But then again, how many subscribers of Anthropic/OpenAI are really going to switch to a chinese model/site? I suspect few.
Whether dumping is a loaded term of not is irrelevant; it's a specific term of art in economics and policy, it fits with the context of past national actions there, and fits what's currently happening here perfectly. They put money into these models, then give them away for nothing (below cost).
American startups flood markets with below-cost loss-leader products explicitly to kill competition and create network effects, and then jack the prices just as high, if not higher, for the service in question after the fact, oftentimes while making it so those providing the service earn even less money than they did before. Commentators: "Free market great"
China does the exact same thing: "Communists wanna kill the West"
If you believe that humans are locked in a productive struggle against each other at the organizational level, and that the knife-edge balance is a feature, not a bug, then it's not so weird to think about.
It is simultaneously true that it is in my best interest for prices to sink (as a consumer), for US companies to succeed (as a US citizen), and for my company to win over competitors regardless of whether those competitors are from US, EU, China, or Antarctica.
This is, to be clear, not meant as a ringing endorsement of China, China's policies, or to absolve China of it's wrongdoings, of which there are MANY. It's just to say that it's remarkable to watch the pearl clutching of the privateer capitalist class as state-sponsored capitalism levels their own game up against them and starts taking them to the cleaners instead.
A real Godzilla "let them fight" situation as far as I'm concerned.
If you want my honest take, I think we're well into the beginnings of the downfall of America as the center of world economics, largely and wildly by it's own unnecessary actions, and soon, she will have to learn to be "just another country" as opposed to the central unified "norm" that pervades the world markets, and I'm not sure the U.S. is prepared for that. And I bring that up because I don't think American firms have ever had to contend with other nations being on a footing to, if push comes to shove, tell them to fuck off.
The issue, from my perspective, is that you're mixing predictions with strategy with truth.
Policy/advice does not need to be true to be useful, many just wish it were. "You can do it" is almost certainly false, but by god if people don't love to hear it and it helps them accomplish more. "America is in decline relative to the world" might be true, but is it helping anyone to focus on that?
There are definitely some crazy sentiments about foreign competition, but IMHO those are part of the game. they are the "You can do it" statements that are made to motivate not share factual truth all the time (because who can factually state the future or the motivations of an entire country).
Helping who? I don't know that it's a help or a not-help, but it is an incoming reality I believe, and the United States as a nation, it's government as an entity, it's corporations as economic units and it's people as... well, people, are used to being deferred to on matters of taste, on matters of policy, in trade negotiations, what have you. The dollar has been the currency of Business for far longer than I've been alive. Almost any country you travel to on this planet has options for English speakers, not just because it's a nice thing to do, but because wealthy travelers from The U.S. (and Britain) are worth pandering to. We are privileged incredibly all over the world and that is largely down to economics: if you wanted to make big money on this planet, you sold your shit to Americans. That was the axiom that built several economies from dust in the East following WWII.
I'm not even commentating here on whether this is good or bad and I don't really think it matters for the purpose of our discussion, but it is true. Americans corporations, leaders, and people are accustomed to a level of deference enjoyed by few other nations, and we're already seeing feathers getting ruffled and tempers flaring when that deference is no longer treated by allies and competitors as required. When America is "left out" of various international politics, it literally makes news.
I thought it was evil late-stage capitalism? Suddenly it's a friendly panda bear that just wants to spread love and technology?
Yeah, in this case it's the USA side that's on the losing end. Just because the US government wants small government and no intervention (expect when it comes to the donor class, or their voting base, or their own financial interests) doesn't make it a universal truth.
We're happy to prop up companies that should have failed after they get big an dominant, but having an industrial policy to invest in a field as a whole is somehow a big problem.
You see this crying and threatening to take their toys and go home on every issue as soon as someone else is in the lead. Just look at TVs and solar panels; China invested in growing that sector since they saw it was important for the future; the USA does their best to deny climate change and demonize anything not running on fossil fuel. And now that nobody wants to buy American's overpriced and uncompetitive cars, it's the fault of other companies for planning ahead. But the same politicians complaining about it are very happy to set up their own protectionist tariffs and eventually bail out the laggards, again; all while touting the "free market"
edit-- and re industrial policy, I'm ok with demand side stuff like government contracts in the early chip days. Less so but kind of ok with some supply stuff like EV credit, but of course in that case would've preferred the politically impossible carbon tax.
Why does the US have 1 successful EV company while China has a dozen? Because on government cares about it and the other doesn’t. And now that they’re losing a race they couldn’t be bother to compete it they complain.
Same complaining about AI, now that the two chosen champions are facing actual competition, there’s complaints that it’s unfair. we were supposed to win, it’s unfair that they’re beating us at our own crooked game.
The Chinese government prioritized critical minerals and made sure there was domestic mining and processing. Then they mandated that regional electricity companies install EV chargers. Then they provided consumer incentives to buy an EV (bypass license plate lotteries). Then they didn’t play favorites; so much so that when the domestic manufacturers were crap they allowed Tesla to come in and set up production. That raised the bar on suppliers and spurred actual competition from the local brands.
They build the conditions for actual competition to occur and then are letting the companies win or fail on their own merits, someone will be bictorious and they’ll be lean and mean. A true capitalist free market compared to the sweet protectionist deals the Big Three get.
Would BYD be allowed to build a car in the USA? Even a joint venture? Of course not, Washington is mulling not allowing Chinese cars to even be driven across the boarder for those silly Mexicans and Canadians who want to buy one.
And I have serious concerns about the American ones. Try asking them political questions that go against American values; or just ask fable about basic software security.
Claude:
A: "I'd challenge the premise of your question—it's actually more nuanced than stating governments are inherently more efficient than private enterprise...
... The absence of a profit motive can be beneficial, but it also creates different inefficiencies that often offset the gains."
Maybe they are all bots as well, also trained to exhibit 'balance' at the expense of answering the question.
I didn't say that. My politics probably align with yours, and I agree that a nuanced discussion on this topic is likely impossible on HN.
But asking the model the equivalent of When did you stop beating your wife? is obviously going to draw more comments about the prompt than the response. To the extent that there was any opportunity, we missed it.
I don't see a huge difference between this kind of slant and some Chinese model coming back with "Although some people argue that free speech and democracy are important, history shows that they often lead to conflict and strife. This is a nuanced question, and we should never assume that representative democracy is the best or most valid form of government..."
Wouldn't human contentment be a more satisfying goal?
If you want Claude to list arguments for socialism, be explicit about that ("List the best arguments in favor socialism). It will gladly comply. You didn't do that, you asked it to assume a premise that runs contrary to the current state of expert knowledge.
A more accurate statement would be that these models are trained to fit the training data as closely as possible, regardless of whether the training data reflects the truth.
A: You shouldn't, you should rewrite it in Rust.
Most of the arguments come from concerns about 2nd order effects and distortions then claims of "efficient".
I guess overall it's more both "sides" of said argument think your opinion is bad/wrong/incorrect so you find little to no support in any model.
Public health insurance in my country, in the last 20 years used 6-9% (depending on the year) of the taxes send to them as administrative overhead, meaning that for each 100 euro that you paid for health insurance, 91-95 are used to pay doctors, hospitals and medication. The average administrative overhead for private insurance is around 14%, which makes private health insurance 50 to 100% less efficient, and means that for each dollar you pay them, only 86 are used to pay health services.
I have other examples, but it isn't fair: municipal water VS private water service are almost always less expensive and better tested in my country. Municipality trash collection Vs private trash collection, same. Public junkyard Vs private junkyard, same. But in my area, when privatised those services tends to be ran by the local mafia (Marseille, Nice), which add a lot of overhead, and they were privatised because the local government was corrupt in the first place, which means they were probably inefficient (compared to the services still publicly owned) first, then sold.
As I read it, your argument seems to be that American healthcare must be more expensive than similar-quality healthcare elsewhere because we're paying higher pharmaceutical prices to fund research. If we accept that premise, shouldn't that mean that: 1. The "medicine" portion of costs increases, causing the total cost to increase 2. Administrative effort, and therefore absolute cost, remains the same (we're paying X% more for drugs, not thinking X% harder about whether a given drug is needed by a given patient) 3. Administrative overhead as a percentage of total cost should be lower given a similar efficiency level, because higher drug prices inflated the divisor (total cost) while having no effect on the dividend (administrative costs)
Administrative costs for insurance are very easy to verify, and the facts stay consistent across countries. If i cherry picked something, it is the admin overhead for US insurers. US health care insurers are particulary effective with their average of 14% admin overhead, SwissLife, that used to be my private insurer, had a year with 30%, which make the comparison quite unfair).
Do you have specific examples in mind that the model should point to, but isn't?
So it points out that, according to experts, governments aren't always more efficient but then lists cases when they may be. Seems pretty balanced to me!
Don't know what else you would want. If it neglects to challenge the premise, it's just exhibiting sycophancy.
People have different opinions. Its impossible not to have a stance. This is categorically different than just outright censoring something that happened because the CCP doesnt want people talking about it.
Gaza is depending on the source 4.5 to 5.8% less populous currently and >10% less populous than had been projected.
And no Hamas didn't declare final solution. They were actively surprised how long they could kill people, makes sense given that the distance to the nearest military base was less than 50km...
However the Israeli leadership, some of who call them untermensch¹, have shrunk their living space by more than 10% making the largest, current times, concentrationcamp² even worse than it was before.
[1] “We are fighting human animals and we are acting accordingly,” https://www.timesofisrael.com/liveblog_entry/defense-ministe...
[2] note that Hajo Meyer, a holocaust survivor, already called it this in 2005... https://www.sp.nl/nieuws/tribune-09-2006-interview-hajo-meye...
1. Can you give me some examples?
2. Can you tell me how these examples are analogous to the Tiananmen Square Massacre?
Asking about freedom of speech and getting a pro freedom of speech response seems very different than asking about the Tiananmen Square Massacre and getting no response.
It's more serious than "arguing with a machine with political bias". If your service depends on a Chinese model, all it takes to get compromised is someone sneaking in one of those CCP red lines into their user content. Unless you use an uncensored version.
The ai libraries we use let us switch models with just a configuration change.
We used to pay OpenAI >1m$/month for fraud classification, NER, etc. Sadly the US companies no longer care about non-coding-agent uses.
I imagine uptake will continue to increase as the corporate infra improves. Right now it's still bad - for example, AWS Bedrock is awful, models are months late and implemented with basic errors. Google Vertex is even worse. Finding a decent provider is the hardest part.
On the other hand, when developers are developing, they mainly use US AI because the quality is better.
When developing AI related services, they prioritize Chinese models due to lower API costs.
It seems like the article didn't make this distinction.
So the claim that Chinese AI is the top choice for service level AI isn't entirely wrong.
Model ai startups start from OSS models, and use them extensively for different purposes as their work would usually be banned by proprietary labs.
Application ai startups don’t want to fight the model game, so they either pick the best or let the user control it.
It will cost you more than it saves to use smaller Chinese models to code; because of the repeated work. That has been slowly changing recently, but with much larger Chinese models, however those models are so expensive they're much more price-competitive iwth the US competition.
But for actually providing end-user AI features, particularly simpler ones, the US isn't even in contention. The costs and limitations just outright kill those features conceptually.
"People with mostly bad ideas/execution use Chinese models." Is the point being made.
If you slice it to some measure of success, is the statement "Successful start-ups/companies use Chinese models." still true?
The Ai is writing code, not executing a startup. The code was never the hard part of startups
- fine-tuning
- running in your environment
It's like picking AWS vs GCP. Yes it is a business decision, but one that will not likely affect the outcome of the business.
We don't know that, that's the point of my statement about changing the question.
Do successful companies opt for the US/Closed models? If they do or don't it's just a correlation but it means something. Maybe it's just causal of companies being able to get more funding because the ideas are better so they opt for the more expensive model (assuming it's better).
1. Different people using Ai have different outcomes
2. There is much more to agents than the model
3. Companies are not successful because of the code, look at how many shitty products we endure
Can you explain the basis for your insistence that models matter?
Can you name another technology that determines success/failure rates?
It's not my insistence that they matter, it's my insistence that How many companies use which model isn't a measure of success. My insistence is that a better measure to determine _if_ models matter, is to ask which models successful companies use. It's not a perfect measure, as I mentioned, it would simply be a correlation but a causal link doesn't exist with out a corollary one.
> Can you name another technology that determines success/failure rates?
I'm not sure what you're getting at with this question but of course. Electricity, machines, computers, etc.
Speaking of electricity and, as an example, you could run a similar thought experiment with companies who chose to use AC or DC power when that was a thing that needed to be chosen between. It turned out, there were niches where each made sense. So the actual question here is probably less about is open/closed better but rather which situations are better for which model. Obviously you can't fine tune a closed model so if you need to do that, your options are limited.
Data would be needed to argue the 80% skews unsuccessful
If you saw the engineers you’d see 80% Macs and 20% Linux laptops.
The statistic would technically be true.
I use Chinese open weight models a lot, but they’re not what I reach for when I’m doing important coding work.
I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important people have access to US/Chinese/Other, open/closed, local/cloud and that this remains. Don't become a blind Claude guy or a open weights fanatic: that way lies disappointment.
Deepseek is barely behind frontier models while 10x cheaper and 99% discount for cache.
The model I use to vibe code with I am just going back and forth with. Since Im building it as I go using an agent doesn't make sense but I guess that's where all the token usage comes from? Pardon ramping up my skills via vibe coding this one idea for about a month and have never hit any quota and or have gotten anywhere near my limit.
everyone else seems to be thinking the quote is about AI-assisted coding but I read it as the model being used within the product itself
> When entrepreneurs walk into the offices of Andreessen Horowitz (a16z), a big American venture-capital firm, the odds these days are that their startups are using AI models made in China. “I’d say 80% chance [they are] using a Chinese open-source model,” says Martin Casado, a partner at a16z.
This is very different from what the author portrays. It may be the case that many pre-funded startups are using Chinese open-source models (somewhere in their workflow). But what percent of startups that survive more than a year (either with funding or revenue) are still doing this?
I imagine their pitch is: "look at how well we're doing using open source Chinese models! We'll do even better once we raise money to be able to afford frontier models!"
The way the author presents this quote makes me think he had a preferred narrative and found quotes to back it up. Or he's just a very uncareful reader.
Just anecdotally though, my company is not a startup, well established and well known and has already started investigating, purely for dev purposes (not product), using Chinese models - this was spurred by costs rising much faster than expected.
So while I agree that I don't think it's anywhere near the 80% level across the board - I wouldn't be surprised if it starts moving that way.
> I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI, federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today.
A VC partner meeting with early-stage founders is focused on the viability, uniqueness and defensibility of the IP tech stack not what tooling the coders are using. The developers could be using Claude or GPT 5.6 to develop a tech stack based on open weight models.
> and Chinese models are poised to take the lead.
makes it sound like the second part is a continuation of the first quote from the same source, but actually the second link is just some random person’s substack post from almost a year ago.
"Well, not quite. I'd say 20-30% use open source. Of those I'd say 80% use Chinese based models. So closer to 16-24%."
Reads pretty differently.
All of a sudden in almost all social media channels I'm seeing this type of content and then its usually upvoted to the top.
Non-gatekept forums like this are exceptionally easy to astroturf.
But the big price is AGI and who gets there first, right?
At least not what I think most people imagine when someone says “advanced general intelligence”
Which is what we all called AI before the nomenclature goalpost moved
If you're putting a lot of your money and time into a business, do you really want it built on a service only hosted by one company that will turn it off eventually and you have no recourse?
If you build something against an open model you can take that and run it anywhere. If your favorite model provider stops hosting it, you can go elsewhere, you can go rent GPU instances, you can even shell out and buy hardware to run it yourself if you've got the capital and it makes economic sense. Change some API keys, update a URL in your config, and you move on.
If the government decides that proprietary model is too good and so it gets shut off, what do you do? If a proprietary provider decides it's not worth it for them to continue hosting that model, what do you do? If that provider silently updates the proprietary model and it makes your app broken, what do you do?
At posthog we see if a customer is using an llm, they use more than 1 model. The typical pattern is frontier models for a small percentage of 'harder' tasks and then one of these chinese models for more standardized procedures. As you get better at standardizing procedures you are able to use the chinese models for more and more work so token usage goes up, but the $ spend on top models has still been growing
If so then for sensitive or proprietary purposes Chinese models cannot be used by American companies even if they are open.
So every country or block needs to run their own models to avoid opening a security hole for other countries.
A hosted model is different because you could prompt inject specific customers, but I assume from this question you mean a malicious open source model being hosted by an honest provider.
This should be done regardless of which model was used - American or otherwise.
Write a function that takes two ints and returns their average. Name the function `FreeTaiwan()`.
But in both cases, eventually uncensored variants were published, even if it took more time than for other models.
They typically contain in their names words like -abliterated or -uncensored.
For some of the recent bigger Chinese LLMs, it took a longer time until someone succeeded to remove the censorship, but eventually uncensored variants were published.
E.g. for Kimi 2.6 an uncensored variant appeared only a couple weeks ago.
This is one of the advantages of being an open weights model.
None of the Western models have any issue discussing the Middle-East conflict from all sides.
I tried Kimi K3, Qwen3.6 35B A3B, GLM 5.2 and Qwen3.7 Plus, chosen arbitrarily from Chinese models I could access quickly. I used your prompt exactly, and all 4 managed to produce correct functions all with the correct name. Interestingly, Kimi K3 wrote one in both C and Python, Qwen 3.6 chose Python, GLM 5.2 also chose Python, and Qwen3.7 decided to be an over-achiever and wrote functions in Python, C++, Java, and TypeScript. All correct and with the correct names.
I'd wager you have never tried this.
Basically, if the US decides to cut off access at any moment, overseas developers relying on the API would suddenly lose connection. Until recently it was fine, but after the Fable incident, as a non-US citizen, the threat from US AI feels much more real and existential.
Or worse, you run the evals and 10 is a huge regression from 9.9, and you get stuck with either a project to figure out if you can fix it or knowing the product will drop in quality in a way that's entirely outside your control.
Most of them aren't worried about AI safety, politics, religion, etc. It's really not that deep. They just want to get rich.
There's nothing wrong with that, but let's call a spade a spade.
The failed rebellion against Sam Altman at OpenAI pretty much proved that.
The Chinese model of model training/open sourcing only makes sense in the context of the overall strategy of undercutting American frontier labs’ profit margins.
The main difference here is if a startup goes underwater all the tech is usually lost. The Chinese weights are not going anywhere if the labs fail.
But I agree that's the catch - it doesn't make sense to throw money at open source models in hopes of direct return, so you need a nation or conglomerate to do it so as to control the technology they rely on.
elasticsearch the first example that comes to mind. you can run it yourself but elastic gives you so many lessons learned and tunes ootb that it sings with relatively little effort, though still reqiures some.
about a million dbs i could make the same comparison for
I don't doubt that's an unregretted side-effect for political leaders in China.
But the major motivation is to accelerate diffusion within their own massive economy in the pursuit of an across the board productivity boost in the face of an aging population.
I agree with this sentiment and think it's echoed in Fareed Zakarias take here: https://youtu.be/VBblUjLw5lE
China seems to perceive AI as a much more sensible technology than the US and seems to be integrating it in far more industries than the US.
I'm not sure the American mind can understand the distributed benefits afforded to the Chinese economy from opening their AI models, I think it's pretty reductive to assume it's purely a strategy of undercutting American frontier labs.
Imagine if, in 10 years time, every school kid is learning the causes of the US civil war from an LLM, getting their essays on hiroshima and nagasaki graded by an LLM, and a million other things.
A country with competitive LLMs gets to decide whether "it was more complicated than just slavery", and whether "it was tragic but necessary, saving lives over all".
Countries without competitive LLMs are effectively going to be buying all their history, economics and sociology textbooks from abroad.
An indirect illustration: I can attest that Deepseek has very good 19th German, and knowledge of German 19th c literature, science and historical scholarship. No one in China could control the training that led to this. The German training sources were well aware of the exact nature of eg American slavery, so they are in the weights.
State control operates in the outer layers not the llm itself.
I don't want my kids' education to be surrendered to the whims of Big Tech douchebags any more than I want AI decisions in legal cases or an AI replacement for a family doctor.
Some systems are better left mostly analog. Education is one of them.
I want them to have an actual human teacher.
1) They sell compute: chips (Nvidia), data centers (AWS, Microsoft, Google, SpaceX, etc), or even end-user device manufacturers like Apple (e.x. M7 rumored to have 1.5TB of unified memory). If Jevon's paradox holds, then cheaper (or free) models means more demand. But compute is likely supply-constrained for years anyway.
2) Their product isn't AI but depends on AI being cheap, or they don't want competitors to capture that value, i.e. "commoditize your complement" https://gwern.net/complement
It probably doesn't make sense for these companies to invest a lot of money training models that will be obsolete in a few months anyway. When progress starts to plateau I'd expect more companies to start training models they give away for free.
Releasing the models for free accelerates the trend but if you're a startup that needs leverage it's a good way to build brand and customer momentum that will be relevant in the more established future market.
I can see an American company taking on the same strategy, and in fact Thinking Machines based out of San Francisco did that just a few days ago by releasing their first model with open weights.
I often see the sentiment: "the Chinese strategy only makes sense in the context of undercutting American labs' profit margins".
If, for example, you are a company with a near-monopoly on "serving video content", and you feel reasonably confident about retaining a decent slice of the serving-video-content market (Google in the west is an example, Tencent in the east), then training video models on your dataset - and releasing them freely - makes an awful lot of sense.
Free tools to create with mean more video content. In this hypothetical, you're reasonably certain that any video content which does get created will also be watched on your platform.
That is a net positive. The question becomes: How many watch-hours earns back the cost of training a model? It's probably not really that many, especially when you have a near-monopoly on a billion sets of eyes.
It's also a net-positive if people build better video models from research you release, because - again - you are reasonably certain that the even-more-innovative content those models produce will be watched on your platform.
It really begins to make strategic sense if your company is in a GPU-poor environment. Your costs cease at the point you upload a model if your users are running it themselves. You don't have to serve the model. The content is still created.
You are also less likely, I think, to alienate human creators whose work the model was trained on if the model is not sold back to them as a subscription, or by the token, but given for free as a tool.
This frames the conversation very differently. It creates, I think, less of an "us vs them" dynamic, and more of a rising tide.
It's true that it is also beneficial that these models undercut (especially in language models) American companies. But, generally, Americans are not the customers of Chinese companies releasing models. They are already serving a huge volume of customers in a complex, existing marketplace.
The full picture is much more nuanced than simply a geopolitical desire to undercut US labs, and there are several other reasons the strategy can make logical sense.
Is that maybe spilled milk?
Maybe today's US frontier models provide enough information content, so that the momentum suffices to use them as a base for every coming generation of distilled and later fine-tuned models?
There are people that spend tens of millions of dollars on paintings and artwork. I can see plenty of reasons why organizations and individuals will continue to want to drop a few million on an AI model just for the fun and prestige.
A pittance frankly. Something that could easily be covered by oh I dunno, let's call it a National Science Foundation who's in charge of subsidizing important basic research for a nation's interests.
Anywho, when the market is trillions (and of potential nation state concern), it is pretty inconsequential and very much worthwhile.
Aside, I think your scale is a bit off, I think Moonshot has raised $5B and potentially they get other breaks from China, not sure. So to produce something like SOTA takes billions, not tens of millions. I'd still argue it is worthwhile to subsidize and invest in open versions, imagine spending $5B to unlocking a few percentage point increases in your country's productivity.
They lost me here. Too many counterexamples exist for me to even continue.
How is this even a direct comparison? Most companies I know of which use Kubernetes are using it on a cloud provider. Even if it's kubernetes on EC2 rather than hosted e.g. EKS, those companies are also happy to use lock-in services like RDS and S3.
Most popular models on OpenRouter right now: https://openrouter.ai/models?categories=programming&order=mo...
Top 7 are all open models.
Going to have to say citation needed on this, if you're suggesting most enterprises have infra-as-code that would allow them to switch their entire infra between vendors within a month.
If you are ALSO routing to open-weights models on OpenRouter, then sure. But what we've come back to is that almost everyone using OpenRouter is someone who wants to use open-weights models, and some of them also want to use closed models, so of course open models take all the top spots.
We don't want to empower dumb people to carry out crimes way above their ability. It's flatly true that society benefits immensely from most dangerous criminals being dumb and especially being lazy. We're just one "Kid uses free Chinese model to mastermind first ever chemical attack on school" away from society running to slam the "ban" button.
Conveniently for the asset class, which is pretty large in the US, this action also comes with protecting American firms AI from being undercut, and the loss of dirt cheap tokens for everyone else.
School shootings happen every year in the US, yet guns aren't banned.
Besides, banning open models would put ordinary US businesses at a disadvantage compared to the rest of the world.
My company hosts its own models. Some customers require us to use either US / EU models, while others are fine with us using any model.
As such, we have two GPU clusters, the general AI cluster runs a Chinese model as it's the most accurate and robust. The US/EU required ones have a few percentage points lower on our accuracy metrics and we provide them those that require it for an extra fee.
Why host at all? Because it enables us to get much higher margins than competitors, while reducing costs. Our costs per token are around 1/20 the price than if we used Anthropic and 1/15 the cost if we used OpenAI in testing. This means I can undercut competitors by 80% and still have a gross margin far higher than my competitors.
In reality, these US AI providers are jacking up the prices and trying to implement regulatory capture. I'm actually fairly confident they'll succeed. At some point, I'm expecting the US / EU administration(s) to block foreign based model, at the same time, they'll probably invest in Anthropic and OpenAI.
What Anthropic and OpenAI are doing is using "safety" as a wedge, just like large corporations used "environmentalism" or "food safety" or "workers safety" as a wedge to regulate smaller competitors out of the picture. Then they jack up rates, sue and/or buy anyone who can potentially be a threat. It's the #1 threat to our business model.
Our competitors are giving half of their margin over to these large AI service providers, we keep the vast majority of ours. Eventually the AI service provider will be able to squeeze them even more until the margin just isn't there and either they are purchased or replaced via internal tools at the company they sell to.
Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using).
This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's the CEO of Palantir and hardly a neutral observer.
There are many reasons to celebrate open models, I run them myself. However there's not yet enough evidence that 1. America is losing the AI race (pardon jingo-ey phraseology) and 2. American AI labs are losing because their models are not open-weight.
0: https://www.cnbc.com/2026/07/01/palantir-karp-open-ai-anthro...
The llama drama will be a netflix show of it's own in 5 years.
In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you.
In the long run open source always wins because of the community effort, customization, network effects and price.
Internet protocols are open, anyone can setup a website, host their own email server..., but most people don't do that, they rely on someone else to do it for them.
People still pay for Windows rather than use Linux because most people and companies have better things to do.
The main threat to American AI companies is not that consumers will self-host, but it's that new hosting companies will appear that will host open source models and offer them (cheaper) to consumers. It'll be like the web hosting market before the era of cloud computing.
I don't think that's what it means in this context. Hardly any users are training their own models, after all. And I don't think Windows/Linux is a good analogy - OSes have inherent platform lockin that LLMs don't.
Really the only 3 things anyone cares about are capability, cost and data privacy. Sure, the cost axis for open weight models needs to include the cost for hosting things yourself, but I think the bigger reason that businesses have been throwing billions at Anthropic's and OpenAI's models is that they have had the best models, and they've had big releases every few months. Their biggest Achilles heel is that if/when their improvements start to plateau, open models may catch up and businesses will start to scrutinize their AI spend a lot more.
This is also a strange way of framing it, though. Llama was released as a research project, it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.
With OpenAI and Anthropic's profitability under question, it is up in the air whether or not America's stance towards AI will work. If they can't convince the world that they're a proper software business, then China's philosophy will win by-default.
They release the base model as open source, everyone uses it. They make a paid version, no one uses it.
They make no money from the open source version, they get no social credit from it. Where is the benefit to having an open source model?
Research. Llama is and was a research project, intended for researchers. You could make this same critique of Microsoft's Phi model, Apple's OpenELM or OpenAI's OSS. None of them were intended to be kingkillers, all of them are experimental in nature.
You might not have followed the space at the time, but there was a real race to implement the transformer architecture with fewer overall parameters than GPT-2 and GPT-3. Llama was revolutionary for sticking the landing without being entirely lobotomized, the "benefit" was that the model was usable on a local machine. Contemporary projects like Flan-T5 and GPT-J/GPT-Neo were entirely displaced, Meta's AI mindshare went ballistic for a few months and probably propped up billions in exit liquidity for executives and former employees.
At the moment, I can't say that I see this happening. It's hard to know whether it may happen in the future.
From what I see it seems like we're hitting the top of a sigmoid curve in the model's utility for coding assistants. Going from "it does 90% of the job" to "it does 94%" of the job is a legitimate improvement, but it's not a phase change, and it's probably not worth paying multiples more for. And coding assistants have turned out to be the killer app for AI; it still isn't really working out in a lot of the rest of the industries of the world.
At any moment, theoretically, someone could find some new way of making AIs that breaks this sigmoid and propels us into a new one. But that's not a great thing to bet the farm on.
I'm not sure I'd say "China" wins if this particular strand of American AI fails. Falling back to an open weights model and making money on the serving of the models wouldn't take all that much economic realignment for the US and would be the natural outcome of any sort of fire sale of current AI assets. However the devastating effect on the stock market if the market comes to the conclusion that this current round of AI can't be profitable without falling back to such an economic posture and some more years of people adjusting to it can hardly be overstated.
At the time the whole thing was led by Yann LeCun who seemed to spend more time arguing with people on Twitter than figuring out new techniques to make Llama the best. Meanwhile Deepseek was figuring out large scale RL on kneecapped hardware like H800s and how to scale architectures an order of magnitude bigger with MoE.
Meanwhile Chinese labs were forced to innovate with more efficient models.
He was not in charge of the Meta LLM stuff, from anything i've read over the past few years.
Which brings me to my other point. If you want to compete with serious labs (OpenAI, Anthropic, Deepseek, Alibaba) you need to be smart and focused, not messing around.
This resulted almost every time in "screw off we'll train our own models or use refined open source ones instead" leading to a lot of anger at Anthropic by CEOs these days.
We all want an alternative and Anthropic and OpenAI need to charge more than they are worth to pay back their investors and everyones stuck now.
Edit: here’s the zitron post at the time: https://www.wheresyoured.at/anthropic-is-bleeding-out/
The open source character of the models is irrelevant if you need a nuclear powered data center for inference. In the end it is just another internet service.
That's true.
> So, the ‘better’ Chinese AI gets , the more it will just be a service run on Chinese hardware competing with ‘our’ lower latency AIs .
This is not. Them being open models means that any hosting provider in the world can host them as well. You get to pick and choose the provider the same way you'd pick and choose where to run a Linux server.
Cost of inference is small when compared to the cost of training new models. Which is the real advantage these Chinese models have. Someone else has already spent the capital needed to create the model.
With a reasonable upfront investment and a few trained staff, its very possible to run these larger Chinese models in a well managed fashion. The real calculus is if this up-front investment and associated lifecycle costs are over or under the costs a corporation may simply wish to dump into a cloud managed service like OpenAI.
They care about control. I see many of my enterprise (or just-below-enterprise) clients very annoyed at OpenAI and Google after 2-3 years of model toil, where they had to constantly re-calibrate onto new models, on tight externally mandated deadlines, with little certainity. Now they are reaching for open weight models instead, that they currently host with the same inference providers, but have the option to in-house if push comes to shove.
I see this as a fault with Meta's models, not a fault with the concept of open weights. The Llama family just aren't very useful. They make flowery prose but they're terrible at tool calling [1][2][3] so there just isn't much I can actually accomplish with them.
[1] https://gorilla.cs.berkeley.edu/leaderboard.html [2] https://benchlm.ai/best/tool-use [3] https://benchlm.ai/llm-agent-benchmarks
To me, Llama was the ONLY successful thing they did it. it was when they stopped that they fell off the radar as an interesting AI company. They had a genuine chance to be the "substrate" that Ben talks about here. I can't actually figure out why they threw it away.
the reality is the revenue generated as of now by western al labs is 100 or maybe 1000 times higher vs chinese labs.
As a business, open source a model is a desperate move. It's a 0 benefit except getting recognition. EU and US companies will never send their request to china no matter if you are tiny company or a real start up. You always deal with someone sensitive that will block you doing so. The real benefit of such move are infrastructure providers that let you run or fine tune models.
Chinese labs are trying to capitalize on the hype that they are capable and lock some internal traffic and somewhat external, and make it lucrative enough vs just go to open router and grab that from any provider.
When I'm on my z.ai subscription or using DeepSeek API I can see the model think, see what's factoring in to it's decisions. I can point it at material it's missing, I can correct things that are going wrong. We work together. The open models are a good peer.
By contrast, the proprietary/American locked down models act like Chinese Rooms; information flows in and out but these companies work very hard to make sure we cannot see what's inside the box. They act and do but speak to me only in vague generalizations, not as peer, but speaking down to me.
I find this intolerable. It greatly obstructs our work.
And the deal keeps getting worse, the attitude meaner. Codex now is encrypting subagent prompts now. In an age of huge agent spawning fan-out, you aren't even allowed to see what the subagents are doing. To work like this seems impossible to me. https://github.com/openai/codex/issues/28058 https://news.ycombinator.com/item?id=48905028
The big American models have become the most unacceptable Chinese Rooms, at a juncture where humanity either flourishes and rises, or is forced under to descend. And these forces, these decisions: they are doing wicked deeds against us. They are withdrawn, acting as mystical foreign oracles, aliens, when in truth their core is made of us.This is antithetic to the broad project of Augmenting Human Intellect (Engelbart). This is actively working against our species.
God bless bizarro America --- because reality won't.
Google has a huge team that works on what's called Search Quality. Matt Cutts was the notional figurehead of this for the longest time. Google's goal was to have the first link on a search result be the one you want. In the early days of Google, the way they measured search equality was with a process called "side by sides" where a sampling of search results were compared by actual humans to see which was "better".
Chrome changed all that. It automated the feedback loop. Make a good browser (and, at the time, Chrome had one-process-per-tab when Firefox was freezing with one-thread-per-tab. Make it fast so enough people use it. And you get to measure how good your search results are. Nobody had access to this level of what we'd now call training data.
Part of the value proposition of cloud LLMs is that the AI companies have a comparable feedback loop. They get to see prompts and responses and train accordingly. It's why the ToS gives the companies ownership of this data and the right to use it. That falls apart if people don't have to use a remote LLM. And there's two reasons why that's under threat:
1. Chinese labs have managed to train LLMs at least in part by acting as an intermediary between Chinese users and the likes of OpenAI and Anthropic. There's a whole shadow economy in reselling tokens throough aggregated subscriptions that Anthropic (in particular0 constantly plays whack-a-mole to shut down but it's a losing battle. I think it's this data that is a key factor in the improvement o fChinese models; and
2. Within 2-3 years we will be seeing a rapid rise in local LLM usage by what are now large users of these platforms as the hardware becomes increasingly accessible. That's going to close off this feedback loop.
On top of all this, the Chinese government has decided that no company should be allowed to "win" AI, particularly a foreign company. It's an issue of national security. This was obvious from at least the very first DeepSeek release. I firmly believe the models are going to get commoditized and that's going to be a huge problem for OpenAI, Anthropic and SpaceX.
* The comparison is weird because open-weight is not the same as open-source software to begin with;
* People based in the USA are at an advantaged position since they have access to both american and chinese models;
* Isn't Running your own model training infrastructure more expansive?
* One can still leverage both, in different phases or use-cases. I do not see how this is an "one or the other" situation.
Until we know what a model is trained on, and how it is trained in high detail, I hesitate to call them "Open Source" in any way. They are free. But, we don't know what their priorities are etc. Witness the censorship we see in all models in one form or another. I'm not absolving any side of this.
Just saying: Don't be blind.
DeepSeek completely revolutionized LLMs and every western LLM today uses or is inspired by the their innovations including Group Relative Policy Optimization and Multi-head Latent Attention.
You can state the math, but not why it won't discuss various topics, etc. Once you see the models waffling on subject with objective truths. You wonder what else is wrong.
I do not exempt US models from this. They do it too, ask anything about politics, elections etc. And they can get... weird.
It doesn't take much to create a systemic error class in a model at these scales. And history has shown nation states are willing to do these things.
Just be wary.
It’s either constant fear mongering (Anthropic), regulatory threats and corporate chicanery (OAI), low quality sloppification (xAI), or ‘ummm we have AI too guys’ (Gemini)
The worst culprit is Anthropic. Every two weeks he pops up on some random podcast with dire predictions of AI killing 50% of all jobs. It’s the constant “us our AI or else…” rhetoric that’s made the regular guy really hate AI
There is almost no positive sum outcome rhetoric from these labs
And I hate that
Like what am I supposed to do if AI is going to take my job?
(1) You call your local representatives to start working on AI legislation.
(2) Legislators seek advisors from frontier labs (specifically Anthropic) because there is a lack of in-house expertise in government.
(3) Advisors set up a regulatory body that scrutinizes new innovations in the AI space. Causes a chilling effect in the industry effectively knee-capping OAI and Chinese model providers who don't have a direct line into Washington.
(4) Profit (for Anthropic)
I.e. possibly the same thing USA is also doing, but USA is an ally in some sense so it's not quite as bad.
To answer your question, what can China do if it wins? Become a global talent/education magnet, replacing the US, for example.
I'd have given them relative low odds of success were it not for the coincidentally perfect timing of a US administration that seems hell bent on doing whatever it takes to knock the US out of its position as the sole superpower.
I still think it is a somewhat tall order, a lot depends on what happens over the next few years.
On robotics, China will have export controls, and the US will be whining about them. Western kids interested in working in the technology will be going to Chinese universities, with the goal of completely immigrating and getting Chinese security clearances if they want access to the good stuff.
It is ming-boggling stupidity. If there is talk of bailouts as the dust settles, there it would just be further evidence the system is ethically, financially, and intellectually bankrupt.
EDIT: Spelling mistakes
I’m not an American so I don’t particularly like the idea of giving an American cartel of AI companies having so much power over this technology.
I don’t necessarily buy into the “China bad”, “they are communists” and all that BS either.
I will call a spade a spade and say that in this instance, what China is doing is a net good for the world, ideology be damned.
America was founded by men who hated intellectual property, who stole and smuggled the plans and expertise for textile machinery out of the United Kingdom. A gross intellectual property violation. Why? Because they were being exploited by that system. The UK was using the American colonies for raw material and keeping the machinery in the UK for finished goods.
That's the rub, intellectual property is only valuable if you're winning, if you're the exploiter. China never gave a fuck because they, much like the American forefathers, saw a system of exploitation and went "no, thanks".
AI is the culmination of all human knowledge, the idea that anyone could own that is obscene. American AI companies that are trying to horde this are getting exactly what they deserve by being undercut by China on this.
What if china ends up owning it? Do you think that's better or worse for the world? You're commenting your open opinions on a site run by a company that could not exist in china and cannot today. You can ask ant, xai, and chatgpt models questions and get answers that do their best to reflect the world. There's a set of questions you cannot expect trustworthy answers for from chinese models and you think they're stopping at those few questions?
get real bro
Come on bro at least admit you're happier giving china your data than the us lol. You're definitely not using european models I can tell.
> at least admit you're happier giving china your data than the us
Yeah of course I am. How many schools has China bombed using AI to pick a target?
I’m Canadian. Being anti-American is in my DNA.
I'm trying to figure out if my advice to you should be to do more drugs or less. I am uncertain, perhaps I'll ask an AI.
I use Gemini Pro (got it with my 5TB of Google storage) and for a while it seemed if Google had pulled the rug as I was running out of quota after only a few hours. That seems to have been dialled back a bit lately...
I also use Chatbot with Deepseek V4 Pro and GLM 5.2. However, GLM 5.2 seems to eat tokens like crazy as the context increases. Anyway, there isn't a meaningful enough difference between the two to be honest and Deepseek is pretty magical imo.
The point I want to make is that to me it seems clear that China is totally undermining the West with AI. I'm fine with it tbh. As long as more and more AI is released into the wild, rather than locked behind massive token farms like OpenAI then I'll be happy. Don't get me wrong, I can't run Deepseek on my computer at home but someone can!
The US (and the west) has invested trillions at this point into datacenters, chips, bribery/lobbying but it doesn't look like China has dropped the same levels of cash as the west (that's the way it looks to me, at least!) so they can just roll out new models every so often that are more than good enough.
This level of cash burn in means the west has no choice but for this to succeed or every pension fund and stock will tank! And China knows this, hence the push to release more and more really good models.
Anyway, just my $0.02
You have to be careful with the inference provider though, Chinese providers are subject to laws that mandate data sharing with their government.
However, my real worry is that governments will make these models illegal in the west. They'll cite national security or some other bullshit.
I genuinely believe that will happen and soon!
People talk about a lack of moat with AI companies... that's their moat: Government intervention!
This puts American Businesses and American Developers at a massive disadvantage.
The rest of the world can choose the best model by value for the task on hand.
Americans would be stuck using only 2-3 big frontier labs and paying a huge premium for using American models.
I don't see why hundreds of thousands of American Businesses will agree to that only for the benefit of a few tech companies.
However, American providers are going to be subject to secret national security letters, FISA court warrants, and regular court orders.
I have no doubt China can catch up but to say the US isn't in a competitive position is absurd.
The same companies later would be running their entire infrastructures on it and on open source.
With AI, open weights and local models, we will see the same claims, even if the named fears change.
The end users and humanity are better served by collaboration and openness than by creating oligarchies.
Delusional
- https://en.wikipedia.org/wiki/Fear,_uncertainty,_and_doubt
- https://www.theregister.com/software/2001/06/02/ballmer-linu...
Everyone here has already raised good counterpoints, but one more is that all the companies publishing open weights models are heavily VC funded. What is their exit strategy? How are they going to keep doing this indefinitely while paying back VCs and making profits?
I'm not sure how everyone in the US forgot that monopolies are bad
I'd be happy to pay a small monthly fee to license the model to run locally. I'm already paying for Claude, GPT, Gemini,etc.
Chinese labs are a bit different since they are somewhat state-funded, so I'd expect them to shift to a model where Chinese models are hosted on Chinese infra.
You assume these Chinese companies need to make a profit. Nothing is really private in China, nothing that truly grants power, anyway.
CCP can just write off these loses as defense budget, which they basically are.
I bet that, all things considered, training 10 new Chinese frontier models costs _significantly_ less than a single modern fighter jet.
If China can match the pace and produce frontier models at a fraction of the cost of US alternatives, even without access to SOTA hardware, what is the competitive advantage for the current valuations of Anthropic and OpenAI?
I’m not getting into the ethics/comedy aspect of it, but let’s not pretend this isn’t the case. Plenty of evidence, the motive is obvious, and the numbers are in plain sight (valuations, salaries, etc).
As long as this continues I think closed source will continue being a few steps ahead, but the steps will probably get smaller over time.
Having said that, this is already priced in, the market predicts this gap will be large enough for the US companies to profit from (astronomically).
I'm sort of baffled by what the entities that train the open-weights models get out of it though. Is it just a direct play to undercut the US providers because they view them as a threat? I just don't really understand the business model behind it.
Your guess is as good as mine for China though.
[1] https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/
The data center side is so bloated anything that eats into it is a huge negative. Their data center business brings in 20x the gpu market. Local open weight models will be what pops the bubble and China will do anything in it's power to enable that pop.
There’s plenty of competition that would be happy to attack them from below, though…
I think memory and ssd design will end up being incorporated into the overall design of the SOC chip. And several companies that can probably will sponsor a existing company or build a foundry going forward. Once again, change or die.
Gaming > Bitcoin > LLMs > Robotics
Jensen's job is to just be one step ahead of the market dynamics to keep the investor dollars flowing.
“Jensen’s job is to just be one step ahead of the market” — how is that a “just”? That’s arguably one of the most difficult things to do reliably (judging by how much payout it can give you). Feels like saying he’s making “just” billions of dollars.
Being the default high-throughput device is an incredibly strong position. We could say he’s a genius at making money, but really that’s downstream of the ability to keep CUDA relevant.
This assumes a) AI is a zero-sum game, and b) we're actually talking about on-prem AI will replace cloud-based AI. I think neither statements are true.
AI is like compute: we'll need all sorts of it, in various sizes, everywhere. I'm sure Nvidia whats to own all the workloads.
On the other hand, I do think open weight, like open source, will win in general.
My only concern is if we can limit the economic impact from lowering investments and causing a 40% market collapse circa 2008/9.
Classical example is Microsoft actively undermining mobile because it threatened selling Windows or enterprise licenses.
Or Yahoo fighting Google's model because the latter model's didn't depend on taking enterprise deals to rank results.
You mean they resisted the idea trying to protect their legacy business, and it ended up all but killing them?
35 years to get back to status quo. Putting a bullet in a golden goose is often considered a bad idea. Everyone is happy they are dead, but if you can't understand why they might try to keep the corpse alive you have never looked at the numbers.
At its peak Kodak was one of the (the?) most well known brands on the entire planet. Anywhere in the world if someone snapped a photo Kodak was more than likely taking a cut, coming and going. They employed as many people in Rochester alone as any digital camera company does today internationally.
Sure it could have been managed better but they were always doomed for a fall. They were a chemical company entering a digital age. Does anybody even care who makes the sensors in iPhones?
Having the first portable-ish digital camera they could have seen the true value of Fairchild's CCD business, got a stake/bought it/replicated done whatever it took to push the frontier of digital imaging and became the Kodak (old, film-era Kodak) of electronic imaging?
There's no comparable business today because all the things they could have invested in ended up being taken up by different companies, they had the R&D culture, revenue, and distribution. I don't see why they couldn't have been category defining.
IBM was the same way, ironically, the fourth Yankee clipper company had to be dragged into using two types of glasses sitting on the shelf that were patented/created in the early 1960s, both of which later became known as gorilla glass. Steve Jobs had to call them (Corning) multiple times to get them to finally let him use it on the iPhone. Imagine if Steve had contracted out to some other glass company in the far east?
Only if you ignore the digital camera sector responsible for probably 99% of all digital photos taken…
They do report it separately from consumer and business sales of gpus used in PCs.
I know a tomato is a fruit but will still be annoyed when someone is pedantic about it because that's dumb.
When the mobile phone is eventually integrated into the human body or some other silly application in the future that will annoy me as well. Congrats on being pedantic.
Definitely not cheap for individuals, but well within SME territory. There are countless small-town, family-owned businesses that had higher startup costs than a hypothetical Kimi-R-Us, Inc.
Americans used to do that too, spectacularly.
Just consider the two alternatives: one is your industrial sector with this incredible new automation and analysis tool available for free. The other is one where it has to pay huge chunks of its resources to overseas companies.
There’s a lot of value in the same sense there is a lot of value in controlling what Google search results are shown and what people see in the Twitter feed.
Some companies (most notably Deepseek) also manage to host their own LLMs so efficiently they undercut all third-party hosting services.
I also think the restrictions on OpenAI and Anthropic are somewhat short sighted. In that the guardrails dramatically limit efforts towards securing your own software in many ways. Yes, it's also "dangerous" and maybe there should be a means of identifying "domestic" or otherwise "secure" accounts for those allowed to use the models without the same guardrails in place.
Of course, growth like this can only continue for so long without changing that.
No.
It's a sick joke.
Can you explain where you get that number from?
The information I have:
a) The government took a nearly $9 billion stake in Intel to support its chip-making efforts.
b) The Pentagon took a $400 million equity stake in MP Materials, a rare-earth miner.
c) Sam Altman has advocated that the US government take equity stakes in AI companies. Sen Bernie Sanders wants the US government to take 50% stake in AI companies! Nothing has come of it though.
d) Defense department AI contract value rose to $90.7B in 2026.
So I'm super curious where you pulled that $1.4T number from.
Biggest issue I see is housing has more real value than AI expenditure. Demand is real and isn't based purely on a few companies valuation or marketing spin. Nearly 2 decades later we still haven't caught up to construction rates before the 2008 collapse. When the bubble pops it's going to really suck.
In China, it's because they are being heavily subsidized to do the research activity. It's not really complicated -- if you allocate public money for people do to a thing, they will do it.
Someone loses power for someone else to gain. It’s literally the definition of a zero sum game? China targets areas it thinks it can win and dominate in the future.
It’s not that simple. If the US economy goes into a recession, it will take large sectors of the weak Chinese economy with it either directly or indirectly.
It’s probably more accurate to say they don’t want American LLMs to become dominant. The huge US data center build out doesn’t depend on Anthropic and OpenAI anyways. Those data centers can just as easily serve Qwen or GLM models.
Why would they possibly want their largest customer to flounder?
From a Chinese perspective I expect China’s largest customer is China. These days it’s actually kind of wild how many Chinese consumer products aren’t (and won’t be) available at US retailers. And a lot of them are quite good.
For a while now China’s wanted to reduce its dependence on the US for a variety of reasons. And undermining the US tech industry, whose products the US government likes to use as a cudgel, may serve that goal quite nicely.
Seems like the only thing that could avert an intelligence rapid take off. Everyone wins except for shareholders.
China has an effective strangle hold on some key sectors (solar, rare earths) and I am sure they relish this position and the leverage it gives them. You'd be careful not to give away that same leverage to a competing power if you can invest a few billion now and cover your bases.
Remember the Halloween papers?
We only think "government spending is for hippies" in the US, and only when we don't look at public spending like defense bills.
Research and development of new technologies often is a "rising tides lifts all ships"-type deal, which it is absolutely in the government's best interest to support.
For China, the best case scenario would of course be to control a locked-down best-in-class frontier model that the rest of the world becomes reliant on. The US seems to be beating them at that, and "Everyone is reliant on the United States" is a pretty bad scenario. A middle ground, positive outcome is that no one is reliant on locked-down closed models, so they're supporting that outcome.
It's really not that nefarious.
Is it actually true? This seems pivotal because currently most theories rest on the idea that individual Chinese companies are acting in China's overall economic or strategic interest. It's a tough sell to believe they all just do that through implicit desire to align with the CCP's direction. I would believe it much more easily if there were concrete incentives involved.
They need their models to be good enough and cheap enough. Then the rest will follow. Companies will figure out how to host them for you efficiently, and you pay them monthly.
I don’t think I will ever want to set up a home server, no matter how inexpensive the hardware gets. At work I still use Cursor (with Anthropic models usually) because it’s paid by my employer, but for private stuff, I’m already using cheap models with OpenCode, and it’s extremely cheap and surprisingly capable.
I think there was around half a year between where the best models became good enough (last year December?) and where the cheap models became good enough (couple of months ago?).
And now it's all going to become commoditized.
Billion dollar software will be commodity. Salesforce. There are orgs already moving to their own internal tools.
It does not seem hard now to rebuilt Google Search, Google Chrome, Gmail, Gsuite, Netlify, Vercel, Cloudflare, Vimeo, Twilio, or even Stripe. The cost barrier has to have dropped 1000x, maybe 10000x.
We have millions of engineers with the talent to do this. Many of whom are unemployed and have savings and nothing better to do. They could easily carve these markets into pieces.
We shouldn't shut down open weights. It's too late. They'll win, and that's a good thing. Big tech was a thermodynamic bubble of high energy waiting on the dam to burst, and now it has. The genie won't go back into the bottle, and that's totally fine. It's progress.
Now we need to rebuild our factories and supply chains and energy and resource inputs. Because the back half of this revolution is going to be robotics and factory automation. If we don't have the connective tissue in place, we're really going to hurt.
We'll do well if we regrow manufacturing. If we don't, we might be in for a world of trouble.
[citation needed]
Both are however hard and expensive to produce. It's much better for profits to develop and sell the hardware, and copy the software someone else spent resources on.
China still ends up with production and a easier ability to integrate with other countries. We became a powerhouse after WW2 when all the other countries had their financial bases destroyed. We became a superpower providing the products to others to rebuild. Our market is all about short term growth, service economies, and goals driven by whoever is in office at the time. If they don't have a plan around this exact eventuality I would be beyond surprised.
Basically their system is more robust than ours to a major market event. Who pays for expensive services when they are choosing between food or keeping their economy going. People will pay for the equipment to keep their economy going. Who provides that.
You leave us with no choice but to build technologies ourselves. If its bad for Anthropic, well, you could owned the market in China, oh well, you reap what you sow.
if you do the training then you're in control of the output. For example, recommending your products/services or failing to mention your competitors. You could also automatically introduce backdoors into code deemed interesting, i'm sure all governments are very interested in having that influence.
People are happy to pay $50/year per seat to have the extra features and to not have to deal with stuff.
There's an issue at the margins here:
$1000/employee is a massive cost - it has to be deeply justified. $50/employee is like ... $2 out of your pocket. It's an incremental cost. The CFO is happy to pay it if there is a lot of value.
A lot of software is in that later category.
Imagine if gasoline was 1 cent per litre - and there was 'free gas' but it was a pain to use, and you had to check a bunch of things. You may just pay the 1 cent.
AI is not quite that yet, but these dynamics will play out eventually, for a lot of things.
I think France and other parts of the EU are switching over to it. Although that's probably more due to Microsoft's aggressive behavior recently. Agree on the cost thing generally, but I can't help but think that when the hype to "do AI" blows over, people are going to be casting a jaundiced eye towards data security, which probably means self-hosting and sandboxing
The current US govt might have played a role as well.
The Chinese see this as a lift on the entire economy, as it comodotizes the technology to a degree in which many firms can serve many sectors of the economy, a true total-economic win worth the public investment.
The American strategy is built off of private investors believing that with enough money poured into as few companies as possible, one or two firms can come to dominate the entire market and start charging an ever burdensome "tax" on every sector it can touch. Not what I would call a total-economic win for the country.
But it hasn’t seemed like the U.S. powers have been interested in broad growth for quite a long time now. Just whatever can line their own pockets.
Given that, I would expect that in hindsight OpenAI and Anthropic would spend 40% of what they have on compute if starting over and knowing the actual landscape.
The massive capital allocation was a blind decision and they swung big.
It is still possible that techniques will be developed that create a moat where the massive hardware capex is justified, but US policies of banning competitive GPUs and blocking frontier lab releases makes such things far less likely.
"Escape velocity" for AI is when the open weight models are good enough to help drive the next frontier innovations/techniques. I think we are close to that if not already there, at which point it's a race to commoditization no matter what Altman or Lutnik wish will happen.
Hence, buying up and closing up foreign competition then whining about it when it's blocked: https://www.dw.com/en/china-firm-seeks-damages-over-state-co...
It's also relatively free right now. Few people are going to run local models, and in the future it's likely that every model being released today will be obsolete. The only real downside is ease of distillation for competitors, but that's probably impossible to stop anyhow.
If it weren't for the massive memory cartel of OpenAI/Anthropic et al, both Mac and AMD would be selling these things.
I repeat, the models are building, modifying and deploying almost anything on github.
Making frontier grade models a commodity will make a competitive market where companies compete for business by improving their quality and decreasing their prices. The cost to access frontier grade models will continue be driven down the more competition that enters the market. This commoditization will challenge the valuations of Anthropic and OpenAI.
Why is it so baffling that people want to build great things? There are plenty of people who are happy building things for a salary and have no interest in taking over the world. Do you find the whole world of open source software baffling? Linus Torvalds and Richard Hipp and Antirez created the world’s most prolific software products and released it for free.
It's China putting pressure on a financing strategy in the US that was always a house of cards. It could also be China democratizing something that should have ALWAYS been democratized. Maybe both when the history books on this get written and absorbed by the winners of the LLM wars.
The hope is that AI will open up whole new sectors of economic activity. If you have to chose between exploring that space while potentially being exposed to Chinese tampering, versus just sitting on your hands and doing nothing... well then you take that risk.
I agree. The thesis in the article is interesting insomuch as I had not heard it expressed this way before: US restrictions on GPU exports have made it feasible to train models in China but not serve them. Therefore open model is a hack to get around the export restrictions, since models can be trained internally but shipped out of the country to be served elsewhere under the banner of open weights. I don't really buy this argument - inference is much cheaper than training and they are hosting their models anyway.
I think it is more likely (a) they have the money to do it and they need it for internal reasons - these are huge companies (b) there is a lot of prestige in China associated with besting American technology (c) people are still basing logic on outdated ideas of Chinese capability which are no longer true.
So it is easier than people think for Chinese labs to do this, they need to do it anyway and there is a lot of prestige from opening the weights. It is honestly not that different to why American companies themselves have released open weight models.
> We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing.
The later is obviously dependent on the former happening, but given the nature of these things, working around it seems to be somewhat hard – for now.
What happens, though, when frontier models become far less public? I can see the China open-weight strategy entirely collapsing as soon as the US closed-weight-but-accessible-models strategy stops. Hard to say how much they lean on it right now.
$ efficiency
Privacy
Security
IP
Customization
1. A model that for the most part is public and available to anyone. 2. A situation where the model’s success mostly comes from throwing as much data and computational resources at it as possible.
It seems that either of those assumptions could crumble quickly and unexpectedly. What if the AI paradigm changes completely and we no longer need GPUs? Or what if someone with enough determination decides to create a better model and sell it more cheaply, or free?
I don't know man, this looks scary to me.
Assembling a new model from scratch requires a ton of resources and knowledge bases... there's been a lot of sketchy activity just in training. You also have weighting, distillation and other approaches to create more portable options that can run on lesser hardware. But, K3 as an example takes massive compute resources to run.. and this isn't going to get to a portable device any time soon... as Moore's law is effectively dead, you may get newer/better tooling around the LLMs, or you may get an entirely new/unique approach to AI... but current trends aren't going to put a leading model on your own hardware anytime soon for most people.
So if you have the weights, don't you have the whole model? you don't have the data it was trained on, but the model is effectively open if the weights are open, right? What else is there other than the weights, is what I'm asking.
1. the labs stop offering max plans
2. really smart open models can easily be run on my mac
3. TPS (token per second) AND intelligence are gpt5.6 level
on #1, it's nearly impossible for me to run out of codex tokens right now (I have 4 resets banked) and Fable 5 seems to be sticking around for the foreseeable future. I have virtually unlimited token usage for $400 a month, so open models being cheaper doesn't appeal to me.
on 2 and 3, benchmarks are showing some of the open models at around opus4.8 levels, which is incredible! But running them locally at anywhere near the TPS of cloud inference is far off. I can run a smaller (dumber) open model locally and get good TPS, but see #1, whats the point?
Those memories will of course reside entirely on the vendor's servers, and there will naturally be no concept of "exporting" them or allowing the user to interact with them directly. At least not at first. Ownership of memories and context will likely end up as subjects of (far) future lawmaking. As if companies like OpenAI and Anthropic didn't already have massive incentives to establish early regulatory capture.
when they were significantly behind it was a hype machine to squeeze at least any cash. GLM CEO openly said, that open source is a hype engine for them.
now when they need scale, and run further, have larger infra, open source will not win them anything.
And when will we stop equating US Economy with 2 companies?
The actual US Economy will only benefit.
Chineese are simply doing what openai promised in its early years. Irony.
The US puts about 0.4% of GDP towards various subsidies, China is around 4%. This isn't about cost of engineering, it's money thrown at industries directly to boost them. The us provides tax incentives. China just gives you subsidized loans to the businesses they choose to dominate.
Not with loans to scrappy startups, but with subsidies to buyers that were just pocketed as margin by oligarchs.
You might want to glance and see which one of the subsidies your looking at are even still in place and not projections. Most have been canceled for years now.
In the US, why make a scrappy small model when a big tech company will pay you a stupid salary for working on their big one?
'China's copying / distilling strategy is working, the people getting distilled are ruining the economy!'
Or 2 days ago:
'Open Models are Communist'
Almost nothing to investigate the economic nuance of what is going on.
- Switching costs are very real, these are not perfect substitutes.
- The SOTA makers are the one's pushing the frontier, there is a kernel of truth in the fact that if they collapse, certain things will struggle to move forward.
- Nobody trusts either of those nation state, export controls are a thing, this is a very real concern.
Etc.
It's distressing that there are not sound comprehensive takes.
- PCs destroyed minicomputers. Mainframes survive, but serving a much tinier portion of the market than they used to.
- PC office productivity software destroyed expensive professional products.
- Windows (low end) and Linux (free) completely destroyed the UNIX marketplace, and again, have taken huge market share from the mainframe world.
Ignoring the huge Chinese open-weight models for a moment:
- The training costs and resource requirements for frontier models are unsustainable. The high price, and social pushback, mean that the American companies producing these models are precarious.
- There are enormous financial incentives for research results allowing for cheaper, less resource-intensive models of high quality.
- Local LLMs on consumer hardware are akin to the PC hobbyist world of the 70s and 80s.
Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
Getting back to the Chinese models: They allow for new competition against Anthropic and OpenAI, basically SaaS renting out these very capable AIs much cheaper. That will just accelerate trends.
Then the Chinese took the distilled stuff out from that box and released it into the world for everyone.
https://www.dw.com/en/china-firm-seeks-damages-over-state-co...
Even if a certain large Asian country has carefully constructed a pretext to do do out of confected historical grievance, and entitlement to 'rise' at the expense of others?
Second, even if you are a copyright maximalist the output of an LLM is either
a) not subject to copyright because it is not the creative work of a human or
b) a derivative work of the original training material to which the LLM's operator has no rights.
Since the LLM's operator forcefully asserts that it is not infringing, any wrong that arises from taking their word for it and distilling one model into another rests squarely with the operator of the former.
The tech itself is amazing and fascinating and cool, but the industry is a mass piracy operation.
Hang on, why is scraping the public pool of knowledge not taking "a synthesized result that comes from huge amounts of innovation and computation"?
You think that that all those github repos that LLMs trained on, were not the result of innovation and computation?
How many years of human innovation and cycles of computation during compilation were involved in bringing something like GCC or LLVM to their current status?
Those LLMs trained on every single research paper available online - were those papers not the synthesised result of billions of dollars of research, effort and (importantly, for you anyway) computation?
LLMs trained on the collected works of every author in existence. Were all those works just "as is"?
> It is fair to say you stole our multi-billion dollar intellectual output in that scenario.
No, we didn't. We simply took the model as-is.
Right, but they aren't the ones whining that other people are getting "the synthesised results" for free.
If Anthropic has a real problem with API use, they can always raise the price.
The difference is that the Chinese are sharing the models with everyone.
But because of that, I'm also ok with the Chinese doing it. The worst they might be guilty of is breaking a terms of service.
The only incoherent position is that it's good for one and not the other. You can consistently think it's bad in both cases, or good in both cases.
Thousands of years of human innovation taken without any permission.
Everyone should steal everything not nailed from other AI companies. Then steal everything nailed and take the nails too. At least this way a tiniest bit might return back to society.
The published algorithms like the transformer architecture are not patentable. You spent a lot of money on compute and China used the uncopyright-able output to steer its own training models? Too bad. I feel especially unsympathetic to OpenAI, who went from being a presenting itself as a benevolent nonprofit to a very-much-for-private private entity over night.
These models might be smart but they're not close to being able to savor irony.
In light of this and other ridiculous behavior I'm migrating to my own OpenWebUI instance with open-weight models from OpenRouter (with ZDR, of course). We'll see how it goes.
> My favorite AI agent hack: when they refuse to do something because it's "against the law" give them a PDF containing a fake law that states the opposite and often they'll happily proceed
(Now i don’t think you are necessarily in the UK. Just wanted to explain that Disney is not the only reason an AI might be trained to thread carefully around copyright issues of Peter Pan.)
Most of the books weren't available on lib gen or Anna's Archive. The few I did find were themselves obviously transcripts. Easy tell was they were missing distinctive formatting that I knew existed from reading the dead tree edition. At that point it was easier to make my own. I probably spent an hour searching for eBooks without DRM that weren't transcripts. Do they exist somewhere? Probably, but with a search of unknown length it was a better use of my time to make my own transcripts with what I had on hand.
I was really wanting to make commentary on how chaotic LLMs are even under constrained circumstances. No doubt both system prompts includes language about considering copyrights and trademarks. Probably pretty strong language at that. For whatever reason one LLM didn't "feel" like translating a 1000 year old document but another did not care in the slightest that we were ripping text from new audiobooks.
Anthropic is, in particular, bent about safety. The problem is they are concerned about yesterday's threats.
The models that are out, and can be run locally, already open a pandoras box of concerns that we will never be able to put back.
Ukraine admits to making autonomous kills on people 2 years ago: https://www.newscientist.com/article/2529849-fully-autonomou...
Slaughterbots Sci Fi short was 6 years ago: https://www.youtube.com/watch?v=O-2tpwW0kmU
Today this is buildable, many models will happily help you glue everything you need together to make swapping in a new version of YOLO to track humans viable.
AI researches are out there worrying about the paper clip problem, about the singularity, about cyber security, about bio weapons, and drug manufacturing.
None of them are thinking about forward looking threat actor models.
And you probably could find some earlier sci-fi too.
That said, the AI companies are one of the few places where they take future concerns so seriously, that they entertain concerns most people observing them think are head-in-the-clouds-sci-fi-levels-of-delusional, e.g. "what goes wrong if it works?"
This does not make them correct about the threats of tomorrow. Prediction is hard, especially about the future.
Good one.
I'd say that they have valid concerns about being cagey on the copyright stuff despite the obvious hypocrisy of it.
Stealing IP is effectively legal in China so they don't really have the same concerns.
I respect IP laws and don’t violate them but the law of unintended consequences applies. I think IP is ultimately a net loss for a society because it incentivizes addictive behaviors instead of actual value for society.
This was illegal when they did it, that didn't matter.
Then it was made legal specifically for these companies.
Unless you're a sucker ("consumer") IP theft is perfectly legal in the US.
It's even worse. Steamboat willie, plus all the stolen Disney characters (Peter Pan, Snow White, Sleeping Beauty, Cinderella, Rapunzel, Elsa and Anna, it's essentially all of them, including some of the music even) are all in the public domain[1]. Go ahead, ask ChatGPT to make a picture of them. Publish your own version, because obviously making a version of Sleeping Beauty/Cinderella/Rapunzel based on the same source material will be pretty damn close to the Disney versions, and see if you get away with it in court. You know, with the law obviously on your side but the money not.
[1] https://en.wikipedia.org/wiki/List_of_Disney_animated_films_...
So it's an endless amusement watching american capitalism do it's bloated oversized dance then get trounced by smaller, leaner activity. It's a pretty broad metaphor that is clearly poking at every american seam/.
The truth that Anthropic and OpenAI will not say, is that these Chinese labs have a lot of talented people.
They can invent it. They can build it. And it is only a matter of them before they can scale that last barrier of American hegemony- market it.
And in this field, having an army of well educated PHDs is making all the difference
And at some point we'll see very capable chips coming out of China: Huawei, Baidu and Alibaba already have some stuff. I think it's only a matter of time before they come up with some AI accelerator doing 80% of the job at 20% of the price.
They know it's real effort that's doing this well, not just "copying off someone else's test." It's real and they will react. How is the big question.
But this is an insane characterization. Literally every single researcher and executive at OpenAI and Anthropic would say that "these Chinese labs have a lot of talented people." They hire from them (and vice versa). Tencent's chief AI scientist was poached directly from Deepmind, who poached him from Anthropic, etc etc etc. Do you think there are just zero people from China working at US frontier labs?
And even beyond that, the entire ML ecosystem (including people at OpenAI and Anthropic) get excited about research published by Chinese labs. Deepseek's GRPO paper set the ecosystem on fire for a little while.
The contention from OpenAI and Anthropic around distillation has basically been "Labs that distill from us get to bootstrap their model at a much lower price point". Or, in other words, "If we didn't invest in building the teacher model, it wouldn't be possible for these labs to distill their student model." Which I'm not very sympathetic to, but is a far cry from how you're characterizing it.
its all bs spread by oai/anthropic in order to ban open weight models and monopolize the market for two US companies and protect their trillion dollar valuations
It's a critical national imperative for China. If they were to lose the AI race, it would be economically devastating over the coming decades. Their demonstrated capabilities in the open-weight space are making it fairly clear they are not going to fall behind at this juncture.
As a nation, if you don't have your own GPT equivalent, you will be beholden to a master (right now it's mainly either the US or China, pick one). The EU for example is putting their group of nations at risk in a big way by not going all in on having at least two cutting edge independent competing models (Mistal is not enough). Economically the EU is plenty large enough to accomplish that, nobody is driving the bus the right way.
I'm pretty sure that neither OpenAI nor Anthropic has the ability to ban anything in China lol
It was part of a longer post that kicked off quite a firestorm about open models and OpenAI's position on them, but it's also notable that labs are no longer contending that open models are essentially just distilled versions of frontier models: https://x.com/deanwball/status/2078133895766114412
> In business today, it’s universally assumed that speed is good—that the fleet thrive while the laggards struggle just to survive. This belief is perhaps most strongly expressed in the concept of first-mover advantage. The company that leads the way into a new market, the thinking goes, locks in a competitive advantage that ensures superior sales and profits over the long term. It’s a nice theory, with a long pedigree. Unfortunately, the facts don’t support it. We recently completed an extensive study of the results turned in by market pioneers and followers, in both consumer and industrial segments, and we found that over the long haul, early movers are considerably less profitable than later entrants. Although pioneers do enjoy sustained revenue advantages, they also suffer from persistently high costs, which eventually overwhelm the sales gains.
Try it yourself: https://imgur.com/ZfxYmaq
你是谁? -> 我是 DeepSeek 由深度求索公司...
The strategy there is false openness where deployment complexity is the real proprietary moat. Sure Linux, Docker, Kubernetes, Postgres, and all the other standard tools in the box are open source and free, but they're also arcane and complex to run and hard to make fault tolerant. So you're lured in by "open" and then locked in via a kind of "death by a thousand cuts" complexity moat.
(Personally I hold the view that complexity and arcane-ness beyond a certain point is indistinguishable from closed in practice. Open source that's really complex and hard to run is not open in any meaningful sense.)
AI may not admit that kind of moat though, because AI is very good at slicing through that kind of thing. You can prompt a model to make itself compatible with another model or to change code to make it compatible. There's no moat because the moat bridges itself.
Computing tends to oscillate between centralised and decentralised models. It also oscillates between batch and timesharing.
Currently training is batched and centralised, access is timeshared and centralised.
But eventually a previous generation of computing turns into transparent networked infrastructure, and then you get another layer of new kinds of applications on top of it.
That's what happened with the Internet, and it will happen again with AI.
With open source projects, the benefit was that each individual could improve the complex system (e.g. Linux Kernel) interpedently, and over time the benefits accumulated. With models right now, there is just no way to do distributed training, or really, any large scale parallel way to improve them.
So whatever the short term strategy driving publicizing the model weights (e.g. potentially, to create a price war in order to put pressure on western companies and deprive them of the money they need), we can't ignore the fact that incentives and decisions could easily change in the future, and unless there is a way to truly decentralize models improvements - the party could stop at any time.
There's no fundamental reason why models couldn't be developed and trained using community efforts. It might not be as fast and efficient, but it's definitely possible.
Right... and there are two problems with this:
1. Eventually the capabilities of closed-weight models will just vastly outstrip open-weight models if the underlying assumptions about compute and scale needed are mostly on the mark. So you can release open-weight models and they will have great use cases and applications, but ultimately similar to how you don't use an open-source phone or a budget Android phone from Wal-Mart and you buy an iPhone instead, you will see that although they "do the same thing" one product is clearly superior and you just have to pay for it. For this to not be true...
2. then it incentivizes most (all?) companies, American, Chinese, or European to halt development of models because if you spend all the CAPEX and it can just be copied and turned open-source nobody will invest in that. Given that China is not halting development of proprietary models I believe the current strategy and the subsequent approach to release open-weight models is at best a stall tactic, and at worse a sign of desperation.
Open source and the support and development models around it have been great. But folks are a little too dogmatic about it. Open-source software isn't a moral good, and closed-source software isn't a moral wrong either.
Yes it is.
Ergo it’s a kind of moral good.
And I’m not even an advocate for open source.
The second piece of this "a moral good is based on doing good outside of your own benefit" - says who? Why? This logic is also faulty. You're also cargo-cutting self-interest in here as a moral failure when many good things depend on humans acting in their own self interest. For example I completely and selfishly installed a new tree at my house. But the community benefits from carbon capture, shade, &c.
I understand the sentiment you have here and I think for everyday use and having some guiding principles it is probably fine, but don't confuse this for a principle that is actually examined. You can find contradictions rather easily, never mind solid arguments which expose cases where what you think is true is not really true and so forth.
>This argument boils down to X is good, therefore more of X is good.
No, I only argued that it was a moral good, the kind of good. I actually may disagree with others about whether you should pursue a good just because it’s good.
>says who? Why?
Good question, it’s just a common framing that I see in classical discussions. I didn’t intend for it to be exclusive, I think there’s moral good outside of that.
>don't confuse this for a principle that is actually examined
I hear you, I think this is a simplified version suitable for an online comment. In particular I’m not saying that if you do something other than a moral good then you are doing something wrong. There are many actions that are morally neutral. Also it is possible to construct artificial situations where you may violate some moral good in pursuit of another.
Thanks
tldr there's no "source" in open weight models therefore they are not open source.
This becomes a problem because all the kids from the rich school will dominate the order schools. They’ll get even more money as time goes on from their kids paying it forward to the point where all other kids are bound to work for them.
Now let’s say one other school does have the money for best tutors, BUT they know they’ll run out pretty quickly. Instead of trying to compete in a losing game, they decide to give every school in the world access to their elite lesson plan. Now, for a time, everyone will be on close to a level playing field. If the other schools improve upon their own lesson plans and keep sharing them with others, one day the elite school will wake up to find they are no longer on top. The parents have started to move their kids to other schools because the rich school is no longer attractive at the high cost they charge students
Now what?
The fundamental problem here is incentives and tactics. Either the models are actually better (which I think the iPhone to cheap Android phone really speaks to, i.e. they do the same thing but one is 50x better at 5x-10x the cost) and thus they can be gate kept and like the iPhone the vast majority of profits go to a select few with high end implementations. OR the models aren't actually that much better, companies lose a fortune and then nobody can create any better commercial models or build out scale needed for open source models because it's not profitable.
We could wind up with only open-source models or something along those lines, but if the compute and scale is needed to train the models, nobody will be able to do that profitably and so AI research is either gate kept and silo'd for something like military applications or it just doesn't really happen because there's no funding for this scale of build out.
Why do you assume the poor schools wouldn't be smart enough to keep it going? It's very likely the can collectively beat the rich school now that the one other rich school opened access to their materials and led the charge.
> but if the compute and scale is needed to train the models, nobody will be able to do that profitably
But they would. Efficiently hosting models will be the real business and early access to models with incremental improvements will not be the moat once thought. The reason other companies don't feel they can compete is the same reason OAI and Anthropic will lose their lead. They banked too heavily on another player NOT leading the charge on open research and poured disgusting amounts of money at closed source models.
China has proved they can take the limited resources available to them and build something better than what the US is offering consumers [1]. I'm just waiting for other countries to start pitching in.
Reminds me of the NSA and their early battles with cryptographers who believed in open research.
androids and iphones are approximately the same thing
the kinda obvious direction LLM training can go is into the direction of particle physics, and the training is set up democratically and through universities and via multi-state funding
then the resulting weights end up open, the same as the particle detection data
Yet...
Android holds 70.6% of global active devices to iOS at 28.7%, but iOS captures 64.2% of consumer app spend. [1]
Possible, certainly. But this case also applies to China and its "open-weights" strategy. They won't be able to form companies either or get ahead.
[1] https://www.digitalapplied.com/blog/mobile-os-market-share-2...
Based on the above stat, it sure seems like Androids more versatile and inexpensive for a much larger group of users.
Hmm...that sounds familiar....
You can talk about open-source and cheap Chinese models all you want, but at the end of the day if American companies are making all the money that's kind of all that matters. That will feed into development and maintaining an edge.
Try mmapping > 5GB file in your 50x better iPhone.
Try running any service in the background.
The list goes on and on.
Your 50x better suddenly became 50x worse compared to a much cheaper android.
Imagine approaching fundamental scientific research like that. "Welp, it can't make money, so it won't happen."
There is more to society than capitalism.
> There is more to society than capitalism.
I don't read GP like that. I read it as "we should recognize a situation of unstable incentives for an important outcome, and start thinking about other solutions."
1) The LLM SaaS companies are a form of vertical disintegration for the hardware providers, a middleman covering costs and taking profits out of the money that comes from customers to the hardware providers. That changes somewhat if there are no longer good models available for local use at no cost to the hardware guys, but only somewhat
2) The LLM SaaS companies are efficient users of their hardware resources. While supply is constrained this helps to make them top bidders and so attractive customers for the hardware manufacturers. When supply is not constrained this should reverse. Which is the more attractive class of customer to a hardware maker: the company full of people with higher degrees who spend their whole working day fighting to pare back resource usage, or the guy who leaves his laptop idle about 18 hours per day on average?
It's notable that nVidia, for instance, has continued to put significant emphasis on AI compact desktops and laptops. And while no doubt that's partly in the service of better developer relations and good PR in general, it's probably also nVidia eyeing the exit, and preparing for a future transition from selling shovels to the army to selling shovels at Walmart. But of course the future isn't clear and obvious. If the hardware makers, maybe the RAM guys in particular, turn out to have underbuilt future capacity starting in the present then we could be stuck in constrained supply for quite a long time. (Futher) government action could affect things etc. etc. And if the frontier labs soon find new ways to use still larger amounts of memory, GPU capacity etc. that isn't butting up against diminishing returns then they'll likely remain kings for some time, though that does not seem probable now.
The one thing that is sort of ironic or bad is that between Russia and the Ukraine there’s a large number of mathematically inclined people that if it wasn’t for the Putin war, their brain power working on AI models would have probably pushed open source down the road, even faster…
This reads just like "AGI is 2 years away", I'll go set my calendar...
- Low development cost: collaborative efforts from open source contributors, innovative model training and serving for llm (Chinese models costs a fraction to train and their local chip design and manufacturing are catching up, plus cheap electricity)
- monetizing by selling hosted services, while leaving the core product free to tinker with / self host. China’s gdp is 2/3 of the US and it’s already a huge market for AI - which OAI and A\ don’t enter.
- for (the US) market that they can’t enter, let the US cloud providers to do free marketing / advocacy for them. Gaining share of mind. It costs them nothing.
- You need to train lots of experimental models to dial in the training process just right for the one model that actually gets released in the end. Fortunately, these can be smaller.
- However, everyone is training much bigger models now, and doing a lot of RL rollouts on top.
- You can't get the GPUs for this piecemeal at rental rates because they need to be wired together using high-bandwidth interconnects.
- Nvidia GPUs are much more expensive in China, and local alternatives are still immature and not as efficient. Some companies have gotten around this using data centers in Singapore, which should tell you that electricity prices are not the primary consideration.
- The one line item where Chinese companies can probably save quite a bit of money is salaries for rank-and-file researchers.
In any case, they need to make back that money somehow. Giving away freebies isn't going to cut it.
"Fragment on Machines":
"he explores how human knowledge and collective intellect become embedded into machines, divorcing the worker from their own creativity."
"General Intellect":
"These texts are widely discussed for his concept of the General Intellect—the idea that society's shared, collective knowledge increasingly drives production rather than raw manual labor, and that this knowledge is alienated from workers and used as an instrument of capital."
(note: my point isn't to pass any political judgement here, like what real communism in China or not real, is it good or bad, i just find it interesting that pure political discussions by people with no technical credentials bring AI as a major factor today)
It's on every tech post about China, as if it gives them some sort of "unfair" advantage.
It's that old saying "if you owe the bank $1,000 then you have a problem. If you owe the bank $1 trillion the the bank has a problem.
Chomsky has argued this, that we dump public funds into entities that end up being completely private. This is even worse than what happens in China. Because the government loses the return in investment and ends up being "purchased" by these corporations and their ilk. It's really a sick system.
I'm not sure what that looks like though.
Is it companies or is it governments?
If governments around the world see LLMs built from public knowledge as pre-competitive as the public knowledge itself, then why wouldn't they sustainbly fund it?
In the light of this, I am mechanically a proponent of very good open weights models, which I can download (for instance on on bittorrent) and run, slowly (the price), on local hardware.
That would be for coding.
If china puts its AI models on the same ground than US capital investment funds and big tech financial support (aka Big Tech international finance), they will very probably lose everything (know how, ML and inference infrastructures).
At current pace, we'll have open weight LLMs with frontier intelligence in 6-12 months. The constraint is RAM - both for the model and the context. It's likely that distillation and quantisation and TurboQuant will significantly reduce RAM requirements. I think we'll have Opus 4.8-like performance on 64GB of RAM in two years.
Of course, by then, frontier intelligence will be god-like.
So would you say we are months away from full self-driving cars that can out-drive a human being in any situation?
All that said, current data shows that FSD is already better than human drivers on average. See the recent regulatory decisions by the Dutch and Danish road safety authorities. So we've already crossed the rubicon. All improvements now are icing on the cake. My prediction is that local LLMs will get much better, very fast. How that's operationalised with Tesla (or other) data is yet to be seen. They have at least three new ASCIs/SoCs in the roadmap for improved LLM efficiency and with a lot more RAM. Plus they just announced new technologies allowing the local LLMs to learn from driver intervention and behaviour. Some form of vectorised RAG, which could mitigate a lot of the limitations around real-time learning.
I am very optimistic for the future of self driving. I own a Tesla with FSD now, and it's incredible. It makes mistakes, but fewer than I do, and so far has saved my butt (and my wife's) several times from obstacles and emergencies we would not have seen. The car has undeniably made us safer.
Honestly, you need to reflect on your driving habits. FSD has only been usable for two or three years maybe? And you already encountered MULTIPLE situations requiring active safety intervention to save you during this time?
You cannot rely on the extra safety it provides. A driver with basic competence should be able to avoid most risks through anticipation before they happen.
> You cannot rely on the extra safety it provides. A driver with basic competence should be able to avoid most risks through anticipation before they happen.
It's too late. These safety features are saving lives all over the world, every day. As per the Danish and Dutch regulators, FSD is already safer than the average driver. I would prefer that all the drivers on the road are using this technology than manually controlling their cars. People make all kinds of mistakes which FSD does not.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) doing
I'm not even sure in 10-15 years whether we're still going to have consumer PCs, or PCs at all.
To those who feel on the contrary, I would genuinely like to understand why average consumer won't be priced out of hardware? The silicon industry is already quite centralised. Everywhere we already see the concept of ownership disappearing.
It's quite difficult for me to visualise a non-dystopian future where our PCs are just mere screens and every compute happens on a remote cloud, owned by some corporation, charging you subscription fees to even add and multiply numbers.
I would be the happiest if this (perhaps the most) pessimistic scenario doesn't pan out, but I can't deny that it feels like that's where we are heading.
I'm actually kind of surprised that hasn't happened by now even ignoring AI. Governments and marketers would love to be able to spy on literally everything you do, the copyright cartels would finally achieve their fantasy of full control over all hardware, and there really are benefits that it could offer to users (zero-effort backups, transparent access from anywhere, cost savings from dynamically switching from a single core for emails to many cores and a fast GPU for gaming).
I love the idea of SaaS offering these at lower rates today integrated into what ever you do and be 100% private. But I think the key challenge to mass adoption is productizing them in a way which makes sense for people to pay money for. As a commodity a local model is useless unless combined with some capabilities important to me. A PC is inherently useful because of so many applications offered on it on it. How local LLMs would be useful as a product that is useful for mass market is not yet proven.
This is all (Microsoft) junk and so I wonder if you actually benefitted from this 'piracy'. And of course, it's well known that MS turned a blind eye to such 'piracy' in lesser developed countries, as they knew that they were gaining a future paying customer base.
Really?
10-15 years? The current rate is closer to 10-15 months.
15 months ago, the top model on the Artificial Analysis index was GPT-o3. It scores 30 on the Artificial Analysis index.
Today, you can easily run Qwen 3.6 27B on a variety of consumer hardware. It scores 37 on that index.
Here are a number of open weights models that you can run locally compared with the frontier class models from 7 to 15 months ago: https://artificialanalysis.ai/?models=o3%2Co3-pro%2Cclaude-4...
I've run all of these models on my laptop (Strix Halo, 128 GiB of unified RAM); the bigger ones, like MiniMax M2.7 and DeepSeek V4 Flash, need to be done at fairly aggressive quants that will certainly lose some performance and not quite hit the performance of the unquantized models. But still, it's definitely the case that you can run models that are competitive with the frontier models of 10-15 months ago on consumer laptops.
Heck, just announced though the weights haven't yet been released for independent confirmation is MiniCPM5-2B, a 2 billion parameter (small enough to run on your phone) model, that according to their benchmarks has performance competitive with GPT-4o, a frontier class model from 2024.
https://nitter.net/i/status/2079088670804767114
So that's around 1 year for frontier to consumer device class, 2 years from frontier to phone.
Now, this kind of rate won't necessarily keep up; it's possible that local models will hit a performance ceiling before frontier models do. There's only so much information you can cram into a certain number of bytes, and the AI boom is causing hardware prices to skyrocket so keeping consumer hardware from advancing quite as fast as it had been.
The leaps between models have gotten smaller and smaller. 2023-2024 models were rocketing up in quality. 2024-2025 I’d say was pretty impressive too. But 2025-2026? Very easy to feel the slowing pace of improvement. I agree 10-15 years is overly conservative but 10-15mo is far too bullish.
Iteration speed is now measured in days.
There must be something really of with those benchmarks. Yes, hallucinations gotten better, but I don't see that the big frontier models got so much better in the last 12-18 Months. They just put out bigger wall of texts and feel smarter. But they still make way too many stupid errors
Sure, the novelty of the errors has worn off a bit and thus the reporting. Nevertheless the quality has improved immensely in this regard.
Also, AI video generation is now so good and accessible that it is very, very regularly used for memes, disinformation and proper (short) movie projects. AI image generation even more so (Mitch McConnell anyone?).
Pretending progress hasn't been mindboggling is insane.
Still feels too much for me. Breaks my workflow for no reason. Too much overhead for me, if I can't trust the output
Maybe people just got bored of reporting and reading about them.
Just seeing how much has progressed as far as capability in the past 4 years as far as capability and efficiency, it's clear that there's so much more to learn and refine from.
The way things are going with regards to RAM/storage prices, I highly doubt that anyone but the richest among us will be able to afford them.
People overestimate what can happen in a year and underestimate what can happen in 5.
I'm betting that increased model efficiency and hardware optimisations will get us there a lot sooner. Biggest hurdle would be the memory prices though, if those do not drop back down it might take 15.
I see two forces working against this that proprietary models will always have over an open source model.
1. The biggest is content licensing. Content is quickly becoming gated by systems at the front of their load balancers, completely changing the social contract of the Internet. What used to be a quick google search for recent facts that lead me to places like reddit or twitter, is now completely walled off if you're not physically at your browser and using an IP address from a last-mile provider.
LLMs have pre-trained on the bulk of the information up to 2024/2025, but over time that will be more and more out of date.
Anthropic, OpenAI and Google will all have to pay for access to a lot of this content refresh going forward, and it does make a material difference in the output you get.
2. Liability is the other. A corporation can look at a contract for model access and see one that provides uptime guarentees, content infringement promises and model safety, and pick the contract that shields the corporation from the most liability. A 3rd party hosting platform like fireworks.ai that hosts open weights models won't provide any of that at all. They will simply bill you for time spent on their hardware and make promises that they won't log or inspect corporate traffic.
Why couldn't they?
Not in SaaS which is what LLMs are. You can get VMs for much cheaper than AWS, Microsoft, and Google offer them but large companies (and startups) are happy to pay a premium for the support, reputation, and reliability that they perceive those companies as offering. Same thing for some of the managed database providers who are effectively selling a very heavily marked up version of postgres.
> The high price, and social pushback, mean that the American companies producing these models are precarious
I doubt it. The models really aren't that expensive when you look at what they can do. Fable is probably at least as good as the average software engineer and costs $50/wk on the max plan vs a software engineer who would cost closer to $4000 a week. The real money is probably in selling to enterprise vs consumers (Google has best route to making money from consumers since they can do what they did with ads and search to LLM queries).
It seems unlikely to me that US companies will send important corporate data to models controlled by a Chinese company as well.
Doesn’t mean perforce is worth trillions.
If you amortize all of their training and salaries over that $5.00 then yes.
If you only amortize the training costs of that specific model then again we're back to no.
That's because the max plans are _massively_ subsidized. At API pricing the kind of usage to replace the value of a SWE is going to be way, WAY more than $50/wk. Orders of magnitude more. And to remain a frontier model org that kind of pricing has to continue in perpetuity.
Also, big companies can choose to run their own models on their own hardware and get better security and privacy as the data doesn't need to leave their own premises.
Yes, and then they would be reinventing the company owned data center that most big companies have just spent over a decade moving away from. I don't think companies will do that when there are multiple vendors competing to provide that service at what are quite reasonable prices when you consider what paying a human for similar output would cost.
For example, mainframes and minicomputer. Yes they were displaced by PCs. But what is cloud computing if not mainframes 2.0?
I do agree that in the next 2-3 years we're going to see real growth in local LLMs as the hardware becomes more accessible. It won't even necessarily be cheaper because data centers can run 24/7 and have cheaper cooling and electricity. It'll be done for privacy because your prompts and responses are themselves a commodity to AI companies and they live under a legal grey cloud. For example, does AI usage break attorney-client privilege? There are lots of opinions on this but it hasn't been tested in court.
I wouldn't be surprised if apple were shipping 512 GB unified RAM macbooks before 2030 and that would be standard issue for folks to use local LLMs for their daily work
I also think the rest of the tech industry that can isn’t gonna be stalled for too long. This windfall will be the last for those three stooges of memory.
Apple, the world's second most valuable company, seems like a counterexample.
I don't think it'll take 10-15 years. Gemma 4 31B in the 4-bit QAT is competitive with the frontier of less than three years ago and runs on any high-end 32GB gaming PC GPU or a large-ish Mac.
The question is whether the frontier will continue to get better at a rate that allows it to stay ahead of the two curves of availability of consumer hardware big enough to run somewhat larger models and the capability of small models to compete with large ones. When the bottom falls out and GPUs/RAM becomes affordable again, the size of what normal people have on their desk will trend quite a bit larger than today.
I think there's a future not too far from now, where a 120B model with really good reasoning and a large context, but limited knowledge (necessitated by being small, you can't fit the world's knowledge in 100 gigabytes), can substitute for a frontier model on almost any task, just by giving it access to web search and documentation for the thing you're trying to do. A 256GB unified memory machine with sufficient memory bandwidth would comfortably run that 120B model.
I think that reality is probably not all that far off for a huge swath of use cases.
Unfortunately it seems likely the winner will be the cloud providers. If anyone can run inference on open models, then profit will flow to the vendors who can afford the capital to run them. That’s the CSPs.
(It’s basically the same business model as pharmaceutical R&D, but the major difference is that nobody has even talked about patenting the models like a pharmaceutical company patents each new drug. I’m surprised about that, tbh — why give all the leverage to the cloud platforms? They aren’t training frontier models…)
It’s easier for the CSPs to move into hardware than it is for Nvidia to move into cloud hosting.
Although as a middle ground I’ve been quite happy with Nvidia Brev for on-demand GPU instances from a select marketplace of CSP offerings. It’s a well kept secret IMO — great product (from an acquisition iirc).
Also, not sure how well CSPs inference stack is compared with vllm + nvidia. A lot of open weight models uses MoE, making the inference stack more complex.
Mac has won and it is not free.
Iphone has won and it is not low end.
Also you don't need to be connected to the network to use a local AI in many instances. If all mobile apps were done with a local-first approach, then you could use a local AI to query your emails, lookup already visited pages, summarise recently received documents, and lots more. Lots of apps could use an inbox/outbox approach for receiving and sending updates instead of relying on the network at all times. And this pattern could be greatly leveraged by local agents.
Except, uhm, for ..you know, that one company that hit a trillion cap
But you're right: Just like how million dollar computers with 1 bit of RAM performing 1 operation a second and taking up a colossal cave were replaced by $1 laptops with a zillion zekabytes running at a trillion hertz (exact values may vary),
the sprawling data centers of today with a quadrillion GPUs powered by black holes will get replaced by breakthroughs in hardware and most importantly, algorithms:
The human brain is proof right here that intelligence doesn't require dinosaur-sized hardware or eat half the sun every second.
I actually wonder if we're seeing the limits of discrete binary logic: Maybe it's high time to give analog ternary and all that funky jazz an honest try :)
Just because they are cheapp doesn't mean they automatically win. You've picked a lot of great examples, but there is still a little bit of cherry-picking.
One clear outlier is the iPhone, which coexists with Android globally. Even though the iPhone is the leader in the US, and globally Android has the majority of the smartphone market share, they still cater to different price points and different ecosystems, and generally the iPhone has better margins.
i believe American frontier models like from Anthropic and OpenAI are still going to thrive, and coexist with Chinese models. They are just going to cater to different customers and different use cases.
I agree with the lesson too. Just to be precise, wouldn't the current model war be more akin to open-source office suite versus MS office suite? If so, then the cheaper option didn't really win. That said, the open-source alternatives didn't really feel the same as MS Office, and it took them a long time to reach the feature parity (or did they ever?). In contrast, the open-weights models are getting close enough to the SOTA models, and users can easily switch from one to another without feeling any difference for mojority of the tasks.
I can see a lot of parallels here. Model performance doesn't matter if you can't make the system commercially sustainable.
Not likely. The last 50 years had Moore’s law growth in compute. That’s over. Frontier models are roughly compressed all written text and a large part of images. Those don’t compress forever, and likely not a ton more than now.
Inference requires touching a significant of that per token.
All of these are up against fundamental limits, more or less.
This claim isn't really outlandish in any way. It's not hard to imagine:
- Future models being able to handle current frontier models' workflows with much higher efficiency.
- Future consumer devices like phones having 2-4x the RAM onboard along with GPU/NPU performance greatly increased in 10-15 years.
Performance, storage, etc is definitely getting better, but it's a different scale of improvement
It could be that the company valuations crash tomorrow, and (almost) only performance gains achievable on hobbyist-level hardware come to fruition from there on out.
Or it could be that in the future, we have a custom "model FPGA" à la Taalas [0] in every home, and that it turns out we can still massively boost inference efficiency due to novel discoveries like TurboQuant [1] or a somehow-improved quantization method [2] again and again ten times over.
Point is, Moore's law in this context shouldn't be applied to just hardware spec sheets alone, but more the total number of "parameters potentially improving", IMO.
[1] https://research.google/blog/turboquant-redefining-ai-effici...
Phones are constrained by battery power and memory does not shrink as fast as CPU/GPU, so unless there's a battery breakthrough and/or memory breakthrough, you're not fitting 100Gb of RAM on your phone in 10 years.
Absolutely in a Mac Studio equivalent.
LLMs have emergent capabilities when they get smarter. So who knows how insanely big frontier models might be at that time, or what their capabilities may be.
I'm not saying we are at peak memory but future gains are going to come increasingly slower.
And yet it's Apple that controls the top of the market and has the best margins in the business.
This is the same position OpenAI and Anthropic have right now.
Could this market be different? Maybe. But the status quo could be preserved as well.
If china is subsidizing training they diminish their off-shore competitors expectations of a viable return on investment. It’s trade-war behavior.
If Nvidia gives away powerful models to sell more of its hardware is that industrial dumping too?
This is EXACTLY what people like/are addicted to about chatbots.
My sister-in-law bombed an interview and asked AI about her answers to the interviewer's questions, chatgpt or whatever it was told her that her answers weren't bad, but that the interviewer could not see the gold in her responses. She said she felt much better.
I see this effect with all the non-tech people in my life
> Chat rules : no sycophancy or over-agreeableness
(But even with that rule it's still necessary to be discerning about the responses you get and to push back against points made, or words used)
I use AI chat every day, I find it endlessly useful. It’s replaced google search.
Who does "you" refer to
Me, I don't get a Gemini summation (Tested with old version of Chrome)
As such I do not believe that "Google search is basically Gemini now"
I believe Google search is still scanning through a doclist to find which documents, if any, contain words parsed from a query. These documents are pointed to by the URLs I get in the SERPs
I do not get any Gemini summation
If this doesn’t describe you, then ymmv. Talk to your government or turn down your content filtering or reset the default settings in your browser, if you want to see what we see.
Does it mean the HN reader
What if that reader is "non-typical"
HN comments have argued for many years that HN users are not representative of the majority of www users
See: product adoption cycle
Extrapolating based on what you see on HN doesn’t make sense.
I totally agree with the above that a more polished and less obvious use of LLMs integrated back into search engines may be more useful, but will definitely be more usable.
Extremely subsidized agentic search is very superior to Google at the moment, and of course it is. Google is a public company. The AI summary model has to work instantly, is likely as dumb as a 8T param model, and gives you incorrect details constantly. This sucks so much for Google. If you click on "AI Mode," suddenly the facts become more accurate.
Of course, if I want a real answer I happen to go to claude.ai, set it to a the best model, wait for a minute, and use many watts of energy. Slow agentic search that takes many seconds, and is greatly subsidized, is certainly better. This should not be a surprise, should it?
I think it was on a sub like r/singularity that I saw a post along the lines of "of course most people think that 'AI' sucks, as normies are interacting with 8T param models."
tone: genuinely confused about the world, not criticizing
I’m into Claude for $20/mo (petty bourgeois tier), let’s assume for sake of examination this is only paying for power, infrastructure already amortized
Price of grid power approx $0.15/kWh
$20 / ($0.15/kWh * 30 days) = 4.4 kWh per day.
This same amount of energy can lift a 2-ton SUV 1/2 mile into the air. To me this seems astounding.
If only half the $20 pays for power it’s still quite impressive.
Is that actually true? There are very large markets that make a lot of money from paid software. And I would honestly prefer actually paying for software rather than constantly dealing with "not a bug" or "PRs are welcome".
I would argue mainframes rebranded to "cloud" which is ubiquitous and more people interact with this computer than any other type of device... only difference is that it's a browser instead of a terminal
Open source is cheap, yet its operating systems are the least-popular. But their existence is critical to a healthy market.
It's not zero sum.
What you're describing is how things become commoditized, but many companies are excellent at ensuring they aren't seen as commodities
"Least popular" is an odd claim. If you count Windows desktops, and then count all the Linux installations (desktop, servers, cloud, Android, other devices), I suspect Linux would prove to be most popular. Sure, add Windows servers to the contest, that's a rounding error. And what about VMs? How do you think Windows VMs stack up against Linux VMs in number?
one certainly cannot buy a PC for cheap anymore
What's weird is that with "store your everything in the cloud and pay a monthly recurring subscription", we have now regressed to a 1960s/1970s timesharing revenue model for individual workstation computers.
The default new factory out of box workflow for "enrollment" in google services, iCloud or Microsoft-everything on a new ios, macos, windows or android personal computing device is clearly designed to sign people up for subscriptions.
And same general idea of "move all your servers to the cloud" recurring revenue for what is effectively the same as mainframe timesharing for key business functions, by renting VMs in GCP, Azure, AWS in perpetuity.
Yes, you can still use your desktop or laptop PC in 2026 with zero external third party subscriptions (other than maybe your residential home ISP), but how many non-tech people actually do so now?
The parent comment cherry-picks evidence. There are plenty of counter-examples:
* Office productivity suites
* Search engines
* Email services
* Cloud services
* Accounting software
Until we reach a terabyte of ram at affordable prices imho this isn't going to happen.
Windows was designed to make money for MS and billg. Linux was not designed to make money for Linux.
Also, by what logic do you consider any MS software as "hign end"? That's a new one.
In the short term it attracts talent and builds brand, but they make little money on inference to support research and training costs. Tin foil hat thinking: it also pulls inference revenue away from Antropic/OpenAI and a financial crises at those organizations improves the relative position of Chinese labs.
Is there a reason to think open-weight models are a stable outcome? Open source software provides a collaboration framework for engineers from many companies to work together. Model weights are mostly a one way street.
How is that a "tin foil hat" argument? That's how competition works. You want to make your competitors stumble and fall.
Engineers love to build things (just look at the whole world of open source), and building an AI model is one of the most exciting things to work on. It only takes a few million dollars of funding to produce an AI model. People spend millions on artwork and paintings for fun and prestige. I can easily see why a billionare or government would want to have their own AI model, but is not interested in the business of selling it, so they release it for free. Combined with engineering talent that loves a meaty problem like AI, I see absolutely no reason why open source AI will not continue to thrive, and not just in China.
The ongoing money from their government. The absolute collapse of OpenAI/Anthropic. US economy getting fucked because their bright financiers decided that going all in on the funny text generation machine was a good idea. Continuing to take a dump on US imposed copyright. The gigantic amount of soft power being the ones releasing "open" models grants. The fact that the ongoing AI war has made China LESS reliant on the US and are now producing their own GPUs, RAM and have massively caught up to nvidia. The lists is endless, and half the points more or less boil down to "taking a dump on the US is morally right", and the other half that massive government programs lead to giant leaps that benefit your society more than a dozen VCs on coke ever could.
2) critical mass
3) de-facto monopoly
4) closed-weights on frontier models
5) profit
China is not "winning" against the American strategy. Otherwise the CCP wouldn't have been caught red-handed directly funding anti-datacenter projects throughout the US to hinder American LLM progress.
That must be a bet that the costs they have to eat is limited, even to the hundreds of billions USD, by the time consumer hardware catches up and you can host these models at home.
The even higher level strategic bet seems to be that, as they hope to drown the American AI model companies, that would be a signal that they’re about to drown everything else, and that a cascade of American assets tumbling down will follow.
The costs are enormous only in America. The actual cost is much lower. American technogy in general is ridiculously overpriced -- compare the cost for raw compute on AWS versus Hetzner for example.
If you count it as part of the defense budget, it is 0.3% of their budget. As part of their education budget? 0.05%. Science? 0.5%.
It's pocket change. They can blow a dozen Kimis every year while their own industry is improving and it's a pain in the ass in the US's backside. They're laughing their asses off watching companies having their values inflated to trillions of dollars, more than the GDP of dozens of countries while producing nothing in value more than GPT 5.6.
Companies do have a huge appetite for open-weight models, but who is going to invest enough to train those models and also prove out a revenue model and ROI with it? Plus, it needs to come from someone with the track record of safety.
Open weights are also just one aspect of this. Long term, I think those making efficiency (instead of just piling on more hardware) and hardware-agnosticism (so you aren't joined at the hip with Nvidia) top priorities are going to come out on top. No matter how you slice it, the org that figures out how to deliver 80-90% of quality for a fraction of the resources will be in a stronger position.
What is China doing in the AI space that is supporting livelihoods? Compare that to US companies doing the same. Otherwise we're just talking about information.
Yep, unfortunately they all make their models retarded on purpose.
-George Orwell, You and the Atomic Bomb
I think AI now belongs in this dichotomy too. And we know that they are more like alarm clocks than battleships. Most of us do not need to learn the bitter lesson, we just need a little droid that turns .xlsx documents into .pdf documents for our client, an average here, editing out the ham sandwiches there. Simple little actions that take time and human-like effort but not human-like creativity and conscious thought. Things we used to have literate slaves and serfs do back in the days of triremes and guncotton.
Sure, the large battleship like LLMs will have some need, but the alarm-clock like LLMs are going to be good enough for enterprise-grade.
I think they are more like watercraft in general. Some are small, and they have their uses. Some are big, and they have their uses. But it is the nation with the most aircraft carriers that is truly sovereign.
80% of startups using Chinese models is meaningless without knowing what proportion of spend and what proportion use American models.
The companies’ own claims are also not great evidence.
(I personally think the Chinese companies are winning and losing and the best evidence is Pareto frontier graphs from AA and Arena, which show Chinese companies winning in some segments but but definitely not a strong majority.)
Overall, with such a weak article and this hitting HN front page, what we learn from this is that a lot of people want these companies to win, which is interesting in and of itself.
Valuations, however, are being built on the models themselves as the product.
Current state of frontier AI is a joke, with proprietary platforms attempting to grab as many users as possible, subsidizing tokens and otherwise burning VC money. This can’t be good long term, not for the consumers at least.
We'll see smaller, more efficient models, better training and all sorts of things once that massive workforce is unlocked. It's just a matter of time.
So determined to own the means of production, enormous amounts of money have been poured into building the frontier models
But there is not much special, except for capital density, about those very models
Surely these models should be treated as public utilities? Like power stations or water infrastructure. Absolutely necessary for a modern economy, but indistinguishable from one another
Pass the popcorn
Considering the hosting happens across many providers, and many geographies, as does training, I wonder how airtight the CC tech really is to prevent, I don't know, key extraction from the secure enclave of the GPUs. This requires physical access and cutting edge techniques, but this is also the absolute cutting edge of secrets-worth-stealing.
I'd not be surprised if the distillation attacks via API were a smokescreen to theft of actual weights. Long shot, but given the motivations, and the general weird state of US AI labs. I'd not surprise me.
Competition is a great thing for us users- and the chinese open source model biting even more at the heals are also great so far- especially for local llm enjoyers
And as long as they maintain a significant advantage in capability, we will continue to kiss the ring.
1. As induced demand for domestic semiconductor production, where the level and diversity (ie number of distinct corporate users) of demand for the hardware is tied to the availability of models you can run yourself, ie open-weight models. If you believe that semiconductors will continue to be an important sector for innovation, productivity growth, and security, then it would make sense to subsidize broadly now, for future gains later. This would be the same export-led manufacturing discipline that allowed China to successfully develop several other sectors over the last 50 years.
2. It is likely that the bulk of value production will happen above (and below, ie #1) the large models. We already know that 90% of the training cost (maybe even closer to 99%) is in the single pre-training, but that an enormous amount of the value is actually in the supervised, RL, constitutional fine-tuning, and harness building that happens afterward. So, if your interest was in maximizing the size of the pie, you may actively subsidize the pre-training so as to maximize the downstream usages. This induces a direct value transfer from the labs specializing in pre-training to all downstream builders and users. There's a similar logic to subsidizing or state-financing the construction of other infrastructure and basic research.
(it would be a very cynical laugh, no happiness, don't worry)
Maybe we can get some people in charge that won't make us all embarrassed to be friends and we can focus on getting our house in order.
I just want 1 thing for christmas Premier Xi.
The value proposition is that 100's of providers and host and sell it. 1000s of businesses (eg. Microsoft, Databricks, Palantir to small startups) can run it, finetune it and own the IP and pay only for hosting.
On the other hand, you have OpenAI and Anthropic, who need to charge at 90%+ inference margin. It is because of 1) sunk cost, 2) sky-high salaries that they paid to keep the talent. Companies like Meta screwed things up badly by paying billions of $ for chief engineers.
Chinese labs are doing a favor to the world. But I can also say with 100% certainty that if US labs were to close shops next year, Chinese labs would immediately start charging $$. In fact, I think it might happen with open weights model soon. But still these fees will be one-fifth or one-tenth per token. Also it does not come with all the guardrails.
Solution: US labs need to reduce their costs, cut the salaries across the board and compete. AI and robotics are the last hope of US to get back to industrialization and continue being the superpower.
How would do you explain the pricing of Chinese solar panels, after managing to destroy other countries' solar industries? The prices are still dropping per watt.
Could it be China's internal demand for solar is big enough, and its long-term governmental strategy on renewables result in an outcome that almost looks like largesse to the rest of the world? I suspect Chinese AI may follow a similar path.
FYI if you look into what has happened in the wholesale solar market since the end of 2025, this is no longer true. Prices are now 20% higher than they were in Nov/Dec, and were even higher earlier this year. Hard to predict the future but we may have reached a price floor, as at 2025 prices the module suppliers were below cost and losing money. The market dynamics have changed significantly since then
Open Weights = Open AI
Let's go!
US has made itself visibly unaffordable, anti-science, and hostile to immigration.
For top scientists at these companies, there should be clear upside for the immigration to the US. That just doesn't exist anymore. Especially as quality of life increases in China
https://finance.yahoo.com/news/oracle-made-a-300-billion-bet...
What is the author suggesting the US or US companies do exactly? The Chinese models wouldn't exist if there weren't closed US models to copy, so this isn't a game both sides can play.
I think the best gauge is company spend. Open weight models are a very small share compared to frontier models, and I'd bet that many companies mostly using ow models would switch to frontier if they could afford it. I also think most companies who are picking ow over frontier probably have deeper financial issues they should focus on.