I do disagree with parts of it, but dreck?
We have only just begun our ascent up the hockey stick and the most intense change is yet to come.
The real danger is how big of a gap will exist once the curve does level off. If we are just at the start of the sigmoid curve and starting our ascent, then many jobs will be thrown off by the time we hit the peak and begin to level off.
No politician or corporation is preparing for this sufficiently.
This. And we don't even need LLMs. Orchestrated SLMs that are actually good at their topical areas of training (imagine that) are more in-tune with what the enterprise wants.
> The real danger is how big of a gap will exist once the curve does level off.
I think we've seen that is has. Parlor tricks like what Anthropic is pulling with Fable / Mythos is ultimately a cheat. Basically what you describe up top: the harnesses and underlying automation built in - and that's exactly what Anthropic didn't tell people Fable was, yet it is. And here we are. Is Fable amazing without Claude Code under the covers? Probably not much outside Opus 4.8. The models themselves aren't accelerating as fast as they were years ago, but the marketing clankers have kicked in and are pushing out benchmarks and numbers and all the things, but in reality they're fractions of a point improved. And they still get the obvious things wrong, even when you're paying the inflated high prices. I just can't wait until Anthropic tells everyone that API access to a model is "dangerous", because you know damn well that's coming. There's no moat unless they keep digging. And they're digging right now, that's very clear.
> Superhuman means unverifiable
is not true for at least large classes of problems. The recent solution of the "unit distance" problem comes to mind, or any future AI-solved math problem that was beyond the capabilities of humans. You can tell it's superhuman (it's doing things humans can't) and you can easily verify its results are correct.
For other classes of problems (eg, policy suggestions for large scale systems like the economy), the point is fair.
The focus is placed on "AI Literacy", but it seems to use this to just mean 'volume of AI use'. The discussion of the Netflix case study is extra perplexing, since the summary here admits they didn't find any actual productivity improvement, just that only a few hours of "training" could induce on the order of $50/person/day on tokens.
That seems... the opposite of literacy?
If you want me to pay for your compiler it must be that much better than the compiler I can get for free that allows me much more freedom of agency in how I use it, if it is not I am going to use the free compiler. repeat for operating systems, word processors, databases and now LLM's
Models on the other hand are just software. You can download Fable / Mythos / Praxia / Concordicon and hide a copy in your shoe. You can make two copies even! One per shoe!
We have lived through decades now of the glorious open source revolution so it’s easy to forget about a different time, in the 1990s, when a black market in CDs jammed full of cracked, slightly off branded copies of Windows / Photoshop / Corel / Office, etc. was all the rage.
Execution isn’t trivial. If anything should be controlled like nuclear weapons, again like the 90s, it ought to be compute hardware. I’m not sure it should.
It then raises a question, which of these two is really the precious thing. (Although there could be a 3rd thing, a memoization of some shortcuts, like tautologies in logic.)
I aint even gonna open source it but someone will.