6 ms·

What revenues do these GPUs generate for OpenAI? OpenAI is not currently profitable, and it is unclear if its business model will ever becomes profitable -- let

by mgh95 1y ago

What revenues do these GPUs generate for OpenAI? OpenAI is not currently profitable, and it is unclear if its business model will ever becomes profitable -- let alone profitable enough to justify this investment. Currently, this only works because the markets are willing to lend and let NVIDIA issue stock to cover the costs to manufacture the GPUs. That's where the belief that we are in a bubble comes from.

davedx 1y ago
OpenAI generates plenty of revenues from their services. Don't conflate revenues with profits
- mgh95 1y ago
  I don't believe I am. Investors (value investors, not pump and dump investors) provide capital to companies on the expectation of profit, not revenue.
  - charcircuit 1y ago
    Sure, and as long the expected profit keeps increasing investors are happy. They don't need to make an actual profit yet.
humanizersequel 1y ago
They're doing about a billion per month in revenue by running proprietary models on GPUs like these. Unless they're selling inference with zero/negative margin, it seems like a business model that could be made profitable very easily.
- mgh95 1y ago
  Revenue != profit, and you don't need to become net negative margin to be net unprofitable. Expensive researchers, expensive engineers, expensive capex, etc. Inference has extremely different unit economics from a typical SaaS like Salesforce or adtech like google or facebook.
  - humanizersequel 1y ago
    All of those expenses could be trimmed in a scenario where OpenAI or other big labs pivot to focus primarily on profitability via selling inference.
    - mgh95 1y ago
      Currently, selling LLM inference is a red queen race: the moment you release a model, others begin distilling and attempting to sell your model cheaper, avoiding the expensive capitalized costs associated with R&D. This can occur because the LLM market is fundamentally -- at best -- minimally differentiated; consumers are willing to switch between vendors ("big labs", as you call them, but they aren't really research labs) to whomever offers the best model at the lowest price. This is emphasized by the distributors of many LLMs, developer tools, offering ways to switch the LLM at runtime (see https://www.jetbrains.com/help/ai-assistant/use-custom-models.html https://www.jetbrains.com/help/ai-assistant/use-custom-model... or https://code.visualstudio.com/docs/copilot/customization/language-models https://code.visualstudio.com/docs/copilot/customization/lan... for an example of this). The distributors of LLMs actively working against LLM providers margin provides an exceptionally strong headwind. This market dynamic begets a low margin race to the bottom, where no party appears able to secure the highly attractive (think the >70% service margin we see in typical tech) unit economics typical of tech. Inference is a very tough business. It is my opinion (and likely the opinion of many others) that the margins will not sustain a typical "tech" business without continual investment to attempt to develop increasingly complex and expensive models, which itself is unprofitable.
      - humanizersequel 1y ago
        I don't disagree but you're moving the goalposts. I never said that they could achieve the profits of a typical tech business, just that they could be profitable. Also, the whole distilling problem doesn't happen if the model is proprietary.
        mgh95 1y ago
- AlexandrB 1y ago
  And even if they are selling inference at negative margin, they'll make it up in scale!
  - kapone 1y ago
    These kinds of phrases are...eerily similar to the phrases heard right before...the .com bust. If you were old enough at the time, that's exactly what the mindset was back then. The classic story of the shoeshine boy giving out stock tips...and all that. We all know how that turned out.
crowcroft 1y ago
The counter point to this is that while not profitable, the cashflow is real, and inference is marginally ROI positive. If you can scale inference with more GPUs then eventually that marginal ROI grows large enough to cover the R&D and other expenses and you become profitable.
- mgh95 1y ago
  "Marginally ROI positive" works in a ZIRP environment. These are huge capital investments; they need to at least clear treasury return hurdles and importantly provide attractive returns. I am fundamentally skeptical of "scaling inference". Margins are not defensible in the market segment OpenAI is in.
  - crowcroft 1y ago
    For some of these tech companies their valuations let them go to the market with their equity in way that is basically a ZIRP environment. In a way you could say this is a competitive advantage someone like Nvidia has at the moment and so they are trying to push that. I'm also pretty skeptical, and could imagine this whole thing blowing up, but it's not like this a big grift that's going to end up like the GFC either.
    - mgh95 1y ago
      I think it's possible we are in datacenter GPU overcapacity already, and NVIDIA is burning its stock to avoid the music stopping. It's already happening in China that datacenters are at GPU overcapacity. I wouldn't be surprised if it occurs here.
theptip 1y ago
OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. I do buy that they are extremely over-valued if they have to slow down on model training. For cloud providers, the analysis is a bit more complex; presumably if training demand craters then the existing inference demand would be met at a lower price, and maybe you’d see some consolidation as margins got compressed.
- mgh95 1y ago
  > OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. But OpenAI can't stop training their next generation models. OpenAI already spends over 50% of their revenue on inference cost [1] with some vendors spending over 100% of their revenue on inference. The real cash cow for them is in the business segment. The problem here is models are rapidly cloned, and the companies adjacent to model providers actively seek to provide consumers the ability to rapidly and seamlessly switch between model providers [2][3]. Model providers are in the situation you imagine cloud providers to be in; a non-differentiated, commodity product with high fixed costs, and poor margins. [1] https://www.wheresyoured.at/why-everybody-is-losing-money-on-ai/ https://www.wheresyoured.at/why-everybody-is-losing-money-on... [2] https://www.jetbrains.com/help/ai-assistant/use-custom-models.html https://www.jetbrains.com/help/ai-assistant/use-custom-model... [3] https://code.visualstudio.com/docs/copilot/customization/language-models https://code.visualstudio.com/docs/copilot/customization/lan...
  - Workaccount2 1y ago
    That's why we are seeing these insane numbers. The competition is "do or die" right now. Zuckerberg said in an interview last week he doesn't mind spending $100B on AI, because not investing carries more risk.
    - mgh95 1y ago
      This only applies if you think one of two things; First, that it is guaranteed that this specific line of inquiry will lead to development of a form of superintelligence or otherwise broadly applicable development; or second, the form of machine learning technologies that unlocks or otherwise enables a market which would otherwise be inaccsesible that justifies this investment. To date, no evidence of either even exists. See Zuckerbergs recent live demo of Facebooks Ray Bans technology, for example.
empath75 1y ago
Amazon lost money every year for the first 9 years of it's existence and people said it was a bubble the entire time.
- mgh95 1y ago
  Amazon was gross margin profitable -- and significantly so -- the entire time. It just turns out they were a server farm subsidizing a gift shop.
  - rhetocj23 1y ago
    Yeah this. Ultimately the marketplace was just an investment that had embedded within it a real option for AWS. Magical really.
cluckindan 1y ago
Wow, diluting stock during a bull run is incredibly short-sighted. NVIDIA is betting there will never be a downturn. If there is, the dilution causes late investors to either be left holding the bag or be forced to sell (potentially at a loss), meaning the stock has the potential to drop like a stone at the first sign of trouble. I guess that’s why they would be gaming their numbers: to convince the next greater fools.
radu_floricica 1y ago
> OpenAI is not currently profitable, and it is unclear if its business model will ever becomes profitable -- let alone profitable enough to justify this investment. Well, yes. Which again is how venture capitalism has worked for ... is it decades or centuries? There is always an element of risk. With pretty solidly established ways to handle: expected value, risk mitigation etc. I haven't lived through the dot com bubble (too young) but i've read about it. The absolutely insane ways they were throwing money at startups were... just insane. The potential of the technology is the same now and then: AI vs Internet. It wasn't the tech that failed the last time, it was the way the money was allocated. The math is actually quite mathing this time around. Most AI companies have solid revenues and business models. They aren't turning a profit because (like any tech startup) they chose to invest all their revenue plus investments into growth, which in this case is research and training new models. They aren't pivoting every 6 months, aren't burning through cash reserves just to pay salaries, and they've already gone through train/deploy cycles several times each, successfully. Are they overvalued? shrug that's between them and their investors, and we'll find that out eventually. But this is not a bubble that can burst as easily as last time, because we're all actually using and paying for their products.

by mgh95 1y ago

davedx 1y ago

OpenAI generates plenty of revenues from their services. Don't conflate revenues with profits

mgh95 1y ago
I don't believe I am. Investors (value investors, not pump and dump investors) provide capital to companies on the expectation of profit, not revenue.
- charcircuit 1y ago
  Sure, and as long the expected profit keeps increasing investors are happy. They don't need to make an actual profit yet.

humanizersequel 1y ago

They're doing about a billion per month in revenue by running proprietary models on GPUs like these. Unless they're selling inference with zero/negative margin, it seems like a business model that could be made profitable very easily.

mgh95 1y ago
Revenue != profit, and you don't need to become net negative margin to be net unprofitable. Expensive researchers, expensive engineers, expensive capex, etc. Inference has extremely different unit economics from a typical SaaS like Salesforce or adtech like google or facebook.
- humanizersequel 1y ago
  All of those expenses could be trimmed in a scenario where OpenAI or other big labs pivot to focus primarily on profitability via selling inference.
  - mgh95 1y ago
    Currently, selling LLM inference is a red queen race: the moment you release a model, others begin distilling and attempting to sell your model cheaper, avoiding the expensive capitalized costs associated with R&D. This can occur because the LLM market is fundamentally -- at best -- minimally differentiated; consumers are willing to switch between vendors ("big labs", as you call them, but they aren't really research labs) to whomever offers the best model at the lowest price. This is emphasized by the distributors of many LLMs, developer tools, offering ways to switch the LLM at runtime (see https://www.jetbrains.com/help/ai-assistant/use-custom-models.html https://www.jetbrains.com/help/ai-assistant/use-custom-model... or https://code.visualstudio.com/docs/copilot/customization/language-models https://code.visualstudio.com/docs/copilot/customization/lan... for an example of this). The distributors of LLMs actively working against LLM providers margin provides an exceptionally strong headwind. This market dynamic begets a low margin race to the bottom, where no party appears able to secure the highly attractive (think the >70% service margin we see in typical tech) unit economics typical of tech. Inference is a very tough business. It is my opinion (and likely the opinion of many others) that the margins will not sustain a typical "tech" business without continual investment to attempt to develop increasingly complex and expensive models, which itself is unprofitable.
    - humanizersequel 1y ago
      I don't disagree but you're moving the goalposts. I never said that they could achieve the profits of a typical tech business, just that they could be profitable. Also, the whole distilling problem doesn't happen if the model is proprietary.
      - mgh95 1y ago
AlexandrB 1y ago
And even if they are selling inference at negative margin, they'll make it up in scale!
- kapone 1y ago
  These kinds of phrases are...eerily similar to the phrases heard right before...the .com bust. If you were old enough at the time, that's exactly what the mindset was back then. The classic story of the shoeshine boy giving out stock tips...and all that. We all know how that turned out.

crowcroft 1y ago

The counter point to this is that while not profitable, the cashflow is real, and inference is marginally ROI positive. If you can scale inference with more GPUs then eventually that marginal ROI grows large enough to cover the R&D and other expenses and you become profitable.

mgh95 1y ago
"Marginally ROI positive" works in a ZIRP environment. These are huge capital investments; they need to at least clear treasury return hurdles and importantly provide attractive returns. I am fundamentally skeptical of "scaling inference". Margins are not defensible in the market segment OpenAI is in.
- crowcroft 1y ago
  For some of these tech companies their valuations let them go to the market with their equity in way that is basically a ZIRP environment. In a way you could say this is a competitive advantage someone like Nvidia has at the moment and so they are trying to push that. I'm also pretty skeptical, and could imagine this whole thing blowing up, but it's not like this a big grift that's going to end up like the GFC either.
  - mgh95 1y ago
    I think it's possible we are in datacenter GPU overcapacity already, and NVIDIA is burning its stock to avoid the music stopping. It's already happening in China that datacenters are at GPU overcapacity. I wouldn't be surprised if it occurs here.

theptip 1y ago

OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. I do buy that they are extremely over-valued if they have to slow down on model training. For cloud providers, the analysis is a bit more complex; presumably if training demand craters then the existing inference demand would be met at a lower price, and maybe you’d see some consolidation as margins got compressed.

mgh95 1y ago
> OpenAI is profitable if they stop training their next generation models. Their unit economics are extremely favorable. But OpenAI can't stop training their next generation models. OpenAI already spends over 50% of their revenue on inference cost [1] with some vendors spending over 100% of their revenue on inference. The real cash cow for them is in the business segment. The problem here is models are rapidly cloned, and the companies adjacent to model providers actively seek to provide consumers the ability to rapidly and seamlessly switch between model providers [2][3]. Model providers are in the situation you imagine cloud providers to be in; a non-differentiated, commodity product with high fixed costs, and poor margins. [1] https://www.wheresyoured.at/why-everybody-is-losing-money-on-ai/ https://www.wheresyoured.at/why-everybody-is-losing-money-on... [2] https://www.jetbrains.com/help/ai-assistant/use-custom-models.html https://www.jetbrains.com/help/ai-assistant/use-custom-model... [3] https://code.visualstudio.com/docs/copilot/customization/language-models https://code.visualstudio.com/docs/copilot/customization/lan...
- Workaccount2 1y ago
  That's why we are seeing these insane numbers. The competition is "do or die" right now. Zuckerberg said in an interview last week he doesn't mind spending $100B on AI, because not investing carries more risk.
  - mgh95 1y ago
    This only applies if you think one of two things; First, that it is guaranteed that this specific line of inquiry will lead to development of a form of superintelligence or otherwise broadly applicable development; or second, the form of machine learning technologies that unlocks or otherwise enables a market which would otherwise be inaccsesible that justifies this investment. To date, no evidence of either even exists. See Zuckerbergs recent live demo of Facebooks Ray Bans technology, for example.

empath75 1y ago

Amazon lost money every year for the first 9 years of it's existence and people said it was a bubble the entire time.

mgh95 1y ago
Amazon was gross margin profitable -- and significantly so -- the entire time. It just turns out they were a server farm subsidizing a gift shop.
- rhetocj23 1y ago
  Yeah this. Ultimately the marketplace was just an investment that had embedded within it a real option for AWS. Magical really.

cluckindan 1y ago

Wow, diluting stock during a bull run is incredibly short-sighted. NVIDIA is betting there will never be a downturn. If there is, the dilution causes late investors to either be left holding the bag or be forced to sell (potentially at a loss), meaning the stock has the potential to drop like a stone at the first sign of trouble. I guess that’s why they would be gaming their numbers: to convince the next greater fools.

radu_floricica 1y ago

> OpenAI is not currently profitable, and it is unclear if its business model will ever becomes profitable -- let alone profitable enough to justify this investment. Well, yes. Which again is how venture capitalism has worked for ... is it decades or centuries? There is always an element of risk. With pretty solidly established ways to handle: expected value, risk mitigation etc. I haven't lived through the dot com bubble (too young) but i've read about it. The absolutely insane ways they were throwing money at startups were... just insane. The potential of the technology is the same now and then: AI vs Internet. It wasn't the tech that failed the last time, it was the way the money was allocated. The math is actually quite mathing this time around. Most AI companies have solid revenues and business models. They aren't turning a profit because (like any tech startup) they chose to invest all their revenue plus investments into growth, which in this case is research and training new models. They aren't pivoting every 6 months, aren't burning through cash reserves just to pay salaries, and they've already gone through train/deploy cycles several times each, successfully. Are they overvalued? shrug that's between them and their investors, and we'll find that out eventually. But this is not a bubble that can burst as easily as last time, because we're all actually using and paying for their products.