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>Despite this, however, they are gaining only in poorer markets They are gaining everywhere. Some more than others, but to say they are only gaining in poorer
by famouswaffles 1y ago
>Despite this, however, they are gaining only in poorer markets
They are gaining everywhere. Some more than others, but to say they are only gaining in poorer markets is blatantly untrue.
>FB global ARPU is about 50 USD. At 700M customers, they do 35B in revenue annually.
Yeah, and that would make them healthily profitable.
>This compares to a publicly stated expected cost of approximately 150B in computing alone over the next 5 years
Yes, because they expect to serve hundreds of millions to potentially billions more users. 'This leaves a profit of 5B per year' makes some very bizarre assumptions. You’re conflating a future-scale spending projection with today’s economics. That number is a forward-looking projection tied to massive scale - it doesn’t prove current users alone justify that spend, and they clearly don't. There is no reality where they are spending that much if their userbase stalls at today's numbers, so it's just a moot point and '5B per year' a made up number.
>Fundamentally, OpenAI does not have the unit economics of a traditional SaaS.
Again, Everything points to their unit economics being perfectly fine.
- mgh95 1y agoNo, the economics are horrible. At current 30Y T-bond rates, your money doubles ever ~15 years. Your money grows faster in USD treasuries then OpenAI. That's disastrous.
- simianwords 1y agoWhy do you think the economics are horrible?
- mangamadaiyan 1y agoThey've explained why. Now why do you think the economics are not horrible?
- simianwords 1y agoI have the same question as my sibling comment. Where did they get the numbers?
- famouswaffles 1y agoYou're moving the post now. Stop making up numbers and substituting them as fact. What you think Open AI's returns will be is your opinion, and not a reason to justify 'poor unit economics'.
- menaerus 1y agoThere's one thing you're missing - inference is not cheap. HW is not cheap. Electricity is not cheap. This is w/o R&D. They show that, in average, they recorded ~2.627B requests/day. This is ~79B requests/month or ~948B requests/year. And this is only for the consumer ChatGPT data, Enterprise isn't included AFAICT. Each request translates to the direct cost that could be even roughly estimated.
- famouswaffles 1y agoNo inference is pretty cheap, and a lot of things point to that being true. - Prices of API access of Open models from third-party providers who would have no motive to subsidize inference - Google says their median query is about as expensive as a google search Thing is, what you're saying would have been true a few years ago. This would have all been intractable. But llm inference costs have quite literally been slashed several orders of magnitude in the last couple of years.
- menaerus 1y agoYou would probably understand if you knew how LLMs are run in the first place but, as ignorant as you are (sorry), I have no interest in debating this with you anymore. I tried to give a tractable clue which you unfortunately chose to counter-argue with non-facts.
- famouswaffles 1y agoTouting the requests per day is pretty meaningless without per query numbers, but sure, I'm the one that doesn't understand. What people with no incentive to subsidize are charging is about as fact as it comes but sure, lol. I've replied to you once man. Feel free to disengage but let's not act like this has been some ongoing debate. No need to make up stories.
- menaerus 1y agoWhich is why I said that it can be roughly estimated. And it can be roughly estimated even without those numbers assuming a fleet of some size X and assuming the number of hours this fleet is utilized per day, for the whole year. Either way, you will end up with a hefty number. Do the math and you'll see that inference is far from being cheap.