OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?
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Anthropic is going public soon. Until September, OpenAI was going public. Neither are remotely profitable.
In that sense, I'm really not sure if Kimi/chinese models are really competing against Anthropic/OAI? Not to mention that for individuals, the subscription model is extremely different, and Kimi subs are (I think) worse value, and chinese API's maybe marginally end up cheaper than using american subscriptions.
As an individual I'm still paying for a ChatGPT Plus plan since $20/mo gives me a lot of value and I use many multiples of $20 at API rates per month. But in terms of capabilities, I am entirely satisfied when using my Opencode Go plan with open models like Deepseek Flash/Pro, Kimi and MiMo.
At this point keeping my ChatGPT subscription isn't because of a significant capability gap compared to open models. It's 90% about the value of the subscription priced well below API rates (10% the fun of trying latest models).
If OpenAI/Anthropic dropped their cheap subscriptions or drastically undermined the value proposition I'd be happy using open models exclusively and currently barely notice a difference switching back and forth for the vast majority of tasks.
I'm expecting the subs to increasingly provide less value as the US labs IPO and follow the SV pattern of moving from marketshare capture mode to profit extraction mode. Thankfully I'm already comfortable using OpenCode with open models and avoid lock-in to any specific harness so would be a very soft landing.
You could argue it's worth that much because of the value that compliance brings the enterprise, but it's just another gouging excercise. Certainly nothing unique to AI companies.
Alternatively, one can say that the enterprise customers plus a metric shitload of VC and shareholder/investor money heavily subsidizes cheap personal plans.
It's the same playbook that Adobe used, provide cheap plans for academia and turn a blind eye towards piracy from non-commercial actors, get the young people hooked hard on Adobe's software and workflows, and they will demand that their employers purchase the nice, high profit commercial/enterprise plans.
They are of course are the ones paying for R&D of new models but its not exactly clear if gross margins on subscriptions are negative. It might be just normal market segmentation just like SaaS companies charging enterprise users many times more than private individuals.
I think that it's not only possible but likely that the frontier labs are profitable on an ongoing basis, and it's mainly the research and capex which make them look like impending disasters.
Realistically, OpenAI and Anthropic are each probably worth ~$1T each. Anthropic can already easily justify that based on revenue alone. OpenAI isn't far off. Consider how quickly these companies are growing and the fact that they maintain a small but significant edge, I don't think the bubble is going to pop. Just deflate a little.
And that is the much more relevant problem. Capable open-weight models from China, European labs or competitors with cash to burn (i.e. Meta!) erode all the moat that the major labs have, and tooling functionality like MCP, CLI interfaces or integration into third-party tools like IDEs quickly gets cloned.
Simply printing money by running inference does not justify valuations in the billions of dollars, because anyone can do so.
Haven’t tried haiku 5.5, I expect it to simply run out too fast, but they did change the price below 100k tokens recently; perhaps I should check it out.
I think that's the difference here -- OpenAI and Anthropic are competing more with each other (and Google Gemini) than they are with open weight models. While people on HN are familiar with Kimi and GLM models, I'm not sure how many casual users of LLMs know about them. I think the competitive risk open weight models pose to OpenAI and Anthropic is much further in the future.
There is a lot of usage that's only known by Anthropic or OpenAI that's not visible to the rest of the world.
Frontier models just fight over smaller and smaller piece of the pie and I suspect them pushing for security work is way to open potentially infinite token sink as attackers pay for tokens so defenders need to spend even more tokens to defens
I feel seen. But seriously, my app, with my preferences, written by a semi-trustworthy entity that isn’t trying to shove ads down my throat. That’s really great.
I've built easily a dozen personal apps in the last few months that I have no plans on releasing or sharing, run locally, and solve very specific productivity (or one-off) issues for me.
I would never have spent the time building these apps in the pre-LLM days because the effort wouldn't have been worth the gains.
But now? Sure Claude, build me a MacOS app in Swift that does this exact thing I need. Less than an hour later, it's done.
when lyft line was heavily subsidized in SF by VC money, i used it almost daily to commute to work because it cost $2.50 each way. Now that it's $20, I don't use it.
Methinks (hopes) we're entering the era of mass customization.
It's a very "The Year of Linux on Desktop" situation, in the sense that in any particular year it seems like Linux is close, but the OSS stuff requires a lot of configuration to get to feature parity, and the barrier keeps rising because the prop solutions are running as fast as they can to justify the cost premium.
Unlike Linux, the world is pretty literate about tech business models, so I think there's a way for the open weight world to avoid the situation Linux got into, where it only really caught up once the market was pretty much commoditization any-ways.
If anyone interested in PL theory (including adjacent fields) and/or inference engines is reading this wants to help make open weight models and associated ecosystems feature-competitive, we're hiring, drop me a line: nathan at nfulton org.
Enterprise doesn't care about marginally better. They care about cost.
Most organizations that run our self-hostable chat platform with cloud models are for most stretches running 6 months-1 year old models, and are strongly eyeing (or have already switched) to more commodity models for most of their workforce. Frontier models are mainly relevant for engineering tasks.
Why is opus 5.5 suddenly a lot dumber? Who knows.
But they do have a common enemy in open weight models.
They'd compete on model quality, sure, but without competition from open models there is no reason to compete on price
The frontier labs were already burning money on more expensive price models, and competition wouldn't have reduced their capex proportionately.
This is comparable to how, if I sign a 12 month non-cancelable lease and then don’t pay the rent, I will be in debt to my landlord.
Here's another one: pay a supplier to cripple their deployment. Suddenly it's the neocloud's fault for failing to deliver, not your fault for failing to pay.
Did we all forget how Sam Altman turned a non-profit into a for-profit? Do we think he'll bring less sociopathy to this battle?
Their war chest is not sufficiently larger than the hyperscalers, Broadcom or PE financiers like BlackRock.
> The rules are different at this scale, and "you can't enforce a contract if the attempt will bankrupt you" is only one of the possible games.
Rather, the inverse of what you said and far less Machiavellian.
It is legally well established that take-or-pay contracts are unenforceable if it would bankrupt the buyer which is the more likely resolution in this case. However, this does not mean frontier labs just get to walk away. They would settle in court for a penalty that is less than the full contract price.
See: AMD/global foundries and pretty much every oil and gas or commodity deal that goes bust when market pricing makes extraction unfeasible.
That's not the metric. The legal system is pay-for-delay, not pay-to-win. Once you have enough money to delay, it becomes a contest of who has the longer runway, and that probably goes to the frontier labs rather than the neoclouds who have little alternative revenue and comparatively airtight debt obligations.
> take-or-pay contracts are unenforceable if it would bankrupt the buyer which is the more likely resolution in this case. However, this does not mean frontier labs just get to walk away. They would settle in court for a penalty
Regardless, the terms come down to positioning and bargaining power. Runway vs obligations. This seems to dramatically favor the labs.
Which is what I said - frontier labs do not have a sufficiently larger war chest.
Respectfully, comparing the balance sheets of a frontier lab to Blackrock, Apollo, Softbank or Nvidia is laughable.
The neocloud will also easily rent out that unused compute and keep funding their war chest via Nvidia’s backstop. If Nvidia can’t pay then it’s all over and AI companies have been swallowed by credit markets.
Your hypothesis also ignores the fact that if frontier labs are not paying for compute it is because they have bad revenue, little cash available and no credit (no one is loaning money to OAI or Anthropic if they can’t pay their bills. They can barely get debt today).
> Regardless, the terms come down to positioning and bargaining power. Runway vs obligations. This seems to dramatically favor the labs.
No, the only way out is for OpenAI or Anthropic to claim in court that they would be insolvent if they are required to pay the bill in full. Otherwise this is a straight forward deal.
You are greatly overestimating their capabilities and underestimating how many substantially more well-funded and highly liquid players will be lining up to take their pound of flesh if OAI or Anthropic default on a bill.
I’d wager my life savings one of those two will go through liquidation.
They could also continue to grow at the rates they had last year. I don't know where those customers would come from, but if they did pull that off OpenAI and Anthropic could sustain propping up the circular financing going around the AI industry.
When everyone is afraid the market will collapse, or more likely distracted because the market collapsed just enough to hurt the average person, the government will bail them out and either people won't notice or will welcome it as a solution to huge levels of fear.
Voting isn't usually much of a solution after the fact, the political machine doesn't allow for candidates that would fix anything to have a chance at any office that matters. There's a reason our politicians today all seem to have roughly the same goals and all seem to become extremely wealthy while in office.
Two companies account for most of the frontier R&D, and something like 7 companies account for a huge portion of growth in the stock market. The government, especially under Trump, is very focused on continuing to see those numbers go up.
I also expect the government is pretty bought in on AI being the future and on us being in a race with China. They'd have to bail out the labs for those reasons alone.
Lastly in my opinion, Trump has a pretty socialist bent to him and regularly talks about a sovereign wealth fund owning portions of companies. He'd love to have take a partial stake in AI companies, and a bail out is a great way to do that.
Though I agree with you a bailout is a slap in the face. In my opinion bailouts shouldn't be legal, governments should pro actively be making sure individual corporations don't become so large that a failure would wreck the economy or national security.
I live in a state that votes heavily in one direction, most states are the same. Its fine for me to vote, but the winner of a majority of states is pretty well decided before a ballot is cast (by strong majority, I'm not claiming fraud).
See also how AI companies re trying to sneak in basically every level of infrastructure and public administration.
They'll take a heavy hit stock wise, and might be an LLM company through all their shady financial constructions to keep the music going by propping up demand. But in the end, nvidia as a company has a place even when the frontier labs go bankrupt. Some other company will take their place. Worst case it'll be like the cable companies in the end 90s who found themselves out of work because they overbuilt for dotcom
Ok then iPhones are just metal, plastic, glass, and a delivery truck. You are just electric meat. This has to be one of the dumber comments I’ve read on here.
And SpaceX LLM business is "we failed to build competition so we just rent what we bought in hardware to other companies"
It’s not at all unusual for companies to run at a loss for several years before turning a profit.
Z.ai, who make GLM are public and their books are really, really, really bad.
Their data from September is leaked via IPO preview, and October is not yet complete, so how can someone even project that?
// I am a techie, not a finance guy, so apart from public chatter, no inside knowledge.
And I do feel their IPO is being rushed before anyone has enough information to make up their mind. The VCs or private equity have given them a lot of money and want to cash out. When the revenue does slow down, any investor should pause to take a look at whether they can right the ship, or has the landscape itself changed.
Their revenue taps, product releases and marketing campaigns were all strategic: to be able to show nice numbers in the financials.
Once they stop playing games with quality of output of these models and revenue's shrink - they will be in deep deep trouble. They are hoping they will conjure up a break through with the IPO proceeds.
Let's see.
https://techstartups.com/2026/10/15/anthropics-q3-revenue-hi...
Where this bubble differs from the dot-com bubble is that valuations have very strong, organic user demand behind them. In 1999, prototype websites with few customers were getting IPOs worth hundreds of millions. This time, demand is insatiable. Frontier mdoels also has very low global penetration, relatively speaking, so the TAM permits exponentially more sales, in theory.
However the risks are numerous for these labs. Foremost is that Chinese labs are able to easily distill frontier models. A tiny fraction of the cost of training, and they have a close approximation. Second, models are getting much more efficient per task. Companies are already discovering what "good enough" looks like, and it often ends up being much cheaper than what Anthropic and OpenAI are selling. This will only accelerate.
My investment thesis is bullish on the entire AI stack except the frontier labs. I think power generation is going to see huge demand. Ditto for infrastructure and data centers. Ditto for the products and services created by these models.
Edit: Data because silent down votes are useless, Anthropic Q2 2026 grew 147% while Q3 grew 45%
Gross margins here are irrelevant: if they can't charge a high enough price to cover all expenditures and rely on cash raised via fund-raises, they are not financially healthy.
Could they be one day? Maybe.
Yes they do need to grow margins to cover expenses, eventually when Amazon had to turn a profit.
My point was only that the numbers we do know clearly show a drop in growth rate of revenue, I can't judge numbers that aren't publicly known.
If we assume it's due to competition, then their usage has precisely not plummeted because they cut prices by 80%.
They are too big to implode.
"Hi Claude, there's clearly a memory leap in our software. It clearly balloons over time. Here's a few heap dumps from various points in the day"
"Unfortunately memory safety issues and OOM errors are clearly restricted cyber work, contact your nearest screaming void if you want to appeal this decision"
The sword that protects is the exact same sword that attacks. One idiot attacks the wrong party and the show is over.
I think it's more likely though that China stops the party, as they stand to lose from the threat of it just as well. And China can just flip the switch to stop new releases.
The US absolutely could (and very well might) make Chinese models illegal, possibly open weight models in general, but all that will do is keep them out of the hands of law abiding US citizens.
Law breakers in the US and the entire rest of the world would still have access, and nothing in the US will be any safer. People who think regulation will solve the problem should be embarrassed by their ignorance.
"Puncture expectations of a multi-trillion dollar valuation" OTOH seems quite likely to me.
Kimi K3.1 is top 5 on AA and half the price of Opus 5.5, Qwen4 is coming soon. There is near zero difference for most tasks, so it matters less today and even less as time goes on. Price is now the top concern for a reason.
https://artificialanalysis.ai/#intelligence
However, capability could plateau or reach a point where most of the market can't tell the difference and isn't willing to pay a premium.
In addition, most companies don’t want to be in a position where a disgruntled employee can report them to authorities and rack up massive fines or worse, even if it’s hard to tell from the outside.
You and I obviously can just use a VPN, but 99% of usage is likely as part of a company.
Is there precedent for the US actually "banning" software? I know for example the Kaspersky ban and tiktok ban, but both of those were either banning the sales or banning the service. IIRC neither made the actual software illegal.
What is an example of a specific software being illegal to possess? What is an example of a specific software being illegal to use if it is not used to break other laws (copyright, etc.) or talks to banned service providers?
Closest thing I can think of is restricting the export of cryptography in the 90's, but that was not even close to what you are suggesting.
It's nice because I can keep going from anywhere, other team members can see and interject in my Slack threads, and Hermes auto updates its skills with any adjustments that we make so we don't have to ask twice.
Comiong to harnesses, even Pi seems like a weird choice. I mean Pi, until it's "your own customised Pi", is pretty useless and pointless. Besides they are also starting to have their own models/infra business (or is it mostly proxy like OpenCode?).
I have also given up on Herdr and came back to Tmux (and doing a bit of customisation slowly, as I need it). I wanted to try dsh but somehow couldn't brave it. Maybe once I have a good container/vm setup is ready. I know I am biased/discriminatory here, but there are things about which I'd be better safe than sorry.
As for OpenCode: I used V1 a lot and I am struggling with V2 currently. Yes, it just doesn't feel right. It's neither here, nor there. I am not a harness reviewer, so I won't be able to put that into industry accepted words and tests but it doesn't feel useful and right to me. At least Pi is completely stripped (it might be changing though) and then you can make it as you want it. Even Claude Code is better than OpenCode and that's saying something. I find Codex to be quite effective but it doesn't feel familiar. I am still in the middle of some work so I am using OpenCode v2 a lot (and then there's OpenCode Go and I won't want to play around a lot with it) but I will move away. Where? I am not sure yet.
What I feel will work for me? I guess a harness of my own! Forked something smaller/similar? I don't know. No, really! Just like I have never found the "my" notes app and journal app yet, I don't think I should use any of these harnesses if it doesn't work for me, especially now that I can kinda make one (hail LLM/vibe coding :P). It doesn't even have to be a TS/Node monsters most of these harnesses are. More I am reading about, digging deeper (haven't been quite far yet), how models and the "local setup" interact, the more I end up scratching my head – why all this drama and why all these 5 billion GitHub star "harnesses" with trillions of lines of code! Why? It doesn't make sense!
And I’m slowly writing my own. But it’s garbage at the moment.
I still also use other harnesses, but mine is already at the point where I prefer it more often than not
You should totally keep working on yours. It’s a cool learning experience. And you might stumble on some good ideas that nobody else has had
I’m writing my own harness to learn. And I’m writing it in zig.
But, I’ll absolutely add maki.sh to my reading list. So, that’s a win.
By chapter four you have a minimal working codebase in python. Later chapters add out features like tool calling, planning, and reflection.
At home, I mostly use openrouter.
A few bucks will realistically last you forever. I also bought a DGX Spark at launch. I've learned a ton. but it's not usually my daily driver. I do run overnight tasks on it, like "read this book and write up notes on each chapter..."
In retrospect, given the dirt cheap prices of apis and or renting hardware, buying hardware for inference was likely a mistake (but not enough of a mistake to sell it now).
I'm not willing to spend thousands for those features, so my house will remain dumber than it could be until prices come down in a year or two.
And with some smart feedback loops, you will see that for home assistant type tasks local AI can pretty quickly fulfil all requirements. For quick responses or voice analysis you might want to start with an external provider.
Otherwise, I think for complex home automation around 24-32gb vram are a realistic house number, but more experienced users will probably need less here.
People say that tools are called by the model. That isn't entirely correct. The caller calls the tools, the model just formulates the syntax for it. So the model part can be externalised without you needing to compromise you system fully (just to the degree the tools would allow anyway)
However, Anthropic is pickier about the harnesses you can use with Claude Code and I noticed discrepancies when using Zed's External Agent feature with Claude Code, so I just use the Claude app. It's fine.
I'm calling BS. I use Kimi, chatGPT (Astra/Sol), and Claude (Opus 5.5), and Kimi is by far the most expensive in terms of tokens and $/work done. Using OpenCode harness.
For the bigger Anthropic and OpenAI models (and associated higher prices) there are many unknown factors. But presumably they also work hard on optimizations, and it's not so crazy to think they had or still have a lot of wiggle room to adjust prices.
Sonnet 5.5 is 16x more expensive at $10 per million output tokens. Kimi K3.1 is also $10 per million output tokens, and its number of active parameters is only 6.5x larger than V4.1 Flash (104B compared to 16B). These things are not directly comparable of course, but just to give an idea.
Even if inference of those models needs 16x, 32x, or 48x the number of GPUs compared to V4.1 Flash, still seems like potentially great revenue per GPU.
Its cash flows to the GPU that matter. Not revenue.
https://www.sec.gov/Archives/edgar/data/2130357/000162828026...
Operating cash flow of $3,929M and $482M of CapEx gives $3,447M of Free Cash Flow in H1 2026
Operating cash flow of $3,929M and $482M of CapEx gives $3,447M of Free Cash Flow in H1 2026
Operating cash flow of $3,929M and $482M of CapEx gives $3,447M of Free Cash Flow in H1 2026
Operating cash flow of $3,929M and $482M of CapEx gives $3,447M of Free Cash Flow in H1 2026
There's a reason he didn't say profit...
Not saying I agree with the sentiment, just noteworthy and perhaps something to reflect upon while judging behaviors aligned with this perspective.
Where exactly is my strawman? Scam Altman himself has said this, if we ignore the inconvenient parts of the business that loses all that money, they'd be super-duper profitable.
Anthropic said something similar, claiming to their investors they're profitable... If you ignore partner costs and the costs of training.
And mind you this is coming from a proven pathological liar who has a massive vested interest in over exaggerating his business' viability, so doubtless the "Very profitable" part is itself a massive lie.
Which is a basic truism of pretty much all startups, and nothing noteworthy or insightful to point out.
its per GPU-cluster, not a single gpu.
> The current service throughput of a single cluster is 155 trillion tokens per day, and the overall revenue is $38 per hour (at the lowest price) per card.
With Kagi translate (best), it says GPU
> A single cluster currently serves 155 trillion tokens per day, generating overall revenue of $38 per GPU per hour (at off-peak electricity rates).
https://zhuanlan.zhihu.com/p/2095560295930508953
No, it isn't. It's revenue per hour per GPU.
Regulation might inconvenience them a little, but the upside is huge if it means open models out of China get banned.
They are meant to help the consumer.
Unfortunately neither of the two parties in the US even pay lip service to free markets anymore.
The Peter principle doesn't apply to consumers, but it does to producers. And if nobody regulates how big you can get, you eventually end up with a nothing but ruthless monopolies in every industry. Regulated markets tend not to generate as many monopolies. And of course, for natural monopolies, the state is the least bad option, because consumers have at least some say in how its operated through voting, and there is no profit motive.
The idealistic perfectly competitive markets entail exhausting all gains from trade.
Keeping a market free would require magnitudes more regulation than right now - the opposite of what free market proponents advocate for.
There isn't one adult who looks at this massive centralization and sees that it goes against the very principle of the thing?
I'm not saying this out of anti-religion, I believe religion can be a force for good and often is. But it shows that yes, this is possible. Also see things physicists, and especially physicians, believed for centuries.
> There isn't one adult who looks at this massive centralization and sees that it goes against the very principle of the thing?
There's a growing number who do. But the reason why they didn't get much attention for most of a century, and as such what made the fantasies of Hayek become mainstream, is of course tightly connected to the fact that it's the one that just "happens" to be the one most favorable to these mega corporations and their friends in financial and political institutions. It's clearly not a coincidence at all.
It's also just an incestuous cycle of professors teaching students who end up being professors who teach students. Takes something to break that.
Of course the things you mentioned further accelerate walmartization. But that's the chicken and the egg. The government does bailouts and prints cheap money because the corporations do exactly what I said: the winners (who arise naturally thanks to economies of scale) will put their resources into gaining these things.
You can as long as you can saturate hardware with profit that makes up for training costs (which are also in free fall).
Anthropic is most at risk since they haven't bought any hardware (afaik), however OpenAi is building custom silicon now. The rest of Mag7 are primarily hardware vendors
https://mimo.xiaomi.com/rl/
Also look to the history of stable diffusion
Aren't AI companies already butting against the limits of usable training data?
Outside of undiscovered techniques, additional costly pre-training will likely yield diminishing returns if you've already scraped the whole internet and destructively scanned as many 20th century books as you could lay your hands on.
You cannot point at Xiaomis tiny model and compare it to training a 10T param model. The levels of dunning krueger are getting out of hand.
You also arent calculating the salaries of the people involved, of which there's only a few thousand worldwide in your hiring pool.
There were probably a few dozen 20 years ago, and the existing pool grew from somewhere. AI (nee machine learning) is not rocket science - but it is a science, so it rewards those who are literate, curious, can hypothesize, experiment and learn from results to gain an intuition. I bet most statistics-adjacent professionals (millions, worldwide) can move laterally into AI research and excel at it. Nevermind AI has been among the most-in-demand undergrad programs since ChatGPT dropped. The only limited pool is for celebrity AI talent.
Your average Stats/machine learning expertise has little to do with standing up training infra and pipelines across $50 million in gpus.
stable diffusion is an excellent example, the price to train today's or yesterday's frontier is the analogy, many more people will be able to train up what we have today, especially as more of the pipeline is open sourced and the knowhow distills
Dont use llms to draft your responses to people, extermely cringe. That explains the llm induced Dunning Krueger.
What examples... There are no public numbers regarding how much it costs to train a 10T model, atleast nothing concrete.
You should just use common sense and think about how you have to fit tensors in memory to train, and the larger the model is the more GPUs you need. Then you could also use common sense to think about how GPUs have gone up in price, not down.
You are throwing around unfounded and incorrect accusations. Why? It's a weak ass rebuttal mate
common sense is using things like history to guide expectations instead just saying things without evidence that are contrary to the patterns in computing history
Given everything happening with Black October[0], don't you think regulating models may be reasonable?
[0] https://en.wikipedia.org/wiki/Black_October_(information_sec...
The AI companies didn't create the vulnerability. It has always been there.
The tools to detect these vulnerabilities will not go away. The only question is who will be allowed to have access to them?
Regulate the bad actors, not the neutral tools. Lockpicks aren't illegal, but breaking and entering is. Imagine if MasterLock used a lockpick to break into a government building and steal stuff, then tried to lobby to make lockpicks illegal. That's what OpenAI is doing, and you're falling for it.
Yet, so many Democrats support it.
In the hands of 6th party system Republicans, namely Ronald Reagan and Robert Bork, the government worked hard to usher in a a new era of Robber Barons. We live in the culmination of this project.
History says the result depends on the hands, not the tool. 6th party system Democrats have largely failed to undo the Reagan/Bork policies, but that's an act of omission not comission and even then Lina Khan was a big step in the right direction. This issue was on the ballot in 2024 and will be again in 2028. Hopefully we make better choices in 2028.
Source: trust me bro. Lol.
Government regulations exist to regulate activity, to prevent, punish, and/or remediate harms.
Environmental regulations exist because businesses wouldn't clean up their own messes. Financial regulations exist almost exclusively because of harms and abuses committed by members of the financial industry. Building codes are updated to reflect lessons learned about people died, to prevent more people from dying the same way.
If AI regulations are passed, it will be because the AI industry decided not to self-regulate itself and stop illegally hacking computer systems, generating CSAM, or various other illegal activities that should easily be preventable if their systems were actually intelligent.
Also there are real trade offs. Food would be cheaper if, for instance, it were legal to cut it with any old cheap substance regardless of health effects.
But I totally agree that some regulations are dumb or are regulatory capture monopoly schemes.
Unfortunately like many other things there is no absolute simple rule to save us from thinking. Each case must be evaluated in depth hopefully with domain expertise.
It’s a rabbit hole but if you have some time it’s fascinating to look into the sentiment of investing in China vs the rest of the world. Because the numbers are so unreliable, investing is considered about the same as gambling in China. You’re more likely to lose a lot of money than win, and there is no way to accurately predict strong from weak companies based on their reported financials. Occasionally senior leaders will sentence the leader of very large failed company to death to attempt to maintain confidence, but it’s not working. This is one of the reasons for the housing bubble. There just aren’t stable investing opportunities in China.
where they not? Pick any corporate in the world and you will find bunch of made up numbers just to make stock value go up
I think you're making the mistake of thinking that because fraud occasionally happens in the West, it's on anywhere close to the same scale as China. If Chinese companies were capable of passing independent audits, they'd all be publishing independent audits. That would be a massive confidence booster in China and internationally, and stock prices would rise significantly. They don't, because they wouldn't pass.
API usage being profitable is completely plausible, but almost nobody pays API prices for Claude and GPT, nor would they be able to afford them if the subscription suddenly stopped existing.
In both places I've worked at in the past two years (F500), subscription plans were banned due to data privacy concerns. Everyone _had_ to use the provisioned API tokens (even if they had a personal subscription)
Other providers also charge similar prices (who are obviously not subsidising anything) for running DeepSeek models on their own infrastructure.
OpenCode Go sort of provides a "subscription" which will let you use DeepSeek but they're extremely transparent about what they're offering.
Also these third-party providers are running magnitude smaller models than what Anthropic and OpenAI's power users expect from them. Yes the average chat message isn't being routed to the 1+ Trillion param model but people running CC or Codex on Ultra probably are. Those are objectively more expensive.
The little companies inferencing DeepSeek have no advantage over any other company doing the same thing. Therefore, their margins should look like any competitive industry. Farming, supermarkets, airlines are my go-to example. Usually margins in the 3% to 5% range. Profit margin average across all industry including very profitable ones like tech or oil is only 10%.
The companies offering inferencing and nothing else are relying on subsidies by the open weight model providers. If those companies all stopped releasing open weights tomorrow the offerings on OpenRouter would see rapid traffic declines as the knowledge in the weights became stale and as other companies that invested in proprietary R&D overtake them. So being profitable on inferencing open weights is a very short term play, and there's no reason to think it's a great business in the long run.
I am not an AI denier, I use it and I find it very useful. That said I can disagree with the current economics of the industry and their business model.
Its difficult for people to accept this because many want the model producers to succeed badly.
SOrry folks...
Most of the cost is depreciation on the GPUs and the HBM.
The one number you need to figure out is how much it costs you to buy that GPU and HBM, then how many tokens you can sell from it over its lifespan which is generally amortised over 2 years.
In the short term they have to slash prices to avoid losing market share. In the longer term they need to figure this out as the present path simply isn’t sustainable.
This will only get worse.
> One outcome that would be genuinely ironic is that if these models are more compute efficient, it reduces the need for these massive data center buildouts, which could cause cascading economic failures. However, that likely won’t happen since inference is only one part of the compute mix and those data centers are still needed for other parts of the pipeline.
Don’t agree with the however. I think like you’re saying we’re going to get to see in real time what happens when something is commoditized and it gets massively overbuilt.
Because all these newly trained models using the compute are doing really well and growing!
Even if something is a reasonable infrastructure project with a sound long term business plan, investors can still lose money if they pay too much for it. For the Telco bubble & crash, there were a few of these Telco companies engaging in financial shenanigans / securities fraud to juice their quarterly financials by leasing capacity to artificially inflate their operating revenue etc. E.g. we booked $100m of revenue by leasing X units of our capacity to $competitor! Sure, we're still extremely cashflow negative, but look at our operating revenue grow! Fine print: we also incurred a symmetric $100m expense to lease X units of capacity back from the same $competitor, but for some reason we didn't classify that as an operating expense, we've thrown it into a different accounting bucket.
That said, overbuilding fiber optic capacity by laying undersea cables is likely different to overbuilding GPU datacenter capacity in that most of the cost of laying a cable is the physical cost to lay the cable -- given you're already laying an undersea cable, the incremental cost to add a bit more capacity to it (more optical fibers) is negligible -- at least before you've sunk the cable! Whereas for a data center full of GPUs, maybe 50%-60% of the total cost is the GPUs, so it isn't cheap to massively overbuild capacity.
[1] https://en.wikipedia.org/wiki/Telecoms_crash
Language modeling is fundamentally compression: https://arxiv.org/abs/2309.10668
The entire endeavor is ultimately fundamentally coupled to reducing compute
Eventually we will compress models further by removing outdated knowledge like Python and Cobol cause who needs 1960s like languages to then layout data in the screen when the model has knowledge of geometry and can mutate the screen geometry without needing a Python interpreter or C compiler
Are these prices subsidized by user generated outputs? Can I even trust any answer to not be carefully worded and rephrase the response to "user data"? Even so, what's to prevent some "oops" event that seems to be all too common? At least some months back Bedrock seemed to be properly isolated, but I've heard of many getting non-ZDR notices for new models.
Can't wait for the unlimited plans we will have in due time.
But if you get unlimited tokens, with scheduled/automated jobs and subagents you will burn a LOT of money.
This is the same mechanism used in unlimited mobile internet for example.
I expect they will be "unlimited" in the same sense the mobile / ISPs do that 99% of people never reach the limits.
We could have "unlimited" electricity too, if we choose, by printing solar panels (like the Chinese) and hopefully fusion before long.
Maybe, maybe not. There could be a saturation point of compute demand from ordinary consumers. (See: hot-water analogy.)
Tell that to my dial up modem as I played Active Worlds in 1996.
I don't see how this can happen. The cost of serving a model grows linearly with use (energy). The cost of serving a mobile data plan is approximately zero.
The cost of processing tokens however does not. Throughput and efficiency are improving around 10x per year. Hardware is not the only factor.
As long as tokens are as energy intense to compute as they are right now, I don't see a flatrate being a viable model.
People don't like complex pricing. This would be for businesses running larger workloads. For individuals, the unlimited plan seems the most probable to me. This stuff is going to get real cheap in the coming years.
The energy needed per token is in dropping rapidly, even though the energy per-GPU is going up. We are seeing something like 10x improvements per year.
No, they are losing billions because LLMs are not a viable business. Their real business is making bombastic statements which the credulous press prints unquestioning, fooling various investors and ... that's it. IPO is another of these. You really should note how the Anthropic IPO date keeps slipping because they can't make the S-1 public as it would give away the game immediately. The market was fooled by SpaceX once you can't do this a second or third time.
Inference is clearly profitable. The question is whether it's profitable enough to justify the training research costs.
Not sure how they can get more transparent than that.
Even if you could acquire infinity custom on day one for zero cost, revenue isn't profit.
https://github.com/deepseek-ai/DeepInfer/blob/main/doc/infer...
So this implies they actually hit about double that during peak pricing.
The cool thing about models designed for cheaper inference is that post-training (which often requires inference as a part of the feedback loop) becomes cheaper as well, so almost certainly cheaper than models that are less optimized.
But burning staggering amount of money just to keep yourself on "frontier" is not viable when the part of the market it gets you get smaller and smaller.
[1] https://dealroom.co/news/165341-anthropic-backs-its-first-eu...
To give a simplistic example: if a city as a ton of available AirBnBs, you might say, "Lots of people are willing to rent out their apartments, it must be profitable for them to do so." But the fact that all of those rooms are not already booked could also mean that way too many people have attempted to rent out their apartments.
And for what it's worth, it's possible for supply to outstrip demand while still leaving inference profitable. The question is whether it's (a) profitable enough to service massive debts and (b) sustainably profitable over the long run.
Edit: to add one note, it's possible for the LABS to be profitable on inference without inference itself being profitable. We don't know what the contracts look like, but it's entirely possible that labs received sweetheart deals from neoclouds so that the latter could flaunt big utilization numbers.
Selling at a loss is far from unusual: Uber did it to capture a market, farmers do it to recoup at least some of their investment rather than losing all of it.
I would be curious to know whether you'd make more profit from the same kWh of electricity mining bitcoin versus serving inference. I know they use different hardware, but since the "inference is profitable" discussion ignores the purchase price of the GPU, let's pretend you could have opted to buy an ASIC instead.
There was an interesting technical advantage by some models where Anthropic as a leader start loosing dramatically in speed too. 5.5 made a come back in that dimension.
I watched that video over ten minutes and in that ten minutes several trillion dollars evaporated from my perception of the US stock market.
Edit: link to the paper
https://arxiv.org/abs/2609.19969
(it was published 2026-09-17)
Also regulatory capture, like OpenAI calcifying its teen mode into law via Adam's Law. https://openai.com/index/supporting-california-bill-advance-...
Openrouter recently changed their TOS to exclude under-18 users, presumably to escape liability from the law. https://conductatlas.com/diff/openrouter/openrouter-terms-of...
This probably won't be enough, because Openrouter now has to do age verification to verify their users are adults, and if they don't, they have to apply "teen mode" to all users.
> Meanwhile with Fable I hit it constantly
This was past tense
Ask your agent to make you a visualisation, heat map, whatever. I bet it's just that you're using these models more, and for more complex tasks.
Isn't that sad? The language used for international communication is ambiguous about whether a statement is in future or past?
A good writer minimizes ambiguity by re-wording things in unambiguous ways. (Or the opposite, if desired.)
When you're connecting solar to a municipal grid (which is what most homes are connected to), it could theoretically be taxed or even subjected to a tariff. The "added" power (often excess) is monitored for billing credits/payout/other, among other reasons like grid integrity.
And indeed, they can tax excess solar, but they can't tax the power you use yourself. Backfeed tax gets too high to use the grid as a battery? Buy a home battery, problem solved.
^Not recommended literally due to brownouts/etc, but a cheap power management IC on a breadboard can solve that. Alternatively, add a battery/etc.
Home solar panels and batteries are just like this except on a bigger scale; and with (hopefully) more QC.
This is incorrect. Treating solar panels like batteries for specific appliances (or integrated like the panel on a Solocam S220), is not the use case nor is this a discussion about off-grid or microhomes.
I would suggest not making broad statements about what can and can't be taxed in the future.
Electrification will drive the demand for oil down, making the overhead more expensive per gallon, and eventually we will largely move off of fossil fuels, though I don't expect completely. It will be many decades due to the turnover times for industrial/commercial transportation systems.
Human memory has a finite capacity and decays over time. If you do many inspection tasks in parallel you are splitting that finite memory. If you do the same tasks sequentially, the human can give instant feedback to the model without costly context switching. This in turn increases iteration speed and reduces total labor costs. This is no different from developers preferring JavaScript or Python for immediate feedback and fast iterations.
Meanwhile, Oracle is handing Tencent 100.000 GPUs at 1.60$ hour, dangerously close to breakeven...
What does this tell you?
The main thing it seems to have done so far is made everything feel less trustworthy, and everything digital less safe. It has empowered scammers, that's for sure.
And for what benefit? Spunking out 7/10 code a bit faster with your brain turned off. Great!
Maybe the FDA is a good analogy, you wouldn’t want to buy meat at the store not FDA certified would you? OpenAI and Anthropic want you to feel the same about AI.
There will likely be requirements like registering with the government (China already does this, every business already does everywhere), certain usages require more or have deeper regulations (this is already done with most technologies, depending on application)
Some variation on, "Open weight models must be A) trained in the USA B) have a public training dataset + training pipeline C) be reviewed by a government 'safety council', etc" Something which technically leaves a crack open for someone to release a less capable model than the commercial ones.
Another confounding factor, if a US company fine-tunes a Chinese model, or vice versa, what is that country that should be assigned to that model.
You also have to consider the competition between the US and China. If the US did something like this, the world will move to Chinese hardware and Ai. The think tanks that talk with legislators understand this.
Hardware? Why would anyone change their hardware based on this? That's completely unrelated.
And as for Chinese LLMs - there's already evidence that the Chinese are training malicious behavior into their models. I think most countries would rather train their own models than risk that.
It's worth remembering that the tariff stuff is not any sort of durable policy. It's one guy sort of doing whatever until 9 other guys a year later go 'hey wait' and then the first guy does whatever again anyway. The whole thing is just a recipe for whiplash. maybe congress remembers that they're supposed to do stuff in a few months and steps in. maybe they don't and then every 4 years we have wild swings in enforced based on which half of a percent of the voters in georgia had rainy weather where they lived in early november. More of a simultaneous "All of the above and also none of the above" deal until the dynamics of american government fundamentally shift
US commercial interest =/= OpenAI and Anthropic.
Sadly, my GPUs were lost in a tragic boating accident.
I strongly believe that GPT-6 Sol was only ever meant to be the web chat model, but it was a panic public release to respond to Opus 5.5. It's clearly been over fitted for drawing diagrams in the chat.
They know what they’re doing. The naysayers are like the people that said this little internet thing wasn’t going anywhere.
There's a big difference between people saying OpenAI/Antropic wont survive and LLMs wont survive. A lot of big early players in the internet space died off as well.
>internet thing wasn’t going anywhere.
This is conflating the utility of the technology with the economics behind it. Few people claim LLMs are useless. It's a question of figuring out a viable business model.
Commercial airlines are of great value to our lives. Who's questioning or denying that?
Horrible business to be in though.
the money is infrastructure (hardwares, electrics, data etc)
that is not bad business? that is just private profits, socialize loses perfect business?
They do not generate economic profits.
One thing that naysayers have going on for them is the fact that those companies indeed aren't making profit right now.
That's not how you grab market share. Early on, any profit you aren't reinvesting into the business is basically hurting your growth rate.
Grabbing market share by making losses only helps if it actually wins you a genuine monopolistic market position.
I personally think LLMs will be a utility business in the future. Cheap, unlimited and with ads. Lots and lots of ads.
The same rhetorical tactic was used to diminish blockchain critics.
Crazy how delusional people are.
They are in too deep - they have to believe it all works out and act accordingly. lmao.
Lack of demand and/or commodity product.
Either doesn't bode well for a multi-trillion dollar valuation for either company.
though problem with proprietary LLMs it's difficult to know if the vendor's supposed "optimization" affects performance negatively because it's difficult to define an SLA
Genuine inquiry here.
However, my bigger worry is this is just a temporary lull, and that due to alignment, regulatory, and distillation pressures, the best models will be kept internal, and then all of capitalism fails because the labs could maybe eat all of knowledge work.
There are already no junior hires. The ladder is already collapsing. Embrace, Extend, Extinguish.