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The data is still mostly 2025 and the 2026 data is mostly from the first few months, and most production deployments (at least in, say, the Fortune 500) lag the frontier by at least several more months (or much more). I suspect it's just not really possible for academic research at its conventional pace to offer an up-to-date perspective on this topic right now.

by simoncion2 hours ago|

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> I suspect it's just not really possible for academic research at its conventional pace to offer an up-to-date perspective on this topic right now.

If we weren't still in a massive hype-and-investment bubble, the burden of proof would be on the LLM manufacturers to demonstrate what deficiencies that studies like this have revealed have been fixed in their latest products.

I mention this because I've been hearing "Yeah, well, that opinion/study is completely invalid because it used a six month old tool. Today's release of SOMNOS 6.9 (Pinnacle Snoozytime) has totally changed the game!" every six months for what feels like the past five years now.

by nilkn2 hours ago|

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> I mention this because I've been hearing "Yeah, well, that opinion/study is completely invalid because it used a six month old tool. Today's release of SOMNOS 6.9 (Pinnacle Snoozytime) has totally changed the game!" every six months for what feels like the past five years now.

Given my own conclusion that it's not possible for conventional academic research to accurately snapshot industrial adoption of this technology, I would expect this pattern to continue for a while until either academia becomes much faster or technology progress slows down (or both). This just isn't yet a situation where you can rely on published papers telling you what to think -- you still need to mix in a bit of your own instinct and experience and extrapolation to get an accurate up-to-date read, even if there's some prediction error as a result.

by geraneum3 hours ago|

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Do you have any references for the models deployed in any of these organizations?

> I suspect it's just not really possible for academic research

But we were told everything changed in fall-winter of 2025 and that when we got “real capable” agents/models. Whatever happened to that?

by nilkn3 hours ago|

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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.

by geraneum3 hours ago|

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If you want to refute a study which presents numbers and statistics, you have to provide it in the form of numbers and statistics.

by nilkn2 hours ago|

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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).

by geraneum2 hours ago|

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> 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?

by nilkn59 minutes ago|

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You're trying to get me to make a claim stronger than what I'm making. The claim I'm making is that capabilities have moved since the paper's data (countless benchmarks support this), and it's not possible to measure the end-to-end effect of that inside industrial applications with complete access, adoption, and internal connectivity at the same pace. Therefore, the study is fundamentally out of date and conclusions about the present or future cannot be drawn from it unless you assume that technological progress cannot have affected the relevant outcomes, which is a strong assumption you'd have to back up separately. Note that the paper's model ties end-to-end outcomes to generated code quality and correctness which is precisely one of the variables that continues to move.

What the study actually shows is that organizations that had very little time to absorb older generations of a disruptive emerging technology experienced an indeterminate change in output (at the 12 month mark, anywhere from down 13% to up 25% -- the paper can't say where reality falls). It certainly doesn't establish any ceiling.