8 ms·

Why current LLM costs are not sustainable

116 points by adityapatadia 3mo ago

chiply314 3mo ago
I think companies will fire 5-10% of people and convert them to token budget. I also believe that before any real companies are running these models locally, they will already have some kind of agentic layer. With the current frontier model lab progress, i do not see any real company which makes real money, running local models. Running local models is easy for me, for sure not that easy for any company. Your DC needs to be able to host GPUs, it needs the cooling power, you need to have a DC. Without a DC, you need to have someone maintaining critical infrastrucutre, taking care of model evaluation etc. For external parties, there might become a new business model: You might not hire an external anymore, but a token budget and the 'operator of the token budget'. The current chip fabs are full, developing a high end / cheapisch local LLM Chip will still take a few years as long as the DC GPU demand is still as high as it is.
- lukebuehler 3mo ago
  I work with large enterprises that _only_ run critical workloads on locally hosted models. Think banks, insurance, etc--businesses that absolutely cannot leak any data. They also have CC and Codex, but their use is extremely restricted; anything of consequence runs on models running on GPU clusters in their own datacenter.
  - chiply314 3mo ago
    I work at large enterprise and they are happy paying Microsoft and AWS for model hosting. But for sure there will be use cases of very critical data, but at the end the question will still be how big they are in comparision to the rest of the market. These cricial workloads also have the cost issue, right? so will they reduce workforce to compensate for the budget?
- netdevphoenix 3mo ago
  I am calling it now. LLM hosting is the new web hosting. You will have a market of hosting providers offering you access to LLM compatible hardware (the Hetzners of the LLM world) as well as virtualised LLM access (the Heroku of the LLM world). These will compete along pricing, ownership axes while frontier labs will compete mostly on performance, integration and ease of use (think Wordpress). That's the only way I can see frontier labs charging high enough to sustain the cash flow needed to operate as racing to the bottom is not possible for them. It is interesting to think whether this is another "Cambrian" era like the smartphone OSes when you had Symbian, Android, iOs, Windows Mobile and so many others competing.
  - chiply314 3mo ago
    I work at a very big company and they just pay azure and aws to host claude and co for them. So the hyperscalers already won for now probably. At the end of the day, you send a lot of personal data to these endpoints. If you already host everything through microsoft already, LLM hosting is then a no brainer.
walrus01 3mo ago
I have already seen a number of people doing the math on what it would take for hardware to self host a Q8XL quantization of GLM5.2 shared between N numbers of people. There's additional advantages that everything you query, all of your context cache and everything it outputs stays private and can't be arbitrarily turned off by external interference. Personally I think it would be a fairly good bet that something with the 1TB of RAM needed to properly self-host GLM5.2 will still be a very usable piece of hardware in 4 to 5 years from now. There will be even larger, newer models available, sure. But there will also be better models that continue to fit in the same size.
- dofm 3mo ago
  Back in the earlier days of the internet, when "dedicated servers" were a competitive advantage, hobbyists and small dev shops definitely shared dedicated hardware. So you could see small LLM co-operatives working out, yeah. But my thinking is that this four-to-five-year scenario just won't come to fruition, because the whole concept of needing to run these massive, massive models will slightly more likely be rendered moot by smaller models with better reasoning capacity, and possibly even in that timescale by hardware innovations. One of the biggest problems I have with the whole "we won't be profitable until 2030" model is that 2030 is almost exactly as far into the future as the launch of ChatGPT is in the past, and in that time, models far more capable than that first ChatGPT have been made available to freely download and run on desktop hardware that existed before it launched, and the entire non-model surrounding functionality of that original ChatGPT plus many more functions is now not much more than a routine weekend coding project. I don't know why the market would entertain the idea that no upset like that is possible in the same period of time again.
  - xienze 3mo ago
    > So you could see small LLM co-operatives working out, yeah. Only on a pay-per-token basis, I think. Unless it's a very tight-knit circle of folks. Fixed monthly subscription costs I doubt would work in that model. Because you'll get the inevitable: someone pegging the service 24/7 because it's "unlimited" while everyone else suffers.
albertgoeswoof 3mo ago
One thing missing here is the maturity of agent harnesses. I’m finding the free deepseek flash model in opencode can handle all of my simple tasks, because the harness is so good. Soon that will be a local model. And the reality is that other industries aren’t finding the use for LLMs as much as programmers are. Sure there are some benefits but you can’t fire your marketing department and replace it with AI
- bflesch 3mo ago
  AI is google-in-a-box, and there will be dedicated hardware to run it locally like there was with the crypto ASICs. I feel the only ones losing are the AI startups and Google. This is why they're trying to morph into a social-media like experience of simulated human interaction that can monetize a certain demographic of vulnerable people.
KronisLV 3mo ago
> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. If you can use a subscription with any of the SOTA models, do that. Instead of around 4k EUR in token costs, my Opus usage costs me 108 EUR (with taxes) per month with their Max 5x plan. It's the same with OpenAI, those are heavily subsidized. It doesn't make sense to pay per-token, unless you must. > What is happening here is that leading AI labs are charging not only for inference but also for research in model architecture, training data collection and curation, model training cost (which can be tens or even hundreds of millions of dollars), paying their employees and recovering the marketing costs. Chances are, they're never getting that money back. Best case scenario, the hype around AI slowly declines, worst case - it crashes and takes a part of the economy with it. Also anyone doing distillation with hundreds or thousands of those subsidized attacks is probably winning big. Especially as the model architectures (e.g. DeepSeek V4) are more oriented towards efficiency. > Last but not least and in fact the most important factor, is the ability of users to run local models. So far, almost everyone is using cloud-hosted models and local models are either too big to deploy or too slow to work with. With advancements in chips, this will change in 4-5 years’ time. Currently beefy hardware to run them fast enough to be competitive with the cloud (at least 60 tps) is expensive and even then the small local models quite suck compared to SOTA or even DeepSeek V4 Pro and GLM 5.2, though they're way better than they used to be (compare Qwen 3.6 with 2.5 for example).
- ReptileMan 3mo ago
  Why do you think that subscriptions are subsidized and not that enterprise tokens are sold at 3000% margin? There are few enough frontier labs that cartel is possible.
  - _flux 3mo ago
    I think this comes from the idea that serving these tokens without paying for training is already expensive, e.g. https://news.ycombinator.com/item?id=46613887 https://news.ycombinator.com/item?id=46613887 self-hosted solution might give you only 10-100x more affordable solution at cost. So, given the SOTA providers with even larger models also need to continously be using considerable resources for training their next models, to fund future data centers, and make profit, the token costs are more likely reflecting the real costs, rather than the subscription costs.
dude250711 3mo ago
Not the end of the world! OpenAI and Anthropic will just go back to entirely healthy valuations of ~$5-10B each and the industry carries on.
arjunchint 3mo ago
There is a wave of users switching over to DeepSeek Flash. There are Reddit threads of users sharing billion token spend for $20. If all of global spend on Anthropic/OpenAI/Gemini APIs just switches over to DeepSeek then easily we can decrease total AI spend by 10x
- pydry 3mo ago
  Probably won't be too long before the government decides to block deepseek's website based on "security" concerns.
  - Mashimo 3mo ago
    Who is "The government"? China or your local one?
    - pydry 3mo ago
      American
  - sharpshadow 3mo ago
    It’s in the doing with the DAAMT Act and soon foreign AI companies will be on the entity list. Circumvent this sanctions and count with assets forfeiture, civil penalty and criminal prosecution. This will eliminate access to Deepseek and so on overnight. Cherry on top most westerners will face similar problems due to secondary sanctions.
  - LUmBULtERA 3mo ago
    Deepseek's models are open-weight and hosted all over the world, how would blocking deepseek's web sight do anything to stop its model's use?
    - sharpshadow 3mo ago
      Deepseek will be sanctioned and therefore no provider will offer it anymore. Only way to use it then will be private but even that will be forbidden if it gets classified as threat to national security.
akie 3mo ago
I am convinced that the combination of capable open weight models and specialized hardware will mean that Apple (and other hardware providers) will start shipping computers with built-in, hardwired, "LLM-on-a-chip" cards that are capable enough to meet 90% of your AI needs. I really believe that in the near-term future we will run our LLMs in hardware, not in software. Hardwire a capable model into a device the size of a graphics card, embed it into a laptop, and you have something that uses less power, does faster inference, doesn't require additional CPU or memory, doesn't cost a monthly fee, and will probably eventually be available for under a (few) hundred bucks.
- FridgeSeal 3mo ago
  I’d put money on Apple buying/acqui-hiring the Talaas people to further this.
karussell 3mo ago
The current costs do not have to be sustainable for the SOTA model providers as they grow their user base. But I really wonder about the future as the costs have to increase at some point (to be sustainable) but at the same time the competition and local models get better and better.
Gingersnap123 3mo ago
[dead]
exizt88 3mo ago
> We are seeing improvements with each model release these days but it’s clear that the improvements are getting smaller and smaller. This is obviously untrue, both with GPT-5.4, and Claude Fable as examples in the last 6 months.
- byzantinegene 3mo ago
  gpt 5.5 regularly wastes tokens on wrong commands, requires lots of handholding. I highly doubt there's substantial improvement
- rubin55 3mo ago
  I would struggle to ascertain the day-to-day difference between GPT-5.4 and GPT-5.5 tbh. Also, imho, Fable is highly hyped, I don't think it is dramatically better than Opus 4.8. Maybe my tasks and interaction with AI is relatively simple (i.e., lots of Rust programming, Linux system engineering stuff).
  - chiply314 3mo ago
    I haven't had enough time with fable, but I had to look back on how i worked with claude just 6 month ago to remind myself that it got a lot better. Like i still used plan mode 6 month ago now I don't. I would argue that with every model release we have a new learning phase.
- skerit 3mo ago
  > the improvements are getting smaller and smaller The AI haters have been saying this for 2 years now.
jillesvangurp 3mo ago
Curren prices will come down. There is a lot of potential for optimization. Energy efficiency, energy generation, self hosting, model size and specialization. Etc. Rught now the state of the art is powering data centers with gas powered turbine generators. That's not very efficient.
- bflesch 3mo ago
  Of course, but will the AI startups with their SaaS business model survive?
  - jillesvangurp 3mo ago
    Not all of them. The mobile internet is a good analogy. Lots of telekom providers that invested heavily in infrastructure and then had to earn that back via subscriptions. But in the end that became a race to the bottom and a lot of their attempts to create walled gardens completely flopped when Apple and others came along. MMS got replaced by iphoto, instagram, etc. SMS got replaced by messaging clients. And so on. I don't believe in the model where OpenAI or Anthropic will own the whole value chain. They'll try and probably fail. They'll own lots of infrastructure and there's going to be a shortage of that for some time. But most of the value creation will happen upstream from them.
jeswin 3mo ago
Would prefer not to offend the author, but I do believe this article has very little for the HN audience. No new insight, and no numbers or new information.
- ludamad 3mo ago
  Is there any place with better curation? I notice quite a few articles summarizing the state of AI that feel redundant with one another
loadcurve 3mo ago
[flagged]
bflesch 3mo ago
Spot on. From an US outsider's perspective there's so much ridiculous stuff going on that you feel like you're watching an episode of "bum fights". I don't think US knowledge workers alone can carry this bubble.
xmstan 3mo ago
There is a good and cheap alternative: R9700 32GB + Qwen 3.5 27B Won't give you SOTA performance, but will be as good as Sonnet a few months back.
arbayi 3mo ago
i think we have the causation backwards here. llms aren't expensive because they have to be — they're expensive because we keep reaching for the expensive model instead of putting any effort into making the cheap one good enough. a surprisingly large fraction of production workloads can be handled by smaller models with the right scaffolding. it's often easier to switch to a larger model than to engineer those pieces, so many teams never bother. my intuition is that a lot of the current "ai cost crisis" is really an orchestration problem rather than a model pricing problem. before asking whether frontier pricing is sustainable, i'd first ask how much of that spend is simple tasks being sent to the smartest available model by default. my bet for the next few years is that the model itself stops being where the value is. frontier models will become more like commodities, and the real difference will be the layer around them as routing each task to the cheapest model that can do it well, verifying the output, and only escalating when needed. eventually, asking "which model do you use?" will sound a bit like asking "which cpu do you use?" the engine still matters, but the system built around it matters a lot more.
- byzantinegene 3mo ago
  Unfortunately, the economics of what you suggest do not justify the trillion dollar valuations of OpenAI and Anthropic.
  - arbayi 3mo ago
    [flagged]
FinnLobsien 3mo ago
The problem space has a few aspects: 1. We're still in the "$5 airport Uber" era of LLMs. They're heavily subsidized, and everyone still complains about costs. 2. There hasn't been a real incentive to work on cost optimization for data centers and the hardware they contain. When/if price hikes happen and send people scrambling to use other models or drastically reduce AI usage, this will suddenly need to happen. 3. We're massively overusing SOTA models. As long as you're on a subsidized subscription, you can use Claude Opus 4.8 high to write blog article meta descriptions. If you paid by token, you wouldn't do that. 4. Open models are a wildcard that could completely change the calculus.
- eru 3mo ago
  Mostly agreed, however I'm not sure about 3: I suspect it works like gym memberships, and the companies mostly make their money from people who don't use the subscriptions all that much.
  - FinnLobsien 3mo ago
    I think the problem is that the companies mostly don't make money, period. They may have better unit economics on underused subscriptions, but I don't see a world in which OAI/Anthropic don't heavily tighten the screws in the future. Right now it's silly to default to frontier models, but it won't bankrupt your company. I believe in the short-medium term future, we'll need to be more deliberate about model choices. In the long-term, of course, tech costs tend to plummet. Is there a future where in 15 years, my Apple Watch locally runs an Opus 4.8-class model? Maybe. And that would obviate this whole discussion.
    - hparadiz 3mo ago
      [flagged]
      - piva00 3mo ago
        You can save it to your favourites, no need to comment at all if it's not going to add to the conversation.
Someone 3mo ago
> What is happening here is that leading AI labs are charging not only for inference but also for research in model architecture, training data collection and curation, model training cost (which can be tens or even hundreds of millions of dollars), paying their employees and recovering the marketing costs. Of course they do. How else do you expect them to pay for that? If you buy a Foo from Acme, Inc, you aren’t only paying construction costs, either. > On the other hand, once an open weight model is released, any inference provider can easily host it and just do some markup on inference cost. This proves way cheaper than running a frontier AI lab. The only logical conclusion for commercial AI labs is to never release their models as open data, and try to stay ahead of open models. One way to do that is by having better models, another by having more users (because that decreases the per-user costs of creating the models, decreasing the price difference with companies running open models). The frontier labs are aiming for a combination of both.
sosojustdo 3mo ago
[flagged]
ramon156 3mo ago
> and Microsoft, Salesforce and Github are taking steps to reduce AI spend by employees. anyone got a source? sounds juicy
simianwords 3mo ago
The author understands well that Opensource is catching up but I think that the gap will remain constant - SOTA models will still be more performant. The author mentions $54 in costs but the reality is that developers are paid around this much per hour. What is likely to happen: LLM performance goes even higher and can do tasks that take humans days to accomplish. You then have to compare LLM cost with human cost - something the Author has forgotten in their analsys.
- xienze 3mo ago
  > The author mentions $54 in costs but the reality is that developers are paid around this much per hour. Sure, but imagine a situation where you've spent an hour going back and forth with the LLM trying to fix a problem and at the end of it you've only made minimal progress. Now you've spent an hour of your time AND $54 with little to show for it. It's a metric I don't think many people track: the cost of going in circles with an LLM for an extended period of time while burning tokens and still not resolving the problem.
  - simianwords 3mo ago
    That happens with humans too and for sure LLMs make it better not worse. I know the number of times I tried to do something where the answer was simple but I took a few days to get there.
rvz 3mo ago
This is no surprise at all and was very predictable. The Chinese open weight models were always winning the AI race to zero where as the likes of Anthropic and OpenAI have no choice but to increase token costs. Even Microsoft wants to use some of the Chinese models only realizing how expensive both the frontier models are. It turns out that Jevon's paradox does not exist in the US (it exists in China). This "Tokenmaxxing" marketing stunt was a scam for the frontier models to raise even more money at unsustainable valuations.
_pdp_ 3mo ago
Prices will go down one way or another. That is of course unless the market gets cornered by restricting model use, restricting supply of essential hardware components or raw materials to make this hardware, etc. In terms of running the model locally vs a service provider, that will be down to convenience more than anything else for the same reason why not everyone is hosting their own website at home on their own box.
- chiply314 3mo ago
  Token prices will go down for sure, but i watched a video interview on yt from cloudflare ceo and apparently the internet traffic of agentics increased and took over human. If we continue this year with a2a, agentic layer and co, there is probably a huge bulk coming up with a lot more agents running a lot longer and talking to each other to solve issues which will increase token usage significanlty.
  - _pdp_ 3mo ago
    My thinking is the same. I believe that AI will be the predominant forms of "intelligence" online probably taking as much as 90-99% of all traffic. It is not hard to see where this is going. The price for tokens will become a proxy of consumed energy in my mind, i.e. tps will be something like kWh almost directly correlated in terms of cost.
swiftcoder 3mo ago
> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. Who in hell would actually do this? That's a level of problem that any of the flash-class models can solve. Hand that sort of thing to GPT-mini, Haiku, or DeepSeek Flash, and save the big guns for big architectural problems.
jhab82 3mo ago
[flagged]
raincole 3mo ago
> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. 1. How much it costs in terms of programmers' salaries? 2. Can DeepSeek do this (I bet it can) and how much it costs? The fact the author ever had the idea of using a SOTA to solve do this means LLMs are actually quite cheap.
veselin 3mo ago
The more I think on the problem, the more I believe this will be solved with US interventions. And the interventions will increase inflation by a lot, so prices will not go down. The other alternatives with LLMs becoming more expensive in an Uber-like move may not work due to a lot of competition. I also don't think usage will increase 10x. I don't always have coding tasks for an LLM despite it being good. My reasons to believe so are outside of what interests HN community and I am neither endorsing this behavior, nor I think it is that simple. But US also has a huge debt that it must service. Wouldn't it be convenient if it was suddenly halved in actual value?
- byzantinegene 3mo ago
  unlikely scenario as the main mandate of the federal reserve is to keep inflation in check. inflation reaching such levels would also cause interest rates to rise astronomically, and this would make the debt harder to service
ajdegol 3mo ago
> doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. Not trying to be harsh, but that sounds like a skill issue. You have the language server to lean on; easy feedback loop; sub agent per type.
charcircuit 3mo ago
>Most AI labs have likely ingested everything available in digital and print media for the model training. This isn't how coding models get better though. Why would this have anything to do with plateauing?
yturijea 3mo ago
I am using perhaps 15% of usage count on Claude with just the normal subscription. And I do full time software engineering and would say I use quite a lot of AI input on thoughts, designs and code drafts. So how these companies and people manage to use these absurd amount of tokens is a mystery to me. It feels like this are just running huge amount of non-vetted data to the LLM's and or running loops against the LLM's which only produce fractional results if not wasted results for insane cost. So really it is the equivalent of just burning money, or heating your house in the winter while having all your windows open.
- mschild 3mo ago
  > So how these companies and people manage to use these absurd amount of tokens is a mystery to me. Fire and forget. They run multiple agents in parallel 24/7. AI isn't just a rubber ducky for them, its their main (only) tool at that point.
- auggierose 3mo ago
  What is a "normal" subscription? Are you using Claude Code, or just Claude??
  - yturijea 3mo ago
    Just Claude, I have seen the weird hallucinations these LLM's make, yes that also means opus, fable etc. so I don't trust it to just run its own clause. Yes that also means I get to inspect and confirm every step of the way, to ensure the design is followed, we are not making unneccesary changes, we have thought about edge cases, testing etc. And I also keep an understanding of what is produced, because I will manually copy it in, I will manually read through it. I will do secondary review of it myself in PR's whatever. But I guess a lot of people just don't and just blow claude code through the roof on ad libitum infinity loop? On subscription, I just checked, I have the Pro Plan, which for Claude I believe is the equal of the normal one?
    - auggierose 3mo ago
      I get where you are coming from (it's me, last year), and that's what I thought. But if you use Claude Code or Codex, you will blow through your pro plan quickly. If you don't use them, you are not really using AI. I know how that sounds, but that is how it is. These models are smart now. Really smart. Yes, they hallucinate, but usually not without reason. I am having long discussions with these models before generating code, and generate markdown from them. These are then the basis for the generated code. I am trying to give the model as much background as possible. I read the generated markdown: if there is something that feels off, like I don't really know what it means, then you need to fix that first, by discussing it with the model. Often, these are real problems in how I was understanding something, the model wasn't really getting it, and just made something up that it hoped would kinda work. And I prefer Codex over Claude Code (prior to Fable, Fable is something else!), it behaves more like a helpful PhD-level colleague and just feels sharper. Claude Code sounds a bit like a mix between an HR person and a therapist that is on vacation too often. I am still looking at code, but only if something came up during high-level discussions with the model that I want to pin down exactly. Otherwise I just talk about the high-level intention of the code, usually not looking at it. What REALLY helps is coming up with the right theoretical frameworks for your work, with practical implementations that the model can use, and that allow some kind of verification. Let's say you want to parse something. For a one-off the model is great at generating "hand-rolled" parsing code, but for something disciplined, giving the model a way to generate context-free grammars and giving it a way to check them for determinism gives great results.
starchild3001 3mo ago
A few thoughts: 1. Chat, being 3 yr old, is a fairly mature and solved problem today. Top companies aren't even talking about it anymore! Gemma 31B does it amazingly well (for $0.4/1M token output). Practically every near-SoTA and SoTA model does simple "chat-like" QA amazingly well -- summarization, basic question answering, single- or few-step search. 2. Tasks -- or knowledge work on a computer -- are the new frontier. Computers have become competent only recently, and only for some of the tasks so far. I'd guess another 2-3 yr development cycle, after which "el cheapo" models will be virtually indistinguishable from SoTA. As tasks are the new game in town, AI labs can still charge a premium for it. That premium has disappeared already for chat; most users cannot tell 99% correct answer from 95% correct answer; nor do they always wish for maximum accuracy. 3. What comes after Tasks? I think today's AI startups should figure that one out and solve it before everyone else.
- nok22kon 3mo ago
  Jobs come next after Tasks
  - starchild3001 3mo ago
    When the overall economy grows, the demand for human labor often grows with it — it doesn’t shrink. That has been the story of 200+ years of industrialization: new technology eliminates some jobs, but it also creates new industries, new demand, and new kinds of work. We heard the same panic about radiologists. In 2016, Geoffrey Hinton famously suggested we should stop training radiologists because AI would outperform them. Yet in 2026, we need more radiologists, not fewer. The job is changing, not disappearing. You even see a similar dynamic with immigration. Immigrants don’t just “take jobs”; they also create demand, start businesses, pay taxes, and expand the market. Remove them, and the economy often shrinks — meaning fewer jobs overall, not more. TL;DR: AI is not simply “coming for your job.” Yes, the nature of work will change. We no longer employ “human calculators,” but society didn’t run out of work. We created better, more productive jobs than doing arithmetic by hand all day.
bvcp 3mo ago
i see all these claims its too expensive but why arent we comparing it to wage costs, is $10-20 an hour actually expensive?
TZubiri 3mo ago
> GPT 5.5, for example, costs $5 per million input tokens and $30 per million output tokens. This is currently the costliest model available as per OpenRouter. claude 5 mythos and fabled are 50$/MOutTok. Previous models were priced at 75$, so presumably they found the "too expensive" price point.
chilmers 3mo ago
It's weird to see people claiming that model capabilities are plateauing. It wasn't until late last year that we even had strong coding models. Imagine if, less than a year after the first iPhone launched, people claimed that smartphone capabilities were "plateauing" because Apple hadn't yet launched a new phone. And it seems the issue is less than "models aren't getting better" than, "models are good enough to handle 99% of the coding tasks people give to them".
- rimliu 3mo ago
  People claim what they see. I see no improvement since opus 4.6, quite the opossite.
  - qtk8 3mo ago
    Which is not even 5 months old.
  - usef- 3mo ago
    That's only a few months old. Just because there's time between big releases doesn't mean progress stopped. Fable seemed very clearly a step up in my one afternoon of usage. I gave it several bugs that other models had failed at repeatedly (in a mess of a vibe coded side project) and it fixed them each in one prompt.
    - jijijijij 3mo ago
      > Just because there's time between big releases doesn't mean progress stopped. No, but progress not stopping doesn't mean it's not plateauing. I believe 'plateauing' is understood as the process of approaching a plateau, not being stuck on a plateau already. So, the question is about the rate of progress, not its existence.
      - usef- 3mo ago
        I guess we draw a different line then. This year has been full of a lot of great releases so far. Most normal people didn't even use Agents before January. It does not at all feel slower than previous years. HN commenters have been saying that LLMs plateaued ever since the first ChatGPT release. 6 months ago: > LLMs are amazing, but they have reached a plateau. https://news.ycombinator.com/item?id=46109534 https://news.ycombinator.com/item?id=46109534 1 year ago: > generative AI has languished in the same place, even in my kindest estimations, for several months, though it's really been years. https://news.ycombinator.com/item?id=43085885 https://news.ycombinator.com/item?id=43085885 2 years ago: > 2024 has seen nothing substantially good and the only notesworthy thing is this article finally hitting into the public consciousness that we are past of the AI peak and beyond the plateau and freefalling has already begun. https://news.ycombinator.com/item?id=42125888 https://news.ycombinator.com/item?id=42125888 --- Many more that I haven't time to look up. I think the present just always feels slow.
virajk_31 3mo ago
I don't agree with Uber & MS buring their AI consumer budget, it is hard to believe they miscalculate something this significant and not realize it within the first month itself.
dipankarsarkar 3mo ago
[flagged]
senectus1 3mo ago
I've posted this anecdote before, but i feel its worth posting again because I've seen this spread further. >amusing side note: >Was in a meeting reviewing a potential new product, it was going well until they showed us that they had added AI to it (of course they have). It was pretty obviously just shoehorned in, and one part of that obviousness was that they had a column that showed how many tokens it took to make each query. >I asked who is paying for the tokens, they said its included in the license. I said, so is there a budget or is it all you can eat. they said good question they didnt know and would get back to me. I said the reason i asked was just one query there had a 250k token burn on it. and it was a fairly simple query about one device. >then, one of the execs on their side was heard saying out loud "Why are we even showing this to the customers?" >it have us quite a chuckle. But lesson learned... the cost of adding AI to anything isnt really being accounted for let alone the true cost of actually running the AI. >all things AI are going to get more expensive. even if you dont want the AI aspect.
arthurofbabylon 3mo ago
As information flows abundantly, and as information processing flows more abundantly, where will the bottlenecks in the system emerge? It surely won't be in design and production. It probably won't be in chips, infrastructure, and energy (already commodities, increasingly competitive). So... what's the bottleneck? Political will? Human discernment/taste? Raw materials?
oezi 3mo ago
A 200 USD Clause 20x subscription gives you 13,000 USD equivalent in API credits. And those are believed to profitable for Anthropic while the 200 USD are properly not, if used to a large extend. If the subscription is gutted by factor 2/5/10/20/65 to make it more profitable for Anthropic it will be harder for users to justify the subscription. On the other hand 13,000 USD in API credits can go a very long way if used ergonomically. For instance using a max context length of 200k is multiplying your reach in comparison to 1m context.
himata4113 3mo ago
Alright, instead of all this yapping here's the real numbers you can use as a guide: 8xB200[1] costs around 250k DIY and 450k from an enterprise builder so that will be our cost factor, these consume around 7.8kw at 100% load with median load of around 7kw (optimistic) which means that a 240kwh solar installation would be enough to supply it (72kwh buffer for bad weeks / winter) and that will set you back around $240k: this includes battery storage, installation and inverters, diy cost would be lower at around $160k. This puts the cost of the entire system anywhere from $410k to $690k. This does not take in any property tax or land ownership into account since honestly it varies too much. The solar is simply used to provide a fixed cost basis for powering hardware instead of monthly recurring payments. Financing a 5 year loan would cost anywhere from $8,313 to $15,700. Now let's do the math for glm-5.2[3], a fully optimized theoretical build can do around 1200tok/s which means that's around 13-14 streams of ~90tok/s on average, pushing batching further and limiting context size to around ~300k with ~150k median) you can achieve up to 37 streams at around ~40tok/s pushing performance envelope to 1400tok/s. This means you are able to generate 2.5B to 2.9B tokens in 4 weeks. Which means putting the numbers together you can serve 1m tokens at $2.86 to $3.32 per million output tokens all else being equal. Considering that glm-5.2 is approaching opus level intelligence it's pretty safe to say that same applies for frontier labs. Input/cache write/cache reads are very difficult to price, so this assumes you're providing input / cache for free[4]. As a very heavy user I generate around 2M to 5M output tokens a day which would put me at $5.72 to $6.6 of cost per day totalling $200 a month[2]. What I also don't mention is that frontier labs have BY FAR the lowest cost per token out of any provider out there due to the amount of money they also invest into efficiency gains. This was proven by the fact that anthropic saw a huge exodus of openai users put strain on their systems and with efficiency optimizations alone they managed to mitigate a bulk of capacity issues, altho they did run into limits and had to begin spreading out the duck curve, but I have zero doubts they're getting percentage points of improvements month to month. [1]: H300's are unobtanium unless you're building rack-rooms, H200's are not that cost effective and saturate too fast while having poorer efficiency, only capable of running flash tier models. [2]: Okay, I didn't expect to arrive at the $200, this is kind of entertaining. [3]: fp8, z.ai serves fp8 according to openrouter. [4]: Assuming you want to charge for input / cache, cache reads make up roughly 30% of the cost, output 20% 50% input so to price it out it would be roughly $.3 for 1m input, $.015 for cache reads and $1.5 for output. Judging by https://openrouter.ai/z-ai/glm-5.2#pricing https://openrouter.ai/z-ai/glm-5.2#pricing, appears that my math checks out.
oggreen 3mo ago
These ridiculous market caps can only be justified if LLMs can ultimately produce outputs that are above human capabilities OR reduce costs and perform human tasks for cheaper than the human rate. Neither of these things have happened thus far and the incremental gains are decreasing more and more. The OpenAIs and Anthropics are going to get eaten by open source, I don't see prices going up, prices are going to crater. The models are going to be more and more commoditized.
offby_one 3mo ago
This entire discussion is on the supply side chips, available weight, and switching cost. What is missing is the demand aspect. The firms that will be the most impacted when there is a drop in price are not Uber or Microsoft; it is that small privately-held firm using spreadsheets and hunches to make business decisions and cannot afford to invest in AI due to the high costs involved currently. The minute inference becomes affordable, an entirely new set of users joins the scene. The real issue is not what will happen to the laboratories it's what gets built for that next wave and whether it's actually useful or just cheaper versions of what already exists.
chonghaoju 3mo ago
[flagged]
buynlarge 3mo ago
Regarding the 'no more training data' point. I agree that text capabilities are maybe hitting the limit of available training data. But, the big AI platforms are now being used by so many people for so many things that this becomes the new data source for new and different types of abilities. Theres also the fact that the AI story means they've been able to fund huge data center builds and hardware innovation. This will lift the existing AI/ML applications (e.g. robotics, sensing) in themselves, as well as the fact that they can be integrated with the text models in probably really useful ways. So I think, maybe text abilities are nearing the end, but intelligence and other interfaces with the real world still have a lot of space to grow.
maplethorpe 3mo ago
Costs will continue to come down. That's how technology works. They will continue to come down further and further until they cross the threshold and become negative, and when that happens, instead of it costing you money to use GPT 5.5, you'll actually receive money! LLMs will become magic money generating machines, and I personally can't wait.
dipankarsarkar 3mo ago
[flagged]
jessinra98 3mo ago
[flagged]
jarodrh 3mo ago
The article's own example makes the point I'm about to make: > To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. That's all because it ran through the most expensive frontier model for a mechanical task that a cheaper model could easily handle. What hardly gets mentioned is that most people don't actually measure what each task costs them on a granular level. A lot of the waste comes from running everything through one expensive model without considering breaking the big task into smaller tasks farmed out to cheaper models. Whether the labs' economics hold is above my paygrade. It costs what it costs. What I can control is my own usage. Like everyone else leaning in heavily on AI usage, my tokens started running out mid week...sometimes within a couple of days. I had to do something about it or double my spend. So I started tracking my cost per task type a few months ago and it completely changed my workflow. The lowest hanging fruit was the mechanical stuff. Moving that to the cheapest models was a game changer, and much faster to boot. Mid tier models take the workhorse tasks. The frontier heavy hitters are now only used for judgment calls like reviewing and planning. Spend dropped dramatically. Freeing up all those tokens made me even more ambitious to explore parallel ways of working, to get even more out of what I was already paying for.
koaw_moi 3mo ago
[flagged]

116 points by adityapatadia 3mo ago

I think companies will fire 5-10% of people and convert them to token budget. I also believe that before any real companies are running these models locally, they will already have some kind of agentic layer. With the current frontier model lab progress, i do not see any real company which makes real money, running local models. Running local models is easy for me, for sure not that easy for any company. Your DC needs to be able to host GPUs, it needs the cooling power, you need to have a DC. Without a DC, you need to have someone maintaining critical infrastrucutre, taking care of model evaluation etc. For external parties, there might become a new business model: You might not hire an external anymore, but a token budget and the 'operator of the token budget'. The current chip fabs are full, developing a high end / cheapisch local LLM Chip will still take a few years as long as the DC GPU demand is still as high as it is.

lukebuehler 3mo ago
I work with large enterprises that _only_ run critical workloads on locally hosted models. Think banks, insurance, etc--businesses that absolutely cannot leak any data. They also have CC and Codex, but their use is extremely restricted; anything of consequence runs on models running on GPU clusters in their own datacenter.
- chiply314 3mo ago
  I work at large enterprise and they are happy paying Microsoft and AWS for model hosting. But for sure there will be use cases of very critical data, but at the end the question will still be how big they are in comparision to the rest of the market. These cricial workloads also have the cost issue, right? so will they reduce workforce to compensate for the budget?
netdevphoenix 3mo ago
I am calling it now. LLM hosting is the new web hosting. You will have a market of hosting providers offering you access to LLM compatible hardware (the Hetzners of the LLM world) as well as virtualised LLM access (the Heroku of the LLM world). These will compete along pricing, ownership axes while frontier labs will compete mostly on performance, integration and ease of use (think Wordpress). That's the only way I can see frontier labs charging high enough to sustain the cash flow needed to operate as racing to the bottom is not possible for them. It is interesting to think whether this is another "Cambrian" era like the smartphone OSes when you had Symbian, Android, iOs, Windows Mobile and so many others competing.
- chiply314 3mo ago
  I work at a very big company and they just pay azure and aws to host claude and co for them. So the hyperscalers already won for now probably. At the end of the day, you send a lot of personal data to these endpoints. If you already host everything through microsoft already, LLM hosting is then a no brainer.

walrus01 3mo ago

I have already seen a number of people doing the math on what it would take for hardware to self host a Q8XL quantization of GLM5.2 shared between N numbers of people. There's additional advantages that everything you query, all of your context cache and everything it outputs stays private and can't be arbitrarily turned off by external interference. Personally I think it would be a fairly good bet that something with the 1TB of RAM needed to properly self-host GLM5.2 will still be a very usable piece of hardware in 4 to 5 years from now. There will be even larger, newer models available, sure. But there will also be better models that continue to fit in the same size.

dofm 3mo ago
Back in the earlier days of the internet, when "dedicated servers" were a competitive advantage, hobbyists and small dev shops definitely shared dedicated hardware. So you could see small LLM co-operatives working out, yeah. But my thinking is that this four-to-five-year scenario just won't come to fruition, because the whole concept of needing to run these massive, massive models will slightly more likely be rendered moot by smaller models with better reasoning capacity, and possibly even in that timescale by hardware innovations. One of the biggest problems I have with the whole "we won't be profitable until 2030" model is that 2030 is almost exactly as far into the future as the launch of ChatGPT is in the past, and in that time, models far more capable than that first ChatGPT have been made available to freely download and run on desktop hardware that existed before it launched, and the entire non-model surrounding functionality of that original ChatGPT plus many more functions is now not much more than a routine weekend coding project. I don't know why the market would entertain the idea that no upset like that is possible in the same period of time again.
- xienze 3mo ago
  > So you could see small LLM co-operatives working out, yeah. Only on a pay-per-token basis, I think. Unless it's a very tight-knit circle of folks. Fixed monthly subscription costs I doubt would work in that model. Because you'll get the inevitable: someone pegging the service 24/7 because it's "unlimited" while everyone else suffers.

albertgoeswoof 3mo ago

One thing missing here is the maturity of agent harnesses. I’m finding the free deepseek flash model in opencode can handle all of my simple tasks, because the harness is so good. Soon that will be a local model. And the reality is that other industries aren’t finding the use for LLMs as much as programmers are. Sure there are some benefits but you can’t fire your marketing department and replace it with AI

bflesch 3mo ago
AI is google-in-a-box, and there will be dedicated hardware to run it locally like there was with the crypto ASICs. I feel the only ones losing are the AI startups and Google. This is why they're trying to morph into a social-media like experience of simulated human interaction that can monetize a certain demographic of vulnerable people.

KronisLV 3mo ago

> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. If you can use a subscription with any of the SOTA models, do that. Instead of around 4k EUR in token costs, my Opus usage costs me 108 EUR (with taxes) per month with their Max 5x plan. It's the same with OpenAI, those are heavily subsidized. It doesn't make sense to pay per-token, unless you must. > What is happening here is that leading AI labs are charging not only for inference but also for research in model architecture, training data collection and curation, model training cost (which can be tens or even hundreds of millions of dollars), paying their employees and recovering the marketing costs. Chances are, they're never getting that money back. Best case scenario, the hype around AI slowly declines, worst case - it crashes and takes a part of the economy with it. Also anyone doing distillation with hundreds or thousands of those subsidized attacks is probably winning big. Especially as the model architectures (e.g. DeepSeek V4) are more oriented towards efficiency. > Last but not least and in fact the most important factor, is the ability of users to run local models. So far, almost everyone is using cloud-hosted models and local models are either too big to deploy or too slow to work with. With advancements in chips, this will change in 4-5 years’ time. Currently beefy hardware to run them fast enough to be competitive with the cloud (at least 60 tps) is expensive and even then the small local models quite suck compared to SOTA or even DeepSeek V4 Pro and GLM 5.2, though they're way better than they used to be (compare Qwen 3.6 with 2.5 for example).

ReptileMan 3mo ago
Why do you think that subscriptions are subsidized and not that enterprise tokens are sold at 3000% margin? There are few enough frontier labs that cartel is possible.
- _flux 3mo ago
  I think this comes from the idea that serving these tokens without paying for training is already expensive, e.g. https://news.ycombinator.com/item?id=46613887 https://news.ycombinator.com/item?id=46613887 self-hosted solution might give you only 10-100x more affordable solution at cost. So, given the SOTA providers with even larger models also need to continously be using considerable resources for training their next models, to fund future data centers, and make profit, the token costs are more likely reflecting the real costs, rather than the subscription costs.

dude250711 3mo ago

Not the end of the world! OpenAI and Anthropic will just go back to entirely healthy valuations of ~$5-10B each and the industry carries on.

arjunchint 3mo ago

There is a wave of users switching over to DeepSeek Flash. There are Reddit threads of users sharing billion token spend for $20. If all of global spend on Anthropic/OpenAI/Gemini APIs just switches over to DeepSeek then easily we can decrease total AI spend by 10x

pydry 3mo ago
Probably won't be too long before the government decides to block deepseek's website based on "security" concerns.
- Mashimo 3mo ago
  Who is "The government"? China or your local one?
  - pydry 3mo ago
    American
- sharpshadow 3mo ago
  It’s in the doing with the DAAMT Act and soon foreign AI companies will be on the entity list. Circumvent this sanctions and count with assets forfeiture, civil penalty and criminal prosecution. This will eliminate access to Deepseek and so on overnight. Cherry on top most westerners will face similar problems due to secondary sanctions.
- LUmBULtERA 3mo ago
  Deepseek's models are open-weight and hosted all over the world, how would blocking deepseek's web sight do anything to stop its model's use?
  - sharpshadow 3mo ago
    Deepseek will be sanctioned and therefore no provider will offer it anymore. Only way to use it then will be private but even that will be forbidden if it gets classified as threat to national security.

akie 3mo ago

I am convinced that the combination of capable open weight models and specialized hardware will mean that Apple (and other hardware providers) will start shipping computers with built-in, hardwired, "LLM-on-a-chip" cards that are capable enough to meet 90% of your AI needs. I really believe that in the near-term future we will run our LLMs in hardware, not in software. Hardwire a capable model into a device the size of a graphics card, embed it into a laptop, and you have something that uses less power, does faster inference, doesn't require additional CPU or memory, doesn't cost a monthly fee, and will probably eventually be available for under a (few) hundred bucks.

FridgeSeal 3mo ago
I’d put money on Apple buying/acqui-hiring the Talaas people to further this.

karussell 3mo ago

The current costs do not have to be sustainable for the SOTA model providers as they grow their user base. But I really wonder about the future as the costs have to increase at some point (to be sustainable) but at the same time the competition and local models get better and better.

Gingersnap123 3mo ago

[dead]

exizt88 3mo ago

> We are seeing improvements with each model release these days but it’s clear that the improvements are getting smaller and smaller. This is obviously untrue, both with GPT-5.4, and Claude Fable as examples in the last 6 months.

byzantinegene 3mo ago
gpt 5.5 regularly wastes tokens on wrong commands, requires lots of handholding. I highly doubt there's substantial improvement
rubin55 3mo ago
I would struggle to ascertain the day-to-day difference between GPT-5.4 and GPT-5.5 tbh. Also, imho, Fable is highly hyped, I don't think it is dramatically better than Opus 4.8. Maybe my tasks and interaction with AI is relatively simple (i.e., lots of Rust programming, Linux system engineering stuff).
- chiply314 3mo ago
  I haven't had enough time with fable, but I had to look back on how i worked with claude just 6 month ago to remind myself that it got a lot better. Like i still used plan mode 6 month ago now I don't. I would argue that with every model release we have a new learning phase.
skerit 3mo ago
> the improvements are getting smaller and smaller The AI haters have been saying this for 2 years now.

jillesvangurp 3mo ago

Curren prices will come down. There is a lot of potential for optimization. Energy efficiency, energy generation, self hosting, model size and specialization. Etc. Rught now the state of the art is powering data centers with gas powered turbine generators. That's not very efficient.

bflesch 3mo ago
Of course, but will the AI startups with their SaaS business model survive?
- jillesvangurp 3mo ago
  Not all of them. The mobile internet is a good analogy. Lots of telekom providers that invested heavily in infrastructure and then had to earn that back via subscriptions. But in the end that became a race to the bottom and a lot of their attempts to create walled gardens completely flopped when Apple and others came along. MMS got replaced by iphoto, instagram, etc. SMS got replaced by messaging clients. And so on. I don't believe in the model where OpenAI or Anthropic will own the whole value chain. They'll try and probably fail. They'll own lots of infrastructure and there's going to be a shortage of that for some time. But most of the value creation will happen upstream from them.

jeswin 3mo ago

Would prefer not to offend the author, but I do believe this article has very little for the HN audience. No new insight, and no numbers or new information.

ludamad 3mo ago
Is there any place with better curation? I notice quite a few articles summarizing the state of AI that feel redundant with one another

loadcurve 3mo ago

[flagged]

bflesch 3mo ago

Spot on. From an US outsider's perspective there's so much ridiculous stuff going on that you feel like you're watching an episode of "bum fights". I don't think US knowledge workers alone can carry this bubble.

xmstan 3mo ago

There is a good and cheap alternative: R9700 32GB + Qwen 3.5 27B Won't give you SOTA performance, but will be as good as Sonnet a few months back.

arbayi 3mo ago

i think we have the causation backwards here. llms aren't expensive because they have to be — they're expensive because we keep reaching for the expensive model instead of putting any effort into making the cheap one good enough. a surprisingly large fraction of production workloads can be handled by smaller models with the right scaffolding. it's often easier to switch to a larger model than to engineer those pieces, so many teams never bother. my intuition is that a lot of the current "ai cost crisis" is really an orchestration problem rather than a model pricing problem. before asking whether frontier pricing is sustainable, i'd first ask how much of that spend is simple tasks being sent to the smartest available model by default. my bet for the next few years is that the model itself stops being where the value is. frontier models will become more like commodities, and the real difference will be the layer around them as routing each task to the cheapest model that can do it well, verifying the output, and only escalating when needed. eventually, asking "which model do you use?" will sound a bit like asking "which cpu do you use?" the engine still matters, but the system built around it matters a lot more.

byzantinegene 3mo ago
Unfortunately, the economics of what you suggest do not justify the trillion dollar valuations of OpenAI and Anthropic.
- arbayi 3mo ago
  [flagged]

FinnLobsien 3mo ago

The problem space has a few aspects: 1. We're still in the "$5 airport Uber" era of LLMs. They're heavily subsidized, and everyone still complains about costs. 2. There hasn't been a real incentive to work on cost optimization for data centers and the hardware they contain. When/if price hikes happen and send people scrambling to use other models or drastically reduce AI usage, this will suddenly need to happen. 3. We're massively overusing SOTA models. As long as you're on a subsidized subscription, you can use Claude Opus 4.8 high to write blog article meta descriptions. If you paid by token, you wouldn't do that. 4. Open models are a wildcard that could completely change the calculus.

eru 3mo ago
Mostly agreed, however I'm not sure about 3: I suspect it works like gym memberships, and the companies mostly make their money from people who don't use the subscriptions all that much.
- FinnLobsien 3mo ago
  I think the problem is that the companies mostly don't make money, period. They may have better unit economics on underused subscriptions, but I don't see a world in which OAI/Anthropic don't heavily tighten the screws in the future. Right now it's silly to default to frontier models, but it won't bankrupt your company. I believe in the short-medium term future, we'll need to be more deliberate about model choices. In the long-term, of course, tech costs tend to plummet. Is there a future where in 15 years, my Apple Watch locally runs an Opus 4.8-class model? Maybe. And that would obviate this whole discussion.
  - hparadiz 3mo ago
    [flagged]
    - piva00 3mo ago
      You can save it to your favourites, no need to comment at all if it's not going to add to the conversation.

Someone 3mo ago

> What is happening here is that leading AI labs are charging not only for inference but also for research in model architecture, training data collection and curation, model training cost (which can be tens or even hundreds of millions of dollars), paying their employees and recovering the marketing costs. Of course they do. How else do you expect them to pay for that? If you buy a Foo from Acme, Inc, you aren’t only paying construction costs, either. > On the other hand, once an open weight model is released, any inference provider can easily host it and just do some markup on inference cost. This proves way cheaper than running a frontier AI lab. The only logical conclusion for commercial AI labs is to never release their models as open data, and try to stay ahead of open models. One way to do that is by having better models, another by having more users (because that decreases the per-user costs of creating the models, decreasing the price difference with companies running open models). The frontier labs are aiming for a combination of both.

sosojustdo 3mo ago

[flagged]

ramon156 3mo ago

> and Microsoft, Salesforce and Github are taking steps to reduce AI spend by employees. anyone got a source? sounds juicy

simianwords 3mo ago

The author understands well that Opensource is catching up but I think that the gap will remain constant - SOTA models will still be more performant. The author mentions $54 in costs but the reality is that developers are paid around this much per hour. What is likely to happen: LLM performance goes even higher and can do tasks that take humans days to accomplish. You then have to compare LLM cost with human cost - something the Author has forgotten in their analsys.

xienze 3mo ago
> The author mentions $54 in costs but the reality is that developers are paid around this much per hour. Sure, but imagine a situation where you've spent an hour going back and forth with the LLM trying to fix a problem and at the end of it you've only made minimal progress. Now you've spent an hour of your time AND $54 with little to show for it. It's a metric I don't think many people track: the cost of going in circles with an LLM for an extended period of time while burning tokens and still not resolving the problem.
- simianwords 3mo ago
  That happens with humans too and for sure LLMs make it better not worse. I know the number of times I tried to do something where the answer was simple but I took a few days to get there.

rvz 3mo ago

This is no surprise at all and was very predictable. The Chinese open weight models were always winning the AI race to zero where as the likes of Anthropic and OpenAI have no choice but to increase token costs. Even Microsoft wants to use some of the Chinese models only realizing how expensive both the frontier models are. It turns out that Jevon's paradox does not exist in the US (it exists in China). This "Tokenmaxxing" marketing stunt was a scam for the frontier models to raise even more money at unsustainable valuations.

_pdp_ 3mo ago

Prices will go down one way or another. That is of course unless the market gets cornered by restricting model use, restricting supply of essential hardware components or raw materials to make this hardware, etc. In terms of running the model locally vs a service provider, that will be down to convenience more than anything else for the same reason why not everyone is hosting their own website at home on their own box.

chiply314 3mo ago
Token prices will go down for sure, but i watched a video interview on yt from cloudflare ceo and apparently the internet traffic of agentics increased and took over human. If we continue this year with a2a, agentic layer and co, there is probably a huge bulk coming up with a lot more agents running a lot longer and talking to each other to solve issues which will increase token usage significanlty.
- _pdp_ 3mo ago
  My thinking is the same. I believe that AI will be the predominant forms of "intelligence" online probably taking as much as 90-99% of all traffic. It is not hard to see where this is going. The price for tokens will become a proxy of consumed energy in my mind, i.e. tps will be something like kWh almost directly correlated in terms of cost.

swiftcoder 3mo ago

> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. Who in hell would actually do this? That's a level of problem that any of the flash-class models can solve. Hand that sort of thing to GPT-mini, Haiku, or DeepSeek Flash, and save the big guns for big architectural problems.

jhab82 3mo ago

[flagged]

raincole 3mo ago

> To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. 1. How much it costs in terms of programmers' salaries? 2. Can DeepSeek do this (I bet it can) and how much it costs? The fact the author ever had the idea of using a SOTA to solve do this means LLMs are actually quite cheap.

veselin 3mo ago

The more I think on the problem, the more I believe this will be solved with US interventions. And the interventions will increase inflation by a lot, so prices will not go down. The other alternatives with LLMs becoming more expensive in an Uber-like move may not work due to a lot of competition. I also don't think usage will increase 10x. I don't always have coding tasks for an LLM despite it being good. My reasons to believe so are outside of what interests HN community and I am neither endorsing this behavior, nor I think it is that simple. But US also has a huge debt that it must service. Wouldn't it be convenient if it was suddenly halved in actual value?

byzantinegene 3mo ago
unlikely scenario as the main mandate of the federal reserve is to keep inflation in check. inflation reaching such levels would also cause interest rates to rise astronomically, and this would make the debt harder to service

ajdegol 3mo ago

> doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. Not trying to be harsh, but that sounds like a skill issue. You have the language server to lean on; easy feedback loop; sub agent per type.

charcircuit 3mo ago

>Most AI labs have likely ingested everything available in digital and print media for the model training. This isn't how coding models get better though. Why would this have anything to do with plateauing?

yturijea 3mo ago

I am using perhaps 15% of usage count on Claude with just the normal subscription. And I do full time software engineering and would say I use quite a lot of AI input on thoughts, designs and code drafts. So how these companies and people manage to use these absurd amount of tokens is a mystery to me. It feels like this are just running huge amount of non-vetted data to the LLM's and or running loops against the LLM's which only produce fractional results if not wasted results for insane cost. So really it is the equivalent of just burning money, or heating your house in the winter while having all your windows open.

mschild 3mo ago
> So how these companies and people manage to use these absurd amount of tokens is a mystery to me. Fire and forget. They run multiple agents in parallel 24/7. AI isn't just a rubber ducky for them, its their main (only) tool at that point.
auggierose 3mo ago
What is a "normal" subscription? Are you using Claude Code, or just Claude??
- yturijea 3mo ago
  Just Claude, I have seen the weird hallucinations these LLM's make, yes that also means opus, fable etc. so I don't trust it to just run its own clause. Yes that also means I get to inspect and confirm every step of the way, to ensure the design is followed, we are not making unneccesary changes, we have thought about edge cases, testing etc. And I also keep an understanding of what is produced, because I will manually copy it in, I will manually read through it. I will do secondary review of it myself in PR's whatever. But I guess a lot of people just don't and just blow claude code through the roof on ad libitum infinity loop? On subscription, I just checked, I have the Pro Plan, which for Claude I believe is the equal of the normal one?
  - auggierose 3mo ago
    I get where you are coming from (it's me, last year), and that's what I thought. But if you use Claude Code or Codex, you will blow through your pro plan quickly. If you don't use them, you are not really using AI. I know how that sounds, but that is how it is. These models are smart now. Really smart. Yes, they hallucinate, but usually not without reason. I am having long discussions with these models before generating code, and generate markdown from them. These are then the basis for the generated code. I am trying to give the model as much background as possible. I read the generated markdown: if there is something that feels off, like I don't really know what it means, then you need to fix that first, by discussing it with the model. Often, these are real problems in how I was understanding something, the model wasn't really getting it, and just made something up that it hoped would kinda work. And I prefer Codex over Claude Code (prior to Fable, Fable is something else!), it behaves more like a helpful PhD-level colleague and just feels sharper. Claude Code sounds a bit like a mix between an HR person and a therapist that is on vacation too often. I am still looking at code, but only if something came up during high-level discussions with the model that I want to pin down exactly. Otherwise I just talk about the high-level intention of the code, usually not looking at it. What REALLY helps is coming up with the right theoretical frameworks for your work, with practical implementations that the model can use, and that allow some kind of verification. Let's say you want to parse something. For a one-off the model is great at generating "hand-rolled" parsing code, but for something disciplined, giving the model a way to generate context-free grammars and giving it a way to check them for determinism gives great results.

starchild3001 3mo ago

A few thoughts: 1. Chat, being 3 yr old, is a fairly mature and solved problem today. Top companies aren't even talking about it anymore! Gemma 31B does it amazingly well (for $0.4/1M token output). Practically every near-SoTA and SoTA model does simple "chat-like" QA amazingly well -- summarization, basic question answering, single- or few-step search. 2. Tasks -- or knowledge work on a computer -- are the new frontier. Computers have become competent only recently, and only for some of the tasks so far. I'd guess another 2-3 yr development cycle, after which "el cheapo" models will be virtually indistinguishable from SoTA. As tasks are the new game in town, AI labs can still charge a premium for it. That premium has disappeared already for chat; most users cannot tell 99% correct answer from 95% correct answer; nor do they always wish for maximum accuracy. 3. What comes after Tasks? I think today's AI startups should figure that one out and solve it before everyone else.

nok22kon 3mo ago
Jobs come next after Tasks
- starchild3001 3mo ago
  When the overall economy grows, the demand for human labor often grows with it — it doesn’t shrink. That has been the story of 200+ years of industrialization: new technology eliminates some jobs, but it also creates new industries, new demand, and new kinds of work. We heard the same panic about radiologists. In 2016, Geoffrey Hinton famously suggested we should stop training radiologists because AI would outperform them. Yet in 2026, we need more radiologists, not fewer. The job is changing, not disappearing. You even see a similar dynamic with immigration. Immigrants don’t just “take jobs”; they also create demand, start businesses, pay taxes, and expand the market. Remove them, and the economy often shrinks — meaning fewer jobs overall, not more. TL;DR: AI is not simply “coming for your job.” Yes, the nature of work will change. We no longer employ “human calculators,” but society didn’t run out of work. We created better, more productive jobs than doing arithmetic by hand all day.

bvcp 3mo ago

i see all these claims its too expensive but why arent we comparing it to wage costs, is $10-20 an hour actually expensive?

TZubiri 3mo ago

> GPT 5.5, for example, costs $5 per million input tokens and $30 per million output tokens. This is currently the costliest model available as per OpenRouter. claude 5 mythos and fabled are 50$/MOutTok. Previous models were priced at 75$, so presumably they found the "too expensive" price point.

chilmers 3mo ago

It's weird to see people claiming that model capabilities are plateauing. It wasn't until late last year that we even had strong coding models. Imagine if, less than a year after the first iPhone launched, people claimed that smartphone capabilities were "plateauing" because Apple hadn't yet launched a new phone. And it seems the issue is less than "models aren't getting better" than, "models are good enough to handle 99% of the coding tasks people give to them".

rimliu 3mo ago
People claim what they see. I see no improvement since opus 4.6, quite the opossite.
- qtk8 3mo ago
  Which is not even 5 months old.
- usef- 3mo ago
  That's only a few months old. Just because there's time between big releases doesn't mean progress stopped. Fable seemed very clearly a step up in my one afternoon of usage. I gave it several bugs that other models had failed at repeatedly (in a mess of a vibe coded side project) and it fixed them each in one prompt.
  - jijijijij 3mo ago
    > Just because there's time between big releases doesn't mean progress stopped. No, but progress not stopping doesn't mean it's not plateauing. I believe 'plateauing' is understood as the process of approaching a plateau, not being stuck on a plateau already. So, the question is about the rate of progress, not its existence.
    - usef- 3mo ago
      I guess we draw a different line then. This year has been full of a lot of great releases so far. Most normal people didn't even use Agents before January. It does not at all feel slower than previous years. HN commenters have been saying that LLMs plateaued ever since the first ChatGPT release. 6 months ago: > LLMs are amazing, but they have reached a plateau. https://news.ycombinator.com/item?id=46109534 https://news.ycombinator.com/item?id=46109534 1 year ago: > generative AI has languished in the same place, even in my kindest estimations, for several months, though it's really been years. https://news.ycombinator.com/item?id=43085885 https://news.ycombinator.com/item?id=43085885 2 years ago: > 2024 has seen nothing substantially good and the only notesworthy thing is this article finally hitting into the public consciousness that we are past of the AI peak and beyond the plateau and freefalling has already begun. https://news.ycombinator.com/item?id=42125888 https://news.ycombinator.com/item?id=42125888 --- Many more that I haven't time to look up. I think the present just always feels slow.

virajk_31 3mo ago

I don't agree with Uber & MS buring their AI consumer budget, it is hard to believe they miscalculate something this significant and not realize it within the first month itself.

dipankarsarkar 3mo ago

[flagged]

senectus1 3mo ago

I've posted this anecdote before, but i feel its worth posting again because I've seen this spread further. >amusing side note: >Was in a meeting reviewing a potential new product, it was going well until they showed us that they had added AI to it (of course they have). It was pretty obviously just shoehorned in, and one part of that obviousness was that they had a column that showed how many tokens it took to make each query. >I asked who is paying for the tokens, they said its included in the license. I said, so is there a budget or is it all you can eat. they said good question they didnt know and would get back to me. I said the reason i asked was just one query there had a 250k token burn on it. and it was a fairly simple query about one device. >then, one of the execs on their side was heard saying out loud "Why are we even showing this to the customers?" >it have us quite a chuckle. But lesson learned... the cost of adding AI to anything isnt really being accounted for let alone the true cost of actually running the AI. >all things AI are going to get more expensive. even if you dont want the AI aspect.

arthurofbabylon 3mo ago

As information flows abundantly, and as information processing flows more abundantly, where will the bottlenecks in the system emerge? It surely won't be in design and production. It probably won't be in chips, infrastructure, and energy (already commodities, increasingly competitive). So... what's the bottleneck? Political will? Human discernment/taste? Raw materials?

oezi 3mo ago

A 200 USD Clause 20x subscription gives you 13,000 USD equivalent in API credits. And those are believed to profitable for Anthropic while the 200 USD are properly not, if used to a large extend. If the subscription is gutted by factor 2/5/10/20/65 to make it more profitable for Anthropic it will be harder for users to justify the subscription. On the other hand 13,000 USD in API credits can go a very long way if used ergonomically. For instance using a max context length of 200k is multiplying your reach in comparison to 1m context.

himata4113 3mo ago

Alright, instead of all this yapping here's the real numbers you can use as a guide: 8xB200[1] costs around 250k DIY and 450k from an enterprise builder so that will be our cost factor, these consume around 7.8kw at 100% load with median load of around 7kw (optimistic) which means that a 240kwh solar installation would be enough to supply it (72kwh buffer for bad weeks / winter) and that will set you back around $240k: this includes battery storage, installation and inverters, diy cost would be lower at around $160k. This puts the cost of the entire system anywhere from $410k to $690k. This does not take in any property tax or land ownership into account since honestly it varies too much. The solar is simply used to provide a fixed cost basis for powering hardware instead of monthly recurring payments. Financing a 5 year loan would cost anywhere from $8,313 to $15,700. Now let's do the math for glm-5.2[3], a fully optimized theoretical build can do around 1200tok/s which means that's around 13-14 streams of ~90tok/s on average, pushing batching further and limiting context size to around ~300k with ~150k median) you can achieve up to 37 streams at around ~40tok/s pushing performance envelope to 1400tok/s. This means you are able to generate 2.5B to 2.9B tokens in 4 weeks. Which means putting the numbers together you can serve 1m tokens at $2.86 to $3.32 per million output tokens all else being equal. Considering that glm-5.2 is approaching opus level intelligence it's pretty safe to say that same applies for frontier labs. Input/cache write/cache reads are very difficult to price, so this assumes you're providing input / cache for free[4]. As a very heavy user I generate around 2M to 5M output tokens a day which would put me at $5.72 to $6.6 of cost per day totalling $200 a month[2]. What I also don't mention is that frontier labs have BY FAR the lowest cost per token out of any provider out there due to the amount of money they also invest into efficiency gains. This was proven by the fact that anthropic saw a huge exodus of openai users put strain on their systems and with efficiency optimizations alone they managed to mitigate a bulk of capacity issues, altho they did run into limits and had to begin spreading out the duck curve, but I have zero doubts they're getting percentage points of improvements month to month. [1]: H300's are unobtanium unless you're building rack-rooms, H200's are not that cost effective and saturate too fast while having poorer efficiency, only capable of running flash tier models. [2]: Okay, I didn't expect to arrive at the $200, this is kind of entertaining. [3]: fp8, z.ai serves fp8 according to openrouter. [4]: Assuming you want to charge for input / cache, cache reads make up roughly 30% of the cost, output 20% 50% input so to price it out it would be roughly $.3 for 1m input, $.015 for cache reads and $1.5 for output. Judging by https://openrouter.ai/z-ai/glm-5.2#pricing https://openrouter.ai/z-ai/glm-5.2#pricing, appears that my math checks out.

oggreen 3mo ago

These ridiculous market caps can only be justified if LLMs can ultimately produce outputs that are above human capabilities OR reduce costs and perform human tasks for cheaper than the human rate. Neither of these things have happened thus far and the incremental gains are decreasing more and more. The OpenAIs and Anthropics are going to get eaten by open source, I don't see prices going up, prices are going to crater. The models are going to be more and more commoditized.

offby_one 3mo ago

This entire discussion is on the supply side chips, available weight, and switching cost. What is missing is the demand aspect. The firms that will be the most impacted when there is a drop in price are not Uber or Microsoft; it is that small privately-held firm using spreadsheets and hunches to make business decisions and cannot afford to invest in AI due to the high costs involved currently. The minute inference becomes affordable, an entirely new set of users joins the scene. The real issue is not what will happen to the laboratories it's what gets built for that next wave and whether it's actually useful or just cheaper versions of what already exists.

chonghaoju 3mo ago

[flagged]

buynlarge 3mo ago

Regarding the 'no more training data' point. I agree that text capabilities are maybe hitting the limit of available training data. But, the big AI platforms are now being used by so many people for so many things that this becomes the new data source for new and different types of abilities. Theres also the fact that the AI story means they've been able to fund huge data center builds and hardware innovation. This will lift the existing AI/ML applications (e.g. robotics, sensing) in themselves, as well as the fact that they can be integrated with the text models in probably really useful ways. So I think, maybe text abilities are nearing the end, but intelligence and other interfaces with the real world still have a lot of space to grow.

maplethorpe 3mo ago

Costs will continue to come down. That's how technology works. They will continue to come down further and further until they cross the threshold and become negative, and when that happens, instead of it costing you money to use GPT 5.5, you'll actually receive money! LLMs will become magic money generating machines, and I personally can't wait.

dipankarsarkar 3mo ago

[flagged]

jessinra98 3mo ago

[flagged]

jarodrh 3mo ago

The article's own example makes the point I'm about to make: > To give an example, just doing Typescript type fixes with this model across 50 files cost me $54 this afternoon. That's all because it ran through the most expensive frontier model for a mechanical task that a cheaper model could easily handle. What hardly gets mentioned is that most people don't actually measure what each task costs them on a granular level. A lot of the waste comes from running everything through one expensive model without considering breaking the big task into smaller tasks farmed out to cheaper models. Whether the labs' economics hold is above my paygrade. It costs what it costs. What I can control is my own usage. Like everyone else leaning in heavily on AI usage, my tokens started running out mid week...sometimes within a couple of days. I had to do something about it or double my spend. So I started tracking my cost per task type a few months ago and it completely changed my workflow. The lowest hanging fruit was the mechanical stuff. Moving that to the cheapest models was a game changer, and much faster to boot. Mid tier models take the workhorse tasks. The frontier heavy hitters are now only used for judgment calls like reviewing and planning. Spend dropped dramatically. Freeing up all those tokens made me even more ambitious to explore parallel ways of working, to get even more out of what I was already paying for.

koaw_moi 3mo ago

[flagged]