There seems to be disturbingly little objective evidence of that. There's certainly a lot of code being written but how much actual value that code is adding is another question. With the scale of resources that have been pumped into AI-supported software development over the past few years where are all the new applications and the quantum leaps in quality and performance in the applications we already had? If anything I seem to be seeing more anecdotes going the opposite way so far.
Disturbingly little evidence? I would hardly call that "disturbingly little"...
This does feel a lot like the "Agile era" that was the last silver bullet that was supposed to fix software development forever and the "Cloud era" that was supposed to fix infrastructure and deployments.
Agile had a few good ideas. Code reviews and automated tests are commonly used today.
It had a few plausible ideas that it took too far. We are better at recognising the value of incremental development over big up-front design today - despite the ironic clash this seems to be having with agentic workflows - but almost everyone hates stand-ups and lots of projects have gone off the rails by breaking things down far too much and never committing to anything and so never getting anywhere.
And it had plenty of ideas that almost all of us have thrown out as unpleasant and/or unworkable. Pair programming comes to mind immediately.
What didn't really change was the fundamental nature of software development. Did we adopt a few useful ideas and get a bit more efficient and improve quality a bit? Certainly. Did we get orders of magnitude better at anything and give up all the tried and tested ways of working in favour of radical new ones that never quite felt right? No.
On our current trajectory I'm expecting a broadly similar outcome for LLMs. They're now very good at some things. But they are still pretty bad at other things that also matter - and some of the reasons they are bad at them are an inherent part of how they work and therefore probably can't be overcome (at least not with incremental improvements on the technology that still basically work the same way). So I am expecting to see a period of consolidation with a lot of organisations pulling back from their dramatic leaps into the unknown and downsizing of dev teams once the big AI companies start squeezing financially and the problems of developer burnout and stagnating skills and low morale that we're already seeing after just a few months become endemic. And then as I suggested in another comment I think it's also increasingly possible that we'll see developers unionise and/or push for professional regulation and licensing so they can resist the pressure to just ship whatever their agent wrote without even bothering to check it but still being responsible when inevitably things then go wrong.
Well, the fancy calculators do. Yes, they still need guidance to keep from going off the rails. But, at least from personal anecdotal experience, I need to go spelunking into the codebase less and less over time. The agents and models really did just start to become that good.