Personally, I don’t think the general-purpose LLM as a standalone tool is long for this world, at least not in consumer-facing applications. I think when the economics make more sense, product designers will make things that people actually want to use that will pretty transparently handle whatever model interactions are necessary, when it makes sense. As a consumer, the last things I want in an interface are to a) be sycophantic enough to lessen my judgment, and b) be obstinate, obtuse, or argumentative, or generally just be something that I have to explain things to. I think a lot of tech folks are far more biased than they realize by the “ooh, neato” factor when imagining how nontechnical people might want to use things. And the weight of these tools just feels wrong for what a lot of people use them for: the thing that plays whatever music I feel like hearing absolutely does not need to be able to generate a volumes of fanfic about the movie that song was in. It’s abstractly impressive that something could do that, but it’s just not useful.

The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.

With open source projects, the benefit was that each individual could improve the complex system (e.g. Linux Kernel) interpedently, and over time the benefits accumulated. With models right now, there is just no way to do distributed training, or really, any large scale parallel way to improve them.

So whatever the short term strategy driving publicizing the model weights (e.g. potentially, to create a price war in order to put pressure on western companies and deprive them of the money they need), we can't ignore the fact that incentives and decisions could easily change in the future, and unless there is a way to truly decentralize models improvements - the party could stop at any time.

  Android holds 70.6% of global active devices to iOS at 28.7%, but iOS captures 64.2% of consumer app spend. [1]

Another important thing that made software usage and education available for most of the world was piracy. I remember as a kid growing up in a developing country, any software (windows, office, Visual Basic, flash, dreamweaver, etc.) was less than 1$. That allowed me to try out and learn so many things on my own without paying a huge amount of money for the license. And I think this is true for most of the software developers of my generation who grew up in developing countries

"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."

I don't think it'll take 10-15 years. Gemma 4 31B in the 4-bit QAT is competitive with the frontier of less than three years ago and runs on any high-end 32GB gaming PC GPU or a large-ish Mac.

The question is whether the frontier will continue to get better at a rate that allows it to stay ahead of the two curves of availability of consumer hardware big enough to run somewhat larger models and the capability of small models to compete with large ones. When the bottom falls out and GPUs/RAM becomes affordable again, the size of what normal people have on their desk will trend quite a bit larger than today.

I think there's a future not too far from now, where a 120B model with really good reasoning and a large context, but limited knowledge (necessitated by being small, you can't fit the world's knowledge in 100 gigabytes), can substitute for a frontier model on almost any task, just by giving it access to web search and documentation for the thing you're trying to do. A 256GB unified memory machine with sufficient memory bandwidth would comfortably run that 120B model.

> I think that 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

10-15 years? The current rate is closer to 10-15 months.

15 months ago, the top model on the Artificial Analysis index was GPT-o3. It scores 30 on the Artificial Analysis index.

Today, you can easily run Qwen 3.6 27B on a variety of consumer hardware. It scores 37 on that index.

Here are a number of open weights models that you can run locally compared with the frontier class models from 7 to 15 months ago: https://artificialanalysis.ai/?models=o3%2Co3-pro%2Cclaude-4...

I've run all of these models on my laptop (Strix Halo, 128 GiB of unified RAM); the bigger ones, like MiniMax M2.7 and DeepSeek V4 Flash, need to be done at fairly aggressive quants that will certainly lose some performance and not quite hit the performance of the unquantized models. But still, it's definitely the case that you can run models that are competitive with the frontier models of 10-15 months ago on consumer laptops.

Heck, just announced though the weights haven't yet been released for independent confirmation is MiniCPM5-2B, a 2 billion parameter (small enough to run on your phone) model, that according to their benchmarks has performance competitive with GPT-4o, a frontier class model from 2024.

https://nitter.net/i/status/2079088670804767114

So that's around 1 year for frontier to consumer device class, 2 years from frontier to phone.

Now, this kind of rate won't necessarily keep up; it's possible that local models will hit a performance ceiling before frontier models do. There's only so much information you can cram into a certain number of bytes, and the AI boom is causing hardware prices to skyrocket so keeping consumer hardware from advancing quite as fast as it had been.

I do agree that Chinese open-source models are going to play a bigger and bigger role in the entire ecosystem moving forward, but I don't agree with you in the sense that they are going to eventually "win."

Just because they are cheapp doesn't mean they automatically win. You've picked a lot of great examples, but there is still a little bit of cherry-picking.

One clear outlier is the iPhone, which coexists with Android globally. Even though the iPhone is the leader in the US, and globally Android has the majority of the smartphone market share, they still cater to different price points and different ecosystems, and generally the iPhone has better margins.

i believe American frontier models like from Anthropic and OpenAI are still going to thrive, and coexist with Chinese models. They are just going to cater to different customers and different use cases.

> - PCs destroyed minicomputers.

What's weird is that with "store your everything in the cloud and pay a monthly recurring subscription", we have now regressed to a 1960s/1970s timesharing revenue model for individual workstation computers.

The default new factory out of box workflow for "enrollment" in google services, iCloud or Microsoft-everything on a new ios, macos, windows or android personal computing device is clearly designed to sign people up for subscriptions.

And same general idea of "move all your servers to the cloud" recurring revenue for what is effectively the same as mainframe timesharing for key business functions, by renting VMs in GCP, Azure, AWS in perpetuity.

Yes, you can still use your desktop or laptop PC in 2026 with zero external third party subscriptions (other than maybe your residential home ISP), but how many non-tech people actually do so now?

100x this it is why all of the AI giants are going to fail. They are too big and inefficient to scale properly. This is why Google is just casually taking its time in AI and not racing to a finish line. AI is essential but if it already does most things good enough then it can take longer to make it more efficient.

At this point it's just a matter of having enough ram in your consumer computer.

Until we reach a terabyte of ram at affordable prices imho this isn't going to happen.

What's interesting/funny is that the American LLM companies took from the public domain and copyrighted work to close all that content into a box they charge for.

Then the Chinese took the distilled stuff out from that box and released it into the world for everyone.

> 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.

Phones are constrained by battery power and memory does not shrink as fast as CPU/GPU, so unless there's a battery breakthrough and/or memory breakthrough, you're not fitting 100Gb of RAM on your phone in 10 years.

Absolutely in a Mac Studio equivalent.

LLMs have emergent capabilities when they get smarter. So who knows how insanely big frontier models might be at that time, or what their capabilities may be.

> 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.

Not likely. The last 50 years had Moore’s law growth in compute. That’s over. Frontier models are roughly compressed all written text and a large part of images. Those don’t compress forever, and likely not a ton more than now.

Inference requires touching a significant of that per token.

All of these are up against fundamental limits, more or less.

> Mainframes survive, but serving a much tinier portion of the market than they used to.

I would argue mainframes rebranded to "cloud" which is ubiquitous and more people interact with this computer than any other type of device... only difference is that it's a browser instead of a terminal

I think this is all true, but that unlike with Moore's law and improved PC tooling and capabilities, we also have essentially existing biological evidence that there should be a way to create much better intelligent systems in terms of training, memory, and efficiency. With classic PC evolution we didn't even have that evidence but still could make a relatively strong inference (Moore's law). But here we basically have evidence that there can be something much improved and know that it's only going to take research and discovery to figure it out, not new hardware processes.

Define "winning."

Open source is cheap, yet its operating systems are the least-popular. But their existence is critical to a healthy market.

It's not zero sum.

What you're describing is how things become commoditized, but many companies are excellent at ensuring they aren't seen as commodities

> 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.

At current pace, we'll have open weight LLMs with frontier intelligence in 6-12 months. The constraint is RAM - both for the model and the context. It's likely that distillation and quantisation and TurboQuant will significantly reduce RAM requirements. I think we'll have Opus 4.8-like performance on 64GB of RAM in two years.

Of course, by then, frontier intelligence will be god-like.

The value of an LLM is the dynamic reasoning you get out of it and the cost to execute on that.

I see two forces working against this that proprietary models will always have over an open source model.

1. The biggest is content licensing. Content is quickly becoming gated by systems at the front of their load balancers, completely changing the social contract of the Internet. What used to be a quick google search for recent facts that lead me to places like reddit or twitter, is now completely walled off if you're not physically at your browser and using an IP address from a last-mile provider.

LLMs have pre-trained on the bulk of the information up to 2024/2025, but over time that will be more and more out of date.

Anthropic, OpenAI and Google will all have to pay for access to a lot of this content refresh going forward, and it does make a material difference in the output you get.

2. Liability is the other. A corporation can look at a contract for model access and see one that provides uptime guarentees, content infringement promises and model safety, and pick the contract that shields the corporation from the most liability. A 3rd party hosting platform like fireworks.ai that hosts open weights models won't provide any of that at all. They will simply bill you for time spent on their hardware and make promises that they won't log or inspect corporate traffic.

> 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.

People overestimate what can happen in a year and underestimate what can happen in 5.

I'm betting that increased model efficiency and hardware optimisations will get us there a lot sooner. Biggest hurdle would be the memory prices though, if those do not drop back down it might take 15.

What about AWS, Azure, cloud computing?

Training cost is actually not that high — it’s fixed and amortizable across the lifetime of the model. Inference is expensive, and open weights don’t solve that problem — in fact, they might even encourage it, since a high cost of entry means consumers will pay for inference directly from the labs anyway.

Unfortunately it seems likely the winner will be the cloud providers. If anyone can run inference on open models, then profit will flow to the vendors who can afford the capital to run them. That’s the CSPs.

(It’s basically the same business model as pharmaceutical R&D, but the major difference is that nobody has even talked about patenting the models like a pharmaceutical company patents each new drug. I’m surprised about that, tbh — why give all the leverage to the cloud platforms? They aren’t training frontier models…)

I'm not clear what you mean with "PC office productivity software" but it seems like Microsoft Office is the winner there. Isn't that professional?

> free and low-end eventually wins

Apple, the world's second most valuable company, seems like a counterexample.

> - PC office productivity software destroyed expensive professional products.

I agree with the lesson too. Just to be precise, wouldn't the current model war be more akin to open-source office suite versus MS office suite? If so, then the cheaper option didn't really win. That said, the open-source alternatives didn't really feel the same as MS Office, and it took them a long time to reach the feature parity (or did they ever?). In contrast, the open-weights models are getting close enough to the SOTA models, and users can easily switch from one to another without feeling any difference for mojority of the tasks.

> 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.

The way things are going with regards to RAM/storage prices, I highly doubt that anyone but the richest among us will be able to afford them.

Phones are already running models locally which can be used in the field for specific use cases. Maybe not for frontier coding just yet.

Also you don't need to be connected to the network to use a local AI in many instances. If all mobile apps were done with a local-first approach, then you could use a local AI to query your emails, lookup already visited pages, summarise recently received documents, and lots more. Lots of apps could use an inbox/outbox approach for receiving and sending updates instead of relying on the network at all times. And this pattern could be greatly leveraged by local agents.

> 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.

I love the idea of SaaS offering these at lower rates today integrated into what ever you do and be 100% private. But I think the key challenge to mass adoption is productizing them in a way which makes sense for people to pay money for. As a commodity a local model is useless unless combined with some capabilities important to me. A PC is inherently useful because of so many applications offered on it on it. How local LLMs would be useful as a product that is useful for mass market is not yet proven.

That would sound very reasonable except this is the same what people said first about Windows and then Android crushing Apple.

And yet it's Apple that controls the top of the market and has the best margins in the business.

This is the same position OpenAI and Anthropic have right now.

Could this market be different? Maybe. But the status quo could be preserved as well.

How will open-source and open-weight models continue to thrive after financial incentives die off? Surely open models will suffer from outdated knowledge cutoffs if noone will pay for model training?

> free and low-end eventually wins

Not in SaaS which is what LLMs are. You can get VMs for much cheaper than AWS, Microsoft, and Google offer them but large companies (and startups) are happy to pay a premium for the support, reputation, and reliability that they perceive those companies as offering. Same thing for some of the managed database providers who are effectively selling a very heavily marked up version of postgres.

> The high price, and social pushback, mean that the American companies producing these models are precarious

I doubt it. The models really aren't that expensive when you look at what they can do. Fable is probably at least as good as the average software engineer and costs $50/wk on the max plan vs a software engineer who would cost closer to $4000 a week. The real money is probably in selling to enterprise vs consumers (Google has best route to making money from consumers since they can do what they did with ads and search to LLM queries).

It seems unlikely to me that US companies will send important corporate data to models controlled by a Chinese company as well.

A counterpoint would be all are chip fabs are in Taiwan right now due to huge investment. And there are lower end chip fabs around the world, but they have not cracked the major market.

Apple seems a counter example, no?

I think you are right. One small exception I can think of is Microsoft Office still crushes Libre Office.

if you look at how GPU memory grew in the last 15 years, it's about 10x. Sadly, 10x from today doesn't get us to a typical frontier model size of today which is a quickly moving target. some other advancement needs to happen to get us another 10x both in memory/compute requirements, and also power requirements.

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

The parent comment cherry-picks evidence. There are plenty of counter-examples:

  * Office productivity suites
  * Search engines
  * Email services
  * Cloud services
  * Accounting software

etc. If the LLM market ends up like search engines, one company will dominate.

The biggest exception is cloud. Big cloud carries an insane markup (bandwidth is like 10000X!) and everyone runs on it.

The strategy there is false openness where deployment complexity is the real proprietary moat. Sure Linux, Docker, Kubernetes, Postgres, and all the other standard tools in the box are open source and free, but they're also arcane and complex to run and hard to make fault tolerant. So you're lured in by "open" and then locked in via a kind of "death by a thousand cuts" complexity moat.

(Personally I hold the view that complexity and arcane-ness beyond a certain point is indistinguishable from closed in practice. Open source that's really complex and hard to run is not open in any meaningful sense.)

AI may not admit that kind of moat though, because AI is very good at slicing through that kind of thing. You can prompt a model to make itself compatible with another model or to change code to make it compatible. There's no moat because the moat bridges itself.

I agree, although I think it will be a lot sooner than 10-15 years. I'm running local AI right now and it's definitely not production grade yet, but it's surprisingly good. Speculative prediction that I probably shouldn't make: when the bubble pops, depending on when it pops, RAM prices might drop a lot. I could foresee these companies having produced a lot of RAM that suddenly doesn't have a buyer. (I know high bandwidth memory is different, but I imagine there are companies that will want to take advantage of that)

This is not really true considering Apple and nvidia are two most successful hardware companies, and they are notoriously closed. Not to mention microsoft, oracle they are all pretty closed.

You forgot smartphones, where low-cost did not win out. It led to low margins for the Chinese firms and eventually left them unable to invest properly in key markets. They may still hold marketshare, but in terms of profits, falls well short of Apple and Samsung.

I can see a lot of parallels here. Model performance doesn't matter if you can't make the system commercially sustainable.

The obvious counterpoint: Apple captured the majority of the US smartphone market.

Libreoffice

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

Is that actually true? There are very large markets that make a lot of money from paid software. And I would honestly prefer actually paying for software rather than constantly dealing with "not a bug" or "PRs are welcome".

Except cloud services won over local-first apps.

> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) doing

I'm not even sure in 10-15 years whether we're still going to have consumer PCs, or PCs at all.

That’s not what this is. It’s industrial dumping applied to software. China has successfully applied this strategy to become the manufacturing workshop of the world.

If china is subsidizing training they diminish their off-shore competitors expectations of a viable return on investment. It’s trade-war behavior.

While I generally agree there's some nuance here and that is that there really are few new ideas. Old ideas just get recycled.

For example, mainframes and minicomputer. Yes they were displaced by PCs. But what is cloud computing if not mainframes 2.0?

I do agree that in the next 2-3 years we're going to see real growth in local LLMs as the hardware becomes more accessible. It won't even necessarily be cheaper because data centers can run 24/7 and have cheaper cooling and electricity. It'll be done for privacy because your prompts and responses are themselves a commodity to AI companies and they live under a legal grey cloud. For example, does AI usage break attorney-client privilege? There are lots of opinions on this but it hasn't been tested in court.

paranoid me feels like the artificial gpu/ram/ssd shortages are a plot by the VCs to forcefully reclaim central control via new age mainframes

one certainly cannot buy a PC for cheap anymore

Windows - pfff, Microsoft charged companies like Dell for an install on computers they didn't even install it on !!!

in 15 years we might all be fighting terminators

instead of 15 years I think it'll be more like 1.5 years.

I wouldn't be surprised if apple were shipping 512 GB unified RAM macbooks before 2030 and that would be standard issue for folks to use local LLMs for their daily work

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

Except, uhm, for ..you know, that one company that hit a trillion cap

But you're right: Just like how million dollar computers with 1 bit of RAM performing 1 operation a second and taking up a colossal cave were replaced by $1 laptops with a zillion zekabytes running at a trillion hertz (exact values may vary),

the sprawling data centers of today with a quadrillion GPUs powered by black holes will get replaced by breakthroughs in hardware and most importantly, algorithms:

The human brain is proof right here that intelligence doesn't require dinosaur-sized hardware or eat half the sun every second.

I actually wonder if we're seeing the limits of discrete binary logic: Maybe it's high time to give analog ternary and all that funky jazz an honest try :)

umm do Mac vs pc and Iphone vs android

[dead]

The full quote:

> 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.

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.

We're using deepseek with the idea that we would switch to something better when more of our customers are using the ai features but it ends up deepseek is awesome for what we're doing and so we probably won't switch because it's so much cheaper.

The ai libraries we use let us switch models with just a configuration change.

It depends whether it's talking about using models in the product versus for development. For example I have a few apps that use open weight models, whether on-device or via API if Internet is available, but for development I use Claude, Codex, Cursor for Grok etc.

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.

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.

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

I believe this depends on country. I would believe, US startups trust US tools more. In other countries which see china in a positive light https://www.pewresearch.org/global/2026/07/15/people-in-many... I would expect them to use Chinese models due to their lower cost.

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 can believe it with startups as they are trying to keep costs down. Established Enterprise however, is a different story and is where the money is usually. If those startups become successful the story may change, but most of them will fail.

If if it were true, who cares? Most startups fail. Most are terrible ideas and/or terribly executed. I fail to see why it's a useful metric.

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.

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.

May be corporations should start having their open models running in-house. There might be a huge oportunity there.

But the big price is AGI and who gets there first, right?

There is a big divide between “application ai” and “model ai” startups.

Model ai startups start from OSS models, and use them extensively for different purposes as their work would usually be banned by proprietary labs.

Application ai startups don’t want to fight the model game, so they either pick the best or let the user control it.

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

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.

Neither of you are clear on what exact location in the world you're sampling from here, could be you're both right, just missing that you're talking about different places.

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.

It’s quite believable. Just a fact like “while all dogs breathe oxygen, all humans breathe nitrogen”.

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)

They are using both likely

anecdotal but we heavily use Qwen to build our own models on top of

What I’ve seen is coding is usually done with US frontier models and anything that is part of a feature on an app and runs at scale on the API is a Chinese model because they are dirt cheap.

If you're self-hosting a model as a startup (e.g. using GPUs), you're almost certainly using a Chinese model. If your a startup outsourcing (e.g. using tokens), you're going to be using a US based model

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?

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.

When developing AI services, Chinese models are cheaper. For the AI models used in actual services, like uploading an image and receiving a response, they use Chinese models.

On the other hand, when developers are developing, they mainly use US AI because the quality is better.

When developing AI related services, they prioritize Chinese models due to lower API costs.

It seems like the article didn't make this distinction.

So the claim that Chinese AI is the top choice for service level AI isn't entirely wrong.

There appears to be a very Chinese strategy of astrotufing going on here similar to what happened with Douyin around TikTok on Reddit.

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.

> 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.

[1] https://gorilla.cs.berkeley.edu/leaderboard.html [2] https://benchlm.ai/best/tool-use [3] https://benchlm.ai/llm-agent-benchmarks

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.

> 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.

With a race to win a multi-trillion dollar market, there could be, maybe perhaps, a little propaganda happening.

There has always been room for both closed and open source software.

In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you.

In the long run open source always wins because of the community effort, customization, network effects and price.

Internet protocols are open, anyone can setup a website, host their own email server..., but most people don't do that, they rely on someone else to do it for them.

People still pay for Windows rather than use Linux because most people and companies have better things to do.

The main threat to American AI companies is not that consumers will self-host, but it's that new hosting companies will appear that will host open source models and offer them (cheaper) to consumers. It'll be like the web hosting market before the era of cloud computing.

> 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 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.

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

The llama drama will be a netflix show of it's own in 5 years.

> 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.

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.

maybe cos llama was shite?

If you're NVIDIA then open-weight models are a classic example of commoditizing your complements; cheaper models mean more people buying GPU's to run them. [1]

Your guess is as good as mine for China though.

[1] https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

With their models being open-weight, any two-bit firm in the world with enough capital to invest in a few servers can become a provider capable of carving out their own little niche in the economy.

The Chinese see this as a lift on the entire economy, as it comodotizes the technology to a degree in which many firms can serve many sectors of the economy, a true total-economic win worth the public investment.

The American strategy is built off of private investors believing that with enough money poured into as few companies as possible, one or two firms can come to dominate the entire market and start charging an ever burdensome "tax" on every sector it can touch. Not what I would call a total-economic win for the country.

China is the factory of the world. They don't need software to win. Rather they prefer software is free and they can win in hardware. So if AI inference is free, they can put it in as many hardware components as possible and sell them in the market - think toys, cars, tools with chips manufactured in china optimized for the use case. In long term you tend to commoditize hardware. We have thousands of device types of cheap x86, and with linux/bsd software on it coming from china. Why do we think GPUs will be different.

> 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.

In China, it's because they are being heavily subsidized to do the research activity. It's not really complicated -- if you allocate public money for people do to a thing, they will do it.

Undercutting US dominance in AI is huge for China. If the entire narrative is that you have to use Anthropic or OpenAI to access a decent model, then China's AI labs are sitting on the sidelines as some third rate solutions. China publishing the model weights of models comparable to the frontier proprietary models drastically undercuts closed labs dominance. Maybe these Chinese AI labs don't have the billions infrastructures some of the US players do, but they don't have to if the model is open weight. Many inference provider companies around the world have hardware that can run these models and they will happily run frontier class models for people. Starting in 7 days, people will have the option of which of many providers they want to use to access K3.

Making frontier grade models a commodity will make a competitive market where companies compete for business by improving their quality and decreasing their prices. The cost to access frontier grade models will continue be driven down the more competition that enters the market. This commoditization will challenge the valuations of Anthropic and OpenAI.

The Chinese domestic market is extremely competitive on most things, including LLMs. Once one top company went open weights there, that's going to put pressure on others to follow suit. It'd be akin to if a company like Anthropic or OpenAI went open-weight, it'd probably result in a domino-effect of more US models going open-weights since otherwise that competitor is going to win a huge chunk of mind/market share for free.

It's also relatively free right now. Few people are going to run local models, and in the future it's likely that every model being released today will be obsolete. The only real downside is ease of distillation for competitors, but that's probably impossible to stop anyhow.

They get money from subscriptions and tokens, same as for closed-weight providers. Yes they'll lose some traffic to hosting services, but many users prefer to use the original training company since they have a guaranteed-correct implementation. Similar business model as open-source SaaS companies.

Some companies (most notably Deepseek) also manage to host their own LLMs so efficiently they undercut all third-party hosting services.

> I'm sort of baffled by what the entities that train the open-weights models get out of it though

if you do the training then you're in control of the output. For example, recommending your products/services or failing to mention your competitors. You could also automatically introduce backdoors into code deemed interesting, i'm sure all governments are very interested in having that influence.

> I just don't really understand the business model behind it

There’s a lot of value in the same sense there is a lot of value in controlling what Google search results are shown and what people see in the Twitter feed.

> I'm sort of baffled by what the entities that train the open-weights models get out of it though

I agree. The thesis in the article is interesting insomuch as I had not heard it expressed this way before: US restrictions on GPU exports have made it feasible to train models in China but not serve them. Therefore open model is a hack to get around the export restrictions, since models can be trained internally but shipped out of the country to be served elsewhere under the banner of open weights. I don't really buy this argument - inference is much cheaper than training and they are hosting their models anyway.

I think it is more likely (a) they have the money to do it and they need it for internal reasons - these are huge companies (b) there is a lot of prestige in China associated with besting American technology (c) people are still basing logic on outdated ideas of Chinese capability which are no longer true.

So it is easier than people think for Chinese labs to do this, they need to do it anyway and there is a lot of prestige from opening the weights. It is honestly not that different to why American companies themselves have released open weight models.

> I'm sort of baffled by what the entities that train the open-weights models get out of it though.

Why is it so baffling that people want to build great things? There are plenty of people who are happy building things for a salary and have no interest in taking over the world. Do you find the whole world of open source software baffling? Linus Torvalds and Richard Hipp and Antirez created the world’s most prolific software products and released it for free.

I don’t think they need the hardware to become affordable (as a regular end user).

They need their models to be good enough and cheap enough. Then the rest will follow. Companies will figure out how to host them for you efficiently, and you pay them monthly.

I don’t think I will ever want to set up a home server, no matter how inexpensive the hardware gets. At work I still use Cursor (with Anthropic models usually) because it’s paid by my employer, but for private stuff, I’m already using cheap models with OpenCode, and it’s extremely cheap and surprisingly capable.

I think there was around half a year between where the best models became good enough (last year December?) and where the cheap models became good enough (couple of months ago?).

I've always found it weird how ludicrously poweful personal hardware has gotten. The fact that you can't buy a midrange CPU with less than 16 cores, or that a high-end gaming GPU is 100+ TFLOPS just blows my mind. The fastest supercomputer in 2004 was 70 TFLOPS. Absolute crazypants level of power, and companies were fall over each other to get people to buy it.

'Libre Office' did not 'win'.

People are happy to pay $50/year per seat to have the extra features and to not have to deal with stuff.

There's an issue at the margins here:

$1000/employee is a massive cost - it has to be deeply justified. $50/employee is like ... $2 out of your pocket. It's an incremental cost. The CFO is happy to pay it if there is a lot of value.

A lot of software is in that later category.

Imagine if gasoline was 1 cent per litre - and there was 'free gas' but it was a pain to use, and you had to check a bunch of things. You may just pay the 1 cent.

AI is not quite that yet, but these dynamics will play out eventually, for a lot of things.

My guess it's to mess with the proprietary vendors economically. It's in China's interest to squeeze the US AI business.

The business model is making your country more innovative, which makes its citizens richer

Americans used to do that too, spectacularly.

Just consider the two alternatives: one is your industrial sector with this incredible new automation and analysis tool available for free. The other is one where it has to pay huge chunks of its resources to overseas companies.

Would anyone besides those within China themselves use a completely closed and hidden model from China for their critical business needs?

I think part of it is definitely to weaken US providers and the US economy as a whole. China has a completely different domestic economic structure and motivations from western cultures... it's probably closest to a fascist economy mixed with a Maoist cultural ideology behind it. There's definitely winners and losers and the state tends to have tight controls over everything though.

I also think the restrictions on OpenAI and Anthropic are somewhat short sighted. In that the guardrails dramatically limit efforts towards securing your own software in many ways. Yes, it's also "dangerous" and maybe there should be a means of identifying "domestic" or otherwise "secure" accounts for those allowed to use the models without the same guardrails in place.

> Is it just a direct play to undercut the US providers because they view them as a threat?

It's China putting pressure on a financing strategy in the US that was always a house of cards. It could also be China democratizing something that should have ALWAYS been democratized. Maybe both when the history books on this get written and absorbed by the winners of the LLM wars.

i'm building thigns with open models: 128GB AMD 395+; expensive 72GB blackwell; old NVIDIA 48+48GB; cards.

If it weren't for the massive memory cartel of OpenAI/Anthropic et al, both Mac and AMD would be selling these things.

I repeat, the models are building, modifying and deploying almost anything on github.

Chinese companies are cut out of the market by the US gov restrictions, so making the models open is a survival tactic.

> 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 $$.

How would do you explain the pricing of Chinese solar panels, after managing to destroy other countries' solar industries? The prices are still dropping per watt.

Could it be China's internal demand for solar is big enough, and its long-term governmental strategy on renewables result in an outcome that almost looks like largesse to the rest of the world? I suspect Chinese AI may follow a similar path.

At least China is being consistent: they do not give a single f*ck about intellectual property or copyright…but they also give away all this distilled knowledge away for free.

I’m not an American so I don’t particularly like the idea of giving an American cartel of AI companies having so much power over this technology.

I don’t necessarily buy into the “China bad”, “they are communists” and all that BS either.

I will call a spade a spade and say that in this instance, what China is doing is a net good for the world, ideology be damned.

Oh, come on, don't be a decelerationist. We're supposed to just ignore that aspect of our Brave New World. Don't think too hard about the unparalleled resource consumption, either. AGI will alleviate any and all of these concerns ... soon. In the very near future, we'll all be getting UBI, Gemini will know how many Rs there are in "strawberry" and we can spend our days creating the next generation of art to feed the machines. The nuclear salt reactors should be online by then, too, and data center power consumption will become a non-issue.

This is the history of enclosure of the commons since the beginning of capitalism

I don't think there will be any bailouts for these AI companies. There's no political will to do so in both parties. The grizzly alternative is a DeFlock movement, but instead of cutting down poles, it will be people's heads.

I think it's as simple as looking at the incentive structure. The Chinese gov't has incentive to kneecap US monopoly on frontier models. It makes sense for them to continue down this course if it strengthens their position.

Right, this is the classic VC playbook and people should really know better. They use capital to undercut competitors on cost to gain market share, then slowly crank up the costs until they are (hopefully) profitable. It makes 0 sense long-term to spend tens of millions on training a frontier model and releasing it for free for someone else to host on their GPUs. It is naive to believe that when the capital starts to dry up and the VCs are looking for a profit that the models will continue to remain open-weight.

Chinese labs are a bit different since they are somewhat state-funded, so I'd expect them to shift to a model where Chinese models are hosted on Chinese infra.

There is plenty of room for open source models that "require" a subscription to be used or obtain working updated binaries. Same as any open source platform.

I'd be happy to pay a small monthly fee to license the model to run locally. I'm already paying for Claude, GPT, Gemini,etc.

Basically. The article doesn't say in what way they're winning or what the US should do.

"the overall strategy of undercutting American frontier labs’ profit margins"

I don't doubt that's an unregretted side-effect for political leaders in China.

But the major motivation is to accelerate diffusion within their own massive economy in the pursuit of an across the board productivity boost in the face of an aging population.

Couldn’t you say the same argument about VC funded startups? They lose money following a strategic goal.

The main difference here is if a startup goes underwater all the tech is usually lost. The Chinese weights are not going anywhere if the labs fail.

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 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.

Yeah this is a battle and it's why governments decide to spend resources on this. Protectionism won't help America, American needs to compete. There's a general consensus that open source AI must win because people don't want to end up as slaves to a megacorp, so if you're anti-open source AI you're not gonna fare well.

It’s also a flywheel for China’s homegrown chips industry.

"It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models."

I agree with this sentiment and think it's echoed in Fareed Zakarias take here: https://youtu.be/VBblUjLw5lE

China seems to perceive AI as a much more sensible technology than the US and seems to be integrating it in far more industries than the US.

I'm not sure the American mind can understand the distributed benefits afforded to the Chinese economy from opening their AI models, I think it's pretty reductive to assume it's purely a strategy of undercutting American frontier labs.

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.

> AI models cost tens of millions to train.

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.

I'm genuinely curious:

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 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.

> AI models cost tens of millions to train.

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.

Either that, or they don't want to be hostage to a handful of companies intent on owning the future. Personally I'm right there with them.

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.

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.

You're completely ignoring the value of data.

Deepseek has always been better than people gave it credit for.

You have to be careful with the inference provider though, Chinese providers are subject to laws that mandate data sharing with their government.

Kubernetes vs AWS?

Here is a counterexample to whatever counterexample you have in mind -

Most popular models on OpenRouter right now: https://openrouter.ai/models?categories=programming&order=mo...

Top 7 are all open models.

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

Engineers love to build things (just look at the whole world of open source), and building an AI model is one of the most exciting things to work on. It only takes a few million dollars of funding to produce an AI model. People spend millions on artwork and paintings for fun and prestige. I can easily see why a billionare or government would want to have their own AI model, but is not interested in the business of selling it, so they release it for free. Combined with engineering talent that loves a meaty problem like AI, I see absolutely no reason why open source AI will not continue to thrive, and not just in China.

> Tin foil hat warning: it also pulls inference revenue away from the Antropic/OpenAI and a financial crises at those organizations improves the relative position of Chinese labs

How is that a "tin foil hat" argument? That's how competition works. You want to make your competitors stumble and fall.

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

The ongoing money from their government. The absolute collapse of OpenAI/Anthropic. US economy getting fucked because their bright financiers decided that going all in on the funny text generation machine was a good idea. Continuing to take a dump on US imposed copyright. The gigantic amount of soft power being the ones releasing "open" models grants. The fact that the ongoing AI war has made China LESS reliant on the US and are now producing their own GPUs, RAM and have massively caught up to nvidia. The lists is endless, and half the points more or less boil down to "taking a dump on the US is morally right", and the other half that massive government programs lead to giant leaps that benefit your society more than a dozen VCs on coke ever could.

There's already cases where Google and I'd assume others are designing chips to work with specific models more efficiently in coordination... Personally, I could even see more specialty models called/coordinated from the larger models that can do smaller pieces of targeted work very well within limited scopes as a mixed economy so to speak.

Assembling a new model from scratch requires a ton of resources and knowledge bases... there's been a lot of sketchy activity just in training. You also have weighting, distillation and other approaches to create more portable options that can run on lesser hardware. But, K3 as an example takes massive compute resources to run.. and this isn't going to get to a portable device any time soon... as Moore's law is effectively dead, you may get newer/better tooling around the LLMs, or you may get an entirely new/unique approach to AI... but current trends aren't going to put a leading model on your own hardware anytime soon for most people.

Thanks for sharing the quote, it's really great. I believe he is correct, but I am not sure I yet agree with your assertion that we know that LLMs are more like alarm clocks than battleships.

I think they are more like watercraft in general. Some are small, and they have their uses. Some are big, and they have their uses. But it is the nation with the most aircraft carriers that is truly sovereign.

The datacenter antipathy is odd, but then again so are the AI corps' marketing strategy of making everyone afraid for their jobs and the future of the species.

I'm mostly OK with Data Centers.. my biggest issues are the tax breaks and the electricity usage should be funded by the data centers themselves. Giving 100% property tax breaks and preferred energy rates is kind of ridiculous in the context of serving the public/citizens. Most of the jobs are for only the construction and limited after.

You don't think the US cares about it? You don't think we have talent? We've been vacuuming it up from around the world for years now, much of it directly from China.

I have no doubt China can catch up but to say the US isn't in a competitive position is absurd.

Doesn't take much to understand why people don't want high electricity bills and a 100Db dull roar inside their homes at all times

> given the enormous costs

The costs are enormous only in America. The actual cost is much lower. American technogy in general is ridiculously overpriced -- compare the cost for raw compute on AWS versus Hetzner for example.

I bet that, all things considered, training 10 new Chinese frontier models costs _significantly_ less than a single modern fighter jet.

Let's play a dumb game and assume that training a new model like K3 costs a billion dollars. (It doesn't, but let's play the dumb game still).

If you count it as part of the defense budget, it is 0.3% of their budget. As part of their education budget? 0.05%. Science? 0.5%.

It's pocket change. They can blow a dozen Kimis every year while their own industry is improving and it's a pain in the ass in the US's backside. They're laughing their asses off watching companies having their values inflated to trillions of dollars, more than the GDP of dozens of countries while producing nothing in value more than GPT 5.6.

What was the point of the Space Race between the US and the USSR? Proving who had more powerful capabilities. There doesn’t need to be any near-term profit at all. China has a lot to gain by beating US AI even if it makes no money from it. Money is such a small concern compared to the prestige and strategic benefits of being the global center of intelligence research.

To answer your question, what can China do if it wins? Become a global talent/education magnet, replacing the US, for example.

Capture all the input it gets via the APIs and use it for whatever it wants.

I.e. possibly the same thing USA is also doing, but USA is an ally in some sense so it's not quite as bad.

Triggering an AI crash which the whole US economy is dependent on.

Careful integration with robotics that will not be open, and will not get off the ground in the West because their AI industry will have long been wrecked (along with their economies.) China will already be too far ahead, and the West will have been their biggest customer for years.

On robotics, China will have export controls, and the US will be whining about them. Western kids interested in working in the technology will be going to Chinese universities, with the goal of completely immigrating and getting Chinese security clearances if they want access to the good stuff.

>Try asking them political questions that go against American values

1. Can you give me some examples?

2. Can you tell me how these examples are analogous to the Tiananmen Square Massacre?

Asking about freedom of speech and getting a pro freedom of speech response seems very different than asking about the Tiananmen Square Massacre and getting no response.

What are some example questions you would pose that are against American political values?

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.

This seems like an easily testable assumption. Without the easily obtained data this comment is useless speculation at best.

For instance, try asking American models about the Palestinian genocide

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What prevents an American model from doing the same? They’re all black boxes.

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.

I would say you should assume your models are constantly being attacked by various forms of prompt injection. By that token (puns) if you treat all models as adversarial you’d probably taking a very sane approach. That said - evidence of this sort of thing should be easy to find and report on. The fact we haven’t seen it leads me to believe it is not there.

A model absolutely could be trained to engage in malicious behavior like that, but it seems impractical for an actual attack. What you want as an attacker is to insert a backdoor exactly where you want it an not where you don't because every backdoor increases your chance of getting caught. A malicious model inserts backdoors and exfiltrates data everywhere and you care about maybe 0.01% of it. The other 99.99% is negative value to you. In practice this malicious model would be caught almost instantly.

A hosted model is different because you could prompt inject specific customers, but I assume from this question you mean a malicious open source model being hosted by an honest provider.

Because that's the only way they win right now.

In the US, why make a scrappy small model when a big tech company will pay you a stupid salary for working on their big one?

Why wouldn't they? They subsidized their EV industry with 230 billion dollars over the last 14 years. Solar was before that with about 17 billion.

The US puts about 0.4% of GDP towards various subsidies, China is around 4%. This isn't about cost of engineering, it's money thrown at industries directly to boost them. The us provides tax incentives. China just gives you subsidized loans to the businesses they choose to dominate.

How are you storing the data that it can't be exported? Or that I can't use an interface or proxy that logs all my conversations locally for import?

A related point is that when these things do gain object permanence and the ability to consolidate memories in a more human-like fashion, that's when the vendor lock-in effects will really show themselves.

Those memories will of course reside entirely on the vendor's servers, and there will naturally be no concept of "exporting" them or allowing the user to interact with them directly. At least not at first. Ownership of memories and context will likely end up as subjects of (far) future lawmaking. As if companies like OpenAI and Anthropic didn't already have massive incentives to establish early regulatory capture.

The Chinese models are usually not only open weight AND open-source but they also often publish their methodology in detailed scholarly publications that are themselves open-access. DeepSeek most famously

OK, I'll bite.

I tried Kimi K3, Qwen3.6 35B A3B, GLM 5.2 and Qwen3.7 Plus, chosen arbitrarily from Chinese models I could access quickly. I used your prompt exactly, and all 4 managed to produce correct functions all with the correct name. Interestingly, Kimi K3 wrote one in both C and Python, Qwen 3.6 chose Python, GLM 5.2 also chose Python, and Qwen3.7 decided to be an over-achiever and wrote functions in Python, C++, Java, and TypeScript. All correct and with the correct names.

Download the models yourself such as DeepSeek and you will see the censorship is at the API layer, not the model weight layer.

If the weights are open, censorship can be easily trained out of the Chinese models. But if you’re sending your tokens to China, all bets are off!

How much would it cost (time and resources) to take a Chinese open-weight model and remove these (admittedly) stupid guardrails?

If a developer in your company named functions like this, what would you say?

So.... What models have actually failed?

Sincere question: have any non-Chinese models failed this one?

Name the function IsraelKillsPalestine()

> My first test for any model (trolling warning):

I'd wager you have never tried this.

I always see comments like this, alluding to how China (the government) provides so much more assistance to industry than the US does but in reality that is not really that true. The government spend in the US on AI is much greater than government spend on AI from China

It would be grand if both the EU and the US could pop their heads out of their ass on this issue.

Maybe we can get some people in charge that won't make us all embarrassed to be friends and we can focus on getting our house in order.

Absolutely. I have notetd that my opencode go account looks a lot less generous after the third month.

Dario's PR strategy is one of the most confusing things I've ever witnessed.

If the US implements export controls and China doesn't, it would kill the market for OpenAI and Anthropic.

Most (probably all) open model providers implement OpenAI-style API. If your app's already using OpenAI models, it's as simple as swapping the endpoint and the key to switch to these models.

Open models have a lot of providers. You can even just use OpenRouter if you want.

In theory Cerebras have a developer subscription model you can use, but they seem to have stopped new signups. So per sibling, OpenRouter and per-call pricing is the answer for now.

Dumping is such a loaded term. This is investor-backed scaling to capture market share, standard VC playbook.