alt Hacker News> But mainly, they are in "I need to kill whole industries to be worth it" tiers of investment.
Yes agreed. Coding is a pretty big industry though in and of itself. Same with healthcare, legal, etc etc etc. Of course we have zero model today that can seriously kill an industry, but if you look at (1) how good things are today (insanely fast and rapid adoption) and (2) robust performance trends from many complimentary sources, it's kind of inevitable and I haven't really heard a coherent steel man argument for why "killing whole industries" is somehow a far-fetched idea.
> still not profitable enough to make up for the costs of either training or the investments gifted away until it became profitable.
Regardless of the weeds of the economics today, you have a clearly valuable asset that at the very least already a must-have for enterprise and will become even more essential over time. There is token economics that either already do or will make sense. You will have some sort of marginal cost + profit margin that things will stabilize at. You can pay a premium for high quality frontier models. "But it costs more in R&D to fund this!" ok but then token costs will increase. Why is this some sort of death knell?
>it's kind of inevitable and I haven't really heard a coherent steel man argument for why "killing whole industries" is somehow a far-fetched idea.
They don't only require "good enough to kill industries" (which is doubtful but certainly feasible), that's just step one. I think about it in terms of potential failure modes:
- if models don't reach worker-substitution levels, they fail
- if models reach that level, but it's too expensive to run and a worker's still cheaper, they fail
- if models reach that level, but the resulting tech is cheap enough to use, they fail (since open models can compete)
- If the models work but there's social rejection leading to regulation (due to mass unemployment for example), they fail
- if the models work but there are significant deal breakers (like a fundamental inability to keep them safeish to use) they fail.
So it's not really a single AI killer reason, it's more that the success case requires things to land in a very specific future where models work, and they're cheap enough, and expensive enough, and valuable enough, and exclusive enough, and safe enough, and...
Each "and" is a multiplier reducing their chances, and there's a ton or factors. Not imposible, but not where I'd put my money.