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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?
It increasingly feels to me like the Tyler Cowen's of the world are right. That the impact will be large, huge even, but take a lot more time to play out then the boosters predict. It will take time, not only for the tech to improve, but for people and companies to learn how to best use it and for complementary infrastructure and skills to build up. The parallels to the personal computer or internet seem increasingly on point, especially the dot com era. People were, rightly, astounded by those. And, for all the jeering pets.com got, and all the (mostly valid!) reasons it wouldn't work, it ended up mostly just ahead of its time. I and everyone I know buy my pet food through an online subscription or auto-recurring purchase. In 20 years I expect AI will be omnipresent in white collar work.
This makes sense to me. The impact of the spreadsheet has been huge, but it took a long time to settle in everywhere, and the accounting department still exists, even though the guys "running the numbers" don't anymore. There are still plenty of operational systems running on DOS or OS/2: if it isn't broken, don't fix it, and things take time to replace.
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