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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 big problem for now is some form of data validation. There are a lot of customer support jobs that can be 99.9% done by AI, but aren’t because of the tail risk that some combination of words will reveal the wrong customer’s information, will allow someone into the account without the right checks, etc, plus general reputational risk like the fact that countries and states are now accusing Facebook LLMs of flirting with minors or whatever. All the stuff that LLM red-teaming groups or ChatGPT jailbreak communities do, essentially. You can fire a bad employee as legal liability, but if its your LLM and the foundation model provider has a big fat liability disclaimer in its contract (which it will), you’re more fucked than you’d be if an employee had just gone rogue.
The eventual solution to this - as with self-driving cars - is to improve accuracy and consistency (by running things through multiple LLMs, including prompt security ones like those slowly coming online through Amazon bedrock and other platforms) until the risks are negligible and therefore insurance costs fall below the $50m a year a big corporation is paying for call centers.
But it will take a few more months, maybe a couple of years, sure.
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