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1019: LGTM, Ship It: The AI Code Review Problem
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Understanding AI-Generated Code
At 3:20 · chapter starts 0:45
The first listener question, from Deep Sea Goose, asks how to actually understand the code an AI agent spits out before making changes. Scott opens with a diagnosis: AI tends toward an 'Effect-brained' style — nesting functions inside functions and over-relying on side effects — that differs from how most developers write. His antidote is inline comments and strict prompting rules, like explicitly banning useEffect in Svelte projects. Wes agrees but draws an important distinction: backend code, where the AI generates hundreds of lines of database logic, is usually reliable enough to accept with less scrutiny. UI code is a different story. It's the reason so many AI-built apps feel terrible to use — they work technically, but the interaction design is neglected. Wes's prescription is to treat UI generation like tab completion: smaller, tighter changes, closer oversight. Both hosts converge on a practical meta-rule: tell the AI where shared utilities live and make it import from there, so the same utility function isn't silently duplicated across a hundred components.
AI-generated backend code is often passable, but UI code is where things fall apart. Drill into smaller changes and use tab completion for UI — that's why so many AI-built apps feel terrible to use.
Wes Bos observed that AI-generated UI code is a major reason many AI-built apps feel poor to use — they may work technically but the interaction design and visual quality are lacking.
