well, yes. But also this is an extremely difficult to price product. 200$/m is already insane, but now you’re suggesting they should’ve gone even more aggressive. It could turn out almost nobody would use it. An optimal price here is a tricky guess.
Although they probably should’ve sold a “limited subscription”. That does give you max break-even amount of queries per month, or 2x of that, but not 100x, or unlimited. Otherwise exactly what happened can happen.
“Our product that costs metric kilotons of money to produce but provides little-to-no value is extremely difficult to price” oh no, damn, ye, that’s a tricky one
I signed up for API access. I run all my queries through that. I pay per query. I’ve spent about $8.70 since 2021.
This seems like a win-win model. I save hundreds of dollars and they make money on every query I run. I’m confused why there are subscriptions at all.
What the LLMs do, at the end of the day, is statistics. If you want a more precise model, you need to make it larger. Basically, exponentially scaling marginal costs meet exponentially decaying marginal utility.
what the locals are probably taking issue with is:
If you want a more precise model, you need to make it larger.
this shit doesn’t get more precise for its advertised purpose when you scale it up. LLMs are garbage technology that plateaued a long time ago and are extremely ill-suited for anything but generating spam; any claims of increased precision (like those that openai makes every time they need more money or attention) are marketing that falls apart the moment you dig deeper — unless you’re the kind of promptfondler who needs LLMs to be good and workable just because it’s technology and because you’re all-in on the grift
Well, then let me clear it up. The statistics becomes more precise. As in, for a given prefix A, and token x, the difference between the calculated probability of x following A (P(x|A)) to the actual probability of P(x|A) becomes smaller. Obviously, if you are dealing with a novel problem, then the LLM can’t produce a meaningful answer. And if you’re working on a halfway ambitious project, then you’re virtually guaranteed to encounter a novel problem.
well, yes. But also this is an extremely difficult to price product. 200$/m is already insane, but now you’re suggesting they should’ve gone even more aggressive. It could turn out almost nobody would use it. An optimal price here is a tricky guess.
Although they probably should’ve sold a “limited subscription”. That does give you max break-even amount of queries per month, or 2x of that, but not 100x, or unlimited. Otherwise exactly what happened can happen.
“Our product that costs metric kilotons of money to produce but provides little-to-no value is extremely difficult to price” oh no, damn, ye, that’s a tricky one
I signed up for API access. I run all my queries through that. I pay per query. I’ve spent about $8.70 since 2021. This seems like a win-win model. I save hundreds of dollars and they make money on every query I run. I’m confused why there are subscriptions at all.
The real problem is believing that you can run a profitable LLM company.
What the LLMs do, at the end of the day, is statistics. If you want a more precise model, you need to make it larger. Basically, exponentially scaling marginal costs meet exponentially decaying marginal utility.
Some LLM bros must have seen this comment and become offended.
guess again
what the locals are probably taking issue with is:
this shit doesn’t get more precise for its advertised purpose when you scale it up. LLMs are garbage technology that plateaued a long time ago and are extremely ill-suited for anything but generating spam; any claims of increased precision (like those that openai makes every time they need more money or attention) are marketing that falls apart the moment you dig deeper — unless you’re the kind of promptfondler who needs LLMs to be good and workable just because it’s technology
and because you’re all-in on the griftlook bro just 10 more
repsgpt3s bro itl’ll get you there bro I swear broWell, then let me clear it up. The statistics becomes more precise. As in, for a given prefix A, and token x, the difference between the calculated probability of x following A (P(x|A)) to the actual probability of P(x|A) becomes smaller. Obviously, if you are dealing with a novel problem, then the LLM can’t produce a meaningful answer. And if you’re working on a halfway ambitious project, then you’re virtually guaranteed to encounter a novel problem.
it doesn’t produce any meaningful answers for non-novel problems either