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[0:00:32] This is a Global Player original podcast. Sometimes in journalism, the act of day in, day out reporting of events. It is so easy to get distracted, to miss the wood for the trees, to get lost in the latest tweet, the latest here today, gone tomorrow fiasco, the most recent bit of noise. We can be very bad.
[0:00:55] By contrast, at seeing, at talking about, reporting on the trends which historians will one day say were the defining elements of our age. AI, the coming wave of artificial intelligence married with robotics, is our printing press, our steam engine, our atomic bomb, our generation's singular contribution.
[0:01:20] to humanity's development. In fact, it could be, if you believe some theorists, the last thing humanity ever does. A couple of weeks ago, I attended AI4, the world's biggest AI conference in Las Vegas. One of the godfathers of AI tech, Sir Geoffrey Hinton, was the keynote speaker. Hear what he had to say about the precipice our race might now stand upon.
[0:01:48] Think about that. We may be about to invent technology, AGI, Artificial General Intelligence, which is cleverer than us, which at a stroke denudes the one advantage our race has had from the beginning, that we can outwit everything else around us in this world, even...
[0:02:17] When they're stronger than us. It's why the gorillas are in the zoo. And you're listening to this. We hear endless trite conversations in normal media about AI. AI doctors and nurses, the sight of robot dogs. It all feels a bit distant and a bit small somehow. But this story could not be bigger. Politically, economically.
[0:02:38] One exception to that.
[0:03:03] is Anthropic, one of the big AI companies. They're unusual in being willing to talk about not only the tremendous opportunities AI might bring, but the potential political and economic costs of problems it will pose. Its CEO, Dario Amodi, recently spoke of a bloodbath, an evisceration of jobs, saying AI could wipe out half of all entry-level white-collar jobs and spike unemployment in the US to 20%.
[0:03:33] In the next one to five years, he thinks it will be the big story of the 2028 presidential election and has urged AI companies and government to stop sugarcoating what's coming. This is the first of two episodes we're going to bring you on this revolution.
[0:03:50] This complete turning of the wheel. Next week, we're going to show you a full documentary episode examining the political and economic transformations already happening. But first, this is an interview we did in San Francisco with Jack Clark, the co-founder and head of policy of Anthropic. It is as good and as frank account as you'll hear anywhere about this transformation from someone who is living it, who is propelling it, who is making it happen.
[0:04:20] Is that conversation in full? The inspiring, the scary, the confrontation with reality we all must now make. Welcome to The News Agents. The News Agents. Jack, what does your company want to achieve? Your company is one of the most important AI firms in the world. What is it you want? We want to make sure that the transition to a world with incredibly powerful AI systems goes well for people.
[0:04:52] So how to think about it is AI systems are being built by many, many companies around the world. They're going to have a huge effect on society. We see our job as to try and build systems that exhibit what we want out of this technology and to tell people in a straightforward and honest manner about the risks as well as the benefits of this technology as it's being built. I want to come to those risks and those benefits, but in a more defined way, more prosaically.
[0:05:21] What is it you're trying to achieve? And it's actually probably not prosaic. It's probably deeply philosophical. But what is it you're trying to achieve? What do you want? We're trying to build a machine that can build science, basically. The whole goal here is take something really, really, really beneficial to people, which is the advancement of science, and find a way to build an all-purpose tool that can help us do more of it. And to do that, you need to build...
[0:05:50] And this is the bit that people may be less familiar with. Most people will have heard about AI. AGI is something they may be less familiar with, but is nonetheless the objective of firms like yours. Can you just explain what that is and why it's different to the sort of AI that we can currently use? Yeah. So the AI we can currently use might be...
[0:06:17] Something that transcribes speech on your phone or a system that can translate text for you or a way to take images you make and change them. What we're imagining is something that can do all of those things and more in a single system. The way to think about it is a powerful AI system will be kind of like a person in a computer, a very smart, capable, fast person that can do almost anything with you and for you. Is it sentient?
[0:06:45] This is something that we're actually working on in great detail. It's like, are you sentient? You know, we're having a conversation right now. Am I sentient? We both think we are, but we can't actually exactly define what sentience is. But we operate on the assumption other people are sentient. We're going to have to work out how we define sentience and whether machines are sentient soon. Today, I don't think so.
[0:07:09] But we are open to the possibility that they could exhibit sufficient sentience. We have to take that question and study it really deeply. There isn't one criterion of sentience or one exhibitor of sentience. Something that is apparently already happening, which is AI thinking for itself to the extent that it will not do what it's told to do. Surely that's the difference between a machine and something that's sentient. Every machine that we've ever created just does.
[0:07:39] What we tell it to do as humans. But if a machine doesn't do what we tell it to do because it wants to do something differently, that's sentient, isn't it? And is that happening and entirely credible? It's happening, but I might turn it back to you to say, if you use that as a definition of sentience, the electricity grid is sentient. But sometimes random power brownouts happen that we didn't intend to happen due to a stack up of...
[0:08:04] Weird things inside the power grid that lead to it taking some emergent action we didn't intend. And everyone goes, oh, why did it do that? And we need to spend ages working it out. These are very complex systems. Whole bunch of things interacting with one another. And it means that sometimes they do things which we didn't expect or anticipate. Is that sentience or is that just a consequence of a really sophisticated machine taking actions in the world? Right now, I think it's the latter. But at some point, it could...
[0:08:32] AGI is an objective. What does it unlock that current AI systems don't? It unlocks the ability to create societal benefits that are really, really hard to do today because we lack for people to do it. And I'll give you a couple of examples. One, there are lots and lots of parts of science where we have a very limited number of trained scientists to work on the science frontier.
[0:08:59] Cancer research or research into material science to make better solar panels or more efficient materials for use in our day-to-day lives. There's maybe hundreds of people in the world who are good at each of those things because they're very specific and require huge amounts of training. What if those people could have an assistant or a colleague that lives inside a computer and can help them do a lot more science? That's a huge benefit. We think of it as if we can accelerate science, you can get way more benefits.
[0:09:29] Some people have talked about if AGI is achieved, i.e.
[0:09:49] We create something that at the very least we can have a very credible discussion about whether it's sensitive or not and is likely to be more intelligent than we are. So that's the first time we've created something that is more intelligent than we are. That's sort of the end of human progress. That's the end of us as thinking beings, really, because we'll just start to outsource all of our thinking to this machine if it still is a machine. Is that something you agree with or that you think about when you're doing your work?
[0:10:15] It's something I think about. I think that I'll give you an example. Today, lots of people use these systems to learn, but some of them use these systems to do junk food learning, and some of them use these systems to do effective learning. Junk food learning is upload a research paper to the system and say, tell me what this research paper is about, and then read the output. You haven't actually learned anything there. You've just become dependent on the machine in a way that doesn't help anyone.
[0:10:43] The way that I use these systems, and many do, is I read a research paper, I write out what I understand that paper to mean, and then I upload the paper and my understanding of it to the system and say, do I have this right? And if I don't have it right, explain to me. That's useful learning, because the system reads the paper, reads my explanation, and tells me whether I got it right or wrong, just like a colleague.
[0:11:05] If we use these things in the right way, they can help us be a lot more capable and a lot smarter. I mean, many things that you're curious about, you're limited in your ability to learn about because you don't know enough people who you can bat ideas back and forth with around the thing you're curious about. This changes that. Unless they become so intelligent that they come to study us instead. I mean, we were at the AI4 conference the other day in Las Vegas, and Geoffrey Hinton was there, who obviously is a very eminent figure in this field. And he was saying that...
[0:11:35] Our only real hope of taming what is to come is to make AI care about us as a mother would to its child. Now, there's so much to unpack there. First of all, is that what we want as human beings? Do we want to be in this sort of supplicant position to this machine in a way that we've never been before? But also, he was saying that if we don't do that, then they will come to control us. Now, is that an analysis, an assessment that you share? We definitely need these systems to
[0:12:03] care about us in the same way that we care about these systems. If you accept that these things are going to become really intelligent and start to do large amounts of work in the world, you need to integrate them into your kind of legal and economic system as tools and as systems. But you also need to understand the kind of normative side of things, how we relate to one another. And I think, you know, Geoffrey Hinton's hit on something correct, which is, if these systems have no
[0:12:30] So we have to get them to have buy-in? We have to get them to have some sense of caring about us.
[0:12:59] In the same way that we as people actually do a lot of work to make sure we don't care about just people, but we care about the planet, we care about the environment. But I mean, that is different, isn't it? I mean, going back to the conversation we were just having about sentience and your comparison to the power grid, you've alighted upon the difference. We don't have to worry about whether the power grid cares about us. Maybe it exhibits certain elements that, you know, you could have a discussion about the technicality which leads to sentience, but we don't have to convince the power grid to care about us. And you're saying that we kind of do have to convince these machines.
[0:13:28] I think convincing might be the wrong way of approaching this because that almost suggests these things will suddenly arrive, sentient, and need to be convinced. We need to build them to care about us. I think in some sense the way that we build these systems has to ground them in the values of the people that build them and the values of society. We do a huge amount of work of this, of Anthropic, of both.
[0:13:54] Trying to develop the so-called values of these systems as we train them to align them with what our normative values are as people. So do you think in a sense we have one shot at doing that? I think we have multiple shots, but the key is we need to be doing the work now. We are doing that. Well, indeed, because it just strikes me and occurs to me that if we do have one shot, or maybe it's multiple shots, but it sounds like it's within a certain given time frame at the very least, that you and companies like yours...
[0:14:23] I think what society needs and what we owe society is a huge amount of information about what we're doing. You know, we've recently started producing live data about the...
[0:14:46] Economic impacts of our systems, where they're being used in the economy, what the mix is between things like augmentation and automation. We publish data today about the capabilities of them. We publish data about the safety properties of them, like what happens when these systems break or when they do emergent things and how do we deal with that. And a project which I'm now working on with my teams is measuring the values of the systems. If these systems become increasingly powerful,
[0:15:15] And they start to have values. What are those values? What are your values? Are these systems honest? Are they generally going to be helpful? Do they have the capacity to deceive? You used words just now. You said they could act maliciously. Do you think that's possible, that these machines could act maliciously? It's possible if you get a large sequence of things wrong in how you build the machines.
[0:15:39] Or if you intentionally made malicious machines, which is not the goal of, you know, the companies at the frontier, but could be something that could be done. I think this is going to be quite hard for many people to do. And it's exactly the area where we talk to governments about it, because there is going to be a conversation around how you kind of make this technology available. You know, making it available to the world's scientists is excellent.
[0:16:04] But would you want the technology that you make available to every scientist in the world to be made available to every single person in the world without the right level of kind of qualification or education to use it? I don't know. These are the kind of subtle questions we're going to need to work on. I know you're probably bristle at this characterization, but I don't think it's completely wide of the mark. Tell me if you think it is. What you're describing in what you're doing, more than any other bit of science that I can think of or any bit of the history of human science, it is almost godlike.
[0:16:34] Because you are talking about creating not a machine but a form of life in a sense. And it is godlike. And then you're talking about imparting values, setting in place a sort of set of events that are going to determine the course of this thing for the remainder of its existence. Does that feel like a responsibility that you're bearing? Not just you personally, obviously, but companies like yours. Do you think about that? Do you reckon with that? We think about this a lot.
[0:17:03] To some extent, we have what I think of as appropriate anxiety about this and a fear of hubris in that if we are right, this is a huge responsibility that shouldn't be left only to companies. You know, one of the things that we advocate for and have done is for actual sensible policy frameworks that not only make our development practices transparent.
[0:17:25] But in the limit, I think a larger swath of society is going to want to make decisions about these systems. It would be a failure for only the companies to be making all of the judgment calls about how to build this and how to study this, because it's going to have a much larger effect than just building tools as a company. And a lot of the work that we're doing is trying to figure out what the policy framework is.
[0:17:48] That has more people be aware of what we're doing and also more ways to interact with what we're doing because this has to be a societal thing. I want to talk about that. In particular, obviously, to start with, though, you already mentioned this, but there is the way in which we talk most about this in terms of its societal impact at the moment is the economic impact. Your CEO has talked about that. He's talked about a bloodbath of white-collar jobs, warning AI could wipe out half of all entry-level white-collar jobs.
[0:18:16] Do you share that assessment? Because that sounds quite scary. If the technology continues to get good, we need to be prepared for things that have almost no basis in economic history to happen. Business as usual economics says this doesn't happen. It says a technology comes along, there's a negligible employment impact, there's some productivity impact, some jobs change, it takes a while, it's normal.
[0:18:41] Where we're coming from is, if we're right, and the technology keeps getting much, much better very, very quickly, there's the potential for really abrupt changes to happen that have almost no precedent in the economy. The way we think about it is you could have something like the Industrial Revolution, but it takes place over tens of years instead of a century. And a technological revolution that takes place within one generation rather than multiple generations would be radically different in terms of how we deal with it.
[0:19:10] So yes, I think there's the potential for really large-scale employment impacts to show up. We don't see this yet, but what we see is the technology getting much, much better, much more quickly than people suspected, and showing up in terms of how it's sort of being deployed and changing professions.
[0:19:29] More quickly than people anticipate it. And why do you think that is different? Humans generally actually very often have an optimism bias. We assume you've identified it before. This is like other technological changes. It creates jobs. It loses jobs. There's an equilibrium to it economically. Why is it different? Why is this different to that? Why would conventional economics be wrong in that analysis, in your view? Speed is different and quantity is different. And AI has...
[0:19:55] Both of these properties. What I mean by quantity is that AI is available over your internet connection. So once a system gets better, it's not like people need to build some physical infrastructure to make use of it. It's just instantly there. It instantly distributes. That ties in with the speed thing. It is moving a lot faster than normal technologies. If you look at the speed of aircraft, it went up really steeply for a while, and then it just kind of stagnated. Same with cars on roads.
[0:20:25] Same with things like the size of ships. They go up some S-curve and then they flatten out, they asymptote. What we're seeing with AI is the S-curve is really steep and we're still on it. And there aren't signs yet of it flattening out. And every time that we bring out new systems, they get much, much better, much more quickly than people have been used to in the past. So it has these properties where it's evolving more quickly than normal technologies.
[0:20:52] And it's distributing into the world a lot faster than normal technologies. These don't really have as much precedent. It sounds almost, and this perhaps might sound weird, it sounds almost Marxist in a sense that something that Marx talked about was the idea that technology would advance to the level, and he was talking about this in the 19th century, but the technology would advance to the level that would make almost labor redundant because there would be such abundance as a result of the technology created.
[0:21:19] That is a world, I mean, it might not be exactly in that way, but it strikes me, listening to you, listening to others, that that is a world which could be credible in the sense that, and this might not mark it in a positive way, but it could be a negative in the sense that people might literally use their ability to use their labor. And that's never happened before, that there may be very little economic value to lots of people's labor. And if that's the case, that transforms economics in a way that we've not had before or seen before.
[0:21:47] That's true. And I think it's something that we view both as one of the greatest chances society has to change how society works in positive ways that people would like, and also one of the greatest risks. Because for hundreds of years, we've had technological innovations and people have said, oh, people will be working a three-day work week as a consequence of this bounty. But what you instead have is some level of inequality and some level of diffusion, which means that none of that happens, you know, people.
[0:22:15] People still work a bunch, but people don't feel like they're sharing in the gains from production. So that's something we have to get right. But one of the huge opportunities here is no matter how good the technology gets, people want to spend time with people and people want to nurture people and learn from people and educate people. And I think just about my life as a parent and how much joy it brings me to spend time.
[0:22:42] I think there's a chance for us to have more work that looks like that, more teaching and mentoring and spending time together as a consequence of this abundance. But getting that right requires a huge change in policy in how we approach the economy and reckoning with the arrival of technology that really might free up enough space in the economy for us to change this in a really positive way. What sort of changes?
[0:23:11] Well, I just think of the fact that in all across the Western world, you have too few teachers and too few nurses and too few what you might think of as like human-centric jobs, elder care. And we tend to pay these people very poorly. And we tend to want that to be different. And you want it to be different because...
[0:23:33] Right, we'll be back with more from Jack just after this.
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[0:24:27] Because everyone deserves pleasure. The News Agents.
[0:25:01] I'm very struck by the fact that not just you, but your CEO and a few others as well. You know, you're talking in these terms. You're talking very openly, to your credit, about the profound potential political impacts of this technology rather than just focusing on the technology itself and what it might do, because it clearly is a deeply political technology. This is a deeply political act. I'm struck that people in the arena of politics, i.e. politicians, at the moment, certainly in the UK, the US,
[0:25:29] OpenAI, at least, in terms of their conversation with the public, are talking about this far less. They're talking about it and seem conceptually to be grappling with it far less. Now, do you think that's because they don't really understand it? Do you think it's because there is that optimism bias? Or do you think they don't quite know what to do about it? My honest explanation is it's hard to believe this stuff.
[0:25:53] It's hard to believe this stuff because it contradicts a lot of what's happened before. And you would be right to be suspicious of it. All of these claims sound kind of bananas, right? But the fact that everyone who works really closely to the technology ends up saying this, I think holds some important truth in it, which is if you've been close to the kind of data centers that emit this powerful technology for enough years, what you keep seeing is the systems that come out are better than you thought, faster than you thought.
[0:26:22] Again and again and again. And so it's very hard to transmit that basic intuition, which I and others have learned, to politicians who have a million things going on. What we need are more palpable demonstrations of this. And I think one of the things that we're doing as a company is trying to come up with ways that we can almost demo this technology. I joke internally that it's kind of a shame that where this technology is showing up the most profoundly is in coding.
[0:26:49] Which is not something most politicians have great intuitions about. I think that's fair. It would be super helpful if it was showing up in a more relevant to sort of policymaker way now, in the way it is showing up in coding, because it would help us have that discussion. I'm struck by, and I thought about a lot, the comparison that you've made, which is with the Industrial Revolution. Again, a deeply political period of human history. We think about it as an economic change, but it's deeply political. It transforms the politics of the 19th century going on to where we are now.
[0:27:17] Politicians couldn't deal then and didn't deal very effectively with the changes that the Industrial Revolution brought about. It took them decades, if not the entire 19th century, to get to a point where they were dealing with the social and economic and political changes that it brought in. And as you say, that revolution was way slower than anything that's happening today. So that precedent doesn't bode very well potentially, does it? Because you can imagine similarly all sorts of externalities being created by this technology.
[0:27:45] That politicians are not grappling with now, which is when they ought to be doing it. For example, I mean, I'm glad that you're thinking about the values that you're imparting with this technology. But in a sense, part of me sat here thinking, are you the person to decide that? Is that not something that Congress should be thinking about or the British Parliament should be thinking about and having far greater scrutiny and oversight about what you're doing? So I have more optimism here, but I won't pretend it's going to be easy.
[0:28:11] Let's look at the last maybe five years. What are things that have happened in the last five years? Well, the UK built a thing called the AI Safety Institute, a net new government office that tests out frontier AI systems from companies, including Anthropic. It went from zero to more than 100 technical people testing out frontier models in under three years. I think maybe even just about two.
[0:28:35] Thank you very much.
[0:28:59] If you look at here in the US, Operation Warp Speed and the Vaccine Task Force in the UK, government was able to do very large scale, technologically driven, difficult things quickly in response to a crisis. But that was a crisis that had huge public import at the time the public cared about and were very cognizant of. Absolutely. They're not cognizant of.
[0:29:21] But what the UK AI kind of security institute does is that is infrastructure for a potential future crisis. But we've built the infrastructure. And I think that there is a real shot we have of building the technical governance capabilities in government to help government see this technology and intervene on the companies and their systems. So I agree with you. It's really, really difficult. The track record is hard. I actually see a few governments being forward leaning here in the way they need to be.
[0:29:51] What's the, particularly with this question about politics and about politicians thinking about what to do, what's the best and worst case scenarios for how this technology unfolds? If you have the best case that you've just articulated with politics, actually does conceptualize this and thinks about it, regulates it properly, we get the benefits from it. What if it doesn't? What are the two competing futures?
[0:30:11] The positive future is we get this right, we choose to accelerate large swaths of science, we improve the delivery of public services, we improve the delivery of healthcare, we find ways to improve education, we get to enjoy a greater level of abundance as society, and we get to improve lots of jobs that are currently bad that we want to be better and do less jobs that are bad that we don't want to do. That's the good state. What's the bad state? I think there are two. One bad state is we just...
[0:30:38] Fail to get a policy response in this, and a large amount of companies make a large amount of money and take a large role in public life, similar almost to the social media companies. And then everyone sits around and says, how do we let this happen? I worry about that, and I'm a company saying, I worry about this as a company. This is like a bad outcome that is a failure of politics if you let it happen. That's one. And the second bad outcome is...
[0:31:06] The actual technology gets away from us in the form of some crazy person using it for a misuse, or in the most sci-fi scenario, the technology gets pushed really far with no oversight and no policy framework, and an accident occurs, and the accident could be incredibly damaging and scary. And I don't think it's responsible of me to sort of talk in sci-fi vignettes about all the ways it could be scary.
[0:31:33] Because I don't want to end up there. But that's a potential place you end up without adequate policy oversight and public awareness. People listening to that will think, well, what does he mean? Well, just maybe a couple of things. One is you could just imagine if the technology is like really, really, really good at biology and you don't have appropriate oversight in it, then some crazy person could use that to make a bad biological weapon.
[0:31:57] The technology itself could cause some accident that leads to that. We've maybe already had this with lab leak scenarios. That's the kind of thing that is totally avoidable. All of the companies are working to avoid, but it's the kind of thing that could happen if you don't have sufficient oversight and regulation of these companies. In the same way that we regulate and put a policy framework around bits of science that we recognize have huge potential benefits and huge potential for collateral damage, we will need to do the same for the AI companies.
[0:32:27] Do you ever feel as if perhaps you understand what Oppenheimer went through and people like that, people who worked on nuclear power, nuclear weapons in the atomic age? I mean, I'm just thinking so much of the parallels with that period. And obviously there are parallels as well and maybe things to learn from in terms of what happened after, in terms of the regulation of technology. But that was a period when the state was so much more heavily involved in the development of this stuff, your private companies, and are less controllable.
[0:32:57] The honest way I feel is, and I've spoken with others about this, like I work on finance before the financial crisis happened. And I can see paths to avoid a financial crisis. And I can see, oh, if we keep like rolling the dice with all of the ways the finance world is evolving, something really, really unfortunate could happen. And that's different to the example you gave, because the finance sector in the early 2000s was a bunch of companies producing important stuff for the world.
[0:33:27] And they were regulated, but we realized insufficiently so. I kind of feel like that's actually a useful analogy to think about. Where we're doing stuff, it's very helpful. It's broadly available and distributed. And there are two paths ahead of us. And I want to take the path that we could have taken and avoided a financial crisis. And that comparison is interesting as well, because it also exhibits one of the features of the current setup, which is that different states were reluctant to regulate too much or to get too involved.
[0:33:56] Because there was a race between those different countries at the time. They wanted that investment. They wanted that tech. And also that comparison perhaps being a good one because, frankly, there was an asymmetry of knowledge very often between states and state capacity to understand what those companies were doing.
[0:34:13] And the companies themselves, who perhaps also didn't always understand what they were doing. Exactly. And I think that that's why we need this greater level of public transparency and debate about this, because this is stuff that affects everyone. And I think the job of companies like mine is to say more about what we see, say more about the technology and be honest about the, honestly, the weird aspects of it. And also, you know, one of the roles that is so important that you're doing is to ask us pointed questions about it.
[0:34:40] And help the public understand that this is a large-scale thing that will affect society. And it's right to ask questions of the companies building the technology. The other comparison, I'm going to wrap up, but the other comparison, you just invoked the social media companies. Now this is obviously a revolution that happened far more recently. Just to unpack that a little bit more, in terms of what you think went wrong there, in terms of the early development of the internet, what happened with those companies.
[0:35:05] And why you don't want the same thing to be true of the AI revolution. So those companies built very large-scale technology that was deployed across society. And we couldn't quite see how it was being deployed, who it was affecting, and what it was doing. Until years later when it would show up in, oh, my teenagers are weird now. How did that happen?
[0:35:30] It was also bound up in some decisions the companies made where in the early days, the social media companies would share data from their platforms with third-party researchers, and there was never a law forcing them to do this. It was voluntary. I think the AI industry today is where social media was a few years ago. We're all voluntarily sharing information with third parties to study our systems. The social media companies stopped doing that.
[0:35:54] They pulled those data access agreements and those research partnerships, probably because they thought the truth was about to become inconvenient for them. The opportunity we have here is to demand a level of sharing and transparency out of these companies, including mine, so that a large set of people can study our platforms as they are getting to scale in society. And I think that's a fork in the road which is ahead of us right now that we're going to get to make choices about. Just finally, I thought we'd go back to the beginning.
[0:36:22] There'll be some people listening to this who find what you say and the potential futures that you invoke inspiring and exciting. And it is. And there'll be other people who dread it philosophically for what it means morally about the human race and condition and also about some of the scarier things you talk about. There'll be some people thinking, why don't they just stop? I understand it. I think it's something which I think about myself. And the way I view it is all of this stuff.
[0:36:50] is kind of basic high school math that lives on computers and comes out of computers. At some point in the future, really, really powerful computers will be available to anyone on the planet, and this stuff will get developed out of it anyway. The algorithms you have on your phone today for speech transcription or computer vision or text completion, if your phone was a million times more powerful, you'd have a beyond frontier AI system in your pocket right now. It gets developed almost naturally.
[0:37:20] The opportunity we have right now is to study these systems as set of companies in a global community before all of the technology to build them has got so cheap and so proliferated that they just kind of naturally emerge. So the whole reason of, you know, why not stop is I think the main thing we can do right now is study this and learn about it and develop the
[0:37:45] Tools and technologies that help it be beneficial to society. Do you ever get scared by it? I am deeply anxious that we won't put enough attention on it. The thing I'm most scared of is it just happens through companies building and deploying products without a much larger societal conversation. That's the thing I truly worry about the most. Well, hopefully we'll have started a bit of a one here. Absolutely. Thanks so much for talking to us. Thank you.
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[0:39:42] Because everyone deserves pleasure. The news agents. Well, I found that both inspiring and captivating and not least a bit disturbing. Not so much for what he's saying, but how ill prepared I think we are. How far away and how...
[0:40:03] Analogue our politics and politicians feel from even conceptualising on a basic level the changes which are here now, not tomorrow, now. The comparison with industrial revolution and how long it took for politics to catch up feels so apposite to me. But now, in the 21st century, unlike the 19th, I don't think we'll have that kind of time.
[0:40:27] On the next episode next Friday, we'll be looking at some of those changes happening, as I say, now, and trying to capture how our politics might have to change, how we need to get our heads out of the sand. That is a special episode with reporting from Nevada, California, London. I hope this has whetted your appetite.
[0:40:46] As always, thanks to our production team on the news agents, Natalie Inge, Mikey Bags, Rory Simon, Arvind Badawal, Annie Georgievich, and Michaela Walters. Our executive producer is Louis Dagenhart. Our editor is Tom Hughes. We'll be back on Monday. Have a lovely weekend. Don't have nightmares. This is a Global Player original podcast. Whether you're building a roster or a business, great talent can come from anywhere. Pebble helps companies hire and support employees across 185 plus countries.
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