Code as a Trained Output: The New Model of AI Coding
In short: AI coding agents are changing the status of code. In mature agentic workflows, code is no longer only written by humans; it is repeatedly generated, tested, corrected, and selected by an optimization loop. That makes tests look like loss functions, production failures look like generalization failures, architecture look like inductive bias, and harness engineering look like optimizer design. Introduction: A Shift We Have Not Yet Named Precisely Over the past eighteen months, software development has undergone a quiet but forceful restructuring. Tools such as Cursor, Claude Code, and Codex are pushing us away from the old workflow of “humans write code, machines assist with completion” toward something structurally different: humans describe intent, define constraints, and provide feedback, while agents repeatedly generate, run, and revise code until some convergence condition is met. Most industry commentary still frames this shift in productivity terms: “AI makes us write code N times faster.” That framing misses a more basic ontological question: in this new workflow, what has happened to the nature of code itself? ...