Inside Anthropic's AI Ambitions with Jared Kaplan

Instead of our usual Friday news rundown, we’re bringing you a conversation from this week’s TC Sessions: AI event out in San Francisco. Our friend and co-host Max Zeff sat down with Jared Kaplan, Co-Founder and Chief Science Officer at Anthropic. If you’ve been following Anthropic, you’ll know it’s been a busy year for the AI startup. Back in March, the company announced it raised $3.5 billion at a $61.5 billion valuation in a round led by Lightspeed Venture Partners. Since then, it’s launched a blog for its Claude models and, according to Bloomberg reporting, partnered with Apple to power a new “vibe-coding” software platform. Listen to the full conversation to hear more about: ⁠Who has direct access to Claude’s AI models⁠, Windsurf’s response, and how it all ties into Anthropic’s broader goals around openness, safety, and sustainability. The company’s pivot away from chatbots and toward agentic AI systems that can perform real tasks. How internal tools like Claude Code are shaping the future of AI-powered development. What it means to build AI that enterprises can actually trust, and how that affects the way humans interact with software, work, and each other. Equity is TechCrunch’s flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday. Subscribe to us on⁠ Apple Podcasts⁠,⁠ Overcast⁠,⁠ Spotify⁠ and all the casts. You also can follow Equity on⁠ X⁠ and⁠ Threads⁠, at @EquityPod. Credits: Equity is produced by Theresa Loconsolo with editing by Kell. We’d also like to thank TechCrunch’s audience development team. Thank you so much for listening, and we'll talk to you next time. Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Anthony Ha?

  This episode of Equity is brought to you by Schneider Electric. Schneider Electric, your energy technology partner.

• 0:17Kirsten Korosec?

  Hello and welcome back to Equity, TechCrunch's flagship podcast about the business of startups. I'm Kirsten Korsak and we're going to mix things up a little bit. Instead of our usual Friday news rundown, we're going to bring you a conversation from this week's TC Sessions AI event out in San Francisco, which I am super bummed to be missing. But luckily for us, Max, one of our co-hosts, is there and actually doing some of the interviews. And specifically, and the one I'm most excited about, is this interview with Jared Kaplan, co-founder and chief science officer at Anthropic. If you've been following Anthropic, and you probably have been, and you've definitely heard us talking about, you know it's been a busy year for the AI startup.

• 1:00Kirsten Korosec?

  Back in March, the company announced it raised $3.5 billion at a $61.5 billion valuation. So that's not small, but they've been doing more than raising money. They've launched a blog for its Claude models. They partnered with Apple to power a new vibe coding software platform. And most recently, Max also wrote about Windsurf that has said that Anthropic is limiting its access to Claude AI models, which are super popular with developers. So back to this interview. Max spoke with Jared about how AI is reshaping the way humans interact with computers, work, and each other.

• 1:41Kirsten Korosec?

  But you can also expect some practical takeaways for businesses of all sizes looking to integrate AI more effectively. Let's take a listen.

• 1:52Speaker 02

  We're going to get right into it. So a couple of weeks ago, Anthropic had your Code with Claude event. You launched Claude 4. And you also said something that kind of flew under the radar about kind of pivoting away from chatbots a little bit, more towards these agentic AI coding systems. Can you talk to me about that decision and how agents and AI coding assistants might be more aligned with Anthropic's mission?

• 2:16Speaker 03

  Yeah, so I guess when we first developed Claude back in 2022, the motivation for us, we were just a research company, we didn't have a product, was to build a dialogue agent because we figured a tremendous amount of sort of what we do can be encapsulated by talking to someone, asking them to do things and sort of seeing how that goes. And that obviously turned into Claude, which is a chatbot. You can go to claude.ai and talk to it. But I think the vision was always that as AI advances, gets better and better, the

• 2:56Speaker 03

  challenge would be to have AI do tasks that take longer and longer, are more sophisticated. And fundamentally, I think the way that we get things done isn't just by giving you the answer, but it's by using tools to go out in the world, learn the information you need to do the task, do the task, iterate. Like if you're a coder, you write code, you run the code, you see if it works. If you're like me, it doesn't work the first time and you have bugs, you see what those bugs are though and you fix them. And so I think that just the natural growth in AI capabilities and sort of AI utility comes from AI agents that can do more and more for you.

• 3:38Speaker 03

  And so I feel like a paradigm where you just ask a question, get an answer, and you're done, like it just feels very limiting. And so it just isn't kind of the focus that we see going forward.

• 3:48Speaker 02

  Right, but I think a lot of your competitors like OpenAI and Google are kind of in this race to get this massive AI chatbot platform, like, you know, to where people will come for their first stop on the internet, kind of to replace Google or maybe even social media. Some of them are kind of thinking very big picture with these consumer apps. And it doesn't feel like that's where Anthropic is competing.

• 4:11Speaker 03

  Yeah, I would say that to a large extent, we were trying to help businesses and individuals sort of integrate AI and use AI in the most useful ways possible. I think that having a static platform like that does feel a little bit limiting. That said, I don't think we know where AI is headed. So I think giving people, giving developers tools to sort of integrate AI, use it in a lot of different ways, feels like the way forward. I think Claude Code is, I think, a cool example of that. We first built that as an internal tool that engineers and researchers at Anthropic

• 4:54Speaker 03

  were using to use Claude, to make themselves more productive. Then we sort of shipped it externally. And I think, obviously, the main use is developers. But I even see people sometimes using Claude Code to do things that are surprising, like just like, almost like an operating system or something. And so I think we're all experimenting with what the best way to use AI is. And I think we're just going to keep experimenting and hopefully keep empowering developers to experiment and build with Claude.

• 5:26Speaker 02

  And on the topic of Claude Code, I mean, you guys have really emerged as a leader in the AI coding space. Your AI models have really shot apps like Cursor to, you know, great fame. You know, they've really benefited from using your models. But now with Claude Code, it feels like there's some tension between Anthropic and these AI coding applications. Are you competing with these companies that are powered by your models?

• 5:57Speaker 03

  I think, the way that I think about a lot of our business is, I mean, a lot of it's built on the API. We really want our customers to be as successful as possible. I think that one reason why we, as I said, like, we were playing with Claude Code internally, one reason we decided, oh, well, we'll ship this, we'll make this public, was that it felt like it was at least a demonstration of how far you could push agentic capabilities. And that's something that we were experimenting with and leaning into on the research side. I think we really, our goal isn't to compete with our customers. Our goal is just to sort of make sure that people are exploring.

• 6:38Speaker 03

  The possibilities with AI. And I always encourage people to sort of experiment with building applications, uses of AI that don't quite work. Because, I mean, we founded Anthropic because we expected AI to keep getting better and better and better. We thought there were safety questions, concerns around that, and that's why we're so focused on safety. But because of that trajectory, because AI is getting better so quickly, I think a lot of things that don't quite work with the model right now are going to work in three months or six months, and it's a way of really kind of getting ahead of the curve.

• 7:14Speaker 02

  Well, one of your customers, Windsurf, was not happy with you this week because they said that Anthropic pulled back some of the direct access that they had to their models. Windsurf is a light cursor competitor, but it's much smaller. They were also reportedly acquired by OpenAI. What's the logic behind kind of pulling that direct access from Windsurf?

• 7:37Speaker 03

  Yeah, so my understanding is that with Windsurf, you can actually bring your own API key and continue to use Claude.

• 7:44Speaker 02

  Right, but it's much more expensive and like complicated. It's my understanding of that.

• 7:48Speaker 03

  Yeah, it might be, it might be more complicated. I mean, I think, as you alluded to, I mean, I think that we were really, we've really been quite constrained in terms of supply. We're, we're hoping to greatly increase the sort of availability of tokens. We want to keep them flowing for Claude over the next couple of months. But we really just are trying to enable our customers who are kind of going to sustainably be working with us in the future.

• 8:15Speaker 02

  You don't think they're going to be sustainably working with you in the future?

• 8:18Speaker 03

  Well, I mean, you, you said it, not me, like, I don't know. I mean, I, I think it's, it would be, it would be odd for us to be sort of selling Claude to, to OpenAI.

• 8:28Speaker 02

  That would be weird. But I think, you know, that deal hasn't been announced yet, and I think in the short term, I think, you know, What a lot of startups and kind of developers saw was that Anthropic can kind of just cut off access, you know, because maybe, you know, your startup isn't competing with Anthropic today, but maybe in the future you will. It feels like AI coding is one of those spaces where you guys are investing a lot. So I think about your relationship with Cursor, which is a much bigger business and really relies on Anthropic, but they're building their own models now. So how do you look at that relationship? I mean, it feels like that is something that might come to a head in the future, too.

• 9:12Speaker 03

  I guess I don't expect it to. I mean, obviously, I mean, there are many, many, many companies training their own models for all sorts of different purposes. I expect we'll be, we'll be working with Cursor for a long time. I hope to be. They've been a great customer, a great partner, great tester of new Claude models. So, yeah, I really think that if you look at us versus a lot of other AI companies, I mean, we're quite invested in our API business. We want people to be able to build with and on top of Claude, and we're definitely not trying to limit that. We're not trying to compete with our customers. We're trying to empower them.

• 9:51Speaker 02

  Got it. And I'm really curious about, kind of, you were a really influential person in the Scaling Laws paper. You were a key author on a key scaling law paper. And I think there's been a lot of questions in the last year about the lasting power of the scaling laws, of how much compute and data you can throw at these models and keep getting gains out of them, or how much we're going to rely on reasoning. Where do you see kind of the value of, you know, continuing to add compute and data to AI models today?

• 10:23Speaker 03

  Yeah, so I think scaling is continuing to pay dividends on both the sort of pre-training and RL side. We've seen a lot of advances in the last year or two on RL. I think I mean, maybe people here are all experts, but I mean, the way we train AI models is that we pre-training, where we teach models to imitate and understand, predict the next word in human-written text and other data. And then we fine-tune them with RL, both with human feedback and with AI being trained to do things like write code that passes tests.

• 11:07Speaker 03

  The, I think both of these show clean scaling laws. The work that I did and many others contributed to, maybe five or six years ago now, was on the pre-training side. It showed really, really clear empirical trends where if you make AI models bigger, give them more data, more compute, then they get better. I think that's continuing. Now, there's a question of, is there enough compute? Is there enough data? I think for now, there is, but I do think that eventually those will be constraints, certainly data. On the RL side, you can go a lot further, and I think RL is much less fully, sort of even close to fully tapped.

• 11:50Speaker 03

  And on the RL side, you're really training models with reinforcement learning to reinforce models doing useful, safe stuff and not doing things that, not making mistakes. And so, very obviously, that's the kind of thing that you're going to want to keep scaling because you want to make AI more useful, not just better at autocomplete. And so, I think that there's a lot of evidence from the last five or six years that you can get clean, kind of log linear scaling with compute in RL. And so, I think that's a major source of investment, as well as basically using new techniques like advancements on constitutional AI and more complex, agentic environments to train AI to get better and better.

• 12:33Speaker 02

  Yeah, I mean, I think I've seen with the pre-training scaling, like the bigger models that have been fed more data, I've seen that, you know, they're better at creative writing and they're better at, like, some of these kind of hard-to-measure tasks, but like on the core benchmarks, like, they are not as stellar as some of the reasoning models. And I'm curious, from your perspective, like, do you think that, you know, by scaling reasoning, that is a reliable path to get to AGI? It feels like that's much better at getting to specific.

• 13:03Speaker 03

  Like tasks and skills.

• 13:05Anthony Ha?

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• 13:42Speaker 03

  Yeah, I guess the way that I think about it is that, at least for hard problems, I think that reasoning is obviously a great way to get more capability out of the model. You give it more compute at test time and you can get a better response. So I definitely think that that's going to be an ingredient. I think that in some ways, like agentic capabilities might be even more important. I mean, maybe they're just both important. In the sense that I think that when we do tasks, when we do work, there isn't a lot of the time that we step back and we like think for an hour about what to do next. I think a lot of what we do is we experiment. We try something. We see what our environment tells us. Like, did we make a mistake?

• 14:26Speaker 03

  Did we write code that passes tests? Does like the paragraph that we wrote make sense? Or can we make it better? So I tend to think of scaling sort of agency, search, tool use as like really important for that as well. But definitely, I think that reasoning can allow us to take models that aren't necessarily at the frontier as pre-trained models, but make them better at.

• 14:56Speaker 03

  Hard problems, solving hard problems.

• 14:58Speaker 02

  I mean, part of the problem with the pre-training scaling is that we're just getting so all of the AI labs are getting so compute restrained right now. It's hard to kind of, you know, level up by another factor of 10. How compute restrained are you guys today? I mean, going back to what you said with Wind Surf, it's like you guys don't have unlimited compute, so.

• 15:16Speaker 03

  I think it's scaling very rapidly, right? I mean, I think AI is on and has been on an exponential for a while, and I think that's continuing. And obviously, you see that in the news with sort of the fundraises in AI going up and up and up. And that's because the value unlocked by AI as AI models get smarter is scaling with it. There are more and more use cases. Customers can use AI to integrate AI more and more. That means that the feedback loop is even faster. So generally speaking, I mean, we're always like every year increasing our supply of compute by a significant integer multiple. That's just continuing and continuing.

• 15:57Speaker 03

  We've just basically started to unlock the capacity on our new Tranium 2 cluster, which is really, really big and continues to scale. So that's what's going to allow us to unlock more compute for customers and for sort of training the next generation of like Claude 5.

• 16:15Speaker 02

  Yeah, and I mean, you mentioned Tranium 2. So I want to talk about some of your partners, you know, Amazon is a big one. Like, I want to, you know, think, talk to you about how you're thinking about Alexa and kind of the ways that Claude might appear in kind of interfaces that are not your own. Like, it seems like Claude is powering some parts of Alexa Plus, their new Alexa, is that right?

• 16:40Speaker 03

  Yeah, I mean, it was sort of announced at a joint event that Claude is contributing to a lot of Amazon products, including Alexa. Generally, I mean, as you were discussing earlier, like, we're excited about Claude being able to power all kinds of products from many different companies and developers.

• 17:02Speaker 02

  Yeah, there's been some rumors that Apple might work with you guys. Do you think that'll happen at WWC next week?

• 17:08Speaker 03

  I mean, I can't comment on ongoing work, but yeah, I mean, we're always, I mean, we're always open to working with a lot of different companies, a lot of different partners. We work with Google as well.

• 17:20Speaker 02

  Google's great, too. Apple, though, you know.

• 17:24Speaker 02

  I really don't like talking to Siri and I do like talking to Claude. I would love to talk to Claude and I'm sure I'm not the first person who's raised this to you. But I mean, I'm curious, like, when you look at, you know, working with people like Apple, like Amazon, you know, like Google, like, what are the troubles with kind of integrating Claude into their voice assistants and why haven't we seen that already?

• 17:48Speaker 03

  Uh, it's a good question. I think it probably depends on all sorts of different factors. I mean, I think, for one thing, obviously, if you're, if you're a startup, you can just move extremely quickly and ship and iterate. But I mean, if you're a giant company with millions and millions of users, then, then, then, then tend to move a bit more slowly. I think a lot of, uh, voice assistants, just, just speaking generally, aren't just voice assistants. That's just something you're chatting with. It needs to be integrated into some larger ecosystem of tools and other applications. You want to be able to like, call something that accesses your calendar. Maybe you want to be able to turn the lights on and off in your house if it's like a physical assistant.

• 18:32Speaker 03

  And so getting really, really high reliability across tens, hundreds, thousands of different tools and applications that are integrated into these assistants, I think, is complicated, requires work and, uh, and the expectations are very high. You don't want to disappoint your customers. So I think, I think those kinds of integrations are complex. But it goes back to the importance of tool use and, and agency. Behind the scenes, a voice assistant maybe needs to be an agent. And maybe that's another reason to focus on those kinds of capabilities.

• 19:05Speaker 02

  I think so. Uh, I wanted to ask you about something that your CEO, Dario Emada, he wrote an op-ed in the New York Times that ran this morning. Title was, "Don't Let AI Companies Off the Hook", which is funny enough, we were also considering naming the panel this. But, uh, no, I, I wanted to ask you about it because he argued against Trump's 10-year moratorium on states regulating AI.

• 19:29Speaker 02

  Why are you guys picking a fight with the Trump administration on this piece?

• 19:33Speaker 03

  I mean, well, my understanding, if you, if you read the op-ed, the op-ed, uh, in, in many ways is, uh, is...

• 19:42Speaker 03

  Saying positive things about a lot of the work that the administration has done in trying to make AI safer, prevent, say, authoritarian governments from getting access to AI. I think that this is really, this is really sort of just a question of states' rights. I think that like, if one state wants to, say, regulate self-driving cars, like, I think it makes sense for them to be able to do that. Maybe, maybe a self-driving car company tests their car in Arizona and then wants to deploy in Michigan. Maybe Michigan is worried that like, that car doesn't know how to drive very well in the snow. Like, that kind of, that kind of use case, I think, I'm not an expert, I'm not a policy

• 20:27Speaker 03

  person, but I think that, that kind of, that kind of thing could, could be blocked. And so I think that many, many folks, I don't think Dario is the most high-profile tech CEO to criticize the legislation, but, but yeah, I think, I think that we, we really want to be able, for governments to be able to move quickly to respond to AI. We think AI is changing very rapidly. We think states are one of our nation's laboratories for experimenting with different possible forms of engagement with industry, with regulation. And so we think that in order to sort of enable government to act with the speed that it needs to, given the speed that AI is moving, we, we think flexibility is, is great.

• 21:12Speaker 02

  Yeah, I mean, one of the things he talks about is the need for transparency among AI model providers. And you guys call out, you know, OpenAI and Google and yourselves as, you know, having some transparent acts. What kind of transparency standards do you think should be put in place for AI labs?

• 21:30Speaker 03

  Yeah, so.

• 21:32Speaker 03

  We have a responsible scaling policy that says that we want to think carefully about what kinds of risks there might be from AI in the future. We want to evaluate pragmatically whether AI models really pose those risks or whether it's too soon, and then we want to sort of put in place various mitigations. I think something that you could ask of AI companies is just to transparently discuss what risks they're considering, what evaluations they've done, what kind of safety testing they've done before deploying their models, and what kind of mitigations they have in place. And at least that would, as kind of a bare minimum, would sort of invite the public,

• 22:17Speaker 03

  regulators, academics, other AI developers to sort of understand what the risks are, what evaluations have happened, and how robust the mitigations are. So I think something like, I'm not a policy person, I'm not a legislator, I don't have a specific policy proposal in mind, but I think that, at least that kind of transparency seems like a good, a good first step. And as you mentioned, I mean, Google and OpenAI also have similar kinds of policies, but I think it would just be good sense if that was something that all advanced frontier AI developers were doing, and I think it would be reasonable for government to sort of ask us to do that and not let us off the hook.

• 22:58Speaker 02

  Are you worried about retaliation from the Trump administration? I mean, you know, their administration that notoriously can, has made things difficult for people who disagree with them.

• 23:08Speaker 03

  I mean, we're, we're trying to work closely with, with all, I mean, state governments, the federal government, et cetera. I mean, I'm not on the policy team, I, I don't have any, any detail, but I mean, we're generally engaged in sort of helping them to, I mean, advance American interests when it comes to AI.

• 23:26Speaker 02

  Yeah, well, I mean, on the, on the topic of transparency, earlier this week Reddit sued Anthropic. They claimed that you guys trained your AI models on Reddit's data, and you did not have authorization to do so, is what they claim. They say that in July 2024, Anthropic said you guys would stop training on Reddit and then continue to scrape their site over 100,000 times. Is that true?

• 23:53Speaker 03

  I can't comment on new, ongoing litigation. I can say that Anthropic puts a lot of effort into carefully obeying sort of requests in robots.txt and kind of industry standard practices.

• 24:11Speaker 02

  Right, but I mean, I think at large, a lot of publishers have kind of brought similar claims against Anthropic and OpenAI and Google and every company and just said, you know, like, you guys are training these models and making billions of dollars. I think you guys are valued at $60 billion or more than that off of, you know, these free kind of works from the internet. And I think Reddit is kind of making its own case that that is not okay and you have to pay them some money for that. I mean, do you think that, generally speaking, like, you know, people should be compensated for their, you know, work when you train AI on it?

• 24:44Speaker 03

  As I said, I think people can prevent Claude and Anthropic from accessing, scraping their data just via robots.txt. We respect that. I think that, more generally though, I think AI training is fair use. It's not copying, it's not reproducing data. And I think that's sort of the basis of, legally, why we think it's reasonable to train AI models on publicly available data that, on the web, that has not, that developers have not requested. We avoid via robots.txt.

• 25:27Speaker 02

  Right, but how can it be fair use if, at the same time, like Reddit has like 60, 70 million dollar deals with Google and OpenAI, like the New York Times has deals with, you know, Amazon? Like, if people are paying for the right to train this content and then at the same time, companies are taking it for free, how does that work? I mean, that's, it seems like a...

• 25:50Speaker 03

  Well, as I said, we obey robots.txt. So if we, that's our policy. So if we've been asked not to scrape data, then that's what we do.

• 26:02Speaker 02

  Got it. I mean, one thing from that lawsuit, and I'll kind of move off of it, but I mean Reddit kind of came off of, they came off kind of saying that like Anthropic has always been this kind of cautious cousin of the AI industry, so to speak. But they kind of argued that that's not exactly true. And I think that with the launch of Claude 4, a lot of people felt that, you know, you guys released a model that had a lot of safety concerns. There was some issues where Claude 4, in a scenario, in one scenario, it blackmailed a developer who tried to take it offline and replace it with another model. What do you say to people who think that Anthropic is not being as cautious as it used to be?

• 26:45Speaker 03

  I don't think that anything has changed. I think that this goes back to what you said about transparency. Our, I mean, you asked like, what would we like to see? So the scenario that you mentioned was not a thing that happened in the world. It was like, we're doing hundreds, thousands, tens of thousands of examples of red teaming, and our team actually calls it weird teaming, where we try to find all sorts of weird scenarios where our model might do something problematic so that we can flag it and so that we can improve it in the future. And then we wrote a 120-page system card where we documented all kinds of these examples in order to encourage others to be transparent, to sort of show what their models are doing, and encourage others to do their own testing.

• 27:30Speaker 03

  So I think that we're going to continue to do that. But I think that like, in being transparent, I think that opens us up to more criticism. We do a lot of other kinds of safety testing, like with respect to our responsible scaling policy as well. We have mitigations in place there. So that may open us up to more criticism, but I think we're going to keep doing that because we think people should understand how AI models are behaving, and they should be encouraged to sort of do their own testing. Tests on other providers as well.

• 28:02Speaker 02

  Just the last question here. I mean, just, you know, Anthropic's been up and going for a while now, a few years, and I'm just curious if, you know, you started this to be kind of, you know, put an eye on safety in AI. And have you seen things, you know, from your competitors out in the wild that have kind of reaffirmed your mission for why you started Anthropic? You said, yes, this is unsafe. This is why we need to exist.

• 28:26Speaker 03

  Yeah, I mean, I think AI is getting more and more capable, and so I think safety risks are becoming more and more salient, more and more real. I think that I'm generally proud of us being transparent, of us being kind of the first to have something like a responsible scaling policy. I think a lot of our competitors want to do the right thing, but I think if we do, we do something like that first, then it sets a standard. It's something that, that, that others can experiment with, iterate on, imitate, and I think we'll keep doing that. I'm happy that we've done it so far.

• 29:01Speaker 02

  All right. That's our time. Jared, thank you so much.

• 29:03Speaker 03

  Cool. Thank you.

• 29:03Speaker 02

  Thank you.

• 29:05Rebecca Bellan?

  Equity is produced by Teresa Loconsolo with editing by Kel. We'd also like to thank TechCrunch's audience development team. Thank you so much for listening, and we'll talk to you next time.