With an LLM you don’t have to try to predict a complex system in advance, experiments are so cheap to can just converge to a solution directly.

We saw a similar philosophy in TDD advocacy many years ago. Search for something like "Sudoku Jeffries" to see how that went. Then search for "Sudoku Norvig" to see what it looks like when you actually understand the problem.

The idea that you can somehow iterate your way to a solution when you have no idea where you're trying to go or even which direction your next step should be in has always seemed absurd to some of us but in the era of LLMs there's no longer any doubt. In the agentic era (can we call a few months an "era"?) I estimate that 90% or more of the writing I've read about how to use agents most effectively came down to making sure there is a clear specification for what they need to implement first and then imposing extensive guard rails to make sure their output does in fact follow that specification. It's all about doing enough design work up front to remove any ambiguity before coding the next part of the implementation and almost everyone claiming any sort of real world success with coding agents seems to have reached a similar conclusion.