A strong statement like this was a reasonable take six months ago, perhaps. But with Claude Opus 4.1, o3-codex/gpt5, and Gemini Pro 2.5 this statement is pretty simply disproven with half an hour with them.
Just last week I spent an afternoon rewriting an old Android app that was done in butterknife and got something functional and tested in a few hours. It involved an 8,000 line diff. The work had been quoted to me by a contractor as likely to take 3-4 months and cost tens of thousands of dollars.
I designed a new website for my wife's band with a custom player widget, carousel photos, fully adaptive to large and small screens, good accessibility features, built from easy to edit JSON, SEO-optimized with microformats, and fast-loading. The first version I got running in 15 minutes. The final polished version took a few more days of ducking onto my laptop a few minutes here and there during an event. Without AI this would have taken me weeks to pull off and wouldn't have looked nearly as nice.
IMO part of the key to the flow here is to avoid a temptation to one shot. First establish ground rules for working together in your AGENTS.md/CLAUDE.md that lays out your software engineering best principles (use git semantics, write out your plans, add lint and tests to commit hooks...). Then have it read through existing code and architecture. Then carefully put together an architecture and set of constraints into your README/PRD. Then build a high level engineering plan divided up into sequences tasks and write it down (vs just keeping in context). Only then do you allow any code to start to get written. And yes, you still need to supervise it (and iterate on your AGENTS/CLAUDE to avoid repeating yourself). But yeah, it's about 10-100x faster than me now with a flow like that. (Context on me: been programming 40 years, Stanford CS, ACM international programming competition finalist.)
This new flow is extremely fun and addictive. It's also a new thing that uses technical knowledge but isn't exactly the same thing as coding. It's like having a pool of hard working inexpensive idiot savant never-worked-in-industry interns at your beck and call - the more you pour into giving them context and process, the better results you are gonna get.
The real secular arc here predating the GenAI rush has been the decreasing ROI of a generic college degree.