Why 'Plan Mode' for AI Coding Failed: A Founder's Postmortem

Ayman Nadeem bet that structured planning would become the center of AI-assisted software development and built a desktop coding app, Nuanced, around that thesis. The premise was sound: models now generate thousands of lines in minutes, leaving developers with a maintenance burden they inherit before they’ve even reasoned about what they’re building or why. Nadeem wanted a persistent, first-class planning artifact—somewhere to specify intent precisely, understand what had been done, and diagnose failures—rather than ephemeral plans that vanished into the chat backscroll.

The product failed, and the postmortem identifies four reasons. First, the team conflated the act of planning with the plan itself; users valued thinking things through but had little appetite for a large preserved spec. Second, models got good enough at exploring codebases and making reasonable assumptions that the need to explicitly instruct them shrank—model capability turned out to compete directly with building better interfaces for human thought. Third, AI-generated specs were long, over-structured, and painful to read, and bolting on a ‘Spec Tour’ to summarize them only added more text demanding attention. Fourth, splitting the flow into rigid stages (chat, spec, review, approve, implement) broke a process that needs to feel continuous, since real understanding emerges by trying things and revising as you learn.

The broader takeaway is a distinction between two jobs plan modes historically served: producing instructions precise enough for an agent, and helping humans keep a coherent mental model of a system machines are changing faster than they can inspect. Nadeem argues the first is being automated away as models improve, while the second matters more than ever—but that rigid plan documents are the wrong abstraction for it, especially as developers run more agents in parallel.