116. Is AI the answer to human suffering? (James Manyika)

26:39Okay, Rory, James, quick break, and then we come back.

26:47I read some of your stuff you've said publicly and written publicly about this. And there's one sentence I wanted to put back at you, which I found particularly interesting. You said, AI is putting a mirror in our face to say, OK, humanity, this is what you look like. How do you want to deal with it? So answer your own question. Well, I think, you know, one of the things with these machine learning algorithms is that they're largely learning from stuff on the Internet. And on the Internet, we have everything. We have incredible poetry, literature, science. We also have some of the very, you know, ugly traits of humanity.

27:28So you've got all of it, all of what humanity has created, good, bad and ugly. And they're learning from all of that. Do they know the difference between good and bad? No. No, because remember what they're learning is... They treat it all the same. Yeah, they're learning the structure of language. They're learning the patterns of words. They're learning things like a word like close lives in the same area as proximity, as near. They're creating these associations about words that seem similar, that seem to speak about the same kinds of things. So they're building associations about language and its structure.

28:06So they don't have any value judgments, good, bad, ugly. They're simply learning the structure of language. Again, just for non-specialist listeners, people often assume that what is happening when they type into one of these AI models and ask it a question is that it's simply going out, searching the internet and reproducing and pasting in something. So if I was to say, you know, draw a three-point comparison between James Manika and Alastair Campbell, and it goes off and produces an answer in two seconds, it somehow found that on the internet. But that's not what it's doing, is it? No, it's not what it's doing at all.

28:46I'd love to know what it would say. It would be fascinating. Keep in mind that the performance and behavior we see from this system is a result of scaling up a very simple process. And that simple process goes something like this. It's when you type in a word, it's trying to predict what comes next. So these are next word predictors. In fact, it's the reason why some people have said, well, this is a very sophisticated version of autocomplete. They're simply predicting the next word. What then happens is that when you scale this process up, meaning you train on a lot of words, it starts to build very complex concepts.

29:27So when you type in a prompt, it's trying to figure out what's the best response to that prompt. So it's not going out to search anything. It's simply trying to predict what comes next. Now, what has happened over time is that in addition to that basic prediction mechanism, as a way to try to fix and address some of the factuality issues, we've now added things where you can ground it on search. go validate what you're about to produce with what's out there first before producing it out is the reason alistair why you find that sometimes these algorithms will seemingly make up things and so-called hallucinations i'm interrupting so here's the answer alistair for you okay oh god three-point comparison james minneke and alistair campbell number one professional persona james minneke with his calm demeanor and analytical rigor and then it praises james then Alistair Campbell, by contrast, exemplifies the intensity of political communication, combining sharp strategic instincts with a combative approach to narrative control.

30:31While they operate in vastly different domains, both are masters of influence. Monika's influence lies in its ability to articulate the transformative potential of technology. Campbell's impact, rooted in the political turbulence of the late 20th century, reflects his capacity to manage media narratives in real time, leaving an indelible mark on political communication. Despite their different approaches, each operates in spheres where perception often outweighs objective facts. And it continues in this way. I'm not going to keep reading it. Wow, that's quite nice. So where did that come from?

31:03I've taken it from ChatGBT. So this is, yeah, ChatGBT4. And you did it very, very quickly. So that comes in two seconds. But explain to Alistair roughly how it does that. It's not lifting it from the internet, is it? No. No, it's building from associations and in what it's been trained on. So there's clearly something in what's on the internet about Alistair. There's clearly on the internet things about me. There's clearly on the internet things about ideas and concepts, maybe even things that we've written and things that we've said. So it's building associations based on those things. And that process, when scaled up dramatically, is able to then give these responses.

31:43It's the reason why you'll often hear people talk about the so-called scaling laws in AI. And the scaling laws simply speak to this idea that these processes, when you make the architectures very large and very complex, and you train them on lots of things and you train them for a very long time you start to have performance and behavior that looks intelligent and is amazing now james i'm gonna sound really stupid here but when you say train i think of sport i think of a coach telling an athlete how to perform so when you see training how are you training and what are you training. So you're giving it lots of examples.

32:25So let me, you know, the expression peanut butter and jelly, which is a very American peanut butter and jam, I guess we would say in England, but it's that expression. So imagine training an algorithm to predict that phrase, peanut butter and jelly. I show the algorithm those words, peanut butter and jelly. It studies them. The next time I cover up the word jelly and I say predict what comes after peanut butter and so initially it guesses it might say peanut butter and I don't know milk or something or cheese and then it looks up the original phrase and said oh I got it wrong let me try again so it keeps trying and then after a while after trying and failing trying and failing it eventually starts to get it right so that's the training process.

33:14Now, you could imagine that training process going on for a very long time with a huge amount of words. All of a sudden, it's now good at predicting that a word like close is similar to near, is similar to proximity. You start to be able to predict those things, so much so that if I then give it a sentence, Alistair, that says, Alistair was riding a bicycle very fast down the hill and he ran over a pothole it can then start to be able to predict well people when somebody's riding a bicycle down the hill and they hit a pothole chances are they fell or they hit something or they might have broken a hand it starts to be able to predict what comes next how does that help me when i'm riding my bike it doesn't help you very much but the algorithm is learning to be able to predict what typically comes after somebody's riding a bike hits a pothole.

34:05There's a finite range of possibilities of what happens next. It's learning to predict those things. And here's something I guess that might help you, Alistair, which I'm going to throw at James, which is that once this thing is really beginning to operate well, it can begin to produce pretty accurate answers to quite sophisticated medical questions. I mean, you can imagine it, maybe not quite yet, maybe now it needs to sit alongside a doctor, but already it'll be able to provide more accurate answers than many doctors on many medical questions. Oh absolutely in fact one of our models MedGemini which is our Gemini model fine-tuned on a medical corpus that's why we call it MedGemini is actually able to perform better than most doctors on predicting and doing diagnostics and diagnosis of things in fact unless you're in the top two or three percent of doctors, it'll do better than you.

34:59You can see why doctors might worry that we cease to have a need for doctors. No, because I think what happens, what we've seen so far, Alistair, is that in fact, when doctors are assisted by these tools, they actually do better than doctors not assisted by these tools. In fact, one of the things that I'm very excited, I think, is what motivates many of us who work in this field are these extraordinary beneficial impacts. I mean, about why do we do all of this? Why does any of this matter? At least I think about it in a few different areas. One are things like how this benefits individuals. Think about language translation.

35:36Think about any kind of assistive individual things. I mean, I don't know if any of you use Google Translate. It works based on AI. That's how it works. And in fact, now we've just gone from, you know, even just four years ago, we could translate about 30 something languages. Now we can do 246. In fact, we're aiming to get to 1 ,000. So think about these extraordinary benefits for individuals. Then think also about the potential benefits for the economy, small businesses, large businesses, even transforming economy sectors, even productivity. And then think about the impacts on science. Some examples on the economy, because that'll be very relevant to a country like Britain that's got economic struggles.

36:18The economic potential. So it's been known for a long time that productivity is a driver of economic growth and prosperity. It's the reason why economists worry about productivity. When productivity is low, we don't have GDP growth. And in fact, if you look at much of what's driven productivity growth throughout human history, a lot of it has been technological innovation. And in fact, in the case of, you know, where do we think we're going to get technological innovation is mostly coming from AI. So we're going to think about transforming sectors like healthcare, industries and manufacturing, and then even small businesses.

36:52The fact that a small business can now use these systems to be able to understand and do better marketing, to be able to understand regulations, to be able to say, you know, I operate in this space. What are the regulations that are applicable to me? As opposed to going to read all hundreds and hundreds of pages of regulatory documents, I can use an AI system to help me understand that. So the economic benefits all the way from small businesses to large businesses, ultimately to economies, especially including workers enabled by the power of this technology is what drives labor productivity and leads to economic prosperity.

37:29One more example, and then I hand back to Alistair again. Tell us a little bit about autonomous vehicles and what that might mean for our future. Oh, so I live in San Francisco. We've had driverless cars, Waymos running around San Francisco for the last year and a half. and they're extraordinary. In fact, I personally have always had a strong interest in this because I remember when I first moved to California, one of the ideas that I toyed with with some of my friends was to use some of the AI and robotics work we've been doing to build autonomous vehicles. There used to be this competition called the DARPA driverless car contest, which used to happen where people used to compete to build cars to drive across the desert in America autonomously.

38:08So this is something I was always very interested in. But now, fast forward to where we are now, we now have fully autonomous cars driving around San Francisco, and they're far safer than your average driver. They're not distracted. They're paying attention to all the rules. So I think from a safety standpoint, these are quite remarkable. But we are going to have to think about what does that mean for our cities and agencies? So if you're solving for safety, you might say that's an extraordinarily wonderful thing. In fact, right now, you know, a week now, Waymo's in San Francisco and a few other cities are doing something like over 175 ,000 rides a week.

38:51So people are getting quite comfortable with these cars for their safety. And in fact, a lot of the users of them tend to be often at night. Women use them a lot at night. They're considered kind of safe and trustworthy. But we're going to have to think about how do we think about agency? How do we think about safety? How do we think about other modes of transportation? I think it's quite an exciting prospect. But the thing I also wanted to mention, by the way, in addition to some of these economic things, is the potential impact for AI to advance science. And so what we're seeing with AI advancing science is happening not just in proteins or structural biology, but in other fields as well, from fusion to material science.

39:34So the potential to benefit in advanced science is really quite exciting. Now, as you know, this podcast is mainly about politics. And I thought it was interesting that you sent us the digitalist papers. And this is a set of essays written by, including yourself, but also our friend Audrey Tang from Taiwan, who we had on the podcast, Reid Hoffman, Eric Schmidt, founder of Google. Lots of kind of big names in your world. The thing that struck me is this goes back to your question, And what do we want to do? How do we want to deal with how humanity is? Very different views about whether this is a threat, whether it is just a threat, whether it is just an opportunity and where we get the balance right between that.

40:22I, for example, I read Lawrence Lessie and I found his essay quite alarming. He seemed to be saying this could kind of destroy democracy. You had John Cochran who was saying it's not AI that will destroy democracy. It's trying to regulate AI. You had Eric Schmidt, who was basically saying, government needs to change the way it works using AI. So all these very different takes. And I guess it shows the level and the intensity of the debate going on. But without blowing smoke up your backside, I thought the one thing that yours did was give a sense of clarity about the balance between the good and the bad.

41:02So where are you on that scale of it's going to destroy humanity and this is going to save humanity? Well, I start with the view that, you know, anytime you have a very powerful technology, if it's powerful enough, it's going to, you know, hopefully bring extraordinary benefits, but also at the same time create some amount of complexity and risk. And I think this is such a technology. So my starting point, Alistair, is I think, you know, where we end up is a function of what we choose to do, which is what I was trying to frame in that essay. Because on the one hand, I see extraordinary benefits to, as I said, to people, to the economy, to advancing science, even tackling things like the SDGs, these big societal things.

41:49And we're seeing examples of that already today. So I see all of that. Interrupting, just reminding people, the Sustainable Development Goals. So this is things like ending extreme poverty, dealing with water, etc. Right, because what I like about the SDGs, by the way, is the fact that it's probably the best expression that humanity has about what we want to improve about the world. And the fact that 193 countries have agreed to them, at least agreed, that's the right list. So I see all those potential benefits. At the same time, Alistair, I also see the complexities and the risks. And there too, you know, we could go into them, right?

42:22I see the potential for harm that could happen if these systems don't perform as expected. So safety risks, accuracy risks, and those kinds of things. I also see the potential risks from misuse. Think about deep fakes. Think about manipulation and all kinds of those kinds of risks. But I also see the risks that could come or the challenges that could come from the changes to our societal structure as a result of this. So think about the impact on how we think about education, how we think about work. So I see all of that, and it's both the positive and the complexities. And I think where we end up is a function of what we choose to do.

43:05So I always think when it comes to how to think about regulation, we should certainly think about how do we address the risks and concerns that we have and all those complexities. But in addition to that, we should also think about how we enable all the extraordinary beneficial uses. You know, this is part of what takes me back, Alistair, to where we began this conversation. You know, I grew up in a township where often people didn't have access to resources, didn't have access to schools, didn't have access to libraries, didn't have access to doctors, and so forth. And so I think this technology has a potential to give access to those things.

43:43I remember when we did the work at the UN, as Rory mentioned, I was co-chairing this body on AI governance. One of the things that many of the people from the global south highlighted quite a bit is that when we talk about the risks of AI, we should talk about certainly misapplication and misuse, but we should also talk about missed use. And the missed use speaks to this idea that there are places where there aren't other alternatives, and this technology could provide alternatives. Antonio Guterres, the General Secretary of the UN, he very kindly sent his speech as well. And the sense I got from him was a real worry that this is just going to increase global inequality, that a very small number of companies in a very small number of countries will dominate this world.

44:33It will have massive consequences for all of us. And the poorer countries of the world and the poorer people of the world will essentially have no say and human rights will suffer and the jobs market will suffer. And I guess that is what I read from your essay that you're worried about. But then I read John Cochran saying regulation is the threat, not AI itself, or Nate personally saying panic is the problem. And I then into my head drop somebody like Elon Musk, sovereign individual, this is all about the powerful being more powerful. And I get a little bit heebie-jeebied about the whole thing.

45:10I certainly worry about the potential that not everybody will benefit from this technology. I would like everybody to benefit both, you know, I'd like to see many more companies participate in this. I'd like to see many more countries participate in this. I'd like to, you see, the thing I worry about, Alistair, is I think about what happened with COVID, where the world invented vaccines and then not everybody got them, at least not initially. And some countries, some people got them and others didn't. I worry that even as we advance the bounty from this in science and knowledge and all of these things, it may end up just benefiting a few.

45:48So I think that's one of the things we have to address. In fact, the work that we did at the UN highlighted this quite a bit. It highlighted that on the one hand, And while much of the rest of the world, the so-called global south, tends to be generally optimistic about this technology, they do have a few concerns. One is they're not participating both in the development of this technology and the governance of it. And then the other is that they don't have the capacity to be able to fully benefit from this. And that capacity ranges from everything from digital infrastructure to even in some cases electricity.

46:24so because if you don't have digital infrastructure and electricity forget ai better from ai there's some basic things you need that need to be in place so i do worry about that quite a bit to this question of how do we make sure everybody and by everybody i don't think it's just in the developing world it even includes in communities inside developed economies i mean And I've spent time, for example, in California, in some communities and districts where kids were promised that somebody was going to show up someday and teach them how to code, never did. And they don't have access to those resources.

47:02Now, some of those kids are starting to use these systems to learn and to write code. I think that's great. So I think when I worry about how do we make sure everybody benefits, it's everybody everywhere, not just in the developing world, but also even in the advanced economies. We're coming to the end, but just to bewilder and astonish listeners even more, we're interviewing you just a few days after you've just announced an extraordinary advance in quantum computing. And, you know, we could do another six hours on quantum computing, but can you give us just a little bit of a sense of what this thing is and why anybody should care about quantum computing and why it might make a difference to the world?

47:48Well, quantum computing is extraordinarily exciting in the sense that it goes back to the person who first came up with the idea of quantum computing was actually Richard Feynman in 1981, who basically said, look, reality is inherently quantum. And so if we want to understand it, at some point we're going to have to build a quantum computer, because if we're going to, in any computer that's going to try to understand the nature of reality, it's going to have to be a quantum computer. Okay, I'm now interrupting again and doing something which James will be able to explain much better than me. But the whole point about the quantum world is it doesn't operate by the rules that we are normally accustomed to.

48:25We have these very strange ideas like uncertainty principles. We can see subatomic particles suddenly appearing and disappearing. Its location in space and its location in time vary. And that's very different to the way we traditionally think about computing because we traditionally think about computing in terms of just ones and zeros, very black and white. Yeah, classic computing systems basically operate on ones and zeros, either in the one state or the zero state. That's what's called Boolean logic, and that's how those work. Quantum computing, on the other hand, allows for the possibility that one and zero can both be true at the same time.

49:03And what that opens up is the possibility of understanding many, many more states, many, many more configurations of things. In fact, you're able to explore computations in a very short amount of time that you couldn't with a classic computer. And this may blow your mind. So first of all, quantum computers, they're not fully functional yet, just to be perfectly clear. There's still a lot of work going on to build what are called fully false tolerant, error corrected quantum computers. That's still a long way away. But we're already in this intermediate stage where our noisy, not quite perfect quantum computers can already do extraordinary things.

49:46One is we did a computation in under five minutes that would take the world's fastest supercomputer, get this, 10 to the power 25 years. That's 10 subtillion years. That's 10 with 25 zeros next to it. That's far, far older than the age of the universe, several times over. And you did it in five minutes. In under five minutes. That's what that quantum computer did. But the really big step, though, towards building a fully error-corrected quantum computer was the fact that we're able to make a big step in what's called error correction to show that as you add more quantum qubits, qubits as in quantum bits as opposed to normal digital bits, you can actually reduce exponentially the errors or the noise associated with them.

50:40So this is an important step towards building quantum computers. I'm handing back to you here and I'm signing up, but I just want to point out that part of the challenge around this is that it would take 10 with 25 zero years to check whether its calculation was correct or not. Over to you, Alistair. James, it's been lovely to talk to you. My last question is this. How many politicians have you met that you think get this properly? Well, I'll give you at least two of the first. I remember back in 2014, I was at least in regular conversation with two leaders who deeply were interested in AI before any of it happened.

51:21One of them was Barack Obama. I used to have these meetings at the White House where he actually deeply wanted to understand this. And I was part of those discussions. The other one was Pope Francis. Oh. Exactly. Who started to convene some of us at the Vatican to talk about. I remember an extraordinary meeting that a few of us read was there, and Sam Altman and Demis Esabes and others went to the Vatican to spend time in 2015 with Pope Francis, who deeply wanted to understand AI and where this was going. And his deep concern was around issues of equity, what it means for people in society, and how we make sure that everybody benefits from this.

52:00So he's been deeply interested in this for quite some time. Am I right that you were one of the experts who signed that statement that got a lot of coverage at the time, saying that the possibility of extinction of humanity via AI was on a par with pandemics and nuclear war? What I signed was that we need to think deeply about, because that was a generally phrased statement. What I signed on to was the idea that we need to think deeply about the societal implications of this technology. And we're doing the work now, long before we get to what's called artificial general intelligence, to think about the risks and the complexities.

52:39To answer the question that you pose in the digitalist papers, between dystopia and helping save the world, you are more confident that it's helping save the world than dystopia. I think it's going to dramatically improve the world, but that's not going to happen automatically. We have to make it so. So the choices that we make, Alistair, about safety, approach, responsibility, I think are going to matter a lot. The applications to which we put this technology to will matter a lot. So I think it's up to us. well look this has been amazing i mean we haven't got on to artificial general intelligence james's friend and colleague demis sabis is already suggesting that within two or three years we could have artificial general intelligence which is a super intelligence able to operate far smarter and autonomous from humans and maybe a conversation for a second interview with james in a couple years time but this has been really wonderful thank you very much over to Alistair.

53:38Yeah, James, honestly, I really enjoyed it. As Rory knows, I have a bit of a blind spot on technological stuff, but I thank you for sending the digital newspapers. We should put them in our newsletter and make sure people have access to them. And thank you for being so, so clear in a way that a lot of people who talk about your world, frankly, are not. Thank you. Well, thank you both for having me. And hopefully we'll talk about quantum computing next time. Definitely, definitely. Thank you. Thanks again. Bye-bye. Thanks so much. So, Alistair, thank you for doing that. As I say, James is a friend of mine.

54:14We've been to Japan together. We've also stood at the South Pole together. He danced and ate some real at the South Pole with me. Sorry? James danced and ate some real. How did two people dance and ate some real? Well, because there were another six people there with us. Ah, good. Okay. But we danced literally around the pole. Wow. Yeah, yeah. With or without music? With music from my iPhone. I was playing the Blackwatch pipe band on my iPhone. Excellent. Trying to dance an Aidsome. Excellent. That's good. I mean, of all the cultures that you could have gone for around the pole, that's great. I'm glad you went for that.

54:48Well done. Thank you. Well, anyway, I liked him. I thought he was a very interesting guy. Anything that surprised you about the AI stuff? Surprises? No, I think nothing that surprised me. Look, I feel that he explains it a lot better than most of these people do. He does explain it well. But I still feel there's a gap between the public political understanding of this and the understanding of people like him. So even though he does his best to try and, you know, explain it very, very clearly, I still feel I need to go on a much deeper educational journey about this stuff. I think you've got an instinctive feel for it.

55:28But I think what was really interesting about the way that he describes it and the way that he talks about it is that he is a kind of almost like an arbiter of all the different debates that are going on. I feel he's somebody who sees all the sides of this. I'll tell you what I did enjoy. I really enjoyed reading those digitalist papers that he sent. I thought they were really interesting. And I guess what came through that is there is no one view, There is no one outcome. This is so much kind of work in progress. But I got a feeling from him about some of the different angles that this thing can go down.

56:02And in a sense, it's like everything is a battle between, you know, this thing being a force for good and this thing being a force for danger. But I think you put your finger on what makes it so interesting for politics, which is that it's very unusual for something that could quite literally change the entire world to require such a strong degree of scientific focus and literacy. And this is going to be an issue for all policymakers. So Demis Hassabis, who just got the Nobel Prize, who's this amazing British AI scientist, is suggesting that we could, within three or four years, have artificial general intelligence.

56:40In other words, machines are not just smarter than us, but which are completely autonomous and are able to make their own decisions to decide what to do. And that really has incredible implications for the economy, for education, for war, for security. And yet, the people who are supposed to be passing the laws, which are going to decide whether to enable this stuff or not, who are supposed to be preparing our country for it, I guess in your and my experience in government, very, very unlikely that many people around the cabinet table are going to have the background to really understand this stuff in depth.

57:16Certainly around any of the cabinet tables I set around. Yeah. One of the most interesting and impressive things he said, which was a statement of the obvious, but it kind of goes to the heart of what you're saying, that in the end, all of us, but policymakers in particular, and governments and leaders in particular, are going to have to decide how we work with this thing. He said, where we end up is a question of what we decide to do. Well, that's absolutely right. But when you hear most politicians talk about this, you feel like they're talking a generation behind where he already is and where these other people who are involved in this already are.

57:50Now, that gives them, if you take this to the debate that's going on with the wretched Musk and Zuckerberg and the social media people, they are essentially, I think, have successfully for a generation now exploited the fact that they understand what they're doing way better than the politicians. they have in a way bamboozled the politicians into thinking this is the stuff that can really help them win campaigns in Trump's case or you know whatever it might be whereas now you're talking about stuff as you say this could transform healthcare around the world transform education around the world but as you said it could also transform the way we do war So if you actually find that generative AI leads to somebody being able to create an army out of nothing, we're then into a completely different sort of world.

58:45That's where I think he is really, really good and really important. He understands the ups and the downs of this. Well, the other thing that we're very, I think, conscious of at the moment when we're focusing on Elon Musk is that by definition, the 100, 200 people at the top of this industry are generally techno optimists. They're generally people who really believe in technology, who really believe that the world has got much better over the last 20 years because of technology. They're also people who've made an enormous amount of money off it. I'm not talking particularly about James here, but certainly with people like Elon Musk and Zuckerberg and others, they've made massive fortunes of it.

59:24And therefore, it's very, very difficult to have a straightforward conversation about it because the people who have the power and the knowledge also have a conflict of interest. It's in their interest for the stuff not to be regulated. It's in their interest for the stuff to be rolled out as quickly as possible. And yet when Biden is sitting down, yes, he's sitting down with James, But many of the other people in the room are people who have a strong financial interest in this stuff. When we talk to Reid Hoffman, for example, he was somebody who has made a vast fortune for himself through his ability, through his talent, through his entrepreneurship, etc.

1:00:01But who clearly now thinks both financially and intellectual firepower, he has to put stuff back. They don't all think like that. Right. James, for example, clearly said that one of his worries about this is that rather than addressing global inequality, it cements and deepens inequality. He said not everyone's going to benefit. And he thinks that everybody should. He made the comparison with the vaccine. Not everybody benefited in the same way. And so I think that risk that AI widens existing inequalities, deepens existing inequalities, I think that's something that hopefully people like him, and he seems to be, genuinely are thinking about.

1:00:41But the Musks and the Zuckerbergs, who strike me just as people who have, you know, really struck lucky in terms of being the right people with the right kind of brains at the right time when this revolution is happening, are they really thinking in those terms? I mean, Zuckerberg, his entire career has talked the talk on that, the social network and all that. But deep down, they're about power. They've got lots of it. They want more. They're about wealth. They've got lots of it. They want more. So getting AI to be of universal humankind benefit, that seems to me is the challenge that the digitalist papers people really need to be focused on.

1:01:18And I think this is where James' own life is very special. so many of the people who dominate Silicon Valley. You know, we interviewed Bill Gates, for example. He was given back a lot, but I think he'd be the first to admit, and he did in the interview, that he grew up in a relatively, well, very comfortable upper-middle-class family, went to a very good private school, went to Harvard, dropped out of Harvard. But these, many of them are the sort of absolute brightest and the best of the Ivy League elite with a very American-centered worldview. whereas James as we discovered in that is somebody who grew up in apartheid Rhodesia in a segregated community and he's understating this to be in a community seeing police raids seeing people being burnt in necklacing seeing the local bars where people hang out I mean all of that gives him I think a sort of humanity and an insight and makes him ask questions which perhaps the others are not asking.

1:02:20And my God, he's got a hundred trillion dollar bill. I mean, the thing about Zimbabwe, and this again goes back to the point about everything in politics and everything in life is about the choices that you make and the short, medium, long-term impact that they have. There was a point at which Zimbabwe could have become a really successful modern country, but it got really bad leaders. I found his account of his childhood and the inspiration of his dad and the fact that he's, you know, I love that image of him, you know, that his dad had a picture of himself in a spacesuit because he'd been a Fulbright scholar and he'd gone to Cape Canaveral and so forth.

1:02:59And that sort of seems to really, really fired him. And also he's clearly somebody as well who had good teachers, again, who inspired him and drove him to have this kind of curiosity that he clearly has. I thought he was a very, very impressive guy. I think Reid Hoffman, Mustafa Suleiman, and now James, I mean, they're all very different, very different sort of people, different kind of backgrounds. I wonder whether they all don't feel that Musk is really damaging them all as a brand right now. Yeah, I definitely think some of them do. Some of the record are really beginning to be appalled by the direction it's going.

1:03:37Well, Alistair, thank you for that. Really appreciate it, and look forward to speaking soon. see you soon bye bye bye bye