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Colton’s piece takes a measured look at the so-called marvel of AI coding assistants. He dismantles the notion that agentic AI can consistently deliver tenfold gains in productivity—pointing out that writing boilerplate or an ESLint rule in minutes doesn’t cut it when the real work involves proper code reviews, testing, and design.
The article reminds us that “a neat text generator” often leaves you back at square one, chasing down hallucinated libraries and wrestling with context limitations (not exactly the stuff of supersonic feats). There’s a dry logic to the math presented—compressing three months of work into a week or two seems a bit like expecting your van to win a Formula 1 race.
The reason I’m linking to this is that it is a good sample of a lot of recent similar posts, and a decent summary of where we are in the AI hype cycle. And keep in mind that this is probably the most scrutinized use case for AI productivity “enhancement”, and pratical issues like diminishing returns and the messy reality of collaborative coding show that while AI can speed up some tasks, the core of engineering remains stubbornly human.
It’s a sober reminder that the hype around “10x engineers” and all the vibe coding mania is more about clever marketing than actual productivity, and that keeping our processes deliberate isn’t a bad thing after all.