just for completeness, I think the closer analogue is probably total expenses: $12.48 billion to $34 billion -- roughly 2.7x. But this is still pretty close to what you said, so I don't particularly disagree with the numbers.
I do wonder if this comparison is really meaningful. It looks like if they can grow infinitely, then at some point they should be profitable. However, that's already a somewhat sad story ("in the limit as x->inf, we'll actually _make_ money!"). And there are of course limitations. Anthropic, Google, open models etc are all real competitors, and it seems to me that there will only be one winner. If openAI is losing money faster than the others, then it may not survive long enough to reach that eventual profitability. And finally, the human population is limited. There isn't a true infinity that the pattern can extend to. If we've only reached 10% of the TAM that's fine, but if we're at like 70% (which personally I suspect is about right), then this looks bad.
The AI companies also have a lot of space to grow their income (more ads, price hikes, ...). It seems realistic for them to turn profitable. But the market expected much more from these companies.
> Revenue went from $3.7B to $13.07B — roughly 3.5x.
> Operating loss went from ~$8.8B to ~$20.9B — roughly 2.4x.
> Doesn't seem like a domesday scenario.
Those two lines are moving up and to the right, but are not parallel.
It all depends on where those two lines meet (the break-even point): too far in the future and the company will be dead anyway. Almost all companies will eventually be profitable; the problem is that the majority of them will need constant cash injections to keep the lights on.
Like the old aviation saying: even a brick will fly if it has enough thrust. doesn't make the brick a plane, though.
Compounding revenue & operating loss at those same rates (3.5x and 2.4x respectfully) puts those two lines meeting at around 2031. That'd be about 9-10 years to profitability, that seems pretty normal. Amazon took 9 years, Uber took 14 years before its first profitable year.
I think it depends on a lot of things, not the least of wish is, this could be the worst their financials get, or depending how competitive this whole thing is, it could be the best:
I'm a little confused here. Cost of revenue is lower than revenue. That's good. R&D is the main contributor to losses here and this seems normal in an industry like this. For OpenAI specifically, I think this is problematic. They were the first movers but despite the large R&D they've lost so much ground to Anthropic despite Anthropic seemingly gifting them with weird PR self owns. But if we were to extrapolate this to the industry as a whole, this seems more positive than negative. Am I reading this incorrectly? Unless there's an assumption that R&D costs have to forever go up in order to increase revenue, I feel like this shows that the AI industry is actually on a path to profitability in the long term.
Whether it can physically be as all encompassing as it makes itself out to be or whether it will just be healthily profitable remains to be seen. Kind of like how Uber went from "We'll autonomously drive the world" to "Look, we deliver food, goods, and people to locations and we figured out how to do that in a way that makes profits. Also, ads".
At the end of his previous article (https://www.wheresyoured.at/ai-is-slowing-down/), Ed hyped this news as "a story that will possibly burst the AI bubble" and "imagine what the worst possible thing for me to get would be and you’re probably close." This news doesn't fit either criteria: OpenAI losing billions of dollars isn't shocking news and both AI boosters and AI skeptics have likely assumed that. If anything, the news that OpenAI has $25B on hand in cash as reported here, plus the $122B raised in March, show that OpenAI won't implode for another year or two if it does...and that doesn't say anything about the AI bubble. There's also the confounder that Codex wasn't released until this year which turbocharged revenue with an uncertain increase in operating costs, so it will be difficult to extrapolate 2025 finances to 2026 and beyond.
When I read "the worst possible thing for me to get" I had assumed it would be evidence that inference/Codex is fundamentally unprofitable (as Ed often blogs about) but there isn't enough information here to support that argument either: revenue is still greater than cost of revenue, and the major losses are clearly delineated.
> I had assumed it would be evidence that inference/Codex is fundamentally unprofitable (as Ed often blogs about)
I'm not sure where they'd get that idea from? If inference was fundamentally unprofitable, I don't think we'd have seen the massive CapEx spend & VC cash flooding into AI, it'd be a negative gross margin trap if that were the case.
It looks unprofitable because of the massive CapEx spend right now to build data centers.
People that think inference is not profitable are mistaking the total compute cost as inference cost, when really it needs separated into training compute vs. inference compute.
The bigger question is, is when does training slow down, if at all? If we hit plateaus with LLMs, at that point inference becomes nearly pure profit once you own the compute (and a hardware refresh cycle every 3-5 years).
LLMs eventually hitting a dead end for more advanced capabilities is what would spell trouble for the labs. Any existing hyperscaler cloud can run inference all day long, as long as they have access to a model. They don't need OpenAI or Anthropic for that. The frontier labs entire valuations rely purely on them staying ahead of the commodity curve. The moment they can't do that, they're done.
> I had assumed it would be evidence that inference/Codex is fundamentally unprofitable (as Ed often blogs about)
Ed's claim is that they haven't shown inference to be profitable. Which is true? And that he personally believe it is unprofitable (his personal opinion, not what his data report).
I think that's a meaningful distinction with your statement
I think Ed would argue that if 90% of your customers are only using your product because their usage is subsidised and the money to cover that subsidisation is coming from unsustainable customers tokenmaxxing then you are “fundamentally unprofitable”.
The question is, can OpenAI survive if customers start tokenminning? A pure inference business could be profitable but that’s not the business OpenAI are in. OpenAI has a billion users that OpenAI loses money on.
What is the right way to deal with Ed Zitron articles because he’s historically extremely inaccurate and makes wild claims.
People ignore all his horrendous takes from last year and still eat this years “analyses” like it’s Gods words.
He has been predicting the doom for years and years now and it is strange to see HN still putting credence here.
This is what he said around a week back
“ One of my sources has come forward and brought me a story that will possibly burst the AI bubble. The reason they brought this to me is that I’ve shown — and will continue to show — that I actually give a shit about this industry and the people in it.
If you’re wondering what the story is, know that it’s the information I’ve wanted for years, delivered as I have always wanted it, and I will treat it with the reverence it deserves. Imagine what the worst possible thing for me to get would be and you’re probably close.
I expect it to be out in the next two weeks, and you’ll know exactly when it runs. There’ll be a podcast and a newsletter, and very likely follow-on coverage elsewhere.
I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.”
This is qanon tier stuff. He’s been pulling this shtick for a while and people still haven’t caught on.
I don't think people ignore anything. Every single Ed Zitron post on HN has dozens of top-level comment exactly like yours, "No no no don't listen to Ed, he's a hack and AI is great".
To be honest I almost think the numbers are irrelevant. In 2024/25 there was a lot going on - will AI replace authors, film makers etc. Will it replace social media (anyone remember Sora?). A tonne of that stuff didn't work out. At the tail end of 2025 a real product market fit emerged. Coding agents. They work. They do a job that you can actually profit from.
So everything else is kind of academic. Of course they were losing money in 2025, they had a technology that was kind of cool - clearly eventually going to deliver something great, but they didn't actually have anything somebody should pay for. Now they have a thing that people will pay for. So who cares what they lost in 2025?
So what's important today is - how competitive are they with Anthropic in delivering that product. How do the economics of companies using AI agents for coding work. That's all. I don't think there's really an argument about them losing money on inference any more.
The free fast-follow situation with open models seems to be a big "if" here. It's not particularly hard to set up OpenCode for your company and plug in one of the myriad inference providers running free models. All of these stacks are one release away from being a dramatically different value proposition, as proved by, well, Anthropic themselves.
I guess we'll see if people will pay a premium for Anthropic in ~6 months, 12 months, etc. If not, well, it's a race to commodity.
> As OpenAI’s worth rose, the increased value of those investor rights created a roughly $30bn charge, added the person. The charge is not expected to recur following the restructuring, they said.
> Stripping out the charge and other non-cash expenses, such as stock-based compensation of staff and computing credits from Microsoft, OpenAI’s losses were $8bn, according to the person.
I think something people are missing in the headlines. The actual losses were 60b with 17b removed from the bottom line figure. To quote a reddit post "removing $17.87 billion in costs via that “net loss attributable to noncontrolling members capital”"
I'm a simple guy and I don't understand the "sales and marketing" cost.
I don't like these products. I have several negative opinions on them. To the extent they work and there is a customer base what marketing could you /possibly/ be engaged in? Doesn't the product sort of market itself? Or another way is this a product that you can market to expand your MAUs?
It's so polarizing I can't imagine how that $5.7B is being spent.
I'm really curious about something: how far will you go to support AI? Clearly they'll need to monetize things further, would you still use [whatever AI you are paying for] if the price was doubled? Tripled? Where would you stop and would you stop using AI altogether or would you look at competitors?
During the internet bubble collapse in the 00s quite some companies went bankrupt. But that's actually a good thing. It doesn't stop progress. It creates new opportunities and new baselines. Same will happen here. AI will not be less or gone or reduced to useless. It will become better , bigger and faster.
My takeaway from this is that it's incredibly validating as a business model. Inference is _highly_ profitable. Of course, like any company that has ever tried to grow at breakneck pace, you run at a loss until you "win."
Look, for coding and a lot of other things, AI is awesome.
But the here's the killer. I have a dinky 16gb VRAM card, and that's kind of the sweet spot for the level of AI I actually want. I don't want it doing too much, I'd rather create slowly than have it one shot something that I have to then pore over later.
Feels like a company investing kazillions in, i don't know, air-conditioning or building wi-fi. Yes, it's going to be around, and also no one's gonna need THAT MUCH.
66 comments
[ 13.1 ms ] story [ 240 ms ] threadThat ship has sailed long ago into the IPO sunset.
Operating loss went from ~$8.8B to ~$20.9B — roughly 2.4x.
Doesn't seem like a domesday scenario.
I do wonder if this comparison is really meaningful. It looks like if they can grow infinitely, then at some point they should be profitable. However, that's already a somewhat sad story ("in the limit as x->inf, we'll actually _make_ money!"). And there are of course limitations. Anthropic, Google, open models etc are all real competitors, and it seems to me that there will only be one winner. If openAI is losing money faster than the others, then it may not survive long enough to reach that eventual profitability. And finally, the human population is limited. There isn't a true infinity that the pattern can extend to. If we've only reached 10% of the TAM that's fine, but if we're at like 70% (which personally I suspect is about right), then this looks bad.
> Operating loss went from ~$8.8B to ~$20.9B — roughly 2.4x.
> Doesn't seem like a domesday scenario.
Those two lines are moving up and to the right, but are not parallel.
It all depends on where those two lines meet (the break-even point): too far in the future and the company will be dead anyway. Almost all companies will eventually be profitable; the problem is that the majority of them will need constant cash injections to keep the lights on.
Like the old aviation saying: even a brick will fly if it has enough thrust. doesn't make the brick a plane, though.
the brick has a lot of thrust but there is a airplane behind it, and it's moving on its own
https://www.reuters.com/technology/openai-considers-drastic-...
Whether it can physically be as all encompassing as it makes itself out to be or whether it will just be healthily profitable remains to be seen. Kind of like how Uber went from "We'll autonomously drive the world" to "Look, we deliver food, goods, and people to locations and we figured out how to do that in a way that makes profits. Also, ads".
> Cost of Revenue: $7.5 billion
It's almost too good to be true. Did OpenAI intentionally leak this? It singlehanded eliminate the biggest concern: that tokens are sold at loss.
When I read "the worst possible thing for me to get" I had assumed it would be evidence that inference/Codex is fundamentally unprofitable (as Ed often blogs about) but there isn't enough information here to support that argument either: revenue is still greater than cost of revenue, and the major losses are clearly delineated.
I'm not sure where they'd get that idea from? If inference was fundamentally unprofitable, I don't think we'd have seen the massive CapEx spend & VC cash flooding into AI, it'd be a negative gross margin trap if that were the case.
It looks unprofitable because of the massive CapEx spend right now to build data centers.
People that think inference is not profitable are mistaking the total compute cost as inference cost, when really it needs separated into training compute vs. inference compute.
The bigger question is, is when does training slow down, if at all? If we hit plateaus with LLMs, at that point inference becomes nearly pure profit once you own the compute (and a hardware refresh cycle every 3-5 years).
LLMs eventually hitting a dead end for more advanced capabilities is what would spell trouble for the labs. Any existing hyperscaler cloud can run inference all day long, as long as they have access to a model. They don't need OpenAI or Anthropic for that. The frontier labs entire valuations rely purely on them staying ahead of the commodity curve. The moment they can't do that, they're done.
Ed's claim is that they haven't shown inference to be profitable. Which is true? And that he personally believe it is unprofitable (his personal opinion, not what his data report).
I think that's a meaningful distinction with your statement
The question is, can OpenAI survive if customers start tokenminning? A pure inference business could be profitable but that’s not the business OpenAI are in. OpenAI has a billion users that OpenAI loses money on.
People ignore all his horrendous takes from last year and still eat this years “analyses” like it’s Gods words.
He has been predicting the doom for years and years now and it is strange to see HN still putting credence here.
This is what he said around a week back
“ One of my sources has come forward and brought me a story that will possibly burst the AI bubble. The reason they brought this to me is that I’ve shown — and will continue to show — that I actually give a shit about this industry and the people in it.
If you’re wondering what the story is, know that it’s the information I’ve wanted for years, delivered as I have always wanted it, and I will treat it with the reverence it deserves. Imagine what the worst possible thing for me to get would be and you’re probably close.
I expect it to be out in the next two weeks, and you’ll know exactly when it runs. There’ll be a podcast and a newsletter, and very likely follow-on coverage elsewhere.
I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.”
This is qanon tier stuff. He’s been pulling this shtick for a while and people still haven’t caught on.
So everything else is kind of academic. Of course they were losing money in 2025, they had a technology that was kind of cool - clearly eventually going to deliver something great, but they didn't actually have anything somebody should pay for. Now they have a thing that people will pay for. So who cares what they lost in 2025?
So what's important today is - how competitive are they with Anthropic in delivering that product. How do the economics of companies using AI agents for coding work. That's all. I don't think there's really an argument about them losing money on inference any more.
I guess we'll see if people will pay a premium for Anthropic in ~6 months, 12 months, etc. If not, well, it's a race to commodity.
> As OpenAI’s worth rose, the increased value of those investor rights created a roughly $30bn charge, added the person. The charge is not expected to recur following the restructuring, they said.
> Stripping out the charge and other non-cash expenses, such as stock-based compensation of staff and computing credits from Microsoft, OpenAI’s losses were $8bn, according to the person.
I don't like these products. I have several negative opinions on them. To the extent they work and there is a customer base what marketing could you /possibly/ be engaged in? Doesn't the product sort of market itself? Or another way is this a product that you can market to expand your MAUs?
It's so polarizing I can't imagine how that $5.7B is being spent.
Alphabet: ~$4.5T value / ~$403B revenue ≈ 11× revenue
Microsoft: ~$2.9T value / ~$282B revenue ≈ 10× revenue
OpenAI: ~$850B value / ~$13B revenue ≈ 65× revenue
Can someone explains that logic?
Look, for coding and a lot of other things, AI is awesome.
But the here's the killer. I have a dinky 16gb VRAM card, and that's kind of the sweet spot for the level of AI I actually want. I don't want it doing too much, I'd rather create slowly than have it one shot something that I have to then pore over later.
Feels like a company investing kazillions in, i don't know, air-conditioning or building wi-fi. Yes, it's going to be around, and also no one's gonna need THAT MUCH.