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.
> 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.
Getting opinionated replies about politics does feel less dangerous than the model shutting up completely when asked about past government atrocities. At least in the west we can freely discuss and criticize.
I had a hn scrapper and feed daily hn front page posts to llms which summarizes stories and comment section consensus. The model (deepseek v4 flash) completely refused to work for this article because there are scattered (like 5?) mentions to the Tiananmen Square incident in the article and comments.
It's more serious than "arguing with a machine with political bias". If your service depends on a Chinese model, all it takes to get compromised is someone sneaking in one of those CCP red lines into their user content. Unless you use an uncensored version.
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.
We use US models for everything in practice, but we are looking at open-source models right now. So it may just be a turn of phrase hiding the reality. I can't imagine anywhere near 80% are relying on open-source as their primary models.
Cursor may make up the difference. A ton of companies use Cursor and Cursor's UI and billing model pushes their in-house "Composer 2.5" model pretty heavily - which is a modded Kimi K2.5 model under the hood. Anyone using Cursor is likely using Chinese models at least some of the time.
It depends, I guess if they still dont care about losing money or they already had subscriptions then they keep using them, likely either openai or anthropic.
If they noticed the bill going up, like with github copilot, they are looking at the alternatives.
It’s a sneaky statistic. You could say that 100% of the startups I worked at used Windows laptops because at least one person had a Windows computer somewhere.
If you saw the engineers you’d see 80% Macs and 20% Linux laptops.
The statistic would technically be true.
I use Chinese open weight models a lot, but they’re not what I reach for when I’m doing important coding work.
Yeah. People should absolutely be _trying_ the Chinese models, and experimenting with running things locally, but the noise in development is genuinely all Claude and Codex.
I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important people have access to US/Chinese/Other, open/closed, local/cloud and that this remains. Don't become a blind Claude guy or a open weights fanatic: that way lies disappointment.
I'm using a ten dollar a month US model to vibe code startup ideas. All my previous startup ideas i had to hire a graphic designer and back-ender or two to help. I use to be a web design front end enigeer since 2009 yet those skills are dumb now, so now Im a vibe coder.
The model I use to vibe code with I am just going back and forth with. Since Im building it as I go using an agent doesn't make sense but I guess that's where all the token usage comes from? Pardon ramping up my skills via vibe coding this one idea for about a month and have never hit any quota and or have gotten anywhere near my limit.
the distinction may be between using the coding agents vs using models for products. for example where i work we're talking about dropping opus for a chinese model for the in-app agent (which is very expensive to run)
> When entrepreneurs walk into the offices of Andreessen Horowitz (a16z), a big American venture-capital firm, the odds these days are that their startups are using AI models made in China. “I’d say 80% chance [they are] using a Chinese open-source model,” says Martin Casado, a partner at a16z.
This is very different from what the author portrays. It may be the case that many pre-funded startups are using Chinese open-source models (somewhere in their workflow). But what percent of startups that survive more than a year (either with funding or revenue) are still doing this?
I imagine their pitch is: "look at how well we're doing using open source Chinese models! We'll do even better once we raise money to be able to afford frontier models!"
The way the author presents this quote makes me think he had a preferred narrative and found quotes to back it up. Or he's just a very uncareful reader.
Not everything runs on paid models. Claude and Codex are frontier models, but some people have much higher usage needs and finite budgets that force them to self-host. And if you're self-hosting, you're very likely running a Chinese model
Our small team (~6 devs) is still using Claude Code because we're still on the cost-per-seat-month plan. If we were being pressed to pay per token, we'd be re-evaluating for sure.
I’m obviously a tiny, insignificant data point as a solo freelance dev, but I watch this space closely and I canceled my Claude Code subscription today.
I don’t think they meant exclusively Chinese models. Many companies including the one I work for uses big US model for most of the work but data sensitive ones are on prem open source ones.
They probably use Claude and Codex for their actual development, but for the products they actually build and deliver to customers I imagine a lot use open-weight models.
If you're putting a lot of your money and time into a business, do you really want it built on a service only hosted by one company that will turn it off eventually and you have no recourse?
If you build something against an open model you can take that and run it anywhere. If your favorite model provider stops hosting it, you can go elsewhere, you can go rent GPU instances, you can even shell out and buy hardware to run it yourself if you've got the capital and it makes economic sense. Change some API keys, update a URL in your config, and you move on.
If the government decides that proprietary model is too good and so it gets shut off, what do you do? If a proprietary provider decides it's not worth it for them to continue hosting that model, what do you do? If that provider silently updates the proprietary model and it makes your app broken, what do you do?
Its a bit more complicated than that quote implies
At posthog we see if a customer is using an llm, they use more than 1 model. The typical pattern is frontier models for a small percentage of 'harder' tasks and then one of these chinese models for more standardized procedures. As you get better at standardizing procedures you are able to use the chinese models for more and more work so token usage goes up, but the $ spend on top models has still been growing
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 think we'll pretty quickly see a best practice emerging that any generated code will be subject to an additional pass scanning for vulnerabilities. The scan will be done by a different model than the one that created the code. That will help catch vulnerabilities created by models, whether intentional or not.
This should be done regardless of which model was used - American or otherwise.
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.
The big problem with US AI is that they deprecate their models after like a year. If I have a routine business process that works with GPT 9.9 and then next year they release GPT10 and 9.9 isn't available anymore, I really do not want to have to drop everything and verify that 10 behaves close enough to 9.9 for my specific task. With an open model I can just host it on whatever hardware or cloud instance forever. Most software you want to keep up to date to avoid security issues but with an LLM you can update the harness and keep the weights forever.
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.
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.
There is a huge cultural influence opportunity too.
Imagine if, in 10 years time, every school kid is learning the causes of the US civil war from an LLM, getting their essays on hiroshima and nagasaki graded by an LLM, and a million other things.
A country with competitive LLMs gets to decide whether "it was more complicated than just slavery", and whether "it was tragic but necessary, saving lives over all".
Countries without competitive LLMs are effectively going to be buying all their history, economics and sociology textbooks from abroad.
There are good reasons to dislike outcomes that involve a single entity pulling well ahead of the pack here. Whether or not it continues to be American labs in the crosshairs and Chinese operators doing the aiming, perhaps it's reasonable to plan for continued efforts of this sort.
I can see two reasons American companies might want to train models they give away for free:
1) They sell compute: chips (Nvidia), data centers (AWS, Microsoft, Google, SpaceX, etc), or even end-user device manufacturers like Apple (e.x. M7 rumored to have 1.5TB of unified memory). If Jevon's paradox holds, then cheaper (or free) models means more demand. But compute is likely supply-constrained for years anyway.
2) Their product isn't AI but depends on AI being cheap, or they don't want competitors to capture that value, i.e. "commoditize your complement" https://gwern.net/complement
It probably doesn't make sense for these companies to invest a lot of money training models that will be obsolete in a few months anyway. When progress starts to plateau I'd expect more companies to start training models they give away for free.
I don't think this is accurate. AI is driving the cost of software towards 0 and these AI models themselves are software.
Releasing the models for free accelerates the trend but if you're a startup that needs leverage it's a good way to build brand and customer momentum that will be relevant in the more established future market.
I can see an American company taking on the same strategy, and in fact Thinking Machines based out of San Francisco did that just a few days ago by releasing their first model with open weights.
I don't think this is necessarily going to prove to be true.
I often see the sentiment: "the Chinese strategy only makes sense in the context of undercutting American labs' profit margins".
If, for example, you are a company with a near-monopoly on "serving video content", and you feel reasonably confident about retaining a decent slice of the serving-video-content market (Google in the west is an example, Tencent in the east), then training video models on your dataset - and releasing them freely - makes an awful lot of sense.
Free tools to create with mean more video content. In this hypothetical, you're reasonably certain that any video content which does get created will also be watched on your platform.
That is a net positive. The question becomes: How many watch-hours earns back the cost of training a model? It's probably not really that many, especially when you have a near-monopoly on a billion sets of eyes.
It's also a net-positive if people build better video models from research you release, because - again - you are reasonably certain that the even-more-innovative content those models produce will be watched on your platform.
It really begins to make strategic sense if your company is in a GPU-poor environment. Your costs cease at the point you upload a model if your users are running it themselves. You don't have to serve the model. The content is still created.
You are also less likely, I think, to alienate human creators whose work the model was trained on if the model is not sold back to them as a subscription, or by the token, but given for free as a tool.
This frames the conversation very differently. It creates, I think, less of an "us vs them" dynamic, and more of a rising tide.
It's true that it is also beneficial that these models undercut (especially in language models) American companies. But, generally, Americans are not the customers of Chinese companies releasing models. They are already serving a huge volume of customers in a complex, existing marketplace.
The full picture is much more nuanced than simply a geopolitical desire to undercut US labs, and there are several other reasons the strategy can make logical sense.
Maybe today's US frontier models provide enough information content, so that the momentum suffices to use them as a base for every coming generation of distilled and later fine-tuned models?
There are people that spend tens of millions of dollars on paintings and artwork. I can see plenty of reasons why organizations and individuals will continue to want to drop a few million on an AI model just for the fun and prestige.
The upfront costs are actually a lot higher, but still absolutely trivial in the big picture. OpenAI pulled in $120,000,000,000 in one round of fundraising and they're closing in on $200,000,000,000 total. Even at 1 billion dollars, new frontier model training is only 0.5% of what they've raised.
A pittance frankly. Something that could easily be covered by oh I dunno, let's call it a National Science Foundation who's in charge of subsidizing important basic research for a nation's interests.
Anywho, when the market is trillions (and of potential nation state concern), it is pretty inconsequential and very much worthwhile.
Aside, I think your scale is a bit off, I think Moonshot has raised $5B and potentially they get other breaks from China, not sure. So to produce something like SOTA takes billions, not tens of millions. I'd still argue it is worthwhile to subsidize and invest in open versions, imagine spending $5B to unlocking a few percentage point increases in your country's productivity.
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.
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.
They'll almost certainly be banned, but to protect our oligarchs. Nobody will be allowed to run unlicensed AI (or OSes), the chips themselves won't allow it.
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.
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.
> considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
This is also a strange way of framing it, though. Llama was released as a research project, it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.
With OpenAI and Anthropic's profitability under question, it is up in the air whether or not America's stance towards AI will work. If they can't convince the world that they're a proper software business, then China's philosophy will win by-default.
2-3 years ago MAIR was on a roll with Llama 1 2 3, Zuck was on his rehab tour to be a cool guy, and Meta as a whole was pumping record numbers after record numbers. I can't believe how that falters so quickly after the addition of Alexdandr Wang.
The behind the scenes element you're not aware of is Anthropic is going to companies reliant on their models and demanding HUGE one time fees (100 million+) to continue using their models or they will be cut off. This has happened to several larger companies I and others are invested in.
This resulted almost every time in "screw off we'll train our own models or use refined open source ones instead" leading to a lot of anger at Anthropic by CEOs these days.
We all want an alternative and Anthropic and OpenAI need to charge more than they are worth to pay back their investors and everyones stuck now.
I mean isn't the explanation simply that llama was never good enough, even when it was released? I hear (no data) lots of people using gemma4, at least a month or two ago.
Isn’t it basically impossible to run the newer high quality Chinese models locally, even for a corporation? The better they get, the more they need a data center. So, the ‘better’ Chinese AI gets , the more it will just be a service run on Chinese hardware competing with ‘our’ lower latency AIs .
The open source character of the models is irrelevant if you need a nuclear powered data center for inference. In the end it is just another internet service.
> Also, enterprises don't give a rip if models are open.
They care about control. I see many of my enterprise (or just-below-enterprise) clients very annoyed at OpenAI and Google after 2-3 years of model toil, where they had to constantly re-calibrate onto new models, on tight externally mandated deadlines, with little certainity. Now they are reaching for open weight models instead, that they currently host with the same inference providers, but have the option to in-house if push comes to shove.
> This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
I see this as a fault with Meta's models, not a fault with the concept of open weights. The Llama family just aren't very useful. They make flowery prose but they're terrible at tool calling [1][2][3] so there just isn't much I can actually accomplish with them.
> considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
To me, Llama was the ONLY successful thing they did it. it was when they stopped that they fell off the radar as an interesting AI company. They had a genuine chance to be the "substrate" that Ben talks about here. I can't actually figure out why they threw it away.
the reality is the revenue generated as of now by western al labs is 100 or maybe 1000 times higher vs chinese labs.
As a business, open source a model is a desperate move. It's a 0 benefit except getting recognition. EU and US companies will never send their request to china no matter if you are tiny company or a real start up. You always deal with someone sensitive that will block you doing so. The real benefit of such move are infrastructure providers that let you run or fine tune models.
Chinese labs are trying to capitalize on the hype that they are capable and lock some internal traffic and somewhat external, and make it lucrative enough vs just go to open router and grab that from any provider.
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.
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.
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.
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.
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.
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[ 0.27 ms ] story [ 85.2 ms ] threadBut then again, how many subscribers of Anthropic/OpenAI are really going to switch to a chinese model/site? I suspect few.
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.
It's more serious than "arguing with a machine with political bias". If your service depends on a Chinese model, all it takes to get compromised is someone sneaking in one of those CCP red lines into their user content. Unless you use an uncensored version.
If you saw the engineers you’d see 80% Macs and 20% Linux laptops.
The statistic would technically be true.
I use Chinese open weight models a lot, but they’re not what I reach for when I’m doing important coding work.
I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important people have access to US/Chinese/Other, open/closed, local/cloud and that this remains. Don't become a blind Claude guy or a open weights fanatic: that way lies disappointment.
The model I use to vibe code with I am just going back and forth with. Since Im building it as I go using an agent doesn't make sense but I guess that's where all the token usage comes from? Pardon ramping up my skills via vibe coding this one idea for about a month and have never hit any quota and or have gotten anywhere near my limit.
> When entrepreneurs walk into the offices of Andreessen Horowitz (a16z), a big American venture-capital firm, the odds these days are that their startups are using AI models made in China. “I’d say 80% chance [they are] using a Chinese open-source model,” says Martin Casado, a partner at a16z.
This is very different from what the author portrays. It may be the case that many pre-funded startups are using Chinese open-source models (somewhere in their workflow). But what percent of startups that survive more than a year (either with funding or revenue) are still doing this?
I imagine their pitch is: "look at how well we're doing using open source Chinese models! We'll do even better once we raise money to be able to afford frontier models!"
The way the author presents this quote makes me think he had a preferred narrative and found quotes to back it up. Or he's just a very uncareful reader.
All of a sudden in almost all social media channels I'm seeing this type of content and then its usually upvoted to the top.
Non-gatekept forums like this are exceptionally easy to astroturf.
But the big price is AGI and who gets there first, right?
If you're putting a lot of your money and time into a business, do you really want it built on a service only hosted by one company that will turn it off eventually and you have no recourse?
If you build something against an open model you can take that and run it anywhere. If your favorite model provider stops hosting it, you can go elsewhere, you can go rent GPU instances, you can even shell out and buy hardware to run it yourself if you've got the capital and it makes economic sense. Change some API keys, update a URL in your config, and you move on.
If the government decides that proprietary model is too good and so it gets shut off, what do you do? If a proprietary provider decides it's not worth it for them to continue hosting that model, what do you do? If that provider silently updates the proprietary model and it makes your app broken, what do you do?
At posthog we see if a customer is using an llm, they use more than 1 model. The typical pattern is frontier models for a small percentage of 'harder' tasks and then one of these chinese models for more standardized procedures. As you get better at standardizing procedures you are able to use the chinese models for more and more work so token usage goes up, but the $ spend on top models has still been growing
If so then for sensitive or proprietary purposes Chinese models cannot be used by American companies even if they are open.
This should be done regardless of which model was used - American or otherwise.
I'd wager you have never tried this.
Ask Claude to do the same with a Palestine Genocide function.
If anything, this is an argument FOR open models, so we can run them and tune them ourselves, for our own needs.
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.
Most of them aren't worried about AI safety, politics, religion, etc. It's really not that deep. They just want to get rich.
There's nothing wrong with that, but let's call a spade a spade.
The failed rebellion against Sam Altman at OpenAI pretty much proved that.
The Chinese model of model training/open sourcing only makes sense in the context of the overall strategy of undercutting American frontier labs’ profit margins.
Imagine if, in 10 years time, every school kid is learning the causes of the US civil war from an LLM, getting their essays on hiroshima and nagasaki graded by an LLM, and a million other things.
A country with competitive LLMs gets to decide whether "it was more complicated than just slavery", and whether "it was tragic but necessary, saving lives over all".
Countries without competitive LLMs are effectively going to be buying all their history, economics and sociology textbooks from abroad.
1) They sell compute: chips (Nvidia), data centers (AWS, Microsoft, Google, SpaceX, etc), or even end-user device manufacturers like Apple (e.x. M7 rumored to have 1.5TB of unified memory). If Jevon's paradox holds, then cheaper (or free) models means more demand. But compute is likely supply-constrained for years anyway.
2) Their product isn't AI but depends on AI being cheap, or they don't want competitors to capture that value, i.e. "commoditize your complement" https://gwern.net/complement
It probably doesn't make sense for these companies to invest a lot of money training models that will be obsolete in a few months anyway. When progress starts to plateau I'd expect more companies to start training models they give away for free.
Releasing the models for free accelerates the trend but if you're a startup that needs leverage it's a good way to build brand and customer momentum that will be relevant in the more established future market.
I can see an American company taking on the same strategy, and in fact Thinking Machines based out of San Francisco did that just a few days ago by releasing their first model with open weights.
I often see the sentiment: "the Chinese strategy only makes sense in the context of undercutting American labs' profit margins".
If, for example, you are a company with a near-monopoly on "serving video content", and you feel reasonably confident about retaining a decent slice of the serving-video-content market (Google in the west is an example, Tencent in the east), then training video models on your dataset - and releasing them freely - makes an awful lot of sense.
Free tools to create with mean more video content. In this hypothetical, you're reasonably certain that any video content which does get created will also be watched on your platform.
That is a net positive. The question becomes: How many watch-hours earns back the cost of training a model? It's probably not really that many, especially when you have a near-monopoly on a billion sets of eyes.
It's also a net-positive if people build better video models from research you release, because - again - you are reasonably certain that the even-more-innovative content those models produce will be watched on your platform.
It really begins to make strategic sense if your company is in a GPU-poor environment. Your costs cease at the point you upload a model if your users are running it themselves. You don't have to serve the model. The content is still created.
You are also less likely, I think, to alienate human creators whose work the model was trained on if the model is not sold back to them as a subscription, or by the token, but given for free as a tool.
This frames the conversation very differently. It creates, I think, less of an "us vs them" dynamic, and more of a rising tide.
It's true that it is also beneficial that these models undercut (especially in language models) American companies. But, generally, Americans are not the customers of Chinese companies releasing models. They are already serving a huge volume of customers in a complex, existing marketplace.
The full picture is much more nuanced than simply a geopolitical desire to undercut US labs, and there are several other reasons the strategy can make logical sense.
Is that maybe spilled milk?
Maybe today's US frontier models provide enough information content, so that the momentum suffices to use them as a base for every coming generation of distilled and later fine-tuned models?
There are people that spend tens of millions of dollars on paintings and artwork. I can see plenty of reasons why organizations and individuals will continue to want to drop a few million on an AI model just for the fun and prestige.
A pittance frankly. Something that could easily be covered by oh I dunno, let's call it a National Science Foundation who's in charge of subsidizing important basic research for a nation's interests.
Anywho, when the market is trillions (and of potential nation state concern), it is pretty inconsequential and very much worthwhile.
Aside, I think your scale is a bit off, I think Moonshot has raised $5B and potentially they get other breaks from China, not sure. So to produce something like SOTA takes billions, not tens of millions. I'd still argue it is worthwhile to subsidize and invest in open versions, imagine spending $5B to unlocking a few percentage point increases in your country's productivity.
They lost me here. Too many counterexamples exist for me to even continue.
Most popular models on OpenRouter right now: https://openrouter.ai/models?categories=programming&order=mo...
Top 7 are all open models.
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.
My company hosts its own models. Some customers require us to use either US / EU models, while others are fine with us using any model.
As such, we have two GPU clusters, the general AI cluster runs a Chinese model as it's the most accurate and robust. The US/EU required ones have a few percentage points lower on our accuracy metrics and we provide them those that require it for an extra fee.
Why host at all? Because it enables us to get much higher margins than competitors, while reducing costs. Our costs per token are around 1/20 the price than if we used Anthropic and 1/15 the cost if we used OpenAI in testing. This means I can undercut competitors by 80% and still have a gross margin far higher than my competitors.
In reality, these US AI providers are jacking up the prices and trying to implement regulatory capture. I'm actually fairly confident they'll succeed. At some point, I'm expecting the US / EU administration(s) to block foreign based model, at the same time, they'll probably invest in Anthropic and OpenAI.
What Anthropic and OpenAI are doing is using "safety" as a wedge, just like large corporations used "environmentalism" or "food safety" or "workers safety" as a wedge to regulate smaller competitors out of the picture. Then they jack up rates, sue and/or buy anyone who can potentially be a threat. It's the #1 threat to our business model.
Our competitors are giving half of their margin over to these large AI service providers, we keep the vast majority of ours. Eventually the AI service provider will be able to squeeze them even more until the margin just isn't there and either they are purchased or replaced via internal tools at the company they sell to.
Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using).
This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's the CEO of Palantir and hardly a neutral observer.
There are many reasons to celebrate open models, I run them myself. However there's not yet enough evidence that 1. America is losing the AI race (pardon jingo-ey phraseology) and 2. American AI labs are losing because their models are not open-weight.
0: https://www.cnbc.com/2026/07/01/palantir-karp-open-ai-anthro...
This is also a strange way of framing it, though. Llama was released as a research project, it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.
With OpenAI and Anthropic's profitability under question, it is up in the air whether or not America's stance towards AI will work. If they can't convince the world that they're a proper software business, then China's philosophy will win by-default.
This resulted almost every time in "screw off we'll train our own models or use refined open source ones instead" leading to a lot of anger at Anthropic by CEOs these days.
We all want an alternative and Anthropic and OpenAI need to charge more than they are worth to pay back their investors and everyones stuck now.
The open source character of the models is irrelevant if you need a nuclear powered data center for inference. In the end it is just another internet service.
They care about control. I see many of my enterprise (or just-below-enterprise) clients very annoyed at OpenAI and Google after 2-3 years of model toil, where they had to constantly re-calibrate onto new models, on tight externally mandated deadlines, with little certainity. Now they are reaching for open weight models instead, that they currently host with the same inference providers, but have the option to in-house if push comes to shove.
I see this as a fault with Meta's models, not a fault with the concept of open weights. The Llama family just aren't very useful. They make flowery prose but they're terrible at tool calling [1][2][3] so there just isn't much I can actually accomplish with them.
[1] https://gorilla.cs.berkeley.edu/leaderboard.html [2] https://benchlm.ai/best/tool-use [3] https://benchlm.ai/llm-agent-benchmarks
To me, Llama was the ONLY successful thing they did it. it was when they stopped that they fell off the radar as an interesting AI company. They had a genuine chance to be the "substrate" that Ben talks about here. I can't actually figure out why they threw it away.
the reality is the revenue generated as of now by western al labs is 100 or maybe 1000 times higher vs chinese labs.
As a business, open source a model is a desperate move. It's a 0 benefit except getting recognition. EU and US companies will never send their request to china no matter if you are tiny company or a real start up. You always deal with someone sensitive that will block you doing so. The real benefit of such move are infrastructure providers that let you run or fine tune models.
Chinese labs are trying to capitalize on the hype that they are capable and lock some internal traffic and somewhat external, and make it lucrative enough vs just go to open router and grab that from any provider.
When I'm on my z.ai subscription or using DeepSeek API I can see the model think, see what's factoring in to it's decisions. I can point it at material it's missing, I can correct things that are going wrong. We work together. The open models are a good peer.
By contrast, the proprietary/American locked down models act like Chinese Rooms; information flows in and out but these companies work very hard to make sure we cannot see what's inside the box. They act and do but speak to me only in vague generalizations, not as peer, but speaking down to me.
I find this intolerable. It greatly obstructs our work.
And the deal keeps getting worse, the attitude meaner. Codex now is encrypting subagent prompts now. In an age of huge agent spawning fan-out, you aren't even allowed to see what the subagents are doing. To work like this seems impossible to me. https://github.com/openai/codex/issues/28058 https://news.ycombinator.com/item?id=48905028
The big American models have become the most unacceptable Chinese Rooms, at a juncture where humanity either flourishes and rises, or is forced under to descend. And these forces, these decisions: they are doing wicked deeds against us. They are withdrawn, acting as mystical foreign oracles, aliens, when in truth their core is made of us.This is antithetic to the broad project of Augmenting Human Intellect (Engelbart). This is actively working against our species.
God bless bizarro America --- because reality won't.
Google has a huge team that works on what's called Search Quality. Matt Cutts was the notional figurehead of this for the longest time. Google's goal was to have the first link on a search result be the one you want. In the early days of Google, the way they measured search equality was with a process called "side by sides" where a sampling of search results were compared by actual humans to see which was "better".
Chrome changed all that. It automated the feedback loop. Make a good browser (and, at the time, Chrome had one-process-per-tab when Firefox was freezing with one-thread-per-tab. Make it fast so enough people use it. And you get to measure how good your search results are. Nobody had access to this level of what we'd now call training data.
Part of the value proposition of cloud LLMs is that the AI companies have a comparable feedback loop. They get to see prompts and responses and train accordingly. It's why the ToS gives the companies ownership of this data and the right to use it. That falls apart if people don't have to use a remote LLM. And there's two reasons why that's under threat:
1. Chinese labs have managed to train LLMs at least in part by acting as an intermediary between Chinese users and the likes of OpenAI and Anthropic. There's a whole shadow economy in reselling tokens throough aggregated subscriptions that Anthropic (in particular0 constantly plays whack-a-mole to shut down but it's a losing battle. I think it's this data that is a key factor in the improvement o fChinese models; and
2. Within 2-3 years we will be seeing a rapid rise in local LLM usage by what are now large users of these platforms as the hardware becomes increasingly accessible. That's going to close off this feedback loop.
On top of all this, the Chinese government has decided that no company should be allowed to "win" AI, particularly a foreign company. It's an issue of national security. This was obvious from at least the very first DeepSeek release. I firmly believe the models are going to get commoditized and that's going to be a huge problem for OpenAI, Anthropic and SpaceX.
* The comparison is weird because open-weight is not the same as open-source software to begin with;
* People based in the USA are at an advantaged position since they have access to both american and chinese models;
* Isn't Running your own model training infrastructure more expansive?
* One can still leverage both, in different phases or use-cases. I do not see how this is an "one or the other" situation.