And the direction is definitely towards removing that subsidy really soon. We can see it with OpenAI's shift to API-equivalent pricing for enterprise customers last month. Anecdotally my company saw OpenAI credit usage grow 2x with stable use across the ChatGPT platform, which is pretty terrifying considering just 2% of the company uses Codex.

For context, ChatGPT business subscriptions give you a fixed pool of credits to use, after which you get billed a la carte at inflated 1.75x rates vs API, or if you don't want to pay, you get access to anything but the non-reasoning models turned off for the month.

We also tried Claude Enterprise, which was unusable as people blew through their monthly limits in a matter of hours.

Depends on what their actual costs are. Either they are losing lots of money on subscriptions, or they make absolute bank on API pricing.

Looking at the pricing of 1-2T models like Kimi or DeepSeek on the open market, I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing.

Especially considering that subscriptions a) distribute load over time via rate limits, and b) will include a lot of users who get only a fraction of the possible value, whether they are on a personal account where they are on the rate limit on the weekend but barely use it during the week, or are corporate users who were issued an account they rarely use. Subscription prices are usually measured on the average case, not the most extreme value a power user can get out of it

> I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing

So just going on vibes?

While some people don't like his content, Ed Zitron shows a lot of evidence for your assumption being very wrong.

These companies are bleeding cash at ungodly rates. It's likely their API pricing is still subsidized if you look at their overall financial picture.

Related, there's a good reason those API prices keep going up a lot every new version and it's not just because the models are better.

Selling inference for more than inference costs is not incompatible with bleeding cash at ungodly rates. They do in fact pay ungodly amounts of cash for other things, like training, marketing, etc. Heck, you can bleed cash while being profitable (in the accounting sense)

Also, API prices going up a lot every new version is more an OpenAI thing, and even there it's a recent trend: GPT 5.0 was a big price drop compared to 4.1, and 4.1 was cheaper than 4o, which itself got a price cut at some point and is cheaper than 4. Meanwhile Anthropic's API pricing stayed stable for many versions, then got slashed to a third with the 4.2 release and have stayed at that level since.

But explain to me how these companies will recoup these costs outside of increasing inference pricing?

Their business model is selling inference but the training and other costs have to be accounted for somehow. Unless I'm missing something obvious, inference costs must go up drastically if these companies are going to survive beyond the subsidy stage.

Sell more. The hope is that there is a huge addressable market that includes huge per-worker demand in almost all white collar work and lots of inference in people's private lives

If that doesn't work, then yes, then prices will have to go up

Both anecdotally for myself and from what I'm reading in the news, it seems just as likely that AI usage has already largely peaked.

There was a lot of hype and exploration of capabilities, but models aren't evolving fast enough to keep that going, so I'm settling down into a familiarity with what an LLM can and can't do that means I am using them less overall that I was 6 months ago when I was throwing everything under the sun at it just to see what happened.

Without either new model breakthroughs or dramatically _lower_ costs, I will be very surprised if the ultimate market doesn't end up within an order of magnitude of where it is today.

> AI usage has already largely peaked.

I think this is minimally likely. While as individuals on the bleeding edge, we're perhaps using these tools less and less, and our echo chamber reinforces that, the penetration of AI into the normal corporate workplace is still very low - emails rewritten with ChatGPT, meeting notes summaries generated by default, etc. There are a million use cases for LLMs which are not yet built out. The tokenmaxxers will begin using AI less, but the penetration into the mass market will continue at a huge velocity.

I agree that more uses will be found and that maybe we're not at the peak. But it also seems very clear a few players have been actively working to inflate usage numbers by margins that might take a while to replace with legitimate uses

Exactly. Like how Meta has a "blow our money on LLMs" leaderboard. Seems like a few companies are attempting to inflate hype enough so all the investors can exit without losing their heads.

Reminds me of the crypto hype but where the hype agents are some of the largest companies in the world.

Yeah from my understanding they'll need to create a few trillion dollars more demand to break into profitability if we look at all the debt/obligations/contracts

Obviously they need more paying users. The entire game in tech is taking advantage of (comparatively)low marginal costs to pay off capex once you corner the market

I do think that's at least part of the strategy. The problem is that we've never seen a single product category so hyped in history, literally trillions of dollars invested. To recoup that, some not so trivial miracles will need to happen.

I think that within 5-10 years most white collar workers around the world will be paying for AI assistants. There are 1.2-1.3 billion such people to sell ai to, so getting more users doesnt really seem like a miracle to me. I do think convincing everyone to use expensive proprietary models instead of open ones hosted cheaply by third parties will be a minor miracle for the AI labs. Definitely not out of the question though.

Considering not one company is in the black yet I don’t really know how we can say anyone is making bank, unless we want to count absurd levels of VC funding (now slowing down) I guess.

Let’s see it first. And without omitting training/infrastructure costs at that. Until then my comment is still accurate.

its a private company, what exactly do you expect to 'see'?

Anthropic IPO's in less than 5 months and I guarantee you any company that officially is in the black will proudly shout it from the rooftops.

> Anthropic IPO's in less than 5 months

pure speculation. about as valuable as my linked wsj reporting i suppose. given thats the case, maybe you shouldnt claim so confidently that they are money incinerators.

“pure speculation” is a bit unfair.

Back to the point: No one is profitable yet. This is a statement of fact. And if you are going to lean on “they will be soon” then I can absolutely say they’re going to IPO soon.

Ease off the gas. We’re just discussing a tech company.

Does Anthropic really expect to double their income without also doubling their expenses?

I’ve been hearing that anthropic is on the verge of profitability for probably a year straight. Until all the companies agree to stop the training arms race I just don’t see how it’s in the cards

This is one of the things people miss. If they double their customers, of course they double their expenses. Unlike SW, the marginal cost here is still high

I mean, it's possible that with the new datacenter from SpaceX, they could onboard more users than it costs them to rent. That's fair. But I kind of doubt that.

One thing that really stinks to me is that various AI boosters have been claiming insane profit margins (40%, 50%, ...), yet apparently Anthropic stands to (possibly) make $500M profit on $11B in expenses, that's clearly nowhere near 50%. Not to mention that they're not making profit on inference now.

So where do people get this confidence to pull random numbers from?

There we go back to the original question: are subscriptions profitable, API pricing wildly profitable, and they just lose all that money on fixed costs like model training; or do they actually barely make money on inference?

That's why talking about the profitability of inference without accounting for model training is interesting, because that is the deciding factor in whether more customers would help getting them in the green

Without actual data I don't know. My gut feeling is that they overall lose money on subscriptions (and especially the free tier that accounts for 95% of all users). And make thin profit (~5%) on API pricing.

But it's just that. A gut feeling.

I don’t think it’s a gut feeling. It seems to be the consensus that subscriptions are still heavily subsidized

its well reported that inference margins on api pricing are 30-50%

No it's not. It's well speculated. Can you give a single source that says they have a 50% margin on inference?

I am conveniently not counting training costs (since they add no marginal costs, selling more tokens doesn't impact them), and hardware and DC costs only amortized

Of course they do have to "make bank" in some way to offset the insane training costs. But whether they go for high prices or high volume, or offer some services as a loss leader to drive profits elsewhere is somewhat orthogonal to that