The AI productivity trap: Why your Best Engineers are Getting Slower
jelizondo writes:
CIO published a very interesting article about how the use of AI by the best engineers actually is slowing them down, and quite not delivering on the promised speed up of production code:
We've all heard the pitch. By now, it's practically background noise in every tech conference: AI coding is solved. We are told that large language models (LLMs) will soon write 80% of all code, leaving human engineers to merely supervise the output.
For a CIO, this narrative is quite seductive. It promises a massive drop in the cost of software production while increasing the engineering speed. It suggests that the bottleneck of writing code is about to vanish.
But as someone who spends his days building mission-critical financial infrastructure and autonomous agent platforms, I have to be the bearer of bad news: it's not working out that way. At least, not for your best engineers.
The deployment of AI copilots into the workflows of experienced engineers isn't producing the frictionless acceleration promised in the brochures. Instead, I'm seeing the emergence of a productivity trap - a hidden tax on velocity that is disproportionately hitting your most valuable technical talent.
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For the first few years of the generative AI boom, we operated on vibes. We had anecdotal evidence and vendor-sponsored studies claiming massive productivity gains. And for junior developers working on simple tasks, those gains were real. If you just need a basic react component for a login button, using AI feels like a miracle.
But we got a reality check in mid-2025. A randomized controlled trial by METR (Model Evaluation & Threat Research) analyzed the impact on senior engineering talent. Unlike previous studies that used toy problems, this one watched experienced developers working on their own mature codebases - the kind of messy, complex legacy systems that actually power your business.
The results were stark. When experienced developers used AI tools to complete real-world maintenance tasks, they took 19% longer than when they worked without them.
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It comes down to what I call the illusion of velocity. In the study, developers felt faster. They predicted the AI would save them huge amounts of time. Even after they finished - and were objectively recorded as being slower - they still believed the AI had been a timesaver.
The AI gives you a dopamine hit. Text appears on the screen at superhuman speed and the blank page problem vanishes. But the engineer's role has shifted from being a creator to being a reviewer and that is where the trap snaps shut.
According to the 2025 Stack Overflow Developer Survey, the single greatest frustration for developers is dealing with AI solutions that look correct but are slightly wrong. Nearly half of developers explicitly stated that debugging AI-generated code takes more time than writing it themselves.
In software engineering, blatantly broken code is fine. The compiler screams, the app crashes upon launch, the red squiggly lines appear. You know it's wrong immediately.
Almost-right code is insidious. It compiles. It runs. It passes the basic unit tests. But it contains subtle logical flaws or edge-case failures that aren't immediately obvious.
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