Designing AI native architecture

“Code is cheap, architecture is expensive” hits different in the AI-native world. The old game was: buy into a heavy framework to save engineer keystrokes. Fewer lines, more magic, lots of hidden coupling. Great for humans, terrible for LLMs that have to guess what you meant. The new game is almost the opposite. You design ultra-explicit, constraint-heavy codebases that feel slightly annoying to write by hand, but incredibly easy for an LLM to extend safely. Think: - Boring, predictable patterns over clever abstractions - Clear boundaries and contracts over “auto-wired” magic - Repetition where it helps the model learn the structure of your system You are not optimizing for your staff engineer writing the perfect one-liner. You are optimizing for a future where 80% of diffs are drafted by a model and your team’s main job is to guardrail, review, and keep the architecture coherent. Humans keep the global mental model. LLMs churn through the local changes. So the real AI-native advantage is not “we use GPT in our stack.” It is “our architecture is shaped so that a model can touch almost any part of the codebase without breaking the whole thing.”