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".
Cost of revenue is lower than revenue. That's good. R&D is the main contributor to losses here
What is counted as R&D is completely arbitrary. These figures are just playing accounting games to attempt to hide the massive ongoing costs.
We’ll see a little better when they IPO and are forced to attempt to make money but I wouldn’t invest in this business.
[flagged]
Ed?
The guy who wrote the post we are discussing
Oh! What’s his reputation?
Up until this post, I thought he was someone with good financial insight, analytical chops, and business sense, stuck with an audience that thinks it's still 2023 and ChatGPT 3 is still the pinnacle of the technology, and that he therefore has to pander to in order to pay the bills.
After this supposedly being the reveal for his bubble-bursting massive revelation that will send the industry flying and lead to journalists kicking in his door for interview requests and exposés, I think... well, not that anymore. I thought "the frontier labs are losing money" was rather universally understood, and this really isn't even as bad as the stuff that's publicly visible; the fact that they keep raising hundreds of billions of dollars that they'll one day supposedly be required to show returns on?
> After this supposedly being the reveal for his bubble-bursting massive revelation that will send the industry flying and lead to journalists kicking in his door for interview requests and exposés
I mean, the fact that lots of expenses are not scaling with revenue (sales and marketing 5xed versus revenue 3xing) and that the losses are very very large is important. More importantly, these are audited figures which haven't been seen before.
Right, but this still isn't exactly new information. I don't think anyone was assuming that the labs are close to being profitable or that the losses wouldn't be rather large. The way this was announced was as if it was going to be a bombshell, but it just confirms what everyone (including the investors) was assuming anyway. Now if he had concrete numbers about whether inference at API pricing is profitable, that'd be a different thing (and it's what that hype bit was heavily implying since it's something he constantly keeps harping on, and rightfully so), but as it stands, nothing about these numbers says anything about whether this fundamentally has a road to profitability. It just says that this is a super high-risk high-reward investment, which isn't new information.
Part of the losses are because of valuation increase and the real operating losses are much lower.
> 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.
Whom would you trust? FT or Ed Zitron?
As a long time FT subscriber, I'm happy you're using them as a source. The Zitron details were more useful to me though.
And none of my points have anything to do with the once off losses. I'm observing that a bunch of costs appear to be scaling with revenue or above revenue, which does not bode well for future profitability.
Also, as an aside, stripping out equity grants is really misleading for a private, high growth tech company.
The losses are scaling with revenue because increase in (expected) revenue increases valuation which increases compensation.
Once expectation stabilises these losses won’t happen because the valuation will remain constant.
A lot of people were paid really high equity grants simply because they started low. You can’t expect them to be paid the same amount each time.
FT themselves point this out and who you believe is up to you.
> The losses are scaling with revenue because increase in (expected) revenue increases valuation which increases compensation.
My original point around equity is that if you pay a substantial fraction of comp in this form, then leaving it out of expenses is pretty bizarre.
Is it your contention that the equity grants are the cause of their increasing losses?
I believe that this is probably not true at all, it's more likely to be S&M (salespeople scale as N not log(N) like engineering/product) particularly given that the product requires tuning for lots of companies (hence all the FDE hires).
More generally, the training costs seem to be increasing which is bad for their future profitability.
It’s not my contention, it’s FT’s conclusion.
Also it should be obvious that you shouldn’t extrapolate stock based compensation in a scale up. People make a one time bounty but that is not recurring obviously.
> Before OpenAI’s switch late last year to become a public benefit corporation, investors in the company received convertible interest rights rather than conventional equity. Under US accounting rules, those interests were treated as liabilities and periodically revalued as the company’s valuation increased.
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 presume that this is what you're talking about, right?
That doesn't actually disagree with what I noted above using the (more detailed) figures from Ed's article. I noted that their revenue scaled by about 3x, while many costs (cost of revenue, sales & marketing, r&d) scaled by either equal (r&d) or greater than their revenue scaled. That's the point I was (apparently badly) making, nothing to do with the stock based compensation causing their losses. In any case, the loss was actually driven by treatment of the non-profit shares.
> Also it should be obvious that you shouldn’t extrapolate stock based compensation in a scale up. People make a one time bounty but that is not recurring obviously.
Correct, in some sense this is a once-off, however, most tech companies continue granting stock over time, so it's definitely worth including in actual margins. (This is a more general point that's not exclusive to Open AI).
> Oh! What’s his reputation?
The people who are completely sold on the belief that AI providers are running at a profit believe him to be utterly, totally and completely wrong in every one of his predictions.
The people who are completely sold on the belief that AI providers are running at a loss they can never recover from believe him to be utterly, totally and completely correct in every one of his predictions.
The reality is that it's not his predictions that matter, but his data, which is almost always correct as of time of writing. If you ignore his opinions, the data presented on liabilities, spend, revenue, loans, commitments, etc across Coreweave, Stargate, Oracle and all of the usual AI companies is, as far as I can tell, correct.
IOW, when it comes to his opinions, it's all about your priors. His data is good, though.
> The reality is that it's not his predictions that matter, but his data, which is almost always correct as of time of writing. If you ignore his opinions, the data presented on liabilities, spend, revenue, loans, commitments, etc across Coreweave, Stargate, Oracle and all of the usual AI companies is, as far as I can tell, correct.
Yeah, I think that he does well with sources and data. I also think that his editorialising can be off-putting for lots of people. I kinda enjoy it, but accept that I have niche tastes.
> Yeah, I think that he does well with sources and data
He's not even good at that, here's him not understanding what ARR means and fumbling a simple calculation and refusing to fix it.
Not only not understanding ARR, he simply doesn't do data analysis properly - he misses some few months and days in his calculation to prop up his point. This is a mistake chatgpt would have caught.
That's the trouble I have with ARR, because there's no standard, people engage in shenanigans. I do find the 5bn lifetime revenue versus their ARR figures pretty sketchy which is why I really want to see the S1.
Can you be more specific on his incorrect calculations please?
Wait. ARR has no precise definition but has a clear social understanding. It’s clear Ed doesn’t get that and ARR not having a clear definition doesn’t absolve him of the mistake. His misunderstanding was on a different axis.
The miscalculations are pretty clearly pointed out in the tweet I linked earlier.
> Wait. ARR has no precise definition but has a clear social understanding.
This is (historically) a recipe for fraud and badness. If ARR is important enough to be reported, then there should be a GAAP definition.
Do you use calendar month or four week rolling? Do you account for seasonality? How do you recognise revenue? (My sense is that Anthropic do sketchy things with credits, as the consumer ones last for like 180 days and then expire).
ARR is a really, really, really easy metric to make sound like whatever you want which is why I am sceptical of it.
EDIT: I looked at the tweet which is a screenshot of a supposed sheet that Ed built. Unless you have a source for the sheet then I'll need to assign this relatively low credibility (don't know the user, it's a screenshot with no link).
> But I’m a curious little critter and went ahead and added up all of the times that Anthropic had talked about its annualized revenue from 2025 onward, and the results — which you can find with links here! — and based on my calculations, just using published annualized revenues gets us to $4.837 billion.
It’s here in the blog.
> This is (historically) a recipe for fraud and badness. If ARR is important enough to be reported, then there should be a GAAP definition.
This is orthogonal to Ed misunderstanding ARR.
I don't think anyone believes the major AI providers are running at a profit? They are openly investing heavily into R&D and building out infrastructure, and according to these numbers way more than revenue. It wouldn't make sense for any of these companies to run at a profit right now as they're still aggressively expanding. The question is whether they will break even in the future, and capture a large enough market segment to sustain the business, allowing revenue to outgrow costs. If these numbers are real, revenue is already higher than COGS which is a really good signal for them.
I think the question is more about whether people believe this is a sound business in the long term, which imo isn't possible to tell based on these numbers yet.
> The people who are completely sold on the belief that AI providers are running at a loss they can never recover from believe him to be utterly, totally and completely correct in every one of his predictions.
It's funny, because you can both believe that these entities are bleeding money on every token and also believe that "financial engineering" will bail them out when they IPO despite this fact.
The fundamentals of running a business that sells products or services for more than the cost to produce them seem increasingly decoupled from the financial success of the company and its owners.
Poor but that doesn't stop innocent from taking his thesis seriously and leaning on to the doom scenario. What do you think of his reputation?
> Cost of revenue is lower than revenue.
I’m not sure how people are looking at numbers that show, even if we wipe off the enormous R&D expenditures, they are still in the red for inference + sales/marketing + admin and responding “this seems positive”.
It’s like being a sold a car and being told “well if you ignore the fact it has no engine it’s a good buy” yet it also has no wheels.
> 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.
There are three futures right, I’ll rank them in order of fantasy -
1. Someone achieves AGI. At that point the economics of an individual company don’t even matter.
2. R&D costs do have to forever continue, because LLMs can be continually iteratively improved. Much like chip development, there is no end in sight, at least not on a near term timescale. If you are not continually at the frontier, customers will use a competitor or open/local alternatives.
3. LLMs reach a plateau of functionality. Further gains are minimal, quality reaches the apex of what the technology permits. In this scenario the hyperscalers have no business because open/local models will rapidly reach that same plateau as well.
The Uber comparison makes no sense. This is the opposite situation. Uber lost money on rides, OpenAI is (possibly) making money on inference. Uber used an R+D moonshot to autonomous driving to justify capturing an established industry without reducing costs meaningfully. OpenAI has a core product that risks becoming a commodity with open source models only 6 months behind.
Google - Uber contribution margin
Amazing how misinformed people write on topics with confidence. Just stop lmao
Uber didn’t lose money on rides other than some edge cases. What’s your source for this claim?
The vast, vast amounts of money they spent on driver incentives city by city would seem to support the OPs claim (source: I was familiar with their spend on ads in the US approximately 10 years ago).
There is no evidence that Uber was systemically losing money per ride instead of at edge cases. Share your evidence please.
> Share your evidence please.
This is an impossible ask unless one works at Uber. I can tell you that i saw how much they were spending on ads back in 2016, and how long it continued and can assure you that they were 100% losing money back then.
Like, even now their margin is around 10% (they made 5bn on 50bn of revenue). Other software companies make a much, much, much better margin because Uber is basically not a real software business, it's an app attached to a low-margin delivery business.
ads =/= rides
Yeah totally. In some ways Google and Facebook being so wildly profitable was very bad for future tech startups.
Nonetheless, that's the bar from a financial perspective, and I honestly don't think Uber has (or will) hit that bar.
Uber kept fares artificially low while simultaneously paying high bonuses to drivers to build a massive network. After burning through roughly $30+ billion over its first decade, Uber then pivoted its business model by raising rider fares, increasing restaurant fees on Uber Eats, and cutting driver pay.
"Cost of revenue" isn't the entire cost of running the company, (ie R&D, operations, sales, marketing, etc). It's just a cost they've associated with revenue IN ADDITION to the other costs I mentioned.
HSBC say they need to turn a 13b revenue to 200b by 2030 AND also find another 204b, in order to become profitable.
> It's just a cost they've associated with revenue
Its a little less arbitrary than that. Cost of Revenue/Cost of Sales/Cost of Goods Sold are clear, if you're following GAAP. To label these expenses as cost of revenue, they must meet the matching principle in that the expenses must be directly tied to the generation of specific revenue. If you didn't make that "sale" then that specific cost would not exist.
Other operating expenses come later on the income statement.
Total Revenue - Cost of Revenue = Gross Profit first, then you subtract OpEx from there for EBIT.
For OpenAI, I'd assume cost of revenue is almost directly inference costs + customer support & app dev.
How in the world could you read that article and think there is anything positive about OpenAI's prospects? We've been hearing for months that these companies need to make trillions of dollars in a handful of years, growing at record rates in order to break even and justify their massive outlay.
It's not going to happen.
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.
> Doesn't seem like a domesday scenario
Ceteris paribus, those figures imply a $45bn loss this year, $90bn loss next year and $110bn loss in 2028 before breakeven in 2029.
That's $250bn of losses to be financed from 2026 onwards. (They raised ~$120bn, $25bn up front and the rest based on milestones. So Another ~$125bn uncovered.) That only works if OpenAI stays a fundraising darling. So not a doomsday sceanario. But perilous, and dependent on short-term trends extending into long-term curves.
You're adding absolute dollars rather than using percentages - that usually isn't how that works.
> rather than using percentages
Not really.
Fractions (7/2), ratios (3.5x) and percentages (+250%) are fundamentally mathematically identical.
There are a lot of problems with this back-of-the-envelope estimate, but I’m not sure the one I understand you presenting is one of them.
Hahaha what a bozo.
Of course you don’t use percentages when the magnitude of the numbers are so high.
> 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.
both amazon and uber used that spending to deliver a network effect moat/almost monopoly.
But openai's chance of a moat on model quality is dropping as we go, not increasing
and again, there are good models racing right behind.
the brick has a lot of thrust but there is a airplane behind it, and it's moving on its own
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:
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.
This news matters because investors should prefer safer investments than: well at least it's not a "doomsday scenario" grade.
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.
> The AI companies also have a lot of space to grow their income (more ads, price hikes, ...).
Ads, maybe, but not only are they already walking back recent price hikes, the paying customers were hitting the brakes even on the original price.
Note that this data you see (their increased revenue) came from a period where they were onboarding customers who were competing to see who used the most tokens.
IOW, this is the best-case scenario for them - customers with no cap on token spend.
But... the caps from customers came in before they hiked prices. Then they hiked prices. That resulted in a short-term boost to revenue to compensate for the caps. Now they are talking about walking back those hikes. That means they are going to find an equilibrium lower than their best-case scenario.
I like this read. Eventually, management did collectively realize that tokens spent leaderboards were a bad idea. That is going to massively reduce the waste that was needlessly being generated to hit work quotas.
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.
coding agents aren't enough to justify the amount of capital invested
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.
Yeah, this pretty much seals it for me that Ed has basically nothing. Sure OpenAI isn’t currently profitable, but this doesn’t say to me that they can’t become so soon(ish).
> 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 had a guaranteed military sale with ED 209, renovation program, spare parts for twenty-five years… Who cares if it worked or not?!?”
It's possible that I'm just not up to date with current news, but I'm having trouble connecting this quote to the article. Or really even understanding the quote at all. Can you elaborate?
The commenter above seems to be describing late stage capitalism, where businesses exist mainly to milk investors, as told by bad boy tech executive Dick Jones in the 1980's action movie RoboCop.
dystopian robocop reference
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.
Yeah if this is his “information he wanted for years” it’s pretty abysmal in terms of crashing the “ai bubble”.
Yeah he has zero credentials and authority and an agenda to push. Not to mention most of his articles are financially and technically illiterate and full of mistakes and inaccuracies.
No idea why his shit keeps getting submitted.
Genuine question, do you have examples of inaccuracies and mistakes? If you ignore his caustic tone and predictions what I’ve seen reported by Ed Zitron has been accurate.
I concur. His tone greatly undermines the value of the facts he reports. Sometimes / oftentimes his analysis is off the mark, but I have not found him to be reporting falsehoods or inaccuracies.
I think there's some fundamental thing in his writing that speaks to people -- they want AI to fail and they want a prophet to give them reasons to think so.
It's simpler than that, some people just like sardonic writing. I don't know if I believe Ed any more than some AI cheerleader. But his writing is proper relaxing compared to hype rants that I wouldn't blame someone for suspecting to be coke-fueled.
Ed Zitron has proven trump wrong so many times it's going to be hilarious how right it will come out on this
They know it is a scam, but it doesn’t matter as it is now too late.
That ship has sailed long ago into the IPO sunset.
That’s absurd. Why couldn’t it still fail, especially when their last raise was at 20x revenue or more? These numbers are horrendous.
It can fail, but the cost will be pushed on small retail investors, pension funds, index funds etc. The investors and managers that made it fail and waste money will be rewarded and will remain rich. It will be the "socialize losses" situation.
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".
Cost of revenue is lower than revenue. That's good. R&D is the main contributor to losses here
What is counted as R&D is completely arbitrary. These figures are just playing accounting games to attempt to hide the massive ongoing costs.
We’ll see a little better when they IPO and are forced to attempt to make money but I wouldn’t invest in this business.
[flagged]
Ed?
The guy who wrote the post we are discussing
Oh! What’s his reputation?
Up until this post, I thought he was someone with good financial insight, analytical chops, and business sense, stuck with an audience that thinks it's still 2023 and ChatGPT 3 is still the pinnacle of the technology, and that he therefore has to pander to in order to pay the bills.
After this supposedly being the reveal for his bubble-bursting massive revelation that will send the industry flying and lead to journalists kicking in his door for interview requests and exposés, I think... well, not that anymore. I thought "the frontier labs are losing money" was rather universally understood, and this really isn't even as bad as the stuff that's publicly visible; the fact that they keep raising hundreds of billions of dollars that they'll one day supposedly be required to show returns on?
> After this supposedly being the reveal for his bubble-bursting massive revelation that will send the industry flying and lead to journalists kicking in his door for interview requests and exposés
I mean, the fact that lots of expenses are not scaling with revenue (sales and marketing 5xed versus revenue 3xing) and that the losses are very very large is important. More importantly, these are audited figures which haven't been seen before.
Right, but this still isn't exactly new information. I don't think anyone was assuming that the labs are close to being profitable or that the losses wouldn't be rather large. The way this was announced was as if it was going to be a bombshell, but it just confirms what everyone (including the investors) was assuming anyway. Now if he had concrete numbers about whether inference at API pricing is profitable, that'd be a different thing (and it's what that hype bit was heavily implying since it's something he constantly keeps harping on, and rightfully so), but as it stands, nothing about these numbers says anything about whether this fundamentally has a road to profitability. It just says that this is a super high-risk high-reward investment, which isn't new information.
Part of the losses are because of valuation increase and the real operating losses are much lower.
https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb... this uses the same sources and answers more honestly and Ed Zitron doesn't touch on this.
> 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.
Whom would you trust? FT or Ed Zitron?
As a long time FT subscriber, I'm happy you're using them as a source. The Zitron details were more useful to me though.
And none of my points have anything to do with the once off losses. I'm observing that a bunch of costs appear to be scaling with revenue or above revenue, which does not bode well for future profitability.
Also, as an aside, stripping out equity grants is really misleading for a private, high growth tech company.
The losses are scaling with revenue because increase in (expected) revenue increases valuation which increases compensation.
Once expectation stabilises these losses won’t happen because the valuation will remain constant. A lot of people were paid really high equity grants simply because they started low. You can’t expect them to be paid the same amount each time.
FT themselves point this out and who you believe is up to you.
> The losses are scaling with revenue because increase in (expected) revenue increases valuation which increases compensation.
My original point around equity is that if you pay a substantial fraction of comp in this form, then leaving it out of expenses is pretty bizarre.
Is it your contention that the equity grants are the cause of their increasing losses?
I believe that this is probably not true at all, it's more likely to be S&M (salespeople scale as N not log(N) like engineering/product) particularly given that the product requires tuning for lots of companies (hence all the FDE hires).
More generally, the training costs seem to be increasing which is bad for their future profitability.
It’s not my contention, it’s FT’s conclusion.
Also it should be obvious that you shouldn’t extrapolate stock based compensation in a scale up. People make a one time bounty but that is not recurring obviously.
> Before OpenAI’s switch late last year to become a public benefit corporation, investors in the company received convertible interest rights rather than conventional equity. Under US accounting rules, those interests were treated as liabilities and periodically revalued as the company’s valuation increased.
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 presume that this is what you're talking about, right?
That doesn't actually disagree with what I noted above using the (more detailed) figures from Ed's article. I noted that their revenue scaled by about 3x, while many costs (cost of revenue, sales & marketing, r&d) scaled by either equal (r&d) or greater than their revenue scaled. That's the point I was (apparently badly) making, nothing to do with the stock based compensation causing their losses. In any case, the loss was actually driven by treatment of the non-profit shares.
> Also it should be obvious that you shouldn’t extrapolate stock based compensation in a scale up. People make a one time bounty but that is not recurring obviously.
Correct, in some sense this is a once-off, however, most tech companies continue granting stock over time, so it's definitely worth including in actual margins. (This is a more general point that's not exclusive to Open AI).
> Oh! What’s his reputation?
The people who are completely sold on the belief that AI providers are running at a profit believe him to be utterly, totally and completely wrong in every one of his predictions.
The people who are completely sold on the belief that AI providers are running at a loss they can never recover from believe him to be utterly, totally and completely correct in every one of his predictions.
The reality is that it's not his predictions that matter, but his data, which is almost always correct as of time of writing. If you ignore his opinions, the data presented on liabilities, spend, revenue, loans, commitments, etc across Coreweave, Stargate, Oracle and all of the usual AI companies is, as far as I can tell, correct.
IOW, when it comes to his opinions, it's all about your priors. His data is good, though.
> The reality is that it's not his predictions that matter, but his data, which is almost always correct as of time of writing. If you ignore his opinions, the data presented on liabilities, spend, revenue, loans, commitments, etc across Coreweave, Stargate, Oracle and all of the usual AI companies is, as far as I can tell, correct.
Yeah, I think that he does well with sources and data. I also think that his editorialising can be off-putting for lots of people. I kinda enjoy it, but accept that I have niche tastes.
> Yeah, I think that he does well with sources and data
He's not even good at that, here's him not understanding what ARR means and fumbling a simple calculation and refusing to fix it.
https://x.com/binarybits/status/2031392856401666362
Not only not understanding ARR, he simply doesn't do data analysis properly - he misses some few months and days in his calculation to prop up his point. This is a mistake chatgpt would have caught.
https://x.com/binarybits/status/2034377838883700953
> He's not even good at that, here's him not understanding what ARR means and fumbling a simple calculation and refusing to fix it.
Do you have a link to his blog where he gets the ARR wrong?
True, I haven't much of his posts, but the one or two I recall reading with ARR in it didn't seem to have fumbled the calculations.
> understanding what ARR means
Can you share me the official meaning of ARR? Preferably on a GAAP basis. Should be no problem, right?
ARR has no official GAAP definition, but is generally understood as the annualized value of a company's current recurring revenue base.
This is something Ed clearly doesn't understand https://x.com/edzitron/status/2031124650474852382
And you haven't addressed the fact that he doesn't do simple data calculations - see his blog https://www.wheresyoured.at/the-beginning-of-history
That's the trouble I have with ARR, because there's no standard, people engage in shenanigans. I do find the 5bn lifetime revenue versus their ARR figures pretty sketchy which is why I really want to see the S1.
Can you be more specific on his incorrect calculations please?
Wait. ARR has no precise definition but has a clear social understanding. It’s clear Ed doesn’t get that and ARR not having a clear definition doesn’t absolve him of the mistake. His misunderstanding was on a different axis.
The miscalculations are pretty clearly pointed out in the tweet I linked earlier.
> Wait. ARR has no precise definition but has a clear social understanding.
This is (historically) a recipe for fraud and badness. If ARR is important enough to be reported, then there should be a GAAP definition.
Do you use calendar month or four week rolling? Do you account for seasonality? How do you recognise revenue? (My sense is that Anthropic do sketchy things with credits, as the consumer ones last for like 180 days and then expire).
ARR is a really, really, really easy metric to make sound like whatever you want which is why I am sceptical of it.
EDIT: I looked at the tweet which is a screenshot of a supposed sheet that Ed built. Unless you have a source for the sheet then I'll need to assign this relatively low credibility (don't know the user, it's a screenshot with no link).
> But I’m a curious little critter and went ahead and added up all of the times that Anthropic had talked about its annualized revenue from 2025 onward, and the results — which you can find with links here! — and based on my calculations, just using published annualized revenues gets us to $4.837 billion.
It’s here in the blog.
> This is (historically) a recipe for fraud and badness. If ARR is important enough to be reported, then there should be a GAAP definition.
This is orthogonal to Ed misunderstanding ARR.
I don't think anyone believes the major AI providers are running at a profit? They are openly investing heavily into R&D and building out infrastructure, and according to these numbers way more than revenue. It wouldn't make sense for any of these companies to run at a profit right now as they're still aggressively expanding. The question is whether they will break even in the future, and capture a large enough market segment to sustain the business, allowing revenue to outgrow costs. If these numbers are real, revenue is already higher than COGS which is a really good signal for them.
I think the question is more about whether people believe this is a sound business in the long term, which imo isn't possible to tell based on these numbers yet.
> The people who are completely sold on the belief that AI providers are running at a loss they can never recover from believe him to be utterly, totally and completely correct in every one of his predictions.
It's funny, because you can both believe that these entities are bleeding money on every token and also believe that "financial engineering" will bail them out when they IPO despite this fact.
The fundamentals of running a business that sells products or services for more than the cost to produce them seem increasingly decoupled from the financial success of the company and its owners.
Poor but that doesn't stop innocent from taking his thesis seriously and leaning on to the doom scenario. What do you think of his reputation?
> Cost of revenue is lower than revenue.
I’m not sure how people are looking at numbers that show, even if we wipe off the enormous R&D expenditures, they are still in the red for inference + sales/marketing + admin and responding “this seems positive”.
It’s like being a sold a car and being told “well if you ignore the fact it has no engine it’s a good buy” yet it also has no wheels.
> 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.
There are three futures right, I’ll rank them in order of fantasy -
1. Someone achieves AGI. At that point the economics of an individual company don’t even matter.
2. R&D costs do have to forever continue, because LLMs can be continually iteratively improved. Much like chip development, there is no end in sight, at least not on a near term timescale. If you are not continually at the frontier, customers will use a competitor or open/local alternatives.
3. LLMs reach a plateau of functionality. Further gains are minimal, quality reaches the apex of what the technology permits. In this scenario the hyperscalers have no business because open/local models will rapidly reach that same plateau as well.
The Uber comparison makes no sense. This is the opposite situation. Uber lost money on rides, OpenAI is (possibly) making money on inference. Uber used an R+D moonshot to autonomous driving to justify capturing an established industry without reducing costs meaningfully. OpenAI has a core product that risks becoming a commodity with open source models only 6 months behind.
Google - Uber contribution margin
Amazing how misinformed people write on topics with confidence. Just stop lmao
Uber didn’t lose money on rides other than some edge cases. What’s your source for this claim?
The vast, vast amounts of money they spent on driver incentives city by city would seem to support the OPs claim (source: I was familiar with their spend on ads in the US approximately 10 years ago).
There is no evidence that Uber was systemically losing money per ride instead of at edge cases. Share your evidence please.
> Share your evidence please.
This is an impossible ask unless one works at Uber. I can tell you that i saw how much they were spending on ads back in 2016, and how long it continued and can assure you that they were 100% losing money back then.
Like, even now their margin is around 10% (they made 5bn on 50bn of revenue). Other software companies make a much, much, much better margin because Uber is basically not a real software business, it's an app attached to a low-margin delivery business.
ads =/= rides
Yeah totally. In some ways Google and Facebook being so wildly profitable was very bad for future tech startups.
Nonetheless, that's the bar from a financial perspective, and I honestly don't think Uber has (or will) hit that bar.
Uber kept fares artificially low while simultaneously paying high bonuses to drivers to build a massive network. After burning through roughly $30+ billion over its first decade, Uber then pivoted its business model by raising rider fares, increasing restaurant fees on Uber Eats, and cutting driver pay.
Basically, win market through subsidy -> establish monopoly -> increase price -> profit.
"Cost of revenue" isn't the entire cost of running the company, (ie R&D, operations, sales, marketing, etc). It's just a cost they've associated with revenue IN ADDITION to the other costs I mentioned.
HSBC say they need to turn a 13b revenue to 200b by 2030 AND also find another 204b, in order to become profitable.
> It's just a cost they've associated with revenue
Its a little less arbitrary than that. Cost of Revenue/Cost of Sales/Cost of Goods Sold are clear, if you're following GAAP. To label these expenses as cost of revenue, they must meet the matching principle in that the expenses must be directly tied to the generation of specific revenue. If you didn't make that "sale" then that specific cost would not exist.
Other operating expenses come later on the income statement.
Total Revenue - Cost of Revenue = Gross Profit first, then you subtract OpEx from there for EBIT.
For OpenAI, I'd assume cost of revenue is almost directly inference costs + customer support & app dev.
How in the world could you read that article and think there is anything positive about OpenAI's prospects? We've been hearing for months that these companies need to make trillions of dollars in a handful of years, growing at record rates in order to break even and justify their massive outlay.
It's not going to happen.
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.
> Doesn't seem like a domesday scenario
Ceteris paribus, those figures imply a $45bn loss this year, $90bn loss next year and $110bn loss in 2028 before breakeven in 2029.
That's $250bn of losses to be financed from 2026 onwards. (They raised ~$120bn, $25bn up front and the rest based on milestones. So Another ~$125bn uncovered.) That only works if OpenAI stays a fundraising darling. So not a doomsday sceanario. But perilous, and dependent on short-term trends extending into long-term curves.
You're adding absolute dollars rather than using percentages - that usually isn't how that works.
> rather than using percentages
Not really.
Fractions (7/2), ratios (3.5x) and percentages (+250%) are fundamentally mathematically identical.
There are a lot of problems with this back-of-the-envelope estimate, but I’m not sure the one I understand you presenting is one of them.
Hahaha what a bozo.
Of course you don’t use percentages when the magnitude of the numbers are so high.
> 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.
both amazon and uber used that spending to deliver a network effect moat/almost monopoly.
But openai's chance of a moat on model quality is dropping as we go, not increasing
and again, there are good models racing right behind.
the brick has a lot of thrust but there is a airplane behind it, and it's moving on its own
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:
https://www.reuters.com/technology/openai-considers-drastic-...
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.
This news matters because investors should prefer safer investments than: well at least it's not a "doomsday scenario" grade.
Tell that to the SpaceX investors.
Challenge accepted:
Facebook: https://www.facebook.com/share/v/1DC1GotK2F/
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.
> The AI companies also have a lot of space to grow their income (more ads, price hikes, ...).
Ads, maybe, but not only are they already walking back recent price hikes, the paying customers were hitting the brakes even on the original price.
Note that this data you see (their increased revenue) came from a period where they were onboarding customers who were competing to see who used the most tokens.
IOW, this is the best-case scenario for them - customers with no cap on token spend.
But... the caps from customers came in before they hiked prices. Then they hiked prices. That resulted in a short-term boost to revenue to compensate for the caps. Now they are talking about walking back those hikes. That means they are going to find an equilibrium lower than their best-case scenario.
I like this read. Eventually, management did collectively realize that tokens spent leaderboards were a bad idea. That is going to massively reduce the waste that was needlessly being generated to hit work quotas.
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.
coding agents aren't enough to justify the amount of capital invested
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.
Yeah, this pretty much seals it for me that Ed has basically nothing. Sure OpenAI isn’t currently profitable, but this doesn’t say to me that they can’t become so soon(ish).
Relevant: https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb... this uses the same sources and answers more honestly and Ed Zitron doesn't touch on this.
> 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 had a guaranteed military sale with ED 209, renovation program, spare parts for twenty-five years… Who cares if it worked or not?!?”
It's possible that I'm just not up to date with current news, but I'm having trouble connecting this quote to the article. Or really even understanding the quote at all. Can you elaborate?
The commenter above seems to be describing late stage capitalism, where businesses exist mainly to milk investors, as told by bad boy tech executive Dick Jones in the 1980's action movie RoboCop.
dystopian robocop reference
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.
Yeah if this is his “information he wanted for years” it’s pretty abysmal in terms of crashing the “ai bubble”.
Yeah he has zero credentials and authority and an agenda to push. Not to mention most of his articles are financially and technically illiterate and full of mistakes and inaccuracies.
No idea why his shit keeps getting submitted.
Genuine question, do you have examples of inaccuracies and mistakes? If you ignore his caustic tone and predictions what I’ve seen reported by Ed Zitron has been accurate.
I concur. His tone greatly undermines the value of the facts he reports. Sometimes / oftentimes his analysis is off the mark, but I have not found him to be reporting falsehoods or inaccuracies.
I think there's some fundamental thing in his writing that speaks to people -- they want AI to fail and they want a prophet to give them reasons to think so.
It's simpler than that, some people just like sardonic writing. I don't know if I believe Ed any more than some AI cheerleader. But his writing is proper relaxing compared to hype rants that I wouldn't blame someone for suspecting to be coke-fueled.
Ed Zitron has proven trump wrong so many times it's going to be hilarious how right it will come out on this
They know it is a scam, but it doesn’t matter as it is now too late.
That ship has sailed long ago into the IPO sunset.
That’s absurd. Why couldn’t it still fail, especially when their last raise was at 20x revenue or more? These numbers are horrendous.
It can fail, but the cost will be pushed on small retail investors, pension funds, index funds etc. The investors and managers that made it fail and waste money will be rewarded and will remain rich. It will be the "socialize losses" situation.