Programming in the AI Epoch: A Manifesto |
We are no longer living in the age in which programming is defined primarily by writing code. That age is ending.
For decades, the centre of the craft was manual implementation. The programmer was the one who knew the syntax, mastered the framework, structured the logic, and built the system line by line. Code was scarce because producing it was slow, expensive, and highly dependent on trained human labour.
That is no longer the world we inhabit.
In the AI epoch, code is abundant. It can be proposed, drafted, expanded, and refactored at a speed no individual programmer can match. The old bottleneck has weakened. The problem is no longer chiefly how to produce code. The problem is how to govern what is produced.
Code is cheap. Judgement is expensive.
This does not mean that earlier forms of software engineering were simple. They were not. Distributed systems, infrastructure, fault tolerance, and large-scale architecture had already pushed programmers beyond mere coding. But AI changes the scale, the accessibility, and the centrality of that condition. What was once the burden of a narrower technical elite is becoming the general condition of programming itself.
Much of the conversation about AI and programming stops at speed. Tools like GitHub Copilot or GPT-4 can draft a function in seconds; what took an afternoon now takes minutes. That productivity gain is real and it matters. But it is not the transformation that concerns this argument. The transformation that concerns this argument is structural: software is increasingly becoming AI-driven.
An AI-driven system is not a normal application with an AI feature attached to it. It is a system in which model-based inference enters........