In April 2000, Stack Overflow founder Joel Spolsky published an essay titled “Things You Should Never Do, Part I”. The occasion was Netscape’s decision to rewrite the code of its then-browser from scratch instead of further developing the existing disciplin
Spolsky’s thesis: This decision was the worst strategic mistake a software company could make. It was based on a fundamental misunderstanding of what programming work actually is. Programmers prefer to rewrite because reading foreign code is tedious and writing feels productive. But this impression is deceptive. The temptation to start over is one of the most expensive temptations in the industry.
More than 25 years later, this text has lost none of its relevance. On the contrary. With the advent of Large Language Models (LLM), the asymmetry between writing and reading has shifted to such an extent that the question becomes acute whether we are systematically underestimating the true senior discipline of software development. Typing is not what will keep teams breathless in the coming years. Reading is. In this article, I want to show why this is the case, where the asymmetry comes from, how it is exacerbated by generative AI, and how a casual accompanying skill must become an independent discipline.
I assume it works better the more local and isolated the code is, since every model only has finite attention heads, giant interleaving programs probably lead to mistakes.
Even with good codebases it feels like they often just consider small parts and reinvent the wheel.
You should not use terms that describe human cognitive processes to talk about what these systems do, because this is misleading. They don’t think, they can’t think. In the same way that a book does not memorizes or forgets stuff, and also isn’t itself intelligent, even if written by Einstein, or a compiler is angry about your syntax errors.
It’s the term used in the field not my invention.