Claude Code's product lead talks usage limits, transparency, and the "lean harness"

Claude Code's product lead talks usage limits, transparency, and the "lean harness" - Ars Technica

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SAN FRANCISCO—Amid an ever-expanding array of surfaces, growing demand for tokens and compute, and a rapidly evolving user base, Anthropic doesn’t have a long-term road map for Claude Code. However, it’s betting that such a plan would be rendered moot by improvements in model capabilities and new signals from developers on how best to use it. That’s the takeaway from a 30-minute conversation Ars had with Cat Wu, Anthropic’s head of product for Claude Code. Last week, in a three-level car rental parking garage meticulously converted into an event space in downtown San Francisco, Anthropic put on its second annual Code with Claude developer conference. As previously reported, the single-day event included a keynote introducing new features for Managed Agents and announcing a compute deal with SpaceX. That compute deal was accompanied by a doubling of usage limits for Claude Code users on the company’s Pro and Max plans—a response to a lot of user frustration about a compute crunch, especially in recent weeks. Anthropic’s products—especially Claude Code, its tool for agentic software development—have seen runaway popularity. “We tried to plan very well for a world of 10x growth per year,” Anthropic CEO Dario Amodei said on stage at the conference. “And yet we saw 80x, and so that is the reason we have had difficulties with compute.” User growth was accompanied by a shift in how people used the company’s models, away from simple chat interfaces to complex, multi-agent workflows that are many times more demanding. During the crunch, Anthropic has been testing solutions to reduce demand, like enforcing stricter limits during peak hours or removing Claude Code from its cheaper subscription plan. And over the past year, Anthropic has released a plethora of new features, products, and surfaces for interacting with its models. Claude Code went from the CLI to the IDE to the desktop, and new tools for managing multiple agents were rolled out, too. The pace at which the company has shipped has been intense and chaotic at times.

Meanwhile, competitors like OpenAI’s Codex, GitHub Copilot, the Cursor IDE, Augment Code, and others are rolling out their own new products and features in this space, sometimes with differentiating hooks like more explicit context, which they claim leads to better results or greater efficiency. At the event, I spoke with Wu about how Anthropic is operating in this context. As head of product for Claude Code, Wu works closely with its creator, Boris Cherny, to identify which features to prioritize and how the teams at Anthropic test, use, and roll out those features. She does not oversee the models, but the product strategy she describes makes a big bet that the models will continue to improve so rapidly that it’s hard to make a plan for what a product like Claude Code should look like in the future. As Wu tells it, the Claude Code team is going through development cycles of just a week or so to roll out new products or features in a Wild West of experimentation, discovering new use cases and methodologies. We discussed user frustrations with usage limits, the role of structured data in making Claude Code work, IDE integration, future capabilities of the tool, and more. A conversation with Cat Wu This interview has been edited for length and clarity. Ars: You’re shipping a lot of things and adding a lot of different surfaces very quickly. There’s the command line, and there’s the IDE integration, there’s the desktop app, and then there’s all this differentiation between Code and Cowork and Managed Agents and so on. Do you still consider the command line the center of gravity for it? Or do you see people moving more to the desktop or web apps more and more?

Wu: We actually find that every developer has a different preference, so our usage is pretty split between all these. All these have a substantial number of users. I would say the center of gravity is still the CLI. It’s still the one that has the most power-user features, it’s where most of our features land first, and it’s also just the fastest for us to iterate on. It’s also what most of our team uses. However, we are seeing a gradual shift in our team from the CLI to desktop because maybe last year people had like one agent and then over the course of the year they started having six terminal tabs, and then people started adding fancy ways to monitor a bunch of terminal tabs and get pings and other notifications, and I think people are now at the point where they feel, “OK, I don’t want to read ten tabs anymore. I just want to, like—I understand why people have graphical interfaces,” so a lot of people are moving over to desktop just to get that rich view.

Ars: At the rate that you’re shipping new surfaces, if you extrapolate that out, it might become unmanageable from a development or product point of view. You would have all these things to maintain for different purposes, and it could also get confusing for users. Do you see a world where you might start consolidating, or do you think this is the best solution because you have something customized for each kind of user? Wu: Yeah, I think of it as a bit of a progression. So most people start in the CLI or IDE, you get to the point where you’re managed against a ton of agents and you want to know which ones are blocks, and you can focus on those, and people go to desktop for that. And then people are like, ‘Wait, I’m just copy and pasting messages from my customer feedback channel into Claude and babysitting it locally.’ So that’s why we added these higher level things like routines that can just watch that Slack channel where you’re getting feedback or data or whatever to kick off these runs… all the products are just a way to help you more easily elicit the intelligence of the models. We actually remove scaffolds. We remove parts of the system prompt and tool descriptions over time as models get smarter. I can definitely see a world where maybe we all just collapse back to the text box again because maybe the model is just always right, so you actually don’t need to follow every step from every prompt. Maybe it doesn’t get blocked. So I can see a world where it collapses, but I think for now we need all these tools to meet people where they are while the models get better. Ars: Is part of that approach following signals from users in real time, as opposed to having, you know, a lot of companies—they have this grand plan, here’s our whole year of everything… Wu: Oh, we have no grand plan! [Laughs.] Ars: I can tell! I don’t mean that in a bad way, because it seems you’re rolling stuff out to meet all these latent demands and signals. It’s different than something like OpenAI, where they’re talking about a super app, right? What’s your thought on that kind of approach of bringing it all into one super app that does everything for everybody? Do you think that’s a bad idea? Wu: There are a few guiding principles for us. One is we believe that models will continue to grow on the exponential, and it’s really important for us to build where the puck is going. I think we’re pretty humble about not knowing exactly what the right form factor is but encouraging our teams to explore that as much as possible to figure out what’s best for the next model.

I don’t know if you’ve read “The Bitter Lesson”? Ars: Mm-hmm, yeah. [The Bitter Lesson is a 2019 essay by computer scientist and reinforcement learning pioneer Richard Sutton. In part, it argues that efforts to bake domain-specific structures into AI systems have often “proved ultimately counterproductive” and that the methods that win out over time are general-purpose ones that scale with available compute.] Wu: Yeah, that is one of our guiding principles for our team. And I think it’s really hard—because the models are changing so quickly—it’s really hard to say that this will definitely be the next form factor. We have a few guesses. We dogfood internally a lot of these ideas, but we’re pretty open-minded to just being wrong, and we just need to stay really close to the model capabilities. Ars: I know some of these things have arisen from seeing users who are just using it this way and then deciding to productize that and make it more convenient. Are there things that you’re seeing right now that are like that, that you haven’t productized yet where