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OpenAI Losses Increased Nearly 8X in 2025, with Spending Hitting $34B

• rvz 4mo ago
  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.
  • thereitgoes456 4mo ago
    That’s absurd. Why couldn’t it still fail, especially when their last raise was at 20x revenue or more? These numbers are horrendous.
    • watwut 4mo ago
      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.
• muglug 4mo ago
  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.
  • JumpCrisscross 4mo ago
    > 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.
    • Schiendelman 4mo ago
      You're adding absolute dollars rather than using percentages - that usually isn't how that works.
      • eiiee 4mo ago
        Hahaha what a bozo. Of course you don’t use percentages when the magnitude of the numbers are so high.
      • JumpCrisscross 4mo ago
        > 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.
        • curio_Pol_curio 4mo ago
          ? https://www.theinformation.com/articles/openai-burned-3-7-billion-first-three-months-2026 https://www.theinformation.com/articles/openai-burned-3-7-bi... ? https://www.theinformation.com/briefings/index-startup-ornn-launches-anthropic-openai-token-benchmarks https://www.theinformation.com/briefings/index-startup-ornn-...
        • Schiendelman 4mo ago
          Any of those three would be fine. They did not use any of them. They simply used absolute dollars.
  • HlessClaudesman 4mo ago
    This news matters because investors should prefer safer investments than: well at least it's not a "doomsday scenario" grade.
    • minimaxir 4mo ago
      Tell that to the SpaceX investors.
      • HlessClaudesman 4mo ago
        Challenge accepted: Facebook: https://www.facebook.com/share/v/1DC1GotK2F/ https://www.facebook.com/share/v/1DC1GotK2F/
  • pinkmuffinere 4mo ago
    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.
  • matusp 4mo ago
    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.
    • lelanthran 4mo ago
      > 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.
      • 0cf8612b2e1e 4mo ago
        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.
  • lelanthran 4mo ago
    > 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.
    • red-iron-pine 4mo ago
      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
    • thewebguyd 4mo ago
      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.
      • deepdarkforest 4mo ago
        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
        • mike_hearn 4mo ago
          Neither Amazon nor Uber have monopolies nor much of a network effect. Amazon retail is or was famously low or near zero margin with their profits driven by AWS. Uber's margins are not much better than any average business.
      • mamonster 4mo ago
  • mikgp 4mo ago
    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-price-cuts-anticipating-war-users-with-anthropic-wsj-2026-06-11/ https://www.reuters.com/technology/openai-considers-drastic-...
• HlessClaudesman 4mo ago
  “I had a guaranteed military sale with ED 209, renovation program, spare parts for twenty-five years… Who cares if it worked or not?!?”
  • pinkmuffinere 4mo ago
    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?
    • HlessClaudesman 4mo ago
      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.
    • red-iron-pine 4mo ago
      dystopian robocop reference
• nstart 4mo ago
  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".
  • Refreeze5224 4mo ago
    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.
    • nstart 4mo ago
      I tend not to focus on that future too much. I used to do so long ago. For example, how could Facebook possibly justify their losses while asking for such a big valuation? Same for Uber. Same for any number of big companies. And it turns out that growth in the future is impossible to predict accurately. Shopify is a good example where at the time the addressable market of online stores was tiny. But it turned out that Shopify created its own market which is huge today. Technology improvements have a way of creating new markets which far surpass today's total addressable market. Factor in currency depreciation and whatnot and sometimes, futures that looked impossible turn out to be possible. Not saying anyone is wrong in pointing at the buildouts for AI and questioning its feasibility. Just making the argument for why I personally only look at operational costs and revenue because it's the only real-ish value I can look at and judge if a business can grow sustainably. As a counter point, the red flag to all of this is R&D costs growing for each model release. If that continues and revenue cannot outstrip it, then these companies have a problem and it'll probably be that just 1-2 frontier labs can survive this once the dust settles.
• minimaxir 4mo ago
  At the end of his previous article (https://www.wheresyoured.at/ai-is-slowing-down/ 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.
  • besterman23 4mo ago
    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).
  • thewebguyd 4mo ago
    > 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.
• simianwords 4mo ago
  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.
  • besterman23 4mo ago
    Yeah if this is his “information he wanted for years” it’s pretty abysmal in terms of crashing the “ai bubble”.
  • saberience 4mo ago
    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.
    • simianwords 4mo ago
      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.
      • dogleash 4mo ago
• sourcegrift 4mo ago
  Ed Zitron has proven trump wrong so many times it's going to be hilarious how right it will come out on this
• Traster 4mo ago
  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.
  • efficax 4mo ago
    coding agents aren't enough to justify the amount of capital invested
    • dofm 4mo ago
      My key realisation from playing with open weights models on my own laptop is that at least where text is concerned, the vast majority of what an average non-programmer consumer thinks AI does, my laptop can now do with the wifi disabled. And arguably where speech and audio is concerned, too. There is, put simply, a huge, huge information gap about the uniqueness of these commercial services. There's an open question about how open weights models will be funded when they can't be used in a war between these companies, but the reality is that the amount Apple is paying Google for the right to distill Gemini, for example, is strongly indicative of the total size of the consumer market. Because pretty soon everyone's phones will be doing what local models can do. Global markets will ultimately learn that coding agents are, at a first approximation, the only source of revenue for this stuff over the medium term at least, and the value proposition for consumer AI in the long term (beyond being a feature of a phone) hasn't yet been invented, and any that might exist depend on micropayments architectures that don't exist.
  • 4mo ago
• simianwords 4mo ago
  Relevant: https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb828 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.
• maskirov 4mo ago
  [dead]
• mfru 4mo ago
  it will be so satisfying to see them crash and burn
• atl_tom 4mo ago
  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”"
• themafia 4mo ago
  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.
  • dylan604 4mo ago
    It costs money to get influencers to set up kool-aid stands on their platforms.
    • deleted 4mo ago
      [deleted]
  • iaaan 4mo ago
    I've seen physical billboards in the Portland, OR area for OpenAI, so I guess that accounts for at least part of it. Not really sure what kind of return they're getting on those but apparently they can just do whatever they want, even if they're losing money.
  • hedgehog 4mo ago
    I didn't look at the financials but the subscription product is heavily discounted relative to the API pricing and that difference could well be booked as a marketing expense. They also have a string of grant and similar initiatives (like $50M each) that could be marketing. There's a lot of stuff they could assign at least partially to marketing, and it sounds like they spend money pretty freely.
  • tomlockwood 4mo ago
    I've seen lots of ads saying I should use chatgpt to plan a workout or give me recipes. Thats apparently the killer app for 95% of the population at this point.
    • 4mo ago
• Mistletoe 4mo ago
  Beginning to see why he needed seven trillion dollars.
• pluc 4mo ago
  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?
  • sorry_outta_gas 4mo ago
    [dead]
  • cj 4mo ago
    I’d easily pay multiple hundreds. Possibly a thousand a month. If I were really forced to. LLMs provide me about the same value as a car does.
    • malux85 4mo ago
      I would probably still pay if the cost doubled, but I would also look at competitors, offline solutions, etc We have benchmarks on our domain and it does there are models that are 2x to 10x cheaper for a small drop in percentage points in accuracy
    • cammikebrown 4mo ago
      Paying a thousand a month for a car is also very stupid.
      • steve_adams_86 4mo ago
        Stretching the analogy, something that gets you from point A to point B for a fraction of the price without the same level of comfort is totally fine for me. For some of my tasks, that means using local models. For others it might mean a frontier-last-year kind of model. That's totally acceptable most of the time. For anything else I guess it's like renting a truck to move; just get the right vehicle as needed and pay the premium.
      • lotsofpulp 4mo ago
        A $50k car used 1,000 miles per month probably costs close to a thousand per month, assuming 200k miles of life. I imagine this is not unusual in the US.
• cliche 4mo ago
  I'm not surprised
• orphereus 4mo ago
  Suspicious lack of pro-AI comments here
  • rvz 4mo ago
    You mean the lack of pro-Anthropic/OpenAI comments, who are gambling tokens at their casinos and won't admit that they are very expensive. This is because people here are quietly realizing that they fell for the "token-maxxing" marketing drive which was complete BS for you to gamble more money on tokens as the big AI labs gave heavily subsidized token prices they cannot afford. Jevon's paradox does not exist at those companies, but it certainly exists at the Chinese AI Labs at Deepseek, Alibaba, z.AI and Xiaomi.
    • operatingthetan 4mo ago
      >This is because people here are quietly realizing that they fell for the "token-maxxing" marketing drive which was complete BS for you to gamble more money on tokens as the big AI labs gave heavily subsidized token prices they cannot afford. Good callout. All these "trends" in AI were definitely from the AI companies themselves in order to push the sales of more tokens. What's after agent orchestration? Whatever it is, it will involve a big spend.
  • pydry 4mo ago
    their PR department is probably still trying to figure out what narrative the bots should follow for this one.
    • NoGravitas 4mo ago
      Apparently the narrative the bots are following is that "this proves that inference is profitable!"
  • thraway3837 4mo ago
    It's the thing to do in HN comments. Downvote anything AI related and armchair diagnosing AI coding as psychosis. :/ Luckily, we've seen this before. Doom and gloom when smartphones came out. And then the same again when mobile development was preferred and there was an outcry from the web dev crowd and constant downvoting of phone apps.
• yieldcrv 4mo ago
  I want to see the person who thought they were losing only hundreds of millions
• holoduke 4mo ago
  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.
• llmslave 4mo ago
  Leaked: OpenAI is a rapidly scaling startup, has economics similar to other startups
  • pooploop64 4mo ago
    If anything this is MORE evidence that the infinite money printer will be coming online any second now! Yep aaaaany second now... OH THERE IT- awww one of you guys wasn't praying hard enough.
• vb-8448 4mo ago
  Almost 6 bln in sales in marketing? It looks an enormous amount given that they used to have the best models and used to give-aways tokens.
  • LaurensBER 4mo ago
    6bn seems excessive but despite GPT 5.5 arguably being better than Claude I don't see a lot of adoption of Codex yet. Some of my coworkers even use Sonnet (the default in Claude Code for the 20 USD subscription) and see no reason to change even though that model is definitely "outdated" compared to current SOTA.
    • dj_axl 4mo ago
      Marketing might help at some workplaces, presumably that are dedicated to Microsoft, for example our network blocks Claude (and DeepSeek) and is slowly rolling out Codex team by team. They should encourage Amazon/AWS to market for them.
    • ai_slop_hater 4mo ago
      most people are behind the curve
      • camdenreslink 4mo ago
        Most people have no need for a SOTA model, and even a model from a year ago would be fine for their needs (a little bit of research, small bits of writing prose, etc).
        • ai_slop_hater 4mo ago
          I meant software engineers, not most of all living people
      • lbrito 4mo ago
        That is a great username right there.
• smashed 4mo ago
  If these numbers are right, it's actually not that bad. Cut r&d costs and they are mostly profitable.
  • vjsrinivas 4mo ago
    Cut down on the one thing they need to keep themselves relevant in this space?
    • pevansgreenwood 4mo ago
      [dead]
    • stogot 4mo ago
      Watch them flare out like a star… but there is lots of questions re the the return on RnD. Is it worth spending another order of magnitude for only marginal frontier gains?
  • deepsun 4mo ago
    I bet any FAANG spend is mostly R&D. If it's not materials, not energy or taxes, not manufacturing, not licensing or rental fees, then I can only think of R&D.
    • windexh8er 4mo ago
      People keep overlooking the fact that costs for these providers scale along with customer acquisition. Most startups don't have that linear expense. Also, training costs are accelerating to get new models out faster. One doesn't simply "get rid of R&D" costs as a comment upstream mentioned. I can't actually imagine R&D goes down anytime soon unless you're willing to play third fiddle. Unless these frontier providers feel some type of squeeze or constraint the Chinese are well positioned to leave the US bag holders of an NVidia bound system. And if anyone has to wonder how one provider for a critical piece of infrastructure will go, well...
  • matt-p 4mo ago
    Even if they keep the R&D costs, more efficient inference and 0 Marketing spend also gets you there. Inference is honestly super inefficient at this point, we can do far better than GPUs, push utilisation up, build more efficient datacentres.
• natas 4mo ago
  Napkin maths. 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?
  • joegibbs 4mo ago
    AI is growing much faster than the other components of MS and Alphabet's business, and OAI is 100% dedicated to AI while the other two only have small portions on AI
  • margalabargala 4mo ago
    Let's say Company 1 has $1B revenue and has grown 5x in the last year, and 20x the last 2 years.. Let's say Company 2 has $1B revenue and that's the same as it was last year and the year before. Should these companies be valued the same?
    • ignoramous 4mo ago
      > Should these companies be valued the same By who? Public money is looking for dividends (profits) not growth?
      • bhelkey 4mo ago
        If indeed the public is looking for dividends, why is Amazon, a company that has never paid a dividend, such a valuable company? Amazon has ~10 Billion outstanding shares and the current market price for one of those shares is ~$240. If folks only care about dividends, why would anyone buy an Amazon share at that price?
        • ignoramous 4mo ago
          > why is Amazon, a company that has never paid a dividend, such a valuable company? You'd hope every publicly-traded long term minded company operates the same way Amazon does. Reinvestment of money they themselves earn in "growth" and still retain a trickle in profits.
• mvkel 4mo ago
  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."
  • trhway 4mo ago
    Yes, it is like a new era - the startups have huge direct revenue on real products instead of "users" which yet to be monetized. And the network effect which ruled for the last 20 years seems to have relaxed its death grip just a bit (of course it is still there as having more customers using your tools and models provides more training data, etc., yet the current network effect doesn't seem to have that high exponential value like before)
  • root-parent 4mo ago
    >> Inference is _highly_ profitable. Totally untrue.
    • cactusplant7374 4mo ago
      How much does 1 million tokens cost OpenAI?
    • mvkel 4mo ago
      "Revenue: 13.07b; cost of revenue: 7.5b." This includes running inference for ~1b free users. What is untrue about this?
      • horticulturist 4mo ago
        [dead]
      • jcranmer 4mo ago
        So far as I'm aware, we don't know that inference for free users is counted as cost of revenue as opposed to sales and marketing. With sales and marketing at 5.7b, which seems unusually high compared to cost of revenue, I think it's deeply irresponsible to ignore that when considering how profitable OpenAI may be. Even taking a charitable interpretation, OpenAI is having to spend considerable budget (more than their gross profit!) to keep people paying for using their services. The less charitable interpretation is that they're deeply unprofitable and they're pulling every legal accounting trick to hide the sources of unprofitability.
• jrm4 4mo ago
  Ha, not a problem. 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.
• mrcwinn 4mo ago
  The scale of the numbers is exceptional, but the shape is pretty typical for a high-growth, scale startup with a big TAM where a winner can take most. And compute, supply constrained as it is for the foreseeable future, is absolutely a moat. I come away from this thinking OpenAI is actually in very good shape given that revenue is growing fast enough that break-even has a clear path without doing anything draconian.
• fsuts 4mo ago
  ”The company reports over 900 million weekly active users of ChatGPT, though only about 50 million of those are paid subscribers.” With so many free models available the ai companies are going to struggle to convert active free users to paid.
  • JimTheMan 4mo ago
    None of the free models offer anything even remotely close to the output you can get on a relatively inexpensive model. I think that AI is going to become just another utility people pay to stay relevant. Same as their internet, electricity or gas.
    • nemomarx 4mo ago
      Will they do it at utility / commodity prices though, or the inflated costs we see now?
      • JimTheMan 4mo ago
        That's a good question. I think there's an assumption that current costs, which are high, will remain high. Most of that seems to be going to model development, rather than model operation, which is the product we all use. To me the most likely option will be, that development slows and prices rise somewhat, as they can't keep burning cash forever. But I'm willing to pay a little bit more, considering how powerful the tool is.
    • 9eLeven 4mo ago
      Sonnet 4.6 is free and works really well for coding for me from just pasting `tree` and `cat` output directly in the chat window on claude.ai
    • GHanku 4mo ago
      [dead]
    • amanaplanacanal 4mo ago
      > another utility people pay to stay relevant I'm guessing that might be so in certain professions, but I would expect the employer to pay for that. For the rest of us, it seems unlikely. At least for me, I don't have a need of a device to generate text for me. And I bet most people are are in the same boat as me.
• deleted 4mo ago
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• eranation 4mo ago
  Is this surprising to anyone? I thought that was a given. I'm getting de-facto unlimited use of a model more expensive than Opus 4.8 for $20 a month.
  • 542458 4mo ago
    I feel like I have a different $20 plan than everyone else. I have no problem hitting my 5 hour and weekly limits. Don’t get me wrong, it’s a great deal compared to API pricing, but it’s a far cry from “unlimited”.
    • uberduper 4mo ago
      I get about 20 minutes of work from my 5h limit with the $20 plan. It wouldn't bother me as much if codex would continue after the token bucket refills instead of waiting for me to show up and tell it to continue. I don't jump to the $100 plan because I would be in the exact same situation.
      • theturtletalks 4mo ago
        Harness matters in this. Using the Codex sub with Hermes eats tokens like nothing. Using it with Pi is much less but you don’t get the long term memory. When you were able to use the Claude subscription with Pi, I barely hit the 5hr limit. When they stopped allowing that, CC harness just chews thru tokens.
    • eranation 4mo ago
      Interesting. I'm mostly using Claude, so perhaps I'm not nearing the limits, but I do use Codex (for coding and reviews occasionally) and use chatgpt for second opinion many times, including "pro" research. Never got to my limits. But again, not my main go to tool.
  • perching_aix 4mo ago
    > de-facto unlimited (...) for $20 a month Would love to hear some details on that one... Or was that a typo and you meant the $200/mo plan instead maybe? That one I could believe, assuming no or frugal subagent use that is.
• atleastoptimal 4mo ago
  Everyone's financial literacy seems to evaporate when discussing AI companies. They assume that companies need to be profitable or they're a bubble waiting to burst. The whole point of the company is that they are investing a huge amount of money upfront in order to make models that are better and better, and thus have a higher productivity multiplier. They are very profitable on inference, they just know that the race to AGI requires a huge amount of investment, compute, getting the best researchers, etc.
  • harimau777 4mo ago
    I think that the issue most people have is that the degree to which they would need to be profitable in order to pay back their debt is not realistic. It is unlikely that they would be able to get that large a portion of US GDP and if they did then there will likely be riots in the streets.
    • atleastoptimal 4mo ago
      WHy would there be riots in the streets?
• marcosdumay 4mo ago
  So... 50% operational costs and about $100 spent on sales for each paying customer. If they manage to keep those customers for several years without more sales, that bit looks like a normal "high-touch" business. They shouldn't look like a "high-touch" business, but their unitary numbers look way better than I expected. They just need to grow some 10 times to star making a profit... Maybe 100 to cover the opportunity cost of their capital. It's just a matter of finding 5 billion people willing to pay US prices :) But it is still better than I expected.
  • ignoramous 4mo ago
    > just a matter of finding 5 billion people willing to pay US prices This is how you know ads are inevitable. YouTube is probably a good indicator of how BigLabs will operate for free users.
    • qntmfred 4mo ago
      I'd be cool with that. YouTube premium is one of the best value subscriptions I have. Steering people toward paying instead of ads-by-default is a net good imo
      • dogecoinbase 4mo ago
        I think we can all understand the ways in which embedded advertisement in LLMs will be fundamentally different than view-based advertisement.
        • qntmfred 4mo ago
          the AI providers will experiment with sustainable ad models and users will demand transparency and responsibility. An equilibrium will be reached, I'm sure.
          • thorbutt 4mo ago
            The AI providers will experiment on their users, and keep going until they lose users It'll be like Facebook; they're not losing money but it's awful to use
• reducesuffering 4mo ago
  Anyone remember how immensely incorrect most of HN commenters were on Uber's eventual profitability? For years we heard endless admonishment of Uber being an unsound business model. They made $10b in profit last year, $150b company at 18 P/E ratio. I would take the average HN opinion of business profitability with a grain of salt.
• apatheticonion 4mo ago
  Seeing that R&D costs are the lion's share, I wonder if we are at a point where the focus can shift to improving the cost of inference. Unless we are genuinely pushing to find AGI, at which point nothing matters, LLMs in their current form don't replace knowledge workers but are an effective force multiplier. How good is enough? For instance, I pay about $1-2 a month for DeepSeek. It's not as sophisticated as Claude, but it still doubles my productivity as a SWE. If Fable comes out and demands 50x the price of DeepSeek in order for Anthropic to make a profit on it, how much more productive would I be compared to my personal experience + DeepSeek? 3x? 50x? Is it cost effective for a business to hire someone without SWE experience + Fable verses hiring someone with SWE experience and DeepSeek? When does R&D hit diminishing returns?
  • SpicyLemonZest 4mo ago
    Even if you discount superhuman AI (which I would emphasize that frontier researchers do not discount and expect to see soon) think it’s still hard to have enough confidence that the ground is solid. Someone in 2024 trying to go down this route would have invested a lot of now-pointless effort into prompt engineering.
  • jaynate 4mo ago
    For clarity, inference is typically a COGS and therefore hits Gross Margin vs model training which would typically be in OpEx (where R&D lives) and would hit operating margin.
  • vlovich123 4mo ago
    > I wonder if we are at a point where the focus can shift to improving the cost of inference. There's always working on improving the cost of inference, but I don't think this is an area of R&D that will slow down. The reason is: 1. A better competitor model risks eating away at how much they can charge for inference (i.e. revenue) 2. Whoever unlocks AGI will unlock even more growth 3. Even when you unlock AGI, you'll want to throw gobs of money at it to improve itself and all sorts of things. > If Fable comes out and demands 50x the price of DeepSeek in order for Anthropic to make a profit on it, how much more productive would I be compared to my personal experience + DeepSeek? 3x? 50x? You're pricing it wrong and looking at it wrong. First, the per token price doesn't consider that a smarter model can end up using fewer tokens overall to achieve a result. Secondly, if the difference is between failing to accomplish the task and accomplishing the task, suddenly that 50x can seem like a bargain. > Is it cost effective for a business to hire someone without SWE experience + Fable verses hiring someone with SWE experience and DeepSeek? When does R&D hit diminishing returns? At this time, someone without SWE experience + <name AI model> vs someone good with SWE experience and <name another AI model> is a no-brainer. The AI model is an accelerant but the "no SWE experience" will be accelerated into a wall. Now maybe that doesn't matter for prototyping and certain other things, but anything in production the lack of experience will hurt them with things they won't even know about or even know how to look for it (e.g. slow, insecure, etc).
• amluto 4mo ago
  These numbers seem insufficiently detailed to really evaluate anything. They’re had $13bn in gross revenue in 2025, and they cost of that revenue was $7.5bn. Both are growing fast (we assume) and the ratio ought to stay roughly constant. But: how are they calculating the cost of revenue? Do they have rapidly depreciating assets that are also needed to produce that revenue? (Starlink has this issue.) Will their cost per arithmetic operation for inference rise or fall? (Anthropic is paying xAI an absolutely insane amount to lease GPUs. They must be betting that they will not need to repeat that.) Is a large portion of the cost allocated to R&D actually being used to support their revenue? I certainly believe that the cost of inference can be plenty low for them to make a profit, but a more granular breakdown would make it easier to evaluate.
  • 3eb7988a1663 4mo ago
    I am also curious what fraction of that revenue is government contracts. If they got say $N billion in government contracts, that is not going to have meaningful growth in the future.
• lbrito 4mo ago
  I wonder how effective the marketing is (not much it seems). I was watching a World Cup match last week and one of the TV ads during half time was something to the tune of ChatGPT being used by kids to improve their street soccer skills. This was Brazilian TV. Anyone even remotely familiar with Brazil would find this ad deeply, thoroughly out of touch. I can't think of a worse chatbot pitch than that.
• aizk 4mo ago
  Sam didn't lie, they are in fact a non profit.
  • bharxhav 4mo ago
    You are an LLM dataset's worst nightmare. XD
  • jcgrillo 4mo ago
    I do not think that means what you think it means
• wxw 4mo ago
  This title is not how I'd actually interpret the results. Glad to see more sane takes in the comments. All these articles on their current financials are missing the point. OpenAI is doing pretty well. Capital expenditure is required to deliver on 1) better models 2) better infra and 3) better products. Insane CapEx is required to do all the above + compete with Google, Meta, Microsoft, Apple, Anthropic, etc. etc. etc. who are all trying to do the same. These financials are sane, considering the scenario.
• YeahThisIsMe 4mo ago
  Pardon my French, but yeah, no shit? AI companies are black holes for money the way delivery companies are (or were, considering the money people are willing to pay these days). Most of them will disappear alongside the money people have bet on them.
• MaysonL 4mo ago
  Who needed leaks to know that?
• romaniv 4mo ago
  The fact that people here are looking at these numbers and saying "this is fine" is absolutely bonkers. Basically, it's a company that's not sustainable for two separate reasons. The first one is that they have an extremely high overhead. SG&A of 55% is really bad. The seconds reason is that their R&D costs are truly astronomical. They could probably cut those costs to some extent, but they're not going to cut them to nothing. They're already losing ground to Anthropic even with this much R&D. To put it differently, even if OpenAI cut its R&D and inference costs by half, they would still be leaking money like a sieve.
  • robocat 4mo ago
    > SG&A = SG&A stands for Selling, General, and Administrative expenses
  • ihsw 4mo ago
    [dead]
  • CPLX 4mo ago
    These companies are clearly calling things that are R&D that aren't R&D. If you're building a model that lasts a few months before it's no longer the most current one, and maybe a year before it's completely unusable by anybody, then that should just be COGS. Doing that, however, would betray the real problem with this business model.
    • lokar 4mo ago
      Calling it capex with an appropriate depreciation schedule is more appropriate.
    • Gigachad 4mo ago
      They are also likely overestimating the useful lifespan of the hardware. They keep extending the number of years on the GPUs to make the accounting look better.
      • BLKNSLVR 4mo ago
• easygenes 4mo ago
  This headline is not what I would read from this. The numbers are more favorable than the general tone of rumors, and point towards the expected shape of a fast-growing R&D heavy business.
• ganelonhb 4mo ago
  People are gonna lose so much money on their upcoming IPO lol
• mamine 4mo ago
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• tty456 4mo ago
  Elon must be licking his chops right now, hoping that the "OpenAI problem" will just solve itself which bumps up X.ai as a competitor to Anthropic but under the guise and financial manipulation of all of SpaceX and it's subsidiaries to fool the public into thinking it is a long term player.
• thraway3837 4mo ago
  Good analysis. But who cares? It takes a long time for companies to figure out how to become profitable. And I honestly believe that OpenAI/Anthropic etc. have done humanity a huge favor. The money they're burning is not yours or mine. They're institutional investor money. So, again, who cares? It will become profitable. Local models and local on-laptop inference will get good enough. This argument has been made for decades. It's not like everyone is walking around hosting email and photos on their personal machines. Sometimes it takes a large investment to make servers and clouds for this stuff possible. We need to get away from this idea that in order for one thing to succeed, the other must fail. We also need to stop thinking in binary and accept that all these things (profitability, local models, powerful laptops, etc.) can all happily coexist.
  • layer8 4mo ago
    Buyers of consumer storage, RAM, and GPUs care. People affected by the data center buildouts care. Workers losing jobs due to underpriced tokens care. People on the receiving end of AI slop care.
    • thraway3837 4mo ago
      People aren't affected by PC component prices. The MacBook Neo was introduced and selling extraordinarily well for $500 during this component price crisis. 99% of the population isn't building their own PCs or smartphones.
      • layer8 4mo ago
        People who don’t want Macs or who want more powerful hardware are.
    • probiz 4mo ago
      [flagged]
  • joshuastuden 4mo ago
    They haven't done humanity a favor at all. The innovation that these LLMs have produced has been small. A few fun math theorems where the answer was gleaned from a pattern in the training data. Great,.but it doesn't change the world one bit. That latest drug for pancreatic cancer? Yeah, all human. After the trillions already spent, AI hasn't come up with any new medications, no new inventions to save lives... Nothing
• dev1ycan 4mo ago
  Remember when Nvidia gave us HBM for the 1080 ti and then took it away because it was "too expensive for consumer products"? I remember. I feel like the 1080 ti is like a prophet of the current crisis, these companies are buying $10k paperweights per user to MAYBE... LUCKILY... charge what... $200 a year? and that is for every 1/100 users. this same 10k hardware will be outdated in a couple of years... It just doesn't make financial sense, if you couldn't sell standalone GPUs that people PAID for with HBM in them, what makes you think that you can sell a POSSIBLE subscription utilizing a $10k+ GPU? This is the most obvious bubble of all time.
• blini-kot 4mo ago
  what a surprise! who would have thought, right?
• jcgrillo 4mo ago
  I'm just here for Ed's victory lap.
• deleted 4mo ago
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• strenholme 4mo ago
  AI is a huge bubble, just as dot-com was a huge bubble (I remember when people spent huge amounts of money to have key .com domains; as much as people paid for linux.com back then, the domain essentially went nowhere), and just as buying houses for too much money with loaned money was a huge bubble. Just as with the two previous bubble, we’re seeing companies hemorrhaging huge amounts of money, and when the dust settles the market is going to crash big time like it did with the two previous bubbles. Unlike previous bubbles, this bubble isn’t giving people high paying jobs until everything crashes (programmers with the dot-com bubble; construction people during the real estate bubble), but it very annoyingly is making memory and SSD storage cost far too much causing computers to cost about 150% as the cost two years ago before the AI bubble was in full force, forcing Apple to make a “MacBook Neo” model with the absolute minimum of ram and SSD storage space. Like the dot-com bubble, we will have very few winners left (with dot-com, the big winners were Amazon and Google) but unlike the previous bubble, it’s incredible how political this particular bubble is (i.e. the controversy around Grok).
• epsteingpt 4mo ago
  A few things to note - the financial literacy here is... sometimes lacking? 1. Revenue GROWTH is 3.5x; Expense GROWTH -> Slightly less than 3x. There's a path to profitability 2. However, the COSTS probably assume a 5 (or longer) year depreciation on GPUs. If that assumption dies, the whole thing goes down. If R&D costs don't go up - where does the moat come from? Cheaper players catch up with 'good enough' and will erode their revenue. Most of human tasks just don't require that much intelligence. They're racing toward 'superintelligence' that recursively self-improves. No indication we're anywhere close to reaching it. Going to be an interesting year to say the least.
• akomtu 4mo ago
  We are watching an experiment: how high the Tower of Babel can stand of it's built with AI slop. "According to the narrative story in Genesis 11, the city received the name "Babel" from the Hebrew verb bālal,[e] meaning to jumble or to confuse, after Yahweh distorted the common language of humankind.[11] According to Encyclopædia Britannica, this reflects word play due to the Hebrew terms for Babylon and "to confuse" having similar pronunciation.[7]" (Wikipedia)
• coldtea 4mo ago
  Of course it does. They all do. Anybody who though otherwise wasn't paying attention.