What exactly would that evidence look like, for you?

It definitely increases some types of productivity (Opus one-shot a visualization that would have likely taken me at least a day to write before, for work) - although I would have never written this visualization before LLMs (because the effort was not worth it). So I guess it's Jevons Paradox in action somewhat.

In order to observe the productivity increases you need a good scale where the productivity would really matter (the same way that when a benchmark is saturated, like the AIME, it stops telling us anything useful about model improvement)

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bwestergard 13 hours ago | parent | next [-]

"What exactly would that evidence look like, for you?"

https://fred.stlouisfed.org/series/MFPPBS https://fred.stlouisfed.org/series/OPHNFB

Productivity is by definition real output (usually inflation adjusted dollars) per unit of input. That could be per hour worked, or per representative unit of capital + labor mix.

I would accept an increase in the slope of either of these lines as evidence of a net productivity increase due to artificial intelligence (unless there were some other plausible cause of productivity growth speed up, which at present there is not).

	▲	no_wizard 13 hours ago \| parent [-]
		There are two sides to this that I see: First, I'd expect the trajectory of any new technology that purports to be the next big revolution in computing to follow a distribution pattern of that similar to the expansive use of desktop computing and productivity increases, such as the 1995-2005 period[0]. There has not been any indication of such an increase since 2022[1] or 2023[2]. Even the most generous estimation, which Anthropic itself estimated in 2025 the following >Extrapolating these estimates out suggests current-generation AI models could increase US labor productivity growth by 1.8% annually over the next decade[3] Which not only assumes the best case scenarios, but would fail to eclipse the height of the computer adoption in productivity gains over a similar period, 1995-2005 with around 2-2.5% annual gain. Second is cost. The actual cost of these tools is multiples more expensive than it was to adopt computing en masse, especially since 1995. So any increase in productivity they are having is not driving overall costs down relative to the gains, in large part because you aren't seeing any substantial YoY productivity growth after adopting these AI tools. Computing had a different trend, as not only did it get cheaper over time, the relative cost was outweighed by the YoY increase of productivity. [0]: https://www.cbo.gov/sites/default/files/110th-congress-2007-... [1]: First year where mass market LLM tools started to show up, particularly in the software field (in fact, GitHub Copilot launched in 2021, for instance) [2]: First year where ChatGPT 4 showed up and really blew up the awareness of LLMs [3]: https://www.anthropic.com/research/estimating-productivity-g...

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bopbopbop7 15 hours ago | parent | prev [-]

Well you would think if there is increased productivity there would be at least a couple studies, some clear artifacts, or increased quality of software being shipped.

Except all we have is "trust me bro, I'm 100x more productive" twitter/blog posts, blant pre-IPO AI company marketing disguised as blog posts, studies that show AI decreases productivity, increased outages, more CVEs, anecdotes without proof, and not a whole lot of shipping software.