Strategy Requires Focus Despite AI Capabilities

At Anthropic, where 90-95% of Claude Code is written by Claude itself, product development has fundamentally changed. Mike Krieger, CPO at Anthropic, shares insights on how AI is reshaping product work, where bottlenecks emerge, and how product teams should adapt.

The New Product Development Reality with AI

• Traditional bottlenecks have shifted dramatically:

  • Code generation is no longer the constraint: "We really rapidly became bottlenecked on other things like our merge queue. We had to completely rearchitect it because so much more code was being written"
  • Over 70% of pull requests are AI-generated: "Over half of our pull requests are Cloud Code generated probably at this point. It's probably over 70%"
  • Review processes have transformed: "They've just realized like Claude is generally right and it's producing pull requests that are probably larger than most people are gonna be able to review"

• The new bottlenecks are now:

  • Upstream decision-making: Deciding what to build and aligning teams
  • Merging and integration: Processing the volume of generated code
  • Maintaining coherence: Ensuring the product remains understandable and cohesive

Where Product Teams Should Focus in the AI Era

• Strategy becomes more critical: "Strategy like how we win where we'll play... figuring out where exactly you're gonna want to spend your time or your tokens or your computation"

• Making AI comprehensible: "Making this all comprehensible... the difference between somebody who's really adept at using these tools in their work and most people is huge"

• Opening people's eyes to possibilities: "We call it overhang... the delta between what the models and the products can do and how they're being used on a daily basis - huge overhang"

• Embedding product people with researchers: "The functional unit of work at Anthropic is no longer take the model and then go work with design and product to go ship a product. It's more like we are at the post training conversations around how these things should work"

The Future of AI Product Development

• AI as thought partner: "My go-to product strategy partner is Claude... opus four I was working on some strategy for our second half of the year... it came back and I was like 'damn you really looked at it in a new way'"

• Accelerating timelines: "I've taken the timelines a lot more seriously now... things continue to improve and the models continue to be able to do more and more and they're able to act agentically"

• Changing review processes: "They would do very line-by-line pull request reviews... and they've just realized Claude is generally right... can you use a different Claude to review it and then do the human almost like acceptance testing"

• Lowering barriers to contribution: "I saw a great comment yesterday in our Slack where somebody had this thing that was driving them crazy about cloud code and they're like 'well I don't know any Typescript I'm just gonna talk to cloud about it' and they went from that to pull request in an hour"

Differentiation Strategy for AI Companies

• Focus on your unique strengths: "How do we figure out what we wanna be when we grow up versus like what we currently aren't or wish that we were or like see other players in the space being?"

• Lean into builder communities: "We have a super strong developer brand... we also have like a builder brand... can we lean into the fact that builders love using cloud?"

• Domain-specific knowledge is defensible: "Understanding of a particular market... differentiated industry knowledge... domains like legal, healthcare... there are very large companies to go and be built there"

• Differentiated go-to-market: "Know not just the company you're selling to but know the person you are selling to at the company"

• Novel interfaces: "I get excited about startups that will get started that have like a completely different take on what the form factor is by which we interface with AI"