why should only profits matter? if i had a killer product today that i just need to sell tomorrow, wouldn't you still invest today knowing i'll probably only start to make money tomorrow (or perhaps next week)?
the expectation is that they'll eventually make money. they can't raise forever. only startups are not profitable for a few years. but most companies that have existed for a long while have been profitable
and since they're expected to make a LOT of money, everyone wants a piece of that future pie, pushing up the valuation and amount raised to admittedly somewhat delusional levels like here
It's well know everyone is making great money on inference. The cost is training.
Whether GPT-5 was profitable to run depends on which profit margin you’re talking about. If we subtract the cost of compute from revenue to calculate the gross margin (on an accounting basis),2 it seems to be about 30% — lower than the norm for software companies (where 60-80% is typical) but still higher than many industries.
(They go on to point out that there are other costs that might mean they didn't break even on other costs - although I suspect these costs should be partially amortized over the whole GPT 5.x series, not just 5.0)
"Most of what we're building out at this point is the inference [...] We're profitable on inference. If we didn't pay for training, we'd be a very profitable company"
"There’s a bright spot, however. OpenAI has gotten more efficient at serving paying users: Its compute margin—the revenue left after subtracting the cost of running AI models for those customers—was roughly 70% in October, an increase from about 52% at the end of last year and roughly 35% in January 2024."
> It's well know everyone is making great money on inference.
That is not, in fact, "well known", but based entirely on the announcements of the inference providers themselves who also get very cagey when asked to show their work and at least look like they're soliciting a constant firehose of investment money simply to keep the lights on. In particular there's a troubling tendency to call revenue "recurring" before it actually, you know, recurs.
> based entirely on the announcements of the inference providers themselves who also get very cagey when asked to show their work
I mean sure, it's self reported.
But the inference prices somewhere like Fireworks or TogetherAI charges is comparable to what Google/AWS/Azure charge for the same model an we know they aren't losing money - they have public accounts that show it, eg:
> If someone has a subscription then yes that is pretty normal.
Not if you've substantively changed rate limits 3 times in the last 5 months while still counting those forecast revenues. In most industries that's called rug-pulling.
It doesn’t matter how you call it. A recurring subscription on the books is a recurring subscription. Yes you can cancel anytime (how generous of them), it also doesn’t matter.
And why do you think twenty competitors can stay competitive for years to come?
Industries always consolidate and winners emerge. SOTA LLMs look like a natural monopoly or duopoly to me because the cost to train the next model keeps going up such that it won't make sense for 20 competitors to compete at the very high end.
TSMC is a perfect example of this. Fab costs double every 4 years (Rock’s Law). It's almost impossible to compete against TSMC because no one has the customer base to generate enough revenue to build the next generation of fabs - except those who are propped up by governments such as Intel and Rapidus. Samsung is basically the SK government.
I don’t see how companies can catch OpenAI or Anthropic without the strong revenue growth.
Google has already surpassed them both in all areas except coding. People on HN only look at benchmarks, but Gemini's multimodal understanding, things like identifying what a plant is, normal user use cases (other than chatting), integration with other tools, is much better.
It's believable that Meta, ByteDance, etc. can catch up too. It is not certain that scaling will meaningfully increase performance indefinitely, and if it stops soon, they surely will. Furthermore, other market conditions (US political instability) can enable even more labs, like Mistral, to serve as compelling alternatives.
Uber, TSMC, etc. have strong moats in the form of physical goods and factories. LLMs have nothing even remotely comparable. The main moat is in knowledge, which is easy to transfer between labs. Do you think all the money that goes into training a model goes into the actual final training run? No, it is mostly experiments and failed ideas, which do not have to be repeated by future labs and offshoots.
>Industries always consolidate and winners emerge.
no, most industries just sell boring generic products, a few industries favor monopolists. Semiconductors are one of them but LLMs are also as far removed from that business as is physically possible.
TSMC makes the most complicated machines humans have ever built, a LLM requires a few dozen nerds, a power plant, a few thousand lines of python and chips. That's why if you're Elon Musk you could buy all of the above and train yourself an LLM in a month.
LLMs are comically simple pieces of software, they're just big. But anyone with a billion dollars can have one, they're all going to be commoditized and free in due time, like search. Copying a lithography machine is difficult, copying software is easy. that's why Google burrowed itself into email, and browsers, and your phone's OS. Problem for openai is they don't have any of that, there's already half a dozen companies that, for 99% of people, do what they do.
The barrier to replicating TSMC isn't just cost, it's supply chain, geopolitics, and talent.
Only one company on Earth can make the UV lithography machines TSMC buys for their highest end fabs, and they're not selling to anyone else.
The PRC tried to brute force this supply chain backed by the full might of the Party's blank check, all red tape cut, literally the best possible duplication scenario, and they failed.
They will succeed eventually since they have proof it’s possible and their plans span decades. I expect them to have working EUV in 10 years. Whether it’ll still be bleeding edge tech is a different question I dare not guess the answer to.
the expectation is that they'll eventually make money. they can't raise forever. only startups are not profitable for a few years. but most companies that have existed for a long while have been profitable
and since they're expected to make a LOT of money, everyone wants a piece of that future pie, pushing up the valuation and amount raised to admittedly somewhat delusional levels like here