AMA: career advice given AGI, how I research ft. Sholto & Trenton

**Dwarkesh Patel** (0:00)
Today, this is going to be an Ask Me Anything episode. I'm joined with my friends Trenton Bricken and Sholto Douglas. You guys do some AI stuff, right?

**Trenton Bricken** (0:09)
Dabble.

**Dwarkesh Patel** (0:12)
They're researchers at Anthropic. Other news, I have a book launching today. It's called A Scaling Era. I hope one of the questions ends up being why you should buy this book. We can come two words with one stone. But okay, let's just get at it. What's the first question that we got to answer?

**Trenton Bricken** (0:28)
Take us away.

**Sholto Douglas** (0:29)
So I want to ask the flyball question that I heard before of why should ordinary people care about this book? Like why should my mom buy and read the book?

**Dwarkesh Patel** (0:38)
Yeah. First, let me tell you about the book, what it is. So you know, these last few years, I've been interviewing AI labs, CEOs, researchers, people like you guys obviously, but also scholars from all kinds of different fields, economists, philosophers. And they've been addressing, I think, what are basically the gnarliest, most interesting, most important questions we've ever had to ask ourselves. Like, what is the fundamental nature of intelligence? What will happen when we have billions of extra workers? How do we model out the economics of that? How do we think about an intelligence that is greater than the rest of humanity combined? Is that even a coherent concept?
And so, what I'm super delighted with is that with Stripe Press, we made this book where we compiled and curated the best, most insightful snippets across all these interviews. And you can read Dario addressing why does scaling work. And then on the next page is Demis explaining DeepMind's plans for whether they're going to go the RL route and how much the AlphaZero stuff will play into the next generation of LLMs. And on the next page is, of course, you guys going through the technical details of how these models work. And then there's so many different fields that are implicated. I mean, I feel like AI is one of the most multi-disciplinary fields that one can imagine because there's no field, no domain of human knowledge that is not relevant to understanding what a future society of different kinds of beings will look like. You can have Carl Schulman talk about how the scaling hypothesis shows up and primate brain scaling from chimpanzees to humans. On the next page might be an economist trying to argue, like Tyler Cowan, explaining why he doesn't expect explosive economic growth and why the bottlenecks will eat all that up. Anyways, so that's why your mom should buy this book. It is the distillation of all these different fields of human knowledge applied to the most important questions that humanity is facing right now.

**Sholto Douglas** (2:40)
I do like how the book is sliced up by different topics and across interviews. So it does seem like a nice way to listen to all of the interviews in one digestible way.

**Dwarkesh Patel** (2:52)
There's two interviews I've done that haven't been released publicly before that are in the book. So one was with Jared Kaplan, who's one of your co-founders. And this is another example where he's a physicist and he's explaining scaling from this very mathematical perspective about data manifolds.
And then on the next page, you have a totally different perspective. It's like Goran talking about, you know, why can't we just have like distilled... Like, why did general intelligence actually evolve in the first place? What is the actual evolutionary purpose of it? And it's like page by page, right? You can just get addresses. Even for me, I mean, like the person who's been on the other end of these conversations, it was actually really cool to read it and just be like, oh, actually, now I realize how these insights connect to each other. Yeah.

**Sholto Douglas** (3:37)
The only other thing that stood out to me as well is the introduction section.

**Dwarkesh Patel** (3:39)
The only thing that stood out to you.

**Trenton Bricken** (3:42)
Yeah.

**Sholto Douglas** (3:43)
That was really the only thing that was noteworthy. I just mean stood out in accessibility, is the introduction section and the diagrams for all the different inputs that enable you to train a machine learning model. Stripe press books are also just beautiful and have these nice side captions for explaining what parameters are, what a model is, these sorts of things.

**Dwarkesh Patel** (4:05)
Actually, when we did our episode together, a bunch of people, I don't know if you saw this, independently made these blog posts and Anki cards and shit where they were explaining the content because we just kind of passed over some things and hopefully we've given a similar treatment to every single interview I've done where you can read a very technical interview with a lab CEO or something or an engineer or a researcher and then the side is like here's more context, here's more definitions, here's more commentary and I feel like it elevated the conversations.