OpenAI Needs $400B In The Next 12 Months
236 comments
·October 17, 2025999900000999
jsheard
> Open source models like deepseek and llama 3 are rapidly catching up
Catching up for how long though? Large models are very expensive to train, and the only reason any of them are open is because ostensibly for-profit companies are absorbing the enormous costs on behalf of the open source scene. What's the plan B when those unprofitable companies (or divisions within companies) pull the ladder up behind them in pursuit of profits?
mgh95
> Catching up for how long though? Large models are expensive to train and the money has to come from somewhere, and for now that somewhere is the charity of ostensibly for-profit companies which release their models for free and just eat the losses. That doesn't seem very sustainable to me.
Catching up enough to execute a business successfully on the open source (and free as in free beer) alternatives to OpenAI. Once you have a single model that works, you can bootstrap your own internal ML infra for just that one use case and grow from there.
mark_l_watson
Martin Casado from a16z stated an opinion that 80% of US startups are likely using less expensive open models, usually from China. Chamath P. on the All In Podcast said that his company is using Chinese models, but hosted in the US and he cited his company as a huge inference user.
tcdent
Important to distinguish that they're likely using them for internal workflows (agents, etc) where the scope is well defined and they can tune their prompts and evals to accommodate a lower-performance model.
Nobody is advocating switching their coding agents to open source (yet), but that's not the bulk of the tokens in companies that have automated workflows integrated into their business.
computerex
I don’t think that’s the point he is making. The argument to me is looking at the numbers and grounding them in real life. It takes time to build data centers, it takes people to run them. The article makes the argument that the timelines are not feasible.
tkz1312
> free if I want to use my own GPU
The hardware required to run something like deepseek / kimi / glm locally at any speed fast enough for coding is probably around $50,000. You need hundreds of gigabytes of fast VRAM to run models that can come anywhere close to openai or anthropic.
edude03
$50k would be the cost to run it un-quantized, 10k could get you for example 4 5090 system, that would run the 671b q4 model which is 90% as good, which was the OPs target
tkz1312
which 671b quants can fit into 96GB VRAM? Everything I’m aware of needs hundreds at least (e.g. https://apxml.com/models/deepseek-r1-671b).
jocaal
> I'm a paid subscriber of open AI, but it's really just a matter of convenience. The app is really good, and I find it's really great for double checking some of my math.
That right there is why they are valuable. Most people are absolutely incompetent when it comes to IT. That's why no one you meet in the real world uses ad blockers. OpenAI secured their position in the mind share of the masses. All they had to do to become the next google was find a way to force ads down the throats of their users. Instead they opted for the inflated bubble and scam investors strategy. Rookie mistake.
array_key_first
The mind share openAI has is next to none.
The reality is they're a paid service, and even if they 10x their prices they're still in the red.
Consumers do actually care about price. They will easily, and quickly, move to a cheaper service. There's no lock in here.
Ekaros
There is talk of 800 million weekly users or whatever. But real question to me is how much actual disposable income they have or willingness to spend it on expensive AI subscription.
zaphirplane
Not true, for the non tech crowd ChatGPT is the AI. There are a few people using Grok or Gemini, fewer outside the coding crowd would know anthropic
raw_anon_1111
Almost a 3rd of users use ad blockers
https://backlinko.com/ad-blockers-users
And just because you have users doesn’t mean it’s easy to create a profitable ad business - ask Yahoo. Besides we still don’t know how much inference costs. But there is a real marginal costs that wouldn’t be covered by ads. They definitely couldn’t make enough on ads to cover their training costs and other costs.
jackcviers3
And adding ads into the responses is _child's play_ find the ad with the most semantic similarity to the content in the context. Insert at the end of the response or every N responses with a convincing message that based on our discussion you might be interested in xyz.
For more subtle and slimier way of doing things, boost the relevance of brands and keywords, and when they are semantically similar to the most likely token, insert them into the response. Companies pay per impression.
When a guardrail blocks a response, play some political ad for a law and order candidate before delivering the rest of the message. I'm completely shocked nobody has offered free gpt use via an api supported by ad revenue yet.
moomoo11
For actual work and not toying around the 10% gap is absolutely worth the cost.
tkz1312
Don’t underestimate the cost of getting locked into a tool that is 100% guaranteed to rugpull you on both cost and privacy.
moomoo11
You know that this is literally not a concern right? It is part of business life to navigate such a situation.
When I was a SWE, I've done migrations between bare metal to AWS to GCP and then both, and then all plus Azure..
It is the cost of doing business. You pick what works best at a price that is optimized for your business needs now. You have a war chest so when they start becoming assholes, you have leverage to fight back or pivot.
I'd rather spend $200-400/mo to unblock myself NOW than do something dumb with 5 or even 100 tokens per second output that isn't actually that good as what the current providers offer. I'm going through millions of tokens a day.. I couldn't do that with local "RIGHT NOW" (<--- important)
Beijinger
Yes. But there is not only OpenAI. There is Gemini, Grok, whatever. If it doesn't become a "the winner takes it all" but a commodity like web hosting, then the payoff breaks down.
thijson
I feel like options for local inference are getting better. AMD has their Strix Halo. Intel's next CPU generation Arrow Lake will have better inference abilities as well.
999900000999
You may be correct, but for my hobbyist projects I tend to use the cheaper models to get started, and then I'll switch to a more expensive one if the cheaper model gets stuck.
Unless the actual race is to create an AI Employee that operates and can deliver work without constant supervision. At that level, of course it would be cheaper to pay $2,000 a month straight to Open AI vs hiring a junior SWE
samastur
This highly depends on the price difference and value you get form those 10%.
paulcole
How clearly are you able to define “actual work” and “toying around?”
Or is this the case of every HN discussion where what you do is “actual work” and what other people do is “toying around?”
moomoo11
Are you making [actual] money? That's literally it.
georgemcbay
> For actual work and not toying around the 10% gap is absolutely worth the cost.
I agree with this... for now. But the hosted commercial models aren't widening the gap as far as I can tell, if anything it appears to be narrowing.
And if the relative delta doesn't increase somehow I don't see any way in which the "AI race" doesn't end in a situation where locally run LLMs on relatively cheap hardware end up being good enough for virtually everyone.
Which in many ways is the best possible outcome, except for the likely severe economic effects when the bubble bursts on the commercial AI side.
moomoo11
Sure. I'm talking about now.
I don't know what will happen tomorrow, let alone 1 year or more down the line.
If it ever becomes economical to run and maintain bare metal GPU compute to run LLMs, then that's what will need to be done..
thenaturalist
This is such a techno-centric view, you're not even remotely aware of your own biases.
> but he completely misses the only real threat to either Open AI or Anthro.
Hard disagree. Economics matter, in fact more than tech.
Tech only gets to shine if the economics work out.
> Then again, open AI is obviously ran by some of the smartest people on planet Earth, with other extremely smart people giving them tons of money, so I can be completely wrong here
Nope.
Money != Intelligence
Open AI, SF and the West-Coast VC scene is run by very opionated, incentiviced people.
Yes, money can make things move, but all the money of the world don't matter if your unit econonmics don't work out.
And the startup graveyard is full of examples of this kind.
bwy
I think you're wrong, in the same way that folks on HN were wrong about Dropbox–HN: why would I pay for something that provides so little value, it's just slightly more convenient file storage?
Just because open source models are almost as good, doesn't mean you can underestimate the convenience factor.
Both can be true: we're in an AI bubble, and the large incumbents will capture most of the value/be difficult to unseat.
hbn
On the other hand, no one has figured out how to make money providing AI yet, and everyone's operating at a loss. At some point they're going to need to monetize, and the cost/convenience compared to alternatives may not be worth it for a lot of people.
At one point you could get a Netflix subscription and it was convenient enough that people were pirating less. Now there's so many subscription services, we're basically back to cable packages, paying ever increasing amounts and potentially still seeing ads. I know I'm pirating a lot more again.
Uber vs cabs, Airbnb vs hotels - We've seen it time and time again, once the VC cashflow/infinite stonk growth dries up and they need to figure out how to monetize, the service becomes worse and people start looking for alternatives again.
VBprogrammer
Yeah, but not just that. I don't expect my mum to go find some high end consumer GPU and install it on a home server in order to run her own local LLM. I expect that people will be throwing chat interfaces running remixed versions of open weight models out on the internet so fast that it's impossible for anyone to monitise it in a reasonable way.
I also wonder whether, similar to bitcoin mining, these things end up on specialist ASICS and before we know it a medium tier mobile phone is running your own local models.
raw_anon_1111
Well seeing how Dropbox is doing now, Steve Jobs was right - it isn’t a product, it’s a feature. For the same price of 2TB of storage on Dropbox you can get the same amount on Google or OneDrive with a full office suite.
People love to quote Dropbox ignoring all of the YC companies that are zombies or outright failed. Just looking at the ones that have gone public.
https://medium.com/@Arakunrin/the-post-ipo-performance-of-y-...
gspetr
Public...? Oh, you mean the ones meant to be left holding the bag.
When there's real money to be made investing in YC is off limits to the public: https://jaredheyman.medium.com/on-the-176-annual-return-of-a...
deaux
I don't get this comparison. The non-Dropbox version was magnitudes less convenient to 99.99% of the population. A non-OpenAI chat interface is, at best, a fracfion less convenient.
A good number of people used to pay for email. Now a tiny fraction does. It all hangs on wbether OpenAI can figure out how to get ad revenue without people moving to a free competitor without them - and there will be plenty of those.
stalfosknight
Does it have to be ads? :/
lcnPylGDnU4H9OF
> folks on HN were wrong about Dropbox–HN: why would I pay for something that provides so little value, it's just slightly more convenient file storage?
https://news.ycombinator.com/item?id=42392302
(Before discussion of your comment devolves into nonsense about this.)
JumpCrisscross
> Open source models like deepseek and llama 3 are rapidly catching up, if I can get 90% of the functionality for significantly less ( or free if I want to use my own GPU), what value does open AI really have
They pay for the hardware and electricity /s.
999900000999
You have several providers who host both deep-seek and llama 3. They pay for the hardware and electricity, you pay for usage but it's significantly cheaper than using OpenAIs models.
jareds
Where are these providers and do they offer batch processing? If they don't how does there cost compare to Gemini and OpenAI batch processing? For the hobby project I'm working on batch processing is a great fit. The only cost comparison tool I've been able to find is openrouter and it doesn't support batch processing for cost savings.
JumpCrisscross
> you pay for usage
Plenty of people don't. That's an enduring advantage of using GPT over anything locally hosted.
null
sillysaurusx
> Even if you think that OpenAI’s growth is impressive — it went from 700 million to 800 million weekly active users in the last two months — that is not the kind of growth that says “build capacity assuming that literally every single human being on Earth uses this all the time.”
I’d argue the other way around: 100M growth in two months suggests literally every single human being on Earth would benefit from using this all the time, and it’s just a matter of enabling them to.
Beware the sigmoidal curve, though. Growth is exponential till it’s not.
emp17344
> 100M growth in two months suggests literally every single human being on Earth would benefit from using this all the time, and it’s just a matter of enabling them to.
This doesn’t make any sense. Popular is not the same as useful. You’d have a more compelling argument if you included data showing that all this increased LLM usage has had some kind of impact on productivity metrics.
Instead, some studies have shown that LLMs are making professionals less productive:
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
og_kalu
>This doesn’t make any sense. Popular is not the same as useful.
If you are using a service weekly for a long period, you find it useful.
>You’d have a more compelling argument if you included data showing that all this increased LLM usage has had some kind of impact on productivity metrics.
Why would you need to do that? Why is a vague (in this instance) notion of 'productivity' the only measure of usefulness? ChatGPT (not the API, just the app) processes over 2.6 B messages every single day. Most of these (1.9B) are for Non work purposes. So what 'productivity' would you even be measuring here ? Do you think everything that doesn't have to do with work is useless ? I hope not, because you'd be wrong.
If something makes you laugh consistently, it's useful. If it makes you happy, it's useful. 'Productivity' is not even close to being the be-all and end-all of usefulness.
[0] https://cdn.openai.com/pdf/a253471f-8260-40c6-a2cc-aa93fe9f1...
yibg
This is free users though. The number of paid users is significantly less (like any other freemium product). Finding it useful enough to use weekly doesn't mean finding it useful enough to pay continuously for use.
deaux
> If you are using a service weekly for a long period, you find it useful.
Do alcoholics find their daily usage of alcohol really useful? You can of course make a case for this, but it's quite a stretch. I think people use stuff weekly for all sorts of reasons besides usefulness for the most common interpretation of the word.
bee_rider
I finally used it for a couple little things, but mostly as a fuzzier replacement for search, where it does do pretty well. Of course nowadays classic search is in shambolic so it is kind like a mediocre prime-aged boxer fighting an 70 year old champion or something.
Anyway, I bet it will be really useful for cool stuff if it can ever run on my laptop!
JumpCrisscross
> nowadays classic search is in shambolic
Not sure how much one should expect or deserve switching from a free search engine to a free chatbot.
If you care about search, use Kagi [1].
[1] https://kagi.com
sysguest
idk "it will be really useful" is a bit too fuzzy and vague -- how do I infer about numbers related to return-in-investment?
of course, it's better than "this is so crap no one would buy it" -- but for investors, they want to know: "if I put X dollars now, would I get 10*X dollars or 1/10 X dollars?"
it's weird that all these comments on "usefulness" doesn't even attempt to explain whether the numbers add up ok or not
baobabKoodaa
OpenAI's bottleneck first shifted from GPUs to energy. Next it will shift from energy to meatbags. I'm sure they will figure out some way to produce more of us to keep the growthrate going.
scarmig
Eventually, we can replace human consumers with LLM agent consumers, and things can scale indefinitely.
sellmesoap
You too can qualify as an "ugly bag of mostly water" just give us your CC number!
stock_toaster
Not just meatbags, but meatbags with _money_.
helsinkiandrew
> 100M growth in two months suggests literally every single human being on Earth would benefit from using this all the time, and it’s just a matter of enabling them to.
For OpenAI I think the problem is that if eventually browsers, operating systems, phones, word processors [some other system people already use and/or pay for] integrate some form of generative AI that is good enough - and an integrated AI can be a lot less capable than the cutting edge to win, what will be the market for a stand alone AI for the general public.
There will always be a market for professional products, cutting edge research and coding tools, but I don’t think that makes a trillion dollar company.
slg
>100M growth in two months suggests literally every single human being on Earth would benefit from using this all the time
In what way does it suggest that? What level of growth is evidence that a product is universally useful?
alemanek
About 10% of the total world population is using it on a weekly basis. Take out those too old or young or illiterate technically or otherwise. Now subtract out the people without reliable internet and computer/phone. That 10% gets a whole lot bigger.
That seems like pretty strong evidence that it is generally, if not universally, useful to everyone given the opportunity.
bdbdkdksk
My work is apparently paying for seats in multiple AI tools for everybody. There's a corporate mandate that you "have to use AI for your job". People seem to mostly be using it to for (a) slide decks with cringe images (b) making their PRs look more impressive by generating a bunch of ineffective boilerplate unit tests.
kamranjon
It’s interesting with the whole quote: “OpenAI has 800 million weekly active users, and putting aside the fact that OpenAI’s own research (see page 10, footnote 20) says it double-counts users who are logged out if they’re use different devices”
The number may not actually be too accurate - but I imagine it’s also paired with what another commentator has said - OpenAI is basically giving their product to companies and the companies are making the employees log in and use it in some way - it’s not natural growth in any sense of the word.
emp17344
Only if you believe popularity is the same as usefulness.
slg
I’m sorry but I don’t see much logic in an argument that boils down to “A lot of people use it and that means it would also be useful to the people who don’t use it”. Maybe the people who don’t use it have an actual reason not to use it.
B56b
Seriously! These two things are laughably far apart. What on earth kind of leap of logic is this?
mrtksn
I just stopped paying for ChatGPT this month, once I found out that they made the projects available to free users too. The free version is just as good and I can shuffle between Grok, Mistral, Deepseek and Gemini when I run out of free quota.
So maybe giving away more and more free stuff is good for growth? The product is excellent, ChatGPT is still my favorite but the competition isn't that behind. If fact, I started liking Grok better for most tasks
lm28469
> 100M growth in two months suggests literally every single human being on Earth would benefit from using this all the time, and it’s just a matter of enabling them to.
Only about 5% see enough value to drop $20 a month... It's like VR and AR, if people get a headset for free they'll use them every now and then, but virtually nobody wants to drop money on these.
LLMs have already been commodified
kbelder
>Only about 5% see enough value to drop $20 a month...
Is that a real number? I'm shocked it's that high. I figured paying customers would be well under 1%.
null
Nevermark
Everybody calm down.
Altman has this.
In the not too distant future, like in 47 days, ChatGPT 6 Recurd is going to knock everyone's socks off. Instead of a better model, it's a recycled model (there's some recursion for you!) but it auto purchases 10 more ChatGPT 6 plans to help it perform much much better.
Then another 38 days later each of those plans upgrades, and scores improve one percent again. And then 24 days later, those plans purchase a cluster of upgrades.
It is very easy to underestimate super-exponentials. But by early 2026, OpenAI is likely to be selling trillions of licenses to these models. At the dawn of agentic computing, the number of human customers is not the limit anymore.
And if you are thinking that the global money supply is going to economically choke off his plan, well, Altman has a coin for that. And an automated loan system extending credit to every human and all those models. And, of course, compute futures (until the real world can catch up) for everybody. But there won't be too much coin for its price to rocket with demand, so get in early. It's a whole new world.
Alt/World!
Back to your local station.
quux
Can someone explain why we measure these datacenters in Gigawatts rather than something that actually measures compute like flops or whatever the AI equivalent of flops is?
To put it another way, I don't know anything but I could probably make a '1 GW' datacenter with a single 6502 and a giant bank of resistors.
martinald
Because that's the main constraint for building them - how much power can you get to the site, and the cooling involved.
Also the workloads completely change over time as racks get retired and replaced, so it doesn't mean much.
But you can basically assume with GB200s right now 1GW is ~5exaflops of compute depending on precision type and my maths being correct!
udkl
As a reference for anyone interested - the cost is estimated to be $10 billion for EACH 500MW data center - this includes the cost of the chips and the data center infra.
jauntywundrkind
Yes! The varying precisions and maths feels like just the start!
Look at next gen Rubin with it's CPX co-processor chip to see things getting much weirder & more specialized. There for prefilling long contexts, which is compute intensive:
> Something has to give, and that something in the Nvidia product line is now called the "Rubin" CPX GPU accelerator, which is aimed specifically at parts of the inference workload that do not require high bandwidth memory but do need lots of compute and, increasingly, the ability to process video formats for both input and output as part of the AI workflow.
https://www.nextplatform.com/2025/09/11/nvidia-disaggregates...
To confirm what you are saying, there is no coherent unifying way to measure what's getting built other than by power consumption. Some of that budget will go to memory, some to compute (some to interconnect, some to storage), and it's too early to say what ratio each may have, to even know what ratios of compute:memory we're heading towards (and one size won't fit all problems).
Perhaps we end up abandoning HBM & dram! Maybe the future belongs to high bandwidth flash! Maybe with it's own Computational Storage! Trying to use figures like flops or bandwidth is applying today's answers to a future that might get weirder on us. https://www.tomshardware.com/tech-industry/sandisk-and-sk-hy...
sedawkgrep
[flagged]
tetha
Mh, in my recently slightly growing, but still tiny experience with HW&DC-Ops:
You have a lot more things in a DC than just GPUs consuming power and producing heat. GPUs are the big ones, sure, but after a while, switches, firewalls, storage units, other servers and so one all contribute to the power footprint significantly. A big small packet high throughput firewall packs a surprisingly high amount of compute capacity, eats a surprising amount of power and generates a lot of heat. Oh and it costs a couple of cars in total.
And that's the important abstraction / simplification you get when you start running hardware at scale. Your limitation is not necessarily TFlops, GHz or GB per cubic meter. It is easy to cram a crapton of those into a small place.
The main problem after a while is the ability to put enough power into the building and to move the heat out of it again. It sure would be easy to put a lot of resistors into a place to make a lot of power consumption. Hamburg Energy is currently building just that to bleed off excess solar power into the grid heating.
It's problematic to connect that to the 10kv power grid safely and to move the heat away from the system fast.
dcre
My understanding is that there is no universal measure of compute power that applies across different hardware and workloads. You can interpret the power number to mean something close to the maximum amount of compute you can get for that power at a given time (or at least at time of install). It also works across geographies, cooling methods, etc. It covers all that.
omgJustTest
Measurement in unit of power because this is the ultimate use-cost, assuming scaling in compute efficiencies, capex costs, etc.
pseudosavant
If you think about it like refining electricity. A data center has a supply of raw electricity, and a capacity for how must waste (heat) it can handle. The quality of the refining improving over time doesn't change the supply or waste capacity of the facility.
null
Ekaros
It simplifies marketing. They probably don't really know how much Flops or anything else they will end up anyway. So gigawatts is nice way to look big.
ctoth
His "$400B in next 12 months" claim treats OpenAI as paying construction costs upfront. But OpenAI is leasing capacity as operating expense - Oracle finances and builds the data centers [1]. This is like saying a tenant needs $5M cash because that's what the building cost to construct.
The Oracle deal structure: OpenAI pays ~$30B/year in rental fees starting fiscal 2027/2028 [2], ramping up over 5 years as capacity comes online. Not "$400B in 12 months."
The deals are structured as staged vendor financing: - NVIDIA "invests" $10B per gigawatt milestone, gets paid back through chip purchases [3] - AMD gives OpenAI warrants for 160M shares (~10% equity) that vest as chips deploy [4] - As one analyst noted: "Nvidia invests $100 billion in OpenAI, which then OpenAI turns back and gives it back to Nvidia" [3]
This is circular vendor financing where suppliers extend credit betting on OpenAI's growth. It's unusual and potentially fragile, but it's not "OpenAI needs $400B cash they don't have."
Zitron asks: "Does OpenAI have $400B in cash?"
The actual question: "Can OpenAI grow revenue from $13B to $60B+ to cover lease payments by 2028-2029?"
The first question is nonsensical given deal structure. The second is the actual bet everyone's making.
His core thesis - "OpenAI literally cannot afford these deals therefore fraud" - fails because he fundamentally misunderstands how the deals work. The real questions are about execution timelines and revenue growth projections, not about OpenAI needing hundreds of billions in cash right now.
There's probably a good critical piece to write about whether these vendor financing bets will pay off, but this isn't it.
[1] https://www.cnbc.com/2025/09/23/openai-first-data-center-in-...
[2] https://w.media/openai-to-rent-4-5-gw-of-data-center-power-f...
[3] https://www.cnbc.com/2025/09/22/nvidia-openai-data-center.ht...
[4] https://techcrunch.com/2025/10/06/amd-to-supply-6gw-of-compu...
dcre
Suffice it to say this is not the first time Ed Zitron has been egregiously wrong on both analysis and basic facts. It's not even the first time this week.
I wrote a post about his insistence that the "cost of inference" is going up. https://crespo.business/posts/cost-of-inference/
lotsofpulp
Finding an audience that wants to believe something, and then creating something that looks like justification for that belief is a method to gain notoriety, which may or may not lead to income. Works doubly well for issues that are "hot" in the public sphere, as you can tap into the supporters and the outraged.
iyn
Solid post, thanks for sharing. Zitron occupies his own echo chamber. I've seen some people share links to his articles with a smirk as a "proof" of how "bullshit LLMs are" — and I know for a fact that they have no understanding of LLMs or how to evaluate limitations, saying nothing about unit economics. Sadly, I don't think it's possible to reason with them.
To be clear, I do expect that the bubble will burst at some point (my bet is 2028/2029) — but that's due to dynamics between markets and new tech. The tech itself is solid, even in the current form — but when there's a lot of money to make you tend to observe repeatable social patterns that often lead to overvaluing of the stuff in question.
onlyrealcuzzo
OpenAI is currently growing WAUs at ~122.8% annualized growth (down from ~461.8% just 10 months ago).
Assuming their growth rate is getting close to stabilizing and will be at ~100% for 3 years to end of 2028 - that'd be $104B in revenue, on 6.4B WAUs.
I wouldn't bank on either of those numbers - but Oracle and Nvidia kind of need to bank on it to keep their stocks pumped.
Their growth decay is around 20% every 2 months - meaning - by this time next year, they could be closer to 1.2B WAUs than to 1.6B WAUs, and the following year they could be closer to 1.4B WAUs than to 3.2B WAUs.
Impressive, for sure, but still well bellow Google and Facebook, revenue much lower and growth probably even.
wrsh07
They don't need to grow users if their acv increases or they grow their enterprise or API businesses
And of course I might pay $20/month for ChatGPT and another $20/month for sora (or some hypothetical future b2c app)
Codex is my current favorite code reviewer (compare to bug bot and others), although others have had pretty different experiences. Codex is also my current favorite programming model (although it's quite reasonable to prefer Claude code with sonnet 4.5). I would happily encourage my employer to spend even more on OpenAI tools, and this is ignoring the API spend that we have (also currently increasing)
jsnell
OpenAI don't monetize the vast majority of their users yet. But the unit costs are really low, and once they start monetizing the free tier with ads, they'll be wildly profitable.
"OpenAI cannot actually afford to pay $60 billion / year" the article states with confidence. But that's the level of revenue they'd be pulling in from their existing free users if monetized as effectively as Facebook or Google. No user growth needed.
And it seems this isn't far off, given the Walmart deal. Of course they'll start off with unobtrusive ad formats used only in situations where the user has definite purchase intent, to make the feature acceptable to the users, and then tighten the screws over time.
Mentlo
Except google and facebook have locked in numbers at times of virtually no competition before they started scaling up ads. If Open AI starts scaling ads next year they will churn people at a rate that will not be offset by growth and will either plateau or more likely lose user numbers, as their product has no material edge to alternatives in the market.
I disagree with Zitron’s analysis on many points, but I don’t see Open AI achieving the numbers it needs. Investors backing it must have seen something in private disclosure to be fronting this much money. Or more precisely, I need to believe they have seen something and are not fronting all this money just based on well wishes and marketing.
wmf
We should also consider the $2,000/month and $20,000/month plans rolling out in the future.
thelastgallon
> His "$400B in next 12 months" claim treats OpenAI as paying construction costs upfront. But OpenAI is leasing capacity as operating expense - Oracle finances and builds the data centers [1].
It is bagholders all the way down[1]! The final bagholder will be the taxpayer/pension holder.
thewebguyd
It's going to be 2008 bailouts again, but much worse.
These companies are doing all sorts of round tripping on top of propping up the economy on a foundation of fake revenue on purpose so that when it does some crumbling down they can go cry to the feds "help! we are far too big to fail, the fate of the nation depends on us getting bailed out at taxpayer expense."
chasd00
I feel like writing that down somewhere because that's pretty close to how the bailout will be pitched. "If you don't bail us out then our adversaries will get to AGI first and it will be game over". Very clever of them.
nevir
The capital cost is even less insane than the fact that power utility companies are the real constraint on this industry.
North American grids are starving for electricity right now.
Someone ought to do a deep dive into how much actual total excess power capacity we have today (that could feasibly be used by data center megacampuses), and how much capacity is coming online and when.
Power plants are massively slow undertakings.
All these datacenters deals seem to be making an assumption that capacity will magically appear in time, and/or that competition for it doesn't exist.
Mentlo
How this is not more examined is beyond me….
nextworddev
Why is this guy so angry?
That aside, his math is wrong
dwedge
He's not angry it's an angsty way of writing, a lot of used to write like that as teenagers. There was a time around 5 years ago where ever best selling book raced to have "fuck" or "vagina" in the title.
shocks
Us British have a unique relationship with profanity as a way to communicate.
edit: Aussies and kiwis too!
iamacyborg
Yeah his writing is emotionally exhausting
deanputney
If you're emotionally on edge when reading it, it's easier to miss that his math is wrong and he's no expert. Writing that way benefits him.
theideaofcoffee
Why are you -not- angry at all of this insanity? I feel the same way as him, hype has blown the bubble bigger and bigger, and it's just a matter of time until it poops out and causes huge amounts of pain.
nextworddev
Yes it’s like watching the titanic. But the question is if the Titanic is 30% of way there or just before hitting an iceberg.
pdmccormick
That seems like a lot of money. How quickly can sustainable capacity be built up in terms of building power plants, data center construction, silicon design and fabrication, etc.? Are these industries about to experience stratospheric growth, followed by a massive and painful adjustment, or does this represent a printing press or industrial revolution like inflection point?
Would anyone like to found a startup doing high-security embedded systems infrastructure? Peter at my username dot com if you’d like to connect.
bcrl
Almost nothing in tech is sustainable outside of gold recycling.
alberth
Why doesn't Anthropic needs similar levels of capital (or do they)?
evandrofisico
Anthropic is more secretive about their costs, Ed Zitron is right now investigating their costs, specifically on GCP
senordevnyc
Sure he is
kachapopopow
because this is for building "AGI", this has little to nothing to do with their current offerings.
This also assumes that intelligence continues to scale with compute which is not a given.
JumpCrisscross
> this is for building "AGI"
I’m increasingly convinced this is AI’s public relations strategy.
When it comes to talking to customers and investors, AGI doesn’t come up. At fireside chats, AGI doesn’t come up.
Then these guys go on CNBC or whatnot and it’s only about AGI.
rediguanayum
I don't think it's AGI, but rather video production. OpenAI wants to build the next video social network / ads / tv / movie production system. The moat is the massive compute required.
gkoberger
I'm sure they're not against building this, and they definitely have competing priorities.
But my personal belief is Sam Altman has a singular goal: AGI. Everything else keeps the lights on.
sillysaurusx
> This also assumes that intelligence continues to scale with compute which is not a given.
Isn’t it? Evidence seems to suggest that the more compute you throw at a problem, the smarter the system behaves. Sure, it’s not a given, but it seems plausible.
B56b
No, that's no longer the case: https://www.newyorker.com/culture/open-questions/what-if-ai-...
IsTom
It also depends on the amount of training data, that isn't really growing much after they scraped all the internet.
kachapopopow
it's not mathematically proven therefore it is not a given.
nutjob2
> a problem
That word is carrying a heavy load. There's no evidence that scaling works indefinitely on this particular sort of problem.
In fact there is no evidence that scaling solves computing problems generally.
In more narrow fields more compute gets better results but that niche is not so large.
deadbabe
In a brute force poorly architected way, perhaps.
But human brains are small and require far less energy to be very generally intelligent. So clearly, there must be a better way to achieve this AGI shit. Preferably something that runs locally in the palm of your hand.
chilipepperhott
I believe they do, but the author seems to focus on OpenAI since they're more of a household name.
wmf
Anthropic will need it if their growth continues.
cma
Anthropic seems more comfortable using TPUs for overflow capacity. The recent Claude degradation was largely due to a bug from implementation differences with TPUs and from their writeup we got some idea of their mix between Nvidia and TPU for inference.
I'm not sure if OpenAI has been willing to deploy weights to Google infrastructure.
torginus
This might be slightly off topic, but after the Sora 2/anime controversy I just looked up how much does it cost to make your average anime - it turns out that top tier 26 ep anime shows like Chainsaw Man, Delicious In Dungeon or box office movies like Demon Slayer cost between $10-$20m to make. Now I don't know how much they spend on Sora 2, but I'd imagine tens of billions. For that money, you could make a thousand such shows.
While this post is full of conjecture, and somewhat unrelated to LLMs, but not their economics - I wonder how the insane capex is going to be justified even if AI becomes fully capable of replacing salaried professionals, they'll still end up paying much much more than what it'd have cost to just hire that armies of professionals for decades.
ksynwa
Can you even make "shows" with sora 2? I haven't used it but everytime I hear about it it's in the context of making "shorts". Making shows would require a technological leap from that point.
udkl
A consequence of tools like Sora and Google Flow is that there will be an increase in amateurs creating professional quality content for comparatively cheap. So a thousand such shows (probably many more) isn't in the realm of the impossible!
null
nithril
Not off topics at all. That massive investment must be baked by something with a bigger ROI than just a chatbot.
Tiberium
> Now I don't know how much they spend on Sora 2, but I'd imagine tens of billions
I think it's magnitudes less, actually.
lumost
How much does the capex model of a datacenter change when the goal is 100% utilization, with no care for node uptime beyond capex efficiency/hardware value mainenance?
I wouldn't be surprised if the cost came down by at least one order of magnitude, two if NVidia and others adjust their margin expectations. If the bet is that OpenAI can ship crappy datacenters with crappy connectivity/latency characteristics in places with cheap/existing power - then that seems at least somewhat plausible.
OpenAI burning 40 billion dollars on datacenters in the next 1 year is almost guaranteed. Modern datacenter facilities are carefully engineered for uptime, I don't think OpenAI cares about rack uptime or even facility uptime at this scale.
wmf
People who have run these numbers still want tier IVish. I haven't seen evidence of crypto mining "tier zero" datacenters being converted to AI despite the obvious advantages.
lumost
Aye- given the margins involved, is imagine you could get quite favorable insurance policies from NVDA on tier 0 facilities.
mark_l_watson
I was explaining the problem of lagging benefits for the huge expenditures for AI research and infrastructure this morning to my wife (my RC airplane flying club is an hour round trip drive so we really had time to get into it). She is not much interested in tech but she found the story of over investment and what it might do to our economy very interesting indeed.
There are many people in the USA who don’t overly care about technology but might care a lot about the economic risks of overly aggressively chasing strong AI capabilities.
I am forwarding this article to a few friends and family members.
downrightmike
The emperor wears no clothes
This was an interesting article, but he completely misses the only real threat to either Open AI or Anthro.
Open source models like deepseek and llama 3 are rapidly catching up, if I can get 90% of the functionality for significantly less ( or free if I want to use my own GPU), what value does open AI really have .
I'm a paid subscriber of open AI, but it's really just a matter of convenience. The app is really good, and I find it's really great for double checking some of my math. However I don't know how they're going to ever truly become a corporate necessity at the prices they're going to need to bill at to become profitable.
Then again, open AI is obviously ran by some of the smartest people on planet Earth, with other extremely smart people giving them tons of money, so I can be completely wrong here