It feels like we're far past the point of where having AI do more faster is helpful.
It's telling that they used "rewrite Bun in Rust" as the proof point here. It's cool! But the vast majority of software engineering doesn't start with tens of thousands of tests, where making them pass is the whole job.
In my experience, AI still drifts from what I meant it to do on anything bigger than building a widget. My time is spent suspiciously reviewing output for changes the agent snuck in, or invariants it broke. I talked with a friend recently where the agent broke the test harness badly enough that none of the tests mattered for 3 weeks. They did pass, though, so CI never complained.
There's something at the intersection of context engineering, managing that sloppy pile of markdown plans, and good old fashioning system understanding that's the real bottleneck.
"In my experience, AI still drifts from what I meant it to do on anything bigger than building a widget."
I've had code bases with tens of thousands of lines of code built from scratch that I hand-reviewed every line of and worked with the AI to improve, and haven't had this issue. I feel like a significant part of this is due to an involved /plan stage -- going back and forth on building out a plan for what you want the AI to do involves surfacing the assumptions that you would have called drift if you asked them to implement it directly from your prompt.
Once the plan has been refined and is what I want it to be, getting it to implement everything in TDD style has for the most part given me 100% working code, as I wanted it to be, without issues. It definitely helps that I'm a principal-level engineer with extensive architectural experience -- but if you're able to tell the AI in detail what you want, have it ask questions for clarifications, and read through a plan before getting it implemented, and have a solid testing plus manual qa process (automated by chrome devtools mcp) in place, I've find that you can one-shot complex features, rewrites, and even not-insignificant applications that would have taken days to write by hand in a few hours.
There are certainly domains where AI is not so effective, but at this point I would agree that at least in terms of web development if you can't get effective results from agents at this point it is a skill issue. That skill can be learned, if you recognize that learning is part of the solution. I do think prior experience in product design, specifications & business analysis as well as engineering leadership are all extremely helpful. Its about putting the agent in a box so small that it really can't screw up; but its also about being able to really look at what its doing, to trace through code quickly, to have intuitions about where its likely to make mistakes, etc. There is really nothing I have to do when working with an agent that I haven't already been doing for decades.