In this newsletter article from « Strategize Your Career », Fran Soto, a software engineer at Amazon, introduces the concept of « developer taste » as a foundational skill in the age of AI-assisted coding. His central thesis: the problem is no longer broken code, but broken judgment.
Soto defines developer taste as « the judgment to know what the right solution looks like before writing a single line of code — and the discipline to pursue it rather than the first output that compiles ». This definition articulates two complementary dimensions: discernment (recognizing quality) and personal rigor (refusing the path of least resistance).
the judgment to know what the right solution looks like before writing a single line of code — and the discipline to pursue it rather than the first output that compiles
The phenomenon he calls « AI slop » — code that compiles, passes tests, appears correct on the surface, but « makes everyone's next six months harder » — represents, in his view, the real danger of the augmented-coding era. This is not a tool problem but a process problem: AI is a tool that can be used well or poorly, and investing zero effort in directing AI's work inevitably leads to poor work.
Soto proposes a reversal of perspective in evaluating engineers. Rather than looking at what a developer built, one should examine what they refused. Taste reveals itself in negative decisions: what was declined, what was pushed back on, what was killed early in the development process. To identify taste in a candidate or colleague, he recommends asking about what they would do differently, the trade-offs they refused, and the solutions they abandoned despite their technical feasibility.
His conclusion is both simple and unsettling: when anyone can generate code, the ability to know which code deserves trust becomes the differentiating skill. The gap between mediocre and excellent is not raw productivity or coding speed, but taste. Yet no one really knows how to hire for this quality — a paradox Soto identifies without claiming to resolve it.
The article had a significant impact within the developer community, « kicking off the conversation on taste » and being widely cited in subsequent discussions on code quality in the AI era, notably in academic articles on « AI slop » as a tragedy of the commons in software development.