An essay comparing AI-assisted software development with cooking a steak argues that faster production has not removed the need for engineering judgment. The author says coding models can generate useful starting points and automate repetition, but they do not independently define what a good result should be.
The culinary metaphor separates something that is merely usable from something produced consistently to a chosen standard. In the author's framing, putting food in a hot pan will eventually make it edible, just as a model can often return working code from a short request. Achieving the intended outcome repeatedly, however, depends on controlling details and understanding why the process works.
That distinction matters because an AI system cannot directly observe the product a user imagines. Requirements, constraints, examples, tests and feedback must communicate the goal. Results also depend on the model's capabilities, the context available to it and the tools surrounding it. A confident answer can still be technically correct in a narrow sense while failing the larger product need.
The essay does not reject AI coding tools. It credits them with accelerating routine work, explaining code, supporting experiments and producing drafts from which a developer can proceed. The criticism is directed at the expectation that purchasing a premium assistant, changing frameworks or adding increasingly elaborate prompts can substitute for learning how software behaves.
The same concern extends beyond individual projects. A small tool built for its creator can succeed with limited polish because its user understands its quirks. Software offered to a broad audience has to account for varying devices, expectations and failure cases. Those last details demand choices about quality and acceptable trade-offs that the model cannot settle on behalf of the people responsible for the product.
The analogy also rejects consistency based on luck. A surprisingly strong generation is not the same as a repeatable process, particularly when later changes must preserve behavior. Documentation and tests help turn an initial success into something a team can maintain.
The author's conclusion is that AI changes the pace and mechanics of building, not ownership of the outcome. Developers still need enough knowledge to evaluate generated code, recognize when an implementation diverges from the real objective and improve it through repeated testing. Expertise is presented less as the ability to type every line manually than as the capacity to specify, inspect and refine a system.
As organizations adopt coding agents, the essay offers a practical warning against confusing volume with reliability. Rapid generation can make more experiments possible, but consistent software still requires humans to articulate the target and decide whether what was generated actually meets it.
