A eulogy for coding, which has always felt like an endlessly deep and rich domain, after ChatGPT swallowed knowledge and skills that take lifetimes to master
Coding has always felt to me like an endlessly deep and rich domain. Now I find myself wanting to write a eulogy for it.
Context & Ripple Effects
This is an early cultural diagnosis of generative AI’s effect on programming: the concern is not merely faster code production, but whether expertise once accumulated through long practice is being compressed into an interface. A contemporaneous counterpoint argued that chatbots can repackage existing ideas while lacking human lateral thinking, underscoring that the dispute was initially about the boundaries of machine assistance rather than a settled replacement story.
Later coverage makes the question more concrete. Reports of junior developers becoming dependent on coding copilots and of developers shifting toward an architect-like role show the debate moving from coding’s cultural status to how skills are learned and work is divided.
First-order effects
- The article gives voice to programmers who experience ChatGPT as a challenge to the value and identity attached to mastering code, rather than simply another productivity tool.
- It reframes coding knowledge as something users can increasingly access conversationally, raising the immediate premium on judgment about what to ask for and how to assess the result.
Second-order effects
- If AI handles more routine implementation, teams can move human attention toward system design, review, and problem framing—the role shift later described as developers becoming more like architects.
- Training becomes a pressure point: reliance on copilots can reduce opportunities to build foundations through hands-on implementation, as later concern over junior-developer dependence illustrates.
Third-order effects
- Software work may stratify more sharply between people who can direct and validate AI-generated output and those whose value is tied primarily to producing routine code; the extent depends on whether validation skills remain broadly taught.
- This is a step toward the assistant becoming the primary work surface for software creation, where accumulated technical knowledge is mediated through AI tools rather than accessed mainly through documentation, languages, and individual practice.
The trend: Generative AI is shifting programming from an implementation-centered craft toward AI-mediated specification, verification, and systems judgment.