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.
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 agree, and find that very plausible. I mean, for the CCP a few billions to subsidize domestic AI companies is a tiny investment with a potential huge payoff. It prevents (or at least make it harder for) US companies to build a monopoly on LLM tech and it could help popping the bubble which would weaken the US economy. In fact, if I remember correctly, the AI infrastructure build-out is what is keeping the US from a technical recession.
> subsidizing use to build market share the same way
To an extent maybe, but that market is almost entirely commoditized already. Besides Cerebras and maybe Groq (which already charge a slight premium) all the other providers are more less interchangeable.
> Maybe it’s a lot of people who already had GPUs for crypto mining
I’m not sure the type of GPUs that were most popular for crypto are at all useful for LLMs?
If there’s a few providers subsidizing, that’s the price ceiling. Everyone who wants to compete has to subsidize.
Now if this market had been operating for years, I’d say that it’s likely all these companies are profitable or close to it. But the market is so new and there’s so much hype, I find it very plausible that none of these guys are making a profit and they all hope to just hang in until all the subsidies go away.
> I’m not sure the type of GPUs that were most popular for crypto are at all useful for LLMs?
There’s some overlap. I’ve definitely read about people repurposing.
> Why are we all whispering about how profitable all this is?
Nobody is whispering about anything. Everyone is loudly assuming what's convenient for their thesis. Even if you have access to the books, the accounting isn't straightforward–there are yet insufficient data for a meaningful answer.
> It is the absolute last thing these firms would keep secret
If you find an optimisation strategy that you don't think your competitors have, you absolutely keep your margins secret for as long as possible. Knowing something is possible is the first step to making it so.
Based on what I said. If e.g. Sonnet (assuming it’s significantly smaller than Opus) is unprofitable why are there a bunch of inference providers on OpenRouter serving very large models way cheaper? They don’t have a pile of money to burn for no reason.
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.
I wouldn't call Kimi K2.6, GLM5.1, DS4 or newer Qwen models "low end". I prefer GPT5.5, but if it disappeared tomorrow, I'd be perfectly fine with any of these chinese models.
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.
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.
At the same time, the training paradigm being scaled, Reinforcement Learning, is significantly less data-efficient than next-token prediction. You basically need to run an agent for minutes (or longer if you want good long-horizon performance), only to give it a binary pass/fail - one bit of information.
Inference compute is definitely scaling fast, but to scale RL, training and R&D compute also needs to scale hard. I don't think it's obvious that inference will overtake R&D/training, unless there's a reputable source that states that.
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
Sure, but if companies don't exit the market and FOSS alternatives don't end up being unable to get near them in quality, they have to keep spending on training. And conversely, if the market becomes uncompetitive and FOSS sucks, the winners of the AI arms race are very strongly incentivised to stick their prices up anyway...
> if companies don't exit the market and FOSS alternatives don't end up being unable to get near them in quality, they have to keep spending on training
Eh, the AI companies still have lots of datacentres. For the guys who funded with equity, they could collapse down to just running those as utilities. (For the guys who funded with debt, they'd have to restructure.)
From the customer's perspective, this situation shouldn't result in a cost spike. (Consolidation, on the other hand, would. But that's a separate argument from the one the article attemptes to make.)
How often do VC funded unicorns collectively decide to stop scaling up, shut down all their departments targeting growth and reach breakeven point by becoming low margin utilities that will never justify their valuation?
Good thing the entire nation's economic growth outlook isn't tied to these companies then. For a second I thought we had a potentially dangerous situation on how we misappropriated trillions of capital.
Not really, because investors will sooner or later want to see real returns on what they invested. Tokens are suddenly not dirt cheap and enterprises are screwed.
It's like selling dope, once they're addicted, a dealer could turn the screw on them
That's why it's an issue for investors. Their investment may not payout. But the things that were built will still have been built and available to sell for related purposes, the models that were trained will still be trained, and so on.
If things don't end up working out a lot of people have already been (and in the future will be) paid. It's the investors that will lose out, not the subscriber.
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.
Do you think it will be the case for the Claude Code/Codex tokens as well? I think those are heavily subsidized, but they're the only ones I find real value in.
If tokens weren't being sold at a loss, Anthropic would be screaming about it from the rooftops. They've been desparately trying to make themselves not look like a money furnace lately, but it's not really working.
They might be sold at-compute-cost, but that of course ignores training, salaries, and everything else.
Obviously I, like basically everyone else here, don't have access to Open AI or Anthropic books so it's just guessing based on public available evidences, but "tokens aren't being sold at a loss" does not imply there is any profit.
And, even if there is some profit, it needs to be big enough to at least pay back the capex spendings and finance the next model iteration.
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.”
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.
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.
In a competitive race, each breakthrough gets copied or illicitly distilled or whatever. That means the frontier models are deprecating assets and the mark up tokens should get smaller and smaller.
Now bigger models are more expensive to run inference on, but today's models, or equivalent ability and size models, shouldn't go up in price.
5.5 is 4x the price, but 5.4 still exists, so its not rug pull, but a big more expensive to run and hopefully more valuable model.
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 come to realize that folks are including "ai-slop" in their ~public use of AI to intentionally signal to others that they're using AI. To some, that signal results in revulsion. To others, that signal results in approval. In my opinion, the approval signal comes from investors, board members, c-suite, and now management. They want us to use AI? Let's make sure they know we are.
I used to think that signalling that I am not using AI would be a good thing, and that people would appreciate that, but now all my public profiles are AI.
I guess the good news may be that if/when there is a major pricing correction, that many of the people using free or $20/mo subscriptions to generate social media commentary may balk at the real cost and go back to writing it themselves.
Something I have noticed is that the people who are using it to write everything are the same people who had a poor level of English writing a year or two ago.
I've never had a problem with direct translation... but the 3 paragraph choppy structure with subheadings full of AI-isms is not ESL users using it faithfully
Would make sense ... writing is a skill, and one that I think most people are proud of if they are good at it.
Maybe it's different if you are doing technical/commercial writing, but for social media where you are writing for fun, and to express yourself, it'd be odd to let AI be your voice unless you realize your own writing is very poor.
> for social media where you are writing for fun, and to express yourself, it'd be odd to let AI be your voice unless you realize your own writing is very poor.
A lot of people post for clout, so something that can skip the difficult process of becoming a good writer (and original thinker) is more than enough. They can churn out think pieces about any topic at an unlimited pace, basically.
It doesn’t add much to the world, but they get a lot of traction (which I cannot understand, given the quality of content.) And that’s what matters to them.
I think if you gave most people the choice between (a) being a thoughtful and original writer (b) being seen as a thoughtful and original writer, the vast majority choose (b). Especially when it is zero effort.
I noticed this from former coworkers who I know couldn't write beyond first grader level a few years ago. They weren't good at their native language either.
Now they write "competent" blog posts on LinkedIn that seem 100% AI slop. Some are employed at AWS, too.
I'm not a native English speaker as I'm sure my writing shows. My point is that I'd rather read genuine posts full of grammar errors instead of slop.
I can't tell from your post that English is not your native language, outside of the Americanisms (I assumed that American English was your native language) :-)
I think there will always be a free tier that they'll be willing to use. Even if it sounds hackneyed, those folks will still use it because many people are not discerning readers anyway.
Despite what I just said, I do hope so, because I'm really not inclined to pay for it, at least not very much. I don't need another $100-200/mo bill in my life, and it doesn't provide that level of value as a chatbot. Google is enough.
I'm not sure that free tier will necessarily continue forever though, unless there is a way to monetize it (presumably by advertising, or by selling data they've gleaned about the user), or perhaps if there is no privacy and the provider is treating you as a source of free data. Right now we're still in the market-share grabbing "never mind the profits, count the users" stage.
A free tier will almost always exist. Mostly for the reasons you already describe. That's a training ground for their small models as well as a way to get full access to new training data (and advertisements). As well as funnel new paying users. Why would you ever give that up?
Thanks for calling that out. I went through and extracted a good handful of those. It’s not a short list. It’s a handful.
“””
The subsidy era is not winding down gracefully. It is showing cracks everywhere.
…
the question is not whether they got a good deal. The question is how long that deal survives.
…
A developer running three or four concurrent coding agents is not consuming 3x or 4x the tokens of a chat conversation. It is an order of magnitude more
…
These are not experiments anymore. They are load-bearing workflows.
…
That is not a rounding error. That is a line item that needs its own budget code.
“””
I hate it. This article starts off well! There is data and it seems well argued, but then halfway through, there it is: example of trend. Another example. Third example. It’s not just X – it’s Y.
It’s as jarring as getting halfway into a well written article, clicking a link to a source, and getting rickrolled.
It’s all you can do to not let it distract you from the fact that in 1998, The Undertaker threw Mankind off Hell In A Cell, and plummeted 16 ft through an announcer's table.
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.
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.
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.
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.
They are available. Seats for team or enterprise plans cost more than the retail prices, but they are fixed prices with resetting usage limits. You can assign seats to members that are the equivalent of $20/$100/$200/mo plans.
You can also do everything metered. There are multiple ways to buy.
Who is selling these with enterprise trappings? What you're describing evaporated 2+ months ago. Everything is metered for enterprise users now. If there happens to be a stray vendor offering this I'd wager 2 things. 1) it's about to be phased out. 2) model limits will be in place so even that $200 plan won't go very far.
Are we talking about the same thing? I just double-checked Anthropic still offers per-seat plans. So does OpenAI though they split the Codex-only plan away from per-seat. Gemini does as well. There’s pooled usage over certain limits but it’s still a good deal to upgrade the seat of a heavy user.
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.
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?
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.
Search costs aren’t trivial and, prior to LLMs, being able to find the piece of information on Wikipedia or software on GitHub that solved your problem took time, a lot of time if you weren’t an expert and unfamiliar with the jargon.
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
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.
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?
> A knowledge worker running a few hours of Claude daily, uploading documents, drafting reports, analyzing data, can easily burn through several million tokens per week. At API rates, that same workload runs somewhere between $200 and $400 a month per seat. Some power users push well beyond that. But on a Pro subscription, the company is paying $20 per head. Anthropic is not the only one eating this cost.
What? Anthropic's costs aren't the API rate. The article never attempts to estimate that cost, which renders its thesis tautology.
Every AI subscription is a ticking time bomb for the frontier provider; within a few years we will be running local models as good as today’s frontier models with almost no cost burden. The floor will fall out of the enterprise market for all the frontier companies.
Or put another way, the frontier models are very quickly deprecating assets, because of the competition in the market.
They have to keep getting better to stay ahead of each other and open weight.
Which means it's the opposite of a timebomb, the article has it completely backwards, tokens at current level of reasoning will continue to get cheaper.
I'm not sure 'local' will be the end state, as hardware needs are high. But certainly competitive forces tend to push profit margins toward zero.
Eventually, we'll see. Frontier models still need some pretty serious hardware which will slowly come down in cost. Smaller models are becoming more capable, which will presumably continue to improve.
I think there's still a pretty big gap, though. Claude estimates Opus 4.6 and GLM-5 need about 1.5Ti VRAM. It puts gpt-5.5 around 3-6Ti of VRAM.
That's 8x Nvidia H200 @ ~$30k USD each. Still need some big efficiency improvements and big hardware cost reduction.
> within a few years we will be running local models as good as today’s frontier models with almost no cost burden
Based on what? The RAM requirements alone are extraordinary.
No, running large models on shared, dedicated hosted hardware at full utilization is going to be vastly more cost-efficient for the foreseeable future.
How do you know this? I'm not trying to attack your statement, I am genuinely curious how anyone knows anything about model performance outside of benchmarks that are already in the training set.
It is not getting easier to obtain hardware that can run models which are sufficiently useful to undercut frontier models, if anything the cost of such hardware has gone up by 25% or more just in the past 6 months.
I think hardware prices will come back down once we start seeing more efficiency improvements in models and hardware, and once more people and companies self-host models (which seems to be happening more and more these days). I think the massive infra/hardware expenditures of OpenAI and the like are going to end up unnecessary, leading to hardware price drops.
If companies decide to self-host, wouldn't that drive the demand and therefore prices up? Most companies currently do not have the needed infrastructure.
I think companies will self host (including on rented hardware) even if it's more expensive, and that, along with efficiency improvements, will drop demand for big AI. I think big AI is overspending on hardware/datacenters at the moment.
I've got a 128GB strix halo staying warm at home, it has nothing on top models with big budget. It's good supplement to low end plans for offloading grunt work / initial triage
> Local modals are 6 months to 18 months behind frontier.
I wish this was true but it is not. And I am working on open source models so if anything, I would have a bias towards agreeing with you.
Frontier closed models (GPT/Claude) are gaining distance to everybody else. Even Google, once the king.
Your claim is a meme coming from benchmark results and sadly a lot of models are benchmaxxed. Llama 4, and most notably the Grok 3 drama with a lot of layoffs. And Chinese big tech... well they have some cultural issues.
"Qwen's base models live in a very exam-heavy basin - distinct from other base models like llama/gemma. Shown below are the embeddings from randomly sampled rollouts from ambiguous initial words like "The" and "A":"
But thank god at least we have DeepSeek. They keep releasing good models in spite of being so seriously resource constrained. Punching well above their weight. But they are not just 6 months behind, either.
I’ve worked, for a long time professionally, in the open model space for 3 years and up to 2 months ago I would have agreed with you. But it’s empirically not the case today. These models (combined with a good harness) have dramatically improved in both power and performance.
Gemma 4 was a major improvement is self-hostable local models and Qwen-3.6-A34B is a beast, and runs great on an MBP (and insanely well on a 4090).
The biggest lift is combining these models with a good agent harness (personally prefer Hermes agent). But I’ve found in practice they’re really not benchmaxxing. I’ve had these agents successfully hand a few non-trivial research projects that I wouldn’t have been able to accomplish as successfully even last year.
When you add in the open-but-not local models, Kimi, GLM, Minimax, you have a lot of very nice options. For personal use anything I don’t use local models for I give to my Kimi 2.6 powered agent.
>running large models on shared, dedicated hosted hardware at full utilization is going to be vastly more cost-efficient for the foreseeable future.
That is only true right now because hundreds of billions of dollars are being burned by these AI companies to try to win market share. If you paid what it actually cost, your comment would likely be very different.
No, it's economies of scale and I don't understand where anyone is coming from that thinks they'll be better off buying their own hardware, why would you get a better deal on MATMULs/watt than the cloud providers ?
Another victim of Goldratt's Theory of Constraints. Some things are more important to optimize for than MATMULs per Watt. What that is I leave as an exercise to the student. May you realize what it is before it is too late.
Some individuals will choose some $10,000 hardware so they can keep freedom and privacy and that's well and good, my point is just that freedom and privacy is not what wins marketshare, and hence, IMHO, local LLMs are not going to catch up and surpass frontier models like some in this thread like to claim
Within 5-10 years you're going to see a box like one of those AMD Halo nodes running homes.
They'll be controlling lights and temperature, they'll be adding calendar reminders that show up on your phone and your fridge. Your phone and devices might sync pictures and videos there instead of the large cloud providers. They'll also be a media server, able to stream and multiplex whatever content you want through the home. They'll also be a VPN endpoint, likely your home router, maybe also a wifi access point.
I think this makes quite a bit of sense. I don't think they'll be ubiquitous, but they could be.
This distributes the power demand where local solar generation can supplement , gives the home user a lot of control, and claims overship of the user data from big tech.
Maybe I'm imagining things but this is what I think is coming.
It's the lmm/data heart of the home. A useful digital tool.
We don't know the parameters but it probably takes at least a H100 and possibly several to run a SOTA model. Given the pricing (25+k per H100 + hardware to run it) and power (700W per H100 + hardware to run it), I don't see how anyone except for a largish company can afford to run this.
I strongly disagree. Humans are so insanely well incentivized here with trillions in market share to make localized AI good enough and that’s the only benchmark they need.
Are they? I don't believe there's that big of a market for local AI. Most people don't care that much, and you'll most likely lose the advertising revenue.
>I don't believe there's that big of a market for local AI. Most people don't care that much,
I agree that the market for local AI is basically limited to nerds at this point, but that's because nobody's really explained why local AI is a good thing and also because the vast majority of people need the $20 paid plan at most. How much time and money would it take to get something half as good as OpenAIs products running locally?
You can now buy 128 GB unified memory computers from AMD as commodity.
They’re still pricey, the world is still scaling up memory production, and a lot of code isn’t yet built for AMD, but we went from the Wright’s brothers first airplane to jet engines in 27 years.
I’m not sure “it’s only a few years away” but we are sure moving there fast.
Non-cynically: the frontier providers have a projection for demand.
Cynically: it’s become an executive-level gpu measuring contest. If you’re not making huge commitments on data centers, you can’t be a serious player.
Realistically: It’s a mix of the two. The recent Claude caps for agentic usage suggest that demand exceeded their immediate compute supply. That they can alleviate it with additional capacity from the existing and small-ish xAI facility suggests that either demand may not be rising quite as fast as anticipated, that they’re okay in the short term until more capacity comes online, or a mix of both.
Open questions:
1. At what price point does demand fall, and are the frontier providers overall profitable before that price point?
2. At what price/performance point do on-prem local models make more sense than cloud models?
> shared, dedicated hosted hardware at full utilization
I must say that the largest dedicated hosted hardware providers now, like Amazon or Google, to a large extent do not produce the software they are offering as a hosted solution (like Linux, Postgres, Redis, Python, Node, etc). Similarly I'm not sure if the producers of the frontier models are going to keep their lead as the service providers for the most widely used models. They would need to have quite a bit of an edge above open-weights models.
Also, models are given very sensitive data to process. For large organizations, the shared dedicated hardware may look like a few (dozens of) racks in a datacenter, rented by a particular company and not shared with any other tenants.
I take it you haven’t actually run any of the current gen local models?
They all fit on fairly accessibility hardware, and their performance is at least on par with what I was paying for last year.
I have one of my agents running entirely from a local model running on a MBP and it has repeatedly shown it’s capable of non-trivial tasks.
Playing around with another, uncensored, local model on my 4090 desktop has me finally thinking about canceling my personal Anthropic subscription. Fully private, uncensored chat is a game changer.
For work it’s still all private models but largely because, at this stage, it’s worth paying a premium just to be sure you’re using the best and it saves the time of managing out own physical servers. But if we got news tomorrow that Anthropic and OpenAI were shutting down, a reasonable setup could be figured out pretty quickly.
There's still going to be plenty of use-case and demand for frontier models running across hundreds or thousands of GPUs. It's just not going to be in the current shape - certainly not accessed by the general public for rote business tasks.
> within a few years we will be running local models as good as today’s frontier
Unless there isn't some important breakthrough in hw production or in models architecture, it's quite the opposite: bigger, more expensive and more energy-intensive hw is needed today compared to 1 or 2 years ago.
I run it on my 4 year old MBP and get 10 tok/s. With the RAM shortage buying anything new today is a nightmare but anyone with a reasonably modern Mac could run it at q6 probably. It is mostly a toy as 4o models weren’t really suitable for real work IMO but at least it won’t ever give me a refusal.
At 10toks, are you using it interactively or do you submit a prompt and come back to it later? I always thought it would make sense to just do conversations over email, asynchronously, the model can take all the time it needs and get back to me when it has an answer.
10 tok/s is around the borderline of interactive being good. I did the math and it is mostly bottlenecked by memory bandwidth, so in the future I can expect to run a similarly sized model on my 4090 once it gets retired from gaming service and get ~25 tok/s which will be very usable.
I can run qwen3.6-27b on a four year-old Macbook Pro that dominates ChatGPT-4o (the frontier model from 2 years ago) and is competetitve against early ChatGPT-5 versions. We are also getting a lot smarter about using and deploying these local models. Your entire AI stack from two years ago would be absolutely crushed by a todays local LLM models and a high-end local inference system when combined with a good modern coding agent.
Today open weights frontier models cannot run locally, unless quantization is used. Deep seek v4 pro require almost 1 TB of RAM in INT4.
I hardly doubt there will be consumer grade HW to run it in 2 years either. And deep seek v4 pro is not even close to OAI or anthropic frontier models.
Per frontier token. You're not calculating the cost of a fixed quality asset here. Old hw running non-frontier models will be very valuable. In fact, we have two direct examples: older server gpus actually appreciating and the very obvious fact that not everyone always use MAX FULL EFFORT BEST MODEL no matter what.
> within a few years we will be running local models as good as today’s frontier models
I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.
Genuine question from a place of ignorance: what in the silicon pipeline makes it take 2-4years to produce chips with a new model on them? Curious what the process bottleneck is.
Without being an insider, I imagine that most global fab capacity is contracted out several years in advance.
You might be interested in the tiny tape out project, which guides you through the process of getting your own design etched on silicon. If you only need larger features and not the next gen single digit nanometer stuff, you may not be so supply constrained.
I think you could get it down to three months between weight changes, if you can encode it in metal layers only. The remaining limits are the fab lead time, and the cost of a metal respin (hundreds of thousands to millions of dollars depending on process).
Why not have a bunch of SRAM and various operations like "Q4 matmul" in silicon? Model weights and even architectures could still evolve on a platform like that.
Doesnt "a bunch of SRAM" top out at maybe a few gigs per chip (with zero area used for logic)? You'd need an order of magnitude more to fit even a fairly weak general purpose LLM model.
If the silicon costs $200-300 and the company throws it away every two years that’s cheaper than a subscription.
Also, how many companies will just buy an M6/M7 MacBook Pro with 32GB+ of RAM in a couple of years and get “free” AI along with the workstation they were going to buy anyway?
> I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.
The big question I'd be asking if I was investing in one of the big players is if those changes are "it can do 99% instead of 97% of the tasks a user will throw at it" (at which point going local and taking back cost control/ownership makes a lot of sense, especially for companies) OR "it will fully replace a human with better output"?
I already don't need Opus for a lot of my tasks and choose instead faster/cheaper ones.
The former is a company that's gonna be trying to sell mainframes against the PC. The latter is a company that is in potentially huge demand, assuming the replaced humans end up with other ways of getting money to still be able to buy stuff in the first place. ;)
Exactly the right argument. Local LLM doesn’t need to outrun the bear (outperform data centers) it only needs to outrun its friend (total cost of ownership).
> I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.
But even if scaling plateaus for the frontier models, maybe distillation will improve to the point where smaller more manageable models can reach the same plateau. That would be great for local.
The economics of local AI just doesn’t make sense. A model like Opus is - supposedly - something like 5T parameters, which is likely something like 3TB of GPU memory.
Local models never reach the % utilization that cloud providers have (80%+), and they’re always going to be much better than local models for this reason.
Capex, opex, quality, and volume are tricky things to balance. On balance, pc/mobile are cheaper to operate than equivalent cloud and on prem deployments.
It’s not unreasonable to suppose that in 2 years time an opus 5 quality model will be etched into silicon for high performance local inference. Then you just upgrade your model every 2-3 years by upgrading your hardware.
I haven't been following anyone baking models into ASICs, is it not still necessary to pack just as many transistors onto a chip, whether it's an NPU or GPU, ASIC or not you still need to hold hundreds of gigabytes in memory, so how is it cheaper to bake it onto custom silicon than running it on commodity VRAM? (Asking because I don't know!)
Running local applications is less efficient than thin clients to the cloud generally, not just in LLMs. The trick is that you can get to the point where it's effective enough, and affordable enough, that the control and availability factors become dominant.
I just don't see how that's different from getting more value by giving all your employees the most stripped-down chromebook-type devices and running everything else in the cloud, than by giving them "proper" laptops with local apps.
It's a measure of a very thin sort of "value/$" that excludes a lot of other things that could be of value to a business, like control, predictability, and availability.
Thin clients have been going away for a long time. The trend has been to continue to push higher levels of compute into ever-smaller and ever-more-portable devices.
I don't know that this is true. The cloud companies are making money, and inferrence is kind of just "hosting an inferrence server and trying to keep it humming 24/7"
But in many cases self hosted or dedicated boxes are cheaper than cloud.
People who are this certain of their predictions should be forced to put real money on them on Kalshi or Polymarket instead of drive-by blowharding on HN.
If that’s true, then it will be even cheaper to provide them as a subscription. Following your logic, every company would be running their own data centers instead of using cloud providers.
I've spent the last month bringing in a small demo of what the future could be like, running Qwen, Gemma, and Deepseek, behind LiteLLM so we can monitor token usage, and instead of some dumb ass "tokenmaxxing" we're actively trying to get the cost of inference both down, and in-house.
Boss is happy, very happy. We're rolling it out more widely now.
I think this is a good under-represented point. Again and again things that could only run on a mainframe get ported to the personal device level. However it looks like the campaign to eliminate the PC (by pre-buying all RAM) is the counter-stroke.
Hard agree - the benefits of local/self-hosted models are not just hardware/cost (it might be more expensive at the moment), but what you get in exchange is unnerfed/unstupified models, full cost/usage transparency, optimized/specialized models, privacy/security, etc.
Not just AI. Every subscription in general can be a time bomb. You grow more dependent on it, and the provider can disappear or take it away at any moment.
I would expand this to any dumped product or service. Whenever the real cost isn't paid now it will sometime in future or will collapse. Just look at how extractive food delivery and taxis are. Start with dumping. Then be last one to survive and fleece all the sides you can.
Not really. Claude Code harness with Sonnet 4.5 model showed you don't really need bigger GPU rollouts, and it's only a matter of time for OSS combos to hit that. Overtime, this will only get better, and the set of enterprise tasks smaller deployments can handle will only go up.
This mirrors my own thoughts. Additionally, for businesses looking to replace people (particularly developers) with agentic AI, this is arguably worse from an accounting perspective as the cost of using these services will likely be pure OpEx vs capitalised per my understanding of US/UK GAAP accounting.
Enterprise customers aren't running 20 bucks a month for claude pro subscriptions. My company provides developers about 1k worth of usage limits a month and best I can tell they get maybe a 30% savings off of API cost tops. That's not an insane subsidy. Many other jobs titles are only allowed 50 a month and those folks are constantly running out.
Github Copilot has been doing this with business and enterprise seats, but that will be coming to a head very soon. I expect a fast follow after june when they re-align consumer pro and pro+ accounts.
OpenAi seems to be trying to throw tokens at clients to get lock in. So i'd be most worried about the rug pull that will come from open AI post IPO. Anthropic is already acting responsibly in this area and github copilot is attempting to remediate their insane subsidies in the next several months.
GitHub Copilot was the only one with absolutely insane subsidies, where they metered by 'request' instead of tokens. A request that costs 3 cents could end up burning $20 worth of tokens or more. That ends this month.
I was actually quite worried, because I've been using GHCP for large chunks of work, but the billing estimator they released shows I was only at about $150-200 a month in API priced tokens. Sure, that's a subsidy for my $20 subscription, but not insane.
Heavy use of agentic coding tools, in a responsible manner, probably lands somewhere around that $200/m mark at API pricing. Assuming that makes the provider money, I don't see that being hard to swallow for businesses employing developers in Western countries, given the hours it can save.
The real risk here is to personal project vibe coders. Building a huge app by abusing subsidized plans is ending.
This is true. At our company they rolled out ChatGPT with Codex. After two months of happily using it, I got a call from the IT OPs telling me I burnt through four hundred million tokens, 200m a month. And created at least a thousand euro bill. That’s after I used all the credit, but I don’t have all details. The guy told me to „watch my usage.“ What does that even mean. He doesn’t use it himself and apparently he doesn’t know how value is created here and how he can monitor and limit usage.
Did OpenAI switch from fixed prices per seat to usage based? This will surprise many companies I reckon.
Personally I use Claude Code, the 200 euro plan. And am a heavy user. A few weeks ago I realized that CC shows the token usage in cli, in the bottom right. Something I never cared about because I thought paying 200 euro a month will give me „unlimited“ access.
But I guess the party is slowly coming to an end? Prices are going to increase slowly? And the flatrates will be removed eventually?
> the gap between what your organization pays for AI today and what it will pay in 18 months is going to be one of the most disruptive line-item increases most companies have ever absorbed
Colour me skeptical on that one. Unless the AI improves a lot so it makes sense to spend more.
I tried out Gemini in Google Sheets the other day. I asked a pretty simple question and the agent ran for like two minutes trying to answer it until I stopped it. I can't imagine these agentic features are cheap to run for what they get you.
The entire problem with "AI" is that it's easy to do without. The AI companies know it, the users know it - even the most pro AI agent manager knows it. Thought experiment: remove AI from the world right now, all of it - what do you have? Business as usual. This article doesn't do enough to underscore that - dreaded be the day I need to get an actual engineer to review a PR, right?
It's always weird when people are suggesting to use some AI tool for the most mundane and generic kind of task. Like it's some kind of pet that will die if it's not used every once in a while.
Isn't that always the case in the early stages of new technology adoption? It becomes less and less true as the new technology becomes more and more integrated.
In the first few years after electric motors became a thing, one could have said the same thing. We would have just gone back to steam. If you tried to "do without them" now, society would collapse.
So the question is not if we can do without them now, it's if we can do without them in 5 to 10 years (or however long it takes for them to be fully integrated)
The current LLM hype started, what, 5 years ago? It's an industry throwing billions of dollars (and teasing at the word trillions) around. It's had super bowl ads. It's a technology that's being mandated in corporate offices. It's basically the only thing the tech world ever talks about anymore. It's sucked all the air out of the room and occupies the whole stage.
Just how "early stage" is that, and how much more integration does this "new technology" need to be?
> Just how "early stage" is that, and how much more integration does this "new technology" need to be?
Based on the way Claude has felt the last few weeks, I'd say we're about 3-6 months away from full AGI. At that point we can start truly replacing white collar workers in earnest and begin deep integration.
The first electric motors in factories just replaced the previously existing steam engine. Power was still distributed throughout the factory through a central shaft and pullies to all the places that needed it. It took decades for the possibilities to get figured out and, more importantly, entirely new factories designed from the ground up around the idea that every machine could have it's own motor and power could be distributed via wires.
AI won't be "integrated" until something similar happens, and new businesses etc. are formed that take advantage of it in a way that can't simply be reversed to the old, pre-AI paradigm. I don't know what that will look like, but someone is going to figure it out and make successful companies with entirely new paradigms that are only made possible by AI.
At some point, every single factory was designed for electric motors, and going back became unthinkable.
-edit- also, the idea that a 5 year old tech that is still rapidly changing and developing deserves quotation marks around "new technology" is hilarious to me.
In my opinion, that's likely a large part of why it's being pushed so hard. Not to drive honest revenue, but to get AI products so deeply embedded that 'just removing AI' won't be seen as an option, even when keeping it has higher and higher costs, up to and beyond airline-style bailouts from the government. An entirely new layer of wealth-extracting intermediary, being sold under false promises.
If it’s replacing developers it makes sense to cost more than 20 or 100 per month. The real issue for these llm companies is that they are yet to show value in other areas. Without that they will be relegated to just coding. That is the rush right now for them. What other workflows can they automate. I guess every paperwork can be automated. Once the other areas are developed they will switch the pricing model
IMO the LLM technology is so poor when it comes to converting text descriptions to visual layout that I can't imagine it could possibly succeed as part of a paid design product.
As inflation plays with 10-year highs, fuel prices go up permanently (thanks to the end of middle east oil), and NIMBYs chase datacenters out of their regions, I think it's inevitable that AI is going to go up in price. It's just a question of how much. Companies should have a fallback plan to either switch AI providers, or replace AI with a pool of new hires quickly.
I had a conversation with Claude yesterday about this very topic. The AI was pretty candid about the issue and said many of the same things the author said. Now I am not sure if I went in with an unintended bias and it just went into full sycophant mode, I tried to be neutral in my prompts, along the lines of the implications of integrating AI into processes when the true cost is not being charged. But it was obvious that even moderate usage is a loss leader, so heavy users with agentic workloads are in a risky situation and should think long and hard about their business model if costs slowly trickle up in the triple, quadruple etc etc range.
I will continue to use it as an assistant that does the menial stuff quicker than I ever could, but it's just too early to let it do stuff that would hurt if it disappeared. Enjoy it while it lasts.
I think a solution could be local hardware acceleration the diffecult thing to achieve is not leaking dmodel data, since yeah that is obviously a nogo for antropic, openai, etc
It's clearly llm-spew in its mannersims, making me wonder if there were any nuggets of wisdom in its core or if it in entirety is part of some llm-driven blog spam project?
This kind of scarce thinking is almost always wrong and will lead you to down a sad dark HN loser path. Tokens will get cheaper. It costs OpenAI less money to serve GPT-5.5 than GPT-4. Ppl don't understand how much efficiency gains are being made with algo breakthroughs as well as hardware improvements that counter balance the demand rise. Open source models are 3-6 months behind. The world is your oyster stop worrying about how things will go wrong start thinking about what you can do today so you don;'t end up like the writer.
Very much agree - efficiency improvements are very real both on model and hardware side. The reliance on proprietary OpenAI/anthropic APIs is a problem though, one that will naturally resolve itself in the favour of self-hosted/open models.
Edit: can't reply but companies aren't selling inference at loss. In the blog post I point to third party hosting of open models like Deepseek which are also going down. They are not VC backed.
I also point to Gemma 31B which you can run on your laptop today that beats most models from 2024.
What they charge people says nothing about what it costs them. Off the top of my head, one confounding factor is trying to win back marketshare from Anthropic.
We will only know the actually situation once Anthropic goes public and we can look at their books.
The price a company charges, _particularly_ a high growth VC-backed one, is a poor signal for their costs.
That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.
The parent comment is correct. They are talking about GPT-4, which was really expensive by today's standard. After GPT4o came out, GPT-4 was completely forgotten.
We used to not know, but now because open source models are being hosted and served by people whose only incentive is making profit on directly running inference, we have a ballpark idea.
There's no reason to think that the latest frontier models have similar inference costs to open source models.
It would be more surprising if the surrounding architecture hasn't significantly diverged. If it _hasn't_ significantly diverged, then given the performance difference it would imply that the frontier models have significantly greater param counts, which would result in a higher cost.
It's just like saying every dependency is a ticking bomb. In a very strict sense, it's true. But it really doesn't matter for most businesses (and absolutely doesn't matter for early stage startups.)
Show, don't tell. Show us that we're wrong and this isn't a VC black hole. The CEO of Enron as late as September 2001 could've called every critic a sad dark loser with nobody challenging him publicly. Jim Cramer famously yelled anyone pulling their money from Bear Sterns in 2008 was "silly, do not be silly" exactly 8 days before their collapse and a -92% stock drop. In COVID, calling everyone paranoid and sensationalist about some mythical new flu was popular in December 2019 and gone by March 2020. How about Uber, the seeming go-to for how VCs can turn a money hole into a profitable business? The average price increase is now 18% per year and still going up, with an over 60% increase in 5 years. Does anyone still talk about the "sad dark HN loser path" of those who doubted VR in 2018? How's your VR startup doing?
I don't think so. AI use is still very limited. For OpenAI and Anthropic and the AI boom to match their valuation, AI adoption needs to increase substantially. The current constraint is data centers. Pricing will be heavily influenced by market dynamics. Plenty of things that should be cheap aren't because of scarcity (simple example: RAM).
This is only true if there is enough competition with equally good SOTA models. Otherwise, the price of the best models will keep increasing until people don't buy them anymore and use humans instead. Regardless of how much it costs to operate in reality. There is a reason why non-profit unnamed company turned to profit company.
Precisely why every bigco is spending $$$$$ buying/reusing GPUs to build their own inference serving stack based on open-source models (usually gpt-oss or one of the LLaMa variants; many bigcos in the US cannot run PRC models). That and having more control over data locality.
Those same companies are getting sweetheart deals with the frontier AI labs in the hope that infrastructure costs go down enough in the future to invert profitability, but it's still a risky position for them to be in. (Having their own infrastructure gives the bigcos huge leverage, even if it's only 80% as good as frontier.)
This is true of every vc backed company they rely on
And some parts of most publicly traded ones.
If it’s not a bootstrapped company with a single offering, there’s a highly likely something there doing is at a loss in the name of growth (and even there, the loss might come in the form of deferred compensation)
Honestly, this isn't too different from any other software or technology nowadays. "What if the service provider pulls the rug on us and jacks up the price exponentially / begins the enshittification" is (and if you aren't doing it, you should be) a factor when procuring and using anything from a third party anymore.
The software world is, by and large, no longer about making products with a focus on the long-term, whether that's about the customer's well being or even the company's own long-term functioning. It's about trapping people, siphoning their money, then running away after setting the building on fire. Founder McBuilder will throw away his entire userbase and tell them "lol idk good luck" about their usage needs if it means he can make an extra dollar.
This is as true for enterprise as it is for consumers. Look at all the lamenting when a liked name gets bought by venture capital or considers an IPO.
Just as a counter example, Midjourney is completely self funded and profitable. But they are images, LLMs might be more expensive to train but their inference is cheaper.
So the frontier model companies might have crazy valuations and they might never reach that. But that might not mean they are actually unprofitable.
It’s a delicate balance currently. Local models are catching up in breaking speeds while OpenAI is publicly stating they want to sell AI like a “utility” aka only through API pricing.
Meanwhile datacenters put out more pollution and use more electricity than all the plane rides Bill Gates took with Epstein combined, for business meetings of course.
Yes actually. After zirp ended, cloud costs got materially more expensive for enough enterprises that there was a good year or so of celebrated "we're moving back on-prem" stories on hn, where companies were announcing savings in the several to tens of millions per year.
My own interest in LLMs increased exponentially when, around 18 months ago, I saw a post somewhere that had a guy who wrote his own inference engine in Rust and demonstrated it running with downloaded open weight models. I tried it out and was quite amazed that even on my laptop (no GPU) I could get an LLM to write Python programs and engage in discussions about Lewis Carroll poetry. It went from "magic thing that needs a data center of unobtanium GPUs to do questionably useful stuff" to "thing that does useful things even on a regular computer".
There's plenty of sand on the planet and clever people (and AI) figuring out how to do more work with less sand and power, so any argument that AI is going to cost so much that it won't be usable, seems just preposterous.
jqpabc123 | 5 hours ago
returnInfinity | 5 hours ago
They maybe running at loss after all the salaries and stock comp, but tokens are in profit now.
iLoveOncall | 5 hours ago
wqaatwt | 5 hours ago
It’s unlikely that Claude is proportionally that bigger and more expensive to serve so profit margins on inference must be pretty decent
sarchertech | 5 hours ago
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.
mvanbaak | 5 hours ago
throwaway-away | 4 hours ago
wqaatwt | 2 hours ago
sarchertech | an hour ago
wqaatwt | 2 hours ago
To an extent maybe, but that market is almost entirely commoditized already. Besides Cerebras and maybe Groq (which already charge a slight premium) all the other providers are more less interchangeable.
> Maybe it’s a lot of people who already had GPUs for crypto mining
I’m not sure the type of GPUs that were most popular for crypto are at all useful for LLMs?
sarchertech | an hour ago
If there’s a few providers subsidizing, that’s the price ceiling. Everyone who wants to compete has to subsidize.
Now if this market had been operating for years, I’d say that it’s likely all these companies are profitable or close to it. But the market is so new and there’s so much hype, I find it very plausible that none of these guys are making a profit and they all hope to just hang in until all the subsidies go away.
> I’m not sure the type of GPUs that were most popular for crypto are at all useful for LLMs?
There’s some overlap. I’ve definitely read about people repurposing.
mpalmer | 5 hours ago
wqaatwt | 5 hours ago
How many times bigger could Opus be than GLM or Kimi, it’s certainly not proportional to the price
mpalmer | 4 hours ago
JumpCrisscross | 4 hours ago
Nobody is whispering about anything. Everyone is loudly assuming what's convenient for their thesis. Even if you have access to the books, the accounting isn't straightforward–there are yet insufficient data for a meaningful answer.
> It is the absolute last thing these firms would keep secret
If you find an optimisation strategy that you don't think your competitors have, you absolutely keep your margins secret for as long as possible. Knowing something is possible is the first step to making it so.
wqaatwt | 2 hours ago
m0llusk | 5 hours ago
riddlemethat | 5 hours ago
rglullis | 5 hours ago
InsideOutSanta | 3 hours ago
hypercube33 | 5 hours ago
ainch | 5 hours ago
[0] https://epoch.ai/data-insights/openai-compute-spend
ml_basics | 4 hours ago
vb-8448 | 3 hours ago
ainch | 29 minutes ago
Inference compute is definitely scaling fast, but to scale RL, training and R&D compute also needs to scale hard. I don't think it's obvious that inference will overtake R&D/training, unless there's a reputable source that states that.
utopiah | 5 hours ago
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.
StevenWaterman | 4 hours ago
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
notahacker | 4 hours ago
JumpCrisscross | 4 hours ago
Eh, the AI companies still have lots of datacentres. For the guys who funded with equity, they could collapse down to just running those as utilities. (For the guys who funded with debt, they'd have to restructure.)
From the customer's perspective, this situation shouldn't result in a cost spike. (Consolidation, on the other hand, would. But that's a separate argument from the one the article attemptes to make.)
notahacker | an hour ago
InsideOutSanta | 3 hours ago
But if there's no more competition, there's no more incentive to keep prices low, which will also be reflected in pricing.
JumpCrisscross | 4 hours ago
But this isn't "a ticking time bomb for enterprise." It's an issue for the AI companies' investors.
shimman | 4 hours ago
airstrike | 3 hours ago
But within that big pie, the "IT-related" investments grew 15.7% whereas non-IT actually shrank 2.0%.
alkyon | 4 hours ago
It's like selling dope, once they're addicted, a dealer could turn the screw on them
mpyne | 3 hours ago
If things don't end up working out a lot of people have already been (and in the future will be) paid. It's the investors that will lose out, not the subscriber.
airstrike | 3 hours ago
manmal | 2 hours ago
cryo32 | 5 hours ago
gobdovan | 4 hours ago
malshe | 4 hours ago
mrweasel | 3 hours ago
jknoepfler | 3 hours ago
parliament32 | 3 hours ago
They might be sold at-compute-cost, but that of course ignores training, salaries, and everything else.
vb-8448 | 2 hours ago
Obviously I, like basically everyone else here, don't have access to Open AI or Anthropic books so it's just guessing based on public available evidences, but "tokens aren't being sold at a loss" does not imply there is any profit.
And, even if there is some profit, it needs to be big enough to at least pay back the capex spendings and finance the next model iteration.
542458 | 5 hours ago
* 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.”
ainch | 5 hours ago
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.
garrickvanburen | 5 hours ago
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.
adamgordonbell | 2 hours ago
Competitive pressure prevents a rug pull.
In a competitive race, each breakthrough gets copied or illicitly distilled or whatever. That means the frontier models are deprecating assets and the mark up tokens should get smaller and smaller.
Now bigger models are more expensive to run inference on, but today's models, or equivalent ability and size models, shouldn't go up in price.
5.5 is 4x the price, but 5.4 still exists, so its not rug pull, but a big more expensive to run and hopefully more valuable model.
gizmodo59 | 5 hours ago
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.
atq2119 | 5 hours ago
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.
fg137 | 5 hours ago
Nobody is going to charge "inference price" for model usage.
hibikir | 4 hours ago
einrealist | 5 hours ago
fg137 | 5 hours ago
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.
Jcampuzano2 | 4 hours ago
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.
ghusto | 5 hours ago
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.
add-sub-mul-div | 4 hours ago
GolfPopper | an hour ago
https://github.blog/news-insights/company-news/github-copilo...
Sharlin | 5 hours ago
baal80spam | 5 hours ago
criley2 | 4 hours ago
ai_slop_hater | 4 hours ago
cbold | 4 hours ago
the_gipsy | 4 hours ago
greesil | 3 hours ago
phendrenad2 | 2 hours ago
knollimar | 4 hours ago
Polizeiposaune | 3 hours ago
LLMs are just parroting relevant documents they've assimilated.
radicalbyte | 9 minutes ago
cpeterso | 4 hours ago
HarHarVeryFunny | 4 hours ago
One can at least hope.
GolfPopper | 3 hours ago
Github Copilot moves to usage-based billing in two weeks.[1]
1. https://github.blog/news-insights/company-news/github-copilo...
radicalbyte | 3 hours ago
It's just "intellectual" botox.
visarga | 3 hours ago
Could be just ESL, it's hard to close the proficient to native gap.
blharr | 46 minutes ago
radicalbyte | 19 minutes ago
HarHarVeryFunny | 3 hours ago
Maybe it's different if you are doing technical/commercial writing, but for social media where you are writing for fun, and to express yourself, it'd be odd to let AI be your voice unless you realize your own writing is very poor.
tyre | an hour ago
A lot of people post for clout, so something that can skip the difficult process of becoming a good writer (and original thinker) is more than enough. They can churn out think pieces about any topic at an unlimited pace, basically.
It doesn’t add much to the world, but they get a lot of traction (which I cannot understand, given the quality of content.) And that’s what matters to them.
I think if you gave most people the choice between (a) being a thoughtful and original writer (b) being seen as a thoughtful and original writer, the vast majority choose (b). Especially when it is zero effort.
the_af | 2 hours ago
Now they write "competent" blog posts on LinkedIn that seem 100% AI slop. Some are employed at AWS, too.
I'm not a native English speaker as I'm sure my writing shows. My point is that I'd rather read genuine posts full of grammar errors instead of slop.
radicalbyte | 17 minutes ago
djeastm | 2 hours ago
HarHarVeryFunny | 2 hours ago
I'm not sure that free tier will necessarily continue forever though, unless there is a way to monetize it (presumably by advertising, or by selling data they've gleaned about the user), or perhaps if there is no privacy and the provider is treating you as a source of free data. Right now we're still in the market-share grabbing "never mind the profits, count the users" stage.
blharr | 25 minutes ago
chandureddyvari | 3 hours ago
oldsecondhand | 2 hours ago
captn3m0 | 2 hours ago
StarlaAtNight | 2 hours ago
“”” The subsidy era is not winding down gracefully. It is showing cracks everywhere. … the question is not whether they got a good deal. The question is how long that deal survives. … A developer running three or four concurrent coding agents is not consuming 3x or 4x the tokens of a chat conversation. It is an order of magnitude more … These are not experiments anymore. They are load-bearing workflows. … That is not a rounding error. That is a line item that needs its own budget code. “””
GeoAtreides | 2 hours ago
sota_pop | an hour ago
“Load-bearing” is a new one for me though, yuck.
jameshart | an hour ago
It’s as jarring as getting halfway into a well written article, clicking a link to a source, and getting rickrolled.
It’s all you can do to not let it distract you from the fact that in 1998, The Undertaker threw Mankind off Hell In A Cell, and plummeted 16 ft through an announcer's table.
fg137 | 5 hours ago
Many companies use models deployed on Azure/Bedrock etc are already paying based on usage (often with discounts).
stego-tech | 5 hours ago
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.
rvcdbn | 5 hours ago
imsofuture | 4 hours ago
plombe | 4 hours ago
photon_collider | 4 hours ago
1123581321 | 4 hours ago
You can also do everything metered. There are multiple ways to buy.
pureliquidhw | 4 hours ago
1123581321 | an hour ago
What happened two months ago?
Bnjoroge | 3 hours ago
PKop | 5 hours ago
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..
megadopechos | 4 hours ago
jeswin | 5 hours ago
zephyreon | 4 hours ago
We all know every frontier AI lab is heavily subsidizing usage, and so do all of the VCs & CEOs funding them.
dmazin | 4 hours ago
But also... is this shit AI written? I'm so tired of this.
fwipsy | 4 hours ago
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?
babajabu | 4 hours ago
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.
4aslk19 | 4 hours ago
derektank | 2 hours ago
babajabu | 57 minutes ago
paoliniluis | 4 hours ago
exabrial | 4 hours ago
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.
smoghat | 4 hours ago
kamranjon | 4 hours ago
oldspleen | 4 hours ago
JumpCrisscross | 4 hours ago
What? Anthropic's costs aren't the API rate. The article never attempts to estimate that cost, which renders its thesis tautology.
andyfilms1 | 4 hours ago
--You lose control over their "salary"
--You lose control over their "schedule"
--Your company becomes reliant on another party that does not share your interests or values, and can stop working for you on a whim for any reason
But AI is definitely good and trade unions are definitely bad, apparently...
nijave | 3 hours ago
That's the same as human workers. In both cases there are contracts/money to help align interests
derektank | 2 hours ago
chermi | 2 hours ago
evo_9 | 4 hours ago
adamgordonbell | 3 hours ago
They have to keep getting better to stay ahead of each other and open weight.
Which means it's the opposite of a timebomb, the article has it completely backwards, tokens at current level of reasoning will continue to get cheaper.
I'm not sure 'local' will be the end state, as hardware needs are high. But certainly competitive forces tend to push profit margins toward zero.
Extended discussion on this topic:
https://corecursive.com/the-pre-training-wall-and-the-treadm...
airstrike | 3 hours ago
nijave | 3 hours ago
Eventually, we'll see. Frontier models still need some pretty serious hardware which will slowly come down in cost. Smaller models are becoming more capable, which will presumably continue to improve.
I think there's still a pretty big gap, though. Claude estimates Opus 4.6 and GLM-5 need about 1.5Ti VRAM. It puts gpt-5.5 around 3-6Ti of VRAM.
That's 8x Nvidia H200 @ ~$30k USD each. Still need some big efficiency improvements and big hardware cost reduction.
throw1234567891 | 3 hours ago
snovv_crash | 57 minutes ago
crazygringo | 3 hours ago
Based on what? The RAM requirements alone are extraordinary.
No, running large models on shared, dedicated hosted hardware at full utilization is going to be vastly more cost-efficient for the foreseeable future.
alsetmusic | 3 hours ago
greesil | 3 hours ago
scragz | 2 hours ago
calvinmorrison | 3 hours ago
lukeschlather | 2 hours ago
aleqs | an hour ago
t-sauer | an hour ago
aleqs | 23 minutes ago
__s | 2 hours ago
I've got a 128GB strix halo staying warm at home, it has nothing on top models with big budget. It's good supplement to low end plans for offloading grunt work / initial triage
manmal | 2 hours ago
__s | 2 hours ago
Thanks for suggestion tho, tool by antirez is always going to pique interest, I'll check it out when I'm finally home again
Tho says Metal / CUDA, so doesn't seem friendly to Linux AMD system
manmal | 35 minutes ago
alecco | 2 hours ago
I wish this was true but it is not. And I am working on open source models so if anything, I would have a bias towards agreeing with you.
Frontier closed models (GPT/Claude) are gaining distance to everybody else. Even Google, once the king.
Your claim is a meme coming from benchmark results and sadly a lot of models are benchmaxxed. Llama 4, and most notably the Grok 3 drama with a lot of layoffs. And Chinese big tech... well they have some cultural issues.
"Qwen's base models live in a very exam-heavy basin - distinct from other base models like llama/gemma. Shown below are the embeddings from randomly sampled rollouts from ambiguous initial words like "The" and "A":"
https://xcancel.com/N8Programs/status/2044408755790508113
---
But thank god at least we have DeepSeek. They keep releasing good models in spite of being so seriously resource constrained. Punching well above their weight. But they are not just 6 months behind, either.
dools | 2 hours ago
janderland | an hour ago
cbg0 | an hour ago
tyre | an hour ago
alecco | 53 minutes ago
[0] US AI firms team up in bid to counter Chinese 'distillation' (Apr 7) https://finance.yahoo.com/sectors/technology/articles/us-ai-...
crystal_revenge | 33 minutes ago
Gemma 4 was a major improvement is self-hostable local models and Qwen-3.6-A34B is a beast, and runs great on an MBP (and insanely well on a 4090).
The biggest lift is combining these models with a good agent harness (personally prefer Hermes agent). But I’ve found in practice they’re really not benchmaxxing. I’ve had these agents successfully hand a few non-trivial research projects that I wouldn’t have been able to accomplish as successfully even last year.
When you add in the open-but-not local models, Kimi, GLM, Minimax, you have a lot of very nice options. For personal use anything I don’t use local models for I give to my Kimi 2.6 powered agent.
leptons | 2 hours ago
That is only true right now because hundreds of billions of dollars are being burned by these AI companies to try to win market share. If you paid what it actually cost, your comment would likely be very different.
jazzyjackson | 2 hours ago
salawat | an hour ago
jazzyjackson | an hour ago
esseph | an hour ago
Digital sovereignty laws may mandate/remove access to LLMs of other countries on economic and national security grounds.
esseph | an hour ago
They'll be controlling lights and temperature, they'll be adding calendar reminders that show up on your phone and your fridge. Your phone and devices might sync pictures and videos there instead of the large cloud providers. They'll also be a media server, able to stream and multiplex whatever content you want through the home. They'll also be a VPN endpoint, likely your home router, maybe also a wifi access point.
I think this makes quite a bit of sense. I don't think they'll be ubiquitous, but they could be.
This distributes the power demand where local solar generation can supplement , gives the home user a lot of control, and claims overship of the user data from big tech.
Maybe I'm imagining things but this is what I think is coming.
It's the lmm/data heart of the home. A useful digital tool.
scheme271 | an hour ago
sshumaker | an hour ago
iwontberude | 2 hours ago
SkiFire13 | an hour ago
GenerWork | 20 minutes ago
I agree that the market for local AI is basically limited to nerds at this point, but that's because nobody's really explained why local AI is a good thing and also because the vast majority of people need the $20 paid plan at most. How much time and money would it take to get something half as good as OpenAIs products running locally?
harrall | 2 hours ago
They’re still pricey, the world is still scaling up memory production, and a lot of code isn’t yet built for AMD, but we went from the Wright’s brothers first airplane to jet engines in 27 years.
I’m not sure “it’s only a few years away” but we are sure moving there fast.
nine_k | an hour ago
Nitpick: more like 36 years, from Wright Flyer in 1903 to Heinkel 178 in 1939. Still quite impressive.
Traubenfuchs | an hour ago
chris_money202 | an hour ago
harrall | an hour ago
The print shop can’t replicate the practicality of local printing and I can’t replicate their scale of investment. Both coexist perfectly.
nnoremap | an hour ago
bluGill | 42 minutes ago
moregrist | an hour ago
Cynically: it’s become an executive-level gpu measuring contest. If you’re not making huge commitments on data centers, you can’t be a serious player.
Realistically: It’s a mix of the two. The recent Claude caps for agentic usage suggest that demand exceeded their immediate compute supply. That they can alleviate it with additional capacity from the existing and small-ish xAI facility suggests that either demand may not be rising quite as fast as anticipated, that they’re okay in the short term until more capacity comes online, or a mix of both.
Open questions:
1. At what price point does demand fall, and are the frontier providers overall profitable before that price point?
2. At what price/performance point do on-prem local models make more sense than cloud models?
nine_k | an hour ago
I must say that the largest dedicated hosted hardware providers now, like Amazon or Google, to a large extent do not produce the software they are offering as a hosted solution (like Linux, Postgres, Redis, Python, Node, etc). Similarly I'm not sure if the producers of the frontier models are going to keep their lead as the service providers for the most widely used models. They would need to have quite a bit of an edge above open-weights models.
Also, models are given very sensitive data to process. For large organizations, the shared dedicated hardware may look like a few (dozens of) racks in a datacenter, rented by a particular company and not shared with any other tenants.
crystal_revenge | 43 minutes ago
I take it you haven’t actually run any of the current gen local models?
They all fit on fairly accessibility hardware, and their performance is at least on par with what I was paying for last year.
I have one of my agents running entirely from a local model running on a MBP and it has repeatedly shown it’s capable of non-trivial tasks.
Playing around with another, uncensored, local model on my 4090 desktop has me finally thinking about canceling my personal Anthropic subscription. Fully private, uncensored chat is a game changer.
For work it’s still all private models but largely because, at this stage, it’s worth paying a premium just to be sure you’re using the best and it saves the time of managing out own physical servers. But if we got news tomorrow that Anthropic and OpenAI were shutting down, a reasonable setup could be figured out pretty quickly.
Leynos | 34 minutes ago
crystal_revenge | 17 minutes ago
simooooo | 24 minutes ago
dandellion | 14 minutes ago
At the same time, $100 a month is A LOT of RAM.
wolttam | 3 hours ago
vb-8448 | 3 hours ago
Unless there isn't some important breakthrough in hw production or in models architecture, it's quite the opposite: bigger, more expensive and more energy-intensive hw is needed today compared to 1 or 2 years ago.
ls612 | 2 hours ago
antisthenes | 2 hours ago
And how many tokens would that buy?
ls612 | 2 hours ago
jazzyjackson | 2 hours ago
ls612 | 56 minutes ago
evgen | 2 hours ago
vb-8448 | 17 minutes ago
I hardly doubt there will be consumer grade HW to run it in 2 years either. And deep seek v4 pro is not even close to OAI or anthropic frontier models.
chermi | 2 hours ago
YesBox | 3 hours ago
slashdave | 2 hours ago
I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.
christopherwxyz | 2 hours ago
We are only 2-4 years away from consumer grade immutable-weight ASICs.
slashdave | 2 hours ago
rogerrogerr | 2 hours ago
jazzyjackson | 2 hours ago
You might be interested in the tiny tape out project, which guides you through the process of getting your own design etched on silicon. If you only need larger features and not the next gen single digit nanometer stuff, you may not be so supply constrained.
https://tinytapeout.com/
pjc50 | 2 hours ago
nixon_why69 | 2 hours ago
ac29 | an hour ago
throwa356262 | an hour ago
The issue is the very huge amount of DRAM and high bandwidth these model require.
dangus | 2 hours ago
Also, how many companies will just buy an M6/M7 MacBook Pro with 32GB+ of RAM in a couple of years and get “free” AI along with the workstation they were going to buy anyway?
majormajor | 2 hours ago
The big question I'd be asking if I was investing in one of the big players is if those changes are "it can do 99% instead of 97% of the tasks a user will throw at it" (at which point going local and taking back cost control/ownership makes a lot of sense, especially for companies) OR "it will fully replace a human with better output"?
I already don't need Opus for a lot of my tasks and choose instead faster/cheaper ones.
The former is a company that's gonna be trying to sell mainframes against the PC. The latter is a company that is in potentially huge demand, assuming the replaced humans end up with other ways of getting money to still be able to buy stuff in the first place. ;)
iwontberude | 2 hours ago
comfysocks | an hour ago
But even if scaling plateaus for the frontier models, maybe distillation will improve to the point where smaller more manageable models can reach the same plateau. That would be great for local.
stingraycharles | 2 hours ago
Local models never reach the % utilization that cloud providers have (80%+), and they’re always going to be much better than local models for this reason.
lumost | 2 hours ago
It’s not unreasonable to suppose that in 2 years time an opus 5 quality model will be etched into silicon for high performance local inference. Then you just upgrade your model every 2-3 years by upgrading your hardware.
jazzyjackson | 2 hours ago
lumost | 57 minutes ago
https://taalas.com/
Is an example startup in this area claiming 16k tok/s on an asic for llama 8b. Qwen has a 27b model at opus 4.5 quality.
jazzyjackson | 25 minutes ago
majormajor | 2 hours ago
stingraycharles | 2 hours ago
majormajor | an hour ago
It's a measure of a very thin sort of "value/$" that excludes a lot of other things that could be of value to a business, like control, predictability, and availability.
Thin clients have been going away for a long time. The trend has been to continue to push higher levels of compute into ever-smaller and ever-more-portable devices.
sroerick | 17 minutes ago
But in many cases self hosted or dedicated boxes are cheaper than cloud.
otterley | 2 hours ago
watwut | 2 hours ago
Not even when that site calls itself "market" to create plausible deniality.
whackernews | an hour ago
planb | 2 hours ago
adrithmetiqa | 2 hours ago
malfist | 2 hours ago
xboxnolifes | 14 minutes ago
intothemild | 2 hours ago
Boss is happy, very happy. We're rolling it out more widely now.
But this is the future.
jmount | an hour ago
himata4113 | an hour ago
It would cost me $300 in normal deepseek v4 pricing (non discounted) PER DAY, but I get it all for $500 worth of subscriptions.
nozzlegear | 9 minutes ago
aleqs | an hour ago
sunaookami | 3 hours ago
clearstack | 3 hours ago
Lapsa | 3 hours ago
carra | 3 hours ago
Ekaros | 3 hours ago
lmeyerov | 2 hours ago
nrawe | 2 hours ago
leemoore | 2 hours ago
Github Copilot has been doing this with business and enterprise seats, but that will be coming to a head very soon. I expect a fast follow after june when they re-align consumer pro and pro+ accounts.
OpenAi seems to be trying to throw tokens at clients to get lock in. So i'd be most worried about the rug pull that will come from open AI post IPO. Anthropic is already acting responsibly in this area and github copilot is attempting to remediate their insane subsidies in the next several months.
briHass | 50 minutes ago
I was actually quite worried, because I've been using GHCP for large chunks of work, but the billing estimator they released shows I was only at about $150-200 a month in API priced tokens. Sure, that's a subsidy for my $20 subscription, but not insane.
Heavy use of agentic coding tools, in a responsible manner, probably lands somewhere around that $200/m mark at API pricing. Assuming that makes the provider money, I don't see that being hard to swallow for businesses employing developers in Western countries, given the hours it can save.
The real risk here is to personal project vibe coders. Building a huge app by abusing subsidized plans is ending.
submeta | 2 hours ago
Did OpenAI switch from fixed prices per seat to usage based? This will surprise many companies I reckon.
Personally I use Claude Code, the 200 euro plan. And am a heavy user. A few weeks ago I realized that CC shows the token usage in cli, in the bottom right. Something I never cared about because I thought paying 200 euro a month will give me „unlimited“ access.
But I guess the party is slowly coming to an end? Prices are going to increase slowly? And the flatrates will be removed eventually?
Too bad, it was nice while it lasted.
tim333 | 2 hours ago
Colour me skeptical on that one. Unless the AI improves a lot so it makes sense to spend more.
wan23 | 2 hours ago
ben8bit | 2 hours ago
skydhash | 2 hours ago
MostlyStable | an hour ago
In the first few years after electric motors became a thing, one could have said the same thing. We would have just gone back to steam. If you tried to "do without them" now, society would collapse.
So the question is not if we can do without them now, it's if we can do without them in 5 to 10 years (or however long it takes for them to be fully integrated)
48terry | an hour ago
Just how "early stage" is that, and how much more integration does this "new technology" need to be?
new_account_100 | an hour ago
Based on the way Claude has felt the last few weeks, I'd say we're about 3-6 months away from full AGI. At that point we can start truly replacing white collar workers in earnest and begin deep integration.
MostlyStable | an hour ago
AI won't be "integrated" until something similar happens, and new businesses etc. are formed that take advantage of it in a way that can't simply be reversed to the old, pre-AI paradigm. I don't know what that will look like, but someone is going to figure it out and make successful companies with entirely new paradigms that are only made possible by AI.
At some point, every single factory was designed for electric motors, and going back became unthinkable.
-edit- also, the idea that a 5 year old tech that is still rapidly changing and developing deserves quotation marks around "new technology" is hilarious to me.
GolfPopper | an hour ago
yalogin | 2 hours ago
niekkamer | 2 hours ago
new_account_100 | an hour ago
siliconc0w | 2 hours ago
phendrenad2 | 2 hours ago
alaudet | 2 hours ago
I will continue to use it as an assistant that does the menial stuff quicker than I ever could, but it's just too early to let it do stuff that would hurt if it disappeared. Enjoy it while it lasts.
niekkamer | 2 hours ago
wegwerper | an hour ago
It's clearly llm-spew in its mannersims, making me wonder if there were any nuggets of wisdom in its core or if it in entirety is part of some llm-driven blog spam project?
wunderlotus | an hour ago
wunderlotus | an hour ago
crorella | an hour ago
throwatdem12311 | an hour ago
bilater | an hour ago
aleqs | an hour ago
blahblaher | an hour ago
simianwords | an hour ago
Input: $30 / 1M tokens
Output: $60 / 1M tokens
GPT-5.5:
Input: $5 / 1M tokens
Output: $30 / 1M tokens
Costs have been reducing by over 5x year over year. Inference cost concern is mostly performative.
https://simianwords.bearblog.dev/conclusive-proofs-that-llm-...
Edit: can't reply but companies aren't selling inference at loss. In the blog post I point to third party hosting of open models like Deepseek which are also going down. They are not VC backed.
I also point to Gemma 31B which you can run on your laptop today that beats most models from 2024.
zamalek | 54 minutes ago
We will only know the actually situation once Anthropic goes public and we can look at their books.
rafaelero | 38 minutes ago
basilgohar | 30 minutes ago
multjoy | 26 minutes ago
alex_sf | 20 minutes ago
alex_sf | 52 minutes ago
That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.
Ygg2 | 51 minutes ago
Pricing has no correlation with profit. It can be artificially lowered to kill competition, and artificially inflated to maximize profit.
IncRnd | 27 minutes ago
GPT-4.1 Input: $2.00 / 1M Tokens Output: $8.00 / 1M Tokens
raincole | 20 minutes ago
ralusek | 27 minutes ago
alex_sf | 21 minutes ago
It would be more surprising if the surrounding architecture hasn't significantly diverged. If it _hasn't_ significantly diverged, then given the performance difference it would imply that the frontier models have significantly greater param counts, which would result in a higher cost.
new_account_100 | an hour ago
raincole | an hour ago
094459 | an hour ago
gjsman-1000 | an hour ago
Assertion assertion assertion wishful thinking assertion.
Show, don't tell. Show us that we're wrong and this isn't a VC black hole. The CEO of Enron as late as September 2001 could've called every critic a sad dark loser with nobody challenging him publicly. Jim Cramer famously yelled anyone pulling their money from Bear Sterns in 2008 was "silly, do not be silly" exactly 8 days before their collapse and a -92% stock drop. In COVID, calling everyone paranoid and sensationalist about some mythical new flu was popular in December 2019 and gone by March 2020. How about Uber, the seeming go-to for how VCs can turn a money hole into a profitable business? The average price increase is now 18% per year and still going up, with an over 60% increase in 5 years. Does anyone still talk about the "sad dark HN loser path" of those who doubted VR in 2018? How's your VR startup doing?
runtime_terror | 52 minutes ago
mmcnl | 26 minutes ago
nicce | 20 minutes ago
nunez | an hour ago
Those same companies are getting sweetheart deals with the frontier AI labs in the hope that infrastructure costs go down enough in the future to invert profitability, but it's still a risky position for them to be in. (Having their own infrastructure gives the bigcos huge leverage, even if it's only 80% as good as frontier.)
AbstractH24 | an hour ago
And some parts of most publicly traded ones.
If it’s not a bootstrapped company with a single offering, there’s a highly likely something there doing is at a loss in the name of growth (and even there, the loss might come in the form of deferred compensation)
48terry | an hour ago
The software world is, by and large, no longer about making products with a focus on the long-term, whether that's about the customer's well being or even the company's own long-term functioning. It's about trapping people, siphoning their money, then running away after setting the building on fire. Founder McBuilder will throw away his entire userbase and tell them "lol idk good luck" about their usage needs if it means he can make an extra dollar.
This is as true for enterprise as it is for consumers. Look at all the lamenting when a liked name gets bought by venture capital or considers an IPO.
prash20026 | an hour ago
So the frontier model companies might have crazy valuations and they might never reach that. But that might not mean they are actually unprofitable.
ninjahawk1 | an hour ago
Meanwhile datacenters put out more pollution and use more electricity than all the plane rides Bill Gates took with Epstein combined, for business meetings of course.
Yhippa | an hour ago
thomasingalls | an hour ago
Havoc | 45 minutes ago
There will be a repricing for sure as any ends of subsidies does but the world will not end
dboreham | 25 minutes ago
There's plenty of sand on the planet and clever people (and AI) figuring out how to do more work with less sand and power, so any argument that AI is going to cost so much that it won't be usable, seems just preposterous.
lol8675309 | 23 minutes ago