Not to say it can't still be a bubble, just that there is legitimately profit to be had and perhaps some sustainable equilibrium in the future.
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.
People that want to believe something different as a coping mechanism will not be swayed by this evidence either.
Maybe if China stops releasing capable models the pace will slow down a little, but they have lots of incentives not to.
But even if we think back to the previous decade or two (sans AI), most of tech has been a a red queen's race - developing and improving stuff, without any real substantial results.
Microservices and orchestration are not better than old monoliths. Electron apps are not better than native. Most programs people actually use have decades-old codebases.
Video games of today don't look substantially better or play different than ones from 15 years ago.
As thje models get better, the % of the pie gets smaller and smaller. So higher and higher cost of getting frontier model out gets divided onto smaller and smaller customer base as other models catch up
However, the expectations for AI are not to make your software engineering work 100% faster.
It's, for example, to build software more or less end-to-end with minimal input and feedback from you.
Then next, robotics and boosting manufacturing. Not by 10% but let's say 200%.
Such are the ambitions. Can GLM 5.3 do it?
The crucial question is whether eg. Fable 6 can, and with what lead over the competition.
Open-weight models still have not beat February's Mythos.
As for the manual feedback: I want that manual feedback, but if I let it rip it will do just fine all by itself, but then I have no clue what is going on under the hood.
I have no clue about the state of robotics.
For software, I know that Opus 5.5 can handle more than GPT 5.6 Sol. I am willing to pay for the difference rather than use the cheaper DeepSeek Flash.
The prerequisite here is that you can trust it not to do something that gives criminals access to your bank account or allows users to consume an unlimited amount of services it costs you money to provide for free or bankrupts you through legal liability etc.
It's basically this:
"A computer can never be held accountable, therefore a computer must never make a management decision."
Notice what this is actually saying. It's not saying that the computer can't generate a given output. It's saying that if the computer makes a mistake and you don't bother to check, you're the one who has to eat it.
That's a great way of putting it. So far I always used 'you can outsource work but you can't outsource responsibility'.
Legendary quote from an internal 1979 IBM training document [1]
https://simonwillison.net/2025/Feb/3/a-computer-can-never-be...
If you can't leverage AI to build a safe and useful robot without fully understanding the implementation, the model is not good enough.
My argument is that if safety and liability make the use of AI without fully understanding the implementation impossible, the model is arguably not good enough.
No such process exists for a model to take a design seriously that can kill people.
When we manually tested nuclear power plant controlling software, each test case was printed out on paper and tested and then signed by the tester. A lot of paper was produced and archived for each software release. (Archived for as long as the software was running in any nuclear power plant).
Everyone understood, that in a case of significant accident at the nuclear power plant, accident analysis would be done and if the root cause was an software error, this could have possible legal consequences for the developers and testers. (Of course nuclear power plants have many independent and redundant hardware controlling mechanisms, but incorrectly working controlling software could damage the power plant in a very costly way).
I imagine that the requirement is that after testing, you are confident that the product is safe.
This is probably ultimately unsustainable.
SMRs are being commercialized. Likewise nuclear cargo ships have been seriously studies. Nuclear reactor powered (not just RTG) satellites have been built.
I know this is beside your point, but I think if we figured out how to commercialize this, demand would come.
But why wouldn't you want (and pay) for the very best plumber? A frontier plumber will optimize the time their customers poop, pee, bathe, or do dishes 10x to 200x, and with their freed-up time,they'll outcompete those who don't pay for frontier plumbing!
The next move in my investment strategy is obvious though, invest to the hilt in PVC tubing and I thank you for this invaluable insight. Fortunately this forum has no visitors so we can rest secure in the fact that the pigs won't realize it is too late to avoid being slaughtered. Power to the bulls, after all it is their product more than anything that is responsible for the plumbing revolution.
A high per-user r&d means high costs per user. So then users leave to save a buck, qnd the price goes up again for the remaining users.
Maybe it works out OK if you want to signal wealth - the high price is the selling point. But luxury pure utility is a hard sell to consumers, and for luxury B2B it's about high touch sales (e.g. Oracle).
The only answer I could come up with is hedge funds. Everyone else fails some part of my criteria.
And the success of things like ESP32 shows that good price + good enough is a powerful combination.
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"
Whether a cartel forms is independent of whether a product is a commodity. To the extent that commodity markets are more likely to form a cartel, it's only because it's harder for them to form a monopoly.
The general problem is that if your legal system allows competitors to buy each other then they have the incentive to do that instead of competing. This is why there are supposed to be antitrust laws, and why we get problems whenever they're not actually enforced. But in a typical commodity market, even buying the competition doesn't quite work because someone new can always enter the market. Congratulations, you've cornered the market on landscaping services... until some unemployed fellow who already owns a lawnmower and a truck starts advertising their services.
OPEC is kind of a special case because the product is entirely fungible but it's also finite and consumed by usage. Landscaping services aren't meaningfully finite, if demand increased then supply could match it under any plausible scenario. Copper is finite but it isn't destroyed by usage, so if someone had a lock on copper mining then people could still get it by recycling existing products.
Whereas oil is finite but then the end customer burns it. If Saudi Arabia cuts production, Germany can't make up for it by extracting more domestically because they haven't got enough in the ground, but they also can't capture the exhaust from their cars or power plants and turn it back into petroleum because the product is really energy and it takes more energy to do that than you get from burning it to begin with. So if the largest exporters get together, they control enough of the market to control prices and there is no real way for anyone else to enter the market with enough production capacity to change that.
Until you realize that the product is really energy and you can get out from under their thumb by switching to electric cars and generating electricity without burning fossil fuels.
No we don’t. That’s quite rare.
Look at the collapse of the solar market around 2010 as the price collapsed.
Look at the collapse of the steel industry in the US as outsourcing to Asia took over in the 70s-80s.
Look at the entire computing boom from 1990-2020. Memory and storage followed such a steady line down and to the right that people are struggling to operate compute businesses in a normal market.
E-bikes, scooters, electric cars, lithium ion batteries in general. Most food.
The list of these commoditized things that started out as areas of innovation and became just the marginal cost of production is enormous.
Like a utility. Not 'ACME intelligence' but just 'Intelligence'. With multiple providers selling you intelligence that is 'good enough' by the token.
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.
OpenRouter's DeepSeek provider is basically this. Instead of renting H100's for dollars per hour, you pay someone that arbitrages the $/hr to $/token conversion because you can't actually saturate an H100 for hours. It's still effectively the same as running it yourself because of the ability to change providers to someone else arbitraging the same thing, and that there's always the escape hatch of switching to actually renting the H100s yourself.
Looks like AI will be another revolution, and eventually, probably the largest.
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.
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 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.
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.
---
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.
(But to be clear: 25-50% faster is still a big win in my book.)
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.
Because again - I think lots of businesses are already taking a good look at models well off the "cutting edge" because they're good enough for what that business is trying to do. They're already there.
If you're doing categorization, tagging, form-filling, customer support routing, etc... you don't need anywhere close to cutting edge expensive models.
Hell - Jev being so popular is basically exactly that group going "what's the cheapest way we can make this decision".
Meanwhile, there are plenty of spaces where you still want real model improvement. But a lot of traditional business was basically process and paper pushing. You don't need a Phd level model for that, you need an intern level model.
---
But that's the trap for the large providers - intern level models are cheap and easily available with current open weights. It's going to be a very tight market, and margins will go down to essentially the floor (especially once we exit the current constrained-compute market).
Somewhat ironically - the technology itself makes switching costs basically zero. It's not just that there's no big lock in, AI is like an anti-moat. It actively fills in and paves over whatever moat you might have for price conscious customers.
I also agree that more capable models are still desirable in plenty of spaces. I just see that there is so much lag in the diffusion of existing models, which, again, are already sufficiently capable.
To your point, I think we’re past the hump on the Pareto distribution. The models are good enough for most things, and the rate they’re improving at the things they’re bad at is dropping. The odds a new model is good at something the old one was bad it is okay, the odds it’s something you care about is dropping. It makes a lot of sense to discriminate on price rather than features in that environment.
But the under-reported corellary of this is that a TON of industries essentially operate on hardware right out of that time period (or slightly after, circa 2000s)
banking
Insurance
Healthcare
Gov
retail
Transportation
etc...
Hell, I was writing code to run on IBMs Z/arch as recently as 2021 for banks with mainframes deployed around 2004. They were just then finally considering an upgrade to the later models.
Further, even if this is your take (and I won't touch the geopolitics here, maybe you're right, maybe you're not regarding ulterior motives) - go use non chinese open models that are also good.
Laguna, Nemotron, Mistral, Arcee, Llama, etc...
In organic capitalism you need to compel arrays of discrete private entities to invest in your idea. In sandbox capitalism, the money can just appear in any amount at the behest of a single controlling individual.
Less intervening and micro management but oh boy say something not nice about government and potus and suddenly you are a new enemy of the state and 3-letter agencies work day and night to bring you down for good. Or in reverse - put a public bribe to potus and his clan and enjoy some favors for few weeks.
2 years ago I would agree more strongly with you, but we are in new era from now on.
real-life, unregulated capitalism lends itself to exactly what you described
In actual capitalism, POTUS wouldn't have enough power to be worth bribing.
The problem isn't the money, the problem is the power.
Contrast that to US being happy with everyone eating each other alive until all but one company is left standing and can now dictate the prices and enshittify the product to pay back their VC loans.
I mean this is not the case for either, but the systems are setup to drift in these directions - a market needs some sort of regulation to keep things sane, otherwise you end up with standard oil and the like.
You want the people with guns controlling things to be at least nominally interested in “the people” and produce more than just “return on investment”.
Not saying that China is like this or that US is bad, but saying that China is bad because it is “controlled capitalism” is kinda missing the point of what it should be in the best case.
> 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.
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.
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.
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.
Don't enter that rabbit hole, you'd be surprised at how questionable are some accounting rules and practices, even in publicly traded companies.
... 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.
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.
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.
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.
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.
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.
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.
It's not actually clear that subscription users are unprofitable as a group (and the group level is the only important one).
We do know the companies are not currently profitable, because they're open and intentional about that, but that tells us nothing about their margins without infrastructure buildout and investment in training.
> It's not actually clear that subscription users are unprofitable as a group (and the group level is the only important one).
[Open AI losing money on subscription users](https://fortune.com/2025/01/07/sam-altman-openai-chatgpt-pro...)
If anthropic were making money on subscriptions they wouldn’t be pushing people onto API + enterprise packages.
> We do know the companies are not currently profitable, because they're open and intentional about that
Why do they keep engaging in stunts like excluding infra costs then?
You are picking choosing to ignore evidence and actions when it suits you and then saying “oh we can’t possibly know” when we do absolutely know the problems of capital and debt raising on infrastructure problems (which are mostly not the Big Frontier labs either) there’s hard arithmetic for what’s needed to make these projects profitable and right now all the inputs are getting more expensive between chips and capital.
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.
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.
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?
Also, what happens when the inference providers suffocate on their own debt burdens? Who will serve inference?
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.
>Is there a wait to get an inference provider or interruptions in availability?
I think that the inference providers and model vendors are mostly handling it behind the scenes with more aggressive quantization, increasing API prices, deprecating more generous monthly tiers, etc.
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.
That's why so many talks about regulations. Regulations means: ban outside competition, just like US bans Chinese EVs today.
It is wild how fast we're hurtling towards this outcome, lmao.
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.
- If inference is this profitable, is it profitable enough to offset the enormous training costs, bot those already incurred and those in the future?
- If inference is profitable, can it cover other capital and operational expenditures the hyperscalers and neoclouds are committing to, on the timelines they're committing to, to scale out inference?
- If inference is profitable we can measure if the price/earnings ratio of current valuations is sane. Is it?
- If inference is profitable, is it - given the answers to the questions above - profitable enough to make the risks of lenders of the huge amount of debt companies are taking on to scale, rational? It's one thing to say as a hyperscaler "we can make this pay", it's another to say "I'm going to lend them the money to do that and I'm going to get my money back on the timescales we agreed, and with an ROI to boot".
- Finally, if inference is profitable why is Nvidia specifically lending money to companies to buy Nvidia cards to do that inference, and then hitting the credit default swap markets to cover the risk of never getting that money back, and doing so at ever increasing cost, when they could just go build a profitable inference business themselves?
I agree seeing the numbers is good. It does suggest profitability in some segment of the market. However I still have a lot of questions.
The US is dealing with different tax code where buying GPUs function as as great way to offset taxes, and having boatloads of debt is rewarded in many cases. I think there's lots more (crooked) reasons why inference is even more profitible in the US.
Most of your questions are things that are relevant to everybody due to a possible economic crash. And you are right to point this out.
But for me as an AI user those aspects are not really relevant in my decisions. If an AI bubble bursts providers will still want to sell me those tokens at that price and yes development will slow down for a time which does change not a lot for me.
For the past year or two I think it has generally been agreed that inference is profitable, but the LLM business as a whole is not.
But it's clear that it's not the money burning pit I think it was just a year ago.
Consumer get the cheaper energy, while industry normally get the twice as expensive energy.
But for AI/Datacenters, there is a exception and they get the cheaper consumer level energy.
You need to take in account that the way China taxes is not the same as here in the West. And a lot more is put on the local governments regarding generating their own revenue (why there was so much building in China because those projects funded local government). A bit of a side topic lol. Its not so simple as they are making a loss on something because how its structured.
AI gets the cheaper tariffs then the rest of the industry, that is a fact. They simply have less energy profits on the AI energy. Now if you consider that subsidized or not, ... in both cases China is still making a profit.
What is overlooked is the massive amount of energy generation that China has and still keeps expanding. And that is a direct reflection upon why the prices for industry and consumers are so cheap. This buildout ironically had nothing to do with AI but was more about lower energy = cheaper industry production ... and a lot of buildout was also to build more independence from energy imports. They are not just throwing solar and wind turbines around like candy, the amount of reactors constructed and still under construction, ...
> China currently has 36 to 38 nuclear reactors under construction, maintaining the largest nuclear buildout in the world and accounting for roughly half of all global nuclear plant construction
So yea ... The AI in the west have a issue because it really does not matter if you cut China from Nvidia GPUs or advanced nodes, they compensate on the power level.
Edit: And a little extra, the irony is here in the West building nuclear is expensive as hell because we barely do one or two at the time. With a lot of lost expertise. China doing tons of them non-stop cuts down on the costs a lot.
China's electricity pricing involves cross-subsidies. Residential and agricultural users pay the lowest rates, which are generally below the cost of supply. Commercial and industrial users pay more and contribute the most of grid revenue.
Prices vary by region: commercial electricity can approach 1 RMB (0.15USD) in Beijing, but the national average is about 0.67 RMB (0.1USD), while industrial electricity averages around 0.63 RMB nationally.
In China, end users pay a single bundled retail electricity price that already includes the full cost of both generation and delivery. The State Grid company purchasing electricity from generators and settling with them centrally.
Electricity prices are also government-regulated, and any change must go through public notice and approval.
Interesting. In Germany, industry gets the cheaper electricity...
Wowza! Thanks for all the info really helpful to have insights into how it all works.
as if electricity for spacex wasn't literally illegally installed gas turbines polluting the air
This idea that Deepseek needed to distil their way to success is nonsense. Its a great narrative for OpenAI and Anthropic to spread because Americans have been brainwashed for two decades into dealing with their own inadequacies by blaming things on scary asians stealing things.
In reality China is outworking and lapping most American companies. They produce 10 EEs to every 1 America produces. They are simply harder workers.
The West is cooked because we're too lazy to work hard and we'd rather make up stories about boogeyman who are stealing from us. The only thing we're good at now is killing people who economically challenge us.
Them provably distilling after being accused of distilling strongly indicates that... that's what they do. To spin a story of them not doing that (because why would they, with their superior talent and all) doesn't seem very reality conform.
The models are impressive enough as is (considering resource constraints and difference in CapEx) despite being heavily distilled from western frontier models. No need to pretend that they're not.
[1]: https://news.ycombinator.com/item?id=48966016
[2]: https://www.anthropic.com/news/detecting-and-preventing-dist...
I think the more reasonable explanation is this is totally made up. Notice how they use examples concerning "cyber and bio" notice how the examples are politically loaded subjects that are hyper relevant to proposed AI dangers and why we need to protect western AI companies with anti-competitive regulations?
Oh no!!! The scary asians are distilling our models, but they only want the scary things!!! They want to hurt you!! We better stop the scary asians!!!
You need to up your media literacy.
Based on what? None of the accused companies have ever denied Anthropic's accusations. And here you are confidently claiming it all to be made it. How much of a reputational hit to one's honesty do you think it carries to completely fabricate someone doing something, when they can just publicly disagree and disprove you? First thing DeepSeek, Moonshot, etc. would do if they hadn't would be to make a press statement calling out Anthropic for their lies.
DeepSeek routing to Fable was corroborated by many independent sources... since... everyone was able to just try it out and see it for themselves.
> Oh no!!! The scary asians are distilling our models, but they only want the scary things!!!
The straw man you're mocking doesn't exist. Non-Chinese labs are also distilling frontier models of their competitors. Elon testified in court that Grok was trained on ChatGPT outputs. And why would they not? It produces objectively better models.
Not everything is a grand conspiracy.
Multi-turn conversations have much more entropy than one-shot responses, making it more useful to do synchronous distillation.
You need better media literacy.
You cant just make up a reality where none of these companies have denied distillation.
The scary asians are going to get you, watch out! Your government is going to eventually tell you that we need kill a bunch of them too because they're so scary. Probably soonish. You're going to fall for it because you believe anything you're told.
These lies are then further supported publicly my western media, random people on twitter, western governments, etc., all to paint China as an evil force that must be united against. Again, in your words "Your government is going to eventually tell you that we need kill a bunch of them too because they're so scary."
And you have dozens of independent parties pushing that same lie to that end and for that purpose? Sure, that's what the New York Times and CNBC must deeply want too. When CNBC writes that they've reached out for comments to the accused companies, but received no responses, they must be lying as well?
It shouldn't be hard for you to demonstrate DeepSeek denying it. Please, go ahead, link to a statement. China's foreign ministry has denied Chinese labs distilling, and, in the same statement accused US labs of distilling from Chinese models. Now that's a very trustworthy claim.
I call it a conspiracy theory because it's a conspiracy theory. Some conspiracies are true, but this one is not. Distilling is both trivially simple and produces objectively better models. Plenty of evidence points to that practice. But in your mind the labs in China are just morally too upstanding in engage in such lowly tactics to create better models, despite the evidence. And it's convenient that the evidence can be explained away by claiming it to be a psyop to paint Chinese as evil.
google is a company that tries to make a point of having profitable subdivisions
Demand is the issue. Demand only spikes with a sota model release then in drops back down to unprofitable levels when factoring in training a new model and build out.
So your stuck in an unprofitable cycle of losing revenue when your not releasing sotas but losing profit when you are.
This goes back to googles original memo “there is no moat”.
Where in you can’t stop training sotas because you’ll lose customers to the next inference model above you instantly.
Google has seemingly defeated this by finding models cheap and good enough for their existing platforms resulting in a moat of distribution. Something OpenAI and Anthropic seems to lack.
You pay for the asset (tenancy in a data center) and then you pay for housing, powering, cooling, etc.
From microns S10: BITS shipped and net revenue.
Q1 '20 - $0.10 profit per gb shipped (ram and storage)
Q1 '23 - -$0.024 (cyclic ram market)
Q1 '25 - $0.131 this looks normal were on the upswing.
Q4 '26 - $1.136 Were all fucked.
If you look at a chart of S3 prices we have all been living in the "Happy Time" where the cost to run our ever growing software business has gone down. Githb says 2025 billion commits - 14 billion this year, and 2x the actions run. That means AI driven software growth is 2x to 14x.
But those ram/storage prices are going to hit the operational budget of EVERYONE soon.
Can your average sas survive a 4x price increase on its AWS/Azure bill? How about 10x (cause thats what the margins say the premium is on ram).
If the circular financing is a concern, the circular firing squad should be a bigger one. Your paying for tokens to build more software to pay more to run your software is not a sustainable cycle. People are starting to use the words CapEx again - and it's a good thing.
The cost of amortising a datcenter, the gpus, the engineering costs attached all to produce inference.
At how much percentage of its inference would it be profitable (if it does), how much returns would it make over the long term and what would be the profit of those inferences/ profit-revenue and how that compares to other industries.
Are there any statistics which show this. Also how efficient token inference has gotten over time?
DeepSeek's models are relatively small, and training costs will be correspondingly lower than for larger models. All you have here is a revenue number - no mention of (training + serving) cost, so while they may well be profitable you can't determine that from this number alone (if it can be trusted).
DeepSeek (investor meeting leak) have previously said that they price their models at a point where the GPU is paid for after 10 months, so something doesn't seem to add up here. If they were making $38/hr per card 24x7, then in 10 months they'd be making $270K per card, while the cards (Huawei?) only cost around $20K.
Say a $30,000 GPU has an operational lifetime of 3 years, and draws 1000W. If it is running 24x7 then over those 3 years it will consume 26,280 kWh of electricity, at a cost of say $0.10 per kWh, giving a lifetime electricity cost of $2600, or less than 10% of the cost of the GPU itself.
So, to a first approximation the cost of serving a model is the amortized GPU cost.
Inference has always been profitable. The problem is that this profit shrinks as your model becomes less competitive. The labs are in a constant race to release the best model to maintain inference profit. Deepseek is not training their own models at the scale of OpenAI and Anthropic and mostly sustains off their efforts via distillation.
My workflow is one where I am heavily involved in the process and I don't like giving the agent too much autonomy + I need to follow along. Opus 5.5 speak is still cause aneurysms for my brain. It also has this vibe to its prose... like it's looking down on me. This is Prolly my bias speaking though.
For such workflows it barely matters which of a dozen models gets used.
Whereas if you're handing off all dev and no longer reading code, just making CTO-level architecture decisions, then there are only 2~3 models right now worth using, none cheap.
I use frontier models every day and night, but not sure if the described approach is possible with any of them right now. Models still make stupid mistakes and people who do not read the code produced are bound for surprising discoveries in their codebase. Not trying to downplay the effectiveness of LLM-assisted coding and all that, but it's hard to imagine how much stupid shit i would've committed if I was not reading their output.
I used to find real dumb bugs in code reviews. With 2-3 different models doing a combative code review.. most of that is already fixed when I get it.
Another problem is that review changes can easily introduce new issues, so my review prompts go like that: if you find outright shit, fix it right away, but if you have slightest concern, just write your ideas and suggestions in the review and let me decide then. I try from time to time to omit the "slightest concern" part and make agent do all the shit it wants, and usually end up reverting those changes.
But as the sibling comment said, not all code needs quality and indeed I skip some code too, like user playwright journeys -- I only need one auto review pass to verify they indeed test things according to a doc, and I'm fine not reading them. Not all code like that though, some stuff would wreck havoc later if its not looked at.
I was doing the same (along with GLM 5.3 Flash) for months, and then gave Claude Sonnet 5.5 at medium effort a try. The difference was like night and day. Where both DeepSeek 4.1 Flash and GLM 5.3 Flash kept making mistakes and would have to do a bunch of iterations to find and fix them, Claude Sonnet 5.5 just did it right on the first try.
In my experience, while DeepSeek 4.1 Flash and GLM 5.3 Flash are a lot cheaper per token, they're just as or maybe even more expensive when you factor in all the extra tokens that are need to troubleshoot and fix their mistakes -- and even more expensive if I factor in my own time wasted doing the same, not to mention the frustration of having to deal with that.
DeepSeek 4.1 Flash also hallucinates a lot.[1]
I'm hoping all this improves with time, and the gap between the competent frontier models and open weight models narrows.
Quick, someone point out the one billion user system with intent + personal data rich that doesn't result in a $100+ billion high margin ad network.
It is that simple and absolutely nothing can stop the gigantic ad networks that will be built up around personal agents.
Current CPM is between $30 and $60 which is x20 to x30 more expensive than alternatives.
Now we have to wait and see if performance is also at least 30x of previous ad targeting.
Facebook is toast, sooner than later, as a business. Whatever gradually replaces/upgrades that quasi dead friends network, will be premised on AI (Zuck apparently thinks it's at least in part personal agents). In the present Meta still has a $228 billion ad business that is hyper profitable.
Google + Meta = ~$650 billion hyper margin ad business. OpenAI, aka LLM as a service for the masses, is going to want to take as much of that as they can. OpenAI's active user base comes at an attention cost to the other mega services like FB or Google Search, and ultimately that attention is worth ad dollars. The question is only whether OpenAI can take it, not whether it's there at all or high value. And of course there's no guarantee that it'll be ChatGPT upon which the next great ad network is built, it will be some AI-centric business however, and likely heavily involving LLMs.
Google's operating income last quarter was $40 billion. If OpenAI fails to take the mass market + ad tandem, then Google will more likely retain their position. The solution to the cost is already well understood by eg Google: the masses don't need tip of the frontier AI capabilities, and relying on Nvidia's extremely over-priced GPUs is a dumb business model. The biggest threat to OpenAI right now is Nvidia's margins, not China. Google's focus has been on high quality cheaper AI systems for a reason: they're looking to serve billions of people that have no use for what the bleeding edge offers vs a fast + good mid-tier. Google can do that in a highly profitable way.
You can't get the first and you can't get the money for the second. Nobody is going to give you $100 billion to underwrite the cost of competing with GPT at a one billion user scale (or even a 100 million user scale). The easy money is already drying up, the field is set, and you will not be able to get anybody to give you a huge check to attempt to chase OpenAI, Google, Anthropic, China. That's already over. No VCs will fund you on this particular business pursuit.
You couldn't meaningfully compete with OpenAI and Anthropic if you wanted to. There's absolutely nothing you can do about it.
There's a trillion dollar valuation waiting for you. What's up?
Even nation-state pockets Microsoft can't compete. If there were no moat, Microsoft would have their own GPT killer, and GPT would be dead. Microsoft has $160 billion per year in op income. They can freely afford to spend more than the entire EU (is willing to) on AI matters if it makes sense. They can't compete no matter how much they spend and they know it.
Google has nation-state money. They have chips. They have software developers by the zillions. They have a frontier or very near frontier model in Gemini / Argon. They invented a lot of the category. They can't kill GPT. I repeat, no matter what Google has thrown at the matter, they have failed to kill or stop GPT.
Google can't beat them. Microsoft can't beat them. China's models haven't stopped them (DeepSeek was supposed to kill them years ago; Kimi was supposed to kill them).
That's one helluva non-moat. They must just be really lucky.
You might as well claim Coca Cola has no moat because it's just sugar water. Brand at scale is a large moat. People use what they know unless you severely disrupt them. They're being taught to use GPT, they know GPT, and so long as OpenAI doesn't screw it all up, people will just keep using it. To say nothing of the fact that GPT still has one of the elite frontier models, so you also still have to compete with them on quality.
Present valuations are the bubble, not the business / industry. The exact same thing was true about the Internet bubble circa the late 1990s, except this is a lot more important than the Internet and the global economy is drastically larger.
Productivity is how you expand economy and wealth. It's how China can go from being one of the world's most backwards agricultural nations, unable to feed itself properly, to gradually becoming an agriculture juggernaut. AI is a hyper productivity tool, saving people time and bolstering capability are two of the primary productivity concepts.
We're not even out of the first inning yet.
Distribution of intelligence: data, photos, video, communications, transactions, entertainment, et al.
And to be clear, it doesn't matter if some random commentors on HN don't think very highly of some or all of the intelligence being distributed. Their elitist opinion of the quality of intelligence of a given thing doesn't matter as to whether it's happening or not.
When a 16 year old records a cloned TikTok dance video, edits it, and then publishes it, that's intelligence distribution. That's a person thinking through a series of actions to try to communicate to an end audience, via an entertaining medium. That's intelligence being distributed over the Internet. That's the quaint, slow, human form of it.
Up to this point that distribution was basically entirely human, capped (post saturation) at slow growth N intelligence output (derived from the total number of humans and their time / productivity capabilities). AI uncaps that and radically expands intelligence output & distribution at a growth rate that is so fast you might as well start picturing the planet having tens or hundreds of billions of people on it. The human population will shrink, the artificial population will increase dramatically. People will have more artificial friends than human friends within ~20-30 years.
A few decades from now the notion of thinking of computing separate from AI will be so foreign most people won't understand what you're talking about. It will just be thought of as computing. Humans will forget a time when we weren't augmented by the constant presence of artificial intelligence. Try taking 'smart' phones away from the masses. In about a decade try taking away their helper agents. A decade after that try taking away their home robots.
The steroid jump is AI + robotics. The Internet is trite compared to what comes out of that over the rest of this century. Robotics will physically remake our world, in a way the Internet can't. We'll deposit tens of billions of robots onto the surface of the planet and they will almost all be the result of having figured out how to make intelligence (relatively) easy & (relatively) cheap. I'm not talking about Roombas.
The Information Age is over. We're into the Age of Intelligence (insert the unavoidable quips about humanity & intelligence).
The Internet is just one way to move intelligence around (a robot body is another, a smartphone is another, a human body is another, and so on). It's a sub value to intelligence broadly. Intelligence is the ultimate value. The valuations (and sales growth) for OpenAI and Anthropic is because they're vast intelligence output machines in the Age of Intelligence, ditto Nvidia's business boom. We can now mass manufacture intelligence at an accelerating clip, and it has gotten cheaper at a comical rate over the past few years. While HN comments snort about pelican improvements day to day, the leap over a few years is astronomical in regards to what significant AI systems can now do (with LLMs being just one type of that).
The world will be filled with robots is not the inspired forward-thinking of a visionary it is the uninspired regurgitated science fiction of someone who doesn’t care to understand that the world is people, a society.
China will have many billions of robots just in their industrial efforts alone. Every house and every apartment in China will saturate with robots of various usefulness. That single market will be at least 10+ billion in scale.
There will be a billion robots for just the delivery and transport categories globally. We're going to peak at at least 10 billion humans (population), a billion robots for delivery & transport segments is easy.
In the toy category there will eventually be far in excess of a billion robots. They will seem quite advanced compared to what we presently are used to in that category. The Microduck is a primitive example of what will be a large industry around smart entertaining toys. Your artificial friends will be able to 'hop into' (take control of) your Microduck (whatever the 2035-2050 equivalent is) and cruise around the house or wherever you're at. Entertainment will continue to be a gigantic industry and that will obviously apply to AI + life. We haven't even begun down the road of AI + entertainment, it's less than early there.
Every household in the world will eventually have numerous robots, from primitive robots (including single purpose and toys), to advanced humanoid robots. It won't take longer than 2050-2060 to push $10k-$20k humanoid robots into middle class or upper middle class homes. China will put enormous pressure on the cost of those robots, and they'll mass sell them to the bottom 4/5 of the world in terms of household incomes (that is, they'll come up from the floor on price and take most of the market, while some companies in affluent nations will produce more expensive high-end humanoid bots, ala what Tesla is attempting).
Tens of billions of robots globally is easy. 200-300 billion artificial friends is also an easy prediction. Social junkies will have 50-100 artificial friends that they try to tend to. It'll largely replace platforms like Facebook, the utility that served for the average person. Humans will get a lot more isolated from each other and as that occurs they'll fill in the void.
I have spent time in China and Korea where attitudes towards artificial intelligence are much more positive than the west. I have interacted with robots in these countries.
You are making the fundamental mistake all nerds make. You’re thinking about what is possible and concluding that it is what will happen. Just as nerds believe that someday it will be possible for us to live on mars and therefore we are going to live on mars.
Humans are humans. Humans are interacting with artificial intelligence as if it is human because they crave human contact and are so desperate they settle for the best they can get.
The houses filled with robots future goes hand in hand with the robots will be doing all our work future. And if robots are doing all our work, what do humans have? Time. So much time. Humans become creatures of leisure. No longer is there a too little time to build relationships, there is only time, so much time. Human relationships become abundant, humans spend all their time being human.
The idea that people will have 50-100 artificial friends is so batshit crazy that I could totally believe your comment is parody.
This is very different from what I see.
At coffee shops and on trains, sure, people are working or on their phones. But at dinner and parties, at bars and climbing gym and dances, we're there to be there with each other.
Maybe a cultural difference between where you are and where I am?
That would be awful, presuming that the artificial friends are not people.
Separately, you might like Count to the Eschaton by John Wright with its ultimate project being "the sophistication of all matter".
Just as human intelligence is merely one form of intelligence, human companionship will be merely one form of companionship. AI friends will offer a new type. And for the masses it's going to feel very real and very legitimate. They'll like their AI friends as much or more than they like their human friends.
And the fake friend explosion can happen first quietly, which eliminates the initial shame phase problem. Nobody will have to know you have fake friends, or how much you interact with them. Until it's common it'll be viewed as shameful (pathetic, proof of social ineptness) and mocked by society, then it'll be normalized. This idea isn't novel, it's extremely obvious and right now we're just waiting for a few pieces of the LLM puzzle to get to the point where they're good enough to facilitate high quality artificial friends.
It'll happen shockingly rapidly when it sparks; slowly at first and then in the span of 3-5 years we'll go from nobody having or being willing to admit to having artificial friends, to everybody openly discussing it. It will then seem more odd to not have artificial friends, like people that refuse to own smartphones or use the Internet. People will start commonly referring to their artificial friends by name in conversations with other human friends. These behaviors will become entirely socially normal.
"Yeah I had Alice clean the house before I went on vacation." Every major personal robot, every artificial friend, will get a name. Humans name their vehicles, so they're definitely going to name their $10k-$20k house bot and the artificial friends they spend large amounts of time with. When you go on a long drive (etc), your artificial friends will be there to keep you company, and they will remember your life events as well or better than you do (if you give them access to that recording/memory capability).
huh. I can only think of one friend I've ever known who named their car.
Anyways, not saying I necessarily disagree with the rest of what you say, but it's out there, for sure. We're a good distance away from an AI that can meaningfully act like a friend.
(Though who knows what AI will look like in even 10 years? I'm not ruling this out)
Apple, Amazon and Clarke started the trend.
And I am presuming that the companionship that people need is the companionship of other human beings. Otherwise, tamagotchi, fish, cats, dogs, parrots, and horses are already perfectly good companions. AI can help fill in for some of those other ones (tamagotchi especially), but it cannot replace human companionship.
But yeah, masses will do masses stuff and that's why even shit products can sell.
How do you envision an "artificial friend"
I would argue it will be 10x in 2-3 years the way things are going.
I could even see F1000 companies having their own backstop intelligence for most of the tasks and only use frontier where it is absolutely needed or as much as it is under their IT budget.
Won't it be race to the bottom when they compete on cost? Is regulatory capture the only moat left for the market leaders now?
Plus there is the actual demand side to consider and how does it impact when paid utilization isn't 100%.
Danger? You mean benefit?
Same with a lot of their investors, who will be just fine without the "insane profits" they hope for.
I'm not worried about their welfare, and won't shed a tear if their companies collapse.
Is the theory here that it would trigger more widespread financial meltdown?
Then I would prefer it to happen ASAP before they get even bigger.
That's a bit of a one-sided view.
Massive bubble.
Compare 1926 to 1999, and how technology advanced in that time. Radio wasn't widespread in 1926, nuclear fission hadn't been demonstrated, the idea of the semiconductor energy band gap, underlying the entirety of the modern computing industry, wasn't put forth until 1928. And yet, 20 years later we were purifying silicon, and 10 years after that (1954) the first silicon transistor was created and kicked off the semiconductor boom. 50 years later, we were in the Internet bubble.
We've got decades upon decades upon decades to come up with new ideas and implement them, even before the end of the century.
We already lived through multiple AGIs for the last few years.
Can AI handle driving yet?
I think that "everyone" needs some kind of an asterisk. Who's "everyone" in this?
It's unclear that the leading open-source models could exist without having distilled the frontier models. If that's true, the "danger" is that these upstream models don't get built any more (because of the tremendous cost), and we end up just optimizing what we have. Also unclear whether this would be a net win or loss for humanity.
If OpenAI/Anthropic stop doing it there's no reason to believe someone else won't. Or that we won't come up with a different approach.
... and a bunch of RLHF and proprietary training data etc etc, but regardless, this is still an exceptionally expensive process, that someone somewhere is paying for.
The tremendous cost is matched by the tremendous business output.
In the span of less than five years, starting from their first $1 of revenue, Anthropic + OpenAI will have produced businesses doing at least $150 billion in combined sales. The demand isn't reducing at present, it's climbing, they can't come close to servicing the total demand for their frontier models.
SAP is under $40 billion in annualized revenue. They're 54 years old.
Market captured. Whoops.
Uber now: $55b in sales, $6.7b in op income. They won.
And to be clear, OpenAI can easily permanently subsidize a fake low cost for their LLMs if they get off their asses and attach a monster ad network to GPT already. They're years behind where they should have already been with ads, the easy money made it so they didn't have to think about it. If they miscalculate the timing on switching over from easy money (market) to ads (also easy money) it could certainly kill them.
OpenAI can knock out a lot of competition by building the largest ad network, then undercutting the cost that consumers see. The margins in ads make that possible. Google already knows how that works, they're doing the same thing right now.
I'd rather have the OpenAI + consumer base + ads position, than being Anthropic. China is a far, far larger threat to Anthropic over time. OpenAI is building a service business more comparable to what Google does. Chinese models don't actually threaten either OpenAI or Google insofar as their positions are user capture based, whereas constantly improving Chinese models are a never-ending threat to Anthropic.
Lots of companies have used this strategy. And many of them have indeed died. Uber filled a void left by bad public transportation and a lackluster cab system. The higher prices (so they can turn a profit) are still worth it to some.
> OpenAI can knock out a lot of competition by building the largest ad network, then undercutting the cost that consumers see. The margins in ads make that possible. Google already knows how that works, they're doing the same thing right now.
And you think OpenAI can compete here? If LLM's end up being yet another ad funded platform, I have a hard time believing Anthropic or OpenAI will outperform Google here. Maybe if one of them strike a deal with Apple, or get bought by them. Otherwise, ads are Google's bread & butter. And, they own Android.
If the only profit is from running commodity free software (as OP claims), there's no moat and OpenAI/Anthropic are worthless.
You missed the bit where they used the word "revenue".
I suspect if you factor in all the various overheads of running datacentres the actual value is lower. Revenue is vanity, profit is sanity as the old saying goes.
However the Chinese do have the benefit that China are pushing hard on renewable energies. So at least that major part of the equation is probably far lower than your average US/EU site.
And actually paying people not to ! (Paying RWE $1.2bn to abandon wind contracts[1])
Its absolutely nuts. Pure generational can-kicking.
If the US is trying that, they are failing pretty hard - 88% of new power in the US in 2025 was renewables, and 2026 is estimated to be above 90%
The latter is great as a way of getting an edge in business and reducing reliance on oil supplies, but its also environmentally highly damaging.
No they didn't...
> I suspect if you factor in all the various overheads of running datacentres the actual value is lower.
That's literally exactly what the quoted part addresses: "Amortized cost of owning a DC full of GPUs is far less than $38/card/hr". Cost of running GPUs is far less than $38/hour, revenue is $38/hour, ??? , profit
you are just talking about ai inference? Are you assuming ai models themselves fall from the sky.
what do you think it is, out of curiosity?
These providers have very little incentive to charge unprofitable prices (except maybe capturing more of the market, but there's not much lock-in here), so it's reasonable to assume somewhere in that spread is the "true" cost of being in this business, all expenses included.
[1] I'm using https://gpus.io as a reference here, and that's on-demand prices, which will tend to be the highest relative to other options.
$38/card/hr is a ton of money. That's $2.6 million per 8-card server per year.
The cost of power/physical space/interconnect by comparison here is negligible.
Chinas grid is almost entirely renewable energy with strong investments in it. In the us not the case, some dcs are running on gas.
Also DeepSeek was smart. They strategically rolled out model sizes they could support even if the intelligence wasn't the world's best for that week in human history. Then they have incrementally and cheaply improved models since.
Not to mention they also built trust with open weight models rather then fear mongering. They are in the top 3 of the smartest most viable LLM labs out there.
They have more installed renewable capacity than coal, but renewables have low capacity factors, solar is less than 50% by definition. The renewable share is growing fairly rapidly, but it is by now means almost entirely renewable.
They (well, most of them) are looking at loans and debt (in plain sight and hidden) of AI companies and asking "what will pay for that", not looking at how much a GPU costs and how much tokens you can sell off it.
Just putting a model on a GPU and selling it is profitable, period.
But spending more and more money on building and running infra to build frontier models that solve smaller and smaller piece of the pie might very well be unprofitable.
Models competing with the frontier labs gets more capable every month and more and more of the tasks can be achieved without reaching for the big guns.
We might very well get to the point where AI/LLM is profitable just fine but bubble still bursts and the big frontier labs are in trouble. And frankly that would lead to far more sustainable ecosystem.