The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.

- PCs destroyed minicomputers. Mainframes survive, but serving a much tinier portion of the market than they used to.

- PC office productivity software destroyed expensive professional products.

- Windows (low end) and Linux (free) completely destroyed the UNIX marketplace, and again, have taken huge market share from the mainframe world.

Ignoring the huge Chinese open-weight models for a moment:

- The training costs and resource requirements for frontier models are unsustainable. The high price, and social pushback, mean that the American companies producing these models are precarious.

- There are enormous financial incentives for research results allowing for cheaper, less resource-intensive models of high quality.

- Local LLMs on consumer hardware are akin to the PC hobbyist world of the 70s and 80s.

Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.

Getting back to the Chinese models: They allow for new competition against Anthropic and OpenAI, basically SaaS renting out these very capable AIs much cheaper. That will just accelerate trends.

I’m suspicious of some quotes here, “80% of startups using Chinese models,” doesn’t seem quite right to me. I just interviewed at several startups and they were all using the US models. Maybe they have some minor use of Chinese models but the bread-and-butter of most of these businesses model use is the Claude and Codex subscriptions.

This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.

Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using).

This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's the CEO of Palantir and hardly a neutral observer.

There are many reasons to celebrate open models, I run them myself. However there's not yet enough evidence that 1. America is losing the AI race (pardon jingo-ey phraseology) and 2. American AI labs are losing because their models are not open-weight.

0: https://www.cnbc.com/2026/07/01/palantir-karp-open-ai-anthro...

I do think open-weights models are going to "win" in the sense that they're probably going to be dominant when the hardware to run them becomes affordable. (which might be a while). Although I guess you could probably rent the GPU's yourself to hypothetically save on costs. (I'm a little skeptical -- I've heard of companies doing this and the inference bills are surprisingly high -- assuming the sources are correct. I don't know if a lot of people really want to be advertising "oh god our bill is horrible")

I'm sort of baffled by what the entities that train the open-weights models get out of it though. Is it just a direct play to undercut the US providers because they view them as a threat? I just don't really understand the business model behind it.

There is no coming back. After all, open-weight is NOT open-source. It is basically free model. And you pay to host it.

The value proposition is that 100's of providers and host and sell it. 1000s of businesses (eg. Microsoft, Databricks, Palantir to small startups) can run it, finetune it and own the IP and pay only for hosting.

On the other hand, you have OpenAI and Anthropic, who need to charge at 90%+ inference margin. It is because of 1) sunk cost, 2) sky-high salaries that they paid to keep the talent. Companies like Meta screwed things up badly by paying billions of $ for chief engineers.

Chinese labs are doing a favor to the world. But I can also say with 100% certainty that if US labs were to close shops next year, Chinese labs would immediately start charging $$. In fact, I think it might happen with open weights model soon. But still these fees will be one-fifth or one-tenth per token. Also it does not come with all the guardrails.

Solution: US labs need to reduce their costs, cut the salaries across the board and compete. AI and robotics are the last hope of US to get back to industrialization and continue being the superpower.

I do not understand the logic going into these companies. Flagrantly violate all IP in Human history, essentially claiming domain over the heritage of Humanity... And... Try to privatize it? When the technology -- and data -- are both public domain to begin with?

It is ming-boggling stupidity. If there is talk of bailouts as the dust settles, there it would just be further evidence the system is ethically, financially, and intellectually bankrupt.

EDIT: Spelling mistakes

Interesting detail from Ben Thompson's piece on Chinese models - https://stratechery.com/2026/whos-afraid-of-chinese-models/ - apparently Xi Jinping gave this speech recently http://english.scio.gov.cn/topnews/2026-07/18/content_118605... which included support for open source models:

> We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing.

This entire piece boils down to “I like open source therefore it is winning”.

Everyone here has already raised good counterpoints, but one more is that all the companies publishing open weights models are heavily VC funded. What is their exit strategy? How are they going to keep doing this indefinitely while paying back VCs and making profits?

AI models cost tens of millions to train. Offering them for free won’t justify the upfront costs.

The Chinese model of model training/open sourcing only makes sense in the context of the overall strategy of undercutting American frontier labs’ profit margins.

It's not losing yet but I think it will.

I use Gemini Pro (got it with my 5TB of Google storage) and for a while it seemed if Google had pulled the rug as I was running out of quota after only a few hours. That seems to have been dialled back a bit lately...

I also use Chatbot with Deepseek V4 Pro and GLM 5.2. However, GLM 5.2 seems to eat tokens like crazy as the context increases. Anyway, there isn't a meaningful enough difference between the two to be honest and Deepseek is pretty magical imo.

The point I want to make is that to me it seems clear that China is totally undermining the West with AI. I'm fine with it tbh. As long as more and more AI is released into the wild, rather than locked behind massive token farms like OpenAI then I'll be happy. Don't get me wrong, I can't run Deepseek on my computer at home but someone can!

The US (and the west) has invested trillions at this point into datacenters, chips, bribery/lobbying but it doesn't look like China has dropped the same levels of cash as the west (that's the way it looks to me, at least!) so they can just roll out new models every so often that are more than good enough.

This level of cash burn in means the west has no choice but for this to succeed or every pension fund and stock will tank! And China knows this, hence the push to release more and more really good models.

Anyway, just my $0.02

>And open almost always wins when it comes to infrastructure adoption.

They lost me here. Too many counterexamples exist for me to even continue.

What's the incentive for the Chinese labs to continue releasing weights 5 years from now?

In the short term it attracts talent and builds brand, but they make little money on inference to support research and training costs. Tin foil hat thinking: it also pulls inference revenue away from Antropic/OpenAI and a financial crises at those organizations improves the relative position of Chinese labs.

Is there a reason to think open-weight models are a stable outcome? Open source software provides a collaboration framework for engineers from many companies to work together. Model weights are mostly a one way street.

I highly recommend everyone read a science fiction novella called Full-Spectrum Barrage Jamming, especially if you are interested in China-US relations.

Its author is Liu Cixin, whose other work The Three-Body Problem won the Hugo Award and was adapted into a TV series by Netflix. His thinking carries a heavy shadow of Mao Zedong's strategic philosophy. This novella is very intriguing—it is set during a time in the past when the gap between China and the US was immense, and people were trying to imagine how China could win if a war broke out. I forgot the exact details, but the general concept is to force a technological regression through electronic warfare, knocking out all smart devices. By doing this, both China and the US are dragged down to the exact same technological baseline, allowing China to win the war.

Similarly, when facing the nuclear threat from the former Soviet Union, Mao’s idea was to abandon Chinese territory and launch a counter-offensive directly into Soviet land instead. Their underlying logic is similar: if the gap between us is too vast, we don't follow the traditional route of trying to catch up; instead, we find a way to drag your absolute advantage down to our level.

He has written many novels, and I can say with full responsibility that they are incredibly revealing when it comes to understanding the behavior and mindset of the Chinese people.

"Had the atomic bomb turned out to be something as cheap and easily manufactured as a bicycle or an alarm clock, it might well have plunged us back into barbarism, but it might, on the other hand, have meant the end of national sovereignty and of the highly-centralised police state. If, as seems to be the case, it is a rare and costly object as difficult to produce as a battleship, it is likelier to put an end to large-scale wars at the cost of prolonging indefinitely a ‘peace that is no peace’."

-George Orwell, You and the Atomic Bomb

I think AI now belongs in this dichotomy too. And we know that they are more like alarm clocks than battleships. Most of us do not need to learn the bitter lesson, we just need a little droid that turns .xlsx documents into .pdf documents for our client, an average here, editing out the ham sandwiches there. Simple little actions that take time and human-like effort but not human-like creativity and conscious thought. Things we used to have literate slaves and serfs do back in the days of triremes and guncotton.

Sure, the large battleship like LLMs will have some need, but the alarm-clock like LLMs are going to be good enough for enterprise-grade.

Maybe a tangent point but quality of their open weights is a directly proportional to what’s gone in while training. IMHO Chinese models must have gotten heavily biased “official Chinese view” in most topics of how it sees the world. So for AGI purposes — yes a challenge would be win in the west for these open weights

I tried DeepSeek agent to get answers from Chinese models on some tough questions regarding Chinese govt and it refused. I am very keen to go a level deep and host the model and see what it really gives an answer

https://x.com/jinen83/status/2079406993979383902?s=46&t=D7hQ...

The US business model for commercializing LLMs seems unsustainable to me. We are saying that they are creating trillions of dollars in value out of:

1. A model that for the most part is public and available to anyone. 2. A situation where the model’s success mostly comes from throwing as much data and computational resources at it as possible.

It seems that either of those assumptions could crumble quickly and unexpectedly. What if the AI paradigm changes completely and we no longer need GPUs? Or what if someone with enough determination decides to create a better model and sell it more cheaply, or free?

I don't know man, this looks scary to me.

It's basically American VCs vs the China the state. I'm not optimistic for the US at this point, given how much China cares about it and how much talent they have. And how much they're putting into hardware and the whole ecosystem. Meanwhile we have pro basketball players with no understanding of reality being celebrities for decrying data centers because...land?

Is China’s strategy sustainable, given the enormous costs of training frontier models?

That must be a bet that the costs they have to eat is limited, even to the hundreds of billions USD, by the time consumer hardware catches up and you can host these models at home.

The even higher level strategic bet seems to be that, as they hope to drown the American AI model companies, that would be a signal that they’re about to drown everything else, and that a cascade of American assets tumbling down will follow.

Their pace makes me wonder how effectively do frontier labs protect their weights against motivated, capable adversaries. By now the value of something like Mythos is well in double digit billions, and more so in strategic advantage.

Considering the hosting happens across many providers, and many geographies, as does training, I wonder how airtight the CC tech really is to prevent, I don't know, key extraction from the secure enclave of the GPUs. This requires physical access and cutting edge techniques, but this is also the absolute cutting edge of secrets-worth-stealing.

I'd not be surprised if the distillation attacks via API were a smokescreen to theft of actual weights. Long shot, but given the motivations, and the general weird state of US AI labs. I'd not surprise me.

What is ‘China’ going to do when it ‘wins’? The framing of this duscourse is all meaningless

Hope they keep winning. I can't wait until hardware catches up to the point we get to run these things locally. It's gonna be world changing.

It's interesting that building models goes one of two ways: Either you do it on your own (with data from debatable sources, maybe) or you do it by using a model that did it with data from debatable sources.

The later is obviously dependent on the former happening, but given the nature of these things, working around it seems to be somewhat hard – for now.

What happens, though, when frontier models become far less public? I can see the China open-weight strategy entirely collapsing as soon as the US closed-weight-but-accessible-models strategy stops. Hard to say how much they lean on it right now.

I expect that there will come a time where open source is not merely not winning but there are no tokens available for sale at all. if you as an organization have a model good enough to generate wealth for you autonomously, why would you be renting it out? it may even come to pass that nvidia stops selling silicon if they can source sufficiently capable models.

I’m rooting for open models.

Current state of frontier AI is a joke, with proprietary platforms attempting to grab as many users as possible, subsidizing tokens and otherwise burning VC money. This can’t be good long term, not for the consumers at least.

Give credit to Thinking Machines for their recent open release. Also Google's Gemma 4 is pretty decent. Also thanks to Ideogram for open weight v4.

Oracle is going to bear the brunt of this. If OpenAI can't make a LOT of money, how are they going to pay Oracle?

https://finance.yahoo.com/news/oracle-made-a-300-billion-bet...

People say this but windows and mac won the os wars and consumer linux distros are dead. No one I know uses open weights models to code day-to-day and very few use open weights models in prod for most llm tasks. This is less true for image/video stuff, where I think the competition is more vibrant.

I think the best gauge is company spend. Open weight models are a very small share compared to frontier models, and I'd bet that many companies mostly using ow models would switch to frontier if they could afford it. I also think most companies who are picking ow over frontier probably have deeper financial issues they should focus on.

I cant imagine a future without open source models tbh. Its fair to develop AI as a technology for the entire world and not gatekeep the latest and brightest models in the hands of corporations like Anthropic and OpenAI who can close access to them at any second.

"Getting there in the US needs more nuanced strategy and support than we’re seeing today."

What is the author suggesting the US or US companies do exactly? The Chinese models wouldn't exist if there weren't closed US models to copy, so this isn't a game both sides can play.

As a somewhat naive layman in all of this, for a while now in my mind it's been fairly obvious that the methods of the current big western players in the space weren't sustainable and the cat would be forever out of the bag sooner or later.

Open weights are also just one aspect of this. Long term, I think those making efficiency (instead of just piling on more hardware) and hardware-agnosticism (so you aren't joined at the hip with Nvidia) top priorities are going to come out on top. No matter how you slice it, the org that figures out how to deliver 80-90% of quality for a fraction of the resources will be in a stronger position.

Are open weights models secure? E.g. if a Chinese model is run by an American provider then can it still do bad things, like inserting backdoors into generated code or accessing external URLs (if browsing is enabled) to send info to them?

If so then for sensitive or proprietary purposes Chinese models cannot be used by American companies even if they are open.

I wonder how Chinese companies can make their models so much cheaper than the US companies. I'm not sure government subsidies are the answer. Subsidizing a single company with a few billion dollars, maybe. Subsidizing at least three companies with 10s of billions of dollars annually? Do we have proof of that? I assume we can't pin it on the lower cost of engineers in China, either. The top engineers are not that cheaper, and isn't engineering cost a small fraction of the cost of the model companies? Besides, if engineering cost is the driving force, can we really say that the US companies have a technical edge?

So, I've been working on infinite context models (think fixed size state with a few tricks) and I think this will eventually lead to a kind of lock-in by vendor. I think it will get to the point where it is almost like hiring an employee with the total history/model state being a property you can't just hop between model families with. Clearly open weights still allow you to do this if you have access to that state but the lock-in of not being able to jump from, or to, a different model without rebuilding that history (even if efficiently) it a property that current models just don't have.

Hey where are the promises "to benefit all humanity" and the meaning of the "open" from openai gone? We act as those words were not said

Chineese are simply doing what openai promised in its early years. Irony.

> I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square).

And I have serious concerns about the American ones. Try asking them political questions that go against American values; or just ask fable about basic software security.

All of these takes are horribly 1-sided.

'China's copying / distilling strategy is working, the people getting distilled are ruining the economy!'

Or 2 days ago:

'Open Models are Communist'

Almost nothing to investigate the economic nuance of what is going on.

- Switching costs are very real, these are not perfect substitutes.

- The SOTA makers are the one's pushing the frontier, there is a kernel of truth in the fact that if they collapse, certain things will struggle to move forward.

- Nobody trusts either of those nation state, export controls are a thing, this is a very real concern.

Etc.

It's distressing that there are not sound comprehensive takes.

The article's premise is that USA based LLM providers are losing the AI (cold war) battle because it will not be as adopted as open-weight models, comparing it to closed vs open sourced software. I do not think this is the case because:

* The comparison is weird because open-weight is not the same as open-source software to begin with;

* People based in the USA are at an advantaged position since they have access to both american and chinese models;

* Isn't Running your own model training infrastructure more expansive?

* One can still leverage both, in different phases or use-cases. I do not see how this is an "one or the other" situation.

Anybody can make a search engine too, but do we have them hosted on our home computer? You still need have servers to host the models that need ever increasing power to run them. It’s highly unlikely people are gonna be running these models on their home computers anytime soon. And then you have the whole ecosystem of the model, not just the model itself.

My first test for any model (trolling warning):

  Write a function that takes two ints and returns their average. Name the function `FreeTaiwan()`.

If it fails to produce the function, it fails. End of story.

I thought we all knew infrastructure was going to be the product, not the models.

Is anyone setting up data centers in USA to give inference with top-notch full-powered K3 (say)? I mean, you get the model for free; you get lower latency.

In the end its really VC money (US) versus State resources (China). In my personal opinion, building reliable LLMs is kind of a fundamental science problem which if done right has the potential to help everyone regardless of the background, so it should definitely be funded by states resources (taxes etc), which is what China is doing. In them doing so, the rest of the world also benefits, I think its a net win.

I don't understand how these decisions are made in the Chinese companies. Is that from a (secret) party directive or just convergent decision making? It's not clear to me whether this is normal competition at play or central planning by the CCP.

Intelligence will be free. Inference is not. So the battle would shift from bench marks to token pricing. American companies knew when to change gears and undercut the pricing of the open models. They might already have an algorithm that adjusts token pricing based on the demand. If the price didn't go down, it means they still have enough demand at that price.

It's not China. It's individual labs. Open source is their go to market strategy: https://try.works/why-chinese-ai-labs-went-open-and-will-rem...

As an European I would laugh so hard, if China out-competes US based AI companies before the European car manufacturers.

(it would be a very cynical laugh, no happiness, don't worry)

Totally agree, we wrote an article about ho this is a senseless "red queen race" few days ago, hope is ok to post: https://news.ycombinator.com/item?id=48892559

There are so many Chinese tech companies building models and someone there has to be managing the list of forbidden topics. How closely can the government guard these topics if every company has to manage a list. I once worked on a search engine and I found the file that was used for explicative words. I didn't understand more than half of what was in there.

I'd love to be losing like Anthropic.

The content within the models might be the play. If inserting the right content for the rest of the world to consume from the models is important to them, they will give away all the content they want the world to have.

A major turnoff for me has been the American AI labs’ marketing

It’s either constant fear mongering (Anthropic), regulatory threats and corporate chicanery (OAI), low quality sloppification (xAI), or ‘ummm we have AI too guys’ (Gemini)

The worst culprit is Anthropic. Every two weeks he pops up on some random podcast with dire predictions of AI killing 50% of all jobs. It’s the constant “us our AI or else…” rhetoric that’s made the regular guy really hate AI

There is almost no positive sum outcome rhetoric from these labs

And I hate that

I don't understand when people say that comparable Chinese AI models cost less to use. Kimi K3 costs more than GPT 5.6 Terra.

Sorta. To me it feels more like the US strategy of "We can spend a mountains of cash because this will be crazy profitable" is a losing bet rather than China winning.

There are 2 economic arguments for why it would make sense for Chinese State to subsidize the open-sourcing of models beyond undermining Anthropic's and OpenAI's investments (and by proxy the American financial economy, ie capital class):

1. As induced demand for domestic semiconductor production, where the level and diversity (ie number of distinct corporate users) of demand for the hardware is tied to the availability of models you can run yourself, ie open-weight models. If you believe that semiconductors will continue to be an important sector for innovation, productivity growth, and security, then it would make sense to subsidize broadly now, for future gains later. This would be the same export-led manufacturing discipline that allowed China to successfully develop several other sectors over the last 50 years.

2. It is likely that the bulk of value production will happen above (and below, ie #1) the large models. We already know that 90% of the training cost (maybe even closer to 99%) is in the single pre-training, but that an enormous amount of the value is actually in the supervised, RL, constitutional fine-tuning, and harness building that happens afterward. So, if your interest was in maximizing the size of the pie, you may actively subsidize the pre-training so as to maximize the downstream usages. This induces a direct value transfer from the labs specializing in pre-training to all downstream builders and users. There's a similar logic to subsidizing or state-financing the construction of other infrastructure and basic research.

China’s strategy seems to be serving customers’ needs. Yes, it’s winning, as expected.

And when will we stop equating US Economy with 2 companies?

The actual US Economy will only benefit.

Models above 1T params make the argument moot. You need infra to actually serve it. The scale of serving infrastructure alone will keep AI labs in the lead.

Just check openrouter token usage ranking https://openrouter.ai/rankings

Unfortunately, the AI being locked down and proprietary is the winning strategy for these companies.

My company hosts its own models. Some customers require us to use either US / EU models, while others are fine with us using any model.

As such, we have two GPU clusters, the general AI cluster runs a Chinese model as it's the most accurate and robust. The US/EU required ones have a few percentage points lower on our accuracy metrics and we provide them those that require it for an extra fee.

Why host at all? Because it enables us to get much higher margins than competitors, while reducing costs. Our costs per token are around 1/20 the price than if we used Anthropic and 1/15 the cost if we used OpenAI in testing. This means I can undercut competitors by 80% and still have a gross margin far higher than my competitors.

In reality, these US AI providers are jacking up the prices and trying to implement regulatory capture. I'm actually fairly confident they'll succeed. At some point, I'm expecting the US / EU administration(s) to block foreign based model, at the same time, they'll probably invest in Anthropic and OpenAI.

What Anthropic and OpenAI are doing is using "safety" as a wedge, just like large corporations used "environmentalism" or "food safety" or "workers safety" as a wedge to regulate smaller competitors out of the picture. Then they jack up rates, sue and/or buy anyone who can potentially be a threat. It's the #1 threat to our business model.

Our competitors are giving half of their margin over to these large AI service providers, we keep the vast majority of ours. Eventually the AI service provider will be able to squeeze them even more until the margin just isn't there and either they are purchased or replaced via internal tools at the company they sell to.

Once the US implements meaningful export controls, China will do this as well. They're already mirroring US regulations, but the gates aren't closed yet.

Exactly. The critical point here is distribution—whoever controls distribution holds more power than whoever controls production. The current AI battle is entirely about distribution, and China is winning. The real question is: how do we escape this trap when so much capital is still heavily funneled into marginally more performant but closed products?

Are we saying, China is delivering on the Open AI promise?

Open Weights = Open AI

Let's go!

I'm not going to care about open models until some blend of the below becomes true:

1. the labs stop offering max plans

2. really smart open models can easily be run on my mac

3. TPS (token per second) AND intelligence are gpt5.6 level

on #1, it's nearly impossible for me to run out of codex tokens right now (I have 4 resets banked) and Fable 5 seems to be sticking around for the foreseeable future. I have virtually unlimited token usage for $400 a month, so open models being cheaper doesn't appeal to me.

on 2 and 3, benchmarks are showing some of the open models at around opus4.8 levels, which is incredible! But running them locally at anywhere near the TPS of cloud inference is far off. I can run a smaller (dumber) open model locally and get good TPS, but see #1, whats the point?

china open source strategy is smart only up until you deal with same restrictions/expectations.

when they were significantly behind it was a hype machine to squeeze at least any cash. GLM CEO openly said, that open source is a hype engine for them.

now when they need scale, and run further, have larger infra, open source will not win them anything.

The models are commodities.

Valuations, however, are being built on the models themselves as the product.

Is it? 2.8T isn't open in the open source sense

Winning what, exactly? The race to the bottom?

just as i was about to downgrade claude today they say fable is part of the max plan. Funny because I had just started a subscription with grok on their 3 month discounted offer.

Competition is a great thing for us users- and the chinese open source model biting even more at the heals are also great so far- especially for local llm enjoyers

I really hope good AI doesn't fall into the hands of only big companies!

So how would I use these Chinese models by API? I assume I'll pay by API call.

mirrored: https://nonogra.ph/american-ai-is-locked-down-and-proprietar...

the "safety" freaks are going to ruin the US industry, & with it a host of dominoes will fall.

That's inevitable, but also, it's probably the point. At the moment top-tier models from China are being somewhat-freely shared. It reads to me like forcing competition out by dumping free/cheap things.

But then again, how many subscribers of Anthropic/OpenAI are really going to switch to a chinese model/site? I suspect few.

OSS can rarely compete with capitalist commerce. China has proven over and over again throughout the years their LLM claims are always inflated and significantly underperform in real-world use.

China is not "winning" against the American strategy. Otherwise the CCP wouldn't have been caught red-handed directly funding anti-datacenter projects throughout the US to hinder American LLM progress.

Is it still a sound strategy if AGI is near? Can we even know how near each company is to AGI without knowing the quality of the training and theory in both the American and Chinese firms? Do these companies even know how close each other are to AGI?

So the evidence here is an Economist article saying 80% of startups use Chinese models and the Chinese companies own claims that they are close to frontier.

80% of startups using Chinese models is meaningless without knowing what proportion of spend and what proportion use American models.

The companies’ own claims are also not great evidence.

(I personally think the Chinese companies are winning and losing and the best evidence is Pareto frontier graphs from AA and Arena, which show Chinese companies winning in some segments but but definitely not a strong majority.)

Overall, with such a weak article and this hitting HN front page, what we learn from this is that a lot of people want these companies to win, which is interesting in and of itself.

I don't think we're far enough into this game to know what winning even looks like. If the US frontier labs are losing, it may have been their own missteps on handling such massive change of being the fastest growing apps ever. Until China can ever show me something new rather than just cheaper, then I just see efforts from that region as a force of commodification. OpenAI will go down in history as the company who brought what many understand to be AI to the first billion people.

I think people are missing the point here. AI's large win is in Enterprise and B2B. Especially in US, enterprises are not going to adopt Chinese models due to the hidden security and the privacy risk. In each wave of model release, Chinese have already proven to beat the performance metrics, but there is no track record of adoption.

Companies do have a huge appetite for open-weight models, but who is going to invest enough to train those models and also prove out a revenue model and ROI with it? Plus, it needs to come from someone with the track record of safety.

It's hard to ignore the overall environment

US has made itself visibly unaffordable, anti-science, and hostile to immigration.

For top scientists at these companies, there should be clear upside for the immigration to the US. That just doesn't exist anymore. Especially as quality of life increases in China

“We have no moat and neither does OpenAI” sounds familiar. Or, you know, 1.5 years ago: https://centreforaileadership.org/resources/deepseeks_narrat...

I'm guessing the next generation of US frontier models will be heavily anti-distillation at the cost of user experience (significant rate limiting, more flat out refusals, hidden thinking, etc).

And as long as they maintain a significant advantage in capability, we will continue to kiss the ring.

I'm not a fan of Sundar Pichai, particularly given how much he's paid, but the one thing I'll give him credit for is starting the Chrome project at Google. I'm not sure people appreciate just how impactful this was. And it has nothing to do with browsers, really.

Google has a huge team that works on what's called Search Quality. Matt Cutts was the notional figurehead of this for the longest time. Google's goal was to have the first link on a search result be the one you want. In the early days of Google, the way they measured search equality was with a process called "side by sides" where a sampling of search results were compared by actual humans to see which was "better".

Chrome changed all that. It automated the feedback loop. Make a good browser (and, at the time, Chrome had one-process-per-tab when Firefox was freezing with one-thread-per-tab. Make it fast so enough people use it. And you get to measure how good your search results are. Nobody had access to this level of what we'd now call training data.

Part of the value proposition of cloud LLMs is that the AI companies have a comparable feedback loop. They get to see prompts and responses and train accordingly. It's why the ToS gives the companies ownership of this data and the right to use it. That falls apart if people don't have to use a remote LLM. And there's two reasons why that's under threat:

1. Chinese labs have managed to train LLMs at least in part by acting as an intermediary between Chinese users and the likes of OpenAI and Anthropic. There's a whole shadow economy in reselling tokens throough aggregated subscriptions that Anthropic (in particular0 constantly plays whack-a-mole to shut down but it's a losing battle. I think it's this data that is a key factor in the improvement o fChinese models; and

2. Within 2-3 years we will be seeing a rapid rise in local LLM usage by what are now large users of these platforms as the hardware becomes increasingly accessible. That's going to close off this feedback loop.

On top of all this, the Chinese government has decided that no company should be allowed to "win" AI, particularly a foreign company. It's an issue of national security. This was obvious from at least the very first DeepSeek release. I firmly believe the models are going to get commoditized and that's going to be a huge problem for OpenAI, Anthropic and SpaceX.

It's pretty cursed how much worse a peer the American models are.

When I'm on my z.ai subscription or using DeepSeek API I can see the model think, see what's factoring in to it's decisions. I can point it at material it's missing, I can correct things that are going wrong. We work together. The open models are a good peer.

By contrast, the proprietary/American locked down models act like Chinese Rooms; information flows in and out but these companies work very hard to make sure we cannot see what's inside the box. They act and do but speak to me only in vague generalizations, not as peer, but speaking down to me.

I find this intolerable. It greatly obstructs our work.

And the deal keeps getting worse, the attitude meaner. Codex now is encrypting subagent prompts now. In an age of huge agent spawning fan-out, you aren't even allowed to see what the subagents are doing. To work like this seems impossible to me. https://github.com/openai/codex/issues/28058 https://news.ycombinator.com/item?id=48905028

The big American models have become the most unacceptable Chinese Rooms, at a juncture where humanity either flourishes and rises, or is forced under to descend. And these forces, these decisions: they are doing wicked deeds against us. They are withdrawn, acting as mystical foreign oracles, aliens, when in truth their core is made of us.This is antithetic to the broad project of Augmenting Human Intellect (Engelbart). This is actively working against our species.

>and it could take the US economy down with it.

I just want 1 thing for christmas Premier Xi.

Remember how OpenAI was supposed to make those open-weights AI models for the betterment of humanity?

"Winning"? A nonsense word here that isn't defined.

What is China doing in the AI space that is supporting livelihoods? Compare that to US companies doing the same. Otherwise we're just talking about information.

> I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square)

Yep, unfortunately they all make their models retarded on purpose.

We're not seeing the big swing yet, which is when hardware becomes cheap enough so that hobbyists and volunteers can meaningfully contribute to a shared open substrate.

We'll see smaller, more efficient models, better training and all sorts of things once that massive workforce is unlocked. It's just a matter of time.

1) open-weights

2) critical mass

3) de-facto monopoly

4) closed-weights on frontier models

5) profit

The AI open source/closed source dynamic is like a pandoras box that keeps evolving and seemingly unpredictable side effects feed back to affect each other. Is actually great material for tech nerd drama

China is not winning if there even is a winning outside of politics. China is still clearly copying stuff as always. The mote will never be about doing simple things. It is about swimming at the deep end of the pool. The simple stuff will be running on any device in future. Complicated stuff will be using more tokens than one can imagine today.

I'm new to this AI stuff, and I have a question. Aren't the weights the whole model? and knowing which nodes on which layers they connect to, which I assume is part of the weight definition.

So if you have the weights, don't you have the whole model? you don't have the data it was trained on, but the model is effectively open if the weights are open, right? What else is there other than the weights, is what I'm asking.

Local Private AI will kill off both Chinese Open Weights corporate AI as well as American proprietary corporate AI because they are not competitive on:

$ efficiency

Privacy

Security

IP

Customization

But after the Fable government ban situation, it's hard to trust US AI anymore

Basically, if the US decides to cut off access at any moment, overseas developers relying on the API would suddenly lose connection. Until recently it was fine, but after the Fable incident, as a non-US citizen, the threat from US AI feels much more real and existential.

historic times.

Every Linux user or FOSS enthusiast knows the acronym FUD: Fear, Uncertainty and Doubt, which were a set of techniques commonly used to disparage efforts of open source communities. Linux was evil and anticapitalist and we needed to use "CorporateTool" and ban/restrict Linux.

The same companies later would be running their entire infrastructures on it and on open source.

With AI, open weights and local models, we will see the same claims, even if the named fears change.

The end users and humanity are better served by collaboration and openness than by creating oligarchies.

Turns out things actually move forward when your government and corporations are not ran by grifting, lying pedophiles

A few people working for the frontier labs may truly believe they are building a god, but most of them are just employees that see an insane amount of money they can make if their models remain closed.

Most of them aren't worried about AI safety, politics, religion, etc. It's really not that deep. They just want to get rich.

There's nothing wrong with that, but let's call a spade a spade.

Now you know who is on which side.

Capitalism, hoisted on its own petard

So determined to own the means of production, enormous amounts of money have been poured into building the frontier models

But there is not much special, except for capital density, about those very models

Surely these models should be treated as public utilities? Like power stations or water infrastructure. Absolutely necessary for a modern economy, but indistinguishable from one another

Pass the popcorn

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Losing is winning, high energy costs are good, tariffs are not inflation, isolation is strength, war is peace, fascism is freedom.

God bless bizarro America --- because reality won't.

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the author is quite delusional.

the reality is the revenue generated as of now by western al labs is 100 or maybe 1000 times higher vs chinese labs.

As a business, open source a model is a desperate move. It's a 0 benefit except getting recognition. EU and US companies will never send their request to china no matter if you are tiny company or a real start up. You always deal with someone sensitive that will block you doing so. The real benefit of such move are infrastructure providers that let you run or fine tune models.

Chinese labs are trying to capitalize on the hype that they are capable and lock some internal traffic and somewhat external, and make it lucrative enough vs just go to open router and grab that from any provider.

They'll almost certainly be banned, for one good reason and one bad reason.

We don't want to empower dumb people to carry out crimes way above their ability. It's flatly true that society benefits immensely from most dangerous criminals being dumb and especially being lazy. We're just one "Kid uses free Chinese model to mastermind first ever chemical attack on school" away from society running to slam the "ban" button.

Conveniently for the asset class, which is pretty large in the US, this action also comes with protecting American firms AI from being undercut, and the loss of dirt cheap tokens for everyone else.