The rule may not hold with AI driven development. The rule exists because it's expensive to rewrite code that depends on a given data structure arrangement, and so programmers usually resort to hacks (eg. writing translation layers or views & traversals of the data) so they can work with a more convenient data structure with functionality that's written later. If writing code becomes free, the AI will just rewrite the whole program to fit the new requirements.

This is what I've observed with using AI on relatively small (~1000 line) programs. When I add a requirement that requires a different data structure, Claude will happily move to the new optimal data structure, and rewrite literally everything accordingly.

I've heard that it gets dicier when you have source files that are 30K-40K lines and programs that are in the million+ line range. My reports have reported that Gemini falls down badly in this case, because the source file blows the context window. But even then, they've also reported that you can make progress by asking Gemini to come up with the new design, and then asking it to come up with a list of modules that depend upon the old structure, and then asking it to write a shim layer module-by-module to have the old code use the new data structure, and then have it replace the old data structure with the new one, and then have it remove the shim layer and rewrite the code of each module to natively use the new data structure. Basically, babysit it through the same refactoring that an experienced programmer would use to do a large-scale refactoring in a million+ line codebase, but have the AI rewrite modules in 5 minutes that would take a programmer 5 weeks.