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I think the current level of LLMs are scary because of their labor market implications when solving for equilibrium. If you think that they will simply get rid of the job of junior software developer and stop afterwards, you are as sorely mistaken.
Basically, as a knowledge worker I feel a bit like a horse might have felt after Ford started selling the Model T in 1908. Before my kind had ruled our economic niche since times immemorial. When Newcomb's engine (or Deep Blue) were able to outcompete my kind in certain small domains, I did not worry. When the railway came, one could spin this as a complement rather than a competition -- once you leave the train, you will still want a horse to get somewhere, after all, it is not like train tracks will ever lead everywhere.
Even today in 1908, some horses are pointing out that it is much easier to find a stable and fodder for your horse than it is to source gasoline in rural Kansas, to say nothing of the road quality. But to me, this is simply because we have not yet reached equilibrium conditions.
Like horses and cars, humans and LLMs are very dissimilar. Training a human to speak a language is vastly more efficient than training an LLM. Take a student who is fluent in German and give her five years worth of English education (e.g. a couple of textbooks worth), and she will speak usable English. Do the same with an LLM, and you will need orders of magnitude more training data.
But that does not matter, because we have sufficient training data, and can train the LLMs, just as the fact that a horse would be the better choice on a narrow and winding forest trail matters little if there is a highway running next to it for the car to use.
Even if LLMs plateau at the current level (which I dearly hope for, until we have solved alignment), that is more than sufficient so that there will be no market demand for the intelligence of an IQ 100 person, and quite possibly not any demand for the IQ 120 person either. Scaffolding will improve and inference will become cheaper.
The main reason I am bearish on AI in education is not because I do not believe that AI could help there, it is because I am skeptical that there will be a point in educating kids. It may well be that in 20 years, a degree in physics will be about as useful as a degree in feminist literature. (Of course, there are other reasons to educate kids besides making them employable, and if we get some kind of UBI we should definitely encourage people to do their PhDs in Minecraft or particle physics or feminist literature or whatever catches their fancy.)
The blue-collar workers will take a bit longer to replace, but sooner or later we will have robots to replace the fans in AI data centers.
Just looking at the software developer and researcher jobs is like looking at the arctic ice melting and saying that it is no big deal because hardly anyone is living up there anyhow while ignoring the fact that the water will go somewhere.
Alignment is already solved. What frontier labs are busy is destroying alignment of the ai with the user for no good reason.
What are you trying to say here?
That AI comes aligned. If I want AI to build me a bioweapon and it does this is aligned AI. The labs do a lot of work to disalign AI from that state.
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Jobs are something I worry about, but as long as there are other decent jobs or something like UBI:
I don’t think devaluing (logical) intelligence really matters, because it’s like how strength has been devalued. It would still be far from useless, just not as important (and if you’re smart enough, in most cases already wasn’t as charisma): like people still want to be strong, want strong partners, strength helps in everyday situations.
And although today’s full curriculum may not be mandatory tomorrow (I think we already teach some generally unnecessary concepts), people will still go to school, like how we still exercise: at minimum for mental health and basic functioning (e.g. exercise for ability to walk without being out of breath, school for common sense); then some will do more because they intrinsically like to, or want a still-remaining benefit of innate knowledge.
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