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Chapter 1: What is the significance of AI's rapid development?
I've been the person who said, you think the singularity is going to happen yesterday?
I think it'll take five years, ten years. And even for me, I've had to admit that the progress has been pretty fast.
Well, I guess the broader point is, is it going to, like, cure cancer at one point? Presumably it will.
I think at some point it will. I mean, just think of it as more people. If civilization had 10 billion more scientists, human scientists, would we make faster progress on aging? I'm sure we would. Here's another angle, mass surveillance. The AIs have the potential to make authoritarian societies much more sustainable and powerful than they have been in the past.
A lot of the reasons that government has not been as authoritarian as it has in the past is that it's just physically not been possible. How do we make sure that humans don't get totally disenfranchised? Right. How do we make sure of that? I think it's a tough question.
Dwarkesh Patel, welcome to Trigonometry. Thanks for having me. It's great to have you on. Actually, I was saying to you before we started, I'm a big fan of your podcast, and I listen particularly to the history episodes.
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Chapter 2: How might AI impact healthcare and disease treatment?
But you've been described as Silicon Valley's favorite podcaster, and you write a lot about AI and tech. And that's actually the conversation we really want to have with you. Partly because a lot of people watching and listening to this, they've got lives, you know, family, work, et cetera. And they haven't been to California. They haven't been to San Francisco.
They haven't seen a third of the cars on the road or 25% of the robots, basically. Like this is happening fast and a lot of people haven't caught up yet. And what we'd love to do is just kind of... connect people like you who really understand what's going on with a much more general audience. That includes us, frankly.
So first of all, can you explain in broad brushstrokes what is happening with AI? I know it's a massive question, obviously, but do your best.
Well, I can explain it very concisely. The models are getting better. And then we can be a little less concise. I think it's, you're correct to point out that there's this huge discrepancy between what people are seeing in Silicon Valley and what people are observing outside. It's frankly because of how useful the models are becoming at certain kinds of things.
So by models, I mean, you've seen Chad GBT. You might have heard of things like Gemini from Google. You might have heard of Clot from Anthropic. And you might be using these models to basically do the equivalent of Google search, using it to replace sometimes any Google search. Instead, I'm going to type it into chat GPT, see what chat says.
What people are now using these models to do in a very powerful way is if you're a developer, Some of the top developers in the world, some of the top researchers in the world, they're not writing code.
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Chapter 3: What are the potential risks of AI in authoritarian regimes?
They haven't touched a line of code since December. They're not looking at a text editor where you'd see lines of code. They're talking to the AI. They tell the AI, hey, I want a feature that does X. Can you build me a new repository or a new code base where I make a certain kind of application, a new website? Can you go and do research for me?
So in the process of building AI, you need to do this research of like, how do you build better algorithms? AI is getting to the point where you can just describe at a high level what you want to happen, and it will go do that software engineering for you. And so to your point, the people in Silicon Valley, they're getting tremendous productivity out of these.
These are people who are getting paid, you know, who are becoming 3x, 4x, 5x more productive as a result of using these models. So far, because these models have been really good at text-in, text-out work, that is software engineering. Software engineering is just a file of text, really, and you can just read every single text file, you can add more to it. AI has been amazing at that.
It's been bad so far at, well, it's terrible at physical work, right? So if you're doing any kind of blue collar work, robots are just not there yet. But then even if you circumscribe it, even if you go in and look at, we're not just gonna look at software engineering, we're gonna look at all kinds of knowledge work, right? All kinds of work that you can do on a computer.
Maybe 40% of the labor force is doing work that you can just do remote work. If COVID happens again, you can put them on Zoom and they can do their work. And AI companies now want to be able to do all of that work. That requires training AIs to just be able to do anything that you can do on a computer an AI should be able to do.
And companies are saying, oh, we think we can get there in a year, maybe two years. But that, I think, is explaining this discrepancy in what people are seeing.
It does. And one of the things I think is also a lot of people would have tried the model at one point to do a Google search or sometimes people have written articles using it. And it turns out that it does make, at the time they tried it, quite a lot of mistakes. And people go, ah, this is all BS. It's not going to work.
But the one thing I think people don't appreciate is how rapidly it's getting better. Can you talk a little bit about that?
Yeah, so I'll maybe tell you a story about my own use of AI. Among the Silicon Valley people, I've been a sort of skeptic. I've been the person who said, you think the singularity is going to happen yesterday? I think it'll take five years, 10 years. By this, I mean this idea that you'll have incredibly powerful AI systems that are able to do basically anything any human being can do.
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Chapter 4: How does AI influence job markets and employment?
And I say, no, it'll take longer. And even for me, I've had to admit that the progress has been pretty fast. And here's a story. So last year, like you guys do, I researched for my podcast. Obviously, I read things, but then I also talked to these LLMs, the equivalents of ChatGPT, to research for a podcast.
And say last year I spent on the order of $100 on... If you add up my subscription to ChaiGBT and Quad and whatever and Gemini, there was a week where I was prepping for two different guests where in that prep, I just threw in a bunch of papers that are relevant to the research and a bunch of books and whatever into a folder. And I just said to...
an LLM, hey, help me understand all this research so I can ask the person good questions about it. And I turned on various things which make the model much more expensive. It's faster, it's smarter, I use a bigger model. And during that week, if you take my spending during that week and you turn it into a yearly spending, how much it would be if I just did that week over the course of a year?
I would be spending over six figures. on AI spend. And I think it's possible next year, it would make sense for me to spend seven figures on this AI research for my podcast. Okay, this is just to say that it's getting to the point where I could hire an analyst. I could hire many different analysts to help me prepare for the podcast or researchers.
And I'm actually realizing, no, it's more useful to hire AIs because there's things AIs can do that humans can't do. They can read 50 different papers that are in my folder that are relevant to an upcoming interview or all these books. They can read it in a second. They have all this compiled knowledge.
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Chapter 5: What are the ethical considerations in AI development?
They know about everything, right? So they don't need to get up to speed. They're incredibly easy to onboard. So you can just keep spinning up more and more AIs and keep coming up with more uses for them. For me, that's been the thing that's given me a bit of psychosis, just seeing how useful they've been for interviews so far. But yeah, lots of people are noticing this kind of usefulness now.
I think the thing is, is when people look at a lot of the eyes, and this is a question, a point that Constantine made, it's the lack of accuracy and the fact that you constantly have to fact check. I'll give an example. I was looking at guests that we could have from America on the show, and I said to Grok, who can we have on that hasn't appeared yet and is based in America?
Number one answer was Douglas Murray. Douglas Murray, I think, is nearly beyond 10 times And practically every suggestion it made is a guess that we've had on the show. So whilst I accept what you're saying, it's also riddled with mistakes and errors. And to the point where I don't trust it at this point as a technology, I would never take something it says as read.
I would always feel that I have to check it. in the way that I wouldn't do with a highly competent human employee.
I'm sure you've seen that meme where a guy wakes up in a hospital with a scar on his stomach, and he says, wait, wait, my appendix on this side, and the robot goes, okay, thank you for that feedback, let me try again.
There's a couple of things there... Just the way that people sometimes have these weird failure modes. You talk to a certain kind of person and their mind just goes in a certain direction. AIs, the way they're trained makes them do that. A big thing they're trained to do is associate things. So they're just like, really, they're just seeing globs of text.
And they see this thing is close to this thing. There's an important relationship there. So Grok has seen that Trigger Neurometry Podcast and Douglas Murray, they just seem to go together a bunch. And that bias in its mind is overriding its ability to sort of think critically about, OK, well, who have they had on? Who have they not had on?
I would be curious, by the way, you should run that experiment with all the other models. I would not be surprised if the other models, if you ask the question, and I want somebody who I've not interviewed before, that they would immediately catch that.
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Chapter 6: How do we ensure AI serves humanity's interests?
And by this point, the models are good enough to be actually giving you novel names. I don't think they'll give you names which you're like, wow, that's an amazing find. I can't believe I didn't think of it. Or I would never have thought of it otherwise. Because they're not amazing and discovering and this...
I think humans are great at this idiosyncratic thing of, like, there's this, like, weird angle that I'm, like, really obsessed with that other people aren't thinking about. Where the models are not going to do that. They're sort of an average. But when you are preparing to interview the person, I also think they're not amazing at coming up with questions.
This is a thing I have to do that you have to do. They're great at just, like, I would have to hire, you know, the ancient Greeks would hire one-on-one tutors for... for their pupils, and this is how you got Alexander being tutored by Aristotle. And there's something powerful about this way of learning, one-on-one, where you can just directly ask questions and learn.
And so they're amazing at just teaching you stuff, because you get this one-on-one tutor that notices your confusion as soon as you have it. You can really probe at your understanding in different ways. But I take your point that some of the models are not there yet on a lot of these kinds of biases that they have.
And one of the things that people are talking about a lot is AGI. Could you explain to people, basically me, what AGI is, what it stands for, and why the potential is to be so transformative for our society?
Yeah. Maybe one way to approach this question is to think about At a very basic level, a system that is an AGI should be able to do anything that a human does. Now, I want to emphasize that current AIs are nowhere close to this. You and I can say, we can do physical work. We can work in a factory, we can go mow the lawn, we can pick up this cup. Robots are not good enough to do that yet.
So the fact that robotics is not there yet already means that we're far from that big definition of AGI. Wait, robots can't pick up a cup? They can, but they have to... My understanding is that they have to be trained in specific environments where they'll have seen, this is what this house looks like, and I need a specific kind of cup that I'm dexterous enough to pick up.
But if you replace this with one that's more circular and is tougher to grip... You and I don't need to have seen that house a hundred times to then just go in and pick it up.
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Chapter 7: What role does surveillance play in the future of AI?
The robot just needs a tremendous amount of data to be flexible in these ways, or it's not flexible in those ways. So being AGI would be able to be able to learn as fast as humans, not just have that distilled knowledge. Now the labs say, okay, fine, robotics is hard, it might take a while, but let's try something easier. We'll just do all knowledge work.
And knowledge work is basically anything that you could do with a Zoom subscription and a Gmail account and Google Drive account. All this work that doesn't involve manipulating the physical world. I still think we're not there yet. There's a tremendous amount of work that has to be done even there. But this is easier because everything that happens on a computer
it's easier to feed that data into an AI, right? It's like all being generated on a computer. You can run simulations, you can just run it millions and millions and millions of times, and AI is just thriving on data, right? The more data you have, the better it gets this thing. This is why it's so good at software engineering because there's all this code out there that you can train it on.
With robotics, there's no equivalent of just billions of lines of code that just exist somewhere. There's not these trillions of video files. It's not just video files. The robot has to feel itself. manipulating the world and seeing the effect it has. I've just described what it is. Let me say what it would mean if that were achieved.
They say that 40% of the labor force is doing work that can be done remotely. That is on the order of tens of trillions of dollars of wages that are being paid to humans every single year to do work that can be done remotely.
Currently, labs are making on the order, if you just add up all the revenues, how much money OpenAI and Anthropic and Google are making, they're making, I don't know, the numbers keep exploding, so who knows what the most recent numbers are, but they'd say close to $40-$50 billion. We're talking about an addressable market here with all knowledge work that is tens of trillions of dollars.
So literally a thousand X bigger than what they're doing right now. So it explains why they're interested in it. It would also mean that a lot of people's jobs would be gone. It would also mean... It would mean a lot of things, right? It would mean... It's a terrible thing in one sense. Lots of people's jobs are gone. In another sense, we can produce a lot more things.
There would be all kinds of, you know... AGI would include automated scientists and researchers coming up with new ideas and new medicines and new drugs. It would mean all kinds of new products for us to enjoy. It would mean that... You and I would have basically an army of extremely smart personal assistants constantly thinking about us and helping us. Okay, so that's the definition of AGI.
Right. And I guess, you know what?
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Chapter 8: How can society adapt to an AI-driven economy?
There is a Duma-ish conversation, which we're definitely going to get to, because I think it's actually the most important conversation that's happening right now. Before we get there, though, I think it's always worthwhile to show the full range. And you... kind of mentioned briefly some of the upsides. But I mean, I think people don't appreciate how big the upsides will be either.
And when it comes to scientific research, healthcare, I mean, I went to the dentist the other day, they already checked like AI does stuff when it measures your gum level relative to the last time you came in and it's all automated and blah, blah, blah, blah, blah. Some dentists don't like it, some do. But like, that's just the very, very, very beginning. Yeah.
I think that's actually a great, like that's, it's a great intuition pump because people are thinking about existing things.
By the way, I love the way you said intuition problem. Like you are already AI. Like I prompted, you already talk like AI.
I'm just doing like a fancy of like, that's a great point. You know, let's build on that. So one thing is just like making going to the dentist more efficient, as you were saying. Another is let's replace a dentist. Why do I need to go and stand in line for three hours to basically be told, you're fine, go home, right?
If I have basically the doctor on my phone and I can talk to it and I explain my specific situations and I can talk back to me and we can have a conversation, I've just like saved myself a bunch of time, saved myself a bunch of money. Society is better off as a result.
Well, I guess the broader point is, is it going to, like, cure cancer at one point? Presumably it will.
I think at some point it will. I'm not... Sometimes people have this idea that you get AGI And tomorrow you cure cancer. I do think, by the way, so in Silicon Valley, people are also working on all these different ideas to stop aging, to reverse aging, to cure diseases. And I think there's a huge... If you just look at the science, there's huge reasons to be optimistic.
And it's also the case that AI will be pretty good at science. It just knows a ton of stuff. It's really smart. That's what's required for science. And... there's a lot of things that i think we get used to that we don't just don't realize how bad it is as a society that this happens if you're just living in europe in the 14th century and half your friends are dying because of the black death um
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