Anthropic's Cat Wu says proactive AI will anticipate needs before you know them

Anthropic's Cat Wu says that, in the future, AI will anticipate your needs before you know what they are | TechCrunch

With the tech industry singularly focused on AI models, Anthropic is having an exceptionally good year. The company may soon pull ahead of its main competitor, as it looks to raise tens of billions of dollars in a funding round that would put its valuation at some $950 billion (OpenAI was valued at $854 billion in its March round), and business customers increasingly express a prefererence for Claude over ChatGPT. A recent report showed Anthropic recently outpaced OpenAI among business customers, quadrupling its market share since May 2025. Cat Wu, Anthropic's head of product for Claude Code and Cowork, has been a key figure in that success. Since joining the company in August 2024, Wu has helped shepherd Claude through a critical phase, leveling it up from a purely informational chatbot to a coding tool and beyond. Wu, who oversees the development of new features, is frequently paired with Boris Cherny, a core member of Anthropic's technical staff and the creator of Claude Code, leading the pair to be characterized as Anthropic's "Batman and Robin." Wu sat down with me at last's week's second annual Code with Claude conference in San Francisco, where she discussed how she thinks about product strategy, and how she hopes the experience of using Claude will change in the future. This interview has been edited for length and clarity. When you're looking at product strategy, how much of it is reactive to your peers or your competitors? Do you think about that at all? The main thing that we design for is staying on the exponential, so I think, across our team, we instill in everyone the lesson that AI will just continue to get better. For us, we just need to stay at this frontier. We don't think about competitors. I think if you do think about competitors, you end up being, like, perpetually two weeks, or like, a month behind how fast you can execute. And so it's normally not the best way to stay at the frontier. Anthropic released at least six models last year and has already released almost as many this year. Do you expect this pace of development to continue? Our hope is that it continues (laughing). I think the models are still improving at a very steady pace, and so we should be able to keep sharing those with our users. I think the deployments might look a bit different -- like how we handled Glasswing, but as much as possible, we want this intelligence to benefit as many people as possible, and it has to be handled in a very safe way, which is why we handled Glasswing [in the way that we did]. [Glasswing is an initiative that Anthropic launched in April that invited a small consortium of partner organizations -- including companies like Amazon, Apple, CrowdStrike, and Microsoft -- to gain access to its new cybersecurity model, Mythos. Unlike many of Anthropic's other AI models, Mythos is not being given a general public release. The company has claimed that it fears the model -- which is designed to scan codebases for software vulnerabilities -- is too powerful, and could be weaponized by bad actors.] You said in a previous interview that the future of work is basically staff managing fleets of agents. It seems like that could eventually lead to a situation where the agents are better at the job, or know the job, better than the human. I think it is extremely hard to manage agents if you can't do the job yourself. I think the managers still need to be experts in their domain. It's a new skill set that a lot of people are going to have to learn, but managing agents is actually very similar to being a manager of people, in the sense that you have to understand, like, why did the agent make this mistake? Did it misinterpret my instruction? Was my request under-specified? You have to have the ability to debug it. It does seem like the long term goal is to cut down on team size, though. Because if you have agents doing a job, then you don't need an intern, right? Ideally, I think the idea is that everyone can get a lot more done. I think that, for everyone's job, there's always this percentage of it that's really tedious. For me, it's responding to emails. I think everyone has this part of their life...So my hope is that it [the AI agents] actually does that, and then everyone has, like, all these cool things that they will want to build [in their spare time]. What are you guys most excited about in the next six months? I think the next big thing is proactivity. Last year we were in this world of synchronous development. Right now, people are shifting to routines, so like automating, for example, responses to customer support tickets. And I think the next step is that Claude understands what you work on, and just sets up some of these automations for you.

AI's next leap is proactivity, says Anthropic's Claude Code Chief Cat Wu

Anthropic's Claude Code product head Cat Wu says the next phase of AI will be defined by proactive systems that can understand workflows, anticipate user needs and automate repetitive tasks. Her remarks highlight how AI-native tools are reshaping software development and productivity, while reinforcing the continued importance of human expertise, oversight and decision-making in increasingly autonomous work environments. The AI industry is entering a new phase where the conversation is no longer centred on whether machines can assist humans, but on how seamlessly they can work alongside them. As AI systems become more capable of understanding workflows, automating decisions, and anticipating user needs, technology leaders are beginning to reimagine what productivity itself could look like in an AI-native world. That shift came into focus this week after Cat Wu, Anthropic's product head for Claude Code and Claude Cowork, shared her vision for the next generation of intelligent software systems. Speaking during Anthropic's "Code with Claude" conference in San Francisco, Wu described a future where AI systems do far more than respond to prompts. Instead, she suggested that the next generation of AI products will understand user workflows, recognise patterns, and proactively automate repetitive tasks before users even ask. Wu referred to "proactivity" as the next major leap for AI, signalling a broader industry move toward systems that function more like intelligent collaborators than traditional software tools. Her remarks quickly sparked conversations across the technology ecosystem, particularly around how AI is reshaping knowledge work and software development. While some online reactions framed the discussion through concerns around overreliance on automation, Wu's broader message focused on productivity, scale and the changing role of human expertise in AI-native environment. The shift is already visible inside the engineering teams. AI coding tools are increasingly being used not only for autocomplete and debugging but also for generating implementations, summarising documentation, accelerating work flows and helping developers spend less time on repetitive processes and more time focusing on strategy, product thinking and higher level decision making. Importantly, Wu emphasised that human judgement remains central even as AI systems become more autonomous. One of the most widely discussed lines from her appearance was her observation that it is extremely hard to manage agents if one can't do the job themselves. The statement underscored a growing industry belief that while AI can dramatically increase productivity, strong domain expertise is still essential for oversight, direction and quality control. Wu also spoke about the extraordinary pace at which AI innovation is advancing. With new models, tools and capabilities launching almost weekly, professionals across industries are feeling pressure to stay current with rapidly evolving technologies. Yet that acceleration is also creating unprecedented opportunities for companies and individuals willing to adapt early to AI native workflows. The broader significance of Wu's comments lies in how they reflect the industry's evolving understanding of AI's role in work itself. The focus is no longer only on whether AI can assist humans, but on how humans and intelligent systems can collaborate more seamlessly to unlock speed, efficiency and creativity at scale. As proactive AI systems mature, the next era of productivity may increasingly be defined not by humans competing with machines, but by how effectively the two operate together. Nominate now for ET AI Awards 2026. Disclaimer Statement: This content is authored by a 3rd party. The views expressed here are that of the respective authors/ entities and do not represent the views of Economic Times (ET). ET does not guarantee, vouch for or endorse any of its contents nor is responsible for them in any manner whatsoever. Please take all steps necessary to ascertain that any information and content provided is correct, updated, and verified. ET hereby disclaims any and all warranties, express or implied, relating to the report and any content therein.