Comment by nilkn
7 hours ago
I have my own experience to go off of, but this can also be inferred easily. There are many large corporations still working on "digital transformation" from 10+ years ago. There's no way such companies have fully onboarded and integrated frontier AI agents throughout their workforces with high adoption. The paper itself reports low adoption even after conditioning on access.
Everything did change after late 2025, but there are tiers of usefulness. Late 2025/early 2026 was the first time agents became clearly useful for personal projects. That's distinct from immediately having economically valuable end-to-end autonomous workflows in teams of median talent, which many would suggest is the threshold we've just recently crossed.
If you want to refute a study which presents numbers and statistics, you have to provide it in the form of numbers and statistics.
I don't, actually. I can simply point out that its data is out of date, and then the numbers themselves are called into question without other numbers to compare them to. And that's what I did: I shared two arguments in two comments that converge on the shared conclusion that the paper's data is not up to date (or, more precisely, cannot be up to date).
> I can simply point out that its data is out of date
You can, but it doesn't make it correct or, evidently, yield a logical argument. Do you notice that your logic is circular? The date doesn't matter if the passage of time has not affected the outcome and that's basically what I questioned, which you refuted without any evidence. So the question still stands. Are there any comparable stats you have access to that change the picture?
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