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Comment by mgh95 | Hacker News Reader

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.

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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.

> 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...

[2] https://www.jetbrains.com/help/ai-assistant/use-custom-model...

[3] https://code.visualstudio.com/docs/copilot/customization/lan...

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.

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.

I agree the market dynamics are weird now, I disagree that says much about the existence of other equilibria.

For example, inference on older GPUs is actually more profitable than bleeding-edge right now; the shops that are selling hosted inference have options to broaden their portfolio the advancement of the frontier slows.

Cloud providers are currently “un-differentiated”, but there are three huge ones making profits and some small ones too. Hosting is an economy-of-scale business and so is inference.

And all of these startups you quote like Cursor that are not free-cash-flow positive are simply playing the VC land grab game. Costs will rise for consumers if VCs stop funding, sure. That says nothing about how much TAM there is at the new higher price point.

The idea that OAI is un-differentiated is just weird. They have a massively popular consumer offering, a huge bankroll, and can continue to innovate on features. Their consumer offering has remained sticky even though Claude and Gemini have both had periods of being the best model to those in the know.

And generally speaking there are huge opportunities to do enterprise integrations and build out the retooling of $10T of economic activities, just with the models we have now; a Salesforce play would be a natural pivot for them.

> Cloud providers are currently “un-differentiated”, but there are three huge ones making profits and some small ones too. Hosting is an economy-of-scale business and so is inference.

Anybody who has worked in a compliance heavy segment (PCI-DSS, HIPAA, etc.) will tell you the big 3 clouds have very significant differences from the smaller players. The differentiation is not on compute itself, but on the product. It's partially why products like AWS Bedrock exist and are actively placing model providers both in competition with eachother and AWS itself which is exactly the market dynamic they should seek to avoid.

> The idea that OAI is un-differentiated is just weird. They have a massively popular consumer offering, a huge bankroll, and can continue to innovate on features. Their consumer offering has remained sticky even though Claude and Gemini have both had periods of being the best model to those in the know.

This is exactly where this line of reasoning goes off the rails. The consumer market is problematic (see the recent post about the segment its growing in; basically young women of limited spend in low income countries); a huge bankroll is also a huge liability, model providers are on a clock to get huge or die, and the innovation we are seeing is effectively attempting to "scale-up" models, not provide novel features.

> Their consumer offering has remained sticky even though Claude and Gemini have both had periods of being the best model to those in the know.

This isn't a good thing with current market mix.

> And generally speaking there are huge opportunities to do enterprise integrations and build out the retooling of $10T of economic activities, just with the models we have now; a Salesforce play would be a natural pivot for them.

Do you have any indication these are achieving buy in or profitable? Most significantly, we have seen a recent study by MIT that 95% of generative AI pilots fail. The honeymoon period is rapidly coming to a close. Tangible results are necessary.

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

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.

Sure, and as long the expected profit keeps increasing investors are happy. They don't need to make an actual profit yet.

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.

"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.

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.

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.

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.

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.

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.

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.

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-model... or 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.

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.

> 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.

In the absence of typical software margins, they will be eroded by providers of "good enough" margins (AWS, Azure, GCP, etc.) who gain more profit from the bundled services than OpenAI does from the primary services. This has happened multiple times in history, either resulting in smaller businesses below IPO price (such as Elastic, Hashicorp, etc.) or outright bankruptcy.

Second, the distilling happens on the outputs of the model. Model distillation refers to the usage of a models outputs to train a secondary smaller model. Do not mistake distillation for training (or retraining) to sparse models. You can absolutely distill proprietary models. In fact, that is how DeekSeek-R1-Distill-Qwen and the DeepSeek-R1-Distill-Llama are trained. This also happens with Chinese startups distilling OpenAI models to resell [2].

The worst part is OpenAI is already having to provide APIs to do this [1]. This is not ideal, as OpenAI wants to lock people into (as much as possible) a single platform.

I really don't like OpenAIs market position here. I don't think it's long term profitable.

[1] https://openai.com/index/api-model-distillation/

[2] https://www.theguardian.com/technology/2025/jan/29/openai-ch...

> Revenue != profit

Indeed. And even if that revenue is net profitable right now (and analysts differ sharply on whether it really is), is there a sustainable moat that'll keep fast-followers from replicating most of OpenAI's product value at lower cost? History is littered with first-movers who planted the crop only to see new competitors feast on the fruit.

And even if they are selling inference at negative margin, they'll make it up in scale!

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.

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

Amazon was gross margin profitable -- and significantly so -- the entire time.

It just turns out they were a server farm subsidizing a gift shop.

Yeah this.

Ultimately the marketplace was just an investment that had embedded within it a real option for AWS. Magical really.

> 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.

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