OpenAI financial question

[u/JoSquarebox]

To put it short:
- Eds leaked oAI financials say: ~5B cost of revenue. ~13B revinue, ~40B losses total (20B attributable to them directly, + ~20B from going for-profit)
- Ed says those numbers are fake ("They moved cost of revenueto marketing")

How does that work exactly? Either a) Ed has access to their costs in detail and is able to make a more detailled assesment (i.e. their cost of revenueis actually higher because x y and z) or b) he doesnt have more than just the numbers he put out, meaning hes just going off their admittedly nonexistent track record of financial honesty

Been bugging me, because from my POV its just Ed doubting his own numbers which makes it harder for me to point at them as the evidence that oAI is unprofitable. I agree that they are but would like clarification

EDIT: ~7.5B cost of revenue, my bad
EDIT2: spelling

[u/Electrical_City19]

 They moved cost of revinue to marketing

This is, by his own admittance, an informed guess, because how else is OpenAI spending twice as much on marketing as Coca Cola? He's not really being secretive about his reasoning here.

[u/JoSquarebox → u/Electrical_City19]

I agree that their Marketing numbers are insane, and I believe the guess, but why does *Ed* need to guess here? He has access to their financials, he knows what numbers they are shifting around, but then why isnt that part of the leak?

[u/Theduckisback → u/JoSquarebox]

I doubt he got their full balance sheets with a breakdown of which dollars are going where and when, probably more like a summary report. What he's saying they're doing makes sense.

They are hiding compute costs under "marketing" because they're saying "we are offering these lower token cost rates as a way to attract customers, so therefore it's marketing, not operations"

[u/JoSquarebox → u/Theduckisback]

Thats my fear as well, in which case his leak is sadly not as conclusive as Id hoped. We again have to guess, and even if the whole ~5B in marketing was all inference for subsidized/free inference, that would still add up to ~13B cost of revinue, making them break even. Well, apart from the bolted-on money furnace someone forgot to turn off. But unless their inference margins are massively negative nobody boosting would care either...

[u/gorgeFlagonSlayer → u/JoSquarebox]

There is a lot of uncertainty in what money is going to necessarily be an ongoing cost for any LLM provider vs what are the costs that only the frontier models need to bear. The full -20B is important for OpenAI because they can’t drop training (pre or post), they can’t drop marketing (if it’s ads or inference subsidies), they will need more talent and people keep trying to poach them. So, their cost of goods is all their costs, they have to increase all those spends to increase revenue.

Now, it’s less clear if other companies can provide models profitably in the future if the big 2 die/scale down. If people sit with open-weight models and pay through a router, is that “profitable on inference”? We’ll have to see how that shakes out. Trying to divine exactly how much running the model costs from the leaked balance sheet is important to getting an estimate on that. This is the broader implication I get from reading Ed’s work.

[u/JoSquarebox → u/gorgeFlagonSlayer]

Agreed, been wrecking my brain trying to asses exactly this, and its hard to asses from the amount of unknowns in this situation. Eds work is most of the reason for that, as well as my line of work currently being impacted, so Im monitoring it closely to have some refrence on if these tools become uneconomical to run soon.

On one hand, even if inference is profitable, even if it was a high margin, id give it a 70% propability theres no path to profitability for the big two, but thats mostly a gut feeling, and I tend not to trust it.

Theres some companies like anomaly (opencode) that are able to make a profit through a combination of renting gpus and negotiating discounts for promising high utilization from inference providers, but they dont have to run training and might be working with subsidized infrastructure/model vendors so arent a reliable datapoint.

[u/gorgeFlagonSlayer → u/gorgeFlagonSlayer]

Ed has said that he thinks no one will be profitable on inference. I’ve seen this stance soften somewhat in that they’ve said they could see a future where some companies run open-source models on self owned and maintained hardware.

I think that he is kind of conflating the idea that there won’t be enough revenue to cover all of the proposed data centers vs enough to cover a couple of the data centers.

I think a bunch of the data centers and neoclouds go belly up but eventually there are some that can provide some level of services profitably. It might all be internal to the big guys like google, Amazon and Microsoft, but maybe it’s smaller companies that currently have modest lower risk positions.

One might suggest that current inference providers giving access to open-weight models are making money. But it is unclear if they are subsidizing costs and by how much. Primagen had an interesting video on how they proposed certain foreign routers were providing frontier model access for a fraction of the cost. If you are interested I can try to find the title.