> Now if that means RAM prices come down (as speculated, not reported on, in the link) or the AI companies just do more things with their extra ram is yet to be determined.

I think it is determined:

https://en.wikipedia.org/wiki/Jevons_paradox

Also, there is zero reason to think that the big labs did not have anything similar to TurboQuant for a long time already.

The recent blog post from Google announcing TurboQuant does not change anything regarding RAM planning for the big labs.

TurboQuant itself is already a year old! So even smaller labs have probably seen and implemented it.

I’m not disagreeing with you, but consumer RAM prices are lagging indicators. If commercial RAM prices are dropping then consumers will see those price drops last, especially given the fact that several consumer manufacturers turned to commercial only.

> almost as bad as when LLMs link things to prove their point, you visit the link, and find it says nothing of the sort or even the opposite

To be fair, they got it from us. This happened to me plenty of times long before modern LLMs.

I agree. The article they link to talks about memory company stocks crashing, not RAM prices crashing. There is some truth to the former: https://www.ft.com/content/e4e15692-187e-4466-832e-ec267e792...

I do wonder how closely prices consumer RAM kits follow the wholesale prices for NAND chips manufacturers see internally. The pcpartpicker graphs you linked show consumer prices have leveled out and may even be starting to fall. Depending on how the economics shake out this could mean we've hit an inflection point.

My personal prediction is that once the VC bill comes due and prices for frontier models starts to climb, competition for efficiency will heat up. The main AI use-cases seem to be falling into buckets, and I doubt serving gigantic, do-it-all general models for every use-case under the sun is remotely cost-effective.

If common use-cases start to be more efficiently served by smaller, more efficient purpose-built models (or systems thereof), it'd make the big frontier models increasingly niche. Cursor's Composer 2 model is a great example of this.

In any case, I think it's pretty fair to speculate we may be seeing RAM prices start falling sooner rather than later.

RAM prices haven't crashed yet and it'll take time because it has to propagate within the supply chain. Micron is -20% from the top already https://www.investing.com/equities/micron-tech

Stock price is the best forward indicator I can think of

Thank you, there are two things I would like to point out:

1) Google releasing something probably means they don't see it as important. 4-bit KV-cache quantization has been known for a long time. The fact there is almost a mass hysteria about this paper makes me think there is a lack of skepticism in this AI mania, even in relatively tech-savvy crowd.

2) But prices for memory companies are crashing! look around, the whole market is crashing.

I would think that we are going to see RAM prices increase even more, given, among other things, pure helium disruptions and increased electricity prices.

I haven't looked closely into TurboQuant, but perhaps it will revolutionize just as much as the 1-bit llm did...

Even if TurboQuant, which was released a year ago, drastically lower RAM requirements, AI labs will just release bigger models.

Jevons Paradox. When are we going to learn that efficiency gains in AI does not decrease hardware usage?

> > RAM prices are crashing because new models won’t need as much

> Reality begs to differ [0] and following the link for that text goes to an article [1] where they talk about Google's TurboQuant which supposedly will lower the RAM requirements. Now if that means RAM prices come down (as speculated, not reported on, in the link) or the AI companies just do more things with their extra ram is yet to be determined. The fact this article links there with text "RAM prices are crashing" throws the entire rest of the article into doubt for me.

I find it fascinating how extremely reactive things have become. One research paper which, to my knowledge, hasn't been externally replicated yet, nor implemented, generate tons of hyperbolic article, tweets and such, and do actually manage to move the market at least temporarily. Not just this, but a simple message in full caps lock by the president of the U.S who is in the habit of lying through is teeth constantly, and the same thing happens. It's like there is a big bubble that threw any form of critical thinking out of the window and is in a hurry to react to anything even if it is not even remotely believable. Now I understand why it happens, there is a lot of money that can be made by capitalizing on FOMO, either by driving traffic to their website, socials, etc, or by simply insider trading (which feels like it has been legalized these days). But I still find it incredible the proportion it started to take.

consumer ram is starved by production capacity shifting to HBMs. Hbms dropping in price would not affect consumer RAM on any immediate timeline. Also, as pointed out by many, Jevons Paradox

If the gains are real why the limits are so bad? Google can barely serve Anti-gravity.

> Reality begs to differ

Honestly you're both wrong. RAM prices spiked speculatively, and they're going down for the same reason. Market people always want to argue in fundamentals, when in practice *ALL* the high frequency components of the signal are down to a bunch of traders trying to guess where it's going in the short term.

At best those guesses are informed by ground truth ("AI needs a lot of RAM!" "Sam cornered the marked!" "TurboQuant needs less RAM!"), but they remain guesses, and even then you can't tell the difference between that and random motion.

Not crashing yet. The article is looking 1 to 5 years to come.

Given Nvidia's CEO's agitation I would give credit to the prediction, and if it's correct the price will go back to what it was, or even lower of investment in capacity are made today.

This feels similar to when Deepseek first debuted with claims of ultra-low cost training, and all the pundits exclaimed that Nvidia was finished, the bubble had burst, etc.

Yeah, I also stopped reading at that point. If I want a bunch of random, made up facts to sell lukewarm opinions or steer the uneducated masses, I'll tune in on a Trump press conference. Why does this feel like someone is desperately trying to make reality mirror his flailing market bets?

There is also demand for ram in others areas of data centers. As we are all pushed deeper into clouds, i can see the rise of ram for data storage (ram drives) continue to eat into the supply. A module of ddr5 will be more useful in a netflix rack streaming movies 24/7 than in a gaming PC where it may only be used an hour or two every day.

Demand of tokens is absolutely skyrocketing.

And unlike the traditional "this will replace humans right away", I think what this introduce is a lot of incentive to spread those token in places where there was never any incentive to hire a software engineer for previously. In turn, that will drive a lot of business activity in those area that will potentially fail given the current quality of the output.

This feels like a boom before bust scenario, and I'm not even sure if it will bust.

> The cost to serve tokens is absolutely profitable today and that’s been true for at least a year.

> For the data center build outs, demand for tokens is still exceeding supply.

Can you provide any numbers for this?

According to open router token demand is growing at something like 10% a week

It’s insane

> "decades of overinflated engineering salaries"

'Overinflated' relative to what? You make some good points but I don't accept this as a premise.

> The cost to serve tokens is absolutely profitable today

How can you possibly say that? Everyone knows that's not the case, these companies are losing money every day selling tokens. Revenue is not the same thing as profit.

My main worry is - once this is all over, the market consolidates and using LLMs will become a requirement in job listings, what's the highest price per million tokens companies will be able to charge us?

Currently on a given day I'm chewing through approximately the equivalent of my lunch money, but where there's opportunity to extract wealth, someone will find a way to do it.

> The cost to serve tokens is absolutely profitable

Can you explain why you know better than the analyst at Cursor cited in this article?

The article is just helpfully illustrating how artisanal you can make your slop if you really try!

This is a classic HN mistaking the map for the territory. R&D and capex absolutely figure into de-facto profitability and sustainability for AI labs, despite their separate treatment in accounting.

> well most of us here on HN have benefited from decades of overinflated engineering salaries being paid by often companies that were not profitable and not only unprofitable

This is a really concerning perspective: people were paid what they were worth. Software is or was one of the few remaining arenas wherein a person can find a middle or upper middle class lifestyle consistently.

I will also note: a startup raising an 8 MM series A and eventually fizzling out is not the same at the hundreds of billions invested in these AI companies without a path to profitability. It is utterly absurd to pretend these are the same thing: any company ingesting that much cash needs to justify its capacity to survive.

step change? how? profitable? where did you read that? people want tokens? really? who are these people?

> The ads aren’t going into your paid plans (except maybe a highly discounted tier, depending on the market). The ads are a play to offer a free version. Having an ad-supported free tier isn’t new.

Sounds like it is new for ChatGPT though. That's also how it started with TV and Youtube, first on the free tier then expanding to the paid ones.

> The ads aren’t going into your paid plans (except maybe a highly discounted tier, depending on the market). The ads are a play to offer a free version. Having an ad-supported free tier isn’t new.

This statement doesn't discount the original statement: that ads are going into GPT, which Sam called a last resort.

> The discussion about being unprofitable also repeats the reductionist view that these companies are losing money and therefore the business model doesn’t work. This happens with every VC cycle where writers don’t understand that funded companies are supposed to lose money while they grow. That’s what the investment money is for.

Usually propped-up companies don't last in the long term once the VC subsidy runs out. There's a difference between getting VC money in order to buy rocket parts, and getting VC money in order to charge $7 when you would really need to charge $10. The latter problem never goes away.

> We have very strong indicators that inference is not a money loser for these companies and is likely very profitable.

Why is OpenAI specifically losing money hand over fist then?

>The “but they’re losing money” argument never seems to be brought out against competitors that literally give away their models for free and for which we can calculate the cost of serving 400B-1T parameter open weight models.

To be fair people aren't exactly bullish on the prospects of deepseek or z.ai either, it's just they're below radar so they don't get mentioned.

> companies are supposed to lose money while they grow

At what point do we declare that a company has "grown" and now must make money? OpenAI is a multi-billion dollar company right now, surely that's a point at which they should be profitable, instead of propped up by further investment and borrowing.

> We have very strong indicators that inference is not a money loser for these companies

All of the economic analysis that I've read strongly states the opposite. Running a GPU is a net loss /even for the data centre operators/. For them to break even, they currently charge OpenAI/Anthropic/Etc more than OpenAI/Anthropic/Etc make per-token.

> This article tries to build upon a lot of half-truths or incorrect facts, like this:

yeah i was wondering why my bullshit detector was going off. This feels as if someone who cooks for Ramsey's kitchen is trying to predict the end of the market hike.

> Having an ad-supported free tier isn’t new having ads shoved in paid tiers isn't new either

I've heard "They're losing money" since the 1990s. About Amazon and nearly every other tech company.

The strategy is always:

* Build something useful

* Give it away for free to get people exited

* Convince investors that this is going to rule the world

* Grow to dominate the world

* Enshittify

Programming is a necessary but not sufficient condition for software products to exist. So while the programming has to be good, so too do many other things, like product vision, product management, project management, and of course there still needs to be feedback between all of the above so that engineering isn't implementing a misunderstood version of the product and that product isn't asking for 5 years and a PhD research team. And on and on and on. Typing the code is like 2-10% of actually ending up with a software project and it's more toward the 2% for a software business.

So while AI made coding maybe 110% faster, it has also made literally every other person in the process lose their gd minds and they're wanting to break or skip everything else in the process to just shit out code faster.

> ⸻

Wow, I'm impressed at your usage of this. Apparently it's 0x2E3B, named "three-em dash".

You must be human!

The tech is still young and projects take time. And there are many slow parts of building that have not been accelerated (mythical man month).

I have started making an indie game, as one does, and it’s easily going 2-4x speed, but even still I’d predict a year of free time development with focus to ‘finish’ this thing. But the latest agentic tech is 3 months old.

But are they actually profitable, or do they employ creative accounting where only parts of overhead expenses are counted against all of inference revenue, similar to what Uber did?

OpenAI's numbers show that they definitely are not profitable on inference, and even worse, revenue growth scaled linearly with inference cost from 2024 to 2025, which means they can't outgrow this problem. See https://www.wheresyoured.at/oai_docs/

Yes I wrote a detailed article about this Forbes claim. https://martinalderson.com/posts/no-it-doesnt-cost-anthropic...

Key points - if you compare it to openrouter costs for ~similar sized models it is ~90% gross margin.

And this claim came from Cursor - not Anthropic!

> Lab executives insist that serving tokens is profitable.

Maybe marginally profitable, but right now they need to give out subsidies for people to use their products (Antigravity, Codex, Claude Code et al) in an actually useful manner that prevents churn and at the scale they need to justify usage growth forecasts, which they need to keep the wheel turning.

Probably if you look at the users who exclusively use the simple chat box interfaces (i.e. ChatGPT, Gemini in UI, Claude in UI) plans it is actually profitable, but I'd also say that's not where most of the usage comes from.

I'd love to actually look at both usage + profitability from each user segment to see if their PxQ growth expectations from non-enterprise usage make any sense.

> Many independent providers price tokens of open-weight models at a fraction of Anthropic's prices.

Are those open-weight models as good as Anthropic? Are they the same parameter class?

The point is that you can’t just serve tokens without also training the next models. It’s an inseparable part of your costs, so naturally you can’t be profitable unless the price you are charging ALSO covers training.

Do tokens just cover ongoing operating costs, or are they also able to pay back the cost of training that model originally?

So these companies will be profitable if training stops? Is that even a real possibility?

Not counting training models as part of your gross margin is just creative accounting. It's an inherent part of being able to provde the service for OpenAI, Anthropic etc.

Even so, their subscriptions are significantly cheaper than the token pricing via API. So at some point they will need to get rid of subscriptions or increase the subscription prices dramatically... And that's assuming their current token pricing is actually profitable. Which it probably isn't.

Lastly, I would not trust one word that comes out of an executive of an AI company (or any other large company, for that matter).

I wouldn't trust those claims from any private companies, even public ones play the most insane tricks in earnings calls to inflate numbers or heck, just make up new ones.

I'm not saying they're wrong, but I don't take much stock in their words.

Buying and driving a new car off the lot costs the manufacturer nothing at that moment, but what happens before that is important to account for.

It’s absolutely not winner take all. LLMs have become a commodity and the cost of switching models is essentially nil.

Even if ChatGPT has brand recognition amongst lay people, your grandparents aren’t the ones shelling out $200/mo for a Claude code subscription and paying for extra Opus tokens on top of that. Anthropic’s revenue is now neck and neck with OpenAI, but if tomorrow they increased the price of Opus by 5x without increasing its capabilities, many would switch to Gemini, GPT 5.4, Cursor, or any cheap Chinese model. In fact I know many engineers that have multiple subscriptions active and switch when they hit the rate limits of one, precisely the tools are so interchangeable.

At some point it could even become cheaper to just buy 8x H100s and host Qwen/Deepseek/Kimi/etc yourself if you’re one of those companies paying $3k/mo per engineers in tokens.

> It's a winner-takes-all market and everyone wants to be the next Google

absolutely isn't! if billed per token, there is no reason to be married to a single model family provider at all. the models have very different strengths and weaknesses, you should be taking advantage of this at all times.

Where to go next? I don't think anyone has gotten close to automating everyday PC usage, likely via screen capture and raw keyboard+mouse inputs. Imagine how much bigger would that market be than vibecoding.

I don't think it's winner-takes-all. Google is Google in 2026 because Lycos and AskJeeves were bad in comparison. The average user doesn't care whose LLM they're using because they're all close enough. It's hard to see past the bubble bursting, but I expect most people will use multiple of them depending on context (Copilot via the integration in windows, Gemini via Siri on their phone, etc), likely without paying.

If I saw a helicopter crashed into a tree, I don't have to be a helicopter pilot to know it's not an ideal state of a helicopter and something/some people failed.

When I'm using MS Word and it takes 20 seconds to cold launch on a machine that's magnitudes faster than any computers 25 years ago where it launched near instantly, I can tell something is going wrong. When all of their software is harassing me to use AI in ways I don't want to use it, I can tell something is going wrong.

Sub par is not the right word, the right word is feature creep.

markdown have much less of that brilliance and thankfully I also needed none of it.

Last time I authored a word document is probably 2 years ago for a government interaction.

You can have an opinion about a tool as a user, without ever having ability to create such a tool yourself, that's literally what every tech and auto reviewer does.

Microsoft's AI, on the other hand, is underwhelming at the moment and might well go the way of Windows Phone. Plus enough people hate the copilot icons everywhere that Microsoft is hinting at dialing down a bit.

MS Office should last a while if they stop calling it "Copilot 365 Office" or whatever it was.

The state of GitHub and Windows 11 certainly qualify as sub-par.

I'm sure Word is full of arcane backwards compatible tricks that 20% of users use, but I find it hard to differentiate the Pareto 80% of the product from Google Docs or any other competitor (LibreOffice?) Adding rich text, tables, headings and colors is pretty much a solved problem for all of these softwares. Adding images or handling more complex layouts sucks everywhere, it's not like that Word has a great user experience and the other don't. All of them are bad. IMHO, if we had any of the competitors being the de-facto standard for word processing, the vast majority of users wouldn't feel the difference. Power users would for sure, but I'm not sure they're many or they use existential features. If Word didn't have a near monopoly in office settings due to aggressive marketing, OS presence and a proprietary file format that constantly changes and never renders well outside of Microsoft products, it could disappear without anyone (save Microsoft) losing much.

Isn’t that covered at the top of the post?

> AI is here to stay. If used right, chances are it will make us all more productive. That, on the other hand, does not mean it will be a good investment.

The dotcom bubble burst and 26 years later we’re all hopelessly addicted to the internet and the top companies on the stock market are almost all what would have been called “dotcoms” then.

The railroad bubble burst in 1846 not because trains were a dead end - passenger number would increase more than 10x in the UK in the following 50 years.

> History doesn't have to repeat

This is high up there on the list of things people say before, you know, it does

they have to fight to stay competitive because mag7 can outspend them, but my hypothesis is that they wont need to ultimately.

The decision is the right one. Scaling at any cost is the right way to go.

You cannot find the efficiency if you haven't been experimenting at scale, this is true personally as well.

If someone haven't been burning a few B tokens per month, everything coming out of their mouth about AI is largely theory. It could be right or wrong, but they don't have the practice to validate what they're talking about.

Not everyone scaling to that degree would have the right answer or outcome, many would be wrong and go bust. But everyone who didn't will not have the right answer.

They're not just betting on the current tech, they're building out infra like this because probably any future tech currently being researched will also require massive data centers.

Like how the gpt llms were kind of a side project at openai until someone showed how powerful they could be if you threw a lot more parameters at it.

There could be some other architecture in the works that makes gpts look old - first to build and train that new ai will be the winner.

I think their current goal is to capture as much market as they can while they still have the best models, their only moat. Look at Anthropic, they are clearly trying to lock their users in their ecosystem by refusing to follow conventions (AGENT.md etc) and restricting their tools exclusively to their own services.

Because whoever wins the AI race (assuming they don't overshoot and trigger the hard takeoff scenario) becomes a living god. Everybody else becomes their slave, to be killed or exploited as they please. It's a risky gamble, but in the eyes of the participants the upside justifies it. If they don't go all in they're still exposed to all the downside risk but have no chance of winning.

I don't expect hardware prices to go down unless the third option (economic collapse) happens before somebody triggers the dystopia/extinction option.

The good news is local models have significantly improved. If it all goes down today, you can still run e.g. Qwen 3.5 at home, and it's "good enough" for most workloads.

With a gaming GPU you can run Qwen3.5-35B-A3B. I use 122B-A10B on my local rig (1x6000 Pro), and 397B-A17B on my 2x6000 Pro server (some spillover into CPU/RAM). It's pricey now but probably within a few years it'll become very affordable.

Don't worry lol. It's not going anywhere. The article is just ragebaitng. Verbatim:

> Anthropic is already in a push to reduce costs and increase revenue

Yeah, it's totally a bad sign when a company tries to... reduce costs and increase revenue.

> Bottom line is that H100 prices are near 3 year highs, A100s are still profitable to run, B200 prices are increasing, no one has enough compute.

Then why aren't the hardware manufacturers of components needed by AI companies making plans yesterday to bring new fabs online to meet demand? That isn't a gotcha question, I genuinely want to know. The money involved isn't that much compared to the money changing hands between Nvidia Microsoft, OpenAI, etc., and it's not like once in-progress data center construction is complete they won't need to buy more RAM and GPUs, especially with any new advances in technology that might happen.

Inevitably someone will reply that hardware manufacturers don't want to be stuck losing money on a facility because the bubble popped and demand disappeared, but if Anthropic and OpenAI are going to "run laps around current big tech", it should be a no-brainer to increase production capacity.

> I think AI agents could completely replace Microsoft Office

How? What do you think lawyers/government will use to write briefs?

> but Anthropic is kicking butt in AI

that's not what the article said:

> They turned to showing ads in ChatGPT, something Sam Altman once called a “last resort”, while Anthropic is crushing them

> I think OpenAI is going to be bigger than Microsoft in market cap within the next 3 years.

I am yet to see how a one-legged business model with just a single product (that is not crude oil), without a plan and money is going to become sustainable. Oh yeah, maybe they'll finally make money on those autonomous lethal weapons. That sounds the easiest.

> The problem with these kind of posts is that "How" is almost useless, I can tell you how the bubble pops: The value of these AI companies crash and take out a lots of other stuff with it.

That's like saying "I know exactly how you're going to die, your heart will stop"

Exponential take-off is great until it stops- genuinely, what are the signals showing any of the large models are performing exponential takeoff and recursive self-improvement?

Currently a lot of that appears to be marketing hype to drive up usage. Is it exponential, or are the labs spending exponentially more for smaller and smaller gains from LLMs?

What recursive self-improvement?

> minus the scandals

Given the tech bros involved, we just don't know about them yet. Also was this comment generated using AI? Look at all the em dashes.

DC infra will be scooped up by cloud guys, that's a given. As for GPUs.. well low-precision tflops have other uses besides inference. You can run Doom for example.

I don't think token demand will shrink because we're still just learning how to use it, demand will skyrocket. The problem is what price we'll be willing to pay for it, specially if competition keeps soaring.

Anthropic isn’t the best by any reasonable measure. They’re the best in some areas and get pwned in others.

In general AI is very much like human intelligence in the regard that no two models are the same just like no two people are the same. IOW if you are a single model shop you might even not have any idea that you’re falling behind.

I think of it like the old mainframes in the 70s

I think this is a good comparison to current AI.

billions of them in our pockets.

AI in your pocket (but first on the desktop) is a real possibility.

A lot of anthropic's recent improvements are coming from the task focus and improved orchestration around the models, not purely massive changes in the models themselves.

This bodes well for us being at a point that even if the bubble burst, we'd still have usable AI going forward.

It’s pretty much undeniable at this point that the sentiment has changed.

About 2 months ago this place was unbearable - filled with doom and hype AI posts. I welcome the calming and eventual slow release of the bubble.

The coming months are the reckoning in which the poor quality of the tooling and the safeguards around them become evident and hopefully eventually rectified.

By which I mean the competent organizations are the ones that will come up with cultural and technical solutions to manage the quantity and quality of the code better.

Others will suffer severe quality issues. Not because the "AI"s produce inherently inferior code but because the volume of the code is too high to manage review of, and to have good internal organizational knowledge of to manage the pages in the middle of the night when servers go down because of code nobody really understood.

I produce masses of independent project work all day long in my spare time using these tools and they blow me away. But in the context of professional work on teams of other coworkers the results are difficult to reason about and often impossible to competently review and it's not clear the results are superior. ' IMHO companies that drink too deep from the well without caution could be burned badly.

Aside:

I hate to say it, but there is no sense in which Anthropic has the clearly better product than OpenAI at this point. I know Claude caught developer's hearts through the fall, but GPT5.4 is a more powerful, careful, and competent model for coding and Codex is a far less buggy and more performant TUI. For the last 3 months I've gone back and forth between the two and I always run anything written by Claude Opus 4.6 by myself and my coworkers through Codex for review and it is constantly finding severe correctness issues to the point where I simply won't subscribe to Anthropic's product anymore.

On top of that, OpenAI provides far higher token limits. Even their $20 plan goes quite far.

If I was just building crud websites, probably Claude Code would be fine, and it does indeed show more "initiative" and "imagination" but I've seen it build way too many race conditions and correctness issues to trust it or the work my coworkers make with it.

Consumers and retail investors will bear most of the brunt from this bubble. Even taxpayers, as the government will most likely bail out the "too big to fail" ai companies in the "race against China". All based on bullshit, hype, and greed.

The current best models are already very capable of disrupting the job of millions of people. I don’t think a scenario where we just go back to pre-Claude Code exists and I’m sure the same models can be tuned for much of other white collar work at similar capability

Yes, that’s what the author wrote in the second sentence of the post: "AI is here to stay."

I guess the point is that without the hype subsiding it enshitification will ensue.

Yeah, I don't think local LLM's will keep up with what the massive corporations put out. But they might get to a level of performance where it just doesn't matter for most users.

And people would prefer to run a model locally for 'free' (not counting the energy cost) rather than paying for an LLM subscription.

Local LLMs don't sound profitable at all for those building them. If you really wanted a SOTA model, you would be paying eye watering amounts to own it unless you got an open sourced one.

bubbles are created by people investing more than reasonable in something, independent of the actual value it will generate for society.

great feedback

> HN is no longer a reliable place for the truth.

"No longer?" It never was.

Especially with AI boosters being allowed to degrade the comments section and shilling their paid blogs and violating the HN guidelines.

Why?