- 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.

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'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.

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.

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

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

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?

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.

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

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

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.

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.

-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.

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.

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...

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.

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.

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.

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.

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.

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

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.

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

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

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

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

'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 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.

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

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

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

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

The actual US Economy will only benefit.

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.

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.

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?

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.

Open Weights = Open AI

Let's go!

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

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

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

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.

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.

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.

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

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

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.

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.

I just want 1 thing for christmas Premier Xi.

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

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

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.

2) critical mass

3) de-facto monopoly

4) closed-weights on frontier models

5) profit

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.

$ efficiency

Privacy

Security

IP

Customization

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 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.

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.

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

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

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.

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.