Comment by Closi
20 hours ago
> hopefully this quells some of the "AI is unprofitable everyone is spending a dollar to make fifty cents" arguments.
I think everyone has known that inference is profitable overall for the major providers for a while (I'm sure not all user accounts, but in aggregate).
The major providers are unprofitable after you add in the training and research costs and other overheads.
This is why the open source models are such a threat if they become good enough - it will destroy the business model of just providing raw chat/intelligence.
Arguably that's also why a few of these providers are trying to diversify into whole-platform approaches rather than just relying on having their whole thing be a great model.
There has always been a lot of evidence that prices are only going down. The fact that you could already get DeepSeek or GLM inference from 20+ providers on openrouter.ai was already sufficient evidence that inference was profitable and being commoditized.
People that want to believe something different as a coping mechanism will not be swayed by this evidence either.
AI will end up being a commodity. That's also why you already see the first rounds of protectionism under the guise of national security. Obviously they do have a point but that point works against them just as easily. Anybody that integrates unreviewed model output into their workflow is asking for trouble.
non frontier intelligence will be a commodity. Frontier intelligence will be a high margin luxury good. this is how most markets work
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> AI will end up being a commodity
what does this even mean, the commoditization... like a thing you can buy from a gas station? AI commoditization is the rock bottoming of pricing.
Usually we end up with cartels in such cases, see OPEC, mafias etc. But we have China throwing sticks into the wheels of the US providers that are attempting to make the cartel and "pacing the frontier"
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I’ve never heard anyone say raw inference was unprofitable. It wouldn’t even make sense, I can’t see a rug pull for renting out commodity GPUs.
People are saying _training + inference_ is unprofitable (I don’t have a dog in the race and don’t really care either way). That’s a lot of GPUs and salaries that aren’t generating revenue at any moment.
Personally, I would guess Anthropic and OAI are profitable. I just suspect that they’re a pretty normal level of profitable and not the absurd, near-alchemical “can turn dirt into gold” level of profitable their valuation seems to have priced in.
To add on, training + a highly paid American salary load + inference is unprofitable, if and especially if you have deepseek dropping weights and engines for anyone to run.
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It's a commodity, and research into how to make it good will be done in the open. What this means is that we should doubt the justifications offered by magnificent-N-whatever companies slurping up all investment dollars to take over the world by being the first to get "general" "super" intelligence. No, there won't be a runaway improvement. Sadly, AI is just a clone of our inner world. Getting interesting information into it is still going to be just as slow as it is in the regular world. You have to build, tinker, experiment, get data, get knowledge, etc. The only thing getting sped up is how much time needs to be allocated to thinking and translating.
there could still be a runaway improvement, but it also almost certainly goes with the decreasing prices
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There may be different thesis out there, but the fact of prices going down definitely doesn't help the case that the major AI labs are are overvalued and need to vastly increase their revenue to stay afloat amidst their contractual obligations.
I honestly don't think there are too many people arguing "AI is actually too expensive to take my job". "AI being too much of a commodity to make meaningful profits" could very well be the case though.
I have seen the former argued many times, though mostly not on HN. It is a cope meme I’ve seen mostly on Twitter and Bluesky, but some on HN and the Elixir forum as well.
They are literally hoping Anthropic & OpenAI go bankrupt (totally possible), and everyone goes back to using their meat brains for work (never happening).
In this context it just doesn’t matter if these companies are profitable after amortizing training cost, because deployment of open weight models that already exist will continue to disrupt economies for at least the next decade if all research ended tomorrow.
> The major providers are unprofitable after you add in the training and research costs and other overheads
The very short lives of all these models also factor in.
I think the funny thing is the more inference, the more tokens a specific version of a model spits out, actually does make it more profitable.
But somehow a lot of people truly think that each token generated increases the cost of OpenAI/Anthropic/... and shout bloody murder about "subsidies"
And I'm certain there are people out there that actually think the API prices of OpenAI and Anthropic somehow equal their electricity costs.
Nobody meets expectations of a 2 trillion dollar IPO selling a fungible commodity like tokens for old models.
And that's the rub - it doesn't matter if inference is profitable, the US labs have promised the whole kitchen sink, and taken serious investment on that pitch. They won't make it back in a cut-throat market for inference. They have to continue pitching the idea that only their models are worth using, for quality/safety/performance/etc reasons.
Which means continuing to develop and release new models. At huge expense.
Side note - while deepseek seems to be doing fine, I'll note that the emphasis on cost effectiveness is very much not a thing for the US labs right now... and it was for non-us labs. I'd be quite interested in knowing if anthropic is pulling in the same returns, or what the margin on their subscription plans actually is.
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I'm already seeing this directly at work - lots of devs still happily use claude for development, but the products we're making aren't. They'll use kimi, glm, gpt-oss, etc... basically - if cloudflare is hosting it, we're exploring it (mainly for contractual/security reasons): https://developers.cloudflare.com/workers-ai/models/.
Because we are price sensitive, and we already have to do all the work of creating very robust evaluation frameworks for our products, so we might as well choose the cheapest model that meets our standards.
whiiiiich is why we're seeing things like Haiku and gpt-luna priced down near the open models at ~.10/mm in, ~.50/mm out. They've been forced to cut prices to keep commercial customers on their cheaper models instead of moving away.
This will continue. If inference is profitable and easy, prices will continue to erode. Go browse openrouter, plenty of providers happy to undercut the competition. Bigger shops with auditing requirements will avoid them, but smaller ones won't.
You know, it's not just cost-effectiveness, it's also iteration time and speed. DeepSeek V4.1 Flash is astoundingly fun to use because it's so quick. Tens of times faster than the standard Frontier models. With, in my experience, practically the same results. Because most of the trouble is not the intelligence of the model. It's the feedback from the world. The quicker you can get feedback from the world, the better your process is, irrespective of raw intelligence metrics of the AI you're using.
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> Which means continuing to develop and release new models. At huge expense.
I guess my question is when do we say a model is "good enough"? For my use cases Opus 5.5 feels as if it is exactly at that point. And at that point of course one of the main factors I will evaluate against is price. So far I am happy with my (heavily subsidized) subscription, but if the time comes, I'll happily switch the inference API any of my agents are calling. So far I have no big lock in on any providers.
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The Chinese labs are just arms of the Chinese government. They have no obligation to be profitable or please (private) investors. If they weaken/collapse American models, then that is the ROI to the lab owners.
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> I think everyone has known that inference is profitable overall for the major providers
> The major providers are unprofitable after you add in the training and research costs and other overheads
It's meaningless to say that selling something is profitable if you are ignoring the cost to produce it.
The profitabiity (or not) of AI models needs to be calculated on a per model lifetime basis - what did the model cost to develop and train, what is the lifetime revenue the model generates, and what is the lifetime operational cost of the model (including amortized GPU cost), etc.
It makes no more sense to calculate "inference" profitability when ignoring training cost than it would to calculate it when ignoring GPU cost.
Exactly. And you can't just stop training models, because your competitors won't, and customers are switching providers on a daily basis (most likely an exaggeration).
Hence "we need to slow down progress for safety" [narrator: it had nothing to do with safety]. The industry is begging for the regulation so that there is a coordinated slow down and consequently move towards profitability.
> coordinated slow down and consequently move towards profitability
100%
Amodei has been promising the "end of the exponential" all year, when they can shift some compute away from cash-burning research and training toward cash-generating inference.
"Pacing the frontier" is how you collude with your competitors to all decelerate at the same time because safety.
I saw last week that Anthropic is currently cash-flow positive with 75% of compute going to research and training. If they can flip a switch and move from 3:1 to 2:1 or 1:1 training:inference, they will be insanely profitable.
As long as they can avoid a price war.
>> calculated on a per model lifetime basis
True only if you never decelerate R&D.
But "pacing the frontier" means every early model doesn't need its own profitable P&L.
Early models can be big money losers while helping build the next generation.
If the industry can coordinate to all decelerate at the same time, then they'll flip from incinerating billions to minting billions, and all that capex will suddenly look completely sustainable.
Unless DeepSeek suddenly gives it all away for free, which is why the big three need government regulation to prevent foreign models from busting the cartel.
> It's meaningless to say that selling something is profitable if you are ignoring the cost to produce it.
At the same time, training costs don’t scale with usage, so no matter how expensive they are, they can be paid for as long as inference is profitable. It is only inherently unsustainable if you lose money on inference.
Of course, it could be that the inference usage required to pay for the training is too high to be possible, but that is a separate argument.
It doesn't just matter that inference is profitable - cash from profitable inference needs to outweigh cash burned by training. If training costs even $1 more than your profits from inference on a model, you lost money on the whole thing.
> It's meaningless to say that selling something is profitable if you are ignoring the cost to produce it.
Don't enter that rabbit hole, you'd be surprised at how questionable are some accounting rules and practices, even in publicly traded companies.
if i can run an existing open model because its good enough for what most people need, I can just spin up an inference business, and put my spend either on trying to optimize it further or on customer experience
... and not worry about training cost at all.
a lot of the cloud businesses of the last decades have relied heavily on open source code, again skipping most of the cost to make the product/service being sold.
>I think everyone has known that inference is profitable overall for the major providers for a while
Have you seen any place that is not as tech-savy as HN? e.g. some Reddit subreddits?
Barely anyone is aware other than people who are already somewhat deep into this whole topic.
The insider baseball in AI is extreme I agree. I still know people who insist large language models are just glorified autocomplete.
Frontier models are still transformer based, so that's still true.
Yes. I've always suspected inference is profitable.
But I'm also interested is retraining needed? how relevant will a model be in N years if it's just "static", and if you do need to retrain and fine-tune over time, is it still profitable? how often is that needed?
For most cases it's probably ok, often times answers are general or basically a web search anyways, but if my Agent started blasting outdated syntax it would suddenly be way worse for coding for example.
It’s helpful for a model to be somewhat static when it’s involved in business processes.
Relying on an LLM's baked-in syntax knowledge is suboptimal, compared to leveraging reasonable context affordances (skills, examples, tool calls, MCP servers, search) to ensure adherence to precise APIs and language specs. Don't get me wrong; a model specifically trained on your desired framework is obviously preferable for more consistent generation of idiomatic code -- but relying on that in a large general-purpose model is a questionable proposition IMHO.
They need to be retrained often is my experience for general usage.
I have SO many cases, for example when shopping, when they make assumptions based on their cutoff date, and they need to redo everything based on lots of searches.
Sure, for many use cases old models will be useful but for general usage a model that's 5 years old will probably be garbage for most people, unless they prompt it extensively, which the general public won't do.
> But I'm also interested is retraining needed? how relevant will a model be in N years if it's just "static", and if you do need to retrain and fine-tune over time, is it still profitable? how often is that needed?
Just wait until what Ilya Sutskever has for Safe Super Intelligence (SSI) to self update the weights without retraining them to bring training costs down close to $0.
> I think everyone has known that inference is profitable overall for the major providers for a while
A lot of AI haters like Ed Zitron and their readers have refused to update on this. I don't know if any of them have yet, but it'll take a year or two for the idea to fully filter out of their podcasts anyway.
Seems a bit of a disingenuous framing. Most of the arguments I see are some mix of
1. Their figures have been pretty dodgy or excluded training costs so why is it trustworthy this time?
2. API inference charged at a profitable level rather than heavily subsidised subscriptions means everyone tightens up their usage + governance Which we very much saw with the rise of openrouter et al.
Personally I think there's a lot of the bait and switch of 2 when people talk about profitability of the 10-20% of API usage for Anthropic while also wanting to lump in subsidised chat users as if they're profitable too.
I think we know if it was this clear cut then the behaviours of companies would be wildly different and more consistent with profitability.
Ed Zitron is a professional apocalypse predictor. Which is to say he's always wrong.
It’s not training and research that kills you here, it’s your own investors ROI expectations that murder you cold when faced with Open Source commoditizing you, from a country that spent 5% of the sum you spent to get to the same position and then gives the recipe away to everyone.
> I think everyone has known that inference is profitable overall for the major providers for a while
Unfortunately while in the tech industry some might know that, the general public are still being misled (especially by certain folks who’ve made a living off downplaying AI) to believe inference itself is unprofitable.
> The major providers are unprofitable after you add in the training and research costs and other overheads.
This is correct for now, but I recently read that 90% of Anthropic's code is written by Claude Code itself. So, if an AI is using its previous version to train the next version, we're not far from the day where these models will take up the decision making process too. For eg: Decisions API by OpenAI or Jev is based exactly on this. What if a model like Jev sits behind Opus 5.5 and guides it to take the next step? Wooh.
Probabilistic predictions right now are at 99% for frontier models. They will never be 100%, but if humans rate themselves 99.9% in decision making, that benchmark can be broken.
As far as I know they haven't been doing any mass layoffs.
So if Claude Code is writing 90% of the code, then why are they keeping probably the most expensive engineers in the world on the payroll?
The machine writing all the code seems to not be enough, even for the company with access to the best models. What's the alternative to explain this discrepancy?
Frequently it's to keep competitors from having them.
In this game you "win" only if you arrive first, so keeping the same headcount to run faster is a valid option for Anthropic. In this context "winning" means mainly that we lose our jobs
Honestly that's an interesting statement because in my experience Claude writes around 5-10x more code than a reasonable person would to accomplish the same thing. If you include comments then that number goes way up as well.
Yeah I think one of the less considered costs when people talk about this is the human cost of training. We all talk about the billions spent on compute, but I don't think anyone's got good numbers on how much RLHF is costing but it's got to be pretty large.
As someone who has long bought into the idea that the labs are not profitable on inference, I have a question. How is it simultaneously true that there is more demand for inference than supply (apparently by orders of magnitude), but also that inference is so cheap that labs can make healthy margins? It seems like the inference providers, not the model providers, should have the leverage. Why haven’t they said, “give me a bigger cut of each token or I won’t serve your model”?
Also, what happens when the inference providers suffocate on their own debt burdens? Who will serve inference?
It's a few things I assume:
1. Inference providers are largely a commodity business. While demand outstrips supply, there are many providers of that supply, which does somewhat constrain how much leverage a provider has.
2. The models are not interchangeable as of yet (though this seems to be changing). A lot of people want the latest Anthropic or OpenAI model; as such, the labs still have considerable leverage.
3. A lot of the inference providers have used their leverage by doing equity deals with the labs, agreements to have some of the deal revenue spent on compute, etc.
What's the evidence that demand outstrips supply? Is there a wait to get an inference provider or interruptions in availability?
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The only moat is in datacenters. The software has no moat, since 1) it's already trained on stolen data, and 2) someone with access to AI will be able to reverse engineer a proprietary implementation eventually, the same AI is being used for developing AI.
That's why so many talks about regulations. Regulations means: ban outside competition, just like US bans Chinese EVs today.
regulation is braindead
I would classify training and research more as capital investment than overhead.
Most people in my experience refuse to believe it for some reason.
Well, as that means there's lots of money to be made lowering costs for research and training! What tools have we used in the past to lower those costs? Automation.. Hm, what if we had AI completely automate AI research? Uh oh, we just rationally found outselves in a trajectory where we lose control of our own trajectory because of economic incentives.
It is wild how fast we're hurtling towards this outcome, lmao.
I doubt that, I got downvoted several times pointing out inferencing is insanely profitable.
Even take account of R&D cost it is still profitable, because you do not actual retrain the base model every month, only distill and post train from the same foundation.
heck, I think even including the giant cost of the top tier researcher salaries, they are still profitable.
then, how are they still losing money? just read what they are actually spend the most budget: always the hardware. those companies will never profitable, because they will never stop spending money to expand their computational prowess.
I don't think so. The number of comments I see from people talking about AI, there are plenty who still think the price is going to shoot up. Some even talk this way about models you can run locally on high end yet still consumer level hardware.
I think the more someone works with AI the more likely they realize that inference does not have to be sold at a loss, but many people's AI knowledge seems quite dated and it sounds almost like it becomes a convenient conspiracy theory for some, a way to convince themselves that the market changes that are already happening a temporary (and I don't mean in a Jevon's paradox sort of way, but that companies will go away from AI).
I suspect a lot of this is fueled by a few stories of companies who went all in on spending as much as possible on AI and then begun to walk that back, without realizing that was more about bad incentives and over promises of what AI could do (especially 1/2 model generations ago). Some also confuse running the SotA models at the fullest capacity costing more and more with a given problem costing more and more to solve, not realizing that the cost per problems is trending down but newer and larger problems can be solved for even more, so the costs at the top are rising despite falling prices. Also things like the subscriptions getting more expensive, though how much of that is because developers are doing far more than they were 6 or 12 months ago (and in some cases there was subsidization that is going away). And maybe a bit with the free models starting to not be upgraded or the harnesses getting worse performance of lowered free limits.
If the frontier labs are struggling under the training costs and the leading open source models are making their progress by distilling from the frontier labs it sounds a bit like there’s something wrong with the economics here or that progress is going to slow quite a bit without the amount of investment.