Wait but he controls the price, not the subscriber number?
Like even if the issue was low subscriber number (which it isn’t since they’re losing money per subscriber, more subscribers just makes you lose money faster), that’s still the same category of mistake? You control the price and supply, not the demand, you can’t set a stupid price that loses you money and then be like “ah, not my fault, demand was too low” like bozo it’s your product and you set the price. That’s econ 101, you can move the price to a place where your business is profitable, and if such a price doesn’t exist then maybe your biz is stupid?
I believe our esteemed poster was referencing the oft-seen cloud dynamic of “making just enough in margin” where you can tolerate a handful of big users because you have enough lower-usage subscribers in aggregate to counter the heavies. which, y’know, still requires the margin to exist in the first place
alas, hard to have margins in Setting The Money On Fire business models
despite that one episode of Leverage where they did some laundering by way of gym memberships, not every shady bullshit business that burns way more than they make can just swizzle the numbers!
(also if you spend maybe half a second thinking about it you’d realize that economies of scale only apply when you can actually have economies of scale. which they can’t. which is why they’re constantly setting more money on fire the harder they try to make their bad product seem good)
Yeah, the tweet clearly says that the subscribers they have are using it more than they expected, which is costing them more than $200 per month per subscriber just to run it.
I could see an argument for an economy of scales kind of situation where adding more users would offset the cost per user, but it seems like here that would just increase their overhead, making the problem worse.
LLM inference can be batched, reducing the cost per request. If you have too few customers, you can’t fill the optimal batch size.
That said, the optimal batch size on today’s hardware is not big (<100). I would be very very surprised if they couldn’t fill it for any few-seconds window.
this sounds like an attempt to demand others disprove the assertion that they’re losing money, in a discussion of an article about Sam saying they’re losing money
Can someone explain why I am being downvoted and attacked in this thread? I swear I am not sealioning. Genuinely confused.
@sc_griffith@awful.systems asked how request frequency might impact cost per request. Batch inference is a reason (ask anyone in the self-hosted LLM community). I noted that this reason only applies at very small scale, probably much smaller than what OpenAI is operating at.
@dgerard@awful.systems why did you say I am demanding someone disprove the assertion? Are you misunderstanding “I would be very very surprised if they couldn’t fill [the optimal batch size] for any few-seconds window” to mean “I would be very very surprised if they are not profitable”?
The tweet I linked shows that good LLMs can be much cheaper. I am saying that OpenAI is very inefficient and thus economically “cooked”, as the post title will have it. How does this make me FYGM? @froztbyte@awful.systems
More like he misjudged subscriber numbers than price.
Wait but he controls the price, not the subscriber number?
Like even if the issue was low subscriber number (which it isn’t since they’re losing money per subscriber, more subscribers just makes you lose money faster), that’s still the same category of mistake? You control the price and supply, not the demand, you can’t set a stupid price that loses you money and then be like “ah, not my fault, demand was too low” like bozo it’s your product and you set the price. That’s econ 101, you can move the price to a place where your business is profitable, and if such a price doesn’t exist then maybe your biz is stupid?
I believe our esteemed poster was referencing the oft-seen cloud dynamic of “making just enough in margin” where you can tolerate a handful of big users because you have enough lower-usage subscribers in aggregate to counter the heavies. which, y’know, still requires the margin to exist in the first place
alas, hard to have margins in Setting The Money On Fire business models
despite that one episode of Leverage where they did some laundering by way of gym memberships, not every shady bullshit business that burns way more than they make can just swizzle the numbers!
(also if you spend maybe half a second thinking about it you’d realize that economies of scale only apply when you can actually have economies of scale. which they can’t. which is why they’re constantly setting more money on fire the harder they try to make their bad product seem good)
please explain to us how you think having less, or more, subscribers would make this profitable
Yeah, the tweet clearly says that the subscribers they have are using it more than they expected, which is costing them more than $200 per month per subscriber just to run it.
I could see an argument for an economy of scales kind of situation where adding more users would offset the cost per user, but it seems like here that would just increase their overhead, making the problem worse.
LLM inference can be batched, reducing the cost per request. If you have too few customers, you can’t fill the optimal batch size.
That said, the optimal batch size on today’s hardware is not big (<100). I would be very very surprised if they couldn’t fill it for any few-seconds window.
i would swear that in an earlier version of this message the optimal batch size was estimated to be as large as twenty.
yep, original is still visible on mastodon
this sounds like an attempt to demand others disprove the assertion that they’re losing money, in a discussion of an article about Sam saying they’re losing money
What? I’m not doubting what he said. Just surprised. Look at this. I really hope Sam IPO his company so I can short it.
oh, so you’re that kind of fygm asshole
good to know
Can someone explain why I am being downvoted and attacked in this thread? I swear I am not sealioning. Genuinely confused.
@sc_griffith@awful.systems asked how request frequency might impact cost per request. Batch inference is a reason (ask anyone in the self-hosted LLM community). I noted that this reason only applies at very small scale, probably much smaller than what
OpenAI is operating at.@dgerard@awful.systems why did you say I am demanding someone disprove the assertion? Are you misunderstanding “I would be very very surprised if they couldn’t fill [the optimal batch size] for any few-seconds window” to mean “I would be very very surprised if they are not profitable”?
The tweet I linked shows that good LLMs can be much cheaper. I am saying that
OpenAI is very inefficient and thus economically “cooked”, as the post title will have it. How does this make me FYGM? @froztbyte@awful.systemsmy god! let me fix that