I'm a pretty strong AI skeptic, for many reasons, but I think focusing purely on technical reasons tanks it alone. Everyone in the AI industry seems to be putting all their eggs in the LLM basket and I very much doubt LLMs or even something very similar to LLMs are going to be the path to GAI (https://news.ycombinator.com/item?id=44628648). I think the LLMs we have today are about as good as they're going to get. I've yet to see any major improvement in capability since GPT-3. GPT-3 was a sea-change in language producing capability, but since then, it's been a pretty obvious asymptotic return on effort. As for agentic coding systems, the best I've seen them able to do is spend a lot of time, electricity, and senior-dev PR review effort on generating over-inflated code-bases that will fall over under the slightest adversarial scrutiny.

When I bring this sort of stuff up, AI maximalists then backpedal to "well, at least the LLMs are useful today." I don't think they really are (https://news.ycombinator.com/item?id=44527260). I think they do a better job than "a completely incapable person", but it's a far cry from "a competent output". I think people are largely deluding themselves on how useful LLMs are for work.

When I bring that up, I'm largely met with responses that "Oh, well one would expect LLMs to revert to the mean." That's a serious goal-post move! AI was supposed to 10x people's output! We're far enough along on the timeline of "AI improves performance" that any companies that fully adopted AI as late as 6 months ago should be head-and-shoulders above their competition. Have we seen that? Anywhere? Any amount of X greater than 1.5 should be visible at this point.

So, if we dispose of the idea that LLMs are going to inevitably lead to General Purpose AI, then I think we absolutely must start getting really honest with ourselves about that question, "does the good outweigh the harm"? I have yet to see any meaningful good, yet I've certainly seen a lot of harm.