Inference is clearly profitable. The question is whether it's profitable enough to justify the training research costs.
Not sure how they can get more transparent than that.
https://github.com/deepseek-ai/DeepInfer/blob/main/doc/infer...
So this implies they actually hit about double that during peak pricing.
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.
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.
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.
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.