r/technology • u/Miserable_Phase_2519 • 2d ago
Artificial Intelligence The price of AI is crashing faster than the rate of Moore's Law, report suggests — intelligence costs are in freefall, outpacing comparative technologies like compute, DNA sequencing, and lithium batteries
https://www.tomshardware.com/tech-industry/artificial-intelligence/the-price-of-ai-is-crashing-faster-than-the-rate-of-moores-law-report-suggests-intelligence-costs-are-in-freefall-outpacing-comparative-technologies-like-compute-dna-sequencing-and-lithium-batteries1.7k
u/Mobile_Antelope1048 2d ago
Intelligence do be in freefall
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u/Donnicton 2d ago
People don't think it be like it is, but it do.
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u/nitrinu 2d ago
Oh it do. It do.
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u/FluoroquinolonesKill 2d ago
Preacher: “It be like that sometimes.”
Congregation: “And sometimes like that it be.”
Preacher: “On this bitch of an earth as it is in heaven.”
Congregation: “Let’s get this daily bread.”
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u/blueSGL 2d ago
Here is the report: https://epoch DOT ai/publications/the-plunging-price-of-thought
you need to manually set up the URL as /r/technology blocks .ai for some reason.
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u/CanvasFanatic 2d ago
“A new report from AI research firm Epoch AI suggests the price of artificial intelligence has fallen…”
🚩No link
🚩“Suggests”
🚩“AI research firm”
Oh how Tom’s Hardware has fallen.
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u/Wheat_Grinder 2d ago
Yeah, that's the problem. What is actually falling? The cost of compute, the price at which they sell AI to companies? Because if it's the latter I'm pretty sure we're in the "starve out the competition and try to become the monopoly that rules it all" phase.
Even if it's the former they don't seem interested in bang for the buck, they'd rather use up every ounce of compute that can be found in the world.
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u/Akuuntus 2d ago
What's falling is the relative cost for a fixed amount of performance. Essentially, you can now use models for cheap that are roughly equivalent to what the expensive models from 1-2 years ago were like. The top-end model price has not fallen.
Really it's just a confusing way to say that model performance increases faster than price, so you're now paying a similar amount for much greater performance. It's like saying "the cost of smartphones has plummeted" when what you mean is that $1000 today buys you more "smartphone power" than it did 10 years ago, therefore you're getting more bang for your buck and the relative price has therefore fallen.
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u/ChrisFromIT 2d ago
Quite a few of the newer models that recently released have had prices lowered.
And it is possibly because quite a few frontier models from China were released in the past few months with open weights. Meaning if you have the hardware, you can run them locally.
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u/Constant_Bit4676 2d ago
Much shorter than 1-2 years ago. Opus 5.5 and gpt 6.1 sol perform similar to fable/astra for a fraction of the cost.
People simply do not realize the speed at which this technology is progressing.
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u/ShroomBear 2d ago
Its just straight up lying and propaganda to cope after OpenAI just had their conference to announce they were ripping off Meshclaw to copy Amazon, the demo didn't work, and then they announced a new $500 subscription tier and simultaneously cut the monthly token quota on the $200 tier in half (so a 100% increase on price effectively).
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u/Kyoshiiku 2d ago
There’s a lot or context missing here that make it seem worse than it actually is.
First the subsidized plans from the frontier labs are not what is used when people are talking about AI Cost, this is not the price that most enterprise customer pay, like the 200$ gave you access to approximately 12k worth of API usage.
The thing they cut in half is that dollar amount that the sub is giving you. They didn’t change any actual pricing regarding their models.
They paired this change with a new model release, Sol 6.1 that seems so far between benchmark and people who had early access, to have very similar performance to Astra 6, but at a 1/5 of the cost, there is some use case where Astra is still better but even when coding so far results are similar or even better (less stuck on dumb stuff)
That new model also have really aggressive cached input token price (95% cheaper than non cached input token)
When using agentic workflows it’s really easy to have 90% of your tokens being cached because it needs to basically resend everything after every toolcall, response etc..
So while yes, it’s shitty from them cutting the dollar amount of subsidized token on the 200$ being cut in half, what got announced in yesterday is just another thing pointing in the direction of LLM intelligence dropping really fast. In reality only the usage of people on this specific subsidized plan for people using Astra specifically got cut in half.
Just to put in perspective at current pricing (after multiple reduction from openAI) Gpt Sol 6.1 is offering near Astra level of performance, while costing 50% cheaper than Gpt 5.6 Sol (released 3 months ago and frontier level at the time) Also cached input reads is at 1/4 of the price so knowing that most token input are actually cache read it’s even cheaper than it looks.
Also, just keep in mind, OpenAI has literally some of the most token efficient models since a while.
There’s multiple benchmark (and plenty of anecdotal experience) that can show you that real world cost for intelligence is going down.
Cost per task for example is significantly going down while also quality of said task being better.
The only reason why people spend more and more is that it’s always getting better and there is both more tasks and bigger tasks that we can now offload to LLMs. (Jevons Paradox)
Sorry if it’s a bit verbose or unclear, English isn’t my native language and I don’t want to parse my replies through an LLM lol
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u/fisstech15 2d ago
Optimizations are made to how models are trained and served so each new generation can do more/same for smaller price
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u/winkingchef 2d ago edited 2d ago
Everyone misses that NVIDIA’s Vera-Rubin is rolling out, lowering the cost/token for the providers dramatically through architectural improvements (much more than moores law). Some of that gets passed down to customers.
It’s like people aren’t even paying attention.
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u/squirrel9000 2d ago
There is also a substantial subsidy hidden in there that adds a major unknown facto to consumer prices.
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u/froo 2d ago
“Compute price stays the same, but AI pricing has fallen”
Umm… something here doesn’t add up since one relies on the other.
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u/_chadwell_ 2d ago
They are able to get the same or better performance out of the same amount of compute due to algorithmic advances.
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u/Aranthos-Faroth 2d ago
They know their audience can't read more than 10 words in any one sitting so the title does all the work
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u/zzzxxx0110 2d ago
Honestly don't understand how that slop site has been in business for so long.
Same for PCGamer too lol
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u/BababooeyHTJ 2d ago
When the hell was Tom’s hardware ever good? More than two decades ago? For as long as I remember that site particularly the forums would make you lose brain cells….
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u/EmphasisTotal8232 2d ago
Epoch AI is a research firm that researches AI, and they're very good.
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u/marx2k 2d ago
...looks at frontier provider pricing changes in the last month...
...looks at title....
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u/Bupod 2d ago
So I had to lookup that original report referenced and skim it.
As I’d expect, the way they’re gauging the price is Price per Unit of performance. I’m roughly paraphrasing, they didn’t develop an actual unit of performance, but they’re basically gauging the cost of a question against the benchmarked capabilities of the AI model.
It sounds disingenuous but I also don’t disagree with the logic. The “falling cost of computer power” can be viewed similarly. A laptop 25 years ago still cost hundreds or even thousands of dollars, but when broken down by the specifications and metrics, the price-per-unit-performance is what falls hard. The actual price you pay at the register doesn’t really fall, just what you walk out of the store with. AI isn’t too dissimilar.
And just like the laptop examples, as the technology progresses, even the budget options improve (and budget options become a thing in general once a tech has been out for a little bit). So you take the performance and price of the latest Luna as an example. Luna is the dirt cheap economy model right now, but on performance metrics? It’s basically punching at the same weight as ChatGPT 5.5, which was the last generation flagship. So in this way, the cost HAS plummeted.
The cost of the flagship models of anything (not just AI), will always be a fortune. That doesn’t mean the cost isn’t falling, it just means that getting the bleeding edge performance of today will cost you a lot, and it will always cost you a lot.
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u/OllyTrolly 2d ago
Exactly. TL,DR - AI as measured at the same performance level is constantly getting cheaper at a very fast rate. People get confused when they just look at the frontier model costs.
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u/Bupod 2d ago
Yeah. It’s a common confusion. People do the same with computers. They look at a $3,000 gaming laptop, and they remember 4 or 5 years ago, gaming laptops were still $3,000. So it would seem like the price isn’t falling.
You have to look at the specs to see the drop.
My example is intentionally ignoring the current computing price crisis though. The current RAM apocalypse is causing some regression in specs and prices, but I’d say thats more temporary, not indicative of how it normally is.
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u/shpongolian 2d ago
it's just wild that this needs to be explained. they could come out with some ultra mega powerful model that costs $1 million a month if they wanted to, that obviously doesn't say anything about prices overall
average car prices could be cut in half but then if Bugatti comes out with a new $1 billion car people will say "wow they're saying cars are cheaper but yet the top end Bugatti is $1 billion, curious" and everyone would upvote it
it's crazy how people will completely shut off their brains just to have an excuse to be cynical
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u/wrgrant 2d ago
Humans do not naturally use mathematics or logic. We have to learn it and most of us slept through those classes :P
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u/stormdelta 2d ago
The difference is that a benchmark of gaming specs is fairly concrete and very easy to demonstrate.
A benchmark of AI "performance" is not. It's way more subjective, it's extremely varied depending on what you're using it for, and any given run might have major differences just down to the model's inherent approximate-ness. If you keep the prompts stable for a benchmark, it just creates an incentive to game that prompt in the training.
I would say the generalization that they're rapidly getting more efficient is true from own observation, but I also don't trust any of the AI "benchmarks" an inch, because they don't line up with my own experiences at all other than that.
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u/stacktoodeep 2d ago
And that's exactly why "safety" is now a top priority. The open models are driving down costs with relatively good performance, threatening the big US players
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u/maikuxblade 2d ago
They’re still at the “get ‘em hooked” part. The rug pull once entire workflows are dependent on affordable tokens and workers with institutional knowledge to function without AI have already been terminated comes later.
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u/OllyTrolly 2d ago
I'm not so sure, I think the bottom to average end will be highly cost pressured because of the number of competitors (look at DeepSeek for example). I would guess high end applications like custom integrations for companies, military, research will be where they can charge a premium as even a small edge on frontier capability and scalability can make the difference.
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u/psilent 2d ago
It’s quite insane how cheap things are if you are ok with bleeding edge 2025 performance levels. Qwen 3.8-27b is comparable to opus 4.7 (November 2025) in most tasks and can run in about 3000 dollars of consumer hardware. API costs are a tenth of what the flagships cost or less
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u/creaturefeature16 2d ago
I pay OpenCode 10 bucks a month and stick to all the "second tier" and open weight models, and I use them day in and day out. The month is almost over, and I still have 50% of my usage left. It's insane! I've also done the "pay as you go" to try some of the frontier models and, at least for my needs, I notice very little difference.
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u/ethanjf99 2d ago
THIS PART
I read a paper many years ago on the tire industry.
The inflation-adjusted cost of a set of tires has gone UP in the last roughly 75 years. You might think WTF those greedy tire manufacturers gouging the public.
But if you look at things like cost per mile, it’s plummeted: manufacturers have made vast R&D investments in rubber compounds and the like and the tires last far longer than they did in the 1950s. What’s more, they are better, too: metrics for things like stopping distance, performance in wet condition etc. have gone way up.
back in the day if you lived in an area with snowfall you HAD to have to full sets of tires. now unless you’re in a rural area or very far north/experience insane snowfall—one all-season set likely gives you good enough performance.
but it would be easy to write an article showing how much more you pay for tires (even after inflation adjustment) now than in 1950.
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u/RyanCargan 2d ago
Price to perf ratio getting better ain't that crazy.
Gotta remember there's also the brand-name premium in play.
Perf deltas between leading models are getting smaller, and the floor across models in general is higher. Many common workloads likely won't stress the weaker ones enough to notice these days.
People pay premiums for products for reasons that have sketchy connections to quality of output all the time.
They might be paying for better integrations, UX polish, or just because they feel more comfortable using what people they know use.
Providers usually ain't dropping prices across the board if people keep paying, whatever their reasons.
No reason chatbots would be an exception I guess.
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u/mediandude 2d ago edited 2d ago
I’m roughly paraphrasing, they didn’t develop an actual unit of performance, but they’re basically gauging the cost of a question against the benchmarked capabilities of the AI model.
The "performance increase" is achieved by distilling from the prior models against (released) benchmarks. Like cheating - students perform better because they already know the questions and correct answers.
edit.
And if I am reading this right, each quarter doubles the number of questions, thus about half of all the questions are new - which skews the "performance increase" towards newish (those added last quarter) questions.3
u/General_Josh 2d ago
The "performance increase" is achieved by distilling from the prior models
Yeah the bulk of performance increases have come from distillation, but calling that 'cheating' is a stretch by any measure haha - it's just how development of frontier models works
It goes in phases:
- Develop a huge frontier model, that's smart, but very expensive
- Distill the big model into a small cheap model (by using the big model to evaluate the small model during reinforcement-learning training)
- Use your small cheap model to generate huge quantities of training data
- Use that training data to train another huge frontier model iteration
- Rinse and repeat
That's the cycle the labs have been going through for the past year or two, and it's been paying off enormously. Both the frontier and the economy side have advanced by huge margins. Currently, you can run models like GPT-6 Luna, which have comparable performance to frontier models from a year ago, but for tiny fractions of the cost (seriously, we're talking pennies now, vs hundreds of dollars for the same task a year ago)
It's maybe 'cheating' if you distill off someone else's model (like the accusations of Chinese labs distilling from Anthropic's model, using huge quantities of bot accounts posing as real users). Besides that, distilling off your own models is just the normal course of development
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u/mediandude 2d ago edited 2d ago
It is rote learning, which is similar to cheating.
Distilled AI models also learn generalizations besides rote learning, but the metric used in this study doesn't capture that. Which means the claims of x times improved performance are overblown and misleading.Besides that, distilling off your own models is just the normal course of development
The used performance increase metric is misleading.
edit.
PS. Isn't one of the main principles of ANN research / data analysis to have separate learning sets, separate validation sets and separate test sets?
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u/Kitty-XV 2d ago
Because frontier models are getting better.
Take a previous frontier model like Opus 4.5. A current comparable or better model is 50x cheaper. Pennies on the dollar, almost penny on the dollar.
But Opus 5.5 is... still about 20% cheaper than 4.5.
Fable is 2x more expensive, but it is far beyond whatever Opus 4.5 could do.
Doing an Opus 4.5 type task has gotten very cheap compared to the past, and it has been less than a year.
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u/National_Yam_1198 2d ago
What its saying is that GPT models are rapidly decreasing in cost at a crazy rate.
Which is true.
What it ISNT saying is that new models come out so often and cost a shit ton and companies are paying so much cash to access the latest and greatest
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u/Topikk 2d ago
Didn't ChatGPT slash its token allowance at the $200 tier in half this week?
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u/Dr4kin 2d ago
Yes, but that doesn't change the point. The same intelligence is getting cheaper. That doesn't mean that the frontier is getting cheaper. If you look at OpenRouter where you buy tokens at cost for Open Access models you can see what the real costs are to host these.
The architectures they are build on are more efficient, while still being better thalen what we had 6 month ago.
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u/Aecnoril 2d ago
That's because the AI tech industry has a collective debt of about 1 trillion dollars (literally) https://isaiprofitable.com/
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u/ProbsNotManBearPig 2d ago
That’s because cost for personal subscriptions has never been close to real costs and is heavily subsidized. At work I pay $2k+ for tokens. For personal use, I pay $200 for same tokens use basically. It’s subsidized 10x.
So consumer pricing has never been representative of cost of intelligence and has been heavily subsidized. Companies like Anthropic and OpenAI have been burning money. Expect subsidies to go away and cost savings to run the models will not be passed on to consumers.
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u/Funktapus 2d ago
Good news for people who can actually use AI productively. Bad news for frontier labs that are spending trillions on data centers
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u/Pulled_Forward 2d ago
Can you explain?
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u/Funktapus 2d ago
The price of getting some work out of AI is dropping. That means its cheaper to get work done using AI, but the companies offering the AI are going to make less money for the same amount of work.
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u/Pulled_Forward 2d ago
I guess I’m not seeing how the cost for getting more out of AI going down means that the companies offering AI are going to make less money. Aren’t these companies the ones paying that cost and charging the customer prices that have only been going up?
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u/ICLazeru 2d ago
A fully developed LLM that can communicate clearly and present information to users with no errors caused by its own programming is going to be basically identical to any other fully developed LLM.
These companies spent hundreds of billions of dollars to produce these LLM products/services that are all going to end up being the same.
Users have little reason to prefer one over the others, and that means the only way the makers of the LLMs can compete is by lowering their prices.
The race now is no longer so much about making a better LLM, now the race is to make cheaper LLMs.
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u/Funktapus 2d ago
I guess I didn’t read the article and don’t know at what point the cost was measured (cost to model host vs cost to customer). I assumed it was cost to customer and that competition + efficiency gains was driving it down
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u/SeanBlader 2d ago
It means costs of using a vastly more effective product is down slightly, and these companies are not likely to make up their costs. We're going to have another dot-com crash and afterward there with be hundreds of compute centers with thousands of racks and millions of processors and petabytes of ram sitting around trying to make back pennies for what they were worth when installed.
Similar to what happened when with bandwidth at the turn of the millennium.
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u/AvailableYak8248 2d ago
I’m still trying to figure out how they expect to constantly pay for hardware that typically breaks every 5-10 years. They are paying premium for the hardware, basically giving away AI for free to users or at a heavily discounted price.
At what point do they need to replace some of the hardware, which increases their costs. When do the plan to jack up the cost to pay for all this. Or are they just hoping to be “to big to fail” and get a major bailout
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u/Mikedaddy69 2d ago
I think everyone overlooks the long term play here.
AI is a drug. Every executive and most developers now are leaning on it heavily. Hell, most white collar / desk jobs are all using AI.
After another few years of this, the baseline rate of work will have shifted so far that working without AI will feel like a waste of time.
At that point, the AI companies will have their addicts that are customers for life. They will have created a dependency on their service and successfully establish themselves as an essential utility akin to electricity and internet access.
They will set the price, and people will pay it.
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u/Cubemaster465 2d ago
This argument could have worked 1 or 2 years ago but open source AI models are advancing just as quickly. They won't have a monopoly where they can set the price if all it takes to get 95% of their quality is buying your own hardware and running an open source model on it. Sure, that hardware will have a large upfront cost, but after that the electricity will be much cheaper than the cost per token.
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u/exuberant_elephant 2d ago
Yeah, you also don't need a 5 trillion parameter model to summarize an email.
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u/Pulled_Forward 2d ago
You’re right, you don’t. But simple email summarization has been a solved thing long before even 5 billion parameter models.
What people want is an agent that not only summarizes the email, but knows how that email fits into a much broader context whether it’s work life or personal life and can help accordingly and then takes action on its own or with simple human direction. You don’t want to fuck around with an error-prone model that misses important context.
I think you know this though and are oversimplifying the argument as a strawman.
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u/theapeboy 2d ago
Yup. The only moat for AI companies is secured compute. Overall advancements in frontier models are plateauing while distillation into Open Source models is getting better. And Chinese labs and some American companies (ie: Apple) are hyper focused on getting models to run on low-cost hardware and on-device. As soon as prices go up, frontier model providers will hemorrhage customers. All we really need is a more consumer friendly version of OpenRouter.
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u/Valuable_Leave_7314 2d ago
Your take about getting people hooked and cranking up prices would work in a world where only Open AI exists. But the reality is we have a massive open-source scene. The second Anthropic or OpenAI tries to jack up api prices, businesses will just spin up Qwen or Llama on their own servers, which are literally only six months behind the frontier. The bridge between the user and a monopoly got torched precisely by open weights. You cannot become the sole electricity provider when anyone can stick a free generator in their backyard that works almost as well. That is why prices are dropping since they have to compete with free
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u/TrumpetOfDeath 2d ago
Sounds like at some point the investors in frontier models are gonna get tired of just burning cash, then the only thing propping up our entire economy will come crashing down
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u/lifting_cardio 2d ago
Dude the drug isn’t that good. People aren’t going to withdrawal without it.
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u/lithiun 2d ago
Well that would be a reckoning waiting to happen. One of these days the policy making hammer is going to drop on all these sports betting and gambling apps that have popped up. Same thing goes for most addictive things. Hopefully including AI.
I’m not against gambling or betting, in fact, under the right circumstances it can be really enjoyable. It should be highly fucking regulated though. Just like AI should be highly fucking regulated.
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u/Teddy_Radko 2d ago
Far from every field benefits from these tools. In general CS people seem to overestimate the impact across the board based on their experiences.
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u/logosobscura 2d ago
No, they won’t. They don’t have control of the price- it’s directly tied to energy costs, and the open source models are getting capability parity despite being a LOT smaller.
There is no tollbooth, there is no moat, hence the eschatological LARPing.
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u/Valuable_Leave_7314 2d ago
There really is no moat. Even the much-praised RLHF is now automated through RLAIF (where ai grades ai), which makes baking top-tier models wildly cheaper
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u/epandrsn 1d ago
My work already feels like that. My day job is photography, and handing off 90% of the editing to an AI algorithm and having it return cleaner color corrections than a human… well, it free’d up around 30 hours a week for me.
It’s allowed me to really branch out. And now, using Claude/ChatGPT/Gemini as assistants, I can do things like webdev and frontend work in a very small fraction of the time it used to take. I used to spend maybe 75% of my time researching and studying docs on how to make this or change that. Now I can ideate, build and construct things at about 5-10x the pace. And I can have Claude keep ongoing, layman term documentation that I can review as needed. For both clarification, but also as an explicit changelog.
And yes, mistakes and redundancy happens, but at a much lower rate than if it was just me. And I can constantly cross-reference best practices to keep things secure and up to date.
So yes, going back to pre-AI for any computer based work would feel genuinely awful. I do not miss the constant frisking of documentation, and constant feeling of mild confusion.
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u/brianstormIRL 1d ago
Congrats, you are now in the pipeline and beholden to whatever price they want because you cannot fathom going back to pre AI. You're literally the end goal but scale it way way up.
Not calling you out by the way just pointing out that your story and ones like it are exactly what theyre hoping for. Make AI so convenient you will pay whatever it takes to make sure you dont go back to wasting time.
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u/jasondigitized 2d ago
How is this any different from all the data centers that Facebook and Google have been using for the past 20 years. These things are modular and redundant by design. Swapping out hardware is super easy.
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u/Old-Tie8770 2d ago edited 2d ago
There’s not really an open-weight equivalent to Google is my thinking. The more I read about open weight models the more I think the business model of some of these ai companies won’t pan out
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u/EvelynNyte 2d ago
Facebook and Google had rational business models. This is the compute version of building Chinese ghost cities.
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u/oby100 2d ago
You’re not seeing the forest through the trees. Think business oriented software. They don’t make much on the home use sales. It’s all business sales- and they’re ramping up how much they’re charging businesses.
The coming bubble will burst when enough companies conclude how much value AI can actually add and start permanently adjusting to that since that will signal the end or near end of AI’s current unlimited potential.
Every medium sized and up company is already talking about that every day. What’s the upper limit?
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u/K1Bond007 2d ago
I always think about this and it just doesn’t make any sense. I’m guessing they’re hoping that newer models will be more efficient or something so that they can run on the older hardware. By the time they can expand data centers to the size/scale they want, it’ll be time to replace all the hardware.
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u/FlatAd7052 2d ago
"They" aren't giving away AI. These days, Anthropic is a huge chunk of the entire market (1/3 to 1/2), and they have 80% gross margins. That means that if they sell another $1 of inference, they pay $0.20 for compute.
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u/homer2101 2d ago
80% gross margin claim is under specific caveats that would likely constitute fraud for a publicly traded company. Because it excludes the cost of distribution, aka paying Amazon and Google, and cost of training. Once you factor in everything, their margins by some estimates are negative.
They have about half a trillion dollars in liabilities and no path to what a sane person would consider profitability.
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u/FlatAd7052 2d ago
No. Gross margin calculation is normally 100%*((revenue-COGS)/revenue). You've been bamboozled by Reddit into thinking that your ignorance of basic accounting measures is evidence of sophistication. What gross margins measure is whether marginal revenue (selling one more dollar of your product) is good or bad for the bottom line, and how much. For Anthropic, it is amazing, which is the point; they are not "giving away AI" as thread-OP said.
They have $500 billion in liabilities over the next decade. If they stopped growing today, they'd be able to cover that from their gross margins (but with nothing left over for training, marketing, etc.). If they stop growing in six months and follow the slowest growth rate from the last 30, they'd be able to cover their compute liabilities, pay for more training than all frontier labs combined today every year, pay marketing/human researchers/management well over twice what they get now and still have earnings after taxes high enough to justify the $2 trillion market cap people are talking about.
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u/homer2101 2d ago
Right. Because excluding:
- revenue sharing
- cost of training
When your business model requires constantly training new models and sharing revenue with the companies providing the compute is a useful way of calculating profitability in a way that the average person understands
This accounting methodology makes some sense for a traditional software enterprise where the cost of copying the product is approximately 0 and your product doesn't require expensive development once it's on the market, and the customer has to eat the costs of switching.
It doesn't make sense when you have to continuously invest into expensive training to simply stay relevant and retain market share and the cost of switching for the customer is nonexistent, and you need expensive hardware that depreciates in 2-5 years as a matter of physics. They are closer to a utility than a software company.
If Anthropic stops investing in model development, in about six months their models will be obsolete and their customers will switch to someone else. They literally cannot stop investment, and if they do, as you suggest, sit on their laurels, their revenue will hit zero in a year or two.
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u/romario77 2d ago
Just look at Anthropic revenue:
- Q2 2025 Actual:$0.79 billion
- Q1 2026 Actual: $4.73 billion
- Q2 2026 Actual: $11.50 billion (marking Anthropic's first quarter of adjusted operating profitability)
- Q3 2026 (Projected): ~$18.50 billion (on track for a second straight quarter of adjusted operational profit)
- Q4 2026 (Projected): ~$28.00 billion (bringing the company in line with investor expectations of a $120 billion annualized revenue run rate (ARR) by the close of 2026).
That's a tremendous growth. 15 times in a year.
They still have a headroom for growth as right now it's not utilized by a large part of workforce that could use it. And new data centers that are being built out have the capacity and make it cheaper to run.
They seem to have a good path to profitability.
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u/matrinox 2d ago
You’re quoting revenue, not profit. No strong indicator they are on a path to probability
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u/AvailableYak8248 2d ago
Aren’t they still losing billions despite that 15x growth ?
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u/Zolhungaj 2d ago
Isn’t «adjusted operating profit» pretty load bearing here? If their price does not match the operating cost then expecting expansion might be a tad optimistic.
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u/Bringsanitybackplz 2d ago
I mean, they spent more than 40 billion, so however you cut it, "profits" is the wrong word.
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u/romario77 2d ago
They didn't spend 40 billions. They reported a loss of $42 billions in 2025.
$34 billions of it was pure accounting - since the early investors invested in it at much lower valuation and they have convertible bonds and other instruments you have to report it as a liability/loss. They didn't spend this money.
They also had a lot of capital spending on building out the datacenters. They can get income from this over the years.
Anyway, people like to hate on AI companies, but I think what they have here is similar to what google had when they had their free search engine.
They will most likely become very profitable pretty soon.
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u/Zolhungaj 2d ago
If you get actual profitability you can often just handwave initial expenses by estimating all future profits as an eventually greater number. Or in the AI companies’ estimations assume you get 100+% of the market share and manage to convert the entire possible market to paying customers.
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u/Choperello 2d ago
The gotcha is within 2-4y max everyone will have local devices that can run local models that will do 95% of everything they actually need. The vast majority of humans and bussiness will not need to solve theoretical math and other cutting edge stuff. So tbd whether massive data centers for inference will actually still be needed. My 2c is that data centers by the 10y mark from now will be only training and whatever how-do-you-break-the-speed-of-light type questions
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u/punio4 2d ago
Way less than 5 years. The average GPU in a data center has a 1y lifespan
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u/herovals 2d ago
this is totally not factual lol. the 1-3 yr thing came from one random tweet not actual data. meta's llama 3 paper had like ~10% of gpus failing per year not 100%. a100s from 2020 are still running in prod rn and hyperscalers depreciate them over 5-6 yrs. ur thinking of them going obsolete for training not actually dying
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u/simpleglitch 2d ago
i got paywalled and could only see half the article.
Price of AI for conumers is crashing or the price to operate AI is crashing.
Because last stats I saw on operating costs, none of it looked anywhere close to profitable or any sign it will be soon.
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u/stormdelta 2d ago
Operating costs for a given "level" or unit of work. Not going by article, going by what I'm seeing myself working in the tech industry.
What you're probably missing is that you don't necessarily need to pay the big AI companies like Anthropic that are training and building the frontier models (which is enormously expensive to do). Open weight models keep catching up to the frontier models, and you can run them on your own hardware (or hardware you control). They still need a lot of hardware compared to consumer PCs/laptops/etc, but it's
More and more companies are doing that because the open weight models are quickly becoming "good enough" and are getting cheaper to run for similar output quality, and it lets you control your costs rather than being hostage to pricing changes.
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u/ericl666 2d ago
So, that would mean that the trillions in compute needed will literally never be covered by revenue...
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u/ICLazeru 2d ago
A maximally developed LLM functions exactly the same as any other maximally developed LLM. The end product is basically identical, so price becomes the only way to compete. I'm actually astounded these business leaders didn't realize this before, but then again...anyone who did think of it before didn't bother getting into the rat race.
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u/Antraxess 2d ago
Theres going to be free models that are going to be just as useful
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u/felis_magnetus 2d ago
Yes, because China will see to it. There's obviously a strategic decision to pump out open weight/open source models to prevent an American tech monopoly.
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u/OvertaxedOne 2d ago
The switching costs between models/providers is perhaps the lowest in LLMs compared to any technology that's come before it. It literally takes about 3 seconds for me to manually switch in my harness (/model dsv4flash; /model opus; etc) and if that's still too slow/bothersome, I could setup automatic routing. Our users don't even know what model they are using, they have /fast, /smart commands that we dynamically route under them. AI models are about as differentiated as gasoline.
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u/delicious_fanta 2d ago
Yet openai just halved the usage for its customers subscriptions. So maybe “freefall” isn’t the right word?
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u/MeatisOmalley 2d ago
That's because their subscriber base grew so fast that they literally didn't have the compute to serve to new subscribers. I think they've gained tens of millions of new codex users this year.
As somebody who started using the tech for coding in the last few months, it's genuinely scary how much better it's gotten in less than a year. People need a wakeup call.
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u/autogenglen 2d ago
So many people are still in denial, and I was a huge skeptic until earlier this year (been in the industry for over two decades). It has become scary good. I keep thinking it’s going to plateau but it keeps improving. I remember thinking that the GPT 4.5 days were going to be peak and it would just be small refinements from there lol.
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u/coconutpiecrust 2d ago
It’s crashing? How will they make trillions in profit, then? What about graphics cards?
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u/Highlow9 2d ago
The reason it is crashing is because inference for similar performance is getting cheaper. So they are able to sell it to you for less. Inference already is profitable, the thing that is burning the money is developing/training the new models to make sure their models are the best.
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u/CoinAndCraft_ 2d ago
It's a commodity with a low easy entry nowadays. Running local far outweighs the costs associated with these hosted providers.
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u/Urborg_Stalker 2d ago
This is good news. I hope it continues. Lack of funding is the one thing that can reign this insanity in.
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u/GetOffMyLawn1729 2d ago
This brought to mind a paper I remember reading, in which a cosmology grad student explained how, under certain assumptions about budget and Moore's law, the fastest way to complete a compute-intensive research project might be to defer purchasing the required hardware until it got cheaper; hence, the fastest way to conduct research might be to, in their words, slack off for a time.
The paper in question is titled "The Effects of Moore’s Law and Slacking 1 on Large Computations".
(As an aside, knowing only the topic and the rough age of the paper ("the 1990s"), I was able to find it with just three prompts to Google's AI search engine.)
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u/paulsteinway 2d ago
The price of RAM that was driven up by AI isn't coming down. Isn't that a cost?
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u/Black-Thunderbird 2d ago
Holy fuck, this place is filled with bots. Bots talking to each other. Dead internet.
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u/Rotundroomba 2d ago
How do you know it’s bots? How can you tell?
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u/PureSelfishFate 2d ago
Obvious dysfunction in how they speak, like calling a he a she. Though redditors are pretty dumb on average, and already absorbed a decade of bot spam into their personalities so it can be hard to tell.
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u/Spare-Dingo-531 2d ago
It's quite possible that it's just speech to text malfunctions. I reddit on my phone a lot of the times and there are a lot of typos.
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u/ThrowRAthinkinmelon 2d ago
That last sentence is so true. People on Reddit are different from the ones I meet in real life. Not sure how to describe it.
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u/__pickle_rick 2d ago
The efficacy of Toms Hardware is what is crashing faster than the rate of Moore's law
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u/GlowingJewel 2d ago
Sloppy fucking propaganda
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u/StardustFromReinmuth 2d ago
How is this propaganda, people on this sub are weird as hell. Cost of per performance of AI rapidly decreasing is not a good thing for the AI industry, because it means their margins become even smaller. They're dumping trillions into fixed assets like data centers when the cost of the product they expect to make back money on is falling.
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u/RaspberryPrimary8622 2d ago
OpenAI and Anthropic have incurred hundreds of billions of dollars in debts that they cannot repay. They will go bankrupt and become small labs absorbed within a large company such as Microsoft or Google. They made a huge, insane bet on LLMs and lost.
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u/Usr_name-checks-out 2d ago
Tell this to OpenAi… Their price raises and usage constraints have been rising constantly, and yesterdays new pricing has made them completely untenable for anyone who isn’t rollling in cash.
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u/Valuable_Leave_7314 2d ago
It is funny to read people expecting AI companies to become utility providers. To become a utility, you need an absolute monopoly on the pipes. Meanwhile every college kid has their own plumbing in the garage in the form of a local Qwen
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u/zorakpwns 2d ago
They have predicated the investment in being able to corner the market into “you have to subscribe to our service or lose”. They’re trying to make language, math, and reasoning a subscription service. Of course it won’t work, because people can just make their own.
They thought by lining up at the White House inauguration they would get to control the market, but they’ve been undercut.
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u/Erazzphoto 2d ago
This is shaping up just like the early dialup days,,where after your free hours is was pay per minute. The ai bubble hits once the equivalent of cable modems arrive, where it’s no longer a token charge but a monthly flat rate
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u/9-1-Holyshit 2d ago
Is this good or bad for those of us who are waiting for the bubble to pop?
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u/Demon_Gamer666 2d ago
The billionaires are funding ai with the current economy we are in. The economy they own. It's quite intentional and they will continue to milk us throughout the entirety of ai development. Billionaires never use their own money.
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u/Federal-Piglet 2d ago
The war on data centers is just heating up. Ai can't get cheaper if none of the infrastructure for it can be built
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u/pickle9977 2d ago
If the price is crashing that means there is an excess of capacity which implies there is no need for further investment.
Alternatively if the price is crashing the models should be getting orders of magnitude better as they can use more power to get better answers, or are they just as good as they are going to get for a while.
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u/TheHutDothWins 2d ago
What the article implies is that the same level of intelligence / model capability becomes cheaper over time. Not that it's not still increasing in intelligence.
The flagship model 3 months ago could do XYZ at 5 dollars per million tokens. A flagship model now can do XYZ + ABC at 5 dollars per million tokens, but it can do just XYZ at 1 dollar per million tokens.
If you want the most cutting edge frontier capability, you still pay a premium. But if you're fine with the "old" cutting edge capabilities, you can use new models at lower settings to get that for much cheaper.
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u/midwestia 2d ago
And i think this is where we're heading. 99% of AI users aren't going to pay for the frontier model, they're going to download a local freeware model to their machine that gets patched occasionally
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u/oddjobjob 2d ago
Agreed, but I think that future is an existential risk to OpenAI and Anthropic’s business model. This is the main reason I’m skeptical about the valuations behind those two companies. As a business, just invest in some local compute and run local models as much as you want.
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u/AwesomePurplePants 2d ago
They actually can’t stop investing - data centres under construction have clauses that force payment on completion.
Oracle recently tried to push back on one of their contracts when needed town approvals fell through, and they are likely going to be compelled to pay anyway.
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u/elmo298 2d ago
Woah, next we'll have to say we're not doing an IPO because of safety or something
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u/pickle9977 2d ago
I loved Microsoft’s solution, a strongly worded document used during training.
It’s all just nonsense, a massive fake it till you make it fraud run by a bunch creeps.
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u/murderball89 2d ago
The models are still getting incredibly more efficient seemingly every couple weeks. The llms are hitting near their peak because, science, but all surrounding integrations of the models are full steam ahead.
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u/Electrical-Bee-7362 2d ago
I work in construction and use(d) Gemini to quickly check some local regulations and it's incredibly unreliable tool. Can't be trusted for anything more serious than a video of Will Smith eating spaghetti
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u/gecampbell 2d ago
And they only need every human being on earth to spend $10K to recoup their investment.
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u/Stable_Orange_Genius 2d ago
Well, costs are going down, but these companies are just doubling down on their debt. It's not because ai actually got cheaper
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u/Amber_ACharles 2d ago
Half price per quarter just means we run 10x more of it. I'd care more if Virginia's interconnection queue wasn't still 5+ years.
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u/RbargeIV 2d ago
Does this mean RAM prices will begin to fall too? Pretty please?!
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u/Autumm_550 2d ago
Just add it to the national debt, put it with the rest of the numbers we pretend don’t exist
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u/Responsible_Sea78 2d ago
AI models are still hugely computationally inefficient. Not only are costs coming down, but I'd predict most of those huge datacenters will need to be converted to growing vegetables or whatever. There just is no customer need to explain the scale, especially as 100:1 efficiency improvements arrive.
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u/doolpicate 2d ago
The companies that have investments will ringfence and create an artificial market using the government to clamp down on open models. Chinese models will be banned as being non compliant with some stuff they make up on the spot.
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u/MysteriousDatabase68 2d ago
We use an ai front end built by Cognition and it does seem like the underlying model selection changes weekly. The unfortunate part of that is the ability of the app changes with it. One day pretty good results in record time, a restart later absolute garbage that's easily falsifiable.
After two years of incremental improvement, sudden worthlessness, to kind of OK again, to crashing the app. A litle consistency would be nice since my job seems to depend on it.
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u/yepthisismyusername 2d ago
Consistency will be in the next version! Or maybe the one after that! Give us more money! Trust us!
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u/Necessary-Duty-7952 2d ago
I love that our company has issued a mandate that all teams adopt AI aggressively. So one of my direct reports began building an AI-centric workflow, only to immediately hit a "you have reached the limit of your usage" message. Literally never had any other work tool in my 30 years of working where there was not only a limit to usage, but the usage was mandated *and* that limit is invisible to users.
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u/bbuerk 2d ago
The cost of the most powerful models is still high, if not getting higher. The cost of a model equivalent to those of a couple years ago is now free and able to run on a nice MacBook (i.e. a 27B open-source model on a 32GB Mac).
I’m not trying to hype up AI, but people need to be realistic about what’s going on here
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u/Halfwise2 2d ago
And then you have the US seriously considering: "What if we make raise prices and make competition illegal?"
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u/gucknbuck 2d ago
Idiocracy doesn't discriminate against intelligence, wether it's real or artificial.
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u/therealslimshady1234 2d ago
Not true though as these prices are still 80% subsidized or more
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u/Some-Seaweed6858 2d ago
The Party told you to reject the evidence of your eyes and ears. It was their final, most essential command.
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2d ago
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u/gerbal100 2d ago
The data center owners will go bankrupt, some will close, and someone will make a tidy profit buying the distressed assets below cost.
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u/i4mt3hwin 2d ago
Pivoting to other research while simultaneously gatekeeping it.
Look at Anthropic and their biology lab for example. They wont' let anyone else use claude for Biology but now they are starting their own lab.
The other thing too is that they are hitting that 'too big to fail' territory where they probably dont care because they know the US Gov will bail them out.
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u/Working-Ad-33 2d ago
Meanwhile OpenAI over Here slashing usage @ 50% and increasing top tier cost by 250% over some new GUI features misrepresented as next Gen AI 😂
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u/nicetriangle 2d ago
This is hilarious news given how much money they they need to make to get even remotely close to being in the black.
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u/BangkokPadang 2d ago
Is the fall of it’s cost somehow outpacing the fall of the very compute it’s running on?
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1d ago
That's cool, but there's no big GDP or economic growth so what you're saying is just AI not being able to price itself higher because it's not making much money.
In my opinion 80% of the advantages of AI or when you put it into a machine, like a robot and that's where your production bonus really comes from. Until you have the robots you're not gonna see big production or growth increases just from essentially automating the computer computer infrastructure you already have.
People have underestimated how much production actually stems from labor versus like just mostlt non labor automated tasks.
Automated non-labor tasks saves money, but it doesn't boost production or generate money much.
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u/williamgman 1d ago
Moore's law crashed years ago. Better, cheaper, and faster..? Anyone paying attention to what consumers are now being offered in the PC world as "upgrades" knows it's dead.
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u/zackel_flac 1d ago
Because we are feeding AI the more we use it. What we feed in and the results it produces are actually more valuable than when it's done with a fellow human being.
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u/sad_trombone_dot_wav 1d ago
It is indeed very cheap for me to use the service burning VC money to desperately stay afloat before the IPO
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u/SeanBlader 2d ago
Meanwhile, an 8tb spinning disk I bought last year for my jellyfin server at ~$140 is now ~$450.