Every major technological shift produces two extreme reactions.
One side says nothing will change. The other says everything will disappear.
AI h...
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"Execution becomes abundant. Decision-making becomes scarce." — this is the right framing.
But I'd push it further: what also becomes scarce is verification. We talk a lot about AI generating code, but not enough about the growing cost of checking whether that code is right.
The METR study is relevant here: developers perceived 24% speedup from AI, but actually measured 19% slowdown. Generation is instant. Verification is where the time goes. And verifying code someone else wrote (including AI) requires a different skill than writing it yourself — you need to understand the intent and the implementation simultaneously.
So the stack you describe — system design, constraint definition, behavior modeling — I'd add "verification infrastructure" to that list. The teams getting the most value from AI right now are the ones investing heavily in tests, type systems, and CI pipelines. Not because AI is bad at code, but because when code generation is cheap, the bottleneck moves to "how do you know it's correct?"
I strongly agree with adding verification infrastructure as a first-class layer alongside system design and constraint definition. Tests, types, CI pipelines, observability, and evaluation loops are becoming the real multipliers, not because AI writes bad code, but because cheap generation increases the surface area that must be trusted.
In a way, AI doesn’t remove engineering rigor; it makes rigor unavoidable. When code is abundant, correctness becomes the scarce resource. Thanks for pushing the framing further, this is a very important evolution of the conversation.
AI is not ending the software engineering. It will redefine it.