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Leaked OpenAI financials show $38.5B loss and compute burn

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Re: Leaked OpenAI financials show $38.5B loss and compute burn

#261

Post by hcheffer »

simonw wrote: post #260
hcheffer wrote: post #254

Earlier quoted context omitted.

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…

If you still think models can't count the Rs in strawberry you're about a year out of date.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

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#262

Post by dofm »

danielmarkbruce wrote: post #258
dofm wrote: post #217

Earlier quoted context omitted.

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…

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!

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#263

Post by dofm »

danielmarkbruce wrote: post #259
dofm wrote: post #200

Earlier quoted context omitted.

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.

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#264

Post by cloudbonsai »

danielmarkbruce wrote: post #257
cloudbonsai wrote: post #237

Earlier quoted context omitted.

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…

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?

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#265

Post by simonw »

hcheffer wrote: post #261
simonw wrote: post #260

Earlier quoted context omitted.

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…

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.

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#266

Post by danielmarkbruce »

cloudbonsai wrote: post #264
danielmarkbruce wrote: post #257

Earlier quoted context omitted.

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 differ…

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

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#267

Post by chrisco255 »

echelon wrote: post #240
chrisco255 wrote: post #115

Earlier quoted context omitted.

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.

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#268

Post by pevansgreenwood »

surgical_fire wrote: post #238
disgruntledphd2 wrote: post #236

Earlier quoted context omitted.

> 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 Ope…

[dead]

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#269

Post by nl »

surgical_fire wrote: post #231
nl wrote: post #8

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.

> 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?…

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.

Re: Leaked OpenAI financials show $38.5B loss and compute burn

#270

Post by cloudbonsai »

danielmarkbruce wrote: post #266
cloudbonsai wrote: post #264

Earlier quoted context omitted.

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 differ…

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

All right. Thank you.
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