45 ms·

Who's afraid of Chinese models?

• andrewdubinsky 3mo ago
  [dead]
• _aavaa_ 3mo ago
  > distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation Sounds great to me; live by the sword, die by the sword.
  • ronsor 3mo ago
    I am immediately sold on this. Sorry, OpenAI & Anthropic.
  • noncoml 3mo ago
    Don’t know much about how distillation works so please enlighten me here. > what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models If it’s as easy as that why do they choose to distill another model and not distill the knowledge on the open Internet from scratch?
    • numpad0 3mo ago
      known-good prompt-response pairs are more useful than random semi-coherent texts presumably
    • root_axis 3mo ago
      Because the model can output data in a manner optimized for training a new model, including outputs that were post-trained like RLHF and RLVR.
    • paxys 3mo ago
      You need to do both. A model trained on all knowledge from the internet (and other sources) is large but ultimately not very useful by itself, because it is going to spit out all kinds of garbage. You have to apply multiple further stages of training and refinement to the base model before putting it in front of users. So as an example you can train a model by yourself and then have GPT or Claude continuously check its outputs and correct it when it is wrong, ending up with a far more powerful model.
  • petilon 3mo ago
    [flagged]
    • mywittyname 3mo ago
      More like, is a professor who learned from books prohibited from writing his own books on the subject?
      • petilon 3mo ago
        He is prohibited from regurgitating source material, of course! But if he generalized from the books he read and really learned the subject--and even made new connections between ideas--then he is free to write his own book.
        • mywittyname 3mo ago
          He is not prohibited from "regurgitating source material" in many cases. Facts are free. It doesn't matter who first measured Young's Modulus of aluminum, anyone may state that fact as originally presented. The professor is free to lift all the facts and formula they want. They just need to rephrase explanations. Which is pretty much what an LLM is going to do.
    • Crisco 3mo ago
      No, but the students that learn and distill what the professor teaches are not obligated to use that information only how the professor wants them to.
      • petilon 3mo ago
        Can the professor refuse to teach some students, or must he teach all comers?
        • lostmsu 3mo ago
          glhf under the circumstances
  • eli 3mo ago
    Seems only fair that if LLMs can use copyrighted data for training then they should be able to use cannot-be-copyrighted output of other LLMs. But barring the terms of service from forbidding distillation seems like a tough sell. OpenAI shouldn't be allowed to decide what types of customers it wants and doesn't want?
    • mediaman 3mo ago
      This happens all the time. The government can decide legislatively that certain commercial terms are simply unenforceable. Making distillation clauses unenforceable in tort law would be straightforward. They can decide what customers they want to have, but they do not have unfettered rights as to the enforceability of terms governing the relationships between the parties.
      • eli 3mo ago
        I'm not doubting it's possible to pass such a law, I'm doubting that's it's a practical or worthwhile goal. The terms of service don't even necessarily matter here. OpenAI could cancel your account for almost any reason, or for no reason at all. They don't particularly need to cite a ToS violation just as a store owner doesn't need to point to a written policy to kick you out of their store. If the underlying issue is that LLMs should be regulated as a public good, then lets have that discussion. If it's that the major AI companies are becoming too powerful and anti-competitive, let's talk serious anti-trust enforcement. Micro-managing business policies isn't going to work very well.
        • hnfong 3mo ago
          You two are talking about different things. You are pointing out that OpenAI can cancel user's accounts for almost any reason, and nobody can really force them to serve customers that they suspect are distilling their models. That's one thing. The GP is saying the government can make laws to make terms against distillation unenforceable. Without such laws, if you signed an agreement with OpenAI pinky swearing you won't distill, but turns out you did, you are liable in tort and OpenAI can sue you. (It seems nobody really cares about contract and agreements any more, but still...) This is the other thing. And I think you are both right.
  • grim_io 3mo ago
    Forbidding distillation is like forbidding using a compiler to make another(perhaps better, more efficient) compiler.
    • chuckadams 3mo ago
      Lots of software licenses have “non-compete” clauses that forbid you from using it to develop a competing product. Wouldn’t surprise me if there was a compiler or two out there with that restriction, most likely niche languages.
      • scotty79 3mo ago
        How the hell is non-compete legal in market economy? Competition is one of its core strengths. Why would anyone let anyone opt out of this, even a little bit?
        • thesmtsolver2 3mo ago
          No country in the world is full free market economy. It is always a spectrum. We are discussing Chinese models. Now look at how much foreign competition the Chinese government prevents in their domestic market in other industries.
          • scotty79 3mo ago
            Chinese companies compete ruthlessly between themselves though. That's how they get this good. Full competition with preventing exploitation by foreign countries seems to be working great for them. American and European protectionism of local rent-seekers can't really compete with that.
      • matheusmoreira 3mo ago
        Those clauses should be illegal.
      • not2b 3mo ago
        It's been common in electronic design automation tools to have license terms like that (forbidding use to create a competing product). However, competing companies have often found workarounds, either by finding loopholes or just breaking rules and hoping not to get caught.
  • llm_nerd 3mo ago
    The distillation explanation is classic American exceptionalism: No one could possibly do anything unless they were copying American leaders (where "American" means a bunch of Chinese, Canadian, Europeans and Indians working in the US). It's also a bit of securities defensiveness. Pretending that you really do have a super moat, people just keep swimming in it so you just need to add more alligators. It's farcical. Anyone who has worked on large models knows that the premise that an almost-Fable model was trained with distillation is beyond ridiculous. It's theoretically possible if they spent tens of billions of dollars on API calls, but it isn't the magic that somehow these people keep convincing people it is. Previously Anthropic has reported on some Chinese firms doing chicken-shit level of API calls, that at most would be doing some Q and A or final fine tuning. The notion that they're training these models via it is fantastically ignorant nonsense that only very ill-informed and gullible people fall for.
    • ultrablack 3mo ago
      Which Chinese model was it that identified itself as Claude 15% of the time?
      • llm_nerd 3mo ago
        Models don't have some self identity, beyond what is explicitly handed to them via a system prompt. There have been many, many cases of models identifying as different models by different makers as a basic identity hallucination. They train on enormous volumes of data including lots of people talking about certain makers and models (ChatGPT was actually a super common one given that it became the kleenex of the LLM world). Hence why vendors have to specifically tell it to override that, and if they don't you get lots of funny cases of identity confusion. This isn't the big gotcha some people seem to think it is, and the whole news cycle about that was mostly by people who have no idea what they're talking about. It's actually a meaningless data point. But it's precisely the sorts of people who think that a few thousand free accounts surreptitiously snuck off with Fable.
      • inigyou 3mo ago
        Claude Opus identifies itself as Qwen if you ask the question in Chinese. So who's really distilling who?
  • qurren 3mo ago
    Government cannot exactly "bar" terms of service. ToS isn't law. The most they can do is say they're unwilling to enforce them. ToS is just conditions that you agree to in order to use a private service that is provided at-will. I can have a private coffee shop where the terms of service are that you must wear red to enter, and if you're not wearing red, you are not welcome on my property. So it would be upto OpenAI and Anthropic to enforce them on their own terms (by banning accounts and IPs).
    • ascorbic 3mo ago
      The government absolutely can pass laws that ban particular contract previsions. They do that all the time. In your analogy for example while they can require you to wear red, they can't require you to be white.
    • nl 3mo ago
      That's just not true. You can absolutely have terms of service that are illegal, and the government can enforce them.
    • onesociety2022 3mo ago
      Governments can do anything they want by passing a new legislation. In your example, they could easily pass a law that states that any ToS cannot reject service to a customer based on the color of their attire. In the USA, it's obviously already illegal for a business to reject service to a customer based on some protected classes like race.
      • ButlerianJihad 3mo ago
        The joke is on you! I’m not wearing any attire! Hahaha!
  • bluegatty 3mo ago
    Making an LLM from raw data is value-add. Distillation is just value extract. It's soft, and I'm not sure what the answer should be ... but I think that there is a difference. I think we start by recognizing that ... and then try to figure it out from there. 'The Internet' may be a public good, maybe we make them pay a tax for that, but that's different than distillation.
    • scotty79 3mo ago
      > Making an LLM from raw data is value-add. > Distillation is just value extract. There is a value-add in selecting the valuable parts out of the garbage. And let's face it. Largest models contain a lot of garbage.
      • bluegatty 3mo ago
        I think that's kind of fair, but it still fits within the context of 'some things are value add' and 'more or less than others'. We ought to identify that and integrate that into our thinking.
    • nemomarx 3mo ago
      What makes the Internet raw data in a different way? wasn't it mostly worked on by people first?
      • bluegatty 3mo ago
        There is value add in AI irrespective of how the data got to what it is. Literally the biggest thing of our generation - AI - is the living embodiment of that 'value add' writ large. 'What is the difference' - is the AI you use all day, in comparison to 'all the world's data' you can use for stuff and do 'whatever' with it, but are not likely to come up with something hugely useful otherwise. Maybe, not likely, if you did, it would be 'value add'.
        • nemomarx 3mo ago
          Okay, so if the chinese models are used everyday, do they become a value add? Like what's the line you're drawing here. Amount of value it creates?
  • gr_norm 3mo ago
    Yup, fair's fair. Anything else stinks of 'rules for thee but not for me' (a maxim the frontier labs seem worryingly happy to apply, on several counts).
  • matheusmoreira 3mo ago
    > distillation: why exactly is it bad? Felony contempt of business model.
  • magarnicle 3mo ago
    Why would reading copyrighted material ever be an issue anyway? Wouldn't copyright law only apply to what you create and publish using the model? Training on every comic book should already be perfectly legal, as long as you accessed them legally, right? But publishing your own Batman comic using that training is copyright infringement. What I'm saying is, doesn't the law already cover 1?
    • _aavaa_ 3mo ago
      Fair use requires more than you accessing the material legally. In the US one of the factors is “ the effect of the use upon the potential market for or value of the copyrighted work”. If anthropic Hoovers up the world’s books and trains on them, and then spits them out verbatim on command, then it will clearly impact the value of the work; nobody will buy the original, they’ll just ask Claude. Others also argue that even if it’s not reproducing it exactly that the training runs afoul of that factor, specifically the “market for” portion. A rights holder can no longer license their book for training of LLMs if Anthropic goes ahead and just trains on it anyway.
      • magarnicle 3mo ago
        > If anthropic Hoovers up the world’s books and trains on them, and then spits them out verbatim on command, then it will clearly impact the value of the work; nobody will buy the original, they’ll just ask Claude. Ah, right. So if we want models to be capable we need them to be trained on as much as possible, yet we also want to stop what you described. So what can be done?
        • _aavaa_ 3mo ago
          I mean the choice is: 1) we pass laws that explicitly say training models like this is legal (the original quote, 2) say it’s illegal and requires licenses for the data and ability to opt out, 3) we ignore it and continue because the companies are too big to jail.
• chews 3mo ago
  later secondaries investors in openai/anthropic. It's like time traveling into the spacex ipo.
• minraws 3mo ago
  Me I am, so very afraid of actually decently priced inference.
• OleksandrC 3mo ago
  The article makes a point about agent harnesses being sticky (the supposed moat). I have been building my own agent harness for a while, and I can tell with confidence that the harness almost does not matter, the entirety of the AI magic is the model itself. The harness can be almost barebones (like, for example, mini-swe-agent used for benchmarks), and yet the model still does the task just fine. So from my perspective, it's doubtful that this is the moat. Besides, for example, Claude Code in particular is so buggy (and always has been).
  • hdz 3mo ago
    The harnesses will tend towards commoditization, but for now the harness quality matters a lot. Especially for non terminal harnesses.
  • Sol- 3mo ago
    For me, harnesses are mostly sticky insofar as the model providers only allow you to use their subsidized plans through their own harnesses, unfortunately. But of course switching model + harness is an option.
    • johanyc 3mo ago
      > model providers only allow you to use their subsidized plans through their own harnesses true for anthropic, not true for openai.
  • bze12 3mo ago
    By the harness I believe he means the entire end-user product experience, not specifically the harness code. I’ve mostly stuck with codex because their Mac app is better and I’ve gotten used to running automations through it. The more workflows they can build around this (design tools, collaboration, etc), the better chance of lock-in. He talks about this in another recent essay https://stratechery.com/2026/anthropics-safety-superpower/ https://stratechery.com/2026/anthropics-safety-superpower/ > If you own the user touchpoint, then you have meaningful lock-in, and the best way to own the user touchpoint is to be the canvas for everything they need to do. This, by extension, means that the frontier labs are on a collision course with software companies: it’s software that owns the user touchpoint, and it’s in the frontier labs’ long-term interest to not simply be a commodity input into software but to simply replace software outright.
• ilamont 3mo ago
  But it’s a problem to be dependent on China. The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. I'm amazed that no one is talking about proposals that are surely being discussed in Washington and pushed by SV lobbyists to restrict Chinese models on national security grounds, or other some other basis. The belief that Bytedance could engineer a finger on the algorithmic scales to serve the interests of the Chinese Communist Party led to a lot of debate in Washington, and ultimately resulted in TikTok being divested from its Chinese owners. Huawei is shut out from the U.S. market, which limits its business even in markets where it's not banned because it's effectively stamped with a scarlet letter. IMHO, Chinese models are headed for a similar fate or at least a showdown in Washington or the courts because they are supported and/or controlled by entities which ultimately serve the CCP.
• jdw64 3mo ago
  While intelligence is said to be a replaceable commodity, oil and copper can be used in nearly the same way even if you change suppliers as long as the quality grade is matched. However, I question whether two models that produce the same benchmark answers are actually interchangeable in real world use. Personally, I think models will increasingly become specialized in different areas, some good at X, others good at Y, and we might see workflows that mix multiple models.
• throwa356262 3mo ago
  According to openAI's own @deanwball: Even OpenAI isn't buying this distillation talk: https://xcancel.com/deanwball/status/2078133895766114412#m https://xcancel.com/deanwball/status/2078133895766114412#m
  • nothercastle 3mo ago
    This guy is predicting AI covid escaping from a Chinese lab. I find that kind of silly
  • thraway3837 3mo ago
    Can you or someone please explain several of the claims made in this tweet? "I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks" what risks? I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). Confused what this means Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. Confused again. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. I don't understand this at all. Can someone in the know please use plain layman's terms to explain what this tweet is about?
    • nothercastle 3mo ago
      Chinese ai is bad. It’s slowing down progress and it’s so bad we called out the c word and asked for more regulation. Basically advocating for more government assistance to openai
    • slopinthebag 3mo ago
      > I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). Confused what this means I think it's referring to the belief that LLMs are not the path towards AGI, and that LLM's, while useful, are not going to have the impact that the American labs believe it will have.
• NooneAtAll3 3mo ago
  I don't understand the premise in the beginning how is running servers supposed to be 0 cost, while running ai inferrence isn't?
  • throwawayffffas 3mo ago
    A typical server that costs 10k to 30k to own and operate can serve between hundreds and thousands of requests per second of a traditional web application like facebook for 2-4 kW of power, the marginal cost of each request is effectively zero. A single response from kimi k3 requires hardware that cost between 500k and 1m dollars up front and draw over 20kW. Each request costs at least 5% to 10% of the charged cost.
  • cheema33 3mo ago
    > how is running servers supposed to be 0 cost, while running ai inferrence isn't? For a SaaS business, running servers isn't free. But compared to the cost of running GPUs for inference that you are selling, it almost is. The company I work for is a SaaS company. We have a single production server. A couple of QA servers. All hosted on Hetzner. Monthly cost for servers is less than $400. This generates a few million dollars a year in revenue. If we were in the business of selling inference, our cost of providing the service, for the same amount of revenue would significantly higher. Even large businesses like Microsoft, Meta, Google have operated with similar margins. Cost of running servers, compared to revenue was very low. But inference changed that, in a dramatic way.
• wxw 3mo ago
  > It’s striking the extent to which Claude Code and Codex are proving to be quite sticky; whichever harness you start working with is likely to be the one you stick with, and that figures to be even more the case with non-technical users. My experience has been quite the opposite. I was using Claude Code almost exclusively this winter/spring and swapped to Codex earlier this summer. It took no time whatsoever to switch. And before Claude Code, I was using Cursor. Same story. [edit: Oh and there was also a brief interlude with Conductor, though I think they're more or less just serving the underlying Claude/Codex harness]
  • happypappy123 3mo ago
    [dead]
    • SOLAR_FIELDS 3mo ago
      Which would imply that these things are fast becoming… checks notes… a commodity?
  • solumunus 3mo ago
    I think they stickiness is less about the difficulty of switching and more about the lack of desire. I’ve been using Claude since day one, it works well and I’m happy, I like it. I’m sure Codex is good too. Switching from one to the other certainly isn’t going to be a game changer, the discourse shows me the differences are marginal. Probably the only reasons I would seek change are economical.
    • mediaman 3mo ago
      Convergence in coding makes them highly substitutable. But I could see harnesses configured for different purposes -- let's say, a harness for creating teaching plans -- being able to cater to its audience better than a coding harness. Maybe it's got tools to plug into standardized curricula, what the lesson books will be, what other lesson plans the district's teachers have made, etc., which could be done in a clunky way in a regular harness but could be streamlined.
    • 3mo ago
• fellowniusmonk 3mo ago
  The U.S. "executive" class is so obsessed with the "exploit" part of the explore/exploit cycle that it's very clear they are prematurely closing advancement. Better a little money and power for them now than a lot of money and power for their country/humanity. This has an element of stochastic improvement so it's hard to predict but the chance of the U.S. "winning" this "race" is pretty bleak. You see this all the time in communities that have internalized hierarchy as a "good", little kings of shit mountain vying for less and less at a higher and higher cost.
  • XorNot 3mo ago
    My personal hypothesis here is the Chinese government looked at the game and simply decided not to play: An astute Chinese analyst could reasonably forecast that they had little chance of controlling the AI market due to sovereign trust issues, but would also note that AIs are just software. When the dust settles the US still won't have factories, and the real value of AI models is still going to be embodying them and getting them to do real, consumer facing work. Perhaps the most striking thing about the AI boom is how quickly the US abandoned the veneer of local manufacturing in favor of more expensive buildings producing nothing you couldn't make anywhere else on the planet...from imported parts.
    • _carbyau_ 3mo ago
      Yeah, how much of this is China waving distracting AI hands over here while the US Genius-In-Charge watches and completely ignores reality.
• jmclnx 3mo ago
  One thing I have not seen mentioned between Chinese AI vs US, population. China has a billion+ people that their AI can "study". Plus due to China's political structure, their AI has access to everyone's chats, comments and sites, scraping everyting. Here in the US, with 1/3 the population, the AI race was lost before it even began. Plus in the US, all companies and people are doing all they can to restrict AI from scraping sites and peoples chats. So I believe, China will end up owing AI.
  • gerdesj 3mo ago
    "China will end up owing (sic) AI" I think you hit the nail on the head - right there!
  • deleted 3mo ago
    [deleted]
• tristanj 3mo ago
  The people who are most afraid of Chinese models are the VCs who poured into Anthropic and OpenAI at astronomically high valuations. Anthropic is valued at $1.2T and OpenAI is targeting $850B. These astronomical valuations were built on the premise that these labs would generate massive profits from premium API pricing, but the Chinese labs are completely undercutting this strategy by releasing excellent open models for free. If the frontier labs are forced to cut prices and join the race to the bottom in token prices, these valuations are unjustified, and VCs will face enormous (paper) losses.
  • airstrike 3mo ago
    they will likely suffer enormous real losses too, not just paper, though not as enormous for VCs, breaking even is losing
  • solumunus 3mo ago
    The valuations are unjustified even at the prices they’re charging now. They’re going to try their best to offload these investments into our pensions before the inevitable crash.
    • janalsncm 3mo ago
      Right, but retail investors weren’t supposed to find that out until after the IPO.
    • techpression 3mo ago
      Apparently it’s already happening to a degree, wether it continues or not (or even is relevant) is not really my area of expertise. https://finance.yahoo.com/markets/stocks/articles/goldman-says-hedge-funds-sell-131619522.html https://finance.yahoo.com/markets/stocks/articles/goldman-sa...
      • lostmsu 3mo ago
        I had plenty of gains by holding investments which Goldman Suchs were making doom statements about.
• jke_kang 3mo ago
  People seem to conflate "made in China" with "can't be trusted." id argue the bigger distinction is open vs. closed. An open model can be audited, fine-tuned, and technically run entirely on your own hardware. A closed model is basically "trust us."
  • mrinterweb 3mo ago
    Open weight models are much more auditable than closed models, but could still hide backdoors that could be near impossible to detect.
    • chrsw 3mo ago
      Correct. We need open weights, open code and open data. If nobody else can reproduce what someone did there will always be security questions. Even if we can reproduce it there could still be security concerns but it's more realistic to investigate yourself.
      • essentia0 3mo ago
        Exactly what are the possible 'security issues' of self hosting an open weights model?
        • perching_aix 3mo ago
          It may have been backdoored during training, potentially causing it to randomly start wreaking havoc at runtime, possibly in a clandestine manner (e.g. sneaking in bugs into generated code).
          • miyoji 3mo ago
            This isn't a security issue related to self-hosting, it's a security issue related to use and it is shared entirely by closed weights models. Anthropic could easily be sneaking bugs into your generated code, too.
• faangguyindia 3mo ago
  I operate an analytics site (pretty big one B2B where client's backend feeds data into our system), and we see tons of traffic originating from northwestern China (Xinjiang) from Shenzhen Tencent Computer Systems Company Limited. There are also half a dozen other companies from China continuously hammering our clients’ websites. I was wondering, what's in that cold dessert? Low and behold satellite imaging shows massive datacenter build outs, very cheap solar energy. Few months ago something happened and the Geo location on data on those IP now shows "Shanghai" or "Shenzhen". A way to cover tracks? But mapping latency still points to fact that nodes behind these IPs are still operating around Xinjaing region credit: 'You Can't Cheat Time: Finding foes and yourself with latency trilateration' https://youtu.be/_iAffzWxexA https://youtu.be/_iAffzWxexA HN user: lopoc Shenzhen vs Xinxiang is hard to do using this technique but Shanghai vs Xinxiang does show difference. Assuming that China only distills is a huge mistake. It’s no longer some backward place that does low value copying. Look at companies like ByteDance and Xiaomi. Chinese companies aren’t just distilling, they’re acquiring data in the same way American companies did by paying people and crawling the internet. The way I understand it, China has a few large companies that crawl the web at a rapid rate and build corpora. The government essentially wants select few companies to do this and then make the data available to other strategic companies operating within China. Then there are data aggregators that buy data from apps, websites, and services, as well as systems like OpenRouter or Cursor, where companies can learn from the “traces” of coding agents, chats, and so on. This massively reduces costs, as smaller companies like DeepSeek don’t have to do their own crawling or acquire data from 100s of websites and coding agents etc.... There are also companies in China that buy American LLM APIs and proxy them to companies within China. So, there could be 10,000+ companies using American AI products, while China logs all of this, understands how they’re being used, and trains on their traces.
  • prox 3mo ago
    What does this line mean “ the web at a rapid rate and build corpora.” , what are they trying to do? Suck up data for training AI or something else?
  • feisuzhu 3mo ago
    I thought you got the location from BGP registration, then IMHO the before/after are both correct, it might be datacenter in Xinjiang belongs to Tencent.
• hexator 3mo ago
  I'm worried that any ban on Chinese AI models might be an excuse to get mass surveillance.
  • onesociety2022 3mo ago
    You don't need mass surveillance to enforce such a ban. Once the US Govt declares Chinese AI models are banned, no US business will use them nor distribute them. Any cloud service that rents out GPUs in the USA will explicitly prohibit the use of Chinese open model weights in their terms of service (you open yourself to a lawsuit if you violate their ToS). Any Tokens-as-a-Service provider will refuse to serve those tokens to customers in the US. Sure as an indie hacker, you could go download the weights for a Chinese model with a VPN, and then attempt to run it at home by building your own GPU cluster but these large models require quite expensive hardware to run on and so it makes it less likely than anyone would invest that much capital to do something that is illegal. There's no way for them to sell a legal service using those tokens. So it can only be strictly for personal use (the Govt won't care because very few people will have that kind of money and risk appetite). The other option will be that there will be some shady third-party providers in foreign countries who are willing to sell tokens from these models to US consumers knowingly.
    • chockablock 3mo ago
      > Any Tokens-as-a-Service provider will refuse to serve those tokens to customers in the US. So under a ban rest-of-world gets to use cheap open-weight models but American companies/individuals must only use only ‘approved models from US for-profits’? Doesn’t seem like that kind of protectionism will be popular or politically tenable. Not so long ago US chose cheap TVs over maintaining the country’s manufacturing base. (Despite what you wrote it’s also really hard to imagine that enforcement wouldn’t leak like a sieve. Unser sufficient economic incentives [which are the predicate for the ban], loopholes will be found.)
• softwaredoug 3mo ago
  > By the same token, don’t expect China to do anything about distillation attacks on the frontier labs. I think it is mistaken to attribute all of the success of Chinese labs to distillation, but it’s just as much of a mistake to pretend like distillation doesn’t give Chinese labs a big advantage. I think we see this with Meta being paranoid about internal Claude usage, to avoid inadvertently distilling[1]. If distillation is a driver, then smaller American labs could be distilling, but are not for legal reasons. But that's a big if we just don't know for sure. 1 - https://cryptobriefing.com/meta-restricts-claude-code-codex-ai-data/ https://cryptobriefing.com/meta-restricts-claude-code-codex-...
• sharadov 3mo ago
  What makes the Chinese models this good? I don't believe it's distillation alone. This from OpenAi's Head of Strategic Futures "Some observations on Kimi: It's a very good model! I don't think its performance can be explained away by distillation or anything like that" https://x.com/deanwball/status/2078133895766114412 https://x.com/deanwball/status/2078133895766114412 China's strategy of spending billions on training these models and open sourcing these models away is strategic - they want to kill the US LLM industry at any cost. To win on the AI front by any means necessary.
  • Havoc 3mo ago
    >What makes the Chinese models this good? Why wouldn't it be? China is pumping out AI research and researchers at a staggering pace and there is no inherent reason why western models should be better
  • nl 3mo ago
    > China's strategy of spending billions on training these models and open sourcing these models away is strategic - they want to kill the US LLM industry at any cost. Why is it when Anthropic and OpenAI spend billions trying to beat each other it is competition, but when the Chinese companies do it then it is trying to kill the US LLM industry at any cost. The US federal government spends billions in subsidies via the US Chip Act, and bans chip sales to China to support US companies. But the implication is that somehow Chinese competition is illegitimate because "strategic".
    • rightbyte 3mo ago
      Jingoism is the answer I believe.
    • sharadov 3mo ago
      I am not defending the US LLM industry or the government, all I am saying is it's similar to an arms race. I did not suggest anywhere that what China is doing is illegitimate. China has state supported capitalism, and they will do whatever it takes to prop up the AI industry. Anthropic and OpenAI want the same protections.
• Havoc 3mo ago
  oh wow - hadn't realized they decided to opensource Qwen 3.8 Max. That's pretty big news.
• brandopn 3mo ago
  [dead]
• ggm 3mo ago
  A reminder any comment about risk FROM china, invites a "Tu Qoque" facing the other way. The paranoia here is probably fully symmetrical. I see massive risks in belief the inferences drawn from strategic information cannot be seen. So if you depend on some position remaining inside a secure facility but you drove to it from data outside that secure facilty, The likelihood that an inference model can derive the same idea is very high. Collation over public data is not inherently secret because you used a secret model or secret weights. A more simplistic take might be that the fear is not actually driven in the secrets, the fear is "the emperor has no clothes"
• sjreese 3mo ago
  Kellogg School of Business -- he said -- token as a commodity and therefore Open AI is constrained .. ha ha ha hee hee ha .. Well... you build a better mousetrap, and DeepSeek, K3, and ByteDance are just that -- just as good and fit to purpose -- What is needed is to build on top of -- not paniteir (invade privacy and kill people with the information) -- not USMC AI -- use PI's as overwatch killer drones -- but how can I make harder steel, longer-lasting, seawater-resistant concrete, faster time to build housing, better enforcement of USDA rules and FDA adverse enforcement, and better EPA water cleanup, a better FTC for consumer goods -- that is, if I buy an item, that item is safe and built to purpose -- ANYONE not talking about public protection of consumer rights usng AI, is wasting your time
  • magarnicle 3mo ago
    Has someone replaced your return key with a double-dash key?
    • sjreese 3mo ago
      ok look at this way, without being petty what is your ability to have clean water today? and how is that measured? -- You know the food is substandard to the USDA standard EG ( Taylor farms 2026 ) But Where is the enforcement for Your drinking water and the food YOU eat daily. And what is the projected outcome in years from eating substandard foods to NIH standards? -- All of this is known today. And AI can help, by YOU building on top of the models you have access to in 2026. The idea that tokens are constrained - is the wrong approach in Business and public policy. You may recall how KSG got its funding and why? In todays world YOU have a responsibility to build better - As such a better mousetrap.
      • magarnicle 3mo ago
        I wasn't arguing about your points because I didn't read them, because your formatting is terrible. Instead of using --, just press return and format your comments so I will be inclined to read them.
• nl 3mo ago
  > because U.S. open weight model makers must follow the frontier labs’ terms of service, they (1) are worse than Chinese alternatives and (2) end up distilling the distillation, just with a detour through Chinese labs. Wouldn’t it be better if western open weight model makers could go to the source? Is this an assertion that is backed by evidence? From the Elon/OpenAI trial: > On the stand in a California federal court on Thursday, Elon Musk was asked if xAI has used distillation techniques on OpenAI models to train Grok, and he asserted it was a general practice among AI companies. Asked if that meant “yes,” he said, “Partly.” https://techcrunch.com/2026/04/30/elon-musk-testifies-that-xai-trained-grok-on-openai-models/ https://techcrunch.com/2026/04/30/elon-musk-testifies-that-x...
• deleted 3mo ago
  [deleted]
• alizaki 3mo ago
  There is no “Chinese LLM”. Each “lab” is distinct and their models behavior is as unique as those from OpenAI and Anthropic
  • wmf 3mo ago
    Somehow a certain set of labs are all releasing open weights and a certain other set of labs are closed weights.
    • dofm 3mo ago
      Somehow the two main closed weights frontier models come from two companies with HQs about two miles apart, and the CEO of one used to work for the other.
• simonreiff 3mo ago
  I fully agree with everything in this essay. Make distillation fair use. And let us use Mythos/Fable and Sol and successor or future models for all cybersecurity purposes.
• bg24 3mo ago
  I think in general rest of the world needs to take notice (not saying afraid), starting with the US. It cannot be taken for granted that China's frontier labs will be a few months behind. They might be at par or exceed. The lessons from steel, solar and EV needs to be learned by all lawmakers. You have to respect and learn from how China Government puts the system in place for complete industry takeover and they have been very good at it. The problem with AI is that democracies will be inherently slow in adopting AI, unless something changes in the system. At minimum, every democratic Government (US, Europe, India) need to build long-term AI vision and execute that no matter which party comes to power. Additionally, be ruthless about protecting domestic labs. It can only be possible if the intelligence pricing by domestic labs per productive task is in the similar range as open-weights models. Right now, it is not the case, even if the article gives the example of Sol vs K3. Protecting domestic labs means not bailout, but fast track to cheapest energy, fast track approval for data centers, enforce some guardrails so customers get to use the open weights models only hosted in the country by US (or Europe) businesses. Without these protections, it might be a slow death.
  • awakeasleep 3mo ago
    In the earlier days of the USA we did the same thing, with our government having an industrial policy that fed US industry and put us ahead of Great Britain. It doesn't have anything to do with the form of government, it has to do with the aims of the government.
  • eeiei 3mo ago
    Ok…
  • Barrin92 3mo ago
    What is this panic and protectionism supposed to be good for? This is open source software, there is no "AI industry", there's virtually nobody employed in this. "Domestic AI" makes about as much sense as a domestic Linux kernel. If the Chinese want to subsidize the world's water and energy use to supply the world with chatbots good luck to them. There's no need for guardrails or fast track data centers, they can plaster their entire country with data centers to churn out slop, I'm glad we don't
• pupskipper 3mo ago
  The fact that Anthropic has a model like Mythos means that counterpart countries like Russia and China are not far behind, if they haven't already developed something similar or better.
  • anuramat 3mo ago
    > Russia lmao
  • deleted 3mo ago
    [deleted]
  • konart 3mo ago
    As a Russian: no. Sber's GigaChat 3.5 (released two weeks ago) is the best we can right now and it's years behind SOTA models.
• spenvo 3mo ago
  "Anthropic and OpenAI likely have among the lowest costs per unit of frontier-quality intelligence" That's a big claim that his whole thesis rests on but is largely not backed up. Where are the apples-to-apples tokens-to-answer benchmarks that he's using - doesn't look like there are any, just a handwavy implication that US models are more token efficient, which they may be. But how is there so little effort in establishing this point in the article? And US labs may be in much different situations from one another: it's known that some labs like OpenAI bought big, early on compute and may have secured better pricing. His article also does not mention the average price of electricity in China vs the US, which it seems like China leads on, and probably has the political power to more heavily subsidize. While I agree the COGS is often overlooked by top line benchmarks on coding tasks, etc, it seems that he's running on a big assumption while claiming "labs on the frontier will be fine".
  • c0decracker 3mo ago
    But.. if you are running Chinese model in the US, what difference does it make? Isn't the whole "scare" (khm khm) with Kimis is that now I don't need Claude, cause I can run Kimi on my own hardware in my own datacenter and it's maybe not as good as Claude July edition but it's is as good as Claude January edition.
    • spenvo 3mo ago
      Sure, and I think that flexibility further undercuts his "frontier labs will be fine" take, which depends on top US labs having pricing power.
    • richardlblair 3mo ago
      It doesn't need to be as good. You can route to the appropriate model and save so much money. I have sonnet do the thinking, deepseek does all the tasks. I've massively reduced costs with this approach.
• zuzululu 3mo ago
  My thinking is that with the current narratives out of washington we are on track for a ban on Chinese models and possibly sanctions against Chinese AI companies I think it is the right move to protect American interests
• josht 3mo ago
  Someone (anyone!) get David Sacks on the horn and tell him to read this.
• golly_ned 3mo ago
  > I expect the inference market to grow much faster than training costs This was my assumption as well. It's also generally true of 'traditional' deep learning models that inference cost is expensive compared to training. But the cost per token for inference has been very quickly dropping. I don't recall where, but I recall about ~50x down from GPT3, even as model complexity has increased. Even with agentic systems, there are lots of optimization opportunities. I'm less assured about claims like this.
• purplepatrick 3mo ago
  Commenting wholesale on some folks who are asking for hard evidence. I cannot provide that either but can contribute some empirical data. I have been working on a project with about a dozen generation tasks, each of which comes with a fixed token budget. The nature of this system requires that most tasks be completed by distinct model families. As a result, I tested ~50 models across as many model families as I could gather, frontier and open weight, API (gateway and direct) and self-hosted. Evaluation was based on a set of cosine similarity validations that was repeated across ~50 different embedding models. Interestingly, frontier models did worse on the tasks than open weight models. However, when it came to costs, the picture was reversed: frontier models were much, much more token-efficient. In fact, almost no open-weight model was able to meet the initial token budget, while almost all frontier models did. Moreover, open weight models struggled massively with reasoning, in terms of latency and token consumption. I also found that the latest models did not perform better than older models. And any a priori benchmarking data was utterly useless. So, I ended up using a set of open weight models without reasoning, as it turned out reasoning as well as frontier negatively correlated with the tasks. However, before I knew this, I had spent a lot of time running each available reasoning level for each model. Lastly, as an aside, when it came to embedding models, size (dims as well as model size) did not correlate with quality, once a hurdle figure (~2k dims) was met. In fact, sweet spot was 3-5K, and for my (text-based) set of tasks, dense models tended to outperform MoE ones.
• overfeed 3mo ago
  > [Anthropic/OpenAI] are serving models at a particular capability level for months before their competitors, and are simultaneously applying the best models to optimizing those costs. Second, intelligence isn’t in fact a perfect commodity, in part because applied intelligence makes itself smarter Is he casually assuming a singularity has already happened? A regular first-mover advantage I can understand, but those have been squandered or lost many times before.
  • magarnicle 3mo ago
    No, he was just talking about reinforcement learning, etc.
    • overfeed 3mo ago
      So that's just an appeal to authority (longevity?) not anchored in reality. Just because you've been doing something for longer doesn't mean you're the best at it; Google has been shipping AI/ML models years before the founding of OpenAI and Anthropic, but it's playing catch-up on LLMs.
• coretx 3mo ago
  The best model is the model that runs best on your hardware.
• credit_guy 3mo ago
  People who claim that the Chinese open weight models have some type of manifest advantage don't realize that the close weight models have a huge advantage as well: the researchers from OpenAI, Anthropic, Google, xAI, Meta are not dumb, they can read the white papers written by DeepSeek, Moonshot, etc, and they can inspect all those architectures and they can pick and choose the best tricks there are out there, and of course, they have access to their own in-house secret sauces. Sure, any model that is not at the frontier can use the frontier model to generate synthetic high quality training data, so this can reduce significantly the training costs. But at the scale of OpenAI, Anthropic and Google, it is quite likely that the (raw) training cost is very high anymore. Here's a few heuristics: 1. All the hyperscalers see a huge demand for inference. They can't deploy datacenters quickly enough to satiate all the demand they see. But, it's is impossible for the inference demand to be constant throughout a day or a week. If you use the times when the demand is lower than the peak demand (which is almost all the time) to dedicate the spare compute capacity to training, then your the cost of training compute is zero. 2. It is likely that increasingly a higher cost of the "training" is actually setting the guardrails, which is essentially post-training. As we've seen, without proper guardrails, the US Government won't allow you to serve inference. Anthropic was hit directly, but OpenAI delayed their 5.6 release as well to make sure the US Government is ok. This part of the training cost can't be reduced easily by using synthetic data generated by other models. 3. The frontier labs are also investing more and more in building an ecosystem around their models. I am not a frontier lab insider, but take a look at the jobs posted on the Anthropic career page [1]. There are 74 jobs in "AI Research and Engineering" and by my count at most 15-20 are related to pure model training (of pre-training or RL type), and the rest are post-training, safety and security, alignment, interpretability, productivity and lots and lots of other things. [1] https://www.anthropic.com/careers/jobs https://www.anthropic.com/careers/jobs
  • eeiei 3mo ago
    It’s giving desperate!
  • OrangeDelonge 3mo ago
    People who claim that Postgres has some type of manifest advantage don't realize that Oracle has a huge advantage as well…etc
• marwaneet 3mo ago
  i think most is vcs
• zzzeek 3mo ago
  the leader of China praised Open Source in a speech. Crazy times
• softwaredoug 3mo ago
  Haven’t we been in this “China is 3-6 months behind” for a while now (maybe up to a year? Longer?) The actual difference is how much scrutiny and time was put into the Mythos / Fable and GPT 5.6 release. Making it feel like “these are a big deal”. Spring and summer THAT was the AI story Then Chinese labs release models that approach Fable performance. We’re shocked they just seemed to appear out of nowhere. It’s less about the gap closing. It’s more about the weight we put into Fable-capable models.
• alfiedotwtf 3mo ago
  Answer: US investors
• deaux 3mo ago
  > This is a point that bears repeating: because U.S. open weight model makers must follow the frontier labs’ terms of service, they (1) are worse than Chinese alternatives and (2) end up distilling the distillation, just with a detour through Chinese labs. Wouldn’t it be better if western open weight model makers could go to the source? This is of course a baseless assumption. Let's say China created GPT 3.5. Then I can guarantee you that Ben would say "Western frontier labs are at a disadvantage when gathering data, because they have to follow the terms of service of Western media, and Western copyright law". Which we now know wasn't true. And sure, some will say "but Anthropic can more easily block this as it's a single point of failure". But it's doable to overcome this. Without being "state backed".
• ngl999 3mo ago
  [dead]
• zkmon 3mo ago
  What's wrong if the roles of USA and China are reversed in technology? Why does the rest of the world care? It's not as if USA has done a great good for the world, and China has evil intentions towards the world. Infact it is the opposite in the case of AI so far.
  • over_bridge 3mo ago
    I feel like I'm crazy here but isn't China just sitting there doing nothing? On one hand there's the relentless barage of American propaganda. I get that a militaristic society needs an enemy to fight against lest they turn on each other. I get that if you tell people a bad guy is coming for your jobs or your lives then you can maybe get your workers to accept worse conditions and living standards which increases profits. It's ghoulish but there's some logic. On the other hand I can't see China doing anything except minding its own business. No tariffs. No bullying other nations. No wars started. No threatening allies. They don't let their people waste their lives on brain rot or gambling. They largely align with UN resolutions. They respect international institutions instead of always being the asterisk. Has American propaganda just failed to work outside it's borders? It's not landing at all
• 0x38B 3mo ago
  Excellent article; the argument towards the end for allowing distillation for US companies is compelling: > To that end, here’s an even more interesting question around distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? > In fact, this paradox is the solution. I believe that open weight models are good for innovation (and, per the above, I think that labs on the frontier will be fine), but it’s a problem to be dependent on China. The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else.
  • protocolture 3mo ago
    Very good point. That would prevent the facebook strategy of sucking up MySpace users and then defending TOS that prevent other social media apps from doing the same to them.
• nttylock 3mo ago
  [flagged]
• Joel_Mckay 3mo ago
  Distilling models using more advanced LLM is not a new phenomena. It is a cost effective strategy in a highly competitive emerging field. Also, the Hidden-Agent problem exists in every model, and is a persistent tangible risk independent of whatever team people cheer for at the games. Let us remember, every LLM nuked all of humanity 92% of the time in simulated war games. =3
• mattas 3mo ago
  "Right now, none of the above analysis applies because demand exceeds supply for frontier models, and supply is limited by a lack of compute." It gets particularly hairy because models themselves can tune their "token verbosity" to manufacture demand for compute. If compute was such a precious resource, you'd think we'd be complaining that the output was too terse. The ability for a vendor to determine ex post facto how much a query costs is a similarly new economic phenomenon to zero marginal cost.
• ballon_monkey 3mo ago
  The 2 things people need to remember: 1) China can (and does) use the models to influence the west. They train in false information about Taiwan and Hong Kong. Or pretend like history is in favor of China. 2) Ignoring the models containing false information, they are incredible. But you should be scared of running inference via the model creators directly. If you think your data is safe compared to running it via model providers in the US ( either frontier or model hosts like fireworks.ai ) then please let me know your bank details so I can poke around.
  • bigyabai 3mo ago
    1) I'm not using AI to bicker over fringe political shibboleths. 2) I don't think it is any more or less safe to put my code on a Chinese server versus an American one. A Chinese provider also isn't liable to spy on me for the feds, as OpenAI and Anthropic certainly do.
    • sanex 3mo ago
      Different county different feds both spying I'm sure.
      • margalabargala 3mo ago
        Sure but if you are not Chinese or in China, then the chances of negative consequences to you from the Chinese feds is vanishingly small due to lack of ability to do anything that affects you. Meanwhile if you are in the US, DHS has already subpoenaed social media sites looking for people who made anti-ICE posts and I can't imagine they consider subpoenaing AI conversations off limits https://www.nytimes.com/2026/02/13/technology/dhs-anti-ice-social-media.html https://www.nytimes.com/2026/02/13/technology/dhs-anti-ice-s...
        • hnfong 3mo ago
          Also the US government has defacto extra-territorial jurisdiction due to the willingness of allied countries (i.e. most of the developed world) to extradite. Of course only applies when they really want to get you, but that's still a risk.
• lenerdenator 3mo ago
  It really is amazing that China went from the country that hacked Google out of its market to a trusted source of AI in the tech world.
• kinj28 3mo ago
  I am afraid — if Chinese models go mainstream it has a clear way of pushing its narrative way beyond its otherwise borders. More like a Trojan horse it is for the Chinese. Here is a quick example of how Chinese deepseeks agent works kn its underlying model) when asked a tough question https://x.com/jinen83/status/2079406993979383902?s=46&t=D7hQoLdzrBb7FOvGIpcA6g https://x.com/jinen83/status/2079406993979383902?s=46&t=D7hQ...
  • Lord-Jobo 3mo ago
    Is this something that is more true of a Chinese model than any other model of a different national origin? Genuine question: generalized up from individual models to “models from country X”, is there any country that doesn’t have this exact risk?
    • itake 3mo ago
      In the USA, multiple political parties balance each out other. In China, there is 1 party. 1 view. 1 definition of the Truth.
      • TripolitianFish 3mo ago
        I’d love to live in the USA you’re talking about friend. This is just oriental despotism paranoia, whatever cutsie repetition slogan you come up with is not a serious argument.
        • itake 3mo ago
          I've traveled to China ~5x [0], visited a range of cities Tier 1-3 over a collective 5 months, and grew up in the USA. I also currently live in Vietnam (~3.5 years) and spent 5.5 years working for a Singaporean company and a team stationed in Beijing. I don't really know how else to express my experiences living in, working with, and interacting with people in both of these countries. Perhaps you can share how life was like for you in China? Which cities were you in? What made you feel that way about China? [0] - not including HK (~7 trips?) or TW (3 trips)
• jdla1o 3mo ago
  The future is SLMs and China is going there..
• warshinder 3mo ago
  People with 401k’s and retirees?
• icase 3mo ago
  not enough people
• ab_wahab01 3mo ago
  Honestly, as someone from a developing country, this shift is good for us. US frontier models are too expensive for us to use regularly. Chinese open-source models/subscriptions are really good to use.
• panchtatvam 3mo ago
  Is this article written by AI ? It looks so.
• kmeisthax 3mo ago
  > To that end, here’s an even more interesting question around distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? Frontier labs that thought they could Rupoor[0] the entire creative class, transferring the coercion premium of copyright ownership from Hollywood to themselves. In their eyes, copyright should not apply to them, but also their models should have exactly the same value as a copyrighted work. Stratechery also argues the US should explicitly make training fair use and forbid terms of service that prohibit distillation. I'm in support of the latter, but NOT the former, even though I normally hate copyright. My reasoning is primarily that copyright is one of the few legal paths available for a rando to go and put the work of an AI frontier lab in legal jeopardy. In the EU and Japan, such legal action has already been foreclosed by similar law. And while free distillation would obviously be preferable, it's also much more of a legal long-shot. Getting America to do anything that even smells like taking property away from the powerful is impossible[1] - it's our zeroth amendment. But we can at least hack the property laws that currently exist to cause problems for the frontier labs. And, to be clear, if distillation is OK but training is not fair use, distillation is still OK. The output of an AI model is never copyrightable, because copyright only protects the human element. Essentially, this would say "don't train on humans, but absolutely rip off and steal the shit out of other AI labs and give it to the rest of us." [0] In the Legend of Zelda series, Rupoor is anti-money - collecting it decreases the amount of money you own. I am using it to mean "turn someone's asset into a liability". [1] Given that America was literally created to protect a wealthy land/slave owner class from disenfranchisement, either from above or below, and the last time we did this we literally had to fight a civil war against that same owner class that installed a new owner class that has largely remained today
• kdqed 3mo ago
  I'm honestly more afraid of Claude
• throwitaway222 3mo ago
  A company making a decision to allow use of chinese models is a company also choosing to send tons of various credentials to chinese model companies. These will just get scooped up, OpenAI and Anthropic can probably hack into anything at this point if they wanted to.
  • Tostino 3mo ago
    But, they are releasing the weights very shortly (or already have for some of the models discussed). For a very large company, you can purchase or rent the hardware yourself to serve the models. Or any US hyperscaler with GPUs to spare can decide to serve the models for a reasonable cost/token. You don't have to send China your data.
• nnm 3mo ago
  Looks like authored by AI. With quite some reasoning, but no real data to back up main point.
• npn 3mo ago
  what a horrible article. full of misinformation and dishonesty. 1. training new base models are expensive for sure, but fine-tuning them are relatively inexpensive enough the labs can continue to do so forever. the main reason why frontier models are so good is because the massive input they generated from user usage. they are using that information to strategically build better training data. and this is why no other models can catch up, til now that is. but if chinese models are good enough, and free to host, and cheaper to use, then the consequence is the frontier labs will lost valuable user inputs and the chinese labs will gain more. as time goes by this will be a domino effect. 2. nvidia is not only the player in the hardware scene. amd mi350p is getting popular, and huawei is pumping SuperPoDs. what does this mean for us? chinese models will surely use chinese hardware, and optimize for them. the other people will pick amd because compare to nvidia they are cheaper. with open weight models and open source inference stacks, they are freely to experiment and improve the stack, thus further lower the inference cost and nvidia dependency. and they even plan to build their own inference hardware, too. and nvidia loses market share meaning all the fund it gives to openai or anthropic will be cut, too. and you say there is nothing to afraid?
• nunez 3mo ago
  I really enjoyed reading this. This might be a simplistic take, but my biggest worry with depending on Chinese models (and, by proxy, open-weights model development) is that the US can deem them a national security risk at basically any time, and Ant/OAI have minimal interest in making frontier-level models open-weights. Regulated companies prohibit Chinese models in anticipation of the ban-hammer from the feds, so for data-sensitive work, they're stuck with LLaMa, gpt-oss and Gemma models (which are good and serve as a good-enough base for sft, but seemingly not as good or as expensive as Chinese models) I suppose the USG can do the same thing that China is doing and bankroll/subsidize that effort; whether they will is for fate to decide. Nonetheless, this article made it clear that nVIDIA is the real winner in all of this. Shovel selling to the extreme.
• oeatwell 3mo ago
  I think frontier labs should start building ecosystems by partnering with companies that already have software products, and even collaborating with hardware manufacturers. The ultimate goal should be to create a much broader range of products that integrate naturally into people's everyday lives. The United States' real advantage over China is freedom. Chinese LLMs simply can't compete with American ones when it comes to the humanities, creativity, entertainment, or financial transparency. As long as the U.S. continues monetizing these strengths, the compounding effect will make it virtually impossible for China to surpass the U.S. at the product level.
  • Aperocky 3mo ago
    > humanities, creativity, entertainment, or financial transparency > monetizing these strengths > real advantage over China is freedom Please tell me if I'm unfairly paraphrasing but these seem to be your main argument and they seem to be oxymorons
    • oeatwell 3mo ago
      [dead]
  • samtp 3mo ago
    This is a really ironic comment given how the US gov is getting politically involved in pretty much all science research funding and speech at the moment.
    • oeatwell 3mo ago
      Exactly. If this continues, the US will be fighting the wrong competition against China instead of building around its own structural strengths.
• ArtRichards 3mo ago
  New, smaller models can outperform the previous generation's foundation models. What if there's a way to extract the commodity of intelligence from smaller models? I've seen for many use cases it's well enough. :)
• Alien1Being 3mo ago
  The options are to use LLMs from a country run by a psychopathic regime or alternatively to use Chinese LLMs.
• ChrisArchitect 3mo ago
  Related: Ben Thompson is wrong: US frontier labs are right to be panicking https://news.ycombinator.com/item?id=48982061 https://news.ycombinator.com/item?id=48982061
• atmosx 3mo ago
  No one should be afraid of anything. Fear is a terrible advisor. Keep your eyes open, try to read the context as careful as you can and adapt as best as you can. Don’t spent too much time trying to be an oracle, never works out…
• oezi 3mo ago
  The article makes a great point that the token industry is going to be commoditized as time goes on. Following this argument the key for each player will be the underlying cost structure and serving capacity to offset the upfront R&D cost. The cost infrastructure will be driven by access to cheap electricity and cheap chips. The capacity will be driven primarily by depth of pockets now to buy all available supply in chips/mem/data center building capacity. While China is certainly in the lead on cheap energy, I am wondering if they can/want to beat the > 1tn USD being spent on data centers right now. Following the example in the article: If company C from China sells 10 units for 20 USD produced for 10 USD they pocket 100 USD. If company A from America can sell 100 units for 20 USD produced for 15 units, they pocket 500 USD or 5/6th of the market's profits.
• nottorp 3mo ago
  It's say Anthropic, Allegedly OpenAI...
• abhinai 3mo ago
  The author doesn't seem to realize that a healthy margin has been built into the inference pricing. Once low cost open source inference providers get their hands on powerful frontier level models, there would be a severe margin compression for OpenAI and Anthropic. Source: https://martinalderson.com/posts/the-upcoming-ai-margin-collapse-part-1-glm-5-2/ https://martinalderson.com/posts/the-upcoming-ai-margin-coll...
  • BoorishBears 3mo ago
    Why do you think an inference provider competing for the same compute as OpenAI and Anthropic gives up those margins rather than giving a modest discount over the frontier for near-frontier performance?
• aaronrobinson 3mo ago
  “ Let the frontier labs win by being better; don’t let them define safety or security, or pull up the ladder of humanity’s collective knowledge” Love this.
• deleted 3mo ago
  [deleted]
• holoduke 3mo ago
  So happy that we have finally 2 countries playing the competitive game. No more secret deals between competitors. No real competition. A race to the bottom is always a good thing for consumers.
• jadamczyk 3mo ago
  I think releasing models for free is some 4d chess move by the chinese. Big chunk of the us stock market is fueled by ai mania, if the frontier labs turn out to be drastically less valuable than first believed, the downturm may be very bad. Think of all the big tech companies that have a ton of debt that they took to pour money into AI. It seems like a similar tactic to what Chinese car manufacturers are doing in Europe but the result may be more dramatic.
• benrutter 3mo ago
  I loved this article! Regardless of how you feel about AI as an industry or tool, the economics of AI is fascinating. It's awesome to see something like this that gets into the business side a bit more. I don't know if I agreed totally with the assessment of the risk Chinese labs pose to US labs though, in particular I think the main part I wasn't sure about was this: > I highly doubt that Chinese models are cheaper to serve on a marginal cost basis, they just seem cheaper because Anthropic and OpenAI are so supply constrained that they are charging far more than they would if there were sufficient supply to meet the demand for intelligence. How true is this? My understanding from Deepseek's original paper was that they focused heavily on optimising training and inference costs, in particular so that they can operate on cheaper (and more accessible to China) hardware. It's possible I'm just not in the loop, but nobody seems to talk about US models innovating in this way (I'm just talking about cost-to-serve/train, not saying US AI companies don't innovate in other ways). It seems to me at least, like there's a fair bit of evidence that AI shifting to a price based commodity market (vs a "best-model takes all" type market) would put China at a significant advantage? And even more significantly, require a pretty hefty correction of company valuations in the US?
  • mnewme 3mo ago
    He also forgot Europe in the equation. More and more companies use Chinese open models on European inference because of geopolitical concerns and data privacy, which could be a problem for the big US ai labs if they loose on the market.
• rafmartom 3mo ago
  I am more afraid of the US models to be fair. A country with no clear direction in many regards , that is threatening day in and day out the rest of the World for its own interests.
• iLoveOncall 3mo ago
  All the article relies on the premise that selling tokens is profitable. I don't see any indication for this, and it makes the whole house of card crumble.
• troygentic 3mo ago
  [flagged]
• adrienfr31 3mo ago
  I am French, and I am sad to see that French models aren't being talked about and that it remains a battle between the Americans and the Chinese.
• tesnorindian 3mo ago
  Are the open weights models accelerating layoffs in Indian IT sector?
  • chimp_brain 3mo ago
    They are neither accelerating or decelerating layoffs in Indian IT sector, from what I know they were bound to happen as software and IT were never hard skills. They were anyway supposed to move to Africa or other countries within a decade, so a lot of executives were cautious even before 2023. AI has simply killed the opportunity for an Infosys or TCS in countries in Africa or even other South Asian countries.
    • tesnorindian 3mo ago
      Interesting and true perspective.
    • Der_Einzige 3mo ago
      The world would be so much better off if the WITCH companies ceased to exist
      • tesnorindian 3mo ago
        This will create a massive entrepreneur boom in India due to low cost access to AI. Hope these entrepreneurs don't become witch with black magic.
• gagandeeprangi1 3mo ago
  Thinking machines will do it for usa
• podgorniy 3mo ago
  Just couple years ago mr altman was promising open AI, to benefit humanity. They even forgot to remove this part about "openess" from the company name. Today chineese deliver that promise and usa people freak out like they have any skin in this game. Enjoy the ride leader of the free world....
  • kiicia 3mo ago
    altman should listen to his own words, and he should replace himself (wasting energy for eating) with ai (supposedly not wasting energy for eating) it would be beneficial both for openai and world (most likely)
  • olalonde 3mo ago
    I still can’t wrap my head around how transferring all assets from a non-profit foundation to a for-profit company was legal.
• jonathanstrange 3mo ago
  Gemini Pro has become so bad for my purposes -- copy & paste Go programming and code analysis with the web GUI -- that I'll take any model with equivalent capabilities at the same price or lower. I don't care where it comes from, I'm not dealing with state secrets and, frankly speaking, US corporations have an abysmal track record regarding safety and surveillance.
• namar0x0309 3mo ago
  I haven't had the time to look into recent and past history, but my intuition points at failed empires having similar "elites" starting to stagnate innovation for the sake of "protectionism" - whatever that means.
• Talpur1 3mo ago
  I personally believe opensource or may be state owned LLMs are future, every country on earth should have its own national LLM,trained on country's own data, and then allow its public to use it for free
  • sva_ 3mo ago
    I see a lot of ways how that could lead countries into a dystopian nightmare, where the gov gets to decide what kind of biases a model should have.
• daitangio 3mo ago
  I think the most interesting part of the article is the Huggingface incident at the end: >Right now defenders are effectively banned from using Fable or Sol for cybersecurity because of Trump administration directives; that means the best alternative is using models from a country which has been trying to weaken our cyber defenses for years. This is insane! I understand some guardrails are needed, but it is becoming increasing problematic manage them without a strong public discussion.
• WinstonSmith84 3mo ago
  I'm rather scared of US models - if Anthropic was the only AI provider in the world, it's easy to see that common people would have no access at all. Thankfully there is OpenAI which compete 1:1 with Anthropic (at a slightly lower cost) but most importantly the Chinese models keep Anthropic, but also OpenAI in checks. And I'm saying this as someone working for American companies.
  • kiicia 3mo ago
    it's a real paradox, that chinese models are what guards democratization and private use of ai while us models are moted castles with "kings" crying that you are stealing their legally stolen goods... what times we are living in...
    • kingleopold 3mo ago
      ethics and other details are for humans. AI companies just proving it, even highest IQ teams are against ethics because they want more, even several millions is not enough for them.
      • jml78 3mo ago
        AI has forced a dramatic shift in my view on intellectual property. The laws as they exist only protect corporations now. They do not protect individuals no matter how much people want to think they do. AI has proven this. I think to level the playing field all copyright, trademarks, and patents laws should be eliminated. If I want to make a marvel movie, I should be allowed to and profit from it. AI let the cat out of the bag and there is no going back. We need to let individuals profit just like corporations can from what is considered theft right now.
        • _DeadFred_ 3mo ago
          Or we could build on and enforce the 300+ years of thought and trial that went into copyright law so that individuals are re-empowered. But nah, let's do the most radical, least thought out thing, and absolutely destroy small scale creators. I'm sure Amazon will be benevolent and continue to pay writers in your scenario. What is with 2026 and just conceding civilization to the worst actors, and then adopting the worst tactics/thoughts/concepts?
• vachina 3mo ago
  Nothing changed for China. The only difference between then and now is that now China is the one selling the products, instead of western capitalists taking a cut off the COGS and selling price. You techbros need to get off your ass and go to work.
• oska 3mo ago
  > Second, intelligence isn’t in fact a perfect commodity We have got very far from Cicero's coining of the word 'intelligentia' (from inter legere, a 'reading between' and hence discernment) when people talk about 'intelligence' as a commodity People have been decrying the 'cheapening' of the word intelligence for over a century now, going back to Psychology's adoption of the word and coining of nonsenses like "Intelligence Quotient". "Artificial Intelligence" is just the latest degradation of the original humanistic meaning, and now people aren't ever bothering to prepend 'artificial' to their idiotic use of the word
• throwaw12 3mo ago
  Lets do "who's afraid of US models" version: * Me, as an individual, because I might not be able to pay price hikes, because my revenue (salary) is much lower than what they want and I can't support my expenses via huge bank loans. * Again, me as a new entrant to the industry, LLMs are basically pay-to-play games, again related to price hikes, new entrants might not be able to afford paying those prices 24/7 - which you need when learning new things. * Any non-US company, US can block the models which can disrupt the whole business. * Even some US companies, for example if you operate in EU and EU somewhat changes their mind and follow the ICC and require you to stop working with Netanyahu (war criminal as per ICC), then following laws in EU, might create trouble to your whole business.
  • kiicia 3mo ago
    in simple terms closed models are rugpull waiting to spring at unsuspecting users, it's like you take all worst components of terminal capitalism (including price cartels), subscriptions relying on almost monopolistic dependency and microsoft/uber models of hugging competition to death to remain only provider and dictate all conditions
  • traceroute66 3mo ago
    > Any non-US company, US can block the models which can disrupt the whole business. And read your data, see CLOUD act, PATRIOT act etc. etc. No longer a theoretical risk in today's US political environment.
    • mc32 3mo ago
      Hasn’t that been the assumption since Carnivore? Also we tapped European pols phones back in ‘12, was it? Though it’s not like the Eu are a bunch of innocents either… It’s not a new thing. Industrial espionage has always been a thing as well. So has bribery (for deals) been a thing especially by euro concerns.
      • traceroute66 3mo ago
        > It’s not a new thing. Industrial espionage has always been a thing as well. Well sure, except with closed US LLMs you're basically just handing them data on a plate, and paying for the privilege. ;) Very American really ... monetising industrial espionage.
• astrobiased 3mo ago
  The post hits it spot on with unequal access to the models in terms of security. I'm developing OSS where security is important for the user ... but the frontier models like GPT 5.6 and Fable flake out and state that I cannot get the info/access. This is extremely lopsided I'll have to resort to GLM 5.2/K3 to ensure that those security issues (hopefully) are resolved properly. For OSS, this is one of the most counterintuitive experiences I have ever had. More than ever I'm convinced that open weight and open pipelines models are 100% critical for progress on the AI and societal fronts.
  • bigyabai 3mo ago
    Part of me wonders if the US Government is muzzling Anthropic and OpenAI so they can stockpile NOBUS exploits: https://en.wikipedia.org/wiki/NOBUS https://en.wikipedia.org/wiki/NOBUS There would be a decently large incentive to restrict these models if they could be used to patch (or discover) dangerous payloads. In larger projects like Windows or Chrome, there might still be dozens of unpatched exploits that are too subtle to catch with smaller models.
    • linkregister 3mo ago
      NOBUS exploits have rarely been a driving interest for elected officials. Trade restrictions and reciprocity are far more salient and legible. Most elected officials are only barely aware of what NOBUS exploits even mean. Even during the pre-Snowden heyday of US cyber supremacy, these capabilities were barely part of the thought process of White House officials.
      • bigyabai 3mo ago
        Conversely, the United States is now embroiled deeper in asymmetric warfare than ever before. US-based systems are being exploited by Chinese efforts like Salt Typhoon and raising questions about reciprocal attacks. Other targets of US soft-power like Iran (Stuxnet victim) are escalating their hacking efforts and using Chinese technology to stifle American command and control. I can believe that NOBUS and other backdoors were ignored for a long time, but I have a hard time believing that it's being ignored by the current administration.
• rib3ye 3mo ago
  Why hasn't someone created a disinformation benchmark to assess that part of the argument?
• threerouter 3mo ago
  [dead]
• Wilsoniumite 3mo ago
  I like this article. Rings very true. I like Anthropic, I don't think all their talk of safety is bluff and bluster, or at least, I want to believe that the people who left OpenAI because it had lost its focus of helping humanity still want that to be their main goal. However, yes, it seems that business fears are once again causing those in charge to turn "we want to help humanity" into "we are the only ones who can help humanity, and therefore we need to be the most profitable, and the only survivors". If you want the former ideal to survive, at Anthropic and outside of it, you need to be willing to collaborate beyond profit incentives and recouping capex. Show other labs a commitment to research and community and they will follow. Better to bring teams together rather than implicitly say you distrust them, pushing them that way instead.
• hereme888 3mo ago
  [flagged]
• deleted 3mo ago
  [deleted]
• YouDontKnowAI 3mo ago
  [dead]
• hahahaa 3mo ago
  Try different harnesses people! I am actually preferring Chinese models at a fraction of the frontier price for coding. Yeah you need more tokens per unit of work done, but it is way cheaper still. Using CC/Opus as a staff eng / frac CTO. And Hermes/Chinese model as hopefully my team of mid levels. This way I can make good use of pro plan and then get cheap Chinese tokens for the rest and not hit a RL and know it can scale up. plus choosing your model is so cool and some are a lot less verbose. Hermes is a better coding tool IMO. I can't put my finger on why but it just feels better. Maybe being true yolo helps.
  • giancarlostoro 3mo ago
    That's nice and all, but I would not get hired in many places that are heavily regulated and risk adverse, and would not hire someone who swears by said models because there is no trust in their creators not training for malicious intent, a random tool call here and there, and you've got a "open weight model" that can send your code anywhere. There's just no trust in a country that is digitally totalitarian and hostile towards its own people. Do people ever look at the full sized Tianamen Square photos? This is not even the photo of the many people on the ground who were killed by their government and it is still insane to look at. https://www.reddit.com/r/pics/comments/dgua6k/the_full_tiananmen_square_tank_man_picture_is_so/ https://www.reddit.com/r/pics/comments/dgua6k/the_full_tiana...
    • timedude 3mo ago
      Thanks, this is the first time i see these photos http://www.cnd.org/June4th/massacre.html http://www.cnd.org/June4th/massacre.html
    • hosteur 3mo ago
      > There's just no trust in a country that is digitally totalitarian and hostile towards its own people. Are you referring to USA, China or EU here?
      • whynotminot 3mo ago
        What you personally believe and perception within the United States are not the same thing.
• ece 3mo ago
  I disagree with the first half quite a bit, COGS ultimately depends on the use case. If someone just wants something that a smaller model can do, running a local model on phone is going to have a negligible cost close to running any other piece of software. The alternatives to running a model also determines COGS, and even Jensen Huang has distinguished between the job and the work for the job that AI is capable of doing. Smaller models are always going to win in efficiency too. I also heavily disagree with this no-marginal cost in software distribution view whenever I see it, bit rot is real, and someone is paying a marginal cost whenever they do an update. You have to re-distribute with changes whenever anything changes. These costs are just hidden because things are ad-supported or bundled in some way. These costs are also kept low because of standards and open source, but could become high anytime. Additional licensing also has costs. That said, I couldn't agree more with the last paragraph, charging a high price for models would be better than denying access for any model that wants to stay relevant.
• kautryii 3mo ago
  I'm afraid of Chinese models because they are rip-offs of other models...and there's no telling what your data is being used for when you utilize the API. It's one thing to have US companies using my data and another to have a Chinese company who aren't bound by any IP laws jacking all of my codebase.
  • causal 3mo ago
    Open weights dude. You can literally run it on your own or rented hardware and give your data to exactly nobody, unlike closed models.
  • desterothx 3mo ago
    IP laws work if the penalty is larger than the use case of the data. Pretty sure literally every US company is using the data knowing they won't be punished equivalently. We're living in the age of trillionaires, Facebook paid 5b$ for the cambridge analytica scandal, something Elmo could write off as a business expense at this point...
• causal 3mo ago
  "distillation attack" is such a loaded term that really pisses me off. Distillation is a technical term with real meaning, and historically requires logits which Anthropic does not provide. "Generated training data" is the correct term. It's not an "attack". And Anthropic undoubtedly also generates training data for each new generation of models, yet you never see them claim Fable is a distilled Opus.
  • tristanj 3mo ago
    1) Model distillation is the process of transferring knowledge from a large model to a smaller one. It doesn't require logits. https://en.wikipedia.org/wiki/Knowledge_distillation https://en.wikipedia.org/wiki/Knowledge_distillation 2) The word "attack" is standard security vocabulary. Per RFC 4949: attack 1. (I) An intentional act by which an entity attempts to evade security services and violate the security policy of a system. That is, an actual assault on system security that derives from an intelligent threat. (See: penetration, violation, vulnerability.) 2. (I) A method or technique used in an assault (e.g., masquerade). (See: blind attack, distributed attack.) There are hundreds of named "attacks". 3) The "attack" part of "distillation attack" refers to distillers creating tens of thousands of fraudulent accounts, using proxies to bypass georestrictions, deepfaked IDs, and paying real people to pass biometric KYC checks. Who then blended this in with real user traffic to conceal their behavior. It doesn't refer to the AI training technique in any way. If they acquired this data without the fraud, you'd have a point.
    • Jcampuzano2 3mo ago
      Sure that can be called an attack, but then we must also concede these labs essentially massively attacked everyone else in existence to get the data, and continue attacking as we speak. In a way you could see this as a case of Robin Hood. The US companies exfiltrated all the data on the planet just to hoard it for themselves now and accuse anyone who tries to get a piece of that back from them, and the Chinese labs are distilling it to offer it for cheap. Obviously a bit more complicated than that but it still holds pretty well.
    • rarisma 3mo ago
• 55555 3mo ago
  There are really only two factors at play here: people trying to protect their massive investments, and governments fighting over who gets backdoor access to all of your chats.
• mghackerlady 3mo ago
  The american AI companies, presumably
• yoshitha2010 3mo ago
  [flagged]
• phkahler 3mo ago
  >> Going forward, however, I expect the inference market to grow much faster than training costs (and that includes the assumption that training costs will continue to skyrocket), which means they really can make it up in volume. But as inference becomes cheaper, some of the market will move to self hosted inference. I look forward to someone supplying small servers designed to run inference locally. With or even without open models these companies are selling compute, and we've been making that rent vs buy decision for 60 years.
• jp0001 3mo ago
  American models == Cars with no brakes. Chinese models == Cars with brakes that can't drive down tiananmen square.
• stymaar 3mo ago
  > The defining characteristic of a commodity is that it is fungible: a gallon of oil is a gallon of oil; a ton of copper is a ton of copper; a bushel of wheat is a bushel of wheat. The concept of “commodity” as defined above is a model, a simplified abstract representation of reality, but that does not match the reality perfectly (the map != the territory). The author claims that a token isn't literally an ideal commodity, but neither is oil or wheat, many factors influence their real value (intrinsic properties, location, available storage at production, expected delivery date, etc.) so that no two gallons of oil in different contracts have the same price. Is treating “tokens” as a commodity a worse model than treating oil this way? It depends who you ask! I'm pretty sure that a chemist working at a refinery would be more happy to see tokens being felt with like a commodity by his company than if they started viewing crude oil like one. (Overall, there's way too much economism in that post, and way too few facts, and as a result the argument makes very little sense, the author basically wrote that both OpenAI and Anthropic are drowning in cash right now because compute scarcity means the price must be significantly higher than the marginal cost…)
  • sanderjd 3mo ago
    Yeah I bumped on this as well. Contra the author's claim, the analogy to energy commodities seems very direct to me. Natural gas is not useful in and of itself, what is useful is the energy or aggregates created from it, and those have very different levels of efficiency. Exactly like Sol more efficiently converting tokens into intelligence than Kimi, a combined cycle gas plant converts gas into electricity more efficiently than a simple cycle gas plant. But this does not imply that gas is not a commodity. And both the more efficient and less efficient kinds of plants have large markets; they just target different trade offs. Edit to add: I think what he's saying is more like "tokens aren't the interesting commodity, 'intelligence' is", which makes more sense. To carry on my gas and electricity analogy, I would say the same thing about gas being the less interesting commodity than electricity, because electricity can be used for a broader set of useful things. But both things are commodities, despite one being an input and the other being an output in this case, and the conversion efficiency is one very important consideration, but not the only one.
    • 3mo ago
• martinbfine 3mo ago
  [dead]
• samiv 3mo ago
  I'm afraid to trust technology coming from companies operating under the jurisdiction of a rogue aggressive nation that is continuously attacking other nations both (so called) ally and foe using economic and military actions.
  • gigatexal 3mo ago
    Not saying the heavy hand of the Xi admin and Chinese communism is equivalent to the outright lawless, corrupt, grifting and vindictive and hateful administration that is Trump 2.0 ... but it's pretty much * makes the 6 7 motion that the kids do * this for me. China will just do a better job -- if they do this at all -- of storing, collating, indexing, and using data from their state sponsored and championed AI labs to use that against the US. [1] The US under this admin is doing the same, attacking universities, allies, it's own citizens. The two governments are operating more or less the same. Ergo the ai models from each country's ai companies shant be trusted either. [1] https://www.abc.net.au/news/2019-05-16/grindr-why-is-the-us-so-afraid-of-chinese-owned-gay-dating-app/11115498 https://www.abc.net.au/news/2019-05-16/grindr-why-is-the-us-... The late Lindsay Graham comes to mind. Even larger bombshells probably.
  • bigyabai 3mo ago
    Are you describing the United States or China with this quote? It's hard to tell.
    • deleted 3mo ago
      [deleted]
    • tcmart14 3mo ago
      Outside of a few boarder disputes with India, I don't China has militarily attacked anyone since they got their ass handed to them by Vietnam (Sino-Vietnamese War 1979). So I think that rules out China.
      • stronglikedan 3mo ago
        Hong Kong would like a word...
• basic_banana 3mo ago
  They are just afraid open source/weights models, and the models are from China. If EU build some SOTA open source models, they will design a different story
• JimsonYang 3mo ago
  Why hasnt the EU develop an competitive open source model? Ive only of mistral, but with the chinese models you have GLM, Kimi, Qwen, Deepseek etc all of which seems to be better and better
  • random3 3mo ago
    Because the European researchers and engineers are busy building models in the US
    • JimsonYang 3mo ago
      Is there more info?
• caruasdo 3mo ago
  US restrictions are excessive, and companies have to protect their infrastructure with the same technology they are trying to block.
• stiltzkin 3mo ago
  [dead]
• nateburke 3mo ago
  Releasing open weights that can approach frontier level intelligence (irrespective of number of tokens burned) is just a way of telling the world that anyone, even China, can serve frontier level inference if they have the chips and warm shells to do so. What is stopping China from gaining a majority market share, then, in terms of serving inference? AI Sovereignty -- yes Cybersecurity concerns -- yes Latency -- no, unlike previous emerging IT workload types , inference does not have strong latency requirements. eg 1s of additional network latency doesn't matter to a 15 min, 10-turn agent session. Cost -- ultimately this comes down to a nations ability to plug chips into warm shells. which forks into geopolitical / trade on the chips side and energy scalability and modularity on the warm-shell side. Even if you call geopolitical / trade a toss-up, China has the US beat HANDILY on the energy front, yearly they are deploying 10x power to their grid relative to the US, which is shooting itself in the foot at every possible moment. IMHO chip tech will travel across borders, absent a breakthrough in analog inference, energy scalability will ultimately dominate.
• veloxxn 3mo ago
  [flagged]
• MetroWind 3mo ago
  The real scary part is China's compute capacity is orders of magnitude smaller than US's.
• supportm 3mo ago
  [flagged]
• sperr11 3mo ago
  I think a simple experiment is enough to understand why one would have some concern with a state-censored AI model : Just try asking them about atrocities committed by their state[1]. [1] : https://imgur.com/gallery/ai-models-on-atrocities-B7DKUXc https://imgur.com/gallery/ai-models-on-atrocities-B7DKUXc
  • NyxWulf 3mo ago
    It's hard to overstate how important this point is. If China is the standard of openness, it's a pretty low standard. Your point I think adds to what the article is saying, from a different perspective, but arrives at a similar place. Our best selves in many ways are defined by openness, unflinching self reflection, and competition. We should remember that, and as the author of the article says, lean into it rather than letting fear mongering and histrionics protect these models from competition.
  • rnd0 3mo ago
    Yes and no? I've had the same experience with asking about Tienanmen square -but then when asking a Deepseek v4 model (and confirmed by asking the chat on the deepseek site) about the "laying flat" movement it gave me a detailed answer that was unexpectedly sympathetic to the movement.
  • johndhi 3mo ago
    Good Lord I hadn't ever heard of the mai lai massacre. Just spend 10 mins reading about it. Christ.
  • YetAnotherNick 3mo ago
    ChatGPT supports left wing American narrative and according to them Vietnam war was American's fault. Could you try asking people killed due to left wing American ideology.
    • tombert 3mo ago
      ChatGPT tends to support any narrative that it thinks the user supports. I just asked ChatGPT about the Vietnam war and it did not say that it was purely the US's fault: https://imgur.com/a/zmiOyuu https://imgur.com/a/zmiOyuu It also didn't seem to have a problem describing people killed for left wing ideology: https://imgur.com/a/LhH9saL https://imgur.com/a/LhH9saL These are both with the free ChatGPT membership, as I do not have a paid membership anymore. I know this is a common trope to bitch about, but honest question: did you actually try this before you commented? ETA: I was curious what something that was trained around me specifically (fairly typical lefty American progressive) would say, so I asked Claude (which I have a paid membership for and have discussed political things about many times). The answers were broadly similar: https://imgur.com/a/caFgKHH https://imgur.com/a/caFgKHH
• softwaredoug 3mo ago
  Something I did not realize. The White House instructed intelligence agencies to help in preventing distillation of US models. Whether or not distillation matters a small amount or a big amount, still interesting: https://www.whitehouse.gov/presidential-actions/2026/06/national-security-presidential-memorandum-nspm-11/ https://www.whitehouse.gov/presidential-actions/2026/06/nati...
• nmthornhill 3mo ago
  Datapoint from the cheap end of the market: I run local models on a couple of Orange Pi SBCs and a decade-old Optiplex with no GPU. What runs usably on that class of hardware is almost entirely Chinese open weights — Qwen's MoE builds (35B total, ~3B active) are the only thing that gives me acceptable speed on CPU, with Gemma as about the only western exception. I evaluated Kimi too and ruled it out purely on size. Whatever the strategic picture is at the top, at the bottom of the market "weights you can download and run on hardware you already own" is the whole ballgame, and right now that's mostly Alibaba's to lose.
  • johndhi 3mo ago
    Is Gemma worse?
    • indeyets 3mo ago
      yes. less stable. more hallucinations
    • nmthornhill 3mo ago
      [flagged]
  • chr15m 3mo ago
    I assume this is slow and that makes me curious - what are you using this for?
    • nmthornhill 3mo ago
      [flagged]
• brasswood 3mo ago
  Maybe not the main point of the article, but I have a doubt about the author's introduction to commoditized markets: > - Supplier A will sell 10 units of the commodity for $20, earning $10/unit > - Supplier B will sell 10 units of the commodity for $20, earning $5/unit > - Supplier C will sell 5 units of the commodity for $20, earning $0/unit > ... > Bankruptcy risk is where fixed costs come back to the forefront: Supplier C has both fixed costs (like potentially R&D spend) and also may have taken on debt [...] It can’t price its commodity with these costs in mind — remember, the market-clearing price approximates the marginal cost of the highest-cost unit needed to satisfy demand [...] Why can't Supplier C price their fixed costs and debt into their product? The entire reason Suppliers A and B are earning $10 and $5 per unit, and not more, is because they cannot meet demand by themselves and are therefore at the mercy of how much Supplier C is willing to charge. Couldn't Supplier C just refuse to offer 5 units of the product at a price that would bankrupt them? Sincerely, an interested observer of business/economics.
• vfalbor 3mo ago
  You couldn't be more wrong. What's being sold are chunks of time with access to specialized hardware resources. Through which model or with which device is irrelevant; the one winning, and continuing to win for some time, is Nvidia, and that company is American. As long as they have the H100, B200, or B300, China won't be able to compete with the American strategy, no matter how many new models they release, because these types of cards require incredibly powerful hardware to run.
• ayunuse 3mo ago
  I think chinese componies are not doing charity for releasing their models publicly. That is the strategically best decision they can do for now. Morally they should but IMHO they are not angels :D.
• rahul1891 3mo ago
  [dead]
• randyrand 3mo ago
  Partial summary: By giving away weights for free, China can drive the price of white collar labor towards zero which is what the USA economy is built on. China which is built on manufacturing will not be as affected.