AI made the first draft cheap.
That changed the economics of software work fast.
But it did not remove the hard part.
In many teams, the scarce skill is no longer generating code.
It is diagnosing why a generated solution is wrong, fragile, incomplete, or impossible to maintain.
Debugging AI systems is bigger than debugging code.
You are debugging assumptions, context, prompts, missing constraints, tool calls, silent hallucinations, and code that looks plausible long before it becomes trustworthy.
The output can be fast and polished and still be wrong in the exact place that matters.
The workflow I trust is boring:
- Isolate the failing behavior.
- Reproduce it with the smallest possible case.
- Inspect logs and diffs before asking for more code.
- Verify every confident claim the model makes.
- Shrink the context if the agent starts wandering.
- Add tests for the edge case that escaped.
If the model is still fighting you, stop prompting and read the source of truth.
Many AI mistakes survive only because humans keep negotiating with them instead of verifying them.
This is why “AI makes developers obsolete” is such a shallow take.
AI lowers the cost of drafting.
It does not lower the cost of responsibility.
The person who can recover from bad generations, trace failures across tools, and turn vague output into reliable systems is becoming more valuable, not less.
In 2026, speed still matters.
But diagnosis is what protects the product.
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