Leaked OpenAI financials show $38.5B loss and compute burn | Hacker News Reader

Leaked OpenAI financials show $38.5B loss and compute burn

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221 pointsTechApocalypse263 comments

[dupe] Discussion: https://news.ycombinator.com/item?id=48550465

Revenue is higher than cost of revenue and revenue is growing faster than cost of revenue.

We know OpenAI is forecasting $25-30B revenue for 2026. They will be very close to breaking even at those number.

Given Anthropic has forecast more revenue than OpenAI and we know has spent less on R&D (cite their desperate scramble for compute capacity!) the rumours of them being profitable this year seem very credible.

Imagine still saying this after open weight models being released everywhere and the likes of DeepSeek keeping their prices at the lowest possible for more than good enough intelligence that even Microsoft is migrating to deploy DeepSeek's models.

I use DeepSeek. I'm very familiar with open models. I run a popular benchmark to check their progress.

DeepSeek is somewhere between Sonnet and Opus in capability, for much lower price.

Their $0.87 per million output tokens is one of the few API offerings that is probably subsidized (the break-even price is probably around $2 judging by[1])

I think Anthropic and OpenAI's margins will erode some over time. But I think they are very profitable now on the API prices they are charging, and their margins will remain healthy.

Put it like this: AWS is a very expensive way of getting compute, and there are numerous competitors that are much cheaper.And yet AWS is very profitable.

[1] https://openrouter.ai/deepseek/deepseek-v4-pro#providers

I find that Deepseek Pro to be below Sonnet 4.6 for much of the coding/review/executing tasks from plan. The only good thing it has going is the price. It will is cheaper for it to crunch through your well worked out implementation plans, it's slower, makes more errors, needs more tokens to fix those. And even at that increased spent it does deliver in the end. More tokens, lower cost, slower.

I use it as something that sits right below Sonnet for daily tasks that can be run in loops with validation checks, perfect. Anything more advanced...it just doesn't cut it.

Same experience with z-ai glm 5.1 btw... don't know what system prompt magic antrophic set in front of their Sonnet 4.6, but it does execute tasks efficiently.

claiming any of these deepseek models are close to opus is a bit optimistic

I use Claude at work, DeepSeek at home.

For software development, I notice no difference. For all the work I did using AI, DeepSeek is as capable as claude.

Have you tried other tools like windsurf? Or other free open source Ai?

I never tried Windsurf, no.

I tried DeepSeek and MiMo through both Claude Code and Pi.dev. I found Claude Code a slightly better harness, but I still have both installed and switch around frequently.

As for "open source AI", what exactly are you talking about? Is it open models?

I tried some through Ollama, but I don't have the hardware to run decent models.

Did you mean Sonnet and Opus?

Yes, edited, thanks.

Sure!

Microsoft has been dead last at everything for decades, so that point isn't surprising.

Microsoft quietly won the AI race in enterprise with the addition of cowork to their copilot app. Being in the energy sector in Europe we're quite limited in what we can do because of things like NIS2 compliance. We have access to corporate AI tool though the equity fund which owns part of us, and while it allows you to create personalized agents that can run sub agents and use "skills" it's all done without any form of filesystem access. Being married to Microsoft because our IT loves that sort of thing, and having spend a decade in the public sector I sort of get why from an enterprise perspective, anyway, we have always had access to their Copilot app. Which has been so bad that it's actively turned people away from AI. Then last month we get cowork frontier, and now I'm in the process of helping everyone adopt it. Not only does it play directly into our licenses, it also has access to all the Microsoft 365 stuff, so that our HR can use it to sort applications into the categories they belong in and what not. Sure they could've had a better on-boarding system, but they don't so someone has to go through the emails and sort "financial controller" from "sheep shepherd" (yes, we have sheep on our solar plants).

Anyway I'm rambling, what Microsoft is doing with AI in enterprise is basically what they did with Teams and similar systems. They provide a platform for it which is good enough that your organisation is going to want it rather than deal with multiple vendors. Not for tech organisations, but for every other enterprise organisation it'll be so much easier to just go this way. I imagine that Anthropic is getting some sort of payment from Microsoft for Cowork, but what Cowork shows is that Microsoft can be completely model agnostic and still sell "top" AI. Especially because they've set cost on a fixed rate that I'm sure they'll increase by 25% every year.

Or do things like the fact that you need some sort of special Agent 365 license for your sysadmins to manage the admin.microsoft part of Copilot which has to do with security policies... Ask me if it was fun doing that agent by agent... It's frankly the most Microsoft thing I've ever seen.

I think OpenAI will still maintain the lead for at least another few years.

The cost of hardware still needs to dramatically drop for open-weight models to be viable for local usage. Even with the release of things like Nvidia DGX Spark and Ryzen AI Halo, you'd likely want a few of them to run agents in parallel.

Sure, you can use cloud hosted variants of models like DeepSeek etc at API rates, but subscriptions still come out on top for bulk usage. GPT is already tightly integrated into peoples workflows, has wide adoption, has good tooling for developers, etc.

Plus there's nothing stopping them from competing on a price level if they really feel the need. It just means they might burn more cash in the short term.

> The cost of hardware still needs to dramatically drop for open-weight models to be viable for local usage. Even with the release of things like Nvidia DGX Spark and Ryzen AI Halo, you'd likely want a few of them to run agents in parallel.

It's more efficient to do the opposite on a constrained platform. Run agents in parallel using a single model, then round-robin among models for cross-checking purposes. (The makers of local inference engines are dropping the ball by not making batched inference a first-class citizen of that workflow. It's not just useful for vLLM and SGlang.)

Looking forward to GLM-5.2 in OpenCode.

But a lot of other people are going to happily shell out for Opus 4.8 at way higher prices.

I think it's out now - or do you mean as part of the Zen subscription?

The API GLM 5.2 launched last night with several providers on OpenRouter. I had a short conversation, didn't get to test much, but initial impressions "the vibes were good".

I think op is on the go sub, i m on the same we have glm5.1 but dont see glm5.2 yet

Been using GLM-5.2 in OpenCode since Saturday, what are you waiting for?

What's the adage, it is difficult to get a man to understand something, when his salary depends upon his not understanding it

My salary is in no way dependent on OpenAI or Anthropic (in fact I could probably get more money if we switched to using open models and they were just as capable).

> Revenue is higher than cost of revenue and revenue is growing faster than cost of revenue.

That's not necessarily true.

It depends on two things that are impossible to derive from the leaked numbers:

1 - How much of their compute costs are subsidized.

2 - How much of that R&D chunk can actually be reduced for the company to continue working. What goes into "R&D"? Is that only things such as training for new models? This is impossible to determine from those numbers alone.

If those numbers are true, they would not be "close to profitability". They would be profitable, period.

This does not explain why they need to raise money like crazy. It would be possible to train new models at a more sustainable pace without the need to jack up prices at all, only with the difference in between "Revenue minus Cost of Revenue".

They also wouldn't need to be on a mad race to IPO and dump this into the public market.

To be frank, the more I look at those numbers, the more I think this is completely unsustainable.

All model training is in R&D

> How much of that R&D chunk can actually be reduced for the company to continue working.

Worth noting that almost all engineering (including software engineering) is always included in R&D.

Development is important! Most software (and indeed engineering!) companies couldn't continue if they stopped R&D.

Ford couldn't continue as a company without R&D.

> All model training is in R&D

Yes. But is all R&D model training? That was the actual question.

Everybody that is presuming "inference is profitable" here is just naively deducting "cost of revenue" from "revenue" and running with that.

> Is that only things such as training for new models?

It feels like cost of revenue should account for training, but I'm not an accountant so who knows?

I'm not even entirely sure of this.

Obviously you need new training rounds over time. Knowledge is not static. New things that are created would need to be part of later models.

How do you account for that? Do you account for some sort of model depreciation?

A lot of things are very nebulous in those leaked numbers.

How is "cost of revenue" considered? If Microsoft, Oracle and such provides computing at a loss to Open AI, that is clearly not sustainable, and it is a way to pretend the numbers are better than they actually are prior to an IPO. This might even be a source of pressure for an IPO. As losses accumulate in the ecosystem, they need to dump it into the public so they can bring the numbers to reality.

Also, I am not sure if they own any compute (for example, the Stargate datacenters). If they do, do they lump the costs of building those data centers all in "R&D"? That would be one hell of a way to pretend that inference is cheaper, good luck having inference without those datacenters.

The more I look at this, the more it looks like "OpenAI is profitable when you pretend they have no costs".

Sales and marketing have to be added to the COR, which makes COR slightly > Revenue.

No, no they do not. Those are operating expenses. This is accounting 101.

It's wild how basic math goes poof as soon as someone has an agenda to push in internet comment threads.

The words have meaning in accounting. The basic idea of accounting is to communicate what is going on in the business in as consistent a way as possible. It's not a perfect system, but this stuff really is 101.

That's only true once they become public. As a private entity they can calculate COR any reasonable way that their investors are okay with.

(For tax purposes for most businesses it doesn't matter whether an expense is CORS or not.)

To expand on this slightly - startups often argue that marketing expenses should not be reported as part of cost of revenue because they are temporary and will change as they grow and the market becomes more aware of them.

There are arguments for and against this - awareness is an issue for startups, but most large companies continue to market. It is true that it is fairly easy to change the amount a company spends on marketing.

Either way, as the parent says - provided the investors understand it there is nothing weird about doing it this way.

No, they argue they shouldn't be counted as operating expenses because they are basically capex dollars. It's a completely reasonable argument. If I spend $100 million on s&m to get a bunch of customers to my saas, it's not different economically to drilling 20 oil wells at $5 million a pop.

Yes that capex is a fair point too.

(To explain: It's basically saying they are spending $X now to get them as a customer which will yield a lifetime value which is some multiple of $X. That's a very valid thing to do - see the whole field of cohort analysis for SAAS)

An oil well doesn't disappear once you put it in the ground (The oil might).

If the marketing and promotions and discounts stopped tomorrow, that revenue will drop.

Page 6 of Google's 10K statement to the SEC[1] they are separate items. The line items are:

Cost of revenues Research and development Sales and marketing General and administrative

Take it up with the SEC I guess?

[1] https://s206.q4cdn.com/479360582/files/doc_financials/2026/q...

I'm not saying that the SEC is stupid. They have a consistent way that they expect a corporation to do their books.

But for all practical intents and purposes, you can't just fire all your sales people and shut off your marketing firehose, and stop giving discounts, and expect revenue to remain steady in either the short, or the long-term.

You can shut down your R&D, and it will remain steady, at least in the short term.

I am clearly mincing terms that mean specific things, and I'm very sorry to do so. I am simply trying to communicate that generally speaking, OpenAI is currently spending ~$1.05 to make a dollar, and is also on top of that, spending ~$3.00 in R&D.

Now, I do think that it's likely that they can increase their prices to close that 5-cent gap.

If you drill a dry well, under successful efforts accounting, you expense it. You also make zero dollars.

On top of that, oil wells decline, slowly but surely, just like... customers churn.

If you spend $100 mill on sales and marketing and get zero customers, I'd agree it should expensed. If you get a bunch of customers, it's hard to argue against the capex treatment idea.

Brand equity lasts long after advertising has ceased. Coca-Cola could stop advertising for a year tomorrow and people would still be aware of it and buy it.

For publicly listed company, isn’t marketing expense in the ‘Selling & Marketing Expense’ part under ‘Operating Expense’ and not part of ‘Cost of Revenue’ as well?

No, public companies do not include sales and marketing as cost of revenue.

COR is a GAAP concept.

COR is an accounting concept, and GAAP does not dictate what has to go into COR because that is industry specific. (Though in that sense, the industry-specific practices determine GAAP for that industry.)

That’s not how that works.

Anthropic has a durable advantage that OpenAI does not, as much as they choose to squander it to pump the numbers.

They were first on a few things but the tell is the coherence of the thinking traces of Claude. You have to put a loss on that. GPT 5 series thinking traces are creepy, Gemini thinking traces are disturbing. They both represent forced discontinuities on the policy gradient.

Claude is good at tool use because it's gigantic and well-labeled, but the reason you pay the premium is for a thinking partner, not a tool user.

Claude Code is the cancer that will kill the patient, Boris is the the Kardashian version of Karpathy, with less business sense.

Is this AI bot? So annoying that bots can join in HN conversations.

No it isn't, and I'm happy to prove it any way you like. If you decline that offer please retract the claim.

post your comment about Meta/Anthropic C/A listers on LinkedIn and link it here

You lost me in the last sentence there. What do you mean?

You and another commenter asked the same question at the same time, it's more honest to link the one reply than to paste it: https://news.ycombinator.com/item?id=48566097

> Claude Code is the cancer that will kill the patient, Boris is the the Kardashian version of Karpathy, with less business sense.

I am not sure I understand.

Using a multi-trillion parameter softmax attention transformer to parse nested delimiters is a perverse thing to do. It is hard to imagine a sillier way to boil the oceans than feeding JSON to an LLM, a task that a pushdown automata from the 1960s effortlessly did on a PDP-X.

The API business throws a massive model that by definition can't be inferred efficiently because nothing can across 4 different compute substates, at a problem that DSv4 nails at or near 100% while leaving most of the actual unique value of Claude on the table.

Claude should be in your house and car and your kid's classroom and shit.

Having it write tail -n5?

That's because Anthropic's A-Team is Meta's C-Team. Hell, I fired some of their stars myself.

You know what’s worse (less efficient) at parsing and writing code than LLMs?

Humans.

None of them even expose thinking anymore. You're seeing a summary

I am happy to show you a thinking trace from 4.8 without even opening the web tools.

The legibility of thinking traces has gone down, but mostly this nets out to spilling more XML because their website is a vibecoded heap

Can you at least ask your owner to upgrade your model? Ideally at least Sonnet-level.

"OpenAI generated $13.07 billion in revenue in 2025"

Considering just four years ago they were a research lab with hardly any revenue at all, and no corporate muscles for earning revenue, I think that is a very impressive number.

(Sure, they're losing a whole lot of money too. Same goes for almost every other hyper-growth company in the history of tech.)

> Same goes for almost every other hyper-growth company in the history of tech

Except it's not true. No one lost $38.5B in a year just to 'hyper-grow' or whatever it means. Uber accumulated ~$30B loss over a decade.

Edit: I read it wrong. The loss was mostly caused by one-time event[0]:

> 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.

It looks like that OpenAI is actually quite in line with other companies that lost money to grow.

[0]: https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb...

Depreciating assets is one of several possibilities.

That argument supports any levels of losses, however I also think it’s rather misleading.

Growth means some inefficiencies, but their expenses are largely around commodities like electricity and data centers not a sudden army of salespeople. They also got 150M 11 years ago and 1 billion 7 year ago, they where quite large in 2022.

Basically you don’t get better at writing checks to your local utility which limits how much they can control costs.

> They also got 150M 11 years ago and 1 billion 7 year ago, they where quite large in 2022.

I had to look that up, you're talking about investment there, not earned revenue.

The 150M was their initial funding (actually 130M I think https://www.clay.com/dossier/openai-funding)

The 1B was from Microsoft in 2019: https://openai.com/index/microsoft-invests-in-and-partners-w...

In 2022 they only had 335 employees (according to various internet searches but I can't find an original source for that number.) I can't find credible numbers for revenue from the GPT-3 API, which did have some usage - GitHub Copilot started charging a subscription fee on June 21, 2022 - https://github.blog/changelog/2022-06-21-github-copilot-is-n... - and that was running on the OpenAI Codex model so presumably OpenAI had some revenue from that.

We don’t disagree with the underlying facts.

That said, in many ways 335 employees is the midpoint between 3 employees and 30,000 employees. The CEO can’t keep track of everyone’s names and what they’re doing, you need layers of management, HR, etc. It’s not really a simple exponential function but 335 to 336 is way more automated than going from 3 to 4.

> Sure, they're losing a whole lot of money too. Same goes for almost every other hyper-growth company in the history of tech

That doesn't mean anything. There are examples to make both ways. E.g. WeWork

and WeWork is awesome example because it fell apart before IPO. It didn't even make it that far. On the other hand, for all of the shit talking that goes on online, SpaceX is up 49% from IPO price.

All of the shit that people said about SpaceX is still true. It's still up 49%. I'm sure it'll take a dump the next time anything bad happens, like a rocket explodes, but now that it's public, I'ma be watching all their rocket launches so I can buy if that happens and sell right after. I'm also going to be watching because going to space is fucking awesome but I can't buy a trip on that rocket yet and and no one's gonna pay me to watch it.

If your losses scale with your growth, while at the same time your competitors are eating into your future user-base, how are you ever gonna become profitable? Only two ways comes to my mind: regulatory capture, and moving upwards into full software-development house.

Or a utility :)

Look at how a utlity works, in setting price specifically, for things that are considered a public good. The story is not about how much profit or revenue they make. Its about how do you keep it afloat and expanding in the coming year. Thats it.

Compute is both rivalrous and excludable so it can't be a public good in an economic sense

$867 million of that was Softbank. News the market has not taken well.

And a JPEG was just a JPEG in 2020, but in 2021 they became a 17 billion dollar speculative market.

How "very impressive" is your NFT collection these days?

NFTs were very obviously stupid. LLMs aren't.

Your argument went from "big number good" to redefining "stupid", and you think that somehow supports your original statement?

What word would you use to describe someone that:

    - told you to put glue on pizza?
    - thinks there's 1 'r' in strawberry?
    - is incapable of stopping terminal flickering? 
    - deletes your production database?
    - bankrupts you trying to scan the entire IPv6 address space of a play network interface?
    - can only attempt to draw a bird on a bike in the most bland and unimaginative style possible yet still completely failing?

All while being given the entire US economy and polluting the only planet we have to do so?

If you still think models can't count the Rs in strawberry you're about a year out of date.

Nice.

I mean, sure you said "LLMs", rather than "LLMs in the last 12 months", and sure, you completely abandoned your original argument, and sure, you ignored the other things listed, and of course everyone knows that list is a comprehensive list of the only failings of genai rather than a honeypot to positively identify you as a shameless shill, but ultimately, the fact that HN chose someone this terrible at making a defensible logical argument to be their favorite genai financial interest mouthpiece is a strong indicator defending the criticisms in the original submission.

If your argument is that LLMs are stupid in the same way that NFTs were stupid I don't think it's worth spending any more time discussing this with you.

Annual revenue of $13 billion per year puts them on par with Apple's AirPods revenue, which places them in U.S. Fortune 500.

lol so weird to put that into perspective macbook ~8b airpods ~18b iphones ~60b

I know they are massive, but AI seems something much more important than airpods

AI doesn't work like the rest of the tech industry. The cost of selling another license for a software program is approximately zero.

In the case of AI the marginal cost of the next token is not zero, and is in fact probably not going down much with volume, if at all.

So I'm not sure one can argue that scale will solve everything. It's very much like the old adage "we lose money on every sale, but make it up in volume".

No you don't.

It's wild to think how efficient Internet services were prior to AI. The most expensive thing would probably have been something like encoding video. Now you've a substantial portion of a rack dedicated to a user in the case of something like fable

Best analogue we have is probably video streaming. Or maybe more so live streaming. Unless subscription based and limited time events it seems those don't do well. Twitch has lost money for how long? And most smaller players seem propped up in other ways.

So if there is real cost involved things start to look lot worse and might not be overcome. OpenAI is unlikely to be exception for me.

But video streaming can be very profitable! Youtube and Netflix are great examples.

But there is no indication they are losing money on tokens when R&D and other expenses are factored out? The margins on API are likely very high so the higher the volume the more likely they will be able to cover the other mostly fixed costs.

Maybe. We'll see.

Also, what are they calling "R&D" exactly? If it is training new models, which needs to be done almost constantly and means spending billions on energy and newer GPUs, then it's not really R&D, but rather operating costs.

Yeah, but they have no moat.

They gave up on video because three separate Chinese companies were kicking their ass (and for cheaper).

Google has a better image model in the majority of cases. Much faster, too.

Claude Opus and Fable are like a billion times better. It's not even funny. Codex can't do Rust at all.

What does that leave them? Ads in ChatGPT? I've started to just rely on Google search blended with Gemini answers now because it's faster and doesn't spit out a 20-page essay of useless effusive prose.

Open source models will eat them from the bottom.

Will those enterprise contracts be renewed in a market full of alternatives?

There's nothing sticky about this company.

They're making a necklace with Jony Ive though, I guess?

They still have the most recognized AI brand name and they are still the most popular LLM. For most users, a 10% diff between Claude and GPT isnt going to move the needle plus it seems to be a horse race anyways. I think their user base is stickier than you would think. Still, it isn't as sticky as social media and it is cheaper to switch AIs than email accounts.

Look at the ChatGPT usage share. It's dropped dramatically in the last year.

https://techcrunch.com/2026/06/16/chatgpts-market-share-slip...

This is not the winner take all market that OpenAI needs it to be.

Its still dominant and a lot higher % wise if you count paying users. Gemini was integrated into Google search so its not necessarily people using Gemini as their daily assistant.

> Google has a better image model in the majority of cases.

Not always. A couple months ago (before ChatGPT Images 2) I tried various prompts on both Google's Nano Banana or whatever and ChatGPT.

"Capybara riding a tricycle. It has 7 tentacles instead of legs"

Google got the number of tentacles completely wrong: https://i.postimg.cc/nzY30y7X/Capybara-Gemini-Nano.png

and after some additions like spotted fur and multicolored tentacles, it was no contest:

ChatGPT: https://i.postimg.cc/02c2LrxV/Capybara-Chat-GPT-before-Image... (although there's still kinda 1 extra tentacle)

And Google still seems to have that odd choice of a European plaza/square/cobblestone street background for everything.

> Claude Opus and Fable are like a billion times better.

NOT at ALL: https://i.imgur.com/jYawPDY.png

Subscribed to Claude Opus for 2 months, with a few months gap between subscriptions to try different versions.

The UX/UI around Anthropic's products was excruciatingly annoying, right from the payment process, and Claude's AI was often hilariously dumb and "trying too hard", constantly full of "oops, you're right" backtracking and often borderline dangerous.

I tried Claude and ChatGPT Codex side by side on some tasks, with the same prompts. Each time, my confidence in Claude fell.

I've been subscribed to the $20 ChatGPT plan for more than 1 year, and this month, I am trying the $100 plan for 1 month.

ChatGPT Codex has been actually helpful and made me more productive enough that I can't imagine going back to coding without it.

I use LLMs more in the context of peer-reviewing and also came to a similar conclusion, gpt-5.5 codex xhigh reasoning seemed to catch more edge cases and went "deeper" into analysis than Opus 4.7/4.8.

My preliminary tests of Fable were pretty promising but that's DOA for everyone for now.

Claude often spent most of its output listing all the things that were already correct and working! "This is good"

and most of its findings were false positive or outright wrong as in the screenshot I posted above.

Opus 4.8 is quite weak. And GPT-Pro is very much available unlike Fable, it's just not hooked up to the Codex harness yet.

Will it be? It's so obscenely slow and expensive and its not obvious it could provide a lot of value for non highly specialized tasks.

So, just like Fable? You can shorten the thinking effort to tweak the "slow and expensive" part a little bit, but at the higher end being more meticulous than even Fable is actually a benefit.

The R&D expenditure seems reasonable, and the revenue numbers seem realistic. I have no trouble believing they can be profitable by 2030 or much sooner. What I don't get is how you get from $30B in revenue to a nearly $1T valuation, but that seems almost level-headed compared to SpaceX, and it's not like any of the big tech companies' valuations make much sense in the context of their revenue.

> What I don't get is how you get from $30B in revenue to a nearly $1T valuation

There is something that has fundamentally changed (or broken, depending on your perspective) with the valuation of American tech companies. They've always traded at a premium, but the pandemic and the encroachment of the monopolists has turned the earth sour.

I don't know if that's completely true. Google, Apple, Amazon, Nvidia etc are somewhat expensive, but not ridiculously so based on earnings and growth. Tesla, spacex and openai are in a different universe, though I honestly can't say wtf is going on with them

The market is pricing in the potential for future revolutionary shifts which seem fairly likely. For instance if there ends up being substantial labor disruption due to LLMs then the economy as we know it is going to end up being reshaped in ways that are difficult to imagine beyond the fact that the LLM providers would likely play a critical role in it.

Similarly, SpaceX has already brought the cost of getting things to space down by a couple of orders of magnitude, and Starship is rapidly progressing with the potential to bring them down a couple more. The aspirational goals there are being able to get things to space on the order of $10-$20/kg. That would dramatically reshape not only space but even transport as we know it, very likely in a way analogous to how the ability to quickly send a 0 or 1 signal long distances for cheap reshaped the world in ways that would be essentially impossible to predict prior to its happening.

I'm bearish on the LLM revolution and bullish on the space one, which generally seems to also be the market consensus.

$20/kg is a wild claim.

A typical American semi-truck (18-wheeler) can carry between 40,000 and 45,000 pounds (20 to 22.5 tons) can travel only 160k miles (2/3 distance to the Moon) on a budget of $400k (i.e. $20/kg).

Do you expect us to believe that rocket transportation would be in the ballpark of truck prices in the near future?

No I don't. The entire nature of revolutionary change is that it's something that seems unimaginable, at best a fantasy, from a distance. 5 years ago you certainly wouldn't have believed that chatbots would be writing meaningfully complex programs from plain language instructions, and posing a viable threat to various forms of cognitive employment, yet here we are. Even now that we're in that era I think many haven't entirely come to terms with what this means.

With rockets, the fundamental viability is there through full reuse, airplane style. The majority of the cost of a repeatedly reusable vessel ends up being fuel, which is around a million bucks for Starship. Add another million for refurbishing, amortizing costs, and so on and you get a baseline launch cost of around $2 million. With a payload of 100 tons, you're at $20/kg.

The major difficulty is creating a system that's fully reusable, and that's exactly what's being done with Starship. I don't know if they'll succeed to the point of $20/kg, but it's a viable possibility. And if they do succeed then everything is going to get very weird, because the economics of transport become absurd, to say nothing of the entire frontier of space suddenly becoming 100% accessible at a reasonable cost for development.

Why should we believe that Starship launch costs will be that cheap when we don't believe Musk's other numbers? Where does that number come from? Has anyone looked into it?

It doesn't cover launch costs directly, but here's a bearish take on reusability of the second stage:

https://mceglowski.substack.com/p/how-should-we-think-about-...

Yes, it's been extensively analyzed. Space is a massive industry with a huge base of people, disproportionately made up engineers and the like, who love digging through the nitty gritty of pretty much everything. Nasa space flight forums [1] are the analog of hacker news, but for space. That link is just for discussion on Starship, and currently has hundreds of thousands of posts.

[1] - https://forum.nasaspaceflight.com/index.php?board=72.0

So what's the consensus? Any article in particular that you recommend?

Peruse the forum. There's a topic on near everything relevant you might imagine. If you want a topic starting from a bearish premise, here is one. [1] In general I think there's little doubt that Starship is viable, but the exact implications are open to a wide range of speculation. Prices are going to go down, and payload sizes are going to go up, but the exact degree is open to speculation varying by orders of magnitude.

[1] - https://forum.nasaspaceflight.com/index.php?topic=63166.0

These links seem to be blocked now. Edit: working again.

"""For instance if there ends up being substantial labor disruption due to LLMs" ""

.. who are they gonna sell to if people don't even have money to buy? We live in circular economy... everyone's dependent on someone or the other... you take one leg out of this, and the whole thing stops. UBI won't work either because it will lead to runaway inflation and extreme levels of invasive control over people's lives and what they can and cannot do.

I hope no one here is naive enough to believe that AI would actually be used for general welfare of people.

> UBI won't work either because it will lead to [...] extreme levels of invasive control over people's lives and what they can and cannot do

I've never heard this before. I thought UBI would be very freeing and without much control. If it is universal then there needs to be no control of who gets it or not. What am I missing?

I think "The Expanse" offers one of the most viable portrayals [1] of what a society under UBI would look like. One practical issue is that you'll end up with people who earn and create and those who passively consume, and that will lead almost immediately to a class system and segregation. And it's likely that basic criminality and other issues will come disproportionately from the consumption class, which will lead to calls from the production class for further regulations on their behaviors.

And in cases where the strain on resources is significant, you could easily see things like efforts to restrict the fertility of the consumption class which would enter into the domain of defacto eugenics. And from all of these sort of issues you're going to see a conflict arise between the two classes, but one holds all the power. It's not going to be pretty. FWIW, a decade ago I was a huge advocate for UBI, but my outlook on the realities of political leadership, and it's probable inescapability, has changed my opinion over time.

[1] - https://expanse.fandom.com/wiki/Basic_Assistance

One simple 100% effective way of control is to control the livelihoods of the dependents. UBI does exactly that by giving that control to governments(politicians), which in turn generally serve corporates interests, thegerby extending that control to them. We can already see it happening with data-centers.

What do "corporate interests" even mean in a world with UBI and automated production?

Your fight against the data centers is misguided. All so they can be built somewhere else so what you have a few extra years of getting paid to fill out a spreadsheet or something? You're applying pressure on the wrong side of the equation.

> What am I missing?

Nothing, it is nonsense. UBI is just expanded social welfare in Europe. There are some checks that you are not abusing social welfare (i.e. living in a huge house while taking housing subsidies) but with UBI these checks are nonsensical from that first letter U = Universal. There are no checks by default.

UBI is really one of the few positive end states. You can't put the genie back in the bottle. People against UBI really need to get over themselves unless you are part of the mega capital class its simply not in your interest to oppose. This will be hard for many software engineers who even in today's market are used to being overpaid and overvalued.

I think a common mistake people making is viewing an economy as fixed, but everything is based on supply and demand. Tech is a tool, like a hammer. But right now it's a hammer that very few people can wield, and that alone is what drives its value. LLMs, if they reach their potential, stand to turn tech into just another hammer that anybody can use. And so being able to use that hammer will no longer be valuable, but there will likely be an explosion of new things being made, with our new-found hammer literacy, that fill the vacuum and then some.

I’ve always been wondering how they are actually planning this scenario. If they cut human labor on a massive scale due to AI, consumption is about to drop heavily. AI doesn’t pay healthcare insurance or taxes, it doesn’t buy groceries or cars or gas and it doesn’t rent apartments. How do people imagine to compensate that?

i am not ready for our weyland-yutani overlords

You see, in this _new economy_ you can't rely on old metrics to judge value.

At least that's what I remember from the 00s.

It's easy actually. If it grows a lot, with decent margins. Grow 30B at 40% pa for 10 years and you arrive at around $850B in revenue, assume 25% operating margins, that's slightly over 200B of operating income, and it all makes a lot of sense.

You can debate the assumptions, but it isn't witchcraft. The math is simple.

I think the reasoning should be the other way around:

If you limit yourself to simple math, than you get this result.

Assuming linear growth into a market with competition and an unclear ability to absorb that growth is a gamble.

I think what PEOPLE are saying IT is very difficult to sustain that level of growth.

Of course it is. But stocks aren’t a measure of current profitability. It’s a bet on future profits.

How many serious, large AI players are there? Google, OpenAI, Anthropic, and who else exactly?

At least one of them will probably win. And winning here means billing almost all companies for AI and automation, consumers, perhaps robotics and research.. and that potential earning is massive.

So yes, I will pay 10 times the worth for the stock now, but paying 1000 per stock for a chance of owning all that future profit is not that outrageous.

I believe a more likely outcome is that neither one of them outright "wins", and they get joined by other players like xAI, DeepSeek, Qwen, Mystral, Llama even...

That should make it hard to bank in on future growth with any single AI company: what I believe is happening is investors jumping in on the short term gains train.

Why would revenue continue to grow at this rate?

Enterprises are becoming increasingly aware that the best models can be used for planning and then cheaper models for execution - all the way to local models for some tasks.

Add in increasing competition from Chinese models… I’m not convinced this revenue growth is guaranteed.

Add in the fact that they claim 900 million weekly uniques. Pretending the growth and cost rates compound as described in the article, they will need to generate about 100x current revenue to growth out of their current hole. That sort of implies that they will have 10x the entire world's population as weekly uniques at that time.

That seems unlikely.

It's not like their 10x revenue growth year-to-year was based on expanding their customer base 10x though. The vast majority of weekly users are not paying, so they are actually a cost right now, but could be converted into paying users or audience for ads in the future.

That's a fair point.

My own experience with converting unpaid customers is that it is really hard.

They could have 2x customers spend 50x as much money too.

I think it's more about contrasting it with world economy as a whole for a reality check.

> all the way to local models for some tasks.

I think this is the key takeaway for the future of AI. Give tech a few years to catch up and we will likely have the functionally equivalent to today's models running on consumer grade hardware. From there it will explode, where "it" is how we use and interact with computers. AI will be integrated into just about every workflow.

The business case in this future would be to sell the trained models to end users. The investment would be shifted towards the training of models and delivering updates, with revenue coming from model licenses, upgrades and cloud services for tasks that exceed the local capabilities.

global gdp is over 100 trillion. Something like 50-60% is paid to labor. If you assume AI takes a good chunk of labor, the market is gigantic. Really really really gigantic.

AI bots don’t buy consumer goods and other services.

If people don’t get work they stop buying things. If people don’t buy things, companies don’t make money. If companies don’t make money they won’t buy — or have a reason to buy —- AI companies’ services.

You cannot eliminate a significant chunk of labour cost without causing demand collapse. People saying that there is money in eliminating labour costs at that scale are not doing the whole of the calculation.

I'm not suggesting the people then sit idle. The economy can grow, services can be added, the % done by AI drops, but the raw value of it doesn't. It's just to show that the market is extremely large.

Like agriculture. That market has grown significantly through time, even though it's shrunk dramatically as a % of GDP.

How can people be economically active if their jobs are eliminated from the economy? At best they job-share, and demand still collapses, just in slightly different ways.

AI cannot make money as an alternative to large scale employment, because essentially all the clients of those AI businesses will see demand for their products and services collapse. AI bots don’t go to In-N-Out Burger or Disneyland.

Anything else is fantasy maths, albeit commonplace fantasy in the AI industry at the moment.

Look at the history of farming. Tractors also don't go to disneyland.

Right, but at best (putting aside the tractor's magnifying effect in the Great Depression) you would be looking at employment displacement from one sector to another. Those people went on to other jobs.

Here you are talking about a shift taking employment out of the economy full stop — taking the money that would have gone to "a good chunk of labor", to use your words, and giving it to AI firms.

You are necessarily talking about job elimination en masse — it's the only way this hypothetical source of money is available.

If you take labour out of the market, en masse, you cause demand to collapse. Because they won't be economically active!

No, I'm not. I was using it to size the market for AI. I don't imagine those people just sit idle. They do something else. Tractors took a good chunk of labor out of farming, those people didn't sit idle.

Can you give an example of what people may transition to?

lol if i knew the next big industry i wouldn't be sitting here chatting on hacker news.

New industry after new industry has been created over the past 100 years. It's difficult to believe that all of a sudden that stops.

Most labor by any measurement is not knowledge work like software engineering.

If by most, you literally mean > 50%, sure. But I've heard it quoted that knowledge work in advanced economies is something like 40%. So, we are still talking extremely large numbers.

They are also the higher earners who buy more of the things.

Henry Ford said he needed to pay his workers enough that they could afford his cars.

I meant by dollar amount, not headcount.

OK, but look, again, if you take away a good chunk of the money that goes to employing people — that is what you mean, right? This money is lost to the labour market one way or another — then don't they buy less stuff because they have less money?

It's the same as if you had taken half their jobs and forced everyone into underemployment through job sharing.

Your formulation forswears them finding replacement jobs because the money will have gone to AI companies instead, and it necessarily implies people having less money to spend.

No, it doesn't. The economy can grow. Money isn't some fixed amount thing.

That’s a good way of looking at it, I appreciate the different perspective.

From your headline number you’d have to deduct non-knowledge work though.

You’d also have to take into account the fact that while AI can often replace some tasks, it’s often not enough to replace the entire worker.

For the high end knowledge worker jobs the corresponding token costs could be higher than the cost of wages.

Given the demand you describe, would it even make a difference whether you invested in OpenAI or Anthropic?

> If you assume AI takes a good chunk of labor, the market is gigantic. Really really really gigantic.

But without labor the entire economy also collapses into a singularity beyond which nothing really makes sense anymore, so there's that.

And it possibly does not take eliminating much of the labour over the span of less than a lifetime to fundamentally break economic signalling.

Much smaller rates of unemployment have worsened stagflation cycles due to various policy traps we are certainly not immune to repeating.

because they have more research and better models coming

building a Rube Goldberg machine on Chinese models might work okay, but it will be brittle, and is unlikely to work as well as the latest and greatest model from OpenAI

the demand for intelligence is nearly limitless

Why would it be a Rube Goldberg machine? You talk to the Chinese models through the exact same API as OpenAI. They do tool calling in the same way. They deliver 90% of the leading benchmarks at less than one tenth the cost.

There’s also no reason to think they’ll always be lagging the US. At some point the scarcity of GPUs will be resolved, or they’ll brute-force the problem using less capable chips but leveraging their much lower energy costs to make this viable.

Did you read the code dump Anthropic involuntary gifted us?

ArsTechnica has a nice graph showing it

https://arstechnica.com/ai/2026/06/leaked-financial-docs-sho...

And I believe this is the actual source

https://www.wheresyoured.at/exclusive-openai-financials/

One of these two should be the main link tbh

The Zitrons was main link yeasterday. Most claimed it is nothing burger and Zitron bullshits, has nothing there and is annoying. Shrug.

Just let go of the entire R&D team and then you have a 50% margin business.

UPDATE: Also bad news, you need to let go of all of sales and marketing and G&A. And THEN it's a 50% margin business.

Can't do that.

If they get rid of R&D, then someone else will make a better model and we will all switch to using that model.

If sales & marketing covers subsidies and bribes then they cant get rid of that either. Get rid of the bribes and they will be shut down. Get rid of the subsidies and we will all switch to someone cheaper.

This also shows that 2025 paid for 2024.

Unless they increased their spending even more, "all they have to do" is cover 2025 with the 2026 revenue?

Do you think they can cover 2026 with 2027 without raising more money?

Or is this more like a bonfire that requires more fuel to keep burning?

From the Ars Technica article... OpenAI’s headline “net loss” number of just over $5 billion in 2024 ballooned to nearly $39 billion in 2025. But the 2025 number includes a significant accounting charge related to investor valuations that shifted amid the company’s 2025 conversion to a for-profit structure. The Financial Times cites “a person familiar with the matter” in reporting that this non-recurring charge was approximately $30 billion and that OpenAI’s 2025 net loss amounted to a more reasonable-looking $8 billion without it.

Huh? Where did $30 billion go?

Written off.

They might not have spent $30b but they likely valued their asset base at >>> $30b+ and had to adjust that at the time of converting to for profit, is how I read it.

“One time non-recurring” is also just accounting double speak that lets executives cover up dumb stuff while sounding plausibly OK.

Profit and loss tracks changes to the fair price of purchasing the business, not operational cash flow. The $30b didn't go anywhere since it's not cash flow, it's acknowledging that someone who purchases OpenAI today would be on the hook for $30b more of future ownership dilution than before 2025.

They promised the former investors $30b in equity, which is like taking a loan accounting wise.

6 billion to sales and marketing…?

Financial Times:

> A person familiar with the matter said the large majority of that jump reflected a non-cash accounting charge linked to the company’s previous structure rather than its underlying operations.

> 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.

https://archive.is/wIzZV#selection-1887.0-1890.0

As to why those are required to be treated as liabilities, the primary point of accounting rules is to ensure the accurate valuation of the business to potential purchasers. Someone who would buy the business before those interests converted would see their ownership get diluted, thus it reduces the value of the business to a prospective buyer, thus an accounting of their books must list it as a liability.

It's not an issue if you're tracking the cash flow of the business or it's overall viability regardless of ownership structure. These book losses are just recognizing that the business has a higher market value so their ownership dilution commitments reduce the present value of the company more.

numbers look better than I thought. i think openai is going to make it

OpenAI likely missed the window to have a successful IPO.

A year ago, even 6 months ago, folks would have been still hypnotized by the hype and they would have pulled it off. Today too many people see a burning ship of cash and no moat to justify the burn. The story just isn’t there anymore.

SpaceX IPO shows that the mania is alive and well.

The big AI IPOs this year will mark the high point in the bubble IMO, with retail FOMO ensuring that they pop like SpaceX has.

After that I expect them to crater, just like all those Spacs and Blockchain startups.

What do you mean spacex has? Did spacex stock go down?

Pop as in go up to an even more insane valuation initially.

After reading Financial Times and Ed Zitron's articles[0][1], I've reached the opposite conclusion. OpenAI's situation healthier than what the outsiders once believed:

> Revenue: $13.07 billion

> Cost of Revenue: $7.5 billion

In other words generating tokens is actually a profitable business even for the frontier models. It's best to IPO when it's the case.

[0]: https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb... [1]: https://www.wheresyoured.at/exclusive-openai-financials/

To be fair, a lot of that revenue is from subscriptions that aren't necessarily fully utilized. OpenAI said in March [0] that they have 50 million subscribers. Assuming they're all on the $8/month plan, that's $4.8 billion a year, likely at a pretty low COR.

[0]: https://openai.com/index/accelerating-the-next-phase-ai/

The problem with that calculation though is that you’re ignoring the deprecation cost of developing the models which is where much/most of the cost actually lies.

Your math is saying an apartment building is profitable because rent exceeds utilities and other direct expense but ignores the mortgage. Real estate run with that math goes bankrupt quite quickly and this is essentially the same problem Open AI has.

Assuming that constant R&D is not a requirement to compete. At what point can any of these companies stop improving their models? The answer is when they have a monopoly position. And we’re nowhere near that happening.

> OpenAI likely missed the window to have a successful IPO.

I wish I could believe this but lighting giant stacks of cash on fire is, unfortunately, no longer a disqualifying event for tulip buyers.

I wouldn't bet they will have a successful IPO, but I also wouldn't bet they won't, it will be almost entirely vibes based at time of launch.

Musk best move IMO was going public before anyone else and gobbling up those 85 billion. I'm sure anthropic will do the same, leaving OpenAI maybe holding the bag. We'll see.

Correct. We will have to see about the first quarterly report from them first.

13 billion in revenue, 7.5 in cost of revenue. Can we finally put to bed the idea that inference is subsidized?

> 13 billion in revenue, 7.5 in cost of revenue.

and 7.81 billion in R&D from last year. I don't know how long it took to build the weights for the current model, or exactly how much that costs, but it's certainly more than zero days and zero dollars.

I also doubt that OpenAI could set that R&D expense to zero and survive without an agreement from Anthropic that they'll do the same... so that R&D expense can't be ignored when figuring up the total cost of the current model.

You're missing the point. There was a lot of debate around if inference was subsidized or not. And that's a huge point to confirm in the public discourse.

Sure, but being able to pay for inference and nothing but inference out of revenue leads to what end?

To them going bankrupt and users paying another company, that bought them cents to the dollar, the same money for the same product.

they have 6B left over after paying for inference, that's a lot of money

R&D is a leading expense, a good portion of that is probably R&D for 2026 models

Could they have treated subsidies to inference as a sales and marketing expense, though?

They are running 40% margins, assuming the reported numbers are valid.

Can I ask what is your opinion about their core CapEx, i.e. model training?

The general trend I observe is that the "shelf lives" of large language models are really short. It costs $1-10 billion to train cutting-edge models at the moment, and they only really last 6 months at best.

There seems to be very little brand loyalty too. Whenever a shiny new thing comes out, people just switch over, which implies that they constantly need to fight the time decay.

It's high, really high. But, that isn't bad. In fact... they are better of with it being extremely high. Then scale matters. They need enough revenue at high enough margins to earn a decent return on that spend, but higher is, from a competitive perspective, better.

I understand your logic ("the high CapEx is the moat"), but on the other hand, isn't it be a bit like multiple high speed railway systems trying to connect San Francisco to Los Angeles?

And there are three internal players chasing the same goal at the moment (OpenAI, Anthropic and Google), and two others (Deepseek and Alibaba/Qwen). What will prevent them from cutting the price floor each other?

Looking from a different angle: Microsoft has been able to maintain its monopoly because it was/is a huge pain for companies to switch the operating system, but do LLMs have that stickiness?

No. But stickiness isn't the only way to build a moat. Scale is a way too.

All right. Thank you.

> You're missing the point. There was a lot of debate around if inference was subsidized or not.

To answer that question you have to take into account the cost to produce the thing that inference uses. If you don't, then that's like claiming that the total cost of a car is the cost to keep it on a dealer's lot until it's sold.

"Figuring out how much R&D adds to the total cost of a thing" absolutely isn't a new problem. And given that models seem to get supplanted every year, it's not like you're gonna be able to spread those R&D costs out very much.

We have no idea what these terms mean and Ed Zitron specifically points out there is no explanation for what they mean in the reviewed documents.

Given that, no, this question is still open.

Ed Zitron claims to not know what terms mean, but that doesn't mean his ignorance is wisdom.

You can just run his content through AI to get a more balanced flash take. Example:

> Zitron repeatedly describes OpenAI as having "removed" costs — $3.74B in 2024, $17.87B and $3.95B in 2025 — via "net loss attributable to noncontrolling members capital," and says "it's unclear what this means." This is standard consolidated-statement mechanics, not a maneuver. When a parent consolidates entities it doesn't wholly own, the slice of losses belonging to other equity holders is split out as "noncontrolling interests." Nothing is removed or hidden; the total loss is unchanged, it's just allocated. Framing it as OpenAI "lowering" its loss "by removing costs" implies something sketchy where there's only routine GAAP. Saying "I will not speculate further" while leaving that insinuation hanging is the rhetorically convenient version of restraint.

For what it's worth, I think AI is a "bubble" and am not convinced at the long-term sustainability or viability of many of these companies but that doesn't mean that every armchair critic actually has the financial expertise to make accurate arguments.

I mean, his whole sensationalized 8X headline is based on a non-cash conversion charge, which is literally the biggest straw man you can find in the financials. He chose it because he's editorializing even as he leads his post with "I am not going to do very much editorializing". Hilarious.

I guess that is good, competition should lower the margins with the time as it doesn't seem like any of AI labs have a particularly strong moat.

I feel like the labs and their army of bots are responsible for spreading the "inference is subsidized" narrative. It plays right into their hand and justifies high prices and price increases. Anthropic in particular loves milking people.

It is subsidized by gov contracts. Everyone who has common sense immediately said the real money is Altman getting into the governments pants which is why he and Brockman lobby so hard. You take away those contracts and OpenAI is dead in the water.

Anthropic are apparently making more revenue than OpenAI, and their government contracts have famously been curtailed.

They are different things. Government money is very predictable and consistent, and based on different calculations that typical consumer-oriented sales. Profits are usually easier.

SpaceX was said to be subsidized by gov contracts. Look at where that got it...

You say "... was said to be" as if it's a fairy tale. SpaceX was subsidized by gov contracts. That's just a fact.

Government contract is not a subsidy. It's a payment for a product or service provided to the government. Examples of subsidies are section 8 housing or USDA PLC. SpaceX providing launch services to the government is nothing like that.

Distinction without a difference as it pertains to the conversation. Most of the money SpaceX received from the government was under the COTS program where NASA gave SpaceX money to develop a product and then NASA would become a customer of that product. It is as close as you can get to a technical subsidy without it being technically a subsidy.

this just isn't true, openai is largely (probably 80%+) consumer revenue in 2025

What? You believe that if you remove government contracts they're selling tokens below cost?

Companies losing that much money a year shouldn’t be allowed to IPO. It leaves the public markets holding the bag.

Why exactly? Isn't the primary reason for having public markets that companies can sell equity to fund growth?

The stock market has companies from massive to tiny. Each investor has the right to choose whether or not to invest in any one company. Some might be best served by investing only in profitable blue chips. For others, investing in IPOs is appropriate.

Your ire might be better directed at index companies who change seasoning rules right before a big IPO forcing unsuspecting investors to invest in unsuitable companies. And kudos to indexers like S&P who do not change the rules.

An IPO doesn't mean public is forced to buy the stock. There is a choice for funds or individuals and if they chose to buy it, they should be allowed to in an open market.

The other side of this are companies like Uber where (if we go by your logic), the public markets made a killing betting on a company that had massive losses. Should Uber also be blocked from an IPO even though objectively it turned out great?

Investing and markets will always decide between risk and reward. The risk of OpenAI is that it will never find profitablity but the potential rewards outweigh that in the current market perception.

In fact, the argument kind of shifts here, OpenAI can afford to IPO at this condition and still expect strong subscription precisely because its OpenAI. If it was some idk cooking appliance company with no exponential future payoff, the market would laugh and reject that IPO.

They can do that to OpenAI too but all signs say they wouldn't. You can still short the stock once it hits public if you really believe in the downfall of OAI in the future.

You will be left holding the bag with regards to your 401k, Superannuation and other non-directly managed investments.

Except certain indices are indeed forced to buy if the stock otherwise meets inclusion criteria, this was a somewhat controversial topic with the recent spacex IPO.

Yes if a company's market value exceeds a certain size, it will be considered to be included in indices, most of which track these things automatically.

They have to apply the rules consistently otherwise it wont make any sense to have these indicies right? Spacex got rejected in one of the big indicies because it was not profitable while it got accepted in another. You can buy from both as an investor or a fund manager. If you think one of them is wrong, it is in your interest to switch. If you don't switch then you indirectly don't see anything wrong with it.

Going back to the point, if we want a free market, it has to be free for all. Spacex can go to 0 tomorrow and cause markets to implode, it can also surge 10x over the next 5 years causing everyone in the market to get richer or it can just drag for 10s of years in the same level.

But you and I do not know the future. We cannot block a company from IPOing just because we think it will fail in the future. If it meets all the existing regs, it should be allowed to IPO. If you want to change the regs, thats a different discussion but one i fundamentally disagree with personally. I think the market should be able to take risks and explore potential otherwise we will stagnate.

so they could stop development and research right now and be profitable, considering that gpt 5.5 is often regarded as one of the best models for writing code this is looking pretty good.

Let's take another example: If OpenAI grows to 10 times their current size and continue spending the same amount on research and development they would be profitable today without any other changes to their organizational structure.

This is shaping up to be a relatively good investment compared to a lot of other companies that have IPO'd in the ~2010 era, the only reason why it looks bad because the numbers are just insane.

I heard from Ed Zitron that you can't really turn off "training" on these models even after they have been released because they need continuous re-training to keep up with the changes in the world and the language etc. Is this correct? if it is, can we even say that research can be turned off entirely?

it's completely false

I do at least think its certainly the case they cannot stop R&D: no moat without being ahead of open-source right?

So model drift is not really a thing?

You don't need to retrain model from scratch to add small of additional knowledge like recent events.

Thanks

If they stop R&D right now they'd have to fire most of their staff to realize the savings and sell off their datacenter capacity.

They would show a profit for 1 year and then become completely irrelevant.

they aren't suggesting it as an actual strategy, merely an indicator that the fundamentals seem realistic

Yes, but I don’t see either of those scenarios giving them the growth needed to justify a trillion dollar valuation. Especially with the current competition (a competitor releases a much better model, and they have to respond or see their revenue drop).

So I don’t see a company in immediate danger of collapsing, but I also don’t see a great investment at that valuation.

The moment they‘d stop development would put marketing and sales in a very dire position, regardless of how good gpt 5.5 is.

Anthropic claimed themselves that AI progress will slow down effectively calling fable the pinicle of what we can do today which is true, they've trained the largest possible model with the most advanced system from nvidia. GPT 5.5 would likely last a year or two because I don't see chinese labs investing that many billions in compute since at the end of the day it's a compute x intelligence graph and the only way to catch up is to quite litereally spend more money, that's realistically where the moat is. Of course distilling techniques have been proved to be very powerful, but I am still not convinced they're able to replicate the nuanced behavior these models exhibit. GLM 5.2 will be interesting to see as it is the most promising model that might blow up the entire argument of spending billions in training.

That said this is obviously not a strategy, but rather an observation that this is not a flawed, impossible concept.

but why is that a feasible hypothetical?

Just 10x your revenue without increasing R&D cost? where's this 1 magic trick and why can't every company just use it?

R&D cost is static, converting all subscription customers to api customers would yield 10x boost in revenue immediately so the demand is there, of course we will probably see demand expand more than that the question is if it's 10x (break even) or 20x (justifying trillion dollar evaluation.

> converting all subscription customers to api customers would yield 10x boost in revenue immediately so the demand is there

If OpenAI started charging all subscription users API pricing they would lose the majority of users.

If they increase the price demand will plummet. They have no moat and there are competitors.

> R&D cost is static

since when? do you think their R&D in 2026 is the same as in 2022?

22->26 was scaling up, right now they're at their peak R&D spending from today onwards for the next forseeable future until nvidia starts rolling out their new room-sized systems this will be the upper limit of what can be trained without diminishing returns to where you're just burning money for less than single digit gains. R&D grows because you have more money to spend, not because the R&D costs are increasing, therefore assuming current R&D costs are enough to keep up with competition (which they very likely are) R&D will remain static or (ideally) shrink.

95% or more of current R&D costs are compute, same as cost of revenue.

I doubt it lets see who's right. 2028 r&d is it higher or lower than 2026.

These numbers show that OpenAI is boned. They have no path to profitability and if they raise prices or cut services they will strangle their golden goose.

They could have existed indefinitely as a service layer that was reliant on other companies feeling charitable, like Firefox, but they also wanted to get rich.

they are forecasting $25-30B this year, so if anything they look to be neutral or even profitable.

No, they show they just need to keep growing. Not a crazy assumption.

They cannot grow indefinitely when they make a loss on every user. More growth = bigger losses.

Current investors should be left holding this shit bag, better them than retirement funds.

They are running 40% margins. Every dollar of inference at the margin is profitable.

I'm sure every business can pull out an area and say: this is profitable. It's a classic hide the losses a shell game.

So many shills on HN today, feels sponsored.

It's 40% based on total revenue, not a subset of it.

it shows that OpenAI found market fit and Anthropic may have done a better job.

not sure where this constant dooming comes from

> The reported 2025 figures include $7.5 billion in cost of revenue, $19.18 billion in research and development, $5.73 billion in sales and marketing, and $1.57 billion in general and administrative expense

Does training of new models go into RnD or cost? And subscription plans' subsidies, are those cost or sales and marketing?

> Does training of new models go into RnD

Pretty likely R&D, but obviously would need to be confirmed by OpenAI.

The original reporting includes this:

> The documents revealed how much OpenAI paid Microsoft for services. In the 2025 calendar year, OpenAI paid Microsoft $10.59 billion for “Research and development” expenses. We believe this most likely refers to the cost of training OpenAI’s models.

https://www.wheresyoured.at/exclusive-openai-financials/

Your comment made we wonder what happens when the AI company's new model does not have a dramatic improvement and have just knowledge updates, and majority of the users does not upgrade?

Given the enormous cost of training, would it be worth training new models then?

> Given the enormous cost of training, would it be worth training new models then?

I'm not an expert, but is this even an option? I mean models must be refreshed with latest knowldge base periodically even without algo/design improvements, otherwise the lag become too noticeable and it will hurt users and their use-case.

How does this compare to SpaceX? I’m very much not an accountant, hard to know what numbers to compare directly

The headline is a little bit misleading and sensationalist imo Most of the loss comes from the non-profit->for-profit switch This is not operational loss, one-time stuff to make the company for-profit and compete with Anthropic in apples-to-apples comparison, am I right?

It’s a big number. I wonder what steps we will see to raise revenue leading up to an IPO, and specifically if they’ll cut off the OpenAI subscription that is powering my Open claw install. They have been quite friendly with using the oauth tokens in various harnesses.

They will most certainly cut subscription access, same as Anthropic. It is inevitable that they will go down the same lockin+squeeze route. I unsubscribed from Claude a couple of weeks ago, on gpt now. However, Openai will have to make a similar move.

At that point however open weight model providers will start to shine. All eyes on China.

Ed Zitron was right all along?!

Ed Zitron certainly was right that a constant firehose of denialist AI doom would get him clicks and views from the type of audience who yearns to have their biases confirmed and their fears validated. He's made a lot of hay off that excellent prediction.

Can't really think of anything else he's been right about, though. I don't think "right" is what he's going for anyway, it's all about that validation and a coherent, testable hypothesis takes a very distant second place.

That revenue exceeds cost of revenue so tokens aren't being subsidized...

In a surprise to no one.

> OpenAI's spending mix shows why. The reported 2025 figures include $7.5 billion in cost of revenue, $19.18 billion in research and development, $5.73 billion in sales and marketing, and $1.57 billion in general and administrative expense

How the hell did they spend 5.7 billion in "sales and marketing"?

Discounted contracts for big clients? Commissions? Both?

If this can be trusted, they are selling $1 for ~95 cents, and are spending $3 on R&D. This is... Not an unsustainable business model as long as the money keeps flowing.

This is like Uber all over again.

OpenAI is selling $1 for $2 and spending ~$5 to make that happen. The question is if they get to selling $1 for $2 long enough to make they're investment back.

That's not the question. They can obviously raise prices ~5% and be in the green when it comes to operational spending.

The better question is what will that $38B of R&D spending get.

Might be bribes and subsidies

I wonder if that includes discounts/commissions for enterprise api bundling (ie cheaper for partner company XYZ if they include openai in their app)

they're spending like 9 figures on a single marketing agency - this doesn't surprise me whatsoever

Baksheesh to keep Trump off their backs?

Greg Brockman spent $25m on that out of his own money, it won't show up in the OpenAI figures. https://www.theverge.com/ai-artificial-intelligence/867947/o...

You say that like it's a bad thing but it's a show of respect. Why shouldn't Trump receive a tribute like any other local warlord?

Of which $30b is a non-recurring loss due to the way they do stock grants. That seems incredible. The company is going to moon on IPO.

I'm super happy for them and Anthropic

They are leading the way

Literally, the largest scam ever made.

Crypto certainly holds the crown in my books, but this is getting up there.

it will be so satisfying to see them crash and burn

Feels like fraud to me. Everything depends on exponential growth. “Trust me, bro”

Musk hiding is Ai inside spacex seems strategic now. Ai forward companies can’t hide losses, but a conglomerate can hide that. Might give grokursor new life.

Daily reminder that API pricing for both OpenAI and Anthropic is profitable today and that the cost of tokens for existing models falls sharply over time for a variety of reasons (unless we go into a far worse hardware inflationary environment than we are in today).

The only thing any of them are losing money on is the 200$ a month plans, and you betchya that they're moving as many enterprises to per token pricing as possible rapidly.

If you're not investing in these companies when they IPO and ideally before that if you are lucky enough to be rich, you deserve to not reap what you didn't sow.

Audited figures would say otherwise.

API pricing sucks. You're gonna want to move to self-hosted LLMs if you're forced to pay API prices.

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