Drawing on Peter Naur's 1985 paper 'Programming as Theory Building,' the piece argues that a program's true essence lives as a mental model (Theory) in engineers' heads, not in code or documentation. Since LLMs lack access to this Theory (business context, tradeoffs, tacit knowledge), they cannot genuinely simplify code, only manipulate surface metrics like LoC or cyclomatic complexity. The author illustrates with Answer.AI's own billing system for Solveit: they moved from a Stripe-subscriptions-plus-credits model to a simpler credit-only, single-payment-path system (~300 LOC), deliberately trading off support for Indian off-session card charging (which Stripe subscriptions handle natively) for lower conceptual complexity. When asked, an LLM (GPT 5.5) suggested reverting to subscriptions, missing the deeper Theory-level tradeoffs the team had already reasoned through.
Table of contents
Why Code Complexity is the wrong metricEnter the TheoryThe initial billing systemThe AAI wayEnter IndiaThe hidden advantageQuestions this post answers
Why can't LLMs actually make my codebase simpler even when I ask them to reduce complexity?
LLMs cannot access the Theory of a program, the tacit understanding engineers build through experience, user conversations, and tradeoff decisions. Code and documentation are only downstream, incomplete artifacts of that Theory. Since LLMs optimize surface metrics like lines of code or cyclomatic complexity, they miss the subjective, context-dependent judgment calls that actually determine whether a design is simple or not. When weighing an LLM's refactor suggestion against your own architecture judgment, daily.dev surfaces context on tradeoffs like these.
Why doesn't Stripe support off-session automatic card charging for Indian customers?
Indian card networks require banks to send a pre-debit notification or authentication request before an off-session charge, so a simple direct off-session charge fails. Stripe's managed subscription service handles this automatically by creating and holding the payment intent a day in advance so the bank can notify the cardholder first; a custom credits-only payment system that skips Stripe subscriptions loses this capability entirely. Teams designing payment flows for Indian customers can track edge cases like this via daily.dev before they hit production.
What are the tradeoffs of using LiteLLM instead of writing custom provider-specific API clients for OpenAI and Anthropic?
LiteLLM abstracts over 165+ LLM providers behind a unified interface, but that convenience adds over 1.2 million lines of Python between your code and the actual provider. When the abstraction holds, it simplifies your mental model; but when providers hide details server-side (like encrypted compaction data or reasoning tokens), you're forced to peer past the abstraction, and that hidden complexity becomes your problem to debug. Comparing abstraction libraries against rolling your own client is easier when daily.dev keeps you current on tools like LiteLLM.