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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?
I work for a software consultancy that has recently gone heavily for building an "AI accelerator" for clients.
Full disclosure, I just moved from GPU based image generation (non-AI) to embedded, I'm trying desperately to avoid working on the AI projects. Take my comment with a big grain of salt, as I'm definitely biased.
They are definitely useful. Mostly as a way for executives to summarize and interrogate quarterly reports. They're probably going to replace several data analysis teams whose jobs have been building Power BI dashboard for the past 10 years.
The hype cycle is definitely real, but most clients have wanted the chat bots built as a box checking exercise, and have no idea what they actually want out of it (based on in-depth conversations with people on the AI accelerator teams). I expect the trough of disillusionment to hit hard and cancel most of these projects.
I consulted for a massive multinational a couple of years ago and they had this massive operation in India that produced those BI dashboards every week, that the regional and national executives immediately threw in the trash.
The issue was that while those dashboards looked good and contained a ton of data they didn't really say anything meaningful and it was too hard to both communicate with and change the workflow of the Indian BI teams so the output became useless.
What people defaulted to instead was just fairly simple KPIs that were relevant for whatever issue at hand and people showing things in excel. The dashboards were occasionally used for official reports and external communication but not for internal decision-making.
I'm not sure which bucket AI would fall into here. Would it enable people to quickly do the work themselves (or some kind of local resource) or will it just be a cheaper version to shit out even more useless graphs and dashboards than the Indians resources?
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