Brad Gerstner confirmed that tokens aren't being sold at a loss. Whatever the formula, API + Subscription split, the companies are making a profit on net token sale.
They maybe running at loss after all the salaries and stock comp, but tokens are in profit now.
It's like witnessing a rocket using the most powerful engine on Earth then once it escaped orbit turn off the engine and said "It is flying without power!".
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.
The problem is, people see "they're not profitable once you account for training" and equate that to "AI will go away soon"
But if all the AI companies stopped training new models, they would all instantly become profitable (and stick around)
The thing that makes them unprofitable, is having to compete (which means training models). If / when enough companies exit the market, the cost to compete goes down and you end up in an equilibrium
They aren't being sold at a loss but they aren't being sold at enough to cover the current losses and the costs. The losses are being passed around in some fucked up circular funding mess which will inevitably collapse into a debt crisis at some point.
Tokens can be sold at profit, but 70% of compute expenditure goes to R&D and model training[0]. Inference needs to cover all of that as well as being profitable in a vacuum.
He's an interested party. His investments are worth a lot more if he says that tokens are sold at a profit. I don't understand how anyone would trust him?
There are plenty of various providers on OpenRouter serving very large Chinese models like GLM for a fraction of what OpenAI/Anthropic. Presumably they are making a profit.
It’s unlikely that Claude is proportionally that bigger and more expensive to serve so profit margins on inference must be pretty decent
Do we know they are making a profit though? They could be subsidizing use to build market share the same way. They might not have billions, but at the volumes they are selling maybe they’ve got the cash to do it.
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.
also, it's very much possible that the chinese companies get heavy investments from the state. Since it's very hard to get this info we have no idea wether they really make a profit or not.
I think for a while this is possible - the models definitely aren't as efficient as they can be as we've seen a lot of promising papers over the last year about how people are changing pieces and parts to do more with less. None of it has come to market yet that I'm aware of so for now it's just a hope I suppose but things like Opus definitely burn a ton of compute to be the leader in benchmarks but the gaps are closing.
Open source models apply pressures on the low end of the market. The paid models are so much better that they can charge based on value for enterprises.
Article is mistaken these subs are not available to businesses. Companies are paying much closer to API prices. The strategy is to get you accustomed to infinite tokens on your personal sub and bet that behavior transfers to work.
Looks more like AI slop with paragraphs like these;
> The pattern is identical across the board. Price for adoption, not for economics. Lock organizations in. Make AI a load-bearing part of every team's daily workflow. Worry about the bill later.
Not only that, but the API rate amounts being pearl clutched over in the article are still relatively trivial. 10k a month is not nothing, but when 10k a month enables a team of ~10-20 engineers, that's pretty good leverage.
Disclaimer: didn't finish tfa, so obviously AI even I could tell.
Perhaps OpenRouter can be used as a benchmark for commodity cost to serve AI. I keep hearing it's better value than Claude, which suggests to me that either Anthropic is especially inefficient for some reason, or they're turning a profit on inference. They could be losing money on training, but I suspect that's just part of the cost of staying a leading lab. If any single one goes under due to debt etc. then companies can just switch?
How do the owners of that site correlate this with their business model, which is to use AI to write articles like this one, so as to get clients in the news?
Eventually, after the seed funding is spent, you will have to pay the real cost of the coal used to power your queries.
The best course of action is to take advantage of subsidy for awhile, but not integrate is so deeply one can’t retreat. You’ll still have full productivity, just be cognizant of the reality of the situation.
Hopefully the market eventually collapses to where companies are hosting their own inference, and you simply lease a model package to run on your own (or rented ) specialty hardware.
The FED will print to infinity as the US gov can’t stop spending, mostly all of that money will keep going to the only industry that’s growing and provides crazy returns for family offices and VC’s right now which is AI. I don’t agree with the authors opinion here as the “time bomb” timer is simply the entire world buying US debt here, which won’t happen in the short/medium term
Not SMBs and SMEs. Big Enterprises would generally be using API buckets or Enterprise-specific consumption models via sales teams and contracts, but most companies would default to subscription tiers - either due to shadow IT paying out of pocket for subscriptions to duck corporate IT, or because they’re too small to negotiate rates and API buckets, or because their IT teams lack the skills needed for the same.
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.
Even if they are momentarily losing money it’s important to note the value add they are providing.
If you increase the price, the value is still astronomical in comparison.
Companies need to find a way to leverage local models in tandem with frontier models to offset the costs.
It’s all about targeting specific workloads with the appropriate AI. These tools are not sentient beings they are tools that need to be properly configured to match the job at hand.
Inference is profitable. Companies lose money because:
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.
Given that it is no a monopoly, and changing providers is very easy, it's not going to be all that easy for anyone to charge a lot more than inference price. It's not someone in cloud A, facing huge costs to migrate to cloud provider B.
Those price increases will increase the pressure to use cheaper / free models (commoditization), thus cutting into the revenue projections of the frontier model vendors. Its going to be exciting to see what happens to these huge investments and valuations.
> increase the pressure to use cheaper / free models
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.
I think part of this is due to the fact that the closest competition cheap but comparable intelligence models are all mostly Chinese models.
Think what you want but even when hosted in the US, at the enterprise level going all in on that would be a legal and/or political death sentence.
We need better open source/cheap but high intelligence western models that are proven to work well in agent if tooling and have strong legal agreements for enterprise to even consider it.
I’ve said this before on HN, but there are two things that make me optimistic that we won’t see a big rug pull where price-to-capability ratio skyrockets relative to today:
* 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.”
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.
The price for a given level of capability will fall, but the frontier has recently been getting more expensive. If you compare GPT-5 to GPT-5.5 on the Artificial Analysis benchmark, it's ~4x more expensive, but achieves a higher score. Claude 4.7 is also more expensive than predecessors because of a tokenizer change.
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.
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.
I think I'm going to puke if I see one more "It's not X. It's Y." phrase or the word "load-bearing" used metaphorically.
It's not metaphorical. It's load-bearing.
load bearing has snuck into my vocabulary, but I work with construction workers so it's slightly more intuitive I guess? :/
[delayed]
It is not human language. It's AI slop!
Managers in my org love using it in "their" Slack messages.
Brad Gerstner confirmed that tokens aren't being sold at a loss. Whatever the formula, API + Subscription split, the companies are making a profit on net token sale.
They maybe running at loss after all the salaries and stock comp, but tokens are in profit now.
It's like witnessing a rocket using the most powerful engine on Earth then once it escaped orbit turn off the engine and said "It is flying without power!".
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.
The problem is, people see "they're not profitable once you account for training" and equate that to "AI will go away soon"
But if all the AI companies stopped training new models, they would all instantly become profitable (and stick around)
The thing that makes them unprofitable, is having to compete (which means training models). If / when enough companies exit the market, the cost to compete goes down and you end up in an equilibrium
They aren't being sold at a loss but they aren't being sold at enough to cover the current losses and the costs. The losses are being passed around in some fucked up circular funding mess which will inevitably collapse into a debt crisis at some point.
Tokens can be sold at profit, but 70% of compute expenditure goes to R&D and model training[0]. Inference needs to cover all of that as well as being profitable in a vacuum.
[0] https://epoch.ai/data-insights/openai-compute-spend
This is the sort of uncritical thinking that inflates bubbles in the aggregate.
Compared to the inference prices for open models it’s highly unlikely OpenAI/Anthropic are not making decent amounts of money from inference.
How many times bigger could Opus be than GLM or Kimi, it’s certainly not proportional to the price
He's an interested party. His investments are worth a lot more if he says that tokens are sold at a profit. I don't understand how anyone would trust him?
There are plenty of various providers on OpenRouter serving very large Chinese models like GLM for a fraction of what OpenAI/Anthropic. Presumably they are making a profit.
It’s unlikely that Claude is proportionally that bigger and more expensive to serve so profit margins on inference must be pretty decent
Do we know they are making a profit though? They could be subsidizing use to build market share the same way. They might not have billions, but at the volumes they are selling maybe they’ve got the cash to do it.
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.
also, it's very much possible that the chinese companies get heavy investments from the state. Since it's very hard to get this info we have no idea wether they really make a profit or not.
That isn't enough. Over time the need for growth and increasing profits will squeeze existing margins.
I think for a while this is possible - the models definitely aren't as efficient as they can be as we've seen a lot of promising papers over the last year about how people are changing pieces and parts to do more with less. None of it has come to market yet that I'm aware of so for now it's just a hope I suppose but things like Opus definitely burn a ton of compute to be the leader in benchmarks but the gaps are closing.
Open source models apply pressures on the low end of the market. The paid models are so much better that they can charge based on value for enterprises.
Have you used any of the recent models? My experience with GLM 5.1 does not make me miss Opus at all.
Article is mistaken these subs are not available to businesses. Companies are paying much closer to API prices. The strategy is to get you accustomed to infinite tokens on your personal sub and bet that behavior transfers to work.
Yeah, I was confused about why it was talking about subscriptions for enterprise. The company I work at is billed on API usage.
Looks more like AI slop with paragraphs like these; > The pattern is identical across the board. Price for adoption, not for economics. Lock organizations in. Make AI a load-bearing part of every team's daily workflow. Worry about the bill later.
Not only that, but the API rate amounts being pearl clutched over in the article are still relatively trivial. 10k a month is not nothing, but when 10k a month enables a team of ~10-20 engineers, that's pretty good leverage.
Disclaimer: didn't finish tfa, so obviously AI even I could tell.
Perhaps OpenRouter can be used as a benchmark for commodity cost to serve AI. I keep hearing it's better value than Claude, which suggests to me that either Anthropic is especially inefficient for some reason, or they're turning a profit on inference. They could be losing money on training, but I suspect that's just part of the cost of staying a leading lab. If any single one goes under due to debt etc. then companies can just switch?
How do the owners of that site correlate this with their business model, which is to use AI to write articles like this one, so as to get clients in the news?
Eventually, after the seed funding is spent, you will have to pay the real cost of the coal used to power your queries.
The best course of action is to take advantage of subsidy for awhile, but not integrate is so deeply one can’t retreat. You’ll still have full productivity, just be cognizant of the reality of the situation.
Hopefully the market eventually collapses to where companies are hosting their own inference, and you simply lease a model package to run on your own (or rented ) specialty hardware.
The FED will print to infinity as the US gov can’t stop spending, mostly all of that money will keep going to the only industry that’s growing and provides crazy returns for family offices and VC’s right now which is AI. I don’t agree with the authors opinion here as the “time bomb” timer is simply the entire world buying US debt here, which won’t happen in the short/medium term
Why does the author assume that enterprises use subscriptions?
Many companies use models deployed on Azure/Bedrock etc are already paying based on usage (often with discounts).
Not SMBs and SMEs. Big Enterprises would generally be using API buckets or Enterprise-specific consumption models via sales teams and contracts, but most companies would default to subscription tiers - either due to shadow IT paying out of pocket for subscriptions to duck corporate IT, or because they’re too small to negotiate rates and API buckets, or because their IT teams lack the skills needed for the same.
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.
Even if they are momentarily losing money it’s important to note the value add they are providing.
If you increase the price, the value is still astronomical in comparison.
Companies need to find a way to leverage local models in tandem with frontier models to offset the costs.
It’s all about targeting specific workloads with the appropriate AI. These tools are not sentient beings they are tools that need to be properly configured to match the job at hand.
You could use "git clone" or Wikipedia for free. If you mean the value of propagandizing gullible people, yes, there is "value".
Since we can't reliably detect AI generated crap, I think it makes sense to penalize their submission. I say this as a generally pro-AI person.
Inference is profitable. Companies lose money because:
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.
Inference at per-token pricing is profitable.
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.
So? How does it change the equation?
Nobody is going to charge "inference price" for model usage.
Given that it is no a monopoly, and changing providers is very easy, it's not going to be all that easy for anyone to charge a lot more than inference price. It's not someone in cloud A, facing huge costs to migrate to cloud provider B.
Those price increases will increase the pressure to use cheaper / free models (commoditization), thus cutting into the revenue projections of the frontier model vendors. Its going to be exciting to see what happens to these huge investments and valuations.
> increase the pressure to use cheaper / free models
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.
I think part of this is due to the fact that the closest competition cheap but comparable intelligence models are all mostly Chinese models.
Think what you want but even when hosted in the US, at the enterprise level going all in on that would be a legal and/or political death sentence.
We need better open source/cheap but high intelligence western models that are proven to work well in agent if tooling and have strong legal agreements for enterprise to even consider it.
I’ve said this before on HN, but there are two things that make me optimistic that we won’t see a big rug pull where price-to-capability ratio skyrockets relative to today:
* 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.”
I agree.
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.
The price for a given level of capability will fall, but the frontier has recently been getting more expensive. If you compare GPT-5 to GPT-5.5 on the Artificial Analysis benchmark, it's ~4x more expensive, but achieves a higher score. Claude 4.7 is also more expensive than predecessors because of a tokenizer change.
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.
Aside from the obvious fact that this is AI slop, the author (prompter?) doesn’t consider the R&D of AI itself. Efficiency gains, more compute, etc.
We all know every frontier AI lab is heavily subsidizing usage, and so do all of the VCs & CEOs funding them.
TL;DR to save you time:
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.
We popularized the term "enshittification" so we wouldn't have to keep explaining this.
As a few commenters already pointed out, IME enterprises aren't paying for subscriptions. They're paying per token.
But also... is this shit AI written? I'm so tired of this.
> is not a rounding error. It is
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..
After reading the third "rounding error" phrase I quit.
It is "bait and switch" --- done on an industrial scale.