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"Any conversation about token costs devolves into an ad-hoc, informally-specified, bug-ridden implementation of half of generally accepted accounting principles."

We have a way of determining if Anthropic is, or has the capability of being profitable, and what the levers to that may be. AI may be world-changing, but the accounting principles behind AI labs are no different than those behind a Pizza Hut.

Even if the cost of "inference + serving" is lower than the cost of selling a token, the relevant question is what is the depreciation schedule of the cost of training. ie, if I spend $1 on training, how long do I have before I have to spend $1 again?

Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable. So the question is:

What can be done to make training depreciate more slowly? Perhaps users can be persuaded to stick around using non-fronteir models for longer, although then there's a shift in the competitive landscape.

If users cannot be persuaded (forced?) to use legacy models, then the entire business model is thrown into question, because there's no reason why training frontier models would ever get cheaper: even if it gets cheaper on the margin, surely that will result in more compute used to generate an even "better" model, resulting in more spend in the aggregate.

This doesn't mean that the AI industry is "doomed". A couple things could happen, and this is where the fronteir labs should be focusing their attention:

1. They could find a way to climb up the value chain and capture more of the consumer surplus.

2. There could be a paradigm shift in compute architecture/compute cost.

3. We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training.

Edit: My assertion of "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is made with no real information, just a gut feeling, and should not be taken seriously.

Dario has made a specific cohort argument here. His numbers (from various interviews) are: you train a model in 2023 for $100M, deploy it, and it earns $200M over its lifetime. Meanwhile you train the 2024 model for $1B, which goes on to earn $2B. Each vintage returns 2x on its training cost.

However, the GAAP P&L tells the opposite story. You book $200M revenue in the same year you spend $1B training the next model, so you report an $800M loss. Next year you book $2B against $10B in training spend, reporting an $8B loss. The business looks like it's dying when every individual model generation actually generates a healthy profit.

That's actually Dario's answer to your depreciation question. If each cohort earns back its training cost within its natural lifespan (however short that lifespan is), the depreciation schedule is already baked in. The model doesn't need to live forever, it just needs to return more than it cost before the next one replaces it. Whether that's actually happening at Anthropic is a different question, and one we can't answer without audited financials, but it's the claim Dario makes (and seems entirely reasonable from a distance).

If those numbers are correct, then my assertion that "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is incorrect.

And I admit that I made that assertion from my gut without actually knowing if it's true or not.

That's an interesting idea. I'm curious, though, are there any other industries and/or companies that have tried to pull this sort of thing off? And what ultimately happened to them?

If you can remember where you read it, could you share a link?

https://youtu.be/GcqQ1ebBqkc?t=1027 is on such but he doesn't actually say that each model has been profitable.

He says "You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish cartoon example that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model is actually, in this example is actually profitable. What's going on is that at the same time"

importantly you'll notice that he's talking revenue, and assumes that inference is cheap enough/profitable enough that 100M + Inferance_Over_Lifetime < 200M

GAAP doesn't work here really. the R&D treadmill means you are always betting on next year and its NOT inventory or something you can defer your cost on. It's an upfront R&D expense.

so what happens on year 10 when Anthropic hits a $10B training and only returns $8T? they're cooked

Yeah, that's kind of what I'm wondering about.

It's an interesting story about how even though all metrics show massive losses actually they have massive gains.

Accounting is a rather mature field, so I figure that someone in the past has tried this stunt and there should probably be ways for dealing with it.

Or do they always flame out after losing all the money? Knowing the history here would be informative.

I'm not accountant, but I would expect Pizza Hut's accounting is significantly more complex than Anthopic's. 50+ year old global franchise with physical supply chain partnerships vs an upstart SAAS company?

Your instincts are good here. Whatever complexity Pizza Hut has it comes from being the weakest of the Yum! Brands siblings — KFC carries the international profit, Taco Bell owns domestic. Pizza Hut is slow growth, perpetual restructuring, and a weird inherited obligation to always serve Pepsi.

> Almost certainly, any reasonable depreciation schedule of the cost of training [...]

Maybe not? This is an argument that has to be made using numbers. We can't do the estimate without the numbers.

This is correct. I regret that assertion and have added a comment reflecting that.

The world labor market is ~35T USD yearly, and so that is roughly the order of magnitude to balance against frontier model training cost. E.g. Dario Amodei has his "data center of PhDs" level where he assumes that's "good enough" to stop training frontier models; so if that can take even 5% of global labor market that's ~1.5T a year revenue, balanced against current model training costs of ~1B. 3 orders of magnitude might get us to PhD level? I think that is ultimately the bet the big AI companies are making. Even if 1T is the cost of PhD level AI then three/four companies could depreciate that over 4-5 years sharing that 5% of global market.

Of course a model does not really depreciate, the problem is they are forced by competitive pressure to offer newer/better models at the same price.

This is what the elites of the gilded age called "ruinous competition", and the solution today will be the same as back then: monopoly power. This has been the business plan of the tech VC industry for 25+ years.

> Qwen 3.5 397B-A17B is a good comparison

It is not. It's a terrible comparison. Qwen, deepseek and other Chinese models are known for their 10x or even better efficiency compared to Anthropic's.

That's why the difference between open router prices and those official providers isn't that different. Plus who knows what open routed providers do in term quantization. They may be getting 100x better efficiency, thus the competitive price.

That being said not all users max out their plan, so it's not like each user costs anthropic 5,000 USD. The hemoragy would be so brutal they would be out of business in months

That's a tautology. People think chinese models are 10x more efficient because they're 10x cheaper, and then you use that to claim that they're 10x more efficient.

Opus isn't that expensive to host. Look at Amazon Bedrock's t/s numbers for Opus 4.5 vs other chinese models. They're around the same order of magnitude- which means that Opus has roughly the same amount of active params as the chinese models.

Also, you can select BF16 or Q8 providers on openrouter.

Opus doubled in speed with version 4.5, leading me to speculate that they had promoted a sonnet size model. The new faster opus was the same speed as Gemini 3 flash running on the same TPUs. I think anthropics margins are probably the highest in the industry, but they have to chop that up with google by renting their TPUs.

The conspiracy theorist side of me whispers "instead of the rumored Sonnet 5.0 you got Opus 4.6...suspicious"

I guess more than a tautology it is an inversion of observed causes and effects?

> That's a tautology. People think chinese models are 10x more efficient because they're 10x cheaper

They do have different infrastructure / electricity costs and they might not run on nvidia hardware.

It's not just the models.

Except there are providers that serve both chinese models AND opus as well. On the same hardware.

Namely, Amazon Bedrock and Google Vertex.

That means normalized infrastructure costs, normalized electricity costs, and normalized hardware performance. Normalized inference software stack, even (most likely). It's about a close of a 1 to 1 comparison as you can get.

Both Amazon and Google serve Opus at roughly ~1/2 the speed of the chinese models. Note that they are not incentivized to slow down the serving of Opus or the chinese models! So that tells you the ratio of active params for Opus and for the chinese models.

Deployments like bedrock have no where near SOTA operational efficiency, 1-2 OOM behind. The hardware is much closer, but pipeline, schedule, cache, recomposition, routing etc optimizations blow naive end to end architectures out of the water.

Do you have evidence for any of this, or are you repeating a bunch of buzzwords you’ve heard breathlessly repeated on Twitter?

Evidence?

AWS and GCP both have their own custom inference chips, so a better example for hosting Opus on commodity hardware would be Digital Ocean.

> Both Amazon and Google serve Opus at roughly ~1/2 the speed of the chinese models

We were responded about 10x not 0.5x.

x86 vs arm64 could have different performance. The Chinese models could be optimized for different hardware so it could show massive differences.

These providers do not run models on CPUs, x86 vs. Arm is irrelevant.

And Microsoft's Azure. It's on all 3 major cloud providers. Which tells me, they can make profit from these cloud providers without having to pay for any hardware. They just take a small enough cut.

https://code.claude.com/docs/en/microsoft-foundry

https://www.anthropic.com/news/claude-in-microsoft-foundry

I mean GN has covered the Nvidia black market in China enough that we pretty much know that they run on Nvidia hardware still.

How is this related to the inference, may I ask? Except for some very hardware-specific optimizations of model architecture, there's nothing to prevent one to host these models on your own infrastructure. And that's what actually many OpenRouter providers, at least some of which are based in US, are doing. Because most of Chinese models mentioned here are open-weight (except for Qwen who has one proprietary "Max" model), and literally anyone can host them, not just someone from China. So it just doesn't really matter.

I mean sure, but in terms of cost per dollar/per watt of inference Nvidia's GPUs are pretty up there - unless China is pumping out domestic chips cheaply enough.

Also with Nvidia you get the efficiency of everything (including inference) built on/for Cuda, even efforts to catch AMD up are still ongoing afaik.

I wouldn't be surprised if things like DS were trained and now hosted on Nvidia hardware.

> unless China is pumping out domestic chips cheaply enough

They are. Nvidia makes A LOT of profit. Hey, top stock for a reason.

> I wouldn't be surprised if things like DS were trained and now hosted on Nvidia hardware

DS is "old". I wouldn't study them. The new 1s have a mandate to at least run on local hardware. There are data center requirements.

I agree it could still be trained on Nvidia GPUs (black market etc), but not running.

> The new 1s have a mandate to at least run on local hardware.

They do? Source?

But if that's true, it would explain why Minimax, Z.ai and Moonshot are all organized as Singaporean holding companies, with claimed data center locations (according to OpenRouter) in the US or Singapore and only the devs in China. Can't be forced to use inferior local hardware if you're just a body shop for a "foreign" AI company. ;)

> with claimed data center locations (according to OpenRouter) in the US or Singapore and only the devs in China

They just have a China only endpoint and likely a company under a different name.

Nothing to do with AI. TikTok is similar (global vs China operations).

This is not a valid argument. TPS is essentially QoS and can be adjusted; more GPUs allocated will result in higher speed.

There are sequential dependencies, so you can't just arbitrarily increase speed by parallelizing over more GPUs. Every token depends on all previous tokens, every layer depends on all previous layers. You can arbitrarily slow a model down by using fewer, slower GPUs (or none at all), though.

With speculative decoding you can use more models to speed up the generation however.

Partially true, you can predict multiple tokens and confirm, which typically gives a 2-3x speedup in practice.

(Confirmation is faster than prediction.)

Many models architectures are specifically designed to make this efficient.

---

Separately, your statement is only true for the same gen hardware, interconnects, and quantization.

Agree, but I guess the Opus 4.6 is 10x larger, rather than Chinese models being 10x more efficient. It is said that GPT-4 is already a 1.6T model, and Llama 4 behemoth is also much bigger than Chinese open-weight models. Chinese tech companies are short of frontier GPUs, but they did a lot of innovations on inference efficiency (Deepseek CEO Liang himself shows up in the author list of the related published papers).

No, Opus cannot be 10x larger than the chinese models.

If Opus was 10x larger than the chinese models, then Google Vertex/Amazon Bedrock would serve it 10x slower than Deepseek/Kimi/etc.

That's not the case. They're in the same order of magnitude of speed.

They serve it about 2x slower. So it must have about 2x the active parameters.

It could still be 10x larger overall, though that would not make it 10x more expensive.

My understanding is that for MoE with top K architecture, model size doesn't really matter, as you can have 10 32GB experts or a thousand, if only 2-3 of them are active at the same time, your inference workload will be identical, only your hard drive traffic will incread.

Which seems to be the case, seeing how hungry the industry lately has been for hard drives.

I agree that Opus almost definitely isn't anywhere near that big, but AWS throughput might not be a great way to measure model size.

According to OpenRouter, AWS serves the latest Opus and Sonnet at roughly the same speed. It's likely that they simply allocate hardware differently per model.

wasn't GPT 4 the model that was so expensive for open AI to run that they basically completely retired it in favor of later models which became much stronger but weren't as expensive for them to run?

GPT-4 was likely much larger than any of the SOTA models we have today, at least in terms of active parameters. Sparse models are the new standard, and the price drop that came with Opus 4.5 made it fairly obvious that Anthropic are not an exception.

Comparing open-source models like Qwen against Anthropic’s models is absolutely foolish. First of all, Anthropic has never disclosed the actual parameter count or architecture of their models. Second, it’s well known that these open-source models more or less distill from other models and use MoE, which allows them to run at much lower computational costs. Using Qwen as a comparison point only proves the blog post author is foolish. The article devoted such a large portion to discussing Qwen on OpenRouter, I find it hard to believe.

Anthropic is obviously also aware of the benefits of MoE and distilling a larger model into a smaller one, so they could run a model of the same size as Alibaba's for the same inference cost if they want to. Or they can run a slightly larger model for slightly higher cost. They definitely aren't running a much larger model (except potentially as a teacher for distillation training) because then they wouldn't be able to hit the output speeds they're hitting.

> Plus who knows what open routed providers do in term quantization

The quantisation is shown on the provider section.

Actually, Opus might achieve a lower cost with the help of TPUs.

>It is not. It's a terrible comparison. Qwen, deepseek and other Chinese models are known for their 10x or even better efficiency compared to Anthropic's.

I find it a good comparison because it is a good baseline since we have zero insider knowledge of Anthropic. They give me an idea that a certain size of a model has a certain cost associated.

I don't buy the 10x efficiency thing: they are just lagging behind the performance of current SOTA models. They perform much worse than the current models while also costing much less - exactly what I would expect. Current Qwen models perform as good as Sonnet 3 I think. 2 years later when Chinese models catchup with enough distillation attacks, they would be as good as Sonnet 4.6 and still be profitable.

> I don't buy the 10x efficiency thing: they are just lagging behind the performance of current SOTA models. They perform much worse than the current models while also costing much less - exactly what I would expect.

Define "much worse".

  +--------------------------------------+-------------+-----------+------------------+
  | Benchmark                            | Claude Opus | DeepSeek  | DeepSeek vs Opus |
  +--------------------------------------+-------------+-----------+------------------+
  | SWE-Bench Verified (coding)          | 80.9%       | 73.1%     | ~90%                 |
  | MMLU (knowledge)                     | ~91         | ~88.5     | ~97%               |
  | GPQA (hard science reasoning)        | ~79–80      | ~75–76    | ~95%             |
  | MATH-500 (math reasoning)            | ~78         | ~90       | ~115%            |
  +--------------------------------------+-------------+-----------+------------------+

Everyone who's used Opus knows it's better than the others in a way that isn't captured by the benchmarks. I would describe it as taste.

Lots of models get really close on benchmarks, but benchmarks only tell us how good they are at solving a defined problem. Opus is far better at solving ill-defined ones.

One of the main edges Anthropic has is that "personality tuning" gap. "Nice to use" is a differentiator when raw performance isn't.

OpenAI can sometimes get an edge over Anthropic in hard narrow STEM tasks. I trust benchmarks over vibes there - and the benchmarks show the teams trading blows release after release. Tracking Claude Code vs OpenAI Codex on SWE-bench Verified feels like watching the back alley knife fight of the AI frontier.

But the vibe of "how easy is that model to interact with" and "how easy it is to get it to do what you want it to" does matter a lot when you are the one doing the interacting. And Opus makes for a damn good daily driver.

The "personality tuning" thing is real and I think it maps to something more concrete than vibes. It's about how predictable the model's failure modes are. When you use Opus enough you develop an intuition for where it'll struggle and you learn to route around it. That mental model doesn't transfer when you switch providers, even if the benchmarks are identical. The switching cost isn't performance, it's losing your calibration.

Dunno, I was using Cursor today and for some reason it decided to swith to GPT 5.3 at some point and I didn't even notice. I was sure that Opus is much better, but who knows?

At this point it's frankly not a fair comparison since DeepSeek 3.2 is now many months old and we're waiting for a newer model which has been rumoured as "any day now" since February. (We'll see).

GLM5, the largest Qwen 3.5 model, and Kimi K2.5 are more fair comparisons, though they are, yes, a bit behind. They're more than capable for routine operations though.

Anyways, I'm back to using Opus & Claude Code after a month on Codex/GPT5.3 and 5.4 and it's frankly a rather obvious downgrade. Anthropic is behind OpenAI at this point on coding models, and there's nothing to say they couldn't fall behind the Chinese models as well.

The moat is very shallow. After the events of the last two weeks there's likely a significant % of international capital very interested in breaching it. I know I would like to see this... Anthropic basically said F U to any non-Americans, and OpenAI is ... yeah.

>Everyone who's used Opus knows it's better than the others in a way that isn't captured by the benchmarks. I would describe it as taste.

Ah, the "trust me bro" advantage. Couldn't it just be brand identity and familiarity?

I have a project where we've had Opus, Sonnet, Deepseek, Kimi, Qwen create and execute an aggregate total of about 350 plans so far, and the quality difference as measured in plans where the agent failed to complete the tasks on the first run is high enough that it comes out several times higher than Anthropics subscription prices, but probably cheaper than the API prices once we have improved the harness further - at present the challenge is that too much human intervention for the cheaper models drives up the cost.

My dashboard goes from all green to 50/50 green/red for our agents whenever I switch from Claude to one of the cheaper agents... This is after investing a substantial amount of effort in "dumbing down" the prompts - e.g. adding a lot of extra wording to convince the dumber models to actually follow instructions - that is not necessary for Sonnet or Opus.

I buy the benchmarks. The problem is that a 10% difference in the benchmarks makes the difference between barely usable and something that can consistently deliver working code unilaterally and require few review interventions. Basically, the starting point for "usable" on these benchmarks is already very far up the scale for a lot of tasks.

I do strongly believe the moat is narrow - With 4.6 I switched from defaulting to Opus to defaulting to Sonnet for most tasks. I can fully see myself moving substantial workloads to a future iteration of Kimi, Qwen or Deepseek in 6-12 months once they actually start approaching Sonnet 4.5 level. But for my use at least, currently, they're at best competing with Athropics 3.x models in terms of real-world ability.

That said, even now, I think if we were stuck with current models for 12 months, we might well also be able to build our way around this and get to a point where Deepseek and Kimi would be cheaper than Sonnet.

Eventually we'll converge on good enough harnesses to get away with cheaper models for most uses, and the remaining appeal for the frontier models will be complex planning and actual hard work.

This is the part nobody wants to talk about in the "just use the cheap model" discourse. The sticker price of the model is not the cost. The cost is the sticker price plus the human intervention rate times engineer salary. When your dashboard goes 50/50, you just doubled your effective cost because someone has to go figure out which half is broken. The models that "cost 10x more" often cost less in production because they don't generate rework.

Thankfully it's not as bad as that. The 50% that goes red means we re-execute those steps, potentially several times, to see if they succeed, before we even bother manually looking at it. But the overall principle holds: First yoou multiply the cost by re-running, then eventually you either need to kick it up to a more expensive model and/or a human.

But of course this is also only viable for non-latency sensitive work, for starters.

Good point on the green/red dashboard. The opportunity cost angle is worth adding though. A failed run isn't just the wasted tokens and retry cost - it's also the task that didn't get done and the engineering required to diagnose why. On anything time-sensitive, that compounds fast.

Exactly. At the moment it's close enough to be a wash for some cases, or tilts seriously one direction or other for others. I expect improved harnesses means more and more we'll just be able to re-run a couple of times, and fall back to "escalating" to Sonnet or even Opus, but whenever it involves egineering time, that's a big deal.

In 12 months, opus will be better than now and you still won't use it lol

I still won't use what? I use Opus now, and I will use Opus then too, but as I clearly stated:

My default model has now dropped to Sonnet, because Sonnet can now do most of my tasks, and we already use Kimi, Deepseek, and Qwen.

They're just not cost-effective enough to be my main driver yet. They are however cheap enough that for things where the Claude TOS does not let me use my subscription, they still add substantial value. Just not nearly as much as I'd like.

The bulk of my tasks won't get harder as time passes, and so will move down the value chain as the cheaper models get better.

For the small proportion of my tasks that benefits from a smarter model, I will use the smartest model I can afford.

The harness makes a difference too.

Where are you getting those benchmark figures from? Math-500 should be closer to 98% for both models: https://artificialanalysis.ai/evaluations/math-500?models=de...

> distillation "attacks"

I find it really funny that anyone can call it this with a straight face when all the American models are based on heaps of illegally pirated books and TOS-breaking website scraping in the first place.

> That being said not all users max out their plan,

These are not cell phone plans which the average joe takes, they are plans purchased with the explicit goal of software development.

I would guess that 99 out of every 100 plans are purchased with the explicit goal of maxing them out.

I’m not maxing them out… I have issues that I need to fix, features I need to develop, and I have things I want to learn.

When I have a feeling that these tools will speed me up, I use them.

My client pays for a couple of these tools in an enterprise deal, and I suspect most of us on the team work like that.

If my goal was to max out every tool my client pays, I’d be working 24hrs a day and see no sunlight ever.

I guess it’s like the all you can eat buffet. Everybody eats a lot, but if you eat so much that you throw up and get sick, you are special.

[flagged]

My employer bought me a Claude Max subscription. On heavy weeks I use 80% of the subscription. And among software engineers that I know, I'm a relatively heavy user.

Why? Because in my experience, the bottleneck is in shareholders approving new features, not my ability to dish out code.

goal? yeah. but in reality just timing it right (starting a session at 7-8am, to get 2 sessions in a workday, or even 3 if you can schedule something at 5am), i rarely hit limits.

if i hit the limit usually i'm not using it well and hunting around. if i'm using it right i'm basically gassed out trying to hit the limit to the max.

There’s absolutely no way that’s true.

In saas this is not true. Most saas is highly profitable or was i suppose because they knew that most of their customers would never max out their plans.

A huge number of people are convinced that OpenAI and Anthropic are selling inference tokens at a loss despite the fact that there's no evidence this is true and a lot of evidence that it isn't. It's just become a meme uncritically regurgitated.

This sloppy Forbes article has polluted the epistemic environment because now theres a source to point to as "evidence."

So yes this post author's estimation isn't perfect but it is far more rigorous than the original Forbes article which doesn't appear to even understand the difference between Anthropic's API costs and its compute costs.

I'd love to be a fly on the wall when this argument is tried in front of a bankruptcy court. It drives me nuts. Of course there's evidence that they're selling tokens at a loss.

The only thing these companies sell are tokens. That's their entire output. OpenAI is trying to build an ad business but it must be quite small still relative to selling tokens because I've not yet seen a single ad on ChatGPT. It's not like these firms have a huge side business selling Claude-themed baseball caps.

That means the cost of "inference" is all their costs combined. You can't just arbitrarily slice out anything inconvenient and say that's not a part of the cost of generating tokens. The research and training needed to create the models, the salaries of the people who do that, the salaries of the people who build all the serving infrastructure, the loss leader hardcore users - all of it is a part of the cost of generating each token served.

Some people look at the very different prices for serving open weights models and say, see, inference in general is cheap. But those costs are distorted by companies trying to buy mindshare by giving models away for free, and of those, both the top labs keep claiming the Chinese are distilling them like crazy including using many tactics to evade blocks! So apparently the cost of a model like DeepSeek is still partly being subsidized by OpenAI and Anthropic against their will. The cost of those tokens is higher than what's being charged, it's just being shifted onto someone else's books. Nice whilst it lasts, but this situation has been seen many times in the past and eventually people get tired of having costs externalized onto them.

For as long as firms are losing money whilst only selling tokens, that means those tokens are selling at a loss. To not sell tokens at a loss the companies would have to be profitable.

This comment defies common usage and accounting practices.

When people say “selling at a loss” they mean negative unit economics. No one ever means this much more expansive definition you’ve invented.

The article is about compute cost though. By "lose money on inference" I mean the assertion that inference has negative gross margins which a lot of people truly believe. This is important because it's common to reason from this that LLM's are uneconomical and a ticking time bomb where prices will have to be jacked up several orders of magnitude just to cover the compute used for the tokens.

But there's no such thing as compute cost in the abstract. What exactly is compute cost for AI? Does it include:

• Inference used for training? Modern training pipelines aren't just gradient descent, there's a ton of inference used in them too.

• Gradient descent itself?

• The CPUs and disks storing and managing the datasets?

• The web servers?

• The people paid to swap out failed components at the dc?

Let's say you try and define it to mean the same as unit economics - what does it cost you to add an additional customer vs what they bring in. There's still no way to do this calculation. It's like trying to compute the unit economics of a software company. Sure, if you ignore all the R&D costs of building the software in the first place and all the R&D costs of staying competitive with new versions, then the unit economics look amazing, but there's still plenty of loss-making software startups in the world.

Unit economics are a useful heuristic for businesses where there aren't any meaningful base costs required to stay in the game because they let you think about setup costs separately. Manufacturing toys, private education, farming... lots of businesses where your costs are totally dominated by unit economics. AI isn't like that.

Gross margins and cost of revenue are well defined accounting terms that apply to any type of business.

> Does it include:

> Inference used for training? Modern training pipelines aren't just gradient descent, there's a ton of inference used in them too.

No because this is training and not inference. Just like how R&D costs for a drug aren't part of COGS either.

> Gradient descent itself?

No

> The CPUs and disks storing and managing the datasets?

Yes

> The web servers?

Yes

> The people paid to swap out failed components at the dc?

Yes to the extent they are swapping for inference and not training. If the same employees do both then the accountants will estimate what percent of their time is dedicated to each and adjust their cost accordingly.

We weren't talking about COGS, we were talking about "cost of compute", which isn't an accounting term.

For the rest, anyone can define and apply an accounting metric but that doesn't mean it tells you anything useful. If you look at the unit cost of any typical IP business it's nearly zero. Yet, many companies lose money on making movies, video games, apps and books.

I'm not familiar with accounting, but I suspect a lot of these cloud infrastructure companies don't throw out hardware for a very long time, just like how AWS sells you their old stuff as whitelabel compute at a markup, behind which I think are mostly old pieces of hardware, I think as long as Anthropic keeps finding uses for the old GPUS provided they dont break, they don't have to write off these assets, which means they don't incur costs using them if they are clever with their books

The marginal cost of the next token. That can include the power, the operating cost of the facility, repair costs, etc.

The API price should hopefully incorporate the capitalized cost of the hardware, the facility rent, the cost to train the model, the r&d, cost of sales, etc., to make it profitable.

Claude Code Max may be able to offer a good price by having a mix of higher and lower utilization of users and ignoring the fixed costs, treating it as a driver of API sales. But it doesn't make sense to essentially pay people to use it.

Your point is that there are more relevant quantities to calculate for checking economic viability is fair, but that doesn't negate the "cost of inference" being an interesting metric in itself.

Actually you can slice out a lot of things. It's even a GAAP metric, i.e. one of the common baseline that public companies are required to report, known as gross margin, literally just (revenue - cogs) / revenue. It is distinct from net margin, but both are useful and low gross vs net margin say very different things concerning the long-term prospects of the business.

This is all true but it isn't really important for the argument people are making. What is more important is the marginal cost per token. If each token sold is at a marginal loss, their losses would scale with usage, that simply can't be happening with API pricing. But in general, yes I agree with you and I'm sure they are taking a huge loss on Claude Code.

It looks to me like their losses have scaled with usage, though? They keep predicting their losses will increase even as usage has gone stratospheric.

One very minor note; Anthropic and others, like most "enterprise" solution, also sell SSO + SCIM + audit logs. Their business plans have lower tokens and higher prices to cover the enterprise features, which should be essentially free to provide in 2026.

I don't think you are an accountant.

It depends how we are looking at the business. Absolutely at the end of the day a company is profitable or not but when thinking about inference, which is largely a commodity these days, you would first think about the marginal cost of it. That is your corner stone of the business. We have pretty clear indication that largely API tokens are being sold above the marginal cost. For especially a brand new business that’s critical and something that many unicorns never even hit.

Your right that all other costs are critical to measuring the profitability of the business but for such a young industry that’s the unknown. Does training get cheaper do we hit a theoretical limit on training. Are there further optimizations to be had.

You don’t have large capex in an industrial and then in year one argue that the business is doomed when your selling the product above the marginal cost but you have not recouped costs yet that have been capitalized.

You're missing costs.

- Amortized training costs.

- SG&A.

- Capex depreciation.

All the above impact profitability over various time horizons and have to rolled into present and projected P&L and cash flow analysis.

We have amortized training cost estimates. Inference to training compute over model lifetime is 10:1 or over for major models at major providers.

In part due to base model reuse and all the tricks like distillation. But mainly, due to how much inference the big providers happen to sell.

So, not the massive economic loss you'd need to push models away from being profitable. Capex and R&D take the cake there.

> A huge number of people are convinced that OpenAI and Anthropic are selling inference tokens at a loss despite the fact that there's no evidence this is true

Theres quite a lot of evidence, no proof I'd agree, but then there's no absolute proof I'm aware to the contrary either, so I don't know where you're getting this from.

The two pieces of evidence I'm aware of is that 1) Anthropic doesn't want their subsidised plans being used outside of CC, which would imply that the money their making off it isn't enough, and 2) last time I checked, API spending is capped at $5000 a month

Like I say, neither of these are proof, you can come up with reasonable arguments against them, but once again the same could be said for evidence on the contrary

> 1) Anthropic doesn't want their subsidised plans being used outside of CC, which would imply that the money their making off it isn't enough, a

Claude Code use-cases also differ somewhat from general API use, where the former is engineered for high cache utilization. We know from overall API costs (both Anthropic and OpenRouter) that cached inputs cost an order of magnitude less than uncached inputs, but OpenCode/pi/OpenClaw don't necessarily have the same kind of aggressive cache-use optimizations.

Vertically integrated stacks might also be able to have a first layer of globally shared KV cache for the system prompts, if the preamble is not user specific and changes rarely.

> 2) last time I checked, API spending is capped at $5000 a month

Per https://platform.claude.com/docs/en/api/rate-limits, that seems to only be true for general credit-funded accounts. If you contact Anthropic's sales team and set up monthly invoicing, there's evidently no fixed spending limit.

> which would imply that the money their making off it isn't enough

I don't think this logically follows. An unlimited buffet doesn't let you resell all of the food out the backdoor. At some level of usage any fixed price plan becomes unprofitable.

I agree the 5k cap is interesting as evidence although as you said I suspect there are other reasons for it.

As for evidence against it: The Information reported that OpenAI and Anthropic are 30%+ gross margins for the last few years. Sam Altman and Dario have both claimed inference is profitable in various scattered interviews. Other experts seem to generally agree too. A quick search found a tweet from former PyTorch team member Horace He: https://x.com/typedfemale/status/1961197802169798775 and a response to it in agreement from Anish Tondwalkar former researcher at OpenAI and Google Brain.

I get the other things, but believing Altmans's words is not high on the list of things to be considered evidence.

But a simple assumption that Anthropic runs a normal large MoE LLM (which it almost certainly does) suggests that the actual price of running it (mostly energy) is pretty small.

> A huge number of people are convinced that OpenAI and Anthropic are selling inference tokens at a loss despite the fact that there's no evidence this is true and a lot of evidence that it isn't.

I think it’s fairly obvious that Anthropic is lighting cash on fire and focusing on whether or not they’re losing money per token on inference is missing the forest for the trees.

Tokens become less valuable when the models aren’t continuously trained and we have zero idea what Anthropic is paying for training.

Does this not count as evidence? I would agree that it sounds a little shaky, but I would not say there is no evidence.

https://www.wheresyoured.at/oai_docs/

They are and they are convinced the cost is not truly baked in because you need to factor in all the training and R&D. It’s a mixture of folks that 1) are convinced AI is terrible, 2) hate Sam Altman and 3) don’t understand how business price products.

We don’t have clear evidence either way but it heavily leans to API pricing at least covering inference cost. Models these days have less and less differentiation and for API use there must be some thought to compete on cost but it’s not going to be winner take all. They leap frog each other with each new model.

I think the wafer scale compute is a massive deal. It's already being leveraged for models you can use right now and the reception on HN has been negligible. The entire model lives in SRAM. This is orders of magnitude faster than HBM/DRAM. I can't imagine they couldn't eventually break even using hardware like this in production.

Nobody really knows but the simple fact is these companies are not making any profit. Far from it.

> Cost remains an ever present challenge. Cursor’s larger rivals are willing to subsidize aggressively. According to a person familiar with the company’s internal analysis, Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute, according to a different person who has seen analyses on the company’s compute spend patterns.

This is the relevant quote from the original article.

This changes... everything.

I calculated only last weekend that my team would cost, if we would run Claude Code on retail API costs, around $200k/mo. We pay $1400/month in Max subscriptions. So that's $50k/user... But what tokens CC is reporting in their json -> a lot of this must be cached etc, so doubt it's anywhere near $50k cost, but not sure how to figure out what it would cost and I'm sure as hell not going to try.

I'm fascinated to know the kind of work that allows you to intelligently allocate so much resources. I use Claude extensively and feel that I great value out of it but I reach a limit in terms of what I can do that makes sense relatively quickly it seems.

Yea basically we have an app that’s like Netflix but for dogs, so people can leave on dog oriented shows for their dogs when they get kombucha or coffee

Omg, I can't believe that's real

I wanted to believe that you're essentially trolling, but no - that service exist. And not an upstart, there is coverage going back several years.

Our societies are seriously fucked.

I haven't tried any of these products, but I do have a senior dog with very severe separation anxiety. She barks and destroys stuff the minute I step out the door until the minute I come back. I could keep her from destroying stuff with a crate, but the neighbors would still throw a fit about the barking.

Effectively, this means that I have to hire a dog sitter every time I leave the house without her, just like an infant. If dog tv could fix this problem for me it would create an enormous amount of economic value.

On the contrary, this gives me hope it's possible to create value for people even when you compete with hundreds of free Youtube playlists. This is good!

They are, but the dogs got it pretty good now. Good shows for good boys.

You see, in any sane world the input box that can answer almost any question in the world should be more profitable than Netflix-for-dogs. But I bet it's not.

Never have I read something that screamed Bay Area more than this lmao.

This product wouldnt be needed if they had a Juicero to dispense refreshing fresh squeezed juice anytime.

Same for me, but I suppose it is letting agents more loose and less checking of the code and rather throw away lots of generated output.

Gemini CLI shows how much was saved through caching each session, and it's usually somewhere around 90%

You can use `npx ccusage` to check your local logs and see how much it would have cost through the API.

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yeah the json token counts are super misleading. i run a bunch of claude agents for automation and like 85% of input tokens end up being cached reads -which cost 1/10th of the sticker price. so your $200k number is probably closer to $25-30k in real cost, and thats before you factor in that anthropics own infra is way cheaper than retail API pricing. the $5k forbes number was always nonsense but even the "corrected" estimates in TFA are probably still too high IMO

I proxy all of my llm completion subscriptions. In a typical 7d span-

model completions read write cached_read cache_write

claude-opus-4-6 11000 16900000 5840000 1312000000 66120000

I'm surprised, isn't it forbidden to use the Max plan as part of a company? Just curious, as I thought it was forbidden by the ToS but I'm not sure if I have a good understanding of it

?

Claude Code has a Teams plan which includes Max tiers. Why would it be forbidden?

There is nothing in the TOS last time I checked forbidding it's use with Claude code. It's only forbidden to utilize it in the running of the business.

So getting Claude code subscriptions for developers should be permissable and not be against anything... However, if you created a rest endpoint to eg run a preconfigured prompt as part of your platform, that'd be against it

But I'm neither a lawyer nor work for anthropic

Ah, that makes sense. I hope they mean that then. We are just devs using it to write code; not selling it on.

Surely that can't be true? The expectation would be that people pay $200 a month for building open source and personal hobby software with Claude?

Yeah, that would end that really quickly. I use Pro for personal stuff. If $200 is not allowed for companies I don't think anyone would use it, at all.

If they believe a sufficient number is locked in then they may consider doing this later.

Most companies forbid it though, since you're not covered by any legal protection - for example, Anthropic can use your data or code to train new models and more.

This maybe was the case year+ ago but this is no longer the case, used to be most; now it is some/few

Any references on this? I hear this argument a lot. In fact, in a talk on AI last week I heard someone say:

"If you click the thumbs up button to rate a chat, the AI provider will use the contents for training, so our company's policy is never to click the thumbs up button"

That seemed so farcical I had a hard time taking this person seriously. Enterprise plans must give some strong guarantees around data usage, right?

This is why most businesses only have ChatGPT subscriptions. Plus their integration into existing Microsoft products and billing.

Obviously I can speak only from my personal experience but just me I have 5 examples of companies that were “no AI, IP and all that” that are now full-on “every developer must use CC, Cursor…”

How many conpanes today don’t have “AI strategy” and are fearing will be left behind etc? In my small circle we went from “most are not using AI” to “none are not using AI” in somewhat short period of time

If that were true, then everyone I know is violating that tos

> but not sure how to figure out what it would cost and I'm sure as hell not going to try.

Ask Opus to figure out how much it would cost. Lol.

If Anthropic's compute is fully saturated then the Claude code power users do represent an opportunity cost to Anthropic much closer to $5,000 then $500.

Anthropic's models may be similar in parameter size to model's on open router, but none of the others are in the headlines nearly as much (especially recently) so the comparison is extremely flawed.

The argument in this article is like comparing the cost of a Rolex to a random brand of mechanical watch based on gear count.

But opportunity cost is not actual cost. “If everyone just kept paying but used our service less we would be more profitable” is true, but not in any meaningful way.

Are Anthropic currently unable to sell subscriptions because they don’t have capacity?

The opportunity cost isn't selling subscriptions, the cost is the gap between what they could sell the GPU time for via their API vs what they're selling it for in a flat rate subscription. If you assume API demand is unlimited and GPU supply is fixed, then the opportunity cost is the 'real' loss of revenue that comes from redirecting supply away from customers willing to pay more to customers willing to pay less.

> If you assume API demand is unlimited

Doing a lot of heavy lifting here. Not everyone on a subscription plan would convert to a 200USD/mo API consumer.

Opportunity costs are real. In many cases they are more real than 'actual costs'. However, I otherwise agree with you.

> Are Anthropic currently unable to sell subscriptions because they don’t have capacity?

Absolutely! Im currently paying $170 to google to use Opus in antigravity without limit in full agent mode, because I tried Anthropic $20 subscription and busted my limit within a single prompt. Im not gonna pay them $200 only to find out I hit the limit after 20 or even 50 prompts.

And after 2 more months my price is going to double to over $300, and I still have no intention of even trying the 20x Max plan, if its really just 20x more prompts than Pro.

This has a absolutely nothing to do with whether they're limited by available compute...

What? Wouldn't they give me more than 1 prompt of compute for my $20, if they had spare?

I don't think that logically follows.

They have a business model and are trying to capture more revenue, fully saturating your computer isn't obviously a good business strategy.

If anything, you are confirming that $170 covers heavy Opus use profitably for the provider.

Opportunity cost is not the same thing as actual cost. They might have made more money if they were capable of selling the API instead of CC, but I would never tell my company to use CC all the time if I didn’t have a personal subscription.

You’re looking through the wrong end of the telescope. An investor is buying opportunity and it is a real cost to them.

Still makes no sense as they’d lose revenue, data, and scale if they don’t subsidize.

> If Anthropic's compute is fully saturated then the Claude code power users do represent an opportunity cost to Anthropic much closer to $5,000 then $500.

I think it's the other way around? Sparse use of GPU farms should be the more expensive thing. Full saturation means that we can exploit batching effects throughout.

If they have spare capacity then there is no opportunity cost to selling $100 subscriptions for exactly that reason. If they don’t have spare capacity then, at the margin, they could replace a subscription user with API calls that make them $5000: that’s opportunity cost.

If you own equity in Anthropic you should care about that cost. Maybe you are willing to tolerate it to win market share, but for you to make the most profit you need that cost to shrink.

You know who also loves to use the term "opportunity cost"?

The entertainment industry. They still tell you about how much money they're leaving on the table because people pirate stuff.

What would happen in reality for entertainment is people would "consume" far less "content".

And what would happen in reality for Anthropic is people would start asking themselves if the unpredictability is worth the price. Or at best switch to pay as you go and use the API far less.

Don’t give them any ideas, please! I need my 100 USD subscription with generous Opus usage!

Google's Antigravity has Opus access, and I suspect it's subsidised.

I prefer car analogies

> The argument in this article is like comparing the cost of a Rolex to a random brand of mechanical watch on gear count

I mean... rolex is overpriced brand whose cost to consumers is mainly just marketting in itself. Its production cost is nowhere close to selling price and looking at gears is fair way of evaluating that

> production cost is nowhere close to selling price

When has production cost had anything to do with selling price?

Not directly. But if production cost is above selling price, you typically tend to get less production. And if production cost is (way) below selling price, that tends to invite competition.

You can rent the GPUs and everything needed to run the model. Opportunity cost is not a real cost here.

Only thing that matters is if the users would have paid $5000 if they don't have option to buy subscription. And I highly doubt they would have.

We'll only know for sure when either of these companies goes public. Google serves inference and Google Cloud is profitable but we don't know how much inference is costing.

If they never go public, there's our answer as well.

There's a huge difference between cost of inference and profit margin of the "big" providers, and the cost of inference for cloud-hosted open-weights. It's the same as R&D cost of the pharmaceutical industry, versus cost of producing generic drugs. One is massively expensive, the other is cheap.

That said, for inference, the margins for OpenAI were estimated at 70% [1] [2], and the margins for Anthropic were estimated between 90% and 40% [3] [4], last year. They will not be profitable for years.

[1] https://phemex.com/news/article/openais-ai-profit-margin-cli... [2] https://www.saastr.com/have-ai-gross-margins-really-turned-t... [3] https://www.theinformation.com/articles/anthropic-projects-7... [4] https://www.investing.com/news/stock-market-news/anthropic-t...

Thank you for real data. Please moderate the use of the word profitable talking to engineers! We get the same circle jerk over and over here.

Profit implies a GAAP accrual of some sort. On any accrual schedule tied to reality, the companies are profitable now - that is, inference margin on each given model has more than paid for capital costs of training and deploying those models.

That the companies get to show a loss is a feature of cash-basis accounting: they made $100m net on that last model? Good news, We’re spending $1b on the next! Infinite tax losses!

The companies will not be cashflow positive for years. Why does this persnickety difference matter? It matters to me because I care about the engineers here - and they seem collectively likely to either short every AI company IPOing, or just quietly ignore AI impact on their livelihood, or head off into a corner and go catatonic - all based on a worldview that “this is collective insanity and everything here is going to eventually go bankrupt” — none of those are good outcomes. Shorting might be, but it should be done judiciously, and understanding the financial factors at play. So, anyway, long plea over - but, allow me to plead: cashflow positive if you want to make the point you were making.

How confident are you in the opus 4.6 model size? I've always assumed it was a beefier model with more active params that Qwen397B (17B active on the forward pass)

Yeah that's a massive assumption they're making. I remember musk revealed Grok was multiple trillion parameters. I find it likely Opus is larger.

I'm sure Anthropic is making money off the API but I highly doubt it's 90% profit margins.

> I find it likely Opus is larger.

Unlikely. Amazon Bedrock serves Opus at 120tokens/sec.

If you want to estimate "the actual price to serve Opus", a good rough estimate is to find the price max(Deepseek, Qwen, Kimi, GLM) and multiply it by 2-3. That would be a pretty close guess to actual inference cost for Opus.

It's impossible for Opus to be something like 10x the active params as the chinese models. My guess is something around 50-100b active params, 800-1600b total params. I can be off by a factor of ~2, but I know I am not off by a factor of 10.

Are you sure you can use tps as a proxy?

In practice, tps is a reflection of vram memory bandwidth during inference. So the tps tells you a lot about the hardware you're running on.

Comparing tps ratios- by saying a model is roughly 2x faster or slower than another model- can tell you a lot about the active param count.

I won't say it'll tell you everything; I have no clue what optimizations Opus may have, which can range from native FP4 experts to spec decoding with MTP to whatever. But considering chinese models like Deepseek and GLM have MTP layers (no clue if Qwen 3.5 has MTP, I haven't checked since its release), and Kimi is native int4, I'm pretty confident that there is not a 10x difference between Opus and the chinese models. I would say there's roughly a 2x-3x difference between Opus 4.5/4.6 and the chinese models at most.

> In practice, tps is a reflection of vram memory bandwidth during inference.

> Comparing tps ratios- by saying a model is roughly 2x faster or slower than another model- can tell you a lot about the active param count.

You sure about that? I thought you could shard between GPUs along layer boundaries during inference (but not training obviously). You just end up with an increasingly deep pipeline. So time to first token increases but aggregate tps also increases as you add additional hardware.

That doesn't work. Think about it a bit more.

Hint: what's in the kv cache when you start processing the 2nd token?

And that's called layer parallelism (as opposed to tensor parallelism). It allows you to run larger models (pooling vram across gpus) but does not allow you to run models faster.

Tensor parallelism DOES allow you to run models faster across multiple GPUs, but you're limited to how fast you can synchronize the all-reduce. And in general, models would have the same boost on the same hardware- so the chinese models would have the same perf multiplier as Opus.

Note that providers generally use tensor parallelism as much as they can, for all models. That usually means 8x or so.

In reality, tps ends up being a pretty good proxy for active param size when comparing different models at the same inference provider.

Oh I see. I went and confused total aggregate throughput with per-query throughput there didn't I.

You can estimate on tok/second

The Trillions of parameters claim is about the pretraining.

It’s most efficient in pre training to train the biggest models possible. You get sample efficiency increase for each parameter increase.

However those models end up very sparse and incredibly distillable.

And it’s way too expensive and slow to serve models that size so they are distilled down a lot.

GPT 4 was rumoured/leaked to be 1.8T. Claude 3.5 Sonnet was supposedly 175B, so around 0.5T-1T seems reasonable for Opus 3.5. Maybe a step up to 1-3T for Opus 4.0

Since then inference pricing for new models has come down a lot, despite increasing pressure to be profitable. Opus 4.6 costs 1/3rd what Opus 4.0 (and 3.5) costs, and GPT 5.4 1/4th what o1 costs. You could take that as indication that inference costs have also come done by at least that degree.

My guess would have been that current frontier models like Opus are in the realm of 1T params with 32B active

Anthropic CEO said 50%+ margins in an interview. I'm guessing 50 - 60% right now.

Even if it's larger, OpenRouter has DeepSeek v3.2 (685B/37B active) at $0.26/0.40 and Kimi K2.5 (1T/32B active) at $0.45/2.25 (mentioned in the post).

Opus 4.6 likely has in the order of 100B active parameters. OpenRouter lists the following throughput for Google Vertex:

    42 tps for Claude Opus 4.6 https://openrouter.ai/anthropic/claude-opus-4.6
    143 tps for GLM 4.7 (32B active parameters) https://openrouter.ai/z-ai/glm-4.7
    70 tps for Llama 3.3 70B (dense model) https://openrouter.ai/meta-llama/llama-3.3-70b-instruct

Yep, you can also get similar analysis from Amazon Bedrock, which serves Opus as well.

I'd say Opus is roughly 2x to 3x the price of the top Chinese models to serve, in reality.

Also curious if any experts can weigh in on this. I would guess in the 1 trillion to 2 trillion range.

Try 10s of trillions. These days everyone is running 4-bit at inference (the flagship feature of Blackwell+), with the big flagship models running on recently installed Nvidia 72gpu rubin clusters (and equivalent-ish world size for those rented Ironwood TPUs Anthropic also uses). Let's see, Vera Rubin racks come standard with 20 TB (Blackwell NVL72 with 10 TB) of unified memory, and NVFP4 fits 2 parameters per btye...

Of course, intense sparsification via MoE (and other techniques ;) ) lets total model size largely decouple from inference speed and cost (within the limit of world size via NVlink/TPU torrus caps)

So the real mystery, as always, is the actual parameter count of the activated head(s). You can do various speed benchmarks and TPS tracking across likely hardware fleets, and while an exact number is hard to compute, let me tell you, it is not 17B or anywhere in that particular OOM :)

Comparing Opus 4.6 or GPT 5.4 thinking or Gemini 3.1 pro to any sort Chinese model (on cost) is just totally disingenuous when China does NOT have Vera Rubin NVL72 GPUs or Ironwood V7 TPUs in any meaningful capacity, and is forced to target 8gpu Blackwell systems (and worse!) for deployment.

Nobody is running 10s of trillion param models in 2026. That's ridiculous.

Opus is 2T-3T in size at most.

What do you think labs are doing with the minimum 10TB memory in NvLink 72 systems that were publicly reported to all start coming online in November/December of last year? And why would this 1 TB -> 10 TB jump matter so much for Anthropic previously being wholly dependent on running Opus 4x on TPUs, if the models were 2-3T at 4bit and could fit in 8x B200 (1.5 TB = 3T param) widely deployed during the Opus 4 era?

You have presented a vibe-based rebuttal with no evidence or or logic to outline why you think labs are still stuck in the single trillions of parameters (GPT 4 was ~1 trillion params!). Though, you have successfully cunninghammed me into saying that while anything I publicly state is derived from public info, working in the industry itself is a helpful guide to point at the right public info to reference.

Could you point at some more public info about active parameter count? You said:

> and while an exact number is hard to compute, let me tell you, it is not 17B or anywhere in that particular OOM :)

I can see ~100B, but that would near the same order of magnitude. I find ~1000B active parameters hard to believe.

Do you have any clues to guess the total model size? I do not see any limitations to making models ridiculously large (besides training), and the Scaling Law paper showed that more parameters = more better, so it would be a safe bet for companies that have more money than innovative spirit.

> I do not see any limitations to making models ridiculously large (besides training)

From my understanding, the "besides training" is a big issue. As I noted earlier[1], Qwen3 was much better than Qwen2.5, but the main difference was just more and better training data. The Qwen3.5-397B-A17B beat their 1T-parameter Qwen3-Max-Base, again a large change was more and better training data.

[1]: https://news.ycombinator.com/item?id=47089780

China is targeting H20 because that's all they were officially allowed to buy.

I generally agree, back of the napkin math shows H20 cluster of 8gpu * 96gb = 768gb = 768B parameters on FP8 (no NVFP4 on Hopper), which lines up pretty nicely with the sizes of recent open source Chinese models.

However, I'd say its relatively well assumed in realpolitik land that Chinese labs managed to acquire plenty of H100/200 clusters and even meaningful numbers of B200 systems semi-illicitly before the regulations and anti-smuggling measures really started to crack down.

This does somewhat beg the question of how nicely the closed source variants, of undisclosed parameter counts, fit within the 1.1tb of H200 or 1.5tb of B200 systems.

They do not have enough H200 or Blackwell systems to server 1.6 billion people and the world so I doubt it's in any meaningful number.

I assure you, the number of people paying to use Qwen3-Max or other similar proprietary endpoints is far less than 1.6 billion.

You don't need to assure me. It's a theoretical maximum.

What people don't realize is that cache is *free*, well not free, but compared to the compute required to recompute it? Relatively free.

If you remove the cached token cost from pricing the overall api usage drops from around $5000 to $800 (or $200 per week) on the $200 max subscription. Still 4x cheaper over API, but not costing money either - if I had to guess it's break even as the compute is most likely going idle otherwise.

Cache definitely isn't free! We're in a global RAM shortage and KV caches sit around consuming RAM in the hope that there will be a hit.

The gamble with caching is to hold a KV cache in the hope that the user will (a) submit a prompt that can use it and (b) that will get routed to the right server which (c) won't be so busy at the time it can't handle the request. KV caches aren't small so if you lose that bet you've lost money (basically, the opportunity cost of using that RAM for something else).

Why do you believe that caches are held in RAM? They don’t need RAM performance, and disk is orders of magnitude cheaper.

Because OpenAI specifically say that:

https://developers.openai.com/api/docs/guides/prompt-caching...

> When using the in-memory policy, cached prefixes generally remain active for 5 to 10 minutes of inactivity, up to a maximum of one hour. In-memory cached prefixes are only held within volatile GPU memory.

You can opt-in to storing the caches on local disk but it's not the default. I haven't done the calculations for why they do this, but given that disaggregated parallel prefill and RDMA can recompute the KV cache very fast, you'd need a huge amount of bandwidth from disk to beat it (and flash drives wear out!).

> What people don't realize is that cache is free

I'm incredibly salty about this - they're essentially monetizing intensely something that allows them to sell their inference at premium prices to more users - without any caching, they'd have much less capacity available.

> [...] if I had to guess it's break even as the compute is most likely going idle otherwise.

Why would it go idle? It would go to their next best use. At least they could help with model training or let their researchers run experiments etc.

inference compute is vastly different versus training, also it has to stay hot in vram which probably takes up most of it. There is limited use for THAT much compute as well, they are running things like claude code compiler and even then they're scratching the surface of the amount of compute they have.

Training currently requires nvidia's latest and greatest for the best models (they also use google TPU's now which are also technically the latest and greatest? However, they're more of a dual purpose than anything afaik so that would be a correct assesment in that case)

Inference can run on a hot potato if you really put your mind to it

They can run any number of inference experiments. Like a lot of the alignment work they have going on.

I am not saying this would be a great use of their compute, but idle is far from the only alternative. (Unless electricity is the binding constraint?)

Electricity is charged whenever you use it or not, so very unlikely, but sure, they can find uses for it. Although they are not going to make that much money compared to claude code subscriptions.

> Electricity is charged whenever you use it or not, [...]

Huh, what? You know you can turn off unused equipment, and at least my nvidia GPU can use more or less Watts even when turned on?

Or does Anthropic have a flatline deal for electricity and cooling?

I think I've heard multiple time that a large % of training compute for SoTA models is inference to generate training tokens, this is bound to happen with RL training

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This is such a well-written essay. Every line revealed the answer to the immediate question I had just thought of

I can’t get past all the LLM-isms. Do people really not care about AI-slopifying their writing? It’s like learning about bad kerning, you see it everywhere.

I had a similar reaction to OP for a different post a few weeks back - I think some analysis on the health economy. Initially as I was reading I thought - "Wow, I've never read a financial article written so clearly". Everything in layman's terms. But as I continued to read, I began to notice the LLM-isms. Oversimplified concepts, "the honest truth" "like X for Y", etc.

Maybe the common factor here is not having deep/sufficient knowledge on the topic being discussed? For the article I mentioned, I feel like I was less focused on the strength of the writing and more on just understanding the content.

LLMs are very capable at simplifying concepts and meeting the reader at their level. Personally, I subscribe to the philosophy of - "if you couldn't be bothered to write it, I shouldn't bother to read it".

Alternate theory... a few months into the LLMism phenomenon, people are starting to copy the LLM writing style without realizing it :(

This happens to non-native English speakers a lot (like me). My style of writing is heavily influenced by everything I read. And since I also do research using LLMs, I'll probably sound more and more as an AI as well, just by reading its responses constantly.

I just don't know what's supposed to be natural writing anymore. It's not in the books, disappears from the internet, what's left? Some old blogs for now maybe.

The wave of LLM-style writing taking over the internet is definitely a bit scary. Feels like a similar problem to GenAI code/style eventually dominating the data that LLMs are trained on.

But luckily there's a large body of well written books/blogs/talks/speeches out there. Also anecdotally, I feel like a lot of the "bad writing" I see online these days is usually in the tech sphere.

Books definitely have natural writing, read more fiction! I recommend Children of Time by Adrian Tchaikovsky

I think you're just hallucinating because this does not come across as an AI article

I see quite a few:

“what X actually is”

“the X reality check”

Overuse of “real” and “genuine”:

> The real story is actually in the article. … And the real issue for Cursor … They have real "brand awareness", and they are genuinely better than the cheaper open weights models - for now at least. It's a real conundrum for them.

> … - these are genuinely massive expenses that dwarf inference costs.

This style just screams “Claude” to me.

It was almost certainly at least heavily edited with one. Ignoring the content, every single thing about the structure and style screams LLM.

> I think you're just hallucinating because this does not come across as an AI article

It has enough tells in the correct frequency for me to consider it more than 50% generated.

Name checks out

It's really unfortunate that we call well-structured writing 'LLM-isms' now.

I don’t see the usual tells in this essay

People care, when they can tell.

Popular content is popular because it is above the threshold for average detection.

In a better world, platforms would empower defenders, by granting skilled human noticers flagging priority, and by adopting basic classifiers like Pangram.

Unfortunately, mainstream platforms have thus far not demonstrated strong interest in banning AI slop. This site in particular has actually taken moderation actions to unflag AI slop, in certain occasions...

It is certainly very obvious a lot of the time. I wonder if we revisited the automated slop detection problem we’d be more successful now… it feels like there are a lot more tells and models have become more idiosyncratic.

Tons of companies do this already. It's not like this is a problem that nobody is constantly revisiting...

Claude subscription is equivalant of spot instance

And APIs are on-demand service equivalant.

Priority is set to APIs and leftover compute is used by Subscription Plans.

When there is no capacity, subscriptions are routed to Highly Quantized cheaper models behind the scenes.

Selling subscription makes it cheaper to run such inference at scale otherwise many times your capacity is just sitting there idle.

Also, these subscription help you train your model further on predictable workflow (because the model creators also controls the Client like qwen code, claude code, anti gravity etc...)

This is probably why they will ban you for violating TOS that you cannot use their subscription service model with other tools.

They aren't just selling subscription, but the subscription cost also help them become better at the thing they are selling which is coding for coding models like Qwen, Claude etc...

I've used qwen code, codex and claude.

Codex is 2x better than Qwen code and Claude is 2x better than Codex.

So I'd hope the Claude Opus is atleast 4-5x more expensive to run than flagship Qwen Code model hosted by Alibaba.

> Claude is 2x better than Codex

This hasn't been true in a long time.

Not only that, but since the release of 5.4 and 5.3 codex I've been running them in parallel and I've been let down by Opus 4.6 with maximum thinking way more than I've been let down with OpenAI models.

In fact I'm more and more inclined to run my own benchmarks from now on, because I seriously distrust those I see online.

Even if the benchmarks are indeed valid, they just don't reflect my use cases, usages and ability to navigate my projects and my dependencies.

imho they're mostly better at a subset of different tasks. I find codex to be better at reasoning through bugs and reviewing code when compared to Opus, but for writing code I find Claude a lot better.

Maybe that's just CLAUDE.md and memory causing the difference of course.

As a matter of preference however I like the way Claude Code works just a lot better, instructing it to work with parallel subagents in work trees etc. just matches the way I think these things should work I guess.

My impression as well, especially since 5.2 which I felt was on par or better than Opus 4.5

> When there is no capacity, subscriptions are routed to Highly Quantized cheaper models behind the scenes.

Have they announced this?

> Have they announced this?

No and indeed they have said they never do this at all.

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I think the main issue I have with the article is that author whole argument is based on 'Qwen wouldn't run at a loss'. But why wouldn't it? Depsite it being a business, there might be a number of arguments why they decide to run without profit for now: from trying to expand the user base, to Chinese government sponsoring Chinese AI business.

Hi, OP here! Even if Qwen wants to run at a loss, why would Together, DeepInfra, SiliconFlow, etc _all_ also want to run at a similar loss?

To capture market.

Nothing differentiates them. Anything they capture is based only on price and when they raise it, they lose it entirely.

Good article! Small suggestions:

1. It would be nice to define terms like RSI or at least link to a definition.

2. I found the graph difficult to read. It's a computer font that is made to look hand-drawn and it's a bit low resolution. With some googling I'm guessing the words in parentheses are the clouds the model is running on. You could make that a bit more clear.

the openrouter comparison is interesting because it shows what happens when you have actual supply-side competition. multiple providers, different quantizations, price competition. the spread between cheapest and priciest for the same model can be 3-5x.

anthropic doesn't have that. single provider, single pricing decision. whether or not $5k is accurate the more interesting question is what happens to inference pricing when the supply side is genuinely open. we're seeing hints of it with open router but its still intermediated

not saying this solves anthropic's cost problem, just that the "what does inference actually cost" question gets a lot more interesting when providers are competing directly

These margins are far greater than the ones Dario has indicated during many of his recent podcasts appearances.

What did he say?

“Cursor has to pay Anthropic's retail API prices (or close to it) for access to Opus 4.6. So to provide a Claude Code-equivalent experience using Opus 4.6, it would cost Cursor ~$5,000 per power user per month. But it would cost Anthropic perhaps $500 max.”

Cursor seems to be in a tough spot. Just heard the swix podcast on their big new cloud agents thing, and it’s looking like a pretty small moat these days.

By the way, one of the charts in the article shows that Opus 4.6 is 10x costlier than Kimi K2.5.

I thought there was no moat in AI? Even being 10x costlier, Anthropic still doesn't have enough compute to meet demand.

Those "AI has no moat" opinions are going to be so wrong so soon.

Claude Code Max obviously doesn't cost 10x more than Kimi. The article even confirms that you can get $5k worth of computer for $200 with Claude Code Max.

So no, Claude would not be getting NEARLY as much usage as it's currently getting if it weren't for the $100/$200 monthly subscription. You're comparing Kimi to the price that most people aren't paying.

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Was anyone under the impression that it does? Serious question. I've never heard that, personally.

Ed Zitron made that claim (in particular here: [1]). In the same article he admits he not a programmer, and had to ask someone else to try out Claude Code and ccusage for him. He doesn't have any understanding of how LLMs or caching works. But he's prominent because he's received leaked financial details for Anthropic and OpenAI, eg [2]

[1] https://www.wheresyoured.at/anthropic-is-bleeding-out/ [2] https://www.wheresyoured.at/costs/

Maybe I'm misreading it, but I don't see him saying it's just the cost of *inference* alone (which is the strawman that the article in the OP is arguing against). He says:

> this company is wilfully burning 200% to 3000% of each Pro or Max customer that interacts with Claude Code

There is of course this meme that "Anthropic would be profitable today if they stopped training new models and only focused on inference", but people on HN are smart enough to understand that this is not realistic due to model drift, and also due to comeptition from other models. So training is forever a part of the cost of doing business, until we have some fundamental changes in the underlying technology.

I can only interpret Ed Zitron as saying "the cost of doing business is 200% to 3000% of the price users are paying for their subscriptions", which sounds extremely plausible to me.

You would be surprised because there are lots of posters here who think that the cost is so enormous that this whole industry is unviable.

Is that conceit somehow intrinsically absurd? Or is everyone just supposed to just know?

Like I wish it was simple as "if it wasn't viable, they wouldn't be in business," but alas that argument is kinda the more naive one in this world. Right?

Or is there some intuition about energy/cost here all the dump posters miss, that you could tell us about?

Please, anything, my company is dying.

It’s the most simple intuition. You can run a 2 year old gpt model equivalent on your laptop today. So cost is obviously going down.

Its so strange to see people still think costs are not going down..

Twitter.

I mean, the very first paragraph of TFA is describing who is under that impression. Literally the first sentence:

> My LinkedIn and Twitter feeds are full of screenshots from the recent Forbes article on Cursor claiming that Anthropic's $200/month Claude Code Max plan can consume $5,000 in compute.

That's claiming that worst case, a subscriber _can_ use that much. It's possible that's wrong too, but in any case a lot of services are built on the assumption that the average user doesn't max out the plan.

So the article's title is obviously sensationalized.

I have no problem believing that a Claude Max plan can consume equivalent to $5000 worth of retail Opus use, but one interesting thing you'll see if you e.g. have Claude write agents for you, is that it's pretty aggressive about setting agents to use Sonnet or even Haiku, so not only will most people not exhaust their plans, but a lot of people who do will do so in part using the cheaper models. When you then factor in Anthropics reported margins, and their ability to prioritise traffic (e.g. I'd assume that if their capacity is maxed out they'd throttle subscribers in favour of paid by the token? Maybe not, but it's what I'd do), I'd expect the real cost to them of a maximised plan to be much lower.

Also, while Opus certainly is a lot better than even the best Chinese models, when I max out my Claude plan, I make do with Kimi 2.5. When factoring in the re-run of changes because of the lower quality, I'd spend maybe 2x as much per unit of work I were to pay token prices for all my monthly use w/Kimi.

I'd still prefer Claude if the price comes down to 1x, as it's less hassle w/the harder changes, but their lead is effectively less than a year.

The article discusses all of that, in great detail.

The title does not seem sensationalized. It's literally a summary of the article.

It does and that is exactly why the article's title is sensationalized.

The title is refuting a strawman argument that wasn't actually made, and that the article itself doesn't claim was made.

> The title is refuting a strawman argument that wasn't actually made

The argument was literally made in Forbes. It's linked to. What are you on about?

Is there something I'm missing here?

The quote from Forbes in the article is a claim that a subscription could use up to 5k worth of tokens.

Which is different from actaully costing 5k in tokens per Claude Code user. As users won't max out their subscriptions. And there doesn't seem to be any stronger claim elsewhere in the article.

But the title is about a strawman that it would cost Anthropic 5k per user which it seems nobody claimed.

What CC costs internally is not public. How efficient it is, is not public.

…You could take efficiency improvement rates from previous models releases (from x -> y) and assume; they have already made “improvements” internally. This is likely closer to what their real costs are.

Is it fair to say the Open Router models aren't subsidized though? They make the case that companies on there are running a business, but there are free models, and companies with huge AI budgets that want to gather training data and show usage.

“The article is right to separate compute cost from retail price — but the retail price baseline itself is arbitrary depending on where you run the model. The same capability (e.g. Llama 3.3 70B with tool calling and 128K context) runs $3.00/1M tokens at model developer list price and $0.22/1M at Fireworks AI — a 93% gap for identical specs. That spread makes any “it costs Anthropic X” estimate depend entirely on which reference price you anchor to. We track this live across 1,625 SKUs and 40+ vendors at a7om.com — the variance across the market is larger than most people realise when they back-calculate provider economics.”

Nobody seems to mention free users who have a pretty small limit but there are a lot of users in this category. Who’s subsidizing them?

But that doesn't say anything about whether they can be profitable or not.

I assume they project that some of the free users will be converted to paying users at some point. Whether it turns profitable in the end it's another story. Have no idea..

I'm using API directly for software developement, i'm on path to pay ~$5k this month per user, some less , some more, with daily use is just growing more and more.

What kind of software development do you do? Are you running a gas town? I assume you make your money back but still, are you sure you’re not wasting your tokens away?

Lots of greenfield sub projects around same product , bug fixes , ticket management etc . With my price per hour it still Make sense, even though it’s on the edge of been not that great.

Yeah , I tried gasTown. Not using it extensively.

The compute cost debate misses a subtler point: the real cost multiplier isn't inference, it's context length. Most agent frameworks naively stuff 6-8k tokens into every prompt turn. If you route intelligently and compress memory hierarchically, you can bring that down to 200-400 tokens per turn with no quality loss. The model cost then becomes almost irrelevant.

Nobody gets RSI typing “iterate until tests pass”

Recursive self improvement and Repetitive Strain Injury being the same initialism is really funny to me

Honest questions: have you never heard of a hyperbole before and are you on the spectum?

One consideration to me, regardless of the exact burn rate on inference is the assumed increase in revenues via higher fees. One of the bull cases I often see is that the hockey stick revenue growth continues longer/higher than the hockey stick cost growth. Then it all prints money because people are spending 10x/100x/1000x what they are today.

In the real world ..

Where I work, AI is used heavily, we are already tipping into cost management mode at a firm level. Users are being aggressively steered to cheaper models, usage throttled, and cost attribution reports sent. This is already being done at the under-$1k/mo per user cost level. So some indications of revenue per user leveling out already.

Meanwhile everyone I know who works anywhere near a computer has had AI shoved down their throat, with training, usage KPIs, annual goal setting and mandated engagement. So we are already pretty saturated, it's not like theres giant new frontiers of new users.

Why does Claude charge 10x for API, compared to subscriptions? They're not a monopoly, so one would expect margins to be thinner.

they're obviously trying to steer customers to the monthly subscription instead of the pay-per-token API.

now, the consensus of the commentards on this website, who don't have access to any of anthropics financial data, is that the monthly subscriptions are a money loser!

so either the leading AI company's business dev team is wrong or the Jacker News comment section is wrong, it is a mystery

Monopoly isn't the only thing that allows you to charge large margins.

API inference access is naturally a lot more costly to provide compared to Chat UI and Claude Code, as there is a lot more load to handle with less latency. In the products they can just smooth over load curves by handling some of the requests slower (which the majority of users in a background Code session won't even notice).

It's why every integration basically tries to piggyback off of a subscription, and why Anthropic has to continuously play whack-a-mole trying to shut those services down.

The comparison with Qwen/Kimi by "comparable architecture size" is doing a lot of heavy lifting. Parameter count doesn't tell you much when the models aren't in the same league quality-wise.

I wonder if a better proxy would be comparing by capability level rather than size. The cost to go from "good" to "frontier" is probably exponential, not linear - so estimating Anthropic's real cost from what it takes to serve Qwen 397B seems off.

Did anthropic do the oldest SaaS sales trick in the 2010s SaaS playbooks ;)

I have very naive question:

People in comments have assumption that Atropic 10 times bigger than chinese models so calc cost is 10 times more.

But from perspective of Big O notation only a few algorithms gives you O(N). Majority high optimized things provide O(N*Log(N))

So what is big O for any open model for single request?

It's a good question. Costs will be lumpy. Inference servers will have a preferred batch size. Once you have a server you can scale number of users up to that batch size for relatively low cost. Then you need to add another server (or rack) for another large cost.

However I think it's fair to say the cost is roughly linear in the number of users other than that.

There may be some aspects which are not quite linear when you see multiple users submitting similar queries... But I don't think this would be significant.

N*Log(N) can be approximated to O(N) for most realistic usecases.

As for LLM, there is probably some cost constant added once it can fit on a single GPU, but should probably be almost linear.

This article is hilariously flawed, and it takes all of 5 seconds of research to see why.

Alibaba is the primary comparison point made by the author, but it's a completely unsuitable comparison. Alibab is closer to AWS then Anthropic in terms of their business model. They make money selling infrastructure, not on inference. It's entirely possible they see inference as a loss leader, and are willing to offer it at cost or below to drive people into the platform.

We also have absolutely no idea if it's anywhere near comparable to Opus 4.6. The author is guessing.

So the articles primary argument is based on a comparison to a company who has an entirely different business model running a model that the author is just making wild guesses about.

What? Aws is a good comparison if you want only infra level costs which is what the post is talking about.

Well, IDK, I have used CC with API billing pretty extensively and managed to spend ~$1000 in one month more or less. Moved to a Max 20x subscription and using it a bit less (I'm still scared) but not THAT less and I'm around 10% weekly usage. I'm not counting the tokens, though.

TL;DR the premise for the calculation is completely flawed even if the conclusion is correct

> Qwen 3.5 397B-A17B is a good comparison point. It's a large MoE model, broadly comparable in architecture size to what Opus 4.6 is likely to be.

I stopped reading here. Frontier models have been rumoured to be in TRILLIONS of parameters since the days of GPT-4. Besides, with agents, I think they’re using more specialized models under the hood for certain tasks like exploration and web searches.

So while their cost won’t be $5000 or anywhere close, I still think it would be in the hundreds for heavy users. They may very well be losing money to the top 5-10% MAX users. Their real margin likely comes from business API customers.

Here’s an interesting bit - OpenAI filed a document with the SEC recently that gave us a peek into its finances. The cost of all infrastructure stood at just ~30% of all revenue generated. That is a phenomenal improvement. I fell off the chair when I first learned that.

Agent teams change everything. I can easily burn through 1m tokens in 15 minutes. There's no way the $200 price will hold once everyone is doing it.

The first clue that an article is likely bullshit is that it’s posted on Forbes. Forbes is now basically a contributor blog wearing the tattered remains of a designer suit.

off-topic, but, please stop hosting websites behind cloudflare, just nginx is enough, cloudflare is cancer.

Equating Cloudflare and NGINX seems a bit weird, they're entirely different tools with different purposes? Cloudflare has never done me any wrong as a web host or a web user. Calling it a cancer is very disingenuous.

tldr: the author argues it is closer to costing 500 USD per month IF a user hits their weekly rate limits every week.

Which is probably a lot more correct than other claims. However it's also true that anybody who has to use the API might pay that much, creating a real cost per token moat for Anthropics Claude code vs other models as long as they are so far ahead in terms of productivity.

And on top of that, Anthropic does not run their own compute clusters do they? They probably get completely ripped by whoever is renting them the processors.

$200 worth of actual computation is an awful lot of computation.

What this doesn't mention is the "cost" to the public: the inevitable bailouts after it all comes crashing down again, the massive subsidies that Datacenters get from tax payers, the fresh water they consume, the electricity price hikes for everyone else, the noise, air and water pollution and the massive health impact on the surrounding population of every datacenter. The jobs that it destroys and the innocent people it kills through use of the technology in military targeting and autonomous weapons usage.

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Tl;dr, their guesstimate:

> Anthropic is looking at approximately $500 in real compute cost for the heaviest users.

Ok but so it does cost Cursor $5k per power-Cursor user?? Still seems pretty rough..

Yes, you could turn it around to say that using Anthropic models in Cursor, Copilot, Junie, etc. is 'subsidising' Claude Code users.

$5 = $5

but $5 that I amortize over 7 years might end up being $1.7 maybe if I don't rapidly combust (supply chain risk)

Cursor may be losing money only on $200 sub people who do over $200 of usage (it grants $400)

Everyone else pays them at API prices

No, to use $5k in Cursor you have to pay $5k.

Oh. Nice! That works out.

I wonder how they are defining a power user. How many tokens, what could be the size the code base?

The $5k power user is the one that consistently uses all input and output tokens available under the Max subscription

> I'm fairly confident the Forbes sources are confusing retail API prices with actual compute costs

Aren't they losing money on the retail API pricing, too?

> ... comparisons to artificially low priced Chinese providers...

Yeah, no this article does not pass the sniff test.

> Aren't they losing money on the retail API pricing, too?

No, they aren't, and probably neither is anyone else offering API pricing. And Anthropic's API margins may be higher than anyone else.

For example, DeepSeek released numbers showing that R1 was served at approximately "a cost profit margin of 545%" (meaning 82% of revenue is profit), see my comment https://news.ycombinator.com/item?id=46663852

Weird that they're all looking for outside money then

They're all looking for outside money because they're all looking for outside money, and so need to keep up with their competitors investments in training. It's a game of chicken. Once their ability to raise more abates, they'll slow down new training runs, and fund that out of inference margins instead, but the first one to be forced to do so will risk losing market share.

Inference is profitable. No one is selling at a loss. It’s training to keep up with competitors that is causing losses.

> Inference is profitable

Eh. We don't really know that, and the people saying that have an interest in the rest of the world believing it's true.

How are we so sure that deep inside the moon isn't made out of cheese?

I remember Enron. Hell, I remember the S&Ls. I've seen this movie too many times to not know how it ends.

I easily go through two pro max $200/m accounts and yesterday got a third pro account when I ran out.

It’s worth it, but I know they aren’t making money on me. But, of course I’m marketing them constantly so…