8 ms·

AI subscriptions are a ticking time bomb for enterprise

• mojosmojo 6mo ago
  They have all switched to usage plus cheap seats based costs for enterprise contracts. the seat costs are typically 20-35% of total spend.
• jqpabc123 6mo ago
  It is "bait and switch" --- done on an industrial scale.
• returnInfinity 6mo ago
  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.
  • iLoveOncall 6mo ago
    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?
    • wqaatwt 6mo ago
      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
      • sarchertech 6mo ago
        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.
        • mvanbaak 6mo ago
          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.
          • throwaway-away 6mo ago
            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.
          • wqaatwt 6mo ago
            The R&D is of course subsidized but a lot/most(?) of these inference providers are not Chinese
            • sarchertech 6mo ago
              If the Chinese companies are subsidized anyone who wants to compete with them has to match their price.
        • wqaatwt 6mo ago
          > 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?
          • sarchertech 6mo ago
            >interchangeable 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 6mo ago
    This is the sort of uncritical thinking that inflates bubbles in the aggregate.
    • wqaatwt 6mo ago
      Compared to the inference prices for open models it’s highly unlikely OpenAI/Anthropic are not making decent amounts of money from inference. How many times bigger could Opus be than GLM or Kimi, it’s certainly not proportional to the price
      • mpalmer 6mo ago
        it’s highly unlikely OpenAI/Anthropic are not making decent amounts of money from inference. Based on what? Why are we all whispering about how profitable all this is? It is the absolute last thing these firms would keep secret.
        • JumpCrisscross 6mo ago
          > 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.
          • kddkkdisis 6mo ago
            [dead]
        • wqaatwt 6mo ago
          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.
  • m0llusk 6mo ago
    That isn't enough. Over time the need for growth and increasing profits will squeeze existing margins.
    • riddlemethat 6mo ago
      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.
      • rglullis 6mo ago
        Have you used any of the recent models? My experience with GLM 5.1 does not make me miss Opus at all.
      • InsideOutSanta 6mo ago
        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.
    • hypercube33 6mo ago
      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.
  • ainch 6mo ago
    Tokens can be sold at profit, but 70% of compute expenditure goes to R&D and model training[0]. Inference needs to cover all of that as well as being profitable in a vacuum. [0] https://epoch.ai/data-insights/openai-compute-spend https://epoch.ai/data-insights/openai-compute-spend
    • benjiro3000 6mo ago
      [dead]
    • ml_basics 6mo ago
      this will change as inference demand increases (which is happening right now faster than many people expected)
      • vb-8448 6mo ago
        do you have some ref?
      • ainch 6mo ago
        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.
  • utopiah 6mo ago
    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.
    • StevenWaterman 6mo ago
      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
      • notahacker 6mo ago
        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...
        • JumpCrisscross 6mo ago
          > 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.)
          • 6mo ago
  • cryo32 6mo ago
    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.
  • gobdovan 6mo ago
    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.
  • malshe 6mo ago
    In other words, AI companies have positive earnings before expenses
    • mrweasel 6mo ago
      "We'd be making money if we didn't have to manufacture the product", something like that?
  • jknoepfler 6mo ago
    Brad Gerstner might need a primer in asset depreciation.
  • parliament32 6mo ago
    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.
  • vb-8448 6mo ago
    Is sounds very much like "trust me bro" ... 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.
  • runtime_terror 6mo ago
    Ignoring the hundreds of billions of investments and debt and the astronomical costs of training and building data centers, sure. This is delusional thinking.
  • dzonga 6mo ago
    so because some1 said something it becomes true ? /o\
• 542458 6mo ago
  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.”
  • ainch 6mo ago
    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.
  • garrickvanburen 6mo ago
    I agree. Between: more efficient models - tuned for the task at hand, the ability to run those models in-house, or even at the edges, plus Google and Microsoft are well positioned to stay ambivalent as they’ve got lots of products to sell and whether or not LLMs are part of the portfolio mix is completely dependent on enterprise customer demand. Anthropic/OpenAI have a number of aggressive downward pressures on their pricing.
  • adamgordonbell 6mo ago
• gizmodo59 6mo ago
  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.
  • atq2119 6mo ago
    Inference at per-token pricing is profitable. The article's point is that if you're relying on flat fee subscriptions, a rude awakening may be coming. That seems plausible to me. Issues around token quotas are a frequent topic on HN.
  • fg137 6mo ago
    So? How does it change the equation? Nobody is going to charge "inference price" for model usage.
    • hibikir 6mo ago
      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.
• simonw 6mo ago
  This article appears to be self-confessed algorithmic clickbait. They say as much in this piece: https://www.thestateofbrand.com/news/Outlever-Owned-Media-Newsroom https://www.thestateofbrand.com/news/Outlever-Owned-Media-Ne... > Our content technology stack is built on AI and proprietary models trained on thousands of hours of executive interviews across industries. As we operate this publication, the system learns from the editorial decisions we make, the audience responses we see, and the distribution patterns that emerge from daily publishing. Those learnings refine the models, improve the workflows, and sharpen the distribution logic.
• einrealist 6mo ago
  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.
  • fg137 6mo ago
    > 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.
    • Jcampuzano2 6mo ago
      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.
• ghusto 6mo ago
  TL;DR to save you time: 1. GenAI companies are making a loss in order to gain adoption and later lock-in 2. ??? 3. They're going to cash-in soon and start milking you now that business critical systems rely on GenAI The "???" denotes a complete failure to offer compelling arguments that link 1 and 3.
  • add-sub-mul-div 6mo ago
    We popularized the term "enshittification" so we wouldn't have to keep explaining this.
    • arnitdo 6mo ago
      Not quite enshittification, that would be the eventual '''realignment and optimization of models to suit business demands and streamline $COMPANY's economic operations while focusing on serving the customers what they need best''' This is just simply economics. Make an early loss to get a customer base, offer addictive features at irresistible prices, then yank them into your profit-making pricing. Although yes, the ??? in the parent comment could be replaced with "A/B test some price gouging or ratty pricing methods to see how far we can go without being noticed"
    • ghusto 6mo ago
      Labels are how brains work, but they carry the inherent danger of eventually leading to lazy thinking. Usage must periodically be checked. I don't see how this is exactly enshittification from the original definition of the word.
  • GolfPopper 6mo ago
    Again, GitHub Copilot is moving to usage-based billing June 1st. https://github.blog/news-insights/company-news/github-copilot-is-moving-to-usage-based-billing/ https://github.blog/news-insights/company-news/github-copilo...
• Sharlin 6mo ago
  I think I'm going to puke if I see one more "It's not X. It's Y." phrase or the word "load-bearing" used metaphorically.
  • baal80spam 6mo ago
    Managers in my org love using it in "their" Slack messages.
    • criley2 6mo ago
      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.
      • ai_slop_hater 6mo ago
        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.
  • cbold 6mo ago
    It is not human language. It's AI slop!
  • d1l 6mo ago
    [dead]
  • the_gipsy 6mo ago
    It's not metaphorical. It's load-bearing.
    • deleted 6mo ago
      [deleted]
• fg137 6mo ago
  Why does the author assume that enterprises use subscriptions? Many companies use models deployed on Azure/Bedrock etc are already paying based on usage (often with discounts).
  • stego-tech 6mo ago
    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.
• rvcdbn 6mo ago
  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.
  • imsofuture 6mo ago
    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.
  • plombe 6mo ago
    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.
  • photon_collider 6mo ago
    Yeah, I was confused about why it was talking about subscriptions for enterprise. The company I work at is billed on API usage.
  • 1123581321 6mo ago
    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.
    • pureliquidhw 6mo ago
      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.
• PKop 6mo ago
  > is not a rounding error. It is Who said it was? > Pull out the napkin. This matters. The article wouldn't exist if you didn't think it mattered, just tell us why. > the question is not whether they got a good deal. The question is Who said that was the question? > This Is Not One Company's Problem Who said it was? Stop telling us what thing aren't, just speak like a normal human and convey your own thoughts. It's an insult to your audience to throw constant AI slop at them. > thousands of companies have woven AI subscriptions deep into their operations. Marketing teams draft copy through ChatGPT Plus. Yea I bet you do..
  • megadopechos 6mo ago
    After reading the third "rounding error" phrase I quit.
• huflungdung 6mo ago
  [dead]
• jeswin 6mo ago
  Since we can't reliably detect AI generated crap, I think it makes sense to penalize their submission. I say this as a generally pro-AI person.
  • deleted 6mo ago
    [deleted]
• freakynit 6mo ago
  [dead]
• zephyreon 6mo ago
  Aside from the obvious fact that this is AI slop, the author (prompter?) doesn’t consider the R&D of AI itself. Efficiency gains, more compute, etc. We all know every frontier AI lab is heavily subsidizing usage, and so do all of the VCs & CEOs funding them.
• dmazin 6mo ago
  As a few commenters already pointed out, IME enterprises aren't paying for subscriptions. They're paying per token. But also... is this shit AI written? I'm so tired of this.
• fwipsy 6mo ago
  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?
• babajabu 6mo ago
  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.
  • 4aslk19 6mo ago
    You could use "git clone" or Wikipedia for free. If you mean the value of propagandizing gullible people, yes, there is "value".
    • derektank 6mo ago
      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.
  • babajabu 6mo ago
    Just to clarify, value in this context is economic value through output. Not considering environmental impact etc.
• paoliniluis 6mo ago
  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
• exabrial 6mo ago
  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.
• smoghat 6mo ago
  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?
  • kamranjon 6mo ago
    It feels like they just pointed an AI model at Ed Zitron’s blog and asked it to make a super engaging and viral post.
• oldspleen 6mo ago
  every infra wave starts with land-grab pricing and ends with metered billing, AI is just running the cycle in 18 months instead of 10 years
• JumpCrisscross 6mo ago
  > 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.
• andyfilms1 6mo ago
  Replacing your workers with AI: --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 6mo ago
    >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 That's the same as human workers. In both cases there are contracts/money to help align interests
    • derektank 6mo ago
      Exactly, the principal-agent problem applies to all agents, be they human, corporate, or robotic
  • chermi 6mo ago
    If only there was a way to think beyond direct substitution.
• evo_9 6mo ago
  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.
  • adamgordonbell 6mo ago
    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. Extended discussion on this topic: https://corecursive.com/the-pre-training-wall-and-the-treadmill-after-it/ https://corecursive.com/the-pre-training-wall-and-the-treadm...
    • airstrike 6mo ago
      Well, it's a timebomb for the companies who get paid per token, so the parent is right and TFA is probably wrong
  • guesswho_ 6mo ago
    [dead]
  • nijave 6mo ago
    >within a few years 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.
    • 6mo ago
• sunaookami 6mo ago
  Isn't EVERY subscription and SaaS a ticking time bomb for enterprise?
  • happosai 6mo ago
    It is, but every enterprise is just looking at the next few quarter results. ROI looks so great when you don't invest in anything and just lease / subscribe / SaaS everything. Time bombs are just a concern for the future.
• guesswho_ 6mo ago
  [dead]
• jeremie_strand 6mo ago
  [dead]
• clearstack 6mo ago
  MSFT, GOOGL, META are spending $60-100B+ annually on AI infra partly to own the cost floor. the moat isnt the model, its the infrastructure.
• Lapsa 6mo ago
  "In 1975, Dr. Joseph Sharp proved that correct modulation of microwave energy can result in wireless and receiverless transmission of audible speech."
• carra 6mo ago
  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.
  • Ekaros 6mo ago
    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.
  • aaronharnly 6mo ago
    This is exactly true and it’s what every SaaS company counts on. They have four basic moves: 1. Sell you X seats, expecting that internal network effects will get you to 3X soon enough. 2. Sell you X seats, noting that as soon you reach 2X seats, you need to move to the Enterprise plan, under which largely the same feature set plus a couple of gated SCIM controls will cost twice as much. 3. Sell you X seats for their core product, then announce that features B and C are actually an additional SKU that requires additional per-seat subscriptions that double the cost. 4. Sell you X seats for their core product, then immediately announce that you’re on a “legacy plan”, but they’re happy to move you now to their updated offering, which is the same product but now structured to cost twice as much.
• lmeyerov 6mo ago
  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.
• nrawe 6mo ago
  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.
• leemoore 6mo ago
  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.
  • briHass 6mo ago
    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.
• submeta 6mo ago
  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? Too bad, it was nice while it lasted.
• tim333 6mo ago
  > 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.
• lezojeda 6mo ago
  [dead]
• wan23 6mo ago
  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.
• nate8bit 6mo ago
  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?
  • skydhash 6mo ago
    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.
  • MostlyStable 6mo ago
    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)
    • 48terry 6mo ago
      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?
      • new_account_100 6mo ago
        > 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.
• yalogin 6mo ago
  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
  • niekkamer 6mo ago
    They are kinda trying to replace figma. That could be valuable.
    • new_account_100 6mo ago
      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.
      • ychnd 6mo ago
        Have you seen the outputs from OpenAI's latest image model? I believe it is called "Image 2.0", is just a few weeks old and - to my untrained eyes - the outputs including text are astonishing.
• siliconc0w 6mo ago
  Both OpenAI and Claude already charge Enterprise usage rates and they're still buying.
• phendrenad2 6mo ago
  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.
• huqedato 6mo ago
  [flagged]
• alaudet 6mo ago
  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.
  • niekkamer 6mo ago
    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
• wegwerper 6mo ago
  Does this article contain any original thought? 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 6mo ago
    No and yes.
• wunderlotus 6mo ago
  This is an (embarrassingly obvious) AI-generated “article” powered by a company whose business model seems to be AISaaS (AI slop as a service).
• throwatdem12311 6mo ago
  Not my problem I just burn the tokens they give me!
• crorella 6mo ago
  wouldn't move to local models in the future remove part of that risk for companies?
• bilater 6mo ago
  [flagged]
  • blahblaher 6mo ago
    "It costs OpenAI less money to serve GPT-5.5 than GPT-4." does it though? do you have the numbers? Or you just making stuff up?
    • simianwords 6mo ago
      GPT-4 (original API): 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-costs-are-going-down/ 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 6mo ago
        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.
        • rafaelero 6mo ago
          I think it's pretty safe to assume they are not losing money on inference.
          • basilgohar 6mo ago
            Based on what? They haven't even IPOed.
• nunez 6mo ago
  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.)
• Jbunga 6mo ago
  [flagged]
• AbstractH24 6mo ago
  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)
• 48terry 6mo ago
  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.
• prash20026 6mo ago
  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.
• ninjahawk1 6mo ago
  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.
• Yhippa 6mo ago
  Wasn't this the same thing when enterprises started using cloud computing? Did the bomb explode for them?
  • thomasingalls 6mo ago
    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.
• Havoc 6mo ago
  Bad attempt to estimate company costs using api sales prices numbers. There will be a repricing for sure as any ends of subsidies does but the world will not end
• dboreham 6mo ago
  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.
• lol8675309 6mo ago
  LOL AND DUH
• pvtmert 6mo ago
  Although I agree with the sentiment in the article, it smells very LLM~y. Especially the sections and punchlines. Such as: `That is not a rounding error. That is a line item that needs its own budget code.`
  • okdood64 6mo ago
    > That is not a rounding error. That is a line item that needs its own budget code. Claude produces this kind of prose SO much. It's pretty annoying. I don't notice it happening on Gemini for the same prompts.
    • pvtmert 6mo ago
      I was working at Amazon until recently. Number of internal documents (PRFAQs, 1-pagers, etc) having these sort of proses boomed since 2024. Punchy titles are also part of the marketing speak. Before Claude or ChatGPT, it would be a delicious read, understanding how they come up with the initial idea for an internal system. Since then, most of reads like "It's not just X, ..." every other paragraph, making it dull...
    • metadat 6mo ago
      It's called "constrastive negation", and both GPT and Claude tend towards it.
  • ashton314 6mo ago
    Pangram flags this as 100% AI generated with high confidence.
    • IshKebab 6mo ago
      We've established that Pangram is bullshit. But it definitely has some strong LLM vibes.
      • sincerely 6mo ago
        Who is "we"? Lol
• Animats 6mo ago
  So, will the AI companies raise prices? That's the article's main claim. Uber ran at a loss to build market share for over five years after the IPO. So it it not impossible for an overhyped IPO to run in that mode. The AI service industry might do that, too. Uber raised prices some, but mostly squeezed drivers harder. When Uber started, driving for Uber was a well paid job. It isn't, now. AI companies are mostly capital cost, so they don't have the oppression option. Hardware price/performance may not improve much near term. Graphics GPU price/performance hasn't improved much in the last decade. DRAM prices have gone up. Fabs are all booked up. NVidia says not to expect better price/performance before 2030. More efficient, specialized models are a strong possibility. Dumping all of human knowledge into a coding tool may be unnecessary. Although this would work a lot better if the LLM crowd figured out how to get a reliable "I don't know" answer out of a small model, then call on a bigger one for help.
• kaydub 6mo ago
  I'm surprised at how many businesses are using subscriptions instead of paying per token.
• edwinjm 6mo ago
  Does the writer understand that for every developer who burns all tokens, there are many people who subscribe just to join the AI revolution, but only ask a couple of questions a day?
  • gampleman 6mo ago
    No. Large co I work at everyone is like running at least 3 concurrent Claude sessions all day every day. Talking to friends in other companies it seems the same. Big difference between professional deployments and personal ones.
• alxndr 6mo ago
  Darkly funny that Pangram 3.3.1 thinks "100% of this text is AI generated"
• rakel_rakel 6mo ago
  The hyperbolic nature of the articles in both AI camps is very exhausting to me. I'd like to get in front of a whiteboard with someone who knows economics and the token providers businesses well enough to answer my "explain to me like I'm five" questions. But I'll start with these in here: Is my observation correct that for the token providers this is a margins game, while for the consumers this is a quality of service/product game? If the quality:margin lines will cross at some point on the x-axis, is the race is to reach this point before running out of money? If yes: What historical examples are there where the delta between these two is huge? I'm guessing LLM's are unique in a sense, since there's really no limit to how good a consumer of the product expects it to get? (Compared to for example email which is much easier to scale in regards to compute.) Also extreme noob at life question: Why would you want to IPO before having a sustainable business model? What's the upside?
  • rstuart4133 6mo ago
    You can ask an AI what it costs to produce an Opus token. The answer I got was $0.216/M Token. That takes into account hardware cost, power, cooling and hosting. Others have posted links here saying inference is about 30% of Anthropic's spend, the other 70% is R&D - things like developing the next model. If you take that into account, add a profit margin, and round to make the figures easy you end up with them selling tokens at $1 / M Token to make money. Their API cost is currently $25 / M Token. There is no question that's profitable. Someone really pushing their $200/mo Max plan can use 10B Tokens per month, which works out at $0.02 / M Token, so they are eating a huge loss there. That is clearly going to go away at some stage. For the rest of us: look at your average monthly token usage. If you are using 100M Tokens a month with the Anthropic $100/mo plan, they are making money out of you. You have to be working very long hours, and be really, really proficient at using AI to achieve 10B Tokens a month. The only way to be that proficient is to have been using it for a long time, years in fact, so it was useful to you long before Opus came along. You would be very disappointed to lose Opus, of course - but you are just the sort of person who can make a less capable model sing. It's not so difficult to see those users moving to an in-house-hosted, open-source model in a few years, and it will cost them what they are paying now - $0.02 / M Token. All that means I'm not convinced by the gloom and doom vibe of the article. Things will change, but it won't mean the end of AI usage.
• blondie9x 6mo ago
  I think one thing the author overlooked in the solutions/hedging section is using open weight models. Enterprises need to be ready to use their own servers for inference and build pipelines to utilize non proprietary models when possible.
• jauntywundrkind 6mo ago
  Good fucking luck DeepSeek. Thoughts & prayers to you with what's about to hit, shit.
• r-johnv 6mo ago
  The article repeatedly uses the API charge to the 'customer' as an estimate of the cost value to Anthropic/OpenAI. Is there any good data on the true cost of inference for these companies?
• aryehof 6mo ago
  The assumption here is that the profitable price (variable costs) is only the api price. I question that. Subscriptions can be equally profitable depending on the total actual tokens used across users. Few subscriptions use 100% of their potential, but these memes that subscriptions are unsustainable always seem to assume it to be so.
• istvanmeszaros 6mo ago
  If it is going to be so cheap that you can run a good LLM model locally, it will certainly cheaper to run it at scale in the cloud. Although, I hope I won't need be dependent on some cloud provider. So I am looking for a day that this comes.
• KoolKat23 6mo ago
  Subscription costs per person going from $20 to $100, may be expensive and potentially unbudgeted expenditure, but it's hardly ruinous. Many big organisations are already on per token pricing purely due to privacy concerns. Once workflows are in place it becomes easier to see what level of "intelligence" is adequate. It no longer becomes important to necessarily keep that workflow on the next state of the art model if the new cheaper models does a good enough job given ongoing improvements. Gemma 4 I'd say is equivalent to 2.5 flash in my opinion/workflows.
• 0xcrevelop 6mo ago
  [flagged]
• noashavit 6mo ago
  It's quite obvious that things are highly subsidized now. What will happen when subscriptions 10x in a year? That's that danger for the enterprise. I don't think the average enterprise will allow users to run local LLMs tbh. Just my POV feel free to tell me that I'm wrong!