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I was browsing through the news today and I found an interesting article about the current state of AI for corporate productivity.
MIT report: 95% of generative AI pilots at companies are failing
There seems to have been a feeling over the last few years that generative AI was going to gut white collar jobs the same way that offshoring gutted blue collar jobs in the 1980s and 90s, and that it was going to happen any day now.
If this study is trustworthy, the promise of AI appears to be less concrete and less imminent than many would hope or fear.
I've been thinking about why that might be, and I've reached three non-exclusive but somewhat unrelated thoughts.
The first is that Gartner hype cycle is real. With almost every new technology, investors tend to think that every sigmoid curve is an exponential curve that will asymptotically approach infinity. Few actually are. Are we reaching the point where the practical gains available in each iteration our current models are beginning to bottom out? I'm not deeply plugged in to the industry, nor the research, nor the subculture, but it seems like the substantive value increase per watt is rapidly diminishing. If that's true, and there aren't any efficiency improvements hiding around the next corner, it seems like we may be entering the through of disillusionment soon.
The other thought that occurs to me is that people seem to be absolutely astounded by the capabilities of LLMs and similar technology.
Caveat: My own experience with LLMs is that it's like talking to a personable schizophrenic from a parallel earth, so take my ramblings with a grain of salt.
It almost seems like LLMs exist in an area similar to very early claims of humanoid automata, like the mechanical Turk. It can do things that seem human, and as a result, we naturally and unconsciously ascribe other human capabilities to them while downplaying their limits. Eventually, the discrepancy grows to great - usually when somebody notices the cost.
On the third hand, maybe it is a good technology and 95% of companies just don't know how to use it?
Does anyone have any evidence that might lend weight to any of these thoughts, or discredit them?
The existence of LLMs makes me a better doctor (and I am studiously silent on whether you could replace me entirely with one). Perhaps this is an artifact of me being relatively junior in my career, but I had an uncle, who is a consultant psychiatrist with more degrees than a heat wave ask GPT-4o questions. He begrudgingly admitted that it gave a more satisfactory answer to one of his thorny questions than the overwhelming majority of other consultant shrinks.
(Said question was on the finer details of distinguishing schizoaffective disorder from bipolar disorder with psychosis. Why 4o? I used the Advanced Voice Mode for the sake of future shock, o3 could have given an even better answer)
Let's just say that my willingness to pay for SOTA LLMs is far higher than their sticker price. Thank god for market competition, and the fact that I don't need nearly as many tokens as vibe coders. The price I pay is comparable to the delicious pork belly I'm eating at a nice Chinese place, and I know what I'd take in a pinch.
I can't find the paper but I was linked recently to a study illustrating that generative AI performance on medical content drops precipitously when "not one of these" is added to the answer list and used.
We aren't dead yet.
https://pmc.ncbi.nlm.nih.gov/articles/PMC12334947/
This the one? It's mildly ironic I found it using GPT-5T, but it's food for thought nonetheless.
Lol I mean I maintain shit ain't ready yet like I always have - it's very common for diseases to present atypically and even more common for patients to poorly explicate things. Neither of these is well captured in the literature and therefore the data set.
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