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LLMs get results is quite the bold statement. If they get results, they should be getting adopted, and they should be making money. This is all built on hazy p

LLMs get results is quite the bold statement. If they get results, they should be getting adopted, and they should be making money. This is all built on hazy promises. If you had marketable results, you wouldn't have to hide 20+ billion dollars of debt financing into an obscure SPV. LLMs are the most baffling piece of tech. They are incredible, and yet marred by their non-deterministic hallucinatory nature, and bound to fail in adoption unless you convince everyone that they don't need precision and accuracy, but they can do their business at 75% quality, just with less human overhead. It's quite the thing to convince people of, and that's why it needs the spend it's needing. A lot of we-need-to-stay-in-the-loop CEOs and bigwigs got infatuated with the idea, and most probably they just had their companies get addicted to the tech equivalent of crack cocaine. A reckoning is coming.

deleted 1y ago
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ACCount37 1y ago
LLMs get results, yes. They are getting adopted, and they are making money. Frontier models are all profitable. Inference is sold with a damn good margin, and the amounts of inference AI companies sell keeps rising. This necessitates putting more and more money into infrastructure. AI R&D is extremely expensive too, and this necessitates even more spending. A mistake I see people make over and over again is keeping track of the spending but overlooking the revenue altogether. Which sure is weird: you don't get from $0B in revenue to $12B in revenue in a few years by not having a product anyone wants to buy. And I find all the talk of "non-deterministic hallucinatory nature" to be overrated. Because humans suffer from all of that too, just less severely. On top of a number of other issues current AIs don't suffer from. Nonetheless, we use human labor for things. All AI has to do is provide a "good enough" alternative, and it often does.
- camillomiller 1y ago
  In this comment you proceeded to basically reinvent the meaning of "profitable company", but sure. I won't even get into the point of comparing LLM to humans, because I choose not to engage with whoever doesn't have the human decency, humanistic compass, or basic phylosophical understanding of how putting LLMs and human labor on the same level to justify hallucinations and non-determinism is deranged and morally bankrupt.
  - ACCount37 1y ago
    You should go and work in a call center for a year, on the first line. Then come back and tell me how replacing human labor with AI is "deranged and morally bankrupt".
    - hitarpetar 1y ago
      red herring. just because some jobs are bad (maybe shouldn't exist like that in the first place) doesn't make this movement humanistic
- ripe 1y ago
  > Frontier models are all profitable. This is an extraordinary claim and needs extraordinary proof. LLMs are raising lots of investor money, but that's a completely different thing from being profitable.
  - ACCount37 1y ago
    You don't even need insider info - it lines up with external estimates. We have estimates that range from 30% to 70% gross margin on API LLM inference prices at major labs, 50% middle road. 10% to 80% gross margin on user-facing subscription services, error bars inflated massively. We also have many reports that inference compute has come to outmatch training run compute for frontier models by a factor of x10 or more over the lifetime of a model. The only source of uncertainty is: how much inference do the free tier users consume? Which is something that the AI companies themselves control: they are in charge of which models they make available to the free users, and what the exact usage caps for free users are. Adding that up? Frontier models are profitable. This goes against the popular opinion, which is where the disbelief is coming from. Note that I'm talking LLMs rather than things like image or video generation models, which may have vastly different economics.
    - hitarpetar 1y ago
      what about training?
      - ACCount37 1y ago
        I literally mentioned that: > We also have many reports that inference compute has come to outmatch training run compute for frontier models by a factor of x10 or more over the lifetime of a model.
        hitarpetar 1y ago
        thank you, I literally can't read
  - jonas21 1y ago
- echelon 1y ago
  > Frontier models are all profitable. They generate revenue, but most companies are in the hole for the research capital outlay. If open source models from China become popular, then the only thing that matters is distribution / moat. Can these companies build distribution advantage and moats?
miohtama 1y ago
OpenAI and Anthropic are making north of 4B/year revenue so some companies have figured out the money making part. ChatGPT has some 800M users according to some calculations. Whether it's enough money today, enough money tomorrow, is of course a question but there is a lot of money. Users would not use them in a scale if they do not solve their problems.
- panja 1y ago
  OpenAI lost 12bn last quarter
- Hendrikto 1y ago
  It’s easy to make 1 billion by spending 10 billion. That’s not “making money” though, it is lighting it on fire.
  - aryonoco 1y ago
    People used to say this about Amazon all the time. Remember how Amazon basically didn’t turn any real profits for 2 decades? The joke was that Amazon was a charitable organisation being funded by Wall Street for the benefit of human kind. That didn’t last. People in the know knew that once you have a billion users and insane revenue and market power and have basically bought or driven out of business most of your competitors (Diapers.com, Jet.com, etc) you can eventually slow down your physical expansion, tighten the screws on your suppliers, increase efficiencies, and start printing money. The VCs who are funding these companies are hoping that they have found the next Amazon. Many will probably go out of business, but some might join the ranks of trillion dollar companies.
    - ambicapter 1y ago
      So every company that doesn't turn any profits is actually Amazon in disguise?
      - aryonoco 1y ago
        If you’ve got nearly a billion users, and are multiplying your revenue on an annual basis, then yes. You’re effectively showing that you’re in hyper growth trajectory. Hyper growth is expensive because it’s usually capital intensive. The trick is, once that growth phase is over, can you then start milking your customers while keeping a lid on costs? Not everyone can, but Amazon did, and most investors think OpenAI and Anthropic can as well.

deleted 1y ago

[deleted]

ACCount37 1y ago

LLMs get results, yes. They are getting adopted, and they are making money. Frontier models are all profitable. Inference is sold with a damn good margin, and the amounts of inference AI companies sell keeps rising. This necessitates putting more and more money into infrastructure. AI R&D is extremely expensive too, and this necessitates even more spending. A mistake I see people make over and over again is keeping track of the spending but overlooking the revenue altogether. Which sure is weird: you don't get from $0B in revenue to $12B in revenue in a few years by not having a product anyone wants to buy. And I find all the talk of "non-deterministic hallucinatory nature" to be overrated. Because humans suffer from all of that too, just less severely. On top of a number of other issues current AIs don't suffer from. Nonetheless, we use human labor for things. All AI has to do is provide a "good enough" alternative, and it often does.

camillomiller 1y ago
In this comment you proceeded to basically reinvent the meaning of "profitable company", but sure. I won't even get into the point of comparing LLM to humans, because I choose not to engage with whoever doesn't have the human decency, humanistic compass, or basic phylosophical understanding of how putting LLMs and human labor on the same level to justify hallucinations and non-determinism is deranged and morally bankrupt.
- ACCount37 1y ago
  You should go and work in a call center for a year, on the first line. Then come back and tell me how replacing human labor with AI is "deranged and morally bankrupt".
  - hitarpetar 1y ago
    red herring. just because some jobs are bad (maybe shouldn't exist like that in the first place) doesn't make this movement humanistic
ripe 1y ago
> Frontier models are all profitable. This is an extraordinary claim and needs extraordinary proof. LLMs are raising lots of investor money, but that's a completely different thing from being profitable.
- ACCount37 1y ago
  You don't even need insider info - it lines up with external estimates. We have estimates that range from 30% to 70% gross margin on API LLM inference prices at major labs, 50% middle road. 10% to 80% gross margin on user-facing subscription services, error bars inflated massively. We also have many reports that inference compute has come to outmatch training run compute for frontier models by a factor of x10 or more over the lifetime of a model. The only source of uncertainty is: how much inference do the free tier users consume? Which is something that the AI companies themselves control: they are in charge of which models they make available to the free users, and what the exact usage caps for free users are. Adding that up? Frontier models are profitable. This goes against the popular opinion, which is where the disbelief is coming from. Note that I'm talking LLMs rather than things like image or video generation models, which may have vastly different economics.
  - hitarpetar 1y ago
    what about training?
    - ACCount37 1y ago
      I literally mentioned that: > We also have many reports that inference compute has come to outmatch training run compute for frontier models by a factor of x10 or more over the lifetime of a model.
      - hitarpetar 1y ago
        thank you, I literally can't read
- jonas21 1y ago
echelon 1y ago
> Frontier models are all profitable. They generate revenue, but most companies are in the hole for the research capital outlay. If open source models from China become popular, then the only thing that matters is distribution / moat. Can these companies build distribution advantage and moats?

miohtama 1y ago

OpenAI and Anthropic are making north of 4B/year revenue so some companies have figured out the money making part. ChatGPT has some 800M users according to some calculations. Whether it's enough money today, enough money tomorrow, is of course a question but there is a lot of money. Users would not use them in a scale if they do not solve their problems.

panja 1y ago
OpenAI lost 12bn last quarter
Hendrikto 1y ago
It’s easy to make 1 billion by spending 10 billion. That’s not “making money” though, it is lighting it on fire.
- aryonoco 1y ago
  People used to say this about Amazon all the time. Remember how Amazon basically didn’t turn any real profits for 2 decades? The joke was that Amazon was a charitable organisation being funded by Wall Street for the benefit of human kind. That didn’t last. People in the know knew that once you have a billion users and insane revenue and market power and have basically bought or driven out of business most of your competitors (Diapers.com, Jet.com, etc) you can eventually slow down your physical expansion, tighten the screws on your suppliers, increase efficiencies, and start printing money. The VCs who are funding these companies are hoping that they have found the next Amazon. Many will probably go out of business, but some might join the ranks of trillion dollar companies.
  - ambicapter 1y ago
    So every company that doesn't turn any profits is actually Amazon in disguise?
    - aryonoco 1y ago
      If you’ve got nearly a billion users, and are multiplying your revenue on an annual basis, then yes. You’re effectively showing that you’re in hyper growth trajectory. Hyper growth is expensive because it’s usually capital intensive. The trick is, once that growth phase is over, can you then start milking your customers while keeping a lid on costs? Not everyone can, but Amazon did, and most investors think OpenAI and Anthropic can as well.