and the simulations that we run. So that's one thing I'd be looking at. The other thing I'd be looking at also is just this, what I mentioned, is the jaggedness. I mean, it's still extraordinary to me

• 19:50Speaker 04

  that there's still some tasks that these models are terrible at. Visual reasoning, for example. To have to do all kinds of kludgy ways to solve for counting, for example, because these systems are not very good at that. So the, once you start to see more consistency across different kinds of activities and tasks, that's something to pay attention to. The final thing I would just also mention, which I think will be a big factor we should watch for in the labor markets, is whether robotics comes along as quickly or not. The AI systems are making a lot of progress in terms of the cognitive activities and, you know, kind of thinking activities. But robotics, even though it's making progress, and we'll see all these demos, is still lagging quite a bit.

• 20:34Speaker 04

  The only real advance we've seen in robotics is in driverless cars, Waymo. You know, in some ways, it's an AI on wheels at some level. But robotics still lags. But if robotics was to fully catch up. That would change it and I was quite a bit because something like if you look around the world, something I think the number is roughly sixty percent of people in work in the world do work where the it's dominated by physical tasks. So if that catches up, that's an important scenario shift that we'd have to think deeply about it, what the implications and opportunities of that would be.

• 21:12Marina Mogilko?

  I'm very excited to talk to you about science because from what I'm getting by talking to scientists and entrepreneurs who work with scientists that we're seeing an incredible progress where we can do more research. But at the same time, the scientist's job is now running multiple experiments and verifying the AI's output. Is that what you see as well?

• 21:35Speaker 04

  The intersection of AI and science, that's one of the things I'm very excited about, very excited because I think it's one of the most underappreciated big advances that are happening today from AI. So that cuts across everything from biology to physics, chemistry, a whole range of the natural sciences, but also recently in some of the formal sciences, so mathematics and algorithms and computer science. So the progress is extraordinary. But I also think it changes often the nature of the work and where the bottlenecks are. So one of the things that's, and we just put out a, you know, a study where we're looking at, so how scientists actually using these tools. And what's interesting

• 22:16Speaker 04

  is you see two patents for now, we'll see whether that persists or not. There's a lot of use of the general models. So I think Gemini, people using Gemini, the frontier models to do a lot of the, you know, the operational tasks, writing papers, researching papers and those kinds of things. So that's kind of roughly half by the study we just published recently with working with some folks at MIT. The other half are scientists still using very specialized models. By specialized models, think Alpha Fold, a Nobel Prize winning model, or any of these specialized models that are either working on proteins or cell structures or connectomics or genomics or any number of areas. So most scientists

• 22:55Speaker 04

  are using both and often in tandem. So they may use, you know, Gemini for the general task, but then come back to these specialized models. So that's what's driving the, a lot of the progress and lots of great results. I'd like to come back to them. But to your question as to where are the new bottlenecks? It's actually quite interesting. This has led to a huge generation of hypotheses, ideas, simulations, things done in silico. For example, one of our, one of our models, genome, was able to kind of predict crystal structures in chemistry that were like over two million structures that were theoretically possible in simulation. But then the task of verifying those is now the bottleneck.

• 23:40Speaker 04

  In fact, after the 2.2 million crystals that were predicted, I think only a few hundred thousand were actually looked like they were viable for, for synthesis. And even then, when people then tried to synthesize them, the number dropped to like a few thousand. So on the one hand, generating ideas and possibilities is almost like easier now, but the bottleneck is lab verification and then often in vitro or in a natural cell structure. We had the same experience where one of our models, C2 scale model, was able to predict these pathways and possibilities in, in, in cancer treatments, for example. But of the possibilities that came up with, we then worked with researchers at Yale to verify

• 24:25Speaker 04

  them in the lab and only a handful were things that were actually reasonable in the lab. So you now have bottlenecks at the back end of verification and validation, but also the, but the process of generating possibilities and hypotheses seems to be much easier and the possibility is much more expanded.

• 24:45Marina Mogilko?

  I wonder if overall then is AI speeding up science or because now we have this new bottleneck of verifying 2.2 million different crystals?

• 24:55Speaker 04

  Well, I think when it comes to science, I think AI is actually doing two different things. There is a part of where it's speeding up the process of research where you can now look at all of it all at once. And like in the protein structure predictions, you can look at all of them. Recently, you may have seen, we actually looked, for example, at all the single letter misspellings in the genome. This is the alpha genome atlas, and we looked at all nine billion of them. So this idea that you can look at all of them quickly is clearly one effect from science. But the other effect is actually being able to do things that we couldn't have done at all before. It's not really a speed up thing. So for example, in the case of some work we're doing in connectomics,

• 25:39Speaker 04

  so connectomics is the area where you're trying to understand, if you like, the wiring diagram of the brain, of brain structures. And actually, we hit a milestone recently, a couple of weeks ago, that we published working with the Howard Hughes Medical Institute, where we did the first full brain mapping of a fruit fly, Drusophelia, was the first time it's ever been done. But the reason why that's interesting, it's also much a speed thing as much as think of the tools we used in that case as more of a, a new kind of microscope, which allows you to look at things you could not have looked at before. We were talking about jobs earlier. I think what's interesting about AI as a technology, it actually has two properties that are interesting. One, which you often think about in the concept

• 26:22Speaker 04

  of economics, which is this idea that it's a general purpose technology, a GPT, which allows you to do in all kinds of economic activities. But the other characteristic, which is I find super interesting, is AI is an invention for making inventions. And so when people make the analogy of AI to the industrial revolution, which is correct, people often think of, think of it as a GPT, a general purpose technology. But I think if you're looking to these inventions as a method of invention characteristics, the Enlightenment might be a better analogy. Because what we did at the Enlightenment was discover new tools, instruments, telescopes, microscopes, the scientific method, things that allowed us to do science in a way that we could not have done before, look at things

• 27:06Speaker 04

  we could not have looked at before. So I think AI has these two remarkable characteristics. And I think the impact on science is just, I'm so excited about that. But also it raises questions about, so what's the role of scientists? Are they, is there all there? It's a bit like the work question. Is it to formulate hypotheses? Is it to explore them? Is it to validate them? Is it to ask questions that haven't been asked before? But I think the possibilities are super exciting.

• 28:37Marina Mogilko?

  James said AI adoption is very wide, but still very shallow. In a typical job, people use AI for only about a fifth of their tasks and mostly as a helper rather than something they actually work with. That's the gap. And the people who close it are the ones whose work becomes more valuable. That's why we put together a free 30 day plan to help you go from using AI like a search engine to actually working with it with a step by step guide on what to set up and in what order. It's available through my newsletter, future proof. So subscribe using the link in the description and you'll get the 30 day plan for free. When do you think we're going to see a scientific breakthrough that's going to affect everybody's lives? Because for now, there are some amazing breakthroughs when we're treating very rare diseases in babies who were just born, right?

• 29:21Speaker 04

  Right.

• 29:21Marina Mogilko?

  But there hasn't been a massive effect on the population where we're like, oh my God, this is thanks to AI.

• 29:27Speaker 04

  I think we're starting to see that. Now, the question is that people think of it as, oh, thanks to AI. So I'll give an example. In healthcare, for example, in health, we're now able to do so many diagnostics that we couldn't have done before, either faster, earlier and see and pick up diseases or evidence of illnesses that we couldn't have done before.

• 29:49Marina Mogilko?

  Can you give an example?

• 29:50Speaker 04

  Yeah. So we just, we just published early this year, one of the largest studies ever done on breast cancer detection. We did this with the NHS in the UK. I think it involved over 180,000 women were part of the year long study. And in that case, we're able to use these tools to do, to pick up evidence of breast cancer that would have been missed before and pick them up much earlier. And we've been-

• 30:13Marina Mogilko?

  By looking at the mammography?

• 30:15Speaker 04

  Yeah, and using AI tools to observe those and evaluate those. We've done the same thing in the colorectal cancer, in lung cancer, and also in tuberculosis. I mean, tuberculosis is interesting because something like thirty to forty percent of the people in the world, we have TB, go undiagnosed. And that's mostly because they live in communities where the infrastructure is just isn't there to diagnose them. And so being able to use these tools in those places, we did a study that involved, we studied in Zambia and India, and we're able to show that, no, you can actually make accurate diagnosis for tuberculosis in those places. Now, in all of these examples, or those people say thanks to AI, they'll

• 30:58Speaker 04

  just think, oh, I've got a great, you know, diagnosis and they discovered I have this disease. So a lot of those effects are going to be a little indirect. It's the same thing with another area we're spending a lot of time on, is trying to help communities be resilient. Imagine if people would not get surprised from natural events and natural disasters. So we've been working down a list from floods to monsoons and cyclones and weather and even earthquakes. One of my favorite examples, just because it's at scale now, is in floods. So there's, there's two kinds of floods you want to worry about in most cases, which are riverine floods and flash floods.

• 31:43Speaker 04

  And in those cases, it's always been known that if you could make a prediction, six plus days in advance, the impact on people will be dramatically halved. Just because people can move out of the way and so forth. And starting three years ago, we did a first experiment using our AI tools in India, and it worked. And now we've expanded that to over one hundred and fifty countries where more than, in places where more than two billion people live. And but again, in those cases, I think those people, what those people will experience is they'll probably get an alert on their phone to get out of the way, there's a flood coming.

• 32:20Marina Mogilko?

  So not necessarily bridging it back to AI.

• 32:22Speaker 04

  Yeah, but but the connection back to AI may not be as direct. And I give another example, recently in the Venezuela earthquake, we've been experimenting with the idea of using our, you know, Android systems with AI sensors, but also AI systems to make predictions. And in that case, I think this was reported in the New York Times as well. We're able to send alerts to about twelve million people ahead of time. In some cases, a few seconds, in some cases, a couple of minutes ahead of time. So we've been working our way down everything from wildfire predictions to monsoons to cyclones to hurricanes. WeatherNext, one of our models, was actually used in the, in Melissa, in Jamaica. And we were able to send alerts.

• 33:05Speaker 04

  So those are examples where the ability to make these predictions.

• 33:09Marina Mogilko?

  Is enhanced really, yeah.

• 33:10Speaker 04

  Are extraordinary. And the impact of people, this is not in the far future, is right now.

• 33:17Marina Mogilko?

  What about the the medical examples that you talked about? Are they available in the US as well? Like, are we using this technology in the US?

• 33:26Speaker 04

  Oh, so we're starting to. So for example, one of our tools called AMU, which is an assistive tool that we've tested with a variety of hospitals and clinics, Beth Deaconess Israel, at Harvard and other places, we're now involved in a nationwide trial. It's an assistive tool for doctors. Another AI-based tool we've developed called for detecting what's called diabetic retinopathy, where you can look at the at the eye as a way to predict all kinds of diseases. We've actually applied that in more than six hundred thousand cases in India. And now we're working at the partner with the goal to scaling that up to be able to do six million assessments. The tuberculosis example that I mentioned, now that we've demonstrated that it works

• 34:08Speaker 04

  with a year-long study, we're now working with various providers in Africa and other places to actually start to deploy that and use that. So a lot of these things are.

• 34:17Marina Mogilko?

  Are happening.

• 34:18Speaker 04

  Here and scaling. That's why these are such important examples. But often, the attribution of those things to AI is often indirect, or at least a step removed from the people experiencing those things. But there's still so much more to do, by the way, don't get me wrong. But I think I worry that when we think about the AI, AI's contributions to science and health and all these areas, it's always positioned as in the far future. Someday, we will do X. And I say, no, no, no, no. We're starting to do that today. Of course, there's a lot more we will be able to do in the future, but don't, this isn't some future promise. We're starting to see those effects today.

• 34:57Marina Mogilko?

  What is the wildest scientific moonshot you think is available now because of AI?

• 35:02Speaker 04

  Oh, what happened with protein folding? I mean, that was a seventy-year grand challenge. That idea was first posed by, you know, Anfinson in his Nobel lecture, like in the seventies, so, so in the sixties. So that's a moonshot that came to be real.

• 35:20Marina Mogilko?

  What about future moonshots? Something you're excited about?

• 35:23Speaker 04

  One thing I'm excited about is the day we are able to build a prediction model that helps understand the dynamics in cells. Cells are very interesting. We don't quite yet understand, nobody's ever, has yet built a fully complete virtual model of the cell, of how cells works, the perturbations that happen at the cell level. So that's still, and there's a lot of great work going on, by the way. So folks at the Broad Institute and a few other places are trying to build the Cell Atlas, and we are contributing to that. Yeah, so the, but once we're able to build models that allows to make predictions about the dynamics in cells, that's going to be pretty exciting. So I'm excited about that.

• 36:03Marina Mogilko?

  What's your timeline for that?

• 36:05Speaker 04

  Oh, I mean, that's the problem with breakthroughs. Scientific breakthroughs, it's hard to make predictions about timeline, because it's one thing, it's easy to make engineering predictions, but when it involves scientific breakthroughs, that's hard. I'm also pretty excited about, by the way, another area in science we haven't talked about, which I'm excited about, is intersection of quantum physics and AI. So we have a team, the Quantum AI Team. The reason I mentioned that, so that's a moonshot, where obviously we're trying to build a quantum computer, and I think we're making progress. And you'll be seeing, and you've seen probably some big milestones we've had from the building a quantum computer point of view. But one of the things that's happening alongside our work in quantum computing is we're now

• 36:47Speaker 04

  able to do extraordinary science alongside trying to build a quantum computer. So we've had some breakthrough results in physics, in understanding the structure of black holes, in understanding electromagnetism. In fact, we've even demonstrated some new results in traversable wormhole possibilities. And also, you know, last year, we had a couple of breakthroughs where we're able to do some work that relates to medical science, where we're able to understand the structure of some very hard to understand molecules and their dynamics, which is so important for some of the work we need to do in science. So I think you're going to see, even from that arena, breakthroughs that are relevant, not just for biomedical research, but also for other parts of science.

• 37:31Speaker 04

  So I'm, I'm pretty excited about that, our moonshots on, on the quantum AI side, too.

• 37:36Marina Mogilko?

  Yeah, it's, it is really fascinating. I feel like it's happening gradually now, but then we're going to probably have a moment in history where it just speeds up immensely.

• 37:43Speaker 04

  I'm convinced of that. I mean, if you asked me, we were talking about bets earlier, I'm willing to bet that when we, you know, ten years from now, when we look back and say, what are some of the biggest things that came out of AI? I think this intersection of AI advancing science in ways that are hugely important for humanity and helps us to tackle questions in health, in well-being, in crises and resilience, I think will probably be some of the biggest things that will have happened. Now, we also have to be super responsible, because just as much as we're making progress on the science of it and the advances, it does raise some complexities we should pay attention to.

• 38:23Speaker 04

  So there'll be some risks related to bio and, and a lot of this work in, in biotech and in chemistry poses some risks. So the responsibility there will be super important as well, so that we're doing this in a safe and responsible manner with regards to the science that we're doing with AI.

• 38:41Marina Mogilko?

  Yeah, which is another huge topic. Do you have a particular scientific discovery that if it's made in your lifetime, you'd say?

• 38:49Speaker 04

  Oh, wow. I've got quite a few. I mean, there's a whole bunch of mathematics, and we're seeing a lot of progress in mathematics these days. I mean, I think if we get to solving the Riemann hypothesis, that'd be great. I'd love to see that. It's been one of the longstanding problems and challenges in mathematics. We've seen some of the things happening in mathematics, so that, that's exciting. But I actually think, for me, the, the ones that I'm, I'd love to see in my lifetime have to do with the structure of biology and of life and our brains and our bodies. So these questions around understanding cells, understanding the dynamics of cells, really understanding what's happening at the genomic level, those, because I think if we make progress on those, it's going to lead to so many more discoveries.

• 39:30Speaker 04

  And one of the things I'm excited about, even with the work on, on AlphaFold, is the fact that what that work has done is opened up the canvas to many more scientists. So today, AlphaFold, I think we reached a milestone a couple of weeks ago, is now being used by four million researchers around the world, because we made those predictions open and available. And these are researchers in a hundred and ninety countries. And what's exciting about that is many of them are working on previously neglected diseases that would have been too costly to even tackle. So the fact that we've expanded the canvas so people can now work on problems, I think that's exciting. So I think for me, part of it is giving tools to scientists to expand the wide range

• 40:14Speaker 04

  of possibilities to solve humanity's problems. Because I think so much of, many of them have just been ignored.

• 40:20Marina Mogilko?

  Absolutely. Because we have no time.

• 40:22Speaker 04

  We have no time, they're too costly, people in other, in various places around the world don't have the infrastructure to be able to do this work. But if we can make these predictions openly available, so that more scientists get to work on these things, I think that's going to be exciting.

• 40:36Marina Mogilko?

  Yeah, it's a community thing, right?

• 40:38Speaker 04

  Oh, absolutely. I mean, I think it's also the reason, Rena, where. I think, you know, we have to do everything we can to make all of this, you know, available, accessible to everybody on earth. It's actually one of the reasons I like, you know, Google's founding mission, right? Organize world's information, make it university accessible and useful. So the more people can use this and access all these benefits. We talked about all these things in health and, you know, we didn't even get to education and all these areas. One of the moonshots that we have, you're asking about moonshots, is actually around languages and accessibility. Because it's one thing to do all these things in health and education and work and learning. But often you want to be able to do that in as many languages as possible.

• 41:22Speaker 04

  So we have a moonshot, our current moonshot on language right now is to get to a thousand languages. Last week, we actually hit a milestone, which I'm very proud of, where we now are at 300 languages that we can handle, trying to make sure that's available in all our products. But a thousand would be great. In fact, if you wanted to get all the languages spoken on earth by everybody, you'd have to do something like 7,000 languages.

• 41:47Marina Mogilko?

  Yeah, I was reading a research with some languages. If you ask something in that language and AI does a translation, sometimes it's up to 60% difference in meaning, which can be critical when it comes to a health question, for example.

• 41:58Speaker 04

  Oh, absolutely. And in fact, it's even more challenging than that. In many languages, we often think of singular languages. Often there's a lot of dialects in single languages, where there's in some cases over 100 dialects of a single language. And so we're doing all this work, for example, in India with the Indian languages, to understand the various dialects and all the languages. We're doing some work in Latin America and in Africa and many. So even understanding the dialects underneath the singular languages and what meaning, what how things, what things actually mean, I think is still a lot of work to do.

• 42:32Marina Mogilko?

  It is.

• 42:32Speaker 04

  But that's a great moonshot.

• 42:34Marina Mogilko?

  Yeah, absolutely. Can you talk to me about AGI? We're hearing so many, I think Demis Hassabis at Google I O said, we're at the foothills of AGI. What does it mean for a regular person?

• 42:46Speaker 04

  One of the things that's always challenged about AGI is this, people have so many definitions, so many definitions. In fact, you know, at some level, if you'd gone back to, you know, the now famous Dartmouth Conference in 1956, where the term AI first came into being, if you actually read the definite, what the scientists who got together at Dartmouth in 1956 were trying to do. It was, if you read that with a modern eye, you'd say, oh, that sounds like a definition of AGI, even though they didn't name it that. And the term AGI itself, I think one people was a big popularizer of that term. It's actually my colleague Shane Legg back in 2001 or two, where he first started to talk about that. And that was initially as a way to distinguish that between

• 43:29Speaker 04

  a more general kind of intelligence, a set of capabilities, compared to more narrow systems that we were building at the time, whether it's for natural language processing or this, that, or vision, machine vision. So there was an attempt to describe a general set of capabilities. And at the time, I think, so, so I think when it comes to AGI, I think of it as a spectrum. So there's a minimal definition, you can think of where you're saying, no, no, you're talking mostly about the kinds of things that Shane was talking about, or even going back to the Dartmouth Conference, where you're describing the general cognitive capabilities that a typical human has. With all the peaks and valleys, I mean, humans have all kinds of peaks and valleys, stuff that I don't know how to do, but some humans maybe you can't, cannot able to do.

• 44:13Speaker 04

  So there's a, there's a jaggedness to it. And I think if that's your definition, you could argue we're close to it. If you look at it in hindsight, but there's some other higher definitions. Demis, for example, likes a much more higher definition, which is a more scientific one, which is more complete. And I think on that base, we're much further away. So I think the AGI question, usually when people ask me, I'll usually start with, well, what's your definition? Then I'm having to give you a prediction, if I know what definition you're working with. But I think the reason it's important to think about it is, you know, regardless of how close we are to it, as we approach it, the effects of it are going to be quite profound, I think.

• 44:55Speaker 04

  I think we're going to have to think about, you know, what does that mean for, you know, everything from the risks and complexities associated with it to, boy, all the amazing things we can use it to do. Science, discovery, all these extraordinary things we can use the technology to do. But also even deeper questions about what does it mean to be human in an age of AGI?

• 45:19Marina Mogilko?

  Well, AI can do everything.

• 45:21Speaker 04

  AI can do most things and no even everything. What do we, how do we think about meaning? And this is, these are the questions that we've been thinking a lot about. You may have seen recently that Demis, Shane and I are now setting up of the Deep Mind Institute. But that's work we've been doing for the last few years to start to think about these deep questions about. So, and we'd love to just engage others and think together on how do we think about these questions? I think this is going to be so profound.

• 45:51Marina Mogilko?

  And also the speed of it, right? If it happens in the next 100 years, then we have plenty of time to think. If it's the next five years with AI models advancing so fast, like, how do we adapt?

• 46:01Speaker 04

  Yeah, I think the speed question is so critical, because I think right now we're starting to see, you know, a whole range of techniques kind of speed up this process of advance of these capabilities, everything from recursive self-improvement and all these different things that, you know, we're now starting to be able to do, that will probably speed things up. But I think there's still many breakthroughs that are going to be needed, many more advances in my view, that are still going to be important to get there. But I think we have to think about how do we do this responsibly and safely? Because, you know, we talked earlier about the risks to, you know, work and jobs and so forth. But AGI would probably pose other kinds of risks too. The questions around alignment, questions around control.

• 46:46Speaker 04

  Those are all things we're going to need to think through and make sure we're being thoughtful about how we build this technology to make sure we still, you know, we solve alignment, we address alignment. I think what I worry the most about Marina is the idea that we shouldn't let the advances in AI capability and performance outrun our ability to control, build this technology safely. So I think that's the, we have to have those two things happen in tandem. But if the capabilities outrun our ability to build this technology and deploy it safely, I worry about that.

• 47:21Marina Mogilko?

  Do you think we're at a stage where capabilities are outrunning?

• 47:25Speaker 04

  We should make sure that doesn't happen. I mean, we're trying very hard, at least on our teams, to think about, you know, how do we keep building our ability to build this technology safely? Making sure that, for example, there's now a whole range of questions that are emerging around, you know, if you think about, you know, a lot of the reasoning capabilities of these systems, we do that through these chain of thought and reasoning and traces. How do we make sure we don't, there's a term now, people are using a lot, this idea of serial depth opacity. How do we make sure we can actually still see what the model is thinking all the way through? And so we don't, there's some, there isn't some other part of it going on, where the thing is going on and we don't have a way to understand that or see that.

• 48:08Speaker 04

  That's work we all have to do. And I think that becomes super important when we start to have these agentic systems, where we may set a benign goal, but then the sub-strategies or sub-goals that the agent then takes on to achieve that goal may be things that we may or may not want.

• 48:29Marina Mogilko?

  Questionable, yeah.

• 48:30Speaker 04

  And so unless we understand it all the way down as much as we can. So I think that's what I mean by we have to keep these two things in tandem. Yes, advance the capabilities because they're useful for science and everything else. But also make sure at least that doesn't outrun our ability to do the safe, responsible, safety work and security work we need to do.

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• 49:51Marina Mogilko?

  Do you agree with some statements that people say we need to start slowing down now? Or you think we're not at the?

• 49:57Speaker 04

  Well, you know, I always worry when people say that as to what they mean. I think if people mean let's not let these two things outpace each other, then I agree with that.

• 50:06Marina Mogilko?

  Slow down the model development because it's getting so complex and we can't trace what the agent is thinking.

• 50:12Speaker 04

  Yeah, either slow down or speed up the safety and responsible work we're doing. I think to me, the key thing is to keep the two things should keep, should not outrun each other. That's the pacing, I think, that I think we need. And so that's the critical thing in my mind. I think we need to make sure that we are doing all the work we should be doing to make sure we're building these systems responsibly and safely.

• 50:34Marina Mogilko?

  Is there an AI risk we're underestimating now? We're talking a lot about 10% chance of destroying humanity. That I've heard so many times in the past few weeks. We're talking about systems escaping. Is there anything else we're not talking about enough?

• 50:47Speaker 04

  We're trying to spend a lot of time thinking about this. I think one of the things we don't spend enough time on is, just at least in the public dialogue, is in how these systems are now interconnected with so many other systems. I think we often will think about AI models in isolation. But keep in mind that we're now connecting these systems, I mean, for useful, good reasons, to grids and other things and other systems. I think, you know, there's so much benefit from that. For example, if we're able to manage grids more efficiently, manage electricity more efficiently. But at the same time, that also raises some new questions about security and safety of infrastructure.

• 51:28Speaker 04

  So I think, thinking about these systems, these AI capabilities as systems, I think is an important lens we should bring to this as opposed to think about, oh, if I evaluate my singular model, how's my model doing? I think we do a lot of that, but we often don't do these system, system-wide evaluations and understanding of the effects. Also, keep in mind that some of the intelligent capabilities, maybe the results, none of a single model, but a collection of models working together. So I think these more systemic kind of questions, I think are important, both on the positive side, it doesn't therefore what that could mean.

• 52:07Speaker 04

  And think about us as humanity, I think we're an extraordinary species, mostly because of all of us, right? And all of complementary intelligences. I think it may very well be when we get to AGI, it won't be the result of somebody's singular model achieving that. It may be actually the collection of agents and models that achieve this. I think these more collective, social components of intelligence, I think is an important area to think about, both on the opportunity side, but also on the challenges and complexity side. I remember a few years ago, a couple of years ago, I was co-chairing this body, the UN High Level Body, looking at AI and all the possibilities of AI. And I remember when we're having discussions about the risks of AI.

• 52:52Speaker 04

  So we went through the typical list that we all worry about. So misuse and misapplication of this technology. And I remember being struck by many of the members of the body, mostly from the global south, say, we should add another risk. And we said, what's that? They said, misuse. I thought that was interesting to think about misuse as a risk. And but I quickly got it, I think, because I think what they're referring to is, there are many communities, not just in the global south, but even in the US, in developed economies, where, you know, there's so many challenges already. Access to healthcare, I mean, if you go on a county by county assessment of the 3,000,

• 53:37Speaker 04

  whatever counties you have in the United States, there isn't equivalent levels of access to healthcare, to education, to all these things. And so if you're in some of these poor communities and, you know, how do we make sure we also don't avoid the misuse possibilities where this could actually make a dramatic difference?

• 53:56Marina Mogilko?

  Absolutely. When it comes to healthcare, something we've discussed today.

• 53:59Speaker 04

  Yeah, to healthcare, to schools, I mean, the kids were promised, somebody was going to come teach them STEM skills and all the rest of it. It never happened. But this technology can actually be useful in some of those communities to address those. So I think that's another important kind of potential risk that's worth thinking about as well.

• 54:16Marina Mogilko?

  And because we're here in New York during the UNGA, I was talking to some representatives from other countries and there was someone who posed this question. It made me think, I don't know if you have any ideas on that, but if we're talking about, okay, AI is amplifying our productivity and we tax large corporations so that people can participate and employees can gain benefit, we're only doing this in the US because the models are being developed here. So we're taxing other corporations in the US and then we're distributing wealth inside the US. What happens to someone in Brazil, right, who is using the same model? Have you ever thought about that, like the distribution of the benefits that we're getting with AI across the globe?

• 54:54Speaker 04

  I think that's such an important question because, you know, in my mind, I think the possibilities of this technology benefit everybody, everywhere. So the question is, how do we increase the possibilities of somebody in Brazil being able to participate in this extraordinary moment? Is it through getting more educated, building the wrong capabilities and entrepreneurial capacity to build, start companies? That's one way of participating. The other is also to think about, you know, do they get better jobs and become more productive? So the question of everybody should have access to participate. Often when I speak to, you know, people in various parts of the world,

• 55:35Speaker 04

  it's actually quite interesting that attitudinally, there's a more positive view about AI around the world.

• 55:44Speaker 04

  But often the gaps and issues that most of them will cite are these questions of access, of infrastructure. How do we get to participate? How do we get infrastructure? How do we get access to the tools, to the models? So their concerns often have to do with just access and participation in this. Whereas attitudinally, they tend to be more positive because they see the possibilities in health, in education, in entrepreneurship and all these things. I think expanding access is one of the clearest ways in my mind.

• 56:13Marina Mogilko?

  And that's open models?

• 56:14Speaker 04

  Open models, languages. In fact, I'm always interested in, in fact, just the other day, since you and I are both here, I was in a conversation where we talked about the infrastructure gaps in other parts of the world. People would point to, yes, access to models and compute, but they'd also point to electricity, to connectivity. They said, we can have all the access to the models we want, but if you don't have access to broadband, connectivity and electricity,

• 56:43Marina Mogilko?

  Basic questions.

• 56:44Speaker 04

  you don't even get to start. And I was also struck by the fact that many of them also pointed to language as infrastructure.

• 56:52Marina Mogilko?

  Because so many things we need to talk about. Wow.

• 56:55Speaker 04

  So I think the more we can, you know, collectively invest, but, you know, team up with others to address those things, I think that expands the possibilities for everyone, everywhere.

• 57:06Marina Mogilko?

  100%. Okay, let's wrap up with you finishing a phrase. As intelligence gets cheaper, I don't know if you agree with that or not, something gets more valuable. What gets more valuable?

• 57:18Speaker 04

  Human creativity, ingenuity, entrepreneurship, exploring possibilities. All those things become more valuable.

• 57:25Marina Mogilko?

  And do you think intelligence gets cheaper or as humans, we're gonna just raise the bar for intelligence?

• 57:32Speaker 04

  I think both will be true. I think intelligence in as far as we've defined it so far. Remember, there was a time we used to define doing mental arithmetic as intelligence. But we got over that with calculators and computers. So I think our definition of intelligence, I think is going to keep evolving. But I think if we're defining intelligence as we've historically defined it, I think that will get cheaper. But I think other forms of intelligence, other ways of knowing, I think are going to become more valuable. And we're going to discover those things. We're going to discover those things.

• 58:04Marina Mogilko?

  Thank you so much, James. I could be chatting to you for another hour because it's so fascinating, like the scope of things that you cover. Thank you so much for your work.

• 58:12Speaker 04

  Thank you for having me. It's such an exciting time, but I think we need to hold both things in mind, that the possibilities are extraordinary, but the challenges and the risks and complexities are also as important to make sure we're working on both, both things.

• 58:25Marina Mogilko?

  Yeah, fascinating and turbulent time, but what a time to be alive, right?

• 58:29Speaker 04

  Thank you.

• 58:30Marina Mogilko?

  Thank you so much.