The biggest problem with “AI” is probably that it’s used as label for two completely different things:
1. Specialized neural networks trained to do highly specific tasks (e.g. cancer screening) which often work reasonably well as a tool to support human experts
2. Generative AI which thoroughly produces the most mid bullshit
It doesn’t help that neither are intelligent in any way, they’re both statistical pattern matching.
Fundamentally, Thomas seems to be arguing that the biggest problem with AI is how it is marketed, or things-that-are-called-AI are marketed as AI. Also that LLMs, by producing s statistical average of their input data, produce output that’s pretty-average (which is, of course, statistically that you’d expect)1.
I’m not sure he’s right: the energy footprint and the copyright issues of generative AI might be the biggest problems. But maybe.
Footnotes
1 That’s not entirely true, of course: sometimes they produce output that’s wild and random, but we describe those as “hallucinations” and for many purposes they’re even worse. At least “mid bullshit” can be useful if you’re specifically looking to summarise existing content (and don’t mind fact-checking it later if it’s important): y’know, the thing people use Wikipedia for.
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