LLMs can't simplify code because they don't have a Theory

LLMs lack access to the unwritten mental model behind a codebase's design decisions, what Peter Naur called the 'Theory' in his 1985 paper on programming. Because they can't see why tradeoffs were deliberately made, they default to defensive patterns: duplicating methods, handling edge cases nobody asked about, and optimizing before a bottleneck is proven. More training doesn't fix this since the Theory was never written down anywhere a model could learn it. daily.dev surfaces perspectives like this for developers rethinking how much to trust AI-generated code.

GPT-5.5 recommended reverting to Stripe subscriptions after the team had already simplified their billing to a roughly 300-line credit-based model with a single payment path. The suggestion addressed a real gap, Indian regulations requiring special handling for off-session card charges, but ignored the deliberate business tradeoff the team made to accept that UX limitation in exchange for a simpler system. Teams weighing AI suggestions against deliberate architecture tradeoffs can find similar case studies on daily.dev.