I've been thinking the same: the smaller the codebase, the better AI performs. So a way to scale AI is to modularize your architecture to maximize the number of leaf nodes in the dependency tree, and split out separate libraries where it makes sense.

It is huge for token usage also, Claude grepping the codebase for context it doesn't have is the main consumer of input tokens from what I can see.

Where do you find the smart developers in 2026. Half are rage quitting and the other half have full-blown psychosis.

The whole AI powered utopia is a bet on two possible outcomes:

- humans and companies somehow stop being greedy, selfish and cutting corners to optimize for revenue and time-to-market

- LLMs are the path to artificial superintelligences that will be able to deal with the exponential increase in tech debt from throwing AI slop at the wall (vibecoding) because no one has time to do things “the proper way”

The former is impossible. The latter is extremely unlikely and an existential threat to humanity.

The so called Luddites are the only ones to have even engaged at all with these concerns. Everybody else is just focused on the selfish game (see bet #1) of staying afloat in a rapidly changing ecosystem.

And interactive feedback rather than having to specify everything up-front.