An AI can write a clean retry function in seconds; it cannot tell you the API call was never safe to retry in the first place. That gap, not typing speed, decides who thrives when AI writes most of the code.
The Details:
The failure mode is specific: ask an AI tool to add a retry with exponential backoff and it will produce something that passes review. If the underlying call is not idempotent, that clean code turns an occasional glitch into silent duplicate charges at scale. A developer who has been burned before asks "what happens if this runs twice" before any code exists, and no model generates that pause on its own.
Execution speed no longer rations judgment. Previously, a veteran's pattern recognition could only touch as many decisions as they could physically type in a day. Paired with AI tooling, that same judgment can now apply to ten or twenty times more decisions per week, because the bottleneck moved from fingers to review.
Speed amplifies whatever judgment is already there. Accelerate a developer who lacks it and the result is not faster software, it is faster mistakes shipped at scale. That is why the market is splitting: experienced builders are not marginally more valuable now, they are the ones who can safely operate the throttle at all.
The real skill shift is architectural, not tactical. Instead of learning prompts like new keystrokes, the developers pulling ahead spend time before code exists: naming constraints out loud, pressure-testing assumptions, asking what breaks under load. Then they review AI output with the same suspicion they'd apply to a confident junior who has never been on call.
Bottom Line: The scarce resource in AI-assisted development was never the code. It is the taste to know what should exist before anyone writes it.
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