One of the hot technologies preceding one of the several “AI Winters” we’ve seen since software was even a thing was called Expert Systems. And they were hot for well over a decade!
…but as the hype started cooling and people looked at the cost/benefit analysis and the real capabilities of such a system in the long term, the whole house of cards came crashing down. Below is the summary the Wikipedia page gives on why they failed. Look familiar?
What I’m getting at is that it doesn’t really matter if LLMs improve the quality of their results to match a novice human worker.
When you remove the subsidies from incredibly rich investors and the massive FAANG war chests, and the momentary early adopter hype from people willing to pay for trash for the novelty, when you take into account the training costs in hardware, energy, and environmental costs…
The math just doesn’t add up, and I don’t believe it ever will. VCs and war chests are able to artificially prop stuff up for a very long time in the (false) hope that it’ll pay off but… the math will never add up. And if and when regulation (like copyright law) catches up, it’s going to be a BLOODBATH
@zkat @neia it’s because they were able to sustain incredible growth over three decades as software was able to “eat the world.” That process is for the most part complete—e.g. social media companies couldn’t keep adding users indefinitely.
Prior to this, they were desperate—e.g. Facebook going so hard on the Metaverse they renamed themselves.
However, the prospect they could replace workers with LLMs is too alluring and profitable to give up on, even if impossible
Katerina Marchán
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