Ant generates $2.08 in revenue for every $1 spent on compute which is up from just $0.40 in Q1 2025.the payback period for compute is just insane, likely less than a year!
Ant generates $2.08 in revenue for every $1 spent on compute which is up from just $0.40 in Q1 2025.the payback period for compute is just insane, likely less than a year!
I wonder how the "It's all heavily subsidized!" people are going to spin this.
Say I open a premium lemonade stand. I buy Walmart lemonade for $1 and resell it for $2. I have around 10 customers a day, but 2 of them buy 1,000 litres of lemonade each. The rest just buy a single cup. I'm making amazing profit.
There's just one detail: those 2 high-volume customers are buying my lemonade and re-selling them at a loss for $1 at another neighbourhood. Supposedly some high-profile Japanese bank is financing their lemonade purchasing expenses for eye-wateringly high interest rates. I've heard its well into the double digits. But that's not important, because the future is right around the corner - I can feel it - and in the future everyone wants lemonade. Thousands of litres of it. So even if those two customers go away, things will be fine!
It will all work out in the end. This is a totally sustainable business model. In fact I'm even thinking of going public.
Lemonade isn't intelligence. Having 4000 liters of lemonade doesn't give you a competitive leg up over a lemonade stand with 2000 liters.
I would unironically invest.
If I'm buying one cup of lemonade a day then it seems highly likely I'm still going to be able to buy it for $2 in the future.
In an alternative world where Walmart is selling it for $10 and I'm buying it from the reseller for $2, that's not the case.
Many people have been saying the second thing is happening.
I'm not really in that camp, but I'm still in the camp of "will they survive long-term if good enough open models trickles down to commodity hardware" (in a way the opposite camp perhaps?).
Sun made bank 25 years ago because their servers was needed for websites, today we run larger workloads on 5$ VPS servers (well maybe the VPS servers has gotten more expensive due to HW shortages but they're not intrinsically expensive anymore).
Think they're going to be like Oracle. Databases were a valuable thing when they were new but became free with postgres. Like Oracle Anthropic et al will move up the value chain.
Switching cost for a DB and switching cost for an inference API are very different things. They’re going to have to create lock-in with something other than model intelligence if they want to play Oracle’s games.
Vendor lock-in is inevitable when negotiating enterprise contracts. Companies will sign one or two-year deals based on headcount, convinced they're going to save a ton of money. And a big part of the appeal will probably come from intelligence capabilities being bundled with services they're already paying for (looking at you Microsoft)
Its nothing like Oracle - for long time there was Oracle and nothing so the army of mostly politically influenced salesman/lobbyist who convinced local councilman its better to spend $250,000 on Oracle license, continue to this days because migration also comes with risk and cost. Here you have Chinese models popping out less than 24 hours later, and they are of similar quality at fraction of cost. Its totally different world that what it was in 80s.
First of all, revenue exceeds compute just for the most recent 2 quarters. Before that, revenue was not exceeding compute. With 500 billion $ in commitments over the next decade, the compute bill will start increasing rapidly and the new commitments are not at the old prices, they're at higher prices.
Second of all, revenue is not profit. So even if they made 2.08x revenue on every 1$ of compute spent in Q2, that's not taking into consideration all the other costs. Hence, the operating income is still negative for 2026 so far.
You can bet that revenue will keep increasing much faster than the more expensive compute commitments come due, but if that doesn't materialize, things go south real fast. Anthropic is going public with a deficit, so more liabilities than assets, so cash is tight to begin with.
Subscriptions are heavily subsidized. 80% of their revenue comes from consumption-based pricing, which is not heavily subsidized (and probably not subsidized at all). The trouble is that this revenue is not especially reliable. If and when OpenAI gets the upper hand again, the consumption-based revenue could crater.
This may also be why OpenAI punted on their IPO: Anthropic has had the lead for most of 2026, so their consumption-based revenue is probably not looking pretty.
> Subscriptions are heavily subsidized.
The only subsidization that matters is if they are selling the tokens for a loss.
Yes the $200 plan provides more than $200 of tokens, but that doesn't mean that it's losing money, either on average or on a case by case basis. The realistic alternative isn't that every $200-plan-user pays the full Api price, it's that they pay Anthropic $0.
I disagree that it only matters if the subsidy results in selling at a loss. Yes, some revenue is better than no revenue. But clearly the demand exists for consumption-based tokens. At its current growth rate, Anthropic could end all of its subscription offerings tomorrow and still be profitable as an enterprise by early next year. Every day that it doesn't do that, it's leaving money on the table, because it has to divert resources to less profitable subscription tokens.
Now, the reason they won't do that is because subscriptions provide a stable floor of revenue. The floor is lower than it could be because of the subsidies, and that's why they matter even if they do not bring prices down to below cost: if the growth of consumption-based spending ever slows or - heaven forbid - DROPS, Anthropic will need to cut into those subsidies to avoid a revenue slowdown, which risks sending away reliable recurring revenues (i.e., customers quitting).
Anthropic is kind of like the bus in Speed: if it stops moving it'll blow up. At least over the next 5 years, massive revenues won't be enough - it needs to sustain massive revenue GROWTH. They can pull two levers to do this: increase uptake or increase prices. So far, the "increase uptake" lever works like an infinite money glitch. But at some point, they'll need to look at the "increase prices" lever. When that happens, we will find out just how big the subsidies are.
I don't think it's that simple. It's similar to why Adobe and Microsoft get their tools into schools and turn a blind eye to certain kinds of piracy; mindshare.
As a software dev, if I'm using codex at home and I'm happy with it, I'll likely choose it at work as well since I don't have to relearn the quirks of other models.
That's a fair point. Maybe the subsidies aren't as relevant as I suspected. Thanks for the insight.
I've heard this a lot that the consumption-based pricing would be easy to switch, but I don't feel like that's how it works in B2B subscriptions. At the company I'm at, we have Anthropic subscriptions for employees. Switching to OpenAI would be a ton of work involving legal, IT, and every employee. I actually think there's a lot of stickiness even if OpenAI has some better frontier models.
F500 company I’m at switched off Claude code to copilot in 30 days across a 5000 person org.
There’s no moat. AI tools actually make the switching that much easier
I’m at +100k people company, and they only spend 20usd a month on copilot and they talk about AI daily. Anyway you can switch Claude to whatever in the drop down they have. But they are moving all away from this as they think 20 usd is too much and want to self host.
That's an interesting perspective. Do you know if the deal with Anthropic has an exclusivity provision? In theory, you don't have to "switch," you could just ADD an OpenAI subscription and start splitting your spend (or not consuming Anthropic tokens at all).
Also, shouldn't your AI efficiency gains shrink the switching costs by a lot? Legal can review for cheaper, IT can implement faster, etc.
> Subscriptions are heavily subsidized
Revenue less compute & infrastructure less sales, marketing & partnerships is positive!
Approximately 80% of revenue comes from consumption-based spending, so your statement would likely be true even if subscriptions were priced below cost. Overall revenue is not especially informative on the price structure of a revenue stream that represents one-fifth of the overall enterprise.
> 80% of revenue comes from consumption-based spending
Why would this be exempt from any of the above categories?
> Overall revenue is not especially informative on the price structure of a revenue stream
It’s pretty informative on the business model of the enterprise!
If you’re quibbling that Anthropic has loss leaders, yes, sure. Whatever. So does Costco. Loss leaders converting into profitable sales is good business.
I'm quibbling that Anthropic has loss leaders and that its profit leaders are extremely unstable. Imagine if there were three Chinese club stores in the same shopping plaza as the Costco. Sure, the regular Costco has the best products, but how long are you going to be able to resist the allure of even cheaper hot dogs?
> quibbling that Anthropic has loss leaders
This is a silly quibble.
> and that its profit leaders are extremely unstable
This is a fair one! Claiming Anthropic’s “subscriptions are heavily subsidized” is not.
The metric we need to question stability is churn rate.
subscriptions use already paid for compute in the holes left by high paying api customers, its hardly subsidised, if you look at it objectively, subscriptions are 100% margin
A podcast posted here recently had this number: 80% of ant revenue comes from just 8 customers. The consensus of the speakers was that if true, this would be indicative of a very vulnerable revenue structure. But this was based on leaks. Maybe we can have the real numbers now?
from the s-1, the top 5 customers make up 25% of revenue
Who would be among those 5? Are these big tech? Or large corporates?
likely mag 7, big tech, megacorps like accenture, etc.
So the leak was BS, or at least now outdated. Top 10 must be under 50%.
In this timeline, can we really trust publicly reported numbers even?
And those 8 customers.. you think they will accept large price increases?
Hahahahahaha.
Those large customers are not a mom and pop store - they’re large market cap firms!!!
As per the S1, 5 companies were responsible for 25% of revenue in the 9months back from end of September.
Which feels like a carefully chosen statistic.
It's interesting to note the anthropic marginal cost of compute is quite high (the SpaceX deal). Claude estimates SpaceX are charging 3x as much as the anthropic average! If that is correct then a few interesting things are going on:
1. Anthropic are probably losing money on the SpaceX deal or not making much at all.
2. To be willing to pay a spot rate so high anthropic likely have no better options.
3. Many of the existing cheaper vendors are likely doing "we will give you cheap compute for now if you give us big commitments"... (cf. hyperscaler capex spends)
Which I suppose is to say that if the spot prices are in the same ballpark as future costs then anthro stops looking profitable pretty quickly. If you're especially bearish you can also throw in the huge reduction in token prices recently and that SpaceX is charging google almost 2x as much as they charge anthropic!
Happy to be corrected by someone more knowledgeable.
Revenue is not profit?
It's very simple, if you just assume they never have to spend money on training ever again, they're wildly profitable.
Compute is by far not the only cost associated with running a service, so, no?
Being sarcastic here. The usual argument everyone brings up "inference is extremely profitable, checkmate AI skeptics!" while dismissing the black hole that is training.
Are these inference & training profitability numbers broken out in the S1?
Sort of? They have “cost of revenue” which is compute and salaries (and whatever) for serving inference, then “research and development” which is cost of compute and salaries for training, then “sales and marketing”, then “operations”. R&D is like half the expenses.
Are they doing the standard SV thing of pretending employee stock comp is free?
Profit is not relevant
It’s still the case. Subscriptions are heavily subsidized
Unfortunately for you - youre the pleb here.
If you’re not generating enough revenue to cover ALL expenditures - you are in effect subsidising as you should raise prices.
Why haven’t they? It would harm their business.
Nah fam, it would harm their IPO price, these are not serious businesses and everyone at the top knows this. They're there to get their bag and leave, that's it.
They’ll just keep saying it is. That’s what they’ve done so far with DS
In page F-7 they write:
Audited, revenue in 2025 is $4.6 with a computing cost of $7.3 (i.e. heavily subsidizing tokens). Unaudited, revenue in 2026 (first six months) is $16.3, computing cost $8.9 (1.83:1). But after adding personnel, sales and administrative costs, they are still spending $17.0 to get that $16.3 revenue (still subsidizing tokens, but less).
my thesis is that it will go down and not up over time. The moat they had in coding which is by far the highest token consumption is not that huge compared to Nov-Feb period where token maxxing was prevalent and no other vendor's model was that good.
Assuming computing costs do not go up and revenue per customer does not go down.
I think the 500 billion $ of compute commitments are at higher prices than what they've been paying so far. Would be a shame if a price war between the AI labs intensifies at the same time.
Hmmm
Less than a year would also match what DeepSeek was reporting, where compute pays off in just 10 months. I think they also said they had an 80% margin on inference.
Does that include R&D?
Over time that margin persists only if they maintain their competitive edge, which is not free.
It includes compute and infrastructure for R&D. Then you have to add just over a third of their compute spend for personnel, another expense of that size for sales and marketing, and half that for other admin/ops costs, for an operating loss of 693M. Then a bunch of accounting stuff to end up losing 80B
I'm not sure I'd call it a great result, but it's a promising one. If they can increase demand without significant changes in margin or R&D spending, they are on the path to becoming very profitable
The way I read it, it does. It looks like total revenue vs total compute expenditure.
Impressive, but probably based on depreciation servers over 5 years. But is that realistic? I wonder how many people are still using 5 year old servers in their AI training or inference.
When will generic AI compute capability trickle down to us plebeians?
In the open weights scene, it is always chasing closely behind. Many leading edge big open models are trained in-part with distillation of large commercial hosted models.
It already has. If you have a gaming GPU you can run AI locally.
At this point though gaming GPUs are not exactly affordable. You need a 32GB GPU to be able to run something genuinely useful for coding, and that's thousands of dollars. Still, happy to see open models make lots of progress. I just hope hardware prices come back to earth.
24 GB 7900 XTX user here. You can do a ton with less than 32 GB. It definitely drops off quick, but 32 GB is not the floor for useful AI.
Realistically I think 16GB is the lower limit and you can find those cheap enough still. Then you run llama.cpp to bond your cards and it's off to the races.
24 GB 3090 user here. What is the best model for coding you have used? Qwen3.8 27B?
I use Qwen 3.8 27b, yeah. I don't really shop around for models a whole lot besides generational jumps, and it doesn't feel like there will be anything too worth switching to until Qwen 4 (hoping they release a similarly sized model, I'm not actually certain they have said they will?)
If you haven't found it yet: ninfer-3090 is worth checking out if you use just one GPU.
Scrappy builds aren't "affordable" but are more so than running Nvidia + optimized builds. You could put together a machine for $5000 or less that competes with frontier models from less than a year ago, albeit a little slower.
And cards like the $1600 (sheesh) R9700 have actually gotten more capable through community efforts at optimization, since their launch.
I think if the bubble can ever deflate/pop, and GPUs and RAM back off a bit, we'll see the dam burst for local AI.
Right now it's only accessible to people who can justify the costs.
If you had been paying any attention at all over the last year, there were 32GB AMD cards at like $1200-1400 and Intel at a little over $1000. I actually hadn't yet noticed they've gone up in price and had last checked only a couple months ago. But even at like $2000, that's still comparable to the nominal price of a mid 90s consumer PC for actual magic.
> for actual magic
Precisely. It is cheap at almost any price.
Can confirm. Our household is involved in raising two natural intelligences. They are quite expensive to maintain. Chances they will offer positive economic return are unclear. The value proposition on comptuer-based intelligence, by-token or through hardware ownership, seems much clearer.
I have to admit I am a sort of enthusiast for the natural intelligences, though.
Likewise, I have four of those. Occasionally there are installation issues and some of the training feels like there is resistance but the final results are definitely worth it, far more satisfying than any piece of technology.
You have a 25K rig.[1] What can a gaming rig do by comparison? One agentic project at a time while yours does twenty? Okay, in that case regular people can do regular serial work (unlike the most agentic setups or whatever is going on).
[1] https://news.ycombinator.com/item?id=50238816
That rig is running single GPU most of the time and like that it is good for 95% of what I'm doing. If I need to evaluate a large model or if the smaller ones get stuck on a problem (which does happen) then I run GLM 5.3 3.86 bpw on all of the GPUs. It's a nice space heater in that configuration, so I see it as co-generation ;)
yeah i trained a 1000 param LLM in my browser literally yesterday. it's game over for Anthropic /s.
Seems like that would imply you should be getting people to adopt now now still even if it costs a stupid amount, if you think it's valuable, rather than limiting token budgets. It'll only get cheaper over time.
ANT stock valuations might be high at listing, but it will be a good stock to add on declines. Great numbers!
Just renting them out as hardware is close to 12 months.
Including training?
Yes, this includes training and internal company usage.
That's their API cost price. No one will pay this on that scale? People are using tokens via their Max Plans?
Enterprise pays the API cost price
And enterprise is furiously working on cost optimization, finding that they can accomplish a lot of what they currently do with frontier models with budget offerings from frontier labs or open source models.
Why are you ignoring the $80.7 Billion loss?
$72 billion of this $80.7 billion loss consists of non-cash accounting charges— related to convertible debt—triggered by a surge in the company’s valuation.
The hardware related liabilities are staggering.
AI psychosis is real.
That's sort of a paper loss though. E.g. they made an agreement to give X% of stock for something, and now the stock has gone up a ton, so that X% of stock is worth a lot more than when they made the agreement. For accounting reasons this gets parsed as a loss. But they're still "paying" that same X% of stock, so it's not cash out the door.
Maybe not great, but a good problem to have if it means your stock is skyrocketing.
> $72 billion of this $80.7 billion loss consists of non-cash accounting charges— related to convertible debt—triggered by a surge in the company’s valuation.
Explain what you think this means.
It means that $72 billion will further dilute the stock price when the debt turns into stock.
It is pump and dump in action.
That is very much not what "pump and dump" means.
I don't think you understand what you quoted. The reason for that loss is because their valuation skyrocketed. If I promise you 1% of my million dollar house, and then the price of my house grows 100x. On paper, I now owe you .99 million more dollars than I owed you before, but that doesn't mean I lost cash.
This is not what "AI psychosis" colloquially refers to. Few people have any firm opinions about the nature of cost accounting on LLM products.
They are getting high off their own supply.