In the S-1 filing Q3 was profitable even with training included, if you exclude stock based compensation.

Q2 was profitable on all operating costs (incl. hardware, employees, training, inference). They were not profitable on non operating costs though. That’s partially interest, but mostly, when their valuation goes up, they’ll have to issue more stock (absolute value) to early investors after IPO.

Their profitability will likely crator as they start building their own data centres after the IPO, so that’s the best results they’ll have in a while.

The median user is using AI as a search engine, meme creator, and/or chatbot… at best.

The additional cost created by the subset of programmers using subscriptions rather than tokens/api can easily be categorized as a marketing expense.

Remember that I referred to median user here.

> Do you have any data you're basing that claim on, or are you just speculating based off of you projecting?

Anecdata? I guess it depends on what you count as “data”.

I am currently training a lot of people directly or indirectly (train the trainer type of stuff) on how to use AI professionally. I always start with “what do you currently use AI for?”. The answers I listed above are largely the categories that people have told me and the folks I train, with the exceptions being a handful of non-technical folks who have leaned into AI and have used agents and/or created apps of some sort.

To add some categories that I didn’t initially mention, some folks also use AI for drafting/writing documents and for creating meeting transcripts.

> AI maximalists would have you believe that people are using these things as therapists and having conversations with it that are more intimate than any lovers'.

Chatbot… this is something I hear, but I’m guessing I don’t hear all of the examples since it’s personal rather than professional. But some folks certainly say something like “I treat AI like a non-judgmental friend that I can ask personal questions to”.

> AI minimalists would have you believe that no one is using these thing except at gunpoint, and even then.

In my experience, this is almost exclusively the domain of old/older people who are happy to let tech pass them by and young people who are virtue signaling to their peers (or at least trying to).

There are precisely zero professionals I k ke

> Unless you have some reason for us to believe you that isn't just "trust me bro", your portrayl of the median user is as suspect as any other.

It seems like you’re new to HN. I understand your skepticism. There is a relatively high density of actual professionals here. Trust me on that, bro.

My data set numbers in the 100s, and it is not a statistically representative cross-section of the US or world market. That said, I’m guessing that I have a fairly good range of data points for the US market.

> some folks certainly say something like “I treat AI like a non-judgmental friend that I can ask personal questions to”.

is why I believe that these things are stickier than I think you believe. I'm obviously biased by my own personal usage of AI and where I live and my own bubble which isn't the same as your bubble. Thank you for the context of your dataset. It's larger and more professional than mine. Mine is not professional at all and definitely a bubble of whom I happen to run into at a non-random selection of events.

We'll have to see what the stock market decides.

Classic

> or are you just speculating based off of you projecting?

This is also a strange thing to say. Why would you assume speculation or jump to projection as the basis for it? Nothing in the comment that you are replying to hints at projection.

It's completely unclear to me why this is the conclusion everyone jumps to instead of "holy cow they are milking it on API pricing"

Welcome to... any subscription system ever? If every person with a gym subscription actually went there say three times a week, or every person living in a dorm paying flat rate for electricity started mining bitcoin 24/7, these systems would collapse. That doesn't mean it's not sustainable, it just is how subscription pricing works, by having some people use its offers a ton and others barely at all

Overall it would go through weekly usage within 40 minutes, if not for 5h session limit.

Running on 10000 computers will just hit all the limits at once.

And there's a hard place that everyone freaks out at API prices and on-demand Opex models outside of a niche (I use API for personal research for quite focused queries and slightly more reassurance on being the customer and not the product) but this is very competitive + not sticky.

I've been running into the same thing, and adding separate OpenAI seats for employees rather than paying obscene overages to Anthropic. Thankfully Opus has decreased usage quite a bit, and made this less of an issue as well.

Anthropic is running 85% gross margins on inference.

You wanna know the real price? It's easy, just check what AWS, GCP or Azure charge for tokens.

The API Gross margin is even higher.

The purpose of the high API prices is, in addition to trying to get more money, to anchor the high price, and then make the subscriptions seem like a great deal. But inference is just not that expensive, not when provided at scale and with huge batches.

You can see this with the various Chinese providers doing inference on open models, which are unlikely to cost less to run than the models at the frontier AI labs. A single 8-GPU GB300 cluster doing high-throughput batch inference on Kimi K3.1 Pro, which is the largest open weights model available, can do something like 300 000 tokens per second, that is, 10T tokens per year. That cluster, with current insane inflated prices, costs something like $1M to buy, and $50k to run per year in electricity. At $10/Mtok, that pays itself back in 4 days. Do you think that that's a reasonable price?

The subscriptions were probably priced with reasonable positive margins.

The huge expenses are in training and pushing the models forwards, with the gigantic RLAIF runs we know they are doing. Then once you got the model, you can scale up inference at low cost and want to serve as many people as possible.

They lure you in to do "automatic top up", which gets quite expensive if you dont watch it :-)

Even if they are a loss leader, they serve a valuable purpose: people who get used to using AI a lot via the subscription then lobby their companies to start using it. IMHO this is one of the reasons Anthropic was able to make major headway into enterprise.

they're setting a precedent that artificial intelligence should be free, or at least at-cost (free from profit), but robot labor (API usage) should not. I am all in favor of this dynamic. They built / maintain the robots, so they profit from their labors.

Anthropic is super opaque with what inference costs at any given moment on the subscription and cache is just super inconsistent.

That is like saying selling hamburgers is profitable if you exclude the cost of beef.

If under the scenario you outline where they don't need to upgrade then AI is commoditized and margins are razor thin. And that is fine. Great even! But it doesn't justify Anthropic's trillion+ dollar valuation.

My guess is a mixture of both... There's a reason why enterprise customers have to use API based billing.

(We also had a stint of using Grok 4.7 and that was the same price for our usage. Opus 5.5 seemed smarter though)