Supremacy: The Race for AI Dominance with Parmy Olson

Fabulous to have you, especially right now. It's all so topical, and, um, we should give the book its proper title, I suppose: Supremacy AI: ChatGPT and the Race That Will Change the World. But we've caught you on a day where you are— I'm sure you're always busy, Parmi, but exceptionally busy because you're just writing up, or in the midst of writing up, um, something about one of the protagonists in your book.

Why don't you tell us more? That's right, yes. Well, so the founder and CEO of Google DeepMind, whose name is Demis Hassabis, is one of the two key protagonists in "Supremacy: AI, ChatGPT, and the Race That Will Change the World." And today he just won a Nobel Prize, the Nobel Prize for Chemistry, and he's sharing it with two other people. And this is for the work that he and the team at DeepMind have done on a process known as protein folding. And proteins are the building blocks of the human body. And the idea is that by using artificial intelligence to predict how these proteins will fold, they can— scientists and medical researchers can use that process for drug discovery and make Breakthroughs in Biology. And this is a project that's been going on for about 8 years or so. And it's, and so it's really amazing that it's been recognized and had this incredible accolade, especially because, as I say in my book, Demis has craved a Nobel Prize for years. In fact, he even told his staff that he wanted DeepMind to win, you know, 3 to 5 Nobel Prizes over the next decade. And this was very much how he measured success. So that's a huge milestone for him. And I think it also adds a really big veneer of credibility to artificial intelligence and particularly to Google, because Demis is actually the first big tech executive to have ever won a Nobel. And I think that really is gonna, in a way, cast a very useful glow on, on Google and perhaps even other tech companies who are kind of struggling at the minute to justify the benefits of artificial intelligence as being good for society, good for humanity, and not just good for them. Which is one of the big arguments I make in my book, that actually right now the wealth benefits, the influence benefits really are just being concentrated in the hands of these few very large companies.

You are right. This takes us very nicely into the heart of your book. 'Cause one of the things I wanted to ask you, maybe it's a jumping off point for you to explain to the, the audience who, who haven't read it yet, but should go and buy it and read it. 'Cause it's excellent. Is, is it a book about the hype versus the doomerism of AI, or is it, a biography about two, you know, potentially, potential geniuses or definitely, you know, amazing entrepreneurs, uh, and maybe engineers and scientists, or is it both? Um, cause it's very interesting. It's quite hard to characterize in that sense.

Yeah. It's tempting to say both, but I would steer it more towards being more of a biography because I think it's, I think that's a really important way to understand technology, which can seem so abstract and so invisible. It's just there. It's just woven into our lives and our infrastructure, is to remember that all technology is designed by humans. And there are people, like you use your phone, every widget, every app, every piece of software you use is a result of a design decision made by a human. And I think if we really want to get a sense of where AI is going, and especially the incentives that are fueling the building of AI today, it's really important just to understand the people behind this technology. And there are so many people behind AI, but for simplicity's sake, and also because I think these two men are the two most important builders of AI, I did focus it on Sam Altman, the founder of OpenAI, and Demis Hassabis, the founder of DeepMind. And the other reason I focused on them was because they were really the, the two people who were the first to start companies aiming to build artificial general intelligence. And this is this really huge ambitious goal to build AI that is as smart as a human, effectively, that can do the same cognitive tasks as a human brain. It's— we're not anywhere near that yet. And there's a big question about whether that's even possible. But it was, it really started off as this humanitarian dream. Like, Demis started this in part because he wanted to make scientific discoveries. He believed that AI could accelerate scientific breakthroughs and that AI could be, and computers could be an extension of the human mind. And he saw it as a way to cure cancer. If he built AGI, he could solve climate change. He was going to solve all these things. And Sam Altman, he was more on the kind of economic benefit of AI. He kind of promoted prosperity, like, "We have AGI, we'll have abundance." They use this word "abundance" all the time, where we'll kind of elevate the wealth of everyone on Earth. And of course, that motivation changed over time, like those objectives changed. Yeah. Yes. Yeah.

Yeah.

But yes, that's why I, that's That's how I would characterize it. It's really a kind of human story. And you're right, it is difficult to characterize the book itself. But, you know, I'm not easy to characterize as a person generally.

It has different dimensions to it. You can come in it through several different ways and it is a complex story. It's told very well. But I think that's nice, there's different levels or layers of interpretation or storytelling in it. I have to ask you, I'm sure people have asked you before, which of them do you prefer?

Oh, people ask me this all the time. I actually, I know the first time I was asked that was like, I was on stage at an event and I hadn't, I was not expecting the question. And I just said, well, I guess I prefer Demis because he just, he does just seem more genuine in his outlook. It does seem like he's stuck a little bit more to the original principles of trying to and the original objectives of trying to, you know, use AI to solve health-related or scientific discoveries and to sort of support science. Whereas Sam, I feel like, you know, the idea of using AGI to improve the wealth of everyone, that just, that feels really like it's just so theoretical and abstract, and it really just feels much more like a marketing point for him. Um, but other people kind of come away with different, like I had someone who read it say to me, wow, I've read this book and it really feels like Demis is the bad guy and Sam is the good guy. So you kind of—

not sure about that.

So you can really come away from it with different interpretations, but I would say that, um, actually the first half of the book is kind of biographical. And then, and then it goes into kind of like the conflict and the tension and the warnings that were coming about this. Technology they were building. And then the final third is actually almost more like a polemic about the risks of AI and just what the— excuse me— what the future could hold and why it all matters.

Yeah, you're right. Obviously, it's your book, so of course you're right. But the first part of it is you are sort of, as you say, exploring the character and the values of each of those individuals, I think, which then goes on to explain quite a lot of how the company, companies look and feel and how they turn out. But one of the interesting bits in the middle, whilst you are discussing all of those ins and outs and, you know, acquisitions and partnerships and all this sort of stuff, particularly with Google and DeepMind, is really interesting, I think. And that does give a lot of illumination into the character of Demis and how much does he want to stick to the original values and the original purpose and the social purpose of DeepMind versus, you know, particularly when you're talking about when they're looking to move into China. Yeah, you know, I think it's after AlphaGo, but maybe you can give a summary of that. I found that a really interesting part of the story, actually, for lots of different reasons.

Yeah, and I think it's this question about sticking to this, like sticking to the, to the social purpose, I think is really interesting, because I think DeepMind has done a really— and Google have done a really good job of publicly framing themselves as sticking to these objectives, when actually if you look at where they put the investment, it's so much more on just the Gemini AI model, the product, as opposed to, and, you know, probably one of the best examples is the latest news about AlphaFold, about the protein folding project. Because actually the number of people who worked on that at DeepMind, for all the publicity that it has received, is tiny. There's something like 1,500 people who work at DeepMind. And actually the number of people who are working on AlphaFold is around 20. It's very, very small. So I think that the accolades that it has received actually is sort of overshadows— maybe not, it doesn't reflect really where Google is putting all the investment and where DeepMind is putting a lot more of its time. And when I interviewed Demis for the book, I was asking him, you know, you started wanting to build AGI because you wanted to unlock the mysteries of reality and the mysteries of the universe and, you know, quantum entanglement and all this kind of stuff. And I said, well, how many people at DeepMind have you actually got working on that now? And he said, well, it's just me. He just, you know, when he comes home from work, he reads research papers late into the night until 2, 3 in the morning. So again, these, I think like these professed goals, which sound really good and which Demis and Sam still talk about in public, are actually not really the focus of the companies. The, you know, even if you look at DeepMind's website a few years ago, It said a lot more, the homepage said a lot more about its efforts in healthcare, its efforts in using, you know, AI machine learning to solve problems around climate change and new energy sources. That's all gone now.

Yeah, Wired used to cover that all the time.

Okay, yeah, yeah.

There was always a story coming out about DeepMind. I don't read so much anymore. It's interesting, it's true.

Yeah, and it's reflected just even, you just look at the website. I mean, there's so much more there about— it's a product-based homepage now. It's talking about Gemini, it's talking about YouTube, it's talking about a new video generation tool. So yeah, I think this is just— it started, you know, DeepMind was always framing itself as this kind of research, AI research lab for scientists who could make discoveries that would lead to making the world a better place and get paid incredibly well because they had Google as a benefactor. And now I think the reality has just come a little bit more to bear now because we're in this AI arms race between the tech companies. So it can't really sort of pretend to be this do-gooding AI research lab anymore. It really is just a company.

And do you see this arms race as very much sort of Microsoft versus Google?

Yes.

Or OpenAI? Yeah, exactly. I wanted to ask you, you know, why you chose them, you focused on them, and what happens to, or what you consider, about, you know, Meta, Apple, exactly where are they in the fray? Because obviously the way you have posited it is this battle between these two big kind of giants.

Yeah, it's interesting because, so I was in Silicon Valley a few weeks ago and I met with a venture capital investor named Reid Hoffman, who was on the board of OpenAI. And, you know, he's played a big role in all the things that have happened with OpenAI and with DeepMind too. And we kind of, he and I are very different, like in our perspectives of the, you know, the benefits of AI to society and big tech's role in that. And he was saying, look, his argument is like, when it comes to AI, if I would be concerned if there was like, if this was only a one or two horse race, but this is a five horse race. And so that's why I'm not worried. But I don't agree with that. I think it actually is more of a two-horse race because to your question about Apple, I don't see Apple really being in this race. I don't really see Facebook being in this race because its model is open source. I mean, again, I should be careful on using the word open source. Strictly speaking. Yes, exactly. Strictly speaking. And, you know, the open source community has kind of taken issue with that definition, but it's not benefiting monetarily in the same way that Google and Microsoft are. You know, even Amazon, maybe we can't completely discount Amazon, but Amazon is sort of up there. I think it tracks really well with who the biggest cloud providers are. There's 3 big cloud providers: Amazon, Microsoft, and Google. And, but in the case of AI, you know, really at the top I think is Google and Microsoft just dominating the market for consumer-based AI products and also AI products for businesses. And DeepMind is a proxy of Google and OpenAI is a proxy for Microsoft. Microsoft owns 49% of OpenAI.

It's really hard to see how even a brilliant sort of smaller organization can cut through because even if they do brilliant work, they end up getting acquired, don't they? And then—

Absolutely.

They're not doing the challenging innovation that you would expect them to do in the market. They just become another just another acquisition. So I can't really see— it's one of the questions I had for you actually. In like 20 years' time, do you still think it will be Microsoft and Google, or do you think there will have been an upstart that has disrupted the market? Or do you think actually these are just— this is just really an oligopoly or potentially even a monopoly in the end?

Yeah, I mean, I think we're just— this is so unprecedented. I was just looking at some market data the other day, and if you look at the S&P 500 index, Like back in 1960, and then you look at the 1970s, then you look at the 1980s, the companies who were in the top 5 stocks in the S&P 500, it was a real diverse array of industries represented there. You had ExxonMobil, you had Exxon, you had General Electric, um, you know, you, you had healthcare companies. So it was, you know, oil, energy, United Fruit.

I don't know.

Um, uh, but then something really odd has happened in the last 2 years.— sorry, in the last decade, which is that the top 5 stocks are all tech companies. The tech industry, there's never been a time where the entire top 5 stocks in the S&P 500 are all from the same industry. And not only that, but the index is weighted increasingly towards those stocks. So I think in 2020, it was something like around 12% of the value of the S&P 500 was tech companies. Now it's 25%. So this is when you ask me like, what will it be like in 20 years? Like, I just think like statistically, like the most likely scenario is it just stays the same, that these tech companies will continue because that's, this is, we're in such an unprecedented situation where they hold so much, the power is concentrated so much among those companies. It would be quite hard to loosen that grip.

Well, yeah, two things could loosen that grip. One, major war which ends up reprogramming the economy either intentionally or accidentally. And then— oh gosh— or two, one thing I wanted to ask you about, the sustainability angle of all of this, because for a long time I've been looking at sort of the resources that are required for sort of training some of these models, and in particular sort of water. Obviously lots of futurists look at these sorts of resource. I mean, it takes as much water to, I don't know, train GPT, ChatGPT, to as it does to cool a nuclear reactor, etc. So these sorts of, you know, basic resources that we need for human survival, are they going to get triaged towards actually the digital public infrastructure, for example, that's all, you know, running on and dependent on AI? Did you look at— because I've read, I've read a lot of the book but I haven't finished it yet— this angle about sustainability and whether that is an obstacle in the future?

It's still, you know, it's funny. I see it as absolutely a valid point of concern, but I'm actually not sure if how big a problem it's gonna be in the next 5 to 10 years. And the reason for that is we keep hearing from people in the tech industry that they are working so hard on efficient, like it's in their interest, not just for the good of, you know, not just for the good of climate, but it's genuinely in their interests to make these models as efficient as possible and not so data hungry because that's, expensive for them. So if you go to an AI conference, you know, just in the past year, that's what all the researchers are talking about. That's what they're working on is how do we make these models more efficient, less data hungry, less power hungry. So although it's a massive problem now, and this is also what I've heard from, you know, I was talking to someone at Google Cloud, one of the executives there, and they, You know, of course they would say it, but this guy was very, very confident, like, yeah, absolutely, these things are gonna become more efficient over time. The chips are gonna become more efficient. Will that actually happen? Will the human ingenuity be there to make it happen? I don't know.

Well, they're gonna have to make it happen, I guess, in a way. So where do you think netting out today, Where do you think OpenAI is versus DeepMind or Google really, or Microsoft and Google? I mean, obviously there've been some recent changes with OpenAI saying, you know, they are literally going, moving from nonprofit to profit. Yeah. And I think in their articles, but you can tell, obviously tell me more about that. Where are we now?

Yeah, it's a great question. And I think it's, You would think maybe OpenAI because they were the first, you know, at the starting gun with ChatGPT. I mean, they were just way ahead of everyone else with this large language model that you could chat to. And it's like literally like talking to a human. It could generate this incredible human-like prose. But since then, I think Google has done a really good job of catching up. It was caught on the back foot, but it has absolutely—

Even though they invented it.

That's right. Yeah. Which I think was probably a little bit humiliating for them. Yeah. Because they, they'd invented it and then they hadn't released it because they were worried about what this, you know, this AI model could say, what weird things it could say when, when the public start using it. But since then, we now, the big advantage that Google has, which OpenAI does not have, is distribution. So even though OpenAI is effectively like a product arm of Microsoft now. So you could say, well, Microsoft, you know, it's everywhere, everyone uses Word, but not in the same way people use Google. It just doesn't have the same kind of reach. Like, if you have an iPhone, the default search engine on your iPhone is Google because Google pays Apple billions of dollars to allow it to be on there. And it's— Android phones are used by, are on the, are on billions of smartphones around the world. And Google's new Gemini model, which is effectively its response to ChatGPT, that's being baked into every Android phone now. So I think that is like a really big advantage. And not only that, but when you use an Android phone and you use Gemini on the system, It's, you know, because Gmail and G Docs and the camera and the calendar, all these things are linked and you can sort of use Gemini as a much more kind of comprehensive assistant that can talk between all those different apps. OpenAI can't do that. It doesn't have access to all those different apps that people use. It's relying on other businesses to build it into their businesses sort of do the same thing, but not as effectively. So it's kind of, I mean, it's hard to say like who's really like ahead right now. It's so close, but if I really had to put money on it, I would say Google because they're the one that have the most distribution.

It's very interesting. It's true. They're like the Coca-Cola, aren't they? It's like the Pepsi and Coca-Cola, I always think.

Yeah.

So, um, when we chatted, uh, originally we talked a little bit about agency. So I did want to ask you about that because it's obviously something that comes up when you're just talking to anybody about the future and what they think the implications of some of these AI tools are. I mean, obviously they're incredibly convenient. You can be really creative. Maybe you can be productive with them. We're not really sure yet. Um, but, uh, people are worried about how much agency it might rob from them. Given that it's working on the basis of their own personal data as inputs, and actually it's making decisions or managing outcomes for them. What's your point of view, having researched this so in depth as you have, what's your point of view on this?

I think there's definitely a, you know, there's definitely the risk of an erosion of human agency. The problem is this is a very, very difficult thing to measure. It's kind of an abstract, squishy subject. Like, how do you measure that my agency as a human has declined? Because I'm—

It's something you feel, isn't it?

Yes. And it's not even really something that people talk about that much, but they recognize it. Like, when you just talk about how addicted we are to our smartphones, for example, does that represent a loss of human agency? I think when it comes to, you know, how we make decisions. I think there are more, there are going to be more situations where companies and organizations and people just outsource their decision-making to generative AI, to AI. And what's really interesting is when you hear companies talk about this and AI makers talk about this, they say, but we don't want AI to take over, to make the decisions. We want them to augment human decisions. But actually, if you think about it, humans are kind of lazy. And if, if the easy option is to let a computer make the decision, then we will. And I'll give you an example of this. So, um, London's Metropolitan Police have been trialing facial recognition, which is a form of AI, for a number of years. And there was a study done of officers who were using facial recognition cameras from a policeman in the middle of a busy street, and it was just— the camera was looking for people as they walked down the street to see if any of them matched a watch list. Now, the problem with facial recognition is that sometimes it's wrong, particularly when it's looking at people of color.

Yeah.

Yes. But what was happening inside the police van is that when the face flashed up on the screen as a match, the officer assumed that the the facial recognition system was correct. They even, rather than sort of double-check, they would go out and just kind of detain the person. And the scientists who were studying this, who were in the van observing the whole phenomenon, actually gave a name to this and they called it deference to the algorithm. And this was a big concern because we assume that computers are often, or more often, right than humans are. Because they are, you know, they are backed by these networks, neural networks and their machines, and they're just like a calculator, it's always correct, right? So you just assume it's correct. And I think that's, that to me is just an example of the possibility that we are actually just going to delegate a lot of decision-making to computers because we trust them and because it's easier for us. Uh, so whether that happens in the criminal justice system, in businesses, in marketing, in customer service, I could see this happening in all sorts of different parts of life. Yeah.

So, and you do cover in the book, um, elements of the kind of trust and safety teams and what has happened to those over time, um, like Tim Geithner and people like that. And what's your point of view on where we are with trust and safety, even, you know, even broader than maybe the two companies you're talking about? Has trust and safety helped? Has it just been a massive distraction? Do we need to start again with it? What, what do you think?

Oh, it's definitely helped for the little resources that these teams have. But when you talk to people who work on these teams and people who have knowledge of these teams, the overwhelming consensus is they're tiny and they are under-resourced and they're largely drowned out in all the product meetings by the people whose greater priority is to get these systems bigger, get them to launch faster. And of course, you know, I'm not going to dismiss all these efforts. Like, Google, I think, really does put a lot of time and effort into trying to build its algorithms responsibly, but at the end of the day, it's not really being regulated for this stuff. And so it's very much its own decision how much investment to put into these. And by and large, these teams are small. And I guess part of the problem is, you know, the other thing is if I went to Google now and said, "How big is your trust and safety team?" they don't have to tell me and they probably wouldn't tell me. I've asked these kinds of questions before. I can't tell you the number of times I've asked Facebook, "How many content moderators do you have?" and they never tell you. They never tell you because they don't have to. And so the hope is that with new regulations coming down the pipe from, especially from the European Union, there's going to be more transparency from these companies over the kinds of risk assessments that they're doing for their algorithms.

This is interesting because at the moment we've also got Draghi's report coming out saying that, you know, well, actually we regulate it. I mean, talking generally, not about this specific case, but generally we're regulating so much that we're stifling innovation. So we need to, you know, maybe think about that and potentially not reverse it, but, you know, is there too much regulation that's stifling innovation? So, I wonder actually what's going to happen with trust and safety in general, you know.

Yeah. Well, I think the approach by the European Union is quite good with the— they have the AI Act, which is coming into force. It's sort of coming into force now, really, but I don't think we're going to see the effect of it for another year or so. But part of the way that act is going to work is that companies like Google, like Apple, OpenAI, DeepMind, they're going to have to be doing these risk assessments on their algorithms. And they're gonna have to share the results of that with auditors, with people from outside the company. This is unprecedented because for years, the only people that company, these companies were accountable to was themselves. They literally, self-regulate. It's so funny, like companies like Uber and Facebook, they have this term transparency report. They all put out these quarterly transparency. It's so funny that they call them transparency reports when there's— it's literally produced by them and you just trust that the numbers are true. They're not. There's no like auditor or like a big consultancy firm like anyone from the Big Five actually checking their homework. It's just the company's doing it themselves. So, it sounds, it seems like this is on course to finally change at some point in the next year.

Okay. Now, two questions, very specific that I wanted to ask you, one from me and one from somebody who I asked, what would you love to ask Parmi? My question is really about Eric Schmidt and what he talked about recently about AI agent-based systems. Actually, you know, you'll almost be able to replace the programmer because you'll be able to give it some kind of prompt or instruction to, you know, I think in the case he was giving, it was copy all of TikTok and do that. And if it, you know, in half an hour it doesn't give you the results, start again and redesign. What, um, where do you think we're going with these AI agent-based systems? Are they going to be in the hands of users so that we can personally affect things? So they can go out and work on our behalf, or actually are they more centralized and they're almost the property of these big companies, the tech giants that you're talking about, and actually the consumer's just on the end of whatever it's been programmed or prompted to do?

Yeah, that's a really good question about whether this is something that consumers are going to have that much control of.

Yes.

I think there's probably going to be products that come out, like ChatGPT, for instance, I think this will, like ChatGPT will probably be given, and I hate this word, but agentic properties.

Oh yeah.

And so the latest version of ChatGPT, which is called 0.1, has these reasoning capabilities, which allows it to do tasks or carry out sort of reasoning tasks, where it actually takes a few seconds to quote unquote, think about something before it does something, and these are kind of more mathematical in nature than the sort of language-based text prediction that it was, that it was doing before. And this has been touted as a precursor to ChatGPT eventually having, being able to carry out tasks like an agent. So that's probably coming. One of the first examples of agent-based AI has actually come from Salesforce, which is this big software as a service company that sells enterprise software to all sorts of companies like banks, British Airways, you know, it's like, and they use oftentimes it's like they use their customer relationship management software. But what their agents can now do is like in customer service, if I'm going on to, let's just say, for example, British Airways's website, and I want to book something, like the agent isn't just going to give me information. It could actually book something for me or it could lodge a complaint. So it can, it can actually take action. And some people are quite concerned about this, like, well, these large language models have been making mistakes, have been hallucinating. What's to say they won't, you know, accidentally, you know, make an incorrect booking, book the wrong flight or take some kind of dangerous action.

Then you're going to go and hire an AI lawyer to sort it.

Yes, that's right. Exactly. Yeah. And that will have agentic. Yeah, exactly. It'll just, we'll really feel like we're in The Matrix at that point. Yeah. It's, it's kind of hard to, I think my big question is what happens when these things make mistakes? You know, like imagine you're, you know, Yeah. Um, you want to book a, book an appointment with the doctor. This is what Salesforce were telling me, like, this is, it'll be able to actually book the appointment for you. Um, so just what happens when these things, uh, actually mess up?

Yeah. It's a new system. We don't have any rules for it yet. And it's going to be very interesting. The question that Chris wanted to ask you was, he said, and he works with a lot of clients And he's obviously trying to bring them with him into the world of AI. But he's saying, I find the most impactful AIs are based on small proprietary models versus general purpose LLMs trained on the public web. So in the battle for supremacy, the title of your book, thank you. How will that square in the future? Small versus large, you know, private versus public?

I don't know, it's funny, I've heard people say the same thing, that these smaller, more specialized models will be more effective. I have yet to see that in practice though, because I have seen examples of small specialized models just not be as good as the generalized models. You know, like the likes of just GPT-4 from OpenAI is better than even a model that A financial company, this is just a real-world example, a financial company made their own model based on their own financial data. It was a large language model, they spent like a year on it, and it just, even based on their own proprietary data, it still wasn't as good as OpenAI's general-purpose model. Why?

Why did they think that was?

It just wasn't as accurate.

Right.

It just didn't, wasn't as effective, it wasn't as capable. Or Lucid. So I don't know how clear it is. I, like, maybe some of these smaller models will still be useful, particularly for companies who don't have the resources to pay for the big models. It's like open source. It's an alternative to having to pay for access to the API of OpenAI or Gemini. But I think, I don't know, I think that the The question is still an open question about whether people will use these. And, you know, one thing I would love right now, which I haven't been able to find, is market share data. Like, in just, in just the market for large language models, who's actually dominating? I, I just anecdotally from what I've read, it seems to be Google's Gemini and OpenAI's GPT. But who, who's behind them? We just don't know yet. So that's hopefully something we'll find out in the next you know, month, few months to a year.

I don't really know anyone who only uses one. So I will use ChatGPT and then I'll put that output into Claude, for example, and get Claude to improve on it and then put it—

oh really?

And then on my mobile I use Perplexity a lot actually. I don't know why, but when I'm on the go and I've got smaller questions, sometimes I use Perplexity. I like to use like all of them because they all do— they all have different strengths and weaknesses, I think. So it's quite good to get a a bit of an adversarial relationship going on. ChatGPT, dear Claude.

Yeah.

ChatGPT, I'm sure you can improve on that. Yeah. Yeah. But I think that's kind of, kind of interesting that the, they've kind of come up through different ways and they say they've got different strengths and weaknesses.

Yeah. And Perplexity was started by academics and which is why they're really big on sources because they're like, well, yeah. And it is really good. But I, you know, another wrinkle to this whole dynamic is none of these really have a strong moat, right? Like, and just like you, I was talking to a startup, an AI startup who were just like, yeah, we use Claude and then sometimes we use, we use OpenAI, you know, for their own product. Their product is like built on top of those things and it's really easy to switch. And that's very unusual in tech, like with, If you're using a cloud provider, for example, like Amazon's AWS, it's really hard to switch to say Microsoft Azure, but it's super easy to switch between foundation models. And apparently the CEO of Anthropic was asked about this at a dinner, at an AI dinner, like, what's your moat? And he was like, well, sorry, this is hearsay by the way. This is what I heard from one person who was at the dinner. And he said, well, Well, our moat is that nobody else can really afford to spend, you know, X billion dollars on compute like we can. So, I mean, yeah, I guess that's a moat, but that's not that great a moat because like, if you, you know, it's just money. The moat's money.

I think just try and use as much of it as possible all across the board while it's still relatively affordable.

Yeah, great point because this is the history of tech, right? Like you start off cheap and then they just jack up the prices once they've got, once they've really established economies scale and have users who are utterly dependent on them.

Yeah. Whilst we're both using each other. Um, so as we close then, and it's been brilliant to spend some time with you and listen to this, um, what should I have asked you about that we haven't covered?

Oh gosh. I don't know. What should you have asked me about? I think you covered, I think you hit all the right notes. Maybe, maybe the question, like I often hear, I worry when I'm talking about my book with people that I just leave people feeling kind of depressed about the future. Maybe, I don't, I feel like we haven't gone into that too much with this discussion, but I would say like, I am hopeful about all the regulatory efforts that are happening. And it's not just regulators, it's also efforts in the courts. You know, there's some really interesting court cases out there, like the one in San Francisco that's fighting websites that produce deepfake porn. And are trying to shut those websites down. That's something that I'm hoping to write about in the next couple of weeks. I think they're doing amazing work. And, you know, there are state laws in the US. There are unions, like think about the Writers Guild and the actors and the writers' strike in Hollywood. And they managed to change the contract language about using AI for scriptwriting and things like that. So I think that there are people, you know, I worry a little bit about civil society and sort of campaign groups with names like AI Now and the Institute for Strategic Dialogue and Open Markets Institute. And these organizations are very poorly funded compared to tech companies and are just trying to fight back against some of the monopolistic practices of tech companies and the AI harms. A lot of these civil society society groups, I feel like their voices are being drowned out a little bit by all the hype in tech. So, what I would love to see is just hearing, to see more of those voices in the public. And just so we're just a little bit more aware. You know, going back to the Nobel Prize for Demis and Geoffrey Hinton, you know, two people linked to Google, two or three people actually, this is great casting a really positive glow on Google, I worry a little bit, it kind of muddies the debate. Like, it kind of eclipses some of the risks that we're trying to talk about with AI and just spotlighting the great stuff that AI does, when actually this is such a tiny part of Google DeepMind's real investment and effort. So yeah, I think we just, we do have to kind of talk about some of these risks and how they're being mitigated.

I think the thing is there's just so many risks at the minute in the news. Like, there's a lot of threats, there's a lot of risks. It's very difficult for people, the general public, to get their heads around the risks of AI, whether it's AGI or whether it's bias or what, whatever it is. Yeah, because there's so many threats at the minute, um, and it's kind of certainly kind of interesting. But we didn't actually talk about China and what, what is going on with AI there, but maybe That's for another time because it could be a column, a column to finish about Demis's Nobel Prize. Yes. Um, that's brilliant, Parmi. Thank you so much for spending a bit of time with us and, um, congratulations on the book. We shall put all the links in the show notes. Is there anything else you'd like to, to add about the book apart from the fact that everybody should go and buy it?

Uh, well, it has just been shortlisted for the Financial Times Business Book of the of the Year Award, which is very, very exciting. So we'll find out in December with the result of that. But it's just been great just being able to talk about the book and tell the story of the people behind the technology. And just, you know, even just hearing what people think, like your questions are so astute. And I think you made a great point at the end just now about actually, yeah, people really kind of struggle to wrap their heads around the risks of AI. And that's a real challenge, I think, in the discourse is how How do we, people like myself, people in civil society, and even tech companies, like, just articulate these risks? Because AI is so abstract, right?