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Comment by nilkn

4 hours ago

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.

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nilkn

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simoncion  2 hours ago

> 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.

  • nilkn  2 hours ago

    > 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.

geraneum  3 hours ago

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?

  • nilkn  3 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.