Comment by diggan
6 days ago
> On the other hand, where I remain a skeptic is this constant banging-on that somehow this will translate into entirely new things - research, materials science, economies, inventions, etc
Does it even have to be able to do so? Just the ability to speed up exploration and validation based on what a human tells it to do is already enormously useful, depending on how much you can speed up those things, and how accurate it can be.
Too slow or too inaccurate and it'll have a strong slowdown factor. But once some threshold been reached, where it makes either of those things faster, I'd probably consider the whole thing "overall useful". Nut of course that isn't the full picture and ignoring all the tradeoffs is kind of cheating, there are more things to consider too as you mention.
I'm guessing we aren't quite over the threshold because it is still very young all things considered, although the ecosystem is already pretty big. I feel like generally things tend to grow beyond their usefulness initially, and we're at that stage right now, and people are shooting it all kind of directions to see what works or not.
> Just the ability to speed up exploration and validation based on what a human tells it to do is already enormously useful, depending on how much you can speed up those things, and how accurate it can be.
The big question is: is it useful enough to justify the cost when the VC subsidies go away?
My phone recently offered me Gemini "now for free" and I thought "free for now, you mean. I better not get used to that. They should be required to call it a free trial."
Inference is actually quite cheap. Like, a highly competitive LLM can cost 1/25th of a search query. And it is not due to inference being subsidized by VC money.
It's also getting cheaper all the time. Something like 1000x cheaper in the last two years at the same quality level, and there's not yet any sign of a plateau.
So it'd be quite surprising if the only long-term business model turned out to be subscriptions.
Can you link to any sources that support your claim?
6 replies →
> The big question is: is it useful enough to justify the cost when the VC subsidies go away?
I won't claim local LLMs as nearly as good as various top models behind paid subscriptions/APIs, but I'm certain I'd be able find a way (for me) of working with them well enough, if the entire paid/hosted ecosystem disappeared over night. Even with models released today.
I think the VC subsidies probably "make stuff happen" faster, and without it we'd see slower progress, but I don't think 100% of the ecosystem would disappear even if 100% of VC funding disappeared. We're bound for another AI winter at one point, and some will surely survive even that :)
So isn't the heuristic that if your job is easily digestible by an LLM, you're probably replaceable, but if the strong slowdown factor presents itself, you're probably doing novel work and have job security?
> So isn't the heuristic that if your job is easily digestible by an LLM, you're probably replaceable
Yeah, that sounds about right to me. I wasn't talking about wholesale replacement though, but as a tool/augmentation, I'm not very confident an LLM would be able replace a software engineer, but I can definitely see many workflows of a software engineer being sped up, like the exploration and validation process.