The Exchanges

Q But so many folks in the AI industry are talking about diminishing returns from scaling now. That really doesn't fit with the vision you just laid out. Are they wrong?

A Yeah, I, I, from what we've seen, I can only speak in terms of the models at Anthropic. Um, but what I've seen in terms of the models at Anthropic, if we look at, you know, let's take coding. Coding is one area where, you know, I think Anthropic models have advanced very quickly. Adoption has been very quick. We're not just a coding company. We're planning to expand to many areas. But if you, if you look at, if you look at coding, um, you know, every, you know, we released 3.5 Sonnet, a model we call 3.5 Sonnet V two. Um, uh, which I, you know, let's call it 3.6 on it now, um, 3.7 sonnet, uh, and then four point O sonnet and four point O opus. And, you know, that series of four or five models, each one got substantially better at coding than, than the last. If you want to look at benchmarks, you can look at, you know, sweet bench growing from, uh, you know, I think 18 months ago is that like three percent or something, um, growing all the way to, you know, 72 to 80%, depending on how you, how you measure it. And, and the real usage has grown up, grown exponentially as well, where we're heading more and more towards autonomously. You can just use these models. I think the actual majority of code, um, uh, uh, that's written, written at Anthropic, uh, is, you know, at this, at this point, uh, written by, or at least with the involvement of one, you know, one of the quad models, um…

AI assessment note: “we see the progress as being very fast, and the exponential is continuing”

Q So you're making the most pure bet on this technology. Like, you know, OpenAI might be betting on ChatGPT and Google might be betting on the fact that No matter where the technology goes, it can, you know, integrate into Gmail and calendar. So why have you made this bet on this, the pure bet on the tech itself?

A Yeah, I mean, I would say, I wouldn't quite put it that way. I think we've, I would describe it more as we've bet on business use cases of the model, um, more so than we've bet on the API per se, and it's just that the first business use cases of the model come through the API. So, you know, as you mentioned, OpenAI is very focused on the consumer side. Google is very focused on kind of the existing products that Google has. Our view is that If anything, the enterprise use of AI is going to be greater even than the consumer use of AI, right? I should say the business use because it's enterprise, it's startups, it's developers, and it's kind of, you know, power users using the model model for productivity. Um, I, I also think that, uh, being a company that's focused on the business use cases actually gives us better incentives to make the models better. Um, a, a thought, a thought experiment that I think is worth running is, you know, suppose I have this model and it's, uh, it's, um, you know, it's, it's as good as an undergrad at biochemistry. Um, and then I improve it and it's as good as a PhD student at, at biochemistry. If I go to a consumer, right, if I give them the chat bot and I say, great news, I've improved the model from, you know, undergrad to graduate level in, in biochemistry. Um, you know, maybe I don't know. One percent of consumers care about that at all, right?…

AI assessment note: “we've bet on business use cases of the model”

Q model, um, has been confounding to some. You can spend 200 dollars a month and get the equivalent, I spoke to one developer, they got the equivalent of 6000 dollars a month, ah, from your API. Um, Ed Zitron has pointed out, the more popular that your models get, the more money you're going to lose if people are super users. Of this technology. So how does that make sense?

A So, um, uh, so actually pricing schemes and rate limits are surprisingly complicated. Um, so, so some of this is basically the result of when we released our, um, uh, when we, when we released Claude Code in the max tier, which we eventually tied together, actually not fully understanding the implications of, you know, the ways in which people could use the models and how much they were actually able to get. Over the last few days, as of the time of this, uh, as of the time of this interview, we've adjusted that particularly on the larger models like Opus. I think it's no longer possible to spend that much, um, uh, uh, with, uh, with a, with a 200 dollar, uh, uh, subscription. And, you know, it's possible more changes will, more changes will come in the future, but we're always going to have a distribution of users who use a lot and, and, and users who lose, who use some amount. And it, it doesn't necessarily mean we're losing money that there are some, Some users who get more, um, uh, you know, who, if you were to measure via API credits, spend, you know, get, get a better deal on the consumer, on the, on the consumer subscription than they would on the API products, right? There's a, there's a lot of assumptions there. Um, uh, and I can tell you that, that some, that some of them, that some of them are wrong. Um, uh, we are not in fact, uh, losing money.

AI assessment note: “we've adjusted that particularly on the larger models... we are not in fact, losing money.”

Q You talked about talent density. What do you think about what Mark Zuckerberg is doing on the talent density front? I mean, combining that with these massive data centers, it seems like he's going to be able to compete. Yeah.

A So, uh, this is, this is actually very interesting because, um, you know, one thing we noticed is that relative to other, uh, companies, um, you know, I think, I think, I think, uh, very, you know, a lot fewer people from Anthropic, uh, have been, have been caught by these. And it's not for lack of trying. I've talked to plenty of people. Uh, you know, who, who got these offers at Anthropic and, and who just turned them down, um, who wouldn't even talk to Mark Zuckerberg, who said, um, you know, uh, uh, no, I'm, I'm, I'm staying at Anthropic. And, and our, our general response to this was, you know, I posted something to the, to the whole company Slack where, where, where I said, look, um, you know, we are not willing to compromise our, you know, our compensation principles, our principles of fairness, To respond individually to these offers. The way things work at Anthropic is there's a series of levels. When candidate comes in, they get assigned a level, and we don't negotiate that level. Um, ah, ah, because, because we think it's unfair. We want to have a systematic way. If, you know, if Mark Zuckerberg, you know, throws a dart at a dartboard and hits your name, that doesn't mean that you should be paid 10 times more than the guy next to you who's, who's, you know, who's, who's just as skilled, who's just as talented. Um, Uh, and, and, and my view of the situation is that, y…

AI assessment note: “I've talked to plenty of people... who got these offers at Anthropic and... turned them down”

Q So what was it like growing up in San Francisco?

A Yeah, um, I, you know, the city, when I first grew up here, I had not really, had not really gentrified, uh, that much. You know, when I grew up, the tech boom hadn't, hadn't happened, uh, hadn't happened yet. Um, you know, it happened as, as I was going through high school, and actually I had no interest in it. Um, it was totally, it was totally boring to me. Um, you know, I was interested in being like a scientist. I was interested in physics and math, and you know, the idea of like, you know, you know, like, Writing some website actually had no interest to me, to me whatsoever. Like founding a company, like those weren't things that I was, uh, that I was interested in at all. Um, you know, I was interested in discovering fundamental scientific truth, and I was interested in like, you know, how can I, how can I do something that like makes the world better? Um, uh, so, so, you know, that was, that was kind of more, and you know, I watched the tech boom happen around me, but I, I feel like, you know, there was all kinds of things I probably could have learned from it that would have been helpful now, but I just, Actually wasn't paying attention and had no interest in it, even though I was like right at the center of it.

AI assessment note: “when I first grew up here, I had not really, had not really gentrified”

Q I wasn't, was, what was the, what didn't you answer?

A I, I probably met the guy four or five times. Um, uh, so I have no great insight, um, into the, you know, what in, you know, into the psychology of SPF or, or, you know, why, why he did things as stupid as stupid or immoral as, as, as, as, as, as he did. I think the only, Uh, the only, you know, the, the only thing I had ever seen ahead of time with, uh, SPF was, uh, you know, a couple of people mentioned to me that he was like hard to work with, that, you know, he was, he was like a bit of a move fast and break things guy. Um, and I was like, okay, you know, there's like plenty of people.

AI assessment note: “I probably met the guy four or five times. Um, uh, so I have no great insight”