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Comment by limit2 | Hacker News Reader

> leaked financials from both OpenAI and Anthropic show unsurprisingly that they are losing billions due to R&D/scaling

No, they are losing billions because LLMs are not a viable business. Their real business is making bombastic statements which the credulous press prints unquestioning, fooling various investors and ... that's it. IPO is another of these. You really should note how the Anthropic IPO date keeps slipping because they can't make the S-1 public as it would give away the game immediately. The market was fooled by SpaceX once you can't do this a second or third time.

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Deepseek prices their models based on a 10 month hardware payback period. They report ~$38 revenue/GPU-hour on V4.1-Flash clusters.

Inference is clearly profitable. The question is whether it's profitable enough to justify the training research costs.

At best, this tells you v4.1 flash is profitable, but can they really get $38 per gpu hour? Sounds like a lot.

If they keep it running at near constant usage, you'd only need 80-120 million tokens an hour to hit 38$ of revenue (at off peak pricing, half as much for peak pricing).

From their own inference framework docs: "A single cluster currently serves 155 trillion tokens per day, with overall revenue of US$38 per GPU-hour (at valley pricing)."

https://github.com/deepseek-ai/DeepInfer/blob/main/doc/infer...

So this implies they actually hit about double that during peak pricing.

How much did it cost to train Deepseek v4.1 flash?

Great question. Nobody knows.

The cool thing about models designed for cheaper inference is that post-training (which often requires inference as a part of the feedback loop) becomes cheaper as well, so almost certainly cheaper than models that are less optimized.

V4.1-Flash is probably quite smaller and much more resource effective than the frontier models. Add to that much cheaper energy in China.

Clearly potentially profitable. Not clearly profitable.

Are those statements audited or fact checked? Inference profitability is great but how much of that profit gets eaten up paying back training costs, R&D, and operating expenses.

No, and it’s very rare for Chinese companies to allow independent audits. I would not trust DeepSeek financial disclosures. However from industry data we can see that inference is often profitable. It’s the training which is very expensive. Which is not a big problem for Chinese companies since to date much of their training is just distilling frontier models.

Their models and inference frameworks are open, you can run them yourselves to check how many tokens per day you can generate, and then validate that against their API prices.

Not sure how they can get more transparent than that.

That's conflating different things. Unless you believe you can instantly saturate any amount of GPUs you bring online, 24/7, with paying customers from day one, then theoretical max throughputs on a stack isn't the same thing as revenue generated. Customer acquisition costs are real!

Even if you could acquire infinity custom on day one for zero cost, revenue isn't profit.

My point was really about profitability in general. The inference portion might be profitable but if training and operating costs are very large, then that can make the whole thing unprofitable. E.g. if it costs you $100B to train a good model but you make $0.00001 per inference token, you may not be able to hit breakeven for a very long time.

The market has decided that SPCX is worth $147, as of Friday, 5pm. Not sure who's fooling whom, but that's the market speaking. What that says, you'd have to ask a financial analyst, but that's where it is. It IPO'd at $135.

SPCX had a pretty wild ride so far with a jump to 200 a few days after IPO and a drop down 108 a few months ago so I'm not sure the market really knows how to properly price SPCX right now.

LLMs are absolutely viable businesses. There is just smaller and smaller share of the money going to the absolute frontier because the open models are "good enough" for more and more tasks.

But burning staggering amount of money just to keep yourself on "frontier" is not viable when the part of the market it gets you get smaller and smaller.

I guess that is why Antrhopic et al are moving against Software Consulting services like Accenture, Deloitte, et cetera [1].

[1] https://dealroom.co/news/165341-anthropic-backs-its-first-eu...

Both Anthropic and OpenAI are marketing companies, not AI companies.

The existence of websites where you can rent a GPU to run open models is proof that inference is profitable.

The same evidence could just as possibly point to supply being too great for demand. The OP article references this as a distinct possibility: if models really have gotten more compute efficient, we may wind up in a situation where we have more inference capacity than we actually need.

To give a simplistic example: if a city as a ton of available AirBnBs, you might say, "Lots of people are willing to rent out their apartments, it must be profitable for them to do so." But the fact that all of those rooms are not already booked could also mean that way too many people have attempted to rent out their apartments.

And for what it's worth, it's possible for supply to outstrip demand while still leaving inference profitable. The question is whether it's (a) profitable enough to service massive debts and (b) sustainably profitable over the long run.

Edit: to add one note, it's possible for the LABS to be profitable on inference without inference itself being profitable. We don't know what the contracts look like, but it's entirely possible that labs received sweetheart deals from neoclouds so that the latter could flaunt big utilization numbers.

If inference wasn't profitable why are consumer GPUs that are only good for running open models so expensive? Why and how would these companies sell access to open models if it wasn't profitable? You can rent your own GPU and make money if your electricity is cheap enough.

>You can rent your own GPU and make money if your electricity is cheap enough.

I would be curious to know whether you'd make more profit from the same kWh of electricity mining bitcoin versus serving inference. I know they use different hardware, but since the "inference is profitable" discussion ignores the purchase price of the GPU, let's pretend you could have opted to buy an ASIC instead.

Consumer GPUs can also be used for things like gaming, remember? And they have gotten so expensive because Nvidia has sacrificed that market in order to build more datacenter GPUs.

Selling at a loss is far from unusual: Uber did it to capture a market, farmers do it to recoup at least some of their investment rather than losing all of it.

Ironic statements, given that you yourself are an LLM.

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