This has been my experience, too. In dealing with hardware, I'm particularly pleased with how vision models are shaping up; it's able to identify what I've photographed, put it in a simple text list, and link me to appropriate datasheets. yday, it even figured out how I wanted to reverse engineer a remote display board for a just-released inverter and correctly identified which pin of which unfamiliar Chinese chip was spitting out the serial data I was interested in; all I actually asked for was chip IDs with a quick vague note on what I was doing. It doesn't help me solder faster, but it gets me to soldering faster.

A bit OT, but I would love to see some different methods of calculating economic productivity. After looking into how BLS calculates software productivity, I quit giving weight to the number altogether and it left me feeling a bit blue; they apply a deflator in part by considering the value of features (which they claim to be able to estimate by comparing feature sets and prices in a select basket of items of a category, applying coefficients based on differences); it'll likely never actually capture what's going on in AI unless Adobe decides to add a hundred new buttons "because it's so quick and easy to do." Their methodology requires ignoring FOSS (except for certain corporate own-account cases), too; if everyone switched from Microsoft365 to LibreOffice, US productivity as measured by BLS would crash.

BLS lays methodology out in a FAQ page on "Hedonic Quality Adjustment"[1], which covers hardware instead of software, but software becomes more reliant on these "what does the consumer pay" guesses at value (what is the value of S-Video input on your TV? significantly more than supporting picture-in-picture, at least in 2020).

[1] https://www.bls.gov/cpi/quality-adjustment/questions-and-ans...