AI can implement a ticket that says "add a discount field to checkout." It can't tell you that the discount field will conflict with the loyalty points system three engineers built last year, because it doesn't have that institutional memory unless someone explicitly feeds it in every time.
When a problem hasn't been solved thousands of times in public code before, AI tends to default to the most common pattern rather than the right one for your specific constraints — team size, scale, compliance requirements, and future roadmap. Architecture decisions are judgment calls under uncertainty, and AI is better at applying known patterns than weighing genuinely novel tradeoffs.
A bug that only shows up under specific load, intermittently, across three services, usually requires building a mental model of how the whole system behaves over time. AI can suggest plausible causes, but the deep reasoning across logs, infra, and business logic that experienced engineers do is still mostly a human strength.
A developer who's been on the team for a year knows when to push back on a request — "this will break X" or "we tried this before and it caused Y." AI doesn't carry that history and won't push back unless it happens to know the relevant facts from the current conversation.
When something breaks in production, someone has to be reachable, accountable, and able to make a judgment call under pressure. AI can help diagnose, but it doesn't carry responsibility — a human still has to own the incident.
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