Right, so you're sane. :D
Unfortunately I think we're entering (have entered?) a period of insanity.
The trouble is AI is being sold as an individual engineering accelerant. I suspect at the most AI pilled orgs you'll then see a commensurate push that starts off with measuring usage (tokens), then measuring output (PRs, code reviews), and then a lot of talk about impact while everyone quietly admits that remains as impossible now as it was fifty years ago.
Why? Because leadership is looking to (and selling, both internally and to the market) AI as the solution to all of their problems, which means they have to prove outcomes that justify their sky high AI budgets.
Higher level metrics at the org/division/product/project level aren't satisfying and flashy enough as they're slow moving and attenuated.
And squishy individual or team level assessments that rely on strong management won't show well on a cost-benefit comparison chart to the board.
At bottom I suspect AI pilled leadership wants to turn software into an assembly line and measure accordingly. Your post perfectly captures why it's still not that easy, and that the real problems in software remains the same and are unsolved by AI: building the right thing, at the right time, and then later figuring out what went well, what didn't, and trying to make those successes more repeatable and failures less likely.