When code is abundant

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I returned from the holiday break in January convinced that something fundamental had changed.Large language models had reached the point where they could produce useful code reliably enough, and cheaply enough, to change the economics of software development. Engineers everywhere seemed to be experimenting with the same thing: not just asking an AI assistant for suggestions, but giving agents real work and seeing how far they could take it.I started thinking about what happens if that continues. What changes when producing code is no longer the primary constraint in building software?I wrote those thoughts down in a board memo in January. In May, I published part of that thesis in GitLab’s Act 2: the cost and time of producing software was collapsing, machines would increasingly build software under human direction, and the architecture underneath software development would have to change with it.In June, at GitLab Transcend, we showed the first pieces of that architecture: source control rebuilt for machine-scale concurrency, GitLab Orbit as a context graph spanning the software lifecycle, and governance around agent identity, policy, approval and audit.Then, on August 21, Anthropic published The AI-Native SDLC Playbook. It opens with a simple statement:“Code is no longer the bottleneck.”I agree.Anthropic’s playbook is a practical description of how the development lifecycle changes when agents can move implementation dramatically faster: planning becomes machine-readable, handoffs become automated, verification moves into the loop, and human judgment concentrates at the …

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