maybe this is why Dario want to slow down AI development and all the big AI labs in the USA is singing the same song.
whey they all singing the same tune. it make me question what is their real motives.
they are afraid of Chinese good enough LLM model killing their margin. we already have story about US companies switch some task to use cheaper Chinese model hosted on Neoclouds.
The reason is money. They want regulation to make it harder for new competitors and competitors from other countries.
They invested billions into training the models but there is no competitive advantage, we see that within a couple of months everyone catches up. There is no way to profitability unless they get some policies to shields them against competitors that can't comply with the regulatory requirements.
That is also why there are things like Claude, Codex and Cursor. They are trying hard to build a customer relationship with a higher switching cost that hopefully sticks.
But the problem is that the AI buildout has become a large percentage of GDP. So obviously the government wants to keep it going because these companies are pumping enormous amounts of money into the economy.
> But the problem is that the AI buildout has become a large percentage of GDP. So obviously the government wants to keep it going because these companies are pumping enormous amounts of money into the economy.
They are pumping enormous amounts of money into each other. Hardly any of that is making its way to people, it's all going to highly automated construction and to energy use.
Seriously, how many jobs did the $1t in venture capital fund?
And, in this case, the dollar-amount increase in GDP serves as a virtual quantitative proxy for the increase in mowed lawns (and the value thereof). In other words, the participants in this economy are collectively ~$200 richer with their mowed lawns than they were without them.
If everyone goes around mowing lawns for each other, the economy is richer in lawn mowing at the expense of all the other things that would have been funded had everyone mowed their own lawns and purchased different services instead.
I am confused with this, if "everyone mowed their own lawns" then the net result will be exactly the same, everyone will be busy the same and not poorer, just without money movement.
This is not the same. If everyone wants mowed lawns, and everyone is busy working on that, there is no opportunity cost, everyone is working on their top priorities. The broken window fallacy is a fallacy because the headline gdp figure doesn't account for the destruction of the window which cancels out the benefit. In the grass mowing analogy nothing has been destroyed, useful and priority work has been done all around.
If the pricing is fair and at arms' length. What's happening in reality is as if they are mowing each others' lawns at wink wink nudge nudge $1000. Not a good proxy for actual value created.
In the real world, you have to pay taxes. So people are incentivized to claim less value for the lawns mowed, or even just do it themselves, instead of benefiting from the division of labour.
Leverage doesn't work that way. (If it were so easy, it would load up my investment portfolio with a lot more leverage than I currently do. And I don't live in the US where regulation T would keep me to a puny 2x leverage.)
But also importantly the government of the residents' country is about 39% ($78) richer, if say the participants are honest in reporting this and the country is the UK and the participants are people like you and me in the tech industry who frequent HN and would think to do something like this.
> Hardly any of that is making its way to people, it's all going to highly automated construction and to energy use.
How do we know that? How automated is the construction really?
In any case, the Fed and other central banks can print as much money as they want in order to hit any aggregate spending or inflation target they have for the economy.
I’m still not at all sure about the “billions” invested claim. How much of that is cloud running the models? How much is pre and post training (which may or may not be part of what we’d want to include in accounting). Etc. Does anyone have links to good reporting about this: not blind recitations of numbers, but analysis and thought mixes with investigation?
Well, if you are right, I just hope their protectionism will only affect the American market, and they leave us unAmericans free to get our models from wherever.
On the other hand (OTOH), China is desperate to keep up and keeps pushing open models (rightfully so), as they understand how far ahead from everyone the US is, and that whoever gets this right first basically is going to become an alien compared to others.
But even with all the open models the US is just insanely ahead in AI buildout and capital allocation (as usual).
Is it a bubble? Is it like the race for the-first-to-the-nuclear bomb? Both?
Unless something has shifted, “everyone catches up” is because these bleeding edge models are distilled. You don’t see this happening with other European and US labs and the problem isn’t something being ignored. I’m not convinced this pattern will continue indefinitely.
I did no such moral claim. I just noted that the foundation labs are working on technical hurdles to thwart distillation efforts and the cost and quality of Chinese models isn’t likely to keep up with the 6 month lag time everyone has assumed.
I don’t follow. Fair use is a copyright defense, and nobody is suggesting distillation attacks are just a copyright violation are they?
Aren’t they alleging these other companies directly entered into a contract and violated the terms, and in cases where question, answer pairs were obtained without such agreement, it was accomplished by outright wire fraud or theft?
I don't understand on what you are basing this reasoning? If a well educated workforce can produce Fable then why couldn't a well educated workforce produce MiMo?
Besides, the latter actually published and open-sourced its RL stack to make it reproducible, which would in fact make it more trustworthy than the models you are speculating were distilled.
No chinese lab has caught up yet. They've tried to fake it by distilling and overfitting on benchmarks to make their models look better than they are, the 'best' models available from chinese labs right now (GLM 5.3 and Kimi K3) fall apart completely when you try to do real work with them. K3 is especially embarrassing because it is larger than Mythos yet performs worse than opus 5 and 5.6 sol in benchmarks they haven't been able to fake yet.
Please explain how putting an upper bound on how good the strongest models can be prevents cheaper less strong models from catching up, rather than enabling it. I do not understand this argument at all.
The general idea is that Anthropic/OpenAI is pushing this narrative as an attempt at "Regulatory Capture"[1] which would allow them to make it prohibitively expensive for anyone but them to enter the market thus stifling competition.
I heard someone analogize token vendors to car manufacturers, where American companies only want to produce expensive options, the people want cheaper/better alternatives, and we ban BYD because those with enough money are more "persuasive"
The analogy is a good one, but your explanation is missing one aspect: the country (USA) does have a reasonable interest in having the capacity to build their own models. The “we need to slow down because it’s getting too dangerous” part is probably more related to “we need to slow our public facing development down so the US government can get the best and the American corporations can trickle out what we decide is safe”
It’s similar with cars. It’s not that American cars are better than Chinese cars on any tangible measurement. But America already shipped most of its manufacturing overseas. Everyone who built those factories is retired. The US should probably hold on to some capacity to make cars, seeing as their entire infrastructure depends on them.
American Ai/Car manufacturers could build cheaper/open models, some do, the big ones do not. It's not an either or, but a spectrum where they have chosen to build only in a subrange
That doesn’t explain the decline of German automotive industry which is now taken over by Chinese cars thanks to massive subsidies by the Chinese government
It’s not a reasonable comparison. In China, every corporation is de facto state run. The Party is in the boardroom and the executives are members of the Party. The CCP will build entire mega cities or pump money into this industry or that according to their plan. China isn't a "state-run" economy but a conditional-autonomy one. Private firms operate freely until they collide with Party priorities. Then the state wins decisively and without due process.
I'm honestly not sure why this is seen so negatively. It seems to be working pretty well for them, perhaps we should do similar instead of whining about others being more effective?
Another point of comparison we might make, how close is Trump's desired increase to the US Defense budget to what China is spending on industrial subsidies? It looks relatively close to numbers in these research papers.
You target the US companies: if they can't use these Chinese models, then they're less of a danger for a now captive audience in the US (and the West generally).
This is already kind of the case: the big enterprises don't really want to touch the latest Chinese models. It's a real pain, personally, I want to use them at work!
Yes, but thats not something a company engaged in regulatory capture for themselves care about: especially if they're worried they'll be outpaced and overtaken by the Chinese labs. Which they will be, IMO.
they care because they know it unlikely open weights will be banned, and thus available to American companies, with regulatory capture (onerous requirements) being a "good enough" "ban" that their big models don't face real competition, regardless of the open weight origin. American companies make open weights too, they are equally threatening to Big Ai financials.
Show them you can burn tokens in seven sessions day and night with comparable results to Opus with less energy and less than 10 dollars a day, per dev.
We have. Unfortunately there are political realities that get in the way, and Bedrock for example doesn't have GLM 5.3 (Flash or otherwise) or anything new/useful
If it's hosted, all they know is "data goes to China".
Until profitable, reputable third parties host open models in the US with ZDR or they become plug-and-play for self-hosting at a modest cost, paying the US models is as much about data protection and liability as performance.
...because everyone saw how well that worked for the Jones act, what with all the naval yards the US has lost over time, and how nearly no US-built ships operate where not legally mandated /s
it wouldn't slow down China as much as make it impossible for American companies to use non-American options, they care about their margins and don't want to be commoditized
> Please explain how putting an upper bound on how good the strongest models can be prevents cheaper less strong models from catching up, rather than enabling it. I do not understand this argument at all.
They are not proposing to regulate only the strongest models. They are proposing to regulate all models. If they are already on top, regulation may stop them from proceeding further, but it also stops the cheaper alternatives from catching up.
If they feel they have reached the asymptote of the curve, then regulation doesn't affect them, it affects those who have yet to reach the asymptote.
Particularly, the route they seem to want to go is "safety".
My guess is that Anthropic and OpenAI will push for "safety" regulations which require byzantine testing that, shocker, Anthropic and OpenAI can pass but the chinese models cannot. The route they'll take is import bans and potentially even general bans on products producing or using "unsafe" models.
They'll further likely try and push AI "safety" treaties from the US to other nations to further lock in their lead.
That's why, IMO, we've been seeing so many "OMG, AI will destroy the world and these AI researchers are so scared" articles.
Cracking down on proliferation of open models which can't be locked down using the kind of guardrails that Anthropic/OpenAI/etc insist are keeping the public safe from all manner of nefarious bioweapons, hacker swarms, propaganda bots, etc. They've discovered they can't meaningfully slow Chinese model progress, so the next best option is to knock them out of competition in the enterprise market for any American company.
Both Anthropic and OpenAI leaders have repeatedly made this exact argument that it's impossible for open models to rigorously enforce the same kind of safety framework as proprietary cloud-served models. It's implicitly part of any regulatory framework they advocate or else it wouldn't be "fair" to American companies since Chinese models would "cheat" (provide weights).
This is true, and it's a good point. I agree that open weight model regulation would either limit the intelligence of open weight models below the frontier or kill them entirely. It would not prevent closed weight Chinese models, but those aren't really tenable in American enterprises unless they strike deals with American cloud providers to deploy them, via products like Bedrock. I unfortunately also have seen no evidence that we can put any sort of guardrails on open weight models whatsoever and so am reluctantly convinced that they should be regulated below the frontier until such a time as someone comes up with a mechanism that is not circumventable.
This is definitely part of it. I think the reports/PR over the past month ended up being a serious unforced error.
Chinese models are increasingly closer to the frontier, while being able to run on much cheaper hardware than what US frontier models run on.
On top of that, both Anthropic and OpenAI showed that they can't really be trusted on data security.
Even if US companies can be forced to not use Chinese models, the rest of the world is going to see the risks and the availability of good enough open weight models for their purposes and be more likely to lean in favor of self-hosted Chinese models or local inference clouds.
> and the availability of good enough open weight models for their purposes
it is childish to believe Chinese are going to give up profits to provide such open weight models forever. the whole idea of being "open" is not compatible with the Chinese culture.
This almost racist read of other cultures has always seemed so bizarre to me. Even moreso when said as an argument on the side of completely closed competitors, some of which are outright seeking to ban open weights.
Chinese companies will continue to provide open weight models as long as it is profitable to do so. Chinese companies are on the more open end in many other industries despite the lack of meaningful foreign competition (for one, 3d printing) so there's plenty of reason to be optimistic as far as I'm concerned.
In a recent Dwarkesh podcast Dylan Patel breaks down how little compute the chinese labs actually have- not even the fact that they don't have access to new Nvidia chips and they're stealing them through shell companies- just that, even if they have cheap electricity, the compute just doesn't compare. Maybe even two orders of magnitude less. They couldn't get it even if they had the money. And if you look at how much more efficient newer chips are, that cuts the effective compute in half again. The conclusion was that they are at least 2-3 years behind.
For frontier labs the current compute seems to be driving model progress (in training) at least to some degree, even without true RSI, and this seems like it'll continue to keep any chinese model from drawing even with the frontier labs, at least for the foreseeable future.
Inevitably the chinese government will drive more funding in chip fab technology and the money will come around to build chinese data centers, but who knows how far off that is. A few different things in the tech tree need to fall into place. It doesn't seem like it'll be next year.
The counterpoint to that, though, is that the Chinese companies have to figure out how to be competitive, regardless of their significant compute deficit. And, as far as I can tell, they're actually doing that. They're trailing the frontiers in model effectiveness, but not by years. It's single digit months.
If there is no upper bound how how these things scale with compute, and if China does really begin to catch up to Nvidia (and they're probably not going to feel encumbered by US patents for domestic AI hardware, given how important AI seems to be to the Chinese government), there will come a day when China leapfrogs the US on AI.
Does anyone know what are the proposed regulations? Controlling software is impossible, so the only option is banning hardware ownership. No more mac studio.
If you pay attention to how these US CEOs talk, it'll be "safety". If I were to guess, they'll try and require a lot of testing, validation, certification before a model is legally allowed to be used in the US or on US products.
It won't be a great moat, they'll probably try and get trade treaties setup to try and expand the moat. But ultimately it won't slow down chinese model development, just limit who can legally use them.
From the frontier labs, the only publicly stated one seemed to be to give them an exception from anti-trust laws to form a cartel and place - incidentally friendly - regulators in charge of monitoring everyone's work.
From politicians like Bernie Sanders, we've had proposals like 20 year imprisonment for anyone researching "ASI".
Dario has always wanted the AI development to slow down and be more careful. Safer AI development was a core reason that Anthropic split off from OpenAI.
What's different today is that now all the big LLM firms want to slow down AI development. When men like Musk and Altman (both known for habitually shooting their mouths off and saying whatever they need to whoever needs to hear it regardless of truth) suddenly agree with Amodei, that's when things start to smell off.
> What's different today is that now all the big LLM firms
not all, just a few American ones (~PayPal Mafia + Google), there are other big American LLM developers (notables include Nvidia, Meta, and Palantir) that do not agree
OAI and Anthropic are forced to release a better model every x months otherwise the Chinese ones will not only be cheaper but also better.
So how could Dario show the investors very nice profit charts representing profit = revenue excluding training costs if it needs to pay a lot of training every x months?
They want to sell the same model for longer(a kind of software subscription where the cost of running /inference is cheap) but the Chinese don’t let them do it. That’s the gist of it. You can see already how they nerf the models just a week or so after release and try all kind of tricks to deliver you shitty performance for the same money. I think it’s part of the same issue of costs and enshitification plan.
In the meantime let’s hope they don’t get to ban the Chinese models(I think they won’t), local AI hardware will get cheaper and the whole AI doom saga will slowly fade to the point that Anthropic becomes a kind of IBM stuff with proprietary data, enterprise certified alignment and enterprise contacts. Think of Accenture junk.
Competition on the provider side—when no single dragon monopolizes the sky—brings fortune for all.
As normal consumers with common sense, we should never naively assume others care for the world out of the goodness of their hearts. Maybe they do, but we should never rely on that.
We can only get good, affordable deals when there is enough competition on the other side.
I mean DSv4.1 Flash and GLM 5.3 kept in check by a supervising frontier like Astra or Fable already in my experience clowns massively on ever using Opus or Sonnet. Opus 5 in particular has been such a stinker that they have to know that they're going to get smoked outside the halo models.
>Nvidia’s financial engineering is partly a response to its biggest customers’ transformation into rivals. “Hyperscalers”, tech giants such as Amazon, Google, Meta and Microsoft, account for roughly half of Nvidia’s revenue
Companies just don't want to pay Jensen's tax. Hyperscalers might still pay Jensen's tax for LLM training but for inference. you don't have to. they are also betting on their own chip for training to replace Nvidia.
this is Nvidia panicking and doing vendor fiance to Neoclouds and buying Hugging Face. even none hyperscalers like Meta is betting on its own chip for AI inference.
it reminds me of a thread I read on PTT, Taiwan's Reddit. AI finally achieved what humans could not. Managers must give exact context for what they want, must pay exact wages (tokens), and can't delay salary payments (which seems to be a problem in China).
Starting with agentic task-time "grounding" being just good documentation, and "skills" being just playbooks and user guides.
Hell, skills are increasingly paired with dedicated CLI tools, that remove jank from actual utilities and adapts them to be token efficient.
So now, any CLI `tool` people want AI to use eventually grows `tool/SKILL.md` and then a `tool-for-llms` wrapper that exposes task-specific, logical, higher level interface, then the skill is rewritten in terms of "for LLMs" wrapper. The procedural knowledge moves from Markdown into the wrapper, making the skill more token efficient, and both skill and the tools are optimized for common tasks and... at this point, we are doing actual UX engineering.
Now the truly interesting part is the difference between what's good UX/DX for LLMs vs humans. Turns out, the conceptual/abstract/cognitive part is pretty much the same: which is why skills still look indistinguishable from well-written documentation for humans, and why the commands exposed by "tool but for LLMs" make sense to us. Same way of grouping ideas into higher level concepts.
No, the main difference is just that LLMs are perfectly content with tightly packed unprettified JSON, or other forms of Perl line noise. The tool output doesn't need to look nice, or to have any spatial structure - they're reading it token by token anyway, and the tokens come from a tokenizer that's reading it byte by byte.
That points at an interesting asymmetry for humans. LLMs are doing I/O the same way in both directions: sequences in, sequences out. Humans only do sequential output - inputs, particularly visual, are processed holistically.
For us, what's easy to read is hard to write, and what's easy to write is hard to read. LLMs don't have this friction.
(I don't know what the implications of this are, I just find this interesting.)
I never quite realized this until just reading this and now it has come into sharp focus. Incredible.
I've spent years trying to convince my director to have our org invest in documentation and monitoring to no avail. Now he is telling us to spend dedicated time on monitoring and documentation so that agents can better diagnose and fix bugs. He is doing this because his boss is mad that our org isn't "agentic" enough.
Except... because we underinvested in the past we have a bunch of services where the institutional knowledge is gone and people are having AI write the documentation...
>I started using Spark 1.2 for development because if you're willing to let Meta train on your data it was dirt cheap
its free on opencode and i use it for personal projects. most of my personal projects are AI generated since its personal projects. nothing important are on them. it is hilarious if Meta is training their AI model with AI generated code.
The useful training data is when you clarify your intent, when you tell the model a different approach would be better, when you consistently refactor towards Y and away from X, and so on. The training data isn’t the code, it’s the session transcript. (Anthropic would call this a “distillation attack” against their model, but in this case the model is you!)
Thats not exactly 'validated'. Feels very noisy, it is not a good bar for either
- does this code do what the user actually asked
- is this code actually 'good'
There would be so many examples of coding projects that these models began or attempted to work in, that were abandoned because the models were floundering.
I would imagine the labs have some decent ways to produce novel requirements and then actually validate they are met, without the noisiness of implicit human feedback.
That said, the more I think about it, you are right, there's probably also very good ways to extract signal for all these sessions.
This is exactly what RLVR is, and the reason that models have improved so much at verifiable domains like coding and math while not so much on unverifiable ones like writing and UI design.
what's the moat for AI labs like OpenAI and Antrhopic as they seek trillion IPOs. a lot of people said Chinese models also distill US LLM models. if it is this easy to do. how do US AI labs justify asking for trillion?
I believe if this was true, we'd already be seeing vibed stuff succeeding everywhere, and pricing out incumbents, but I guess I'm not seeing this? Like at all?
Even if they have Astra or whatever completely to themselves, how do they deal with the -product- side? Or sales, or account management?
They've been focused on juicing model's coding capabilities, but it's absolutely -not- "gen ai" enough to be doing the whole thing in agents, even 3 or 5 years from now, if only because there's so much context we _can't_ give to these models, without reverse centauring ourselves with cameras and mics and oodles of compute everywhere, and so far that hasn't exactly been playing out the way the frontier labs and the singularity folk hoped it would (meta glasses? humane? really?)
>we'd already be seeing vibed stuff succeeding everywhere, and pricing out incumbents, but I guess I'm not seeing this? Like at all?
As a small business owner I see this. Random people contacting me to sell a software where I can instantly tell it is vibe coded. Subscription prices half or 1/4th of incumbents. Domain names registered in the last few months.
They don’t need to make you or me, people who actually make software, believe that every anthropic employee is actually and in fact, right now, a „10x software factory controlling thousands of agents“, they need to make investors and CEOs believe, and then hope that making that true is possible and that they can keep the fiction up long enough and get enough money and infra built out to make it actually true.
Like Uber replaced all drivers with self driving cars [1] in its 20 years of operation? Just because I want to paint the moon pink, doesn't mean it's feasible.
[1] If anything, Waymo has a higher chance and global Waymo adoption is probably 20 years into the future, at least.
> I believe if this was true, we'd already be seeing vibed stuff succeeding everywhere, and pricing out incumbents, but I guess I'm not seeing this? Like at all?
I'm substituting my own[0] vibe coding for buying[1] apps. Language mini-games to help with German? A few prompts. Fluid dynamics simulation for an airzooker? Vibed. A web app listening for a MIDI keyboard, upon which you can drop some .midi files, and get a rhythm action game to learn the piano? Vibed. Webcam for my Raspberry Pi? Vibed. Getting Marathon 2 (well, the open sourced and upgraded engine, Aleph One) working as a web app? Vibed. Isochrone maps? Vibed.
Half of this I can even get done with the free models.
> without reverse centauring ourselves with cameras and mics and oodles of compute everywhere, and so far that hasn't exactly been playing out the way the frontier labs and the singularity folk hoped it would (meta glasses? humane? really?)
Yeah, so fortunate that cameras are expensive and there aren't 6 on my table right now between laptops and phones. :P
Seriously though, what's saving humanity collectively from everything getting automated from the panopticon we'd already built before Transformer models got good enough for even the most basic of classification and translation tasks, let alone anything we now use them for, is that machine learning takes an obscene number of examples before getting competent. Any living creature that needed so many examples would starve to death before learning how to eat.
This difficulty is why, for all the billions of miles that Tesla cars have collectively driven, perhaps pushing trillions now, they're still not sold to the public without steering wheels. Tesla claim to make such vehicles now in the form of the Cybercab, but they're not for sale, and even then some of the pictures that get in the press still show steering wheels.
[0] if you can call anything vibe-coded "my own"
[1] or worse, given the popularity of subscription models in this era, leasing some SaaS
> I'm substituting my own[0] vibe coding for buying[1] apps. Language mini-games to help with German? A few prompts. Fluid dynamics simulation for an airzooker? Vibed. A web app listening for a MIDI keyboard, upon which you can drop some .midi files, and get a rhythm action game to learn the piano? Vibed. Webcam for my Raspberry Pi? Vibed. Getting Marathon 2 (well, the open sourced and upgraded engine, Aleph One) working as a web app? Vibed. Isochrone maps? Vibed.
When do you have time for all of this? I don’t mean the vibing part but the using the app part.
I agree that agents are super good for one shotting throwaway code for tasks that would have required manual human actions previously but I would never have bothered buying an app for that.
Not having to deal with IT support for relatives is a win though since now I can just throw it at an LLM!
> When do you have time for all of this? I don’t mean the vibing part but the using the app part.
Mix of this being spread over more than a year, that I'm not doomscrolling because HackerNews and Telegram are my main social media presences, and being unemployed/prematurely retired (which one depends on what one thinks of €1k/month passive income and no rent).
I am also weirdly unmotivated by opportunities to spend money, which is both how I got this passive income and lack of rent, and why it's borderline enough for me. FIRE is very easy when all your working life, you only spend rent+50%, the rest of your paycheque going to savings and investments; but most people can't do this.
Oracle makes billions selling SQL when you can just use a free version. This is no different. Enterprise has enterprise needs. It's really not that complicated or irrational.
Anthropic wants you to think that models that distill from them would be worthless otherwise. It’s not true, it’s just one part of the process. The whole narrative that Chinese and other models are only good because they distill is nonsense.
A lot of the freakout and "bye" posts on the OpenCodeCLI subreddit due to DeepSeek raising prices.
Stripe is a middleman. So is OpenRouter. So is OpenCode. Unless you own the data center and the hardware, how cheap can a middleman's tokens really be compared to the hyperscalers, without massive model compression?
Even DeepSeek itself is raising token prices. How much margin is there for a middleman like Stripe buying tokens in bulk from a data center and reselling them? Last i check Stripe do not run or own physical data center
That's old economy thinking. If you have shit tons of data about the behavior of large groups of people, someone will pay you for it. HFT firms paying for satellite pictures of shopping mall parking lots in order to get an advantage over their competitors is old news, but it gives you an idea of the game being played. Why do you think meta bought gif keyboard?
OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.
Whether AI works or is useful or not isn’t even the question anymore. It can fulfil every promise Sam Altman has been making and will still make no financial sense to justify these valuations.
I take it from [1] (transcript of recent DeepSeek CEO discussion with investors) that DeepSeek would disagree on the immediate catastrophic impact to the likes of OpenAI or Anthropic. The reason is even though technology parity mostly exists, only OpenAI, Anthropic et al have the inference capacity to gain market share and generate revenue. Chinese vendors don't have the chips needed to scale up inference and gain market share, and the DeepSeek CEO doesn't think this would happen in optimistic circumstances in the next 3 years, but thinks it might be possible in 5 years.
In summary, regardless of country of origin, availability of inference capacity is the moat protecting the likes of OpenAI and Anthropic, not technology superiority.
It only justifies their long term valuations if they can leverage that temporary monopoly for technological superiority (they can't) or lasting market share (they can't).
Chinese models prove there's no technical advantage, and the software side is heavily commoditized so there's not much advantages to market share either.
The question mark in my mind over the technological superiority is whether the additional volume of data they see due to capturing the top of the market allows them to do recursive self-improvement in a way nobody else can match, before any of the other labs can figure it out. That's the only runaway outcome I can see.
If you have exponentially increasing use of your harness, then it's true that every day you capture exponentially more data, but it's also true that every day exponentially more data will slip through the cracks of your would-be monopoly and that data arrives at your competitors via various channels (competitor harnesses, subsidized reselling, etc)
The very exponential that you are relying on to give you runaway improvement is also giving exponentially increasing data to your competitors. All else being equal your competitors stay a step behind but you never develop a monopoly either. That's the best case for Anthropic/OpenAI. In reality, training data is just one variable, exponentials don't last forever, and your competitors will get better at capturing a bigger slice of training data.
If user data would become such a key ingredient (which it might, i actually remember noam shazeer talking about the importance of user data), i think chinese labs can still get it from china, as keep in mind it ahs a billion people behind the great firewall banned from using us llms. And btw broadly for any gap like this, you really gotta consider that if its becoming a bottleneck, chinese labs will find a way to buy it from one of the labs unless theres strict regulation at the government level
But is that data good? That's the question. As in, is my usage at work:
a) indicative of problems that aren't already out there in the wild? (no)
b) are the responses I'm getting so good and novel that the model can improve itself? (no)
It's the garbage in garbage out idea, just scaled up. If the model gave a bad answer, and I didn't catch it, and you now train on that I/O pair (my perhaps crappy prompt, the bad output), then you're not going to improve anything.
Yes, RSI seems to be the new AI industry McGuffin of 2026, just as agentic capability has become table stakes and scaremongering has become a punchline.
The Chinese models are adopting licensing quite rapidly and Xi will soon enough close them for security reasons. The most widely used model, integrated across Bytedance apps and operations, has never been open and is most closely associated with the state.
I would add that it is not just capacity, but also negotiation ability. With scale comes the ability to negotiate better prices than everyone else. Even if you can find capacity for your smallish user base, your inference cost can not match these companies unless you have a technical advantage for your inference cases. Squeezing the hardware requires request batching and caching which are far easier at scale and sustained user activity.
People can host these models is doing a lot of lifting here, these are models that depends on 5 digits on specialized installation to run on.
IMHO, this has the impact of softening the impact of data centers sitting unused in the long term if they can still serve open weight models, even if Anthropic or OAI have to scale down their expansion rate to pay the bills.
Regardless, reality has to give at some point; these valuations don't make any sense. We've been valuing GenAI as disruptive work, when in reality they're much closer to cloud providers with a beefy, one-pony-trick R&D department.
Is lack of inference chips due to the trading blocks by trump administration? What if Trump agrees to sell chips to china, would they collapse then? That's not a very strong position to be at
Most discussion in recent years about chip fabrication shortages, expansion, etc has focussed on leading nodes (<7nm) and AI/computer chips. But perhaps more quietly in the background, China has been rapidly building other semiconductor capacity such as power semiconductors used in electric vehicles, wind turbines, solar modules, train traction systems, etc. For example, Chinese-produced motor vehicles (37% of global motor vehicle production in 2025) in a year or two are targeted to use 100% domestically produced chips, and this production is decreasingly dependent on imports, even for factory tooling.
The report at [1] is a good summary of long term trends for China's rise in domestic self-sufficiency for semiconductor manufacturing. The report predicts "At current pace, China may achieve self-sufficiency in semiconductor manufacturing by 2027-2028, though trailing at leading-edge nodes". By contrast, before the first Trump presidency in 2017, a chart shows China importing 30% of all globally manufactured semiconductors (and increasing). Other reports on semiconductor fabrication equipment sales show the means, which is China having been and continuing to be in number (1) position for expenditure on semiconductor fabrication equipment.
The reports at [2] and [3] are also a good summary of long term trends for semiconductor foundry capacity predictions to 2031. A prediction is made that China's current 12% global semiconductor foundry supply capacity (across all semiconductor categories) in 2025 will expand to ~30% by 2031.
I have already begun winding down my spend on claude and OAI to make room for infra budget. Anecdotal, but I have no doubt a lot of others are doing the same, I very much agree the US players have major issues looming. What an exciting time to be alive!
No time like the present to pull out and reduce your exposure. I brought this up in my employer's forums 4 months ago and honestly it's been clear even before then. In particular, the upcoming IPOs of both oAI and Anthropic will likely be disastrous for the public - the floor is falling from under them and I don't know if they can be scrappy and work with fewer resources - their internal culture may not support this. We all knew in our hearts they're a commodity - just see how easily you can switch between the 2 of them - and now there are 10 more options costing a fraction.
When Xi Jinping did the announcement of their open weights push, they might as well cancelled their IPOs....
Yes buuuut…. I do quite a bit of day trading (maybe closer to scalping) for the first few hours the market is open, everyday. Anecdotally: despite everyone knowing its valuation was ridiculous, I rode that SpaceX train pretty hard and made a pretty penny.
I close-out all my positions by end-of-trading everyday… so when the day came when there was a very clear and very scary indicator during early trading hours, quickly followed by SpaceX’s catastrophic fall right after opening bell, that was the end of my involvement….
And I fully expect oAI and anthro to be the same way. They’re being propped up with private loans, subsidies, and other tricky bookkeeping techniques. You would think their CEOs would pivot away from their current public personas. Ironically, they are like a poor man’s Elon Musk… and that doesn’t bode well for their companies
The frontier labs will do well if they pivot their offering towards more capable, larger-scale models that are inherently harder to both train and deploy for commodity suppliers. Their existing investments in gigawatt-scale datacenters are quite optimal for this. "Commodity" inference need not comprise the whole market.
I don’t think this works, for a few reasons. First, intelligence gains from scaling the models bigger is sublinear now. So they could eke out a little extra performance, but the increased cost will eventually eclipse the economic value gained from this.
Second, humongous models are impractical even for them to deploy widely. They’re best used as teachers for smaller, more efficient models that can crank out the volume they need to sell.
Finally, there is a data wall. Sure, they can keep scaling RL on math problems and code. But with everything else, where will the supervision come from when they need several orders of magnitude more?
I agree. And even if they were able to do it for one more round, it's not a sustainable strategy. What they (Anthropic and OpenAI) need to do is build platforms and integrate verticals.
assuming the technology of model architectures does not gain any further breakthroughs that returns us back to the gains previously seen. I'm of the opinion that we still have some discoveries on the mathematical side of the fence to go that will improve models further.
> I'm of the opinion that we still have some discoveries on the mathematical side of the fence to go that will improve models further.
That's assuming the infrastructure needed to develop models stays available financially and supply wise. A lot of the services used to train and develop models are supplied and funded by people who are looking for multiple returns of investment. If/when OpenAI and Anthropic valuations fall and they inevitably get acquired, will Meta/Alphabet/Microsoft still want to spend lots of money for unclear returns in the short-term? Nvidia and co are on a one way train service to hype town. I don't think they will be happy to get on a coach to hype town Temu version. The shareholders likely won't.
Also, the backlash against LLMs is growing rapidly. AI content, data centres, etc is quickly gaining negative connotations outside of visual and music artists circles. While existing models are going nowhere, developing more advanced models is very quickly getting unpopular. LLMs Data centres increasing people's bills, Anthropic destroying old books, chat bots giving unethical advice to vulnerable people, etc. It won't be long before LLM infrastructure becoming an electoral issue.
Will a small research oriented community be big enough justify maintaining the apparatus needed to produce infra tech at a profitable level post OpenAI?
Most of the money will come from companies/corporations who will be required to buy safe AI. The public will be just banned from buying which might make it hard (ie: site/payment blocked) but not impossible. It could be good enough for the big whales.
A large portion of the economy is currently tied up in the musical chairs shell game that is AI hype. When the music stops there are going to be CEOs looking for handouts and justifying it with spooky national security buzzwords. How we respond to that will depend on whether it happens in an admin that is famously captured by the industry or not.
Many believe, including myself, that the market is currently propped by a massive AI bubble. Nearly a US $1 trillion is being spent this year, and more is planned for next year. All of this is for a "build up". There is no pay out. The major AI companies are taking in massive losses in the hopes that they will eventually be able to cash out.
The math is not looking good to me. The effect will be like the dotcom bubble. But much much bigger. Because the numbers are so much bigger.
Well, the dotcom bust wasn't all that bad for the wider economy. No financial crisis. A shallow recession (and even that could have been avoided.)
Btw, the dotcom bust was real, but there was no dotcom bubble. Skeptics back then said that the valuations only made sense if tech companies were to dominate the economy in the future. Well, that future arrived more than a decade ago.
(More formally, if you had invested in a broad index of tech companies throughout the dotcom boom years, and had held this, you would have done reasonably well over the next twenty years.)
Just keep in mind that it can take a whole for things to play out. I’m someone who believe the US AI industry is completely unsustainable and built on sand, and will crash even if the current AI itself turns out to be very successful. But that doesn’t mean everything will burn to the ground next week. In a history book things will look very sudden but at normal speed that can easily take months to years to fully play out.
Also, take in consideration that the AI trade infected a lot of other trade in the economy, if you decide at some point to move your money to a place that is safe in case of a downturn be sure to carefully evaluate that’s actually the case
These crises are manufactured by the central banks.
Compare and contrast how the dot-com bust did _not_ lead to global financial crises. Nor did Black Monday, nor the recent string of bank failures in the US.
('Manufactured' above means that central banks are responsible. I make no judgement on intent here. Around 2008 it was incompetence by the Fed and ECB as far as I can tell. The Fed started paying interest on excess reserves and the ECB even increased rates. Twice. Amongst quite a few other missteps.)
A lot of the performance of these open source models might come from distilling the closed frontier models. If those can't raise the funds anymore to train newer and better models then the whole improvement cycle might slow down.
Another interesting potential market here will be 'LLM in a box'. All the hardware and other tooling in a prebuilt, but modular, package ready to go. Pay one up-front cost, get a system running [whatever open LLM] with a token rate of [x], optionally configured to be immediately ready for distributed usage. Basically the opposite of cloud stuff: no rent, no dependency, 100% guaranteed uptime, guaranteed security/privacy (at least subject to your own actions), and so on.
Palantir already offers a "turnkey AI datacenter", i.e. a rack with "NVIDIA Blackwell Ultra systems with eight NVIDIA Blackwell Ultra GPUs and NVIDIA Spectrum-X™ Ethernet networking for AI training and inference".
It is said that it comes with all hardware and software required to run inference or training with an open weights LLM.
The existence of this product, which competes with cloud-based offerings like those of OpenAI and Anthropic, is presumably the reason why the Palantir CEO criticized very harshly some time ago the business model of OpenAI/Anthropic.
While I doubt that the ethics of Palantir is any better than of OpenAI/Anthropic, in this particular case I have to agree with Alex Karp about "Sovereign AI", i.e. that only losers will make their business completely dependent on an external entity like OpenAI or Anthropic, who are certainly not trustworthy.
It is just a dedicated computer system, which should be managed by its owner, like any other on-prem servers.
I doubt that it has a good price/performance ratio, but it is a solution for those who feel that they do not want to search, buy, assemble, install and configure every HW/SW component.
Fair but the idea of "running your LLM setup" at every "need" level and corresponding cost does make sense.
For a lot of people (and orgs I'd guess) who just go and buy ≈$20 per month plans (or more for teams), they might not even need a fraction of that cost or capability. A lot of them don't even need it for coding or graphics. Even the API access based pricing aren't great from these frontier US AI houses. The distribution of "LLM being" offered will also give rise to many open-router like offering but at the end point level - direct interfaces to the customers. Pick your vendor sort.
AI shouldn't become another "search means Google".
It comes in a full sized shipping container and costs around $10M but money has stopped being connected to reality now anyway with all the AI company valuations being floated around, so who cares about a few million here or there.
“100% guaranteed downtime when you least can afford it and the support tickets are your problem.”
We’ve a hybrid shop, including hosting our own ML infra, and we save a ton from cloud spend with local ML. Easily one million USD over past three years. But it’s not “free”, you are shifting a lot of labor into your plate.
Still has to break even on the balance sheet, especially at a bootstrapped startup. We actually made most of the financial windfall in translation API fees oddly enough.
For our own model training we needed to do some large scale translation tasks of a large dataset (1M or so documents, 10 or so target languages), running full-size NLLB on-prem saved us an absurd amount of money vs Google Translate API.
(For reference doing 1M target docs into a single language in Google Translate API is roughly $120k list price. You can run full size NLLB on an 48GB NVIDIA A600 and the major difference for us was speed, but for this task time to completion wasn’t an issue.)
Exactly! As I've argued here on HN before, such an "LLM in a box" might end up being serviced/upgraded once or twice a year by a company very similar to the one servicing the coffee machine at the office. In contrast to databases, storage, etc. it doesn't matter much if the box breaks at some point – they'll just come by and replace it with a new one – and there's barely any software on the box to speak of, at least none that requires continuous development and feature upgrades, beyond rolling out security patches. This makes the business case drastically different from cloud and SaaS offerings, where most of the moat is in the software and the state maintenance (and the vendor lock-in of course). The LLM in a box is destined to become a commodity.
What makes that kinda complicated is that multi-user throughput of LLMs scale well but single-user performance often stays constant at low ends. If you could saturate e.g. 16 concurrent session-month of demand, you can just go buy 16 of 32GB GPUs and start charging monthly for inference. That could work if you had e.g. over thousand total employees with hundreds of devs eager to trying it out, but only if the company is also interested in a private inference experiment.
You're talking about multi-session vs. single-session throughput. A single user can easily leverage multiple sessions via e.g. subagent swarms, especially on a lower-end setup where any single session is going to be quite slow. Saturating utilization during off-hours is harder but potentially quite feasible by assigning lower priority, unattended tasks/inference loops.
I think at this point the question is: will the US government be willing and capable to justify the trillion dollar valuation for _one_ of the companies via regulatory capture? The US has a workforce of 170m, so 1.7 trillion would come down to 10k per person, or a discounted cashflow at 3% of 25 USD per month - not including private use, students etc.
It’s a common denominator if you want to do napkin-math for a whole national economy. Regulatory capture is like a tax on those people not on the beneficiary side, so if the government were to nationalize both supply (no export license for SOTA models) and demand (no foreign or self-hosted LLMs allowed), they’d end up making everyone else pay for it in some way or the other. The governmental utility function will then include only those using the services for direct economic benefit.
It is impossible to justify the absurd private valuations they have given themselves in collusion with investors.
I wish they had tried to IPO because then we’d see the judgement of the market on this. But that’s why they didn’t this year. How long can they keep up the charade that their models are uniquely valuable and on the path to AGI?
its interesting, as it's typically the banks and against the public at large because the goal is to jimmy up valuations to justify IPOs then sell on opening; just like spacex.
It's what enron was doing; it's what most of crypto's offshoots were doing.
Sure you can blame the marks of the grift and say "well the public should know they're faking all this cash flow expectation".
It seems like you're either driving the grift economy or part of the collusion.
It's similar to how a cult operates, so I'll be frank: your skepticism seems biased.
Which is not unreasonable. Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
> Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
It's been a few years. Has anyone done this successfully yet?
There are a over a dozen EU open-weight providers. I’m not sure if they are even charging that much of an extra. EU-based clients have little reason to use non-EU inference providers.
I don’t have user statistics but my mail/domain registrar Infomaniak advertises Qwen 3.5 and Apertus, “a Swiss open-source AI model, developed by EPFL, ETH Zurich and CSCS”
Most are not necessarily free to host and monetize. At least one of them has a license that says if you are re-hosting the model then you need a license with that company that made the model.
"I just don't see how you justify a trillion valuation for US AI"
- military applications
- financial applications
- medical
- applied science
In all those cases it is achievable for those who have needed training data, and Chinese are not going to get them easily. US AI Labs are showing: give us the data, we will do wonders, promising "singularity"-level future achievements.
I'm sure US billionaires will find a way to extract those trillions from the public. They're smart, they can handle it. After all, they can ask AI for advice on how to do it.
Could you just tell us why you think they were worth billions before ChatGPT, instead of suggesting that we think on it? You seem to know the answer already, so please share it with the class.
agentic coding is real. If AI labs can take over the coding tool market, that's a billion+ market. LLMs work in coding, and AI labs can slowly expand into other white collar work. Vaporware? Hardly.
The American attitude is generally to let private companies build up a new industry so it can create jobs and pay taxes. However, in the LLM race, the Chinese open weight playbook pretty much killed that. China has basically commoditized LLMs. Chinese models are good enough, so the race has come down to who can offer the cheapest tokens.
Chinese open weight models are great for this turn, but American private models generate orders of magnitude more cashflow. This cashflow = investment in training future models. It's unclear how Chinese open weight companies are going to compete in future rounds if they can't raise the same capital for training runs.
The American business model is exceedingly efficient at building large businesses from zero. I wouldn't dismiss it as just a jobs creation thing.
It’s unclear where American labs future capital will come from. They pretty much exhausted private options at that point and it’s not clear how successful an ipo would be at the current time
Obviously to me, I express things from my point of view.
Raising too much from debt is a bit dangerous if you plan to go public relatively soon and don’t have a good story for it (I don’t believe they have one). You can continue raising from VCs, but at some point the valuation and dilution starts to become a real issue, and will make your ipo even more difficult. Their options are pretty much limited to raising money from hyperscalers (with required compute spending, so more circular funding), which is what they are doing, but you cannot do that infinitely without having a good story to tell Microsoft/Google/Amazon investors. The market is more skeptical than it was a few months ago, I’m not convinced you can do that for years to come
I think as a counter point we continue to see investment and buildout. What do you mean the market is more skeptical? Of course the market doesn’t really have an opinion per se and aren’t all of these companies growing in valuation, revenues, and profits? At least the public ones.
Why are Anthropic's and OpenAI's annualized revenue about $50B each?
LLMs need massive amounts of compute to compete, so I wouldn't claim that the great (and leading, and likely to continue to lead) LLMs are commodities end-to-end, even if the non-executing-at-scale LLMs files and IP are commoditized. The execute, the compute, that is what breathes life into the model, which is otherwise weak or dead.
Astra is the best. Luna is cheapest then it seems like Sol is the middle child like Terra.
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