Comment by eleventhborn

I feel there is a set of codebases in which LLMs aren't showing the 2-10x lift in productivity.

There is also a set of codebases in which LLMs are one-shotting the most correct code and even finding edgecases that would've been hard to find in human reviews.

At a surface level, it seems obvious that legacy codebases tend to fall in the first category and more greenfield work falls in the second category.

Perhaps, this signals an area of study where we make codebases more LLM-friendly. It needs more research and a catchy name.

Also, certain things that we worry about as software artisans like abstractions, reducing repeated code, naming conventions, argument ordering,... is not a concern for LLMs. As long as LLMs are consistent in how they write code.

For e.g. One was taught that it is bad to have multiple "foo()" implementations. In LLM world, it isn't _that_ bad. You can instruct the LLM to "add feature x and fix all the affected tests" (or even better "add feature x to all foo()") and if feature x relies on "foo()", it fixes every foo() method. This is a big deal.