They maybe running at loss after all the salaries and stock comp, but tokens are in profit now.
It’s unlikely that Claude is proportionally that bigger and more expensive to serve so profit margins on inference must be pretty decent
Even if they are “profitable” how many Uber drivers are “profitable” because they aren’t correctly calculating asset depreciation. Maybe these guys are doing the same thing.
Maybe it’s a lot of people who already had GPUs for crypto mining, and they’ve moved over to this, so that if they need to grow and buy new GPUs the costs would dramatically grow.
How many times bigger could Opus be than GLM or Kimi, it’s certainly not proportional to the price
Yes, sure, right now it is ... but that's NOT how it got here.
There are trillions invested to recoup and at most billions in sales. It doesn't add up to tokens making a profit any time soon.
* People keep finding ways of cramming more intelligence into smaller models, meaning that a given hardware spec delivers more model capability over time. I remember not that long ago when cutting edge 70B parameter models could kinda-sorta-sometimes write code that worked. Versus today, when Qwen 27BA3B (1/23 of the active parameters!) is actually *fun* to vibe code with in a good harness. It’s not opus smart, but the point is you don’t need a trillion parameters to do useful things.
* Hardware will continue to improve and supply will catch up to demand, meaning that a dollar will deliver more hardware spec over time. Right now the industry is massively supply constrained, but I don’t see any reason that has to continue forever. Every vendor knows that memory quality and memory bandwidth and the new metrics of note, and I expect to start seeing products that reflect that in a few years.
I hope that one day we’ll look back on the current model of “accessing AI through provider APIs” the same way we now look back on “everyone connecting to the company mainframe.”
As the AI labs become more reliant on enterprise adoption, it makes sense to push capabilities at a cost that makes sense for businesses. Even if it prices out consumers or hobbyists.
Between: more efficient models - tuned for the task at hand, the ability to run those models in-house, or even at the edges, plus Google and Microsoft are well positioned to stay ambivalent as they’ve got lots of products to sell and whether or not LLMs are part of the portfolio mix is completely dependent on enterprise customer demand.
Anthropic/OpenAI have a number of aggressive downward pressures on their pricing.
1. Training is expensive. Not just compute but getting the data, researchers salaries etc 2. You have to keep producing new models to ensure people use your inference and there seems to be no end to this. So they have to pour more billions to keep the cycle going on 3. People salary and other admin cost are not that high compared to 1 and 2.
The article's point is that if you're relying on flat fee subscriptions, a rude awakening may be coming. That seems plausible to me. Issues around token quotas are a frequent topic on HN.
Nobody is going to charge "inference price" for model usage.
Not necessarily. Many factors go into what models are available at enterprise level. If you look around, not many companies (everywhere around the world) use DeepSeek models even though they are significantly cheaper.
1. GenAI companies are making a loss in order to gain adoption and later lock-in
2. ???
3. They're going to cash-in soon and start milking you now that business critical systems rely on GenAI
The "???" denotes a complete failure to offer compelling arguments that link 1 and 3.
Many companies use models deployed on Azure/Bedrock etc are already paying based on usage (often with discounts).
Remember that enthusiasts leaning on API keys and large enterprises are the exception, not the norm, and even some large customers may lean on subscriptions for at-scale adoption and wait for teams to report hitting usage caps before buying more token buckets. Subscriptions are predictable, reliable, and above all else a contractable way to acquire service.
Truth be told, this has been my red flag in orgs and with peers elsewhere for several years, now. Those orgs leaning on subscriptions are in for a nasty surprise within a year or two (like the author, I predict sooner than later), especially if those subscriptions power internal processes instead of AI buckets.
Hell, this is why I think there’s a sudden focus on the “Forward Deployed Engineer” nonsense role: helping organizations migrate from subscriptions to token buckets for processes so the bill shock doesn’t send them running away screaming.
Who said it was?
> Pull out the napkin. This matters.
The article wouldn't exist if you didn't think it mattered, just tell us why.
> the question is not whether they got a good deal. The question is
Who said that was the question?
> This Is Not One Company's Problem
Who said it was?
Stop telling us what thing aren't, just speak like a normal human and convey your own thoughts. It's an insult to your audience to throw constant AI slop at them.
> thousands of companies have woven AI subscriptions deep into their operations. Marketing teams draft copy through ChatGPT Plus.
Yea I bet you do..
jqpabc123•48m ago