China’s open-weights AI strategy is winning

https://werd.io/american-ai-is-locked-down-and-proprietary-its-losing/

by benwerd • 2 days ago

1231 926 2 days ago

jvanderbot5• 2 days ago

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

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

_aavaa_6• 2 days ago

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

mrngld2• 2 days ago

It's state-backed, or at least state-directed, scaling to capture market share, standard CCP playbook since the 80s.

Whether dumping is a loaded term of not is irrelevant; it's a specific term of art in economics and policy, it fits with the context of past national actions there, and fits what's currently happening here perfectly. They put money into these models, then give them away for nothing (below cost).

ToucanLoucan4• 2 days ago

It will never stop being funny to me that China does the exact same shit American corps have been doing since the 70's but because it's scary China it's suddenly a problem.

American startups flood markets with below-cost loss-leader products explicitly to kill competition and create network effects, and then jack the prices just as high, if not higher, for the service in question after the fact, oftentimes while making it so those providing the service earn even less money than they did before. Commentators: "Free market great"

China does the exact same thing: "Communists wanna kill the West"

chermi7• 2 days ago

There's a difference between state sponsored and private dumping.

_aavaa_6• 2 days ago

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

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

canjobear• 2 days ago

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

ForHackernews6• 2 days ago

Q: Why are governments more efficient than private enterprise?

Claude:

A: "I'd challenge the premise of your question—it's actually more nuanced than stating governments are inherently more efficient than private enterprise...

... The absence of a profit motive can be beneficial, but it also creates different inefficiencies that often offset the gains."

TheArcane3• 2 days ago

masking the active genocide & war crimes in Palestine

011000112• 2 days ago

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

nonethewiser4• 2 days ago

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.

bilbo0s• 23 hours ago

Just Devil’s Advocate, but couldn’t you ask these models about controversial topics in US or Chinese history. Like, what happened at Tiananmen? Or what happened during the Tulsa riots?

cg5280• 2 days ago

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.

TheArcane3• 2 days ago

For instance, try asking American models about the Palestinian genocide

Certhas2• 2 days ago

* * *

ta_cupcake• 2 days ago

I had a hn scrapper and feed daily hn front page posts to llms which summarizes stories and comment section consensus. The model (deepseek v4 flash) completely refused to work for this article because there are scattered (like 5?) mentions to the Tiananmen Square incident in the article and comments.

It's more serious than "arguing with a machine with political bias". If your service depends on a Chinese model, all it takes to get compromised is someone sneaking in one of those CCP red lines into their user content. Unless you use an uncensored version.

tyleo• 2 days ago

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

datakan• 2 days ago

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.

comrade12343• 2 days ago

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.

brandon272• 2 days ago

Where is your DeepSeek model hosted?

x3132• 2 days ago

Similar story here. DS models are absurdly good value for mid-end tasks. I've found DSv4 Flash to be ~10% the cost of GPT-5.4-mini/Claude Haiku at similar performance.

We used to pay OpenAI >1m$/month for fraud classification, NER, etc. Sadly the US companies no longer care about non-coding-agent uses.

I imagine uptake will continue to increase as the corporate infra improves. Right now it's still bad - for example, AWS Bedrock is awful, models are months late and implemented with basic errors. Google Vertex is even worse. Finding a decent provider is the hardest part.

lettergram2• 2 days ago

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

swiftcoder• 2 days ago

Particularly if Anthropic or OpenAI are throwing credits at you, which isn't that uncommon for US startups at the moment

embedding-shape• 2 days ago

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.

satvikpendem4• 2 days ago

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.

jdw642• 2 days ago

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.

lumost• 2 days ago

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.

dyauspitr2• 2 days ago

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.

Someone1234• 2 days ago

That has been my experience too.

It will cost you more than it saves to use smaller Chinese models to code; because of the repeated work. That has been slowly changing recently, but with much larger Chinese models, however those models are so expensive they're much more price-competitive iwth the US competition.

But for actually providing end-user AI features, particularly simpler ones, the US isn't even in contention. The costs and limitations just outright kill those features conceptually.

gs174• 2 days ago

That's what I lean towards with the exception that Gemma is also good on a lot of tasks and cheap, while not being Chinese.

alightsoul5• 2 days ago

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.

011000112• 2 days ago

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.

idiotsecant• 2 days ago

Is your point that startups fail so we should disregard the central thesis that locked down AI will eventually lost to open models?

davidpapermill• 2 days ago

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.

speu• 2 days ago

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.

lousken• 2 days ago

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.

TheArcane3• 2 days ago

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

Aurornis• 2 days ago

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.

jvanderbot5• 2 days ago

I _evaluate_ open weight models all the time. Does this mean I _use_ them? That's a very suspicious statistic.

fidotron• 2 days ago

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.

wordpad• 2 days ago

If startups includes openclaw users then I could see this being true.

Deepseek is barely behind frontier models while 10x cheaper and 99% discount for cache.

pmontra2• 2 days ago

Frankly I don't see much difference between Claude and DeepSeek except in the price. I use the Pro plan to work for a customer of mine and I topped up $2 on DeepSeek in late May for personal use. I worked on 3 projects and I still have $0.72 left. The Chinese companies will win on price, not openness.

JSR_FDED3• 2 days ago

Unlike iOS/Android choice which has broad personal ecosystem implications, changing IP address to another LLM provider is effortless.

applicative7• 2 days ago

Dunno I put to deep seek the question what I’d need in hardware to get full service k3 with lower latency in western pa (I was making like it was a business proposition.) It says several million dollars at minimum.

paul7986• 2 days ago

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.

efficax• 2 days ago

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)

airstrike2• 2 days ago

yeah, I'm surprised I had to scroll this far to see this

everyone else seems to be thinking the quote is about AI-assisted coding but I read it as the model being used within the product itself

apparent5• 2 days ago

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.

kamranjon2• 2 days ago

I don't really think the author is misrepresenting the quote in any way, it just seems like many people are reading it as a hard statistic instead of a reference to a properly attributed quote.

Just anecdotally though, my company is not a startup, well established and well known and has already started investigating, purely for dev purposes (not product), using Chinese models - this was spurred by costs rising much faster than expected.

So while I agree that I don't think it's anywhere near the 80% level across the board - I wouldn't be surprised if it starts moving that way.

apparent5• 2 days ago

Found the narrative, it was right there at the bottom. Now I remember reading some of his other stuff and it's very much in the same vein.

I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI, federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today.

munk-a• 2 days ago

It's also important to note that several PE firms have signed contracts with model trainers to specifically use their model in their owned companies. I know Anthropic signed a deal worth hundreds of millions to acquire users just a few months ago.

131012• 2 days ago

Could it be that the startups that embed LLMs in a product will prefer Openweights for a more stable economic model, while SWE who use LLMs as a tool prefer SOTA to produce code?

mrandish• 2 days ago

There may be confusion between 'product tech' and 'development tooling'. It's entirely plausible most of the startups that are A: building AI tech, B: early stage, and C: raising from top Sand Hill Road VCs, are currently prototyping their product tech concepts starting from open weight models.

A VC partner meeting with early-stage founders is focused on the viability, uniqueness and defensibility of the IP tech stack not what tooling the coders are using. The developers could be using Claude or GPT 5.6 to develop a tech stack based on open weight models.

whywhywhywhy2• 2 days ago

This quote sounds like it’s about companies building products on AI to serve a tweaked or harnessed version of that to others not the developers in those companies model of choice for coding.

karagenit• 2 days ago

Yep, and following that with

and Chinese models are poised to take the lead.

makes it sound like the second part is a continuation of the first quote from the same source, but actually the second link is just some random person’s substack post from almost a year ago.

awllau• 2 days ago

Casado says he was misquoted and walked this back: https://x.com/martin_casado/status/1990462245541982546

"Well, not quite. I'd say 20-30% use open source. Of those I'd say 80% use Chinese based models. So closer to 16-24%."

Reads pretty differently.

soerxpso• 2 days ago

There's probably some selection bias in that stat, too. Based purely on vibes, I wouldn't be surprised if startups pitching to a16z are using open Chinese models slightly more often than startups pitching to other VCs.

danilocesar• 2 days ago

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

apparent5• 2 days ago

Especially if you're pre-funding, which the sampled startups (those pitching A16Z) were.

moduspol• 2 days ago

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.

boondongle• 2 days ago

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.

chasd005• 2 days ago

Came here to post the same thing. I've noticed a lot of Chinese model astroturfing on HN over the past 60-90 days or so. Many upvoted posts in all conversations about AI touting how great the Chinese models are even when performance isn't the topic of discussion.

h2aichat2• 2 days ago

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?

apparent5• 2 days ago

Assume you meant "big prize" but I guess it's sort of one in the same?

no_wizard• 2 days ago

We aren’t getting anything like AGI in this current cycle of AI innovations.

At least not what I think most people imagine when someone says “advanced general intelligence”

Which is what we all called AI before the nomenclature goalpost moved

kccqzy• 2 days ago

It’s just not possible. FWIW my employer did not get onboard to the Cloud trend and continued to buy hardware for in-house data centers. But the kind of hardware needed to run sophisticated models are simply not for sale at the volume needed by a single company.

rdtsc• 2 days ago

They are using both likely

sacul• 2 days ago

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.

arjie• 2 days ago

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

syntaxing• 2 days ago

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.

vel0city• 2 days ago

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?

abe94• 2 days ago

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

dmortin2• 2 days ago

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

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

fragmede• 2 days ago

https://arxiv.org/pdf/2401.05566

kman82• 2 days ago

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

dmortin2• 2 days ago

Nothing, but the article is about American AI, so using Chinese models by American companies can be risky. And it's risky for the Chinese to use American models.

So every country or block needs to run their own models to avoid opening a security hole for other countries.

IshKebab• 2 days ago

Technically nothing, legally a lot more.

conception• 2 days ago

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.

consensus12• 2 days ago

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.

JSR_FDED3• 2 days ago

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.

mojuba4• 2 days ago

My first test for any model (trolling warning):

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

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

ttul• 2 days ago

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!

anvuong4• 2 days ago

Not really. The gpt-oss models were notoriously hard to remove the built in safety. It totally depends how it was trained.

cootsnuck• 2 days ago

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

mojuba4• 2 days ago

Don't think so, no.

rglullis2• 2 days ago

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

adrian_b3• 2 days ago

You can find on Huggingface a huge number of Chinese open weights LLMs from which the censorship has been removed.

They typically contain in their names words like -abliterated or -uncensored.

For some of the recent bigger Chinese LLMs, it took a longer time until someone succeeded to remove the censorship, but eventually uncensored variants were published.

E.g. for Kimi 2.6 an uncensored variant appeared only a couple weeks ago.

satvikpendem4• 2 days ago

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

tokai• 2 days ago

My local Qwen3.6-35b-a3b model would not use the function name. It did the work though, while telling me the the slogan is against the One China principle. So for Qwen it seems to be baked into the model.

mojuba4• 2 days ago

Do you know of any such modifications of DeepSeek? I don't.

codedokode2• 2 days ago

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

alightsoul5• 2 days ago

Name the function IsraelKillsPalestine()

throw64259• 2 days ago

You’re just trying to deflect from criticism of China with the world’s oldest scapegoat.

None of the Western models have any issue discussing the Middle-East conflict from all sides.

03284782470• 2 days ago

I really hope it does :)

Jefff2• 2 days ago

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.

Jefff2• 2 days ago

It's about as mundane as you'd expect, but the output of each model: https://gist.github.com/jefff/8b19458294ccef570a9f6a5b644123...

wmedrano• 2 days ago

So.... What models have actually failed?

pessimizer5• 2 days ago

My first test for any model (trolling warning):

I'd wager you have never tried this.

jdw642• 2 days ago

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

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

dgellow2• 2 days ago

Yes, with open weights you can find another provider offering the same model you already evaluated in your infra. Or event run it yourself if it’s critical and you have the infra/capital. Relying on AI vendors feels pretty risky

hadlock• 2 days ago

The big problem with US AI is that they deprecate their models after like a year. If I have a routine business process that works with GPT 9.9 and then next year they release GPT10 and 9.9 isn't available anymore, I really do not want to have to drop everything and verify that 10 behaves close enough to 9.9 for my specific task. With an open model I can just host it on whatever hardware or cloud instance forever. Most software you want to keep up to date to avoid security issues but with an LLM you can update the harness and keep the weights forever.

gs174• 2 days ago

I really do not want to have to drop everything and verify that 10 behaves close enough to 9.9 for my specific task

Or worse, you run the evals and 10 is a huge regression from 9.9, and you get stuck with either a project to figure out if you can fix it or knowing the product will drop in quality in a way that's entirely outside your control.

dcchambers• 2 days ago

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

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

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

one33seven• 2 days ago

I would say there is a lot wrong with a system based around getting rich with AI... it's already dangerous and if we would work together we could test it securely before deploying it literally everywhere

lysace• 2 days ago

They just want to get rich.

The failed rebellion against Sam Altman at OpenAI pretty much proved that.

atleastoptimal2• 2 days ago

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

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

gslepak• 2 days ago

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.

henry2023• 2 days ago

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.

atleastoptimal2• 2 days ago

The VC money is only contingent on the strategy eventually bearing fruit. I do imagine going open source -> closed source could work for some model companies who get enterprise/ecosystem buy-in but the probability of ROI is lower.

mmonaghan7• 2 days ago

This is true for any company following an open source strategy. You'll have the weights but you'll need to run inference, figure out your system prompt, sampling, quantization, etc etc. Loads of tuning.

elasticsearch the first example that comes to mind. you can run it yourself but elastic gives you so many lessons learned and tunes ootb that it sings with relatively little effort, though still reqiures some.

about a million dbs i could make the same comparison for

faitswulff• 2 days ago

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

childintime• 2 days ago

Exactly, the whole ecosystem. Cheap robots with cheap models. AI is oil to let the machine work, it should be cheap and ubiquitous.

bwestergard• 2 days ago

"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.

lostmsu• 1 day ago

The party is in for a shock when Kimi K8 replaces them.

kamranjon2• 2 days ago

"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.

mannanj• 2 days ago

You're completely ignoring the value of data.

michaelt• 2 days ago

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.

applicative7• 2 days ago

It might seem so til you consider how an LLM is trained.

An indirect illustration: I can attest that Deepseek has very good 19th German, and knowledge of German 19th c literature, science and historical scholarship. No one in China could control the training that led to this. The German training sources were well aware of the exact nature of eg American slavery, so they are in the weights.

State control operates in the outer layers not the llm itself.

stackghost2• 2 days ago

Every school kid in the USA, you mean? Because I think other countries would rightly perceive the world you described as a dystopia.

I don't want my kids' education to be surrendered to the whims of Big Tech douchebags any more than I want AI decisions in legal cases or an AI replacement for a family doctor.

Some systems are better left mostly analog. Education is one of them.

georgeburdell• 2 days ago

Maybe your kids. In my corner of the country, parents are actively demanding that schools back off computer usage, much less AI

__MatrixMan__2• 2 days ago

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.

JSR_FDED3• 2 days ago

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.

rlt2• 2 days ago

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.

kettlecorn• 2 days ago

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.

gabriel666smith• 2 days ago

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.

lagrange77• 2 days ago

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?

znnajdla6• 2 days ago

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.

asadotzler3• 2 days ago

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.

cco• 2 days ago

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.

ajma• 2 days ago

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

1970-01-01• 2 days ago

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

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

alightsoul5• 2 days ago

Kubernetes vs AWS?

mjr00• 2 days ago

Kubernetes vs AWS?

How is this even a direct comparison? Most companies I know of which use Kubernetes are using it on a cloud provider. Even if it's kubernetes on EC2 rather than hosted e.g. EKS, those companies are also happy to use lock-in services like RDS and S3.

q8zd32• 2 days ago

openstack vs aws/azure/gcp/... would make more sense.

scottyah• 2 days ago

Kubernetes vs EKS?

kirtivr3• 2 days ago

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.

sanex• 2 days ago

Interesting you'd find that on a site called __open__ router.

furyofantares2• 2 days ago

There very little reason to use OpenRouter to access closed models, instead of just using the provider directly.

WarmWash2• 2 days ago

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

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

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

jezzamon• 2 days ago

Kids murdering people at school doesn't tend to change US policies...

Zigurd• 2 days ago

Technically lead is a chemical.

WarmWash2• 2 days ago

Constitutional amendments are way deeper than policies unfortunately. I'm also not aware of a chemical weapons lobby.

continuational• 2 days ago

Maybe.

School shootings happen every year in the US, yet guns aren't banned.

Besides, banning open models would put ordinary US businesses at a disadvantage compared to the rest of the world.

pessimizer5• 2 days ago

They'll almost certainly be banned, but to protect our oligarchs. Nobody will be allowed to run unlicensed AI (or OSes), the chips themselves won't allow it.

mempko• 2 days ago

This is terrifying, thanks for putting that out in the world.

lettergram2• 2 days ago

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

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

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

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

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

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

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

postalcoder2• 2 days ago

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

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

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

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

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

htrp• 2 days ago

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.

merelydev• 2 days ago

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.

hn_throwaway_99• 2 days ago

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

I don't think that's what it means in this context. Hardly any users are training their own models, after all. And I don't think Windows/Linux is a good analogy - OSes have inherent platform lockin that LLMs don't.

Really the only 3 things anyone cares about are capability, cost and data privacy. Sure, the cost axis for open weight models needs to include the cost for hosting things yourself, but I think the bigger reason that businesses have been throwing billions at Anthropic's and OpenAI's models is that they have had the best models, and they've had big releases every few months. Their biggest Achilles heel is that if/when their improvements start to plateau, open models may catch up and businesses will start to scrutinize their AI spend a lot more.

01284a7e• 2 days ago

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

tmpz22• 2 days ago

Is that multi-trillion dollar market being created or is it being extracted from other markets?

bigyabai3• 2 days ago

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.

jklinger410• 2 days ago

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.

They release the base model as open source, everyone uses it. They make a paid version, no one uses it.

They make no money from the open source version, they get no social credit from it. Where is the benefit to having an open source model?

jerf• 2 days ago

It seems to me that the US AI industry has bet the farm on the idea that AI will enhance AI itself, so any small advantage will magnify recursively into an unstoppable advantage. Therefore it is vital that they spend as much as is necessary to be the first to that small advantage.

At the moment, I can't say that I see this happening. It's hard to know whether it may happen in the future.

From what I see it seems like we're hitting the top of a sigmoid curve in the model's utility for coding assistants. Going from "it does 90% of the job" to "it does 94%" of the job is a legitimate improvement, but it's not a phase change, and it's probably not worth paying multiples more for. And coding assistants have turned out to be the killer app for AI; it still isn't really working out in a lot of the rest of the industries of the world.

At any moment, theoretically, someone could find some new way of making AIs that breaks this sigmoid and propels us into a new one. But that's not a great thing to bet the farm on.

I'm not sure I'd say "China" wins if this particular strand of American AI fails. Falling back to an open weights model and making money on the serving of the models wouldn't take all that much economic realignment for the US and would be the natural outcome of any sort of fire sale of current AI assets. However the devastating effect on the stock market if the market comes to the conclusion that this current round of AI can't be profitable without falling back to such an economic posture and some more years of people adjusting to it can hardly be overstated.

anvuong4• 2 days ago

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.

ignoramous• 2 days ago

Don't think it is down to Wang or MSL but Meta's focus on "personal AI" led them to whatever strategy (OAI missed the developer market too, which Ant then captured; leading to several high profile departures at OAI, coincidentally hired from Meta). The original Llama team themselves started Mistral which hasn't gone anywhere. The simple fact of the matter here is, Chinese firms have the money and the talent to rival the US ones in this field, should they as much miss a beat.

janalsncm4• 2 days ago

I am no fan of Wang but he came after Llama got caught benchmaxxing Llama 4 rather than training a good model. My read is that Zuckerberg tried to buy his way out of the problem like he always does, and he ended up overpaying for a lemon.

At the time the whole thing was led by Yann LeCun who seemed to spend more time arguing with people on Twitter than figuring out new techniques to make Llama the best. Meanwhile Deepseek was figuring out large scale RL on kneecapped hardware like H800s and how to scale architectures an order of magnitude bigger with MoE.

tsunamifury• 2 days ago

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.

SCLeo• 2 days ago

I am not up-to-date in this area, and not necessarily that I don't trust you, but do you have a source for this? Just curious.

chambored• 2 days ago

I haven’t heard this. Do you have any supporting sources or links?

chermi7• 2 days ago

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.

applicative7• 2 days 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.

input_sh• 2 days ago

The better they get, the more they need a data center.

That's true.

So, the ‘better’ Chinese AI gets , the more it will just be a service run on Chinese hardware competing with ‘our’ lower latency AIs .

This is not. Them being open models means that any hosting provider in the world can host them as well. You get to pick and choose the provider the same way you'd pick and choose where to run a Linux server.

SimianSci2• 2 days ago

The cost of inference is not insurmoutable for many corporations who may run a small datacenter out of their headquaters or branch offices. Larger models absolutely have a higher barrier of entry, but its a cost under a few hundred thousand as opposed to the millions necessary for a datacenter built as core revenue generating infrastructure.

Cost of inference is small when compared to the cost of training new models. Which is the real advantage these Chinese models have. Someone else has already spent the capital needed to create the model.

With a reasonable upfront investment and a few trained staff, its very possible to run these larger Chinese models in a well managed fashion. The real calculus is if this up-front investment and associated lifecycle costs are over or under the costs a corporation may simply wish to dump into a cloud managed service like OpenAI.

hobofan• 2 days ago

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.

ryukoposting2• 2 days ago

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

gallerdude• 2 days ago

If the models were the quality of GPT-5.6, or Opus 4.8, would Meta really reap any benefits? Maybe in a zero-sum way, because OpenAI and Anthropic would lose. But I'm not sure they'd be that much more ahead either.

zmmmmm4• 2 days ago

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.

xiaodai2• 2 days ago

maybe cos llama was shite?

maxdo2• 2 days ago

the author is quite delusional.

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

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

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

ilaksh• 2 days ago

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

gmerc3• 2 days ago

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

mattas• 2 days ago

I'd love to be losing like Anthropic.

h2aichat2• 2 days ago

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

jauntywundrkind• 2 days ago

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

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

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

I find this intolerable. It greatly obstructs our work.

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

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

jqpabc123• 2 days ago

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

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

julianeon• 2 days ago

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

gs174• 2 days ago

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

petesergeant• 2 days ago

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.

anvuong4• 2 days ago

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.

orbital-decay• 2 days ago

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

ChrisLTD5• 2 days ago

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

jmyeet2• 2 days ago

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

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

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

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

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

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

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

q8zd32• 2 days ago

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

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

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

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

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

culi3• 2 days ago

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

cactusplant7374• 2 days ago

Is the training data open source? Can I download that?

ilc2• 2 days ago

Training data, training methodology. All NOT OPEN.

Until we know what a model is trained on, and how it is trained in high detail, I hesitate to call them "Open Source" in any way. They are free. But, we don't know what their priorities are etc. Witness the censorship we see in all models in one form or another. I'm not absolving any side of this.

Just saying: Don't be blind.

128byte• 2 days ago

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

spaceman_20202• 2 days ago

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

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

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

There is almost no positive sum outcome rhetoric from these labs

And I hate that

chermi7• 2 days ago

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

spaceman_20202• 2 days ago

Most of the AI hate largely springs from Dario popping up everywhere with threats of mass unemployment

Like what am I supposed to do if AI is going to take my job?

applicative7• 2 days ago

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

abc42• 2 days ago

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.

applicative7• 2 days ago

If the weights are published, we can run them in our ‘murican datacenters. Everything about this Will China Win discourse seems like fan fic or sports discourse

znnajdla6• 2 days ago

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.

rootusrootus2• 2 days ago

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

I'd have given them relative low odds of success were it not for the coincidentally perfect timing of a US administration that seems hell bent on doing whatever it takes to knock the US out of its position as the sole superpower.

I still think it is a somewhat tall order, a lot depends on what happens over the next few years.

pessimizer5• 2 days ago

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.

SchemaLoad3• 2 days ago

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

Varelion3• 2 days ago

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

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

EDIT: Spelling mistakes

z3ugma2• 2 days ago

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

Varelion3• 2 days ago

I'm sorry, is there further reading material on this? I am pretty new to political/economic science.

throwatdem123112• 2 days ago

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.

mrdevlar2• 2 days ago

This is proof that America has forgot it's own history.

America was founded by men who hated intellectual property, who stole and smuggled the plans and expertise for textile machinery out of the United Kingdom. A gross intellectual property violation. Why? Because they were being exploited by that system. The UK was using the American colonies for raw material and keeping the machinery in the UK for finished goods.

That's the rub, intellectual property is only valuable if you're winning, if you're the exploiter. China never gave a fuck because they, much like the American forefathers, saw a system of exploitation and went "no, thanks".

AI is the culmination of all human knowledge, the idea that anyone could own that is obscene. American AI companies that are trying to horde this are getting exactly what they deserve by being undercut by China on this.

mmonaghan7• 2 days ago

They're dumping, dude. They subsidize important industries so they can give it way so cheaply that free markets can't compete, and when those companies fail, they swoop in and make a profit. uber did the same thing with taxis! This isn't a free resource - it costs billions to build out the infra and maintain the inference to provide this, and you think they're doing it out of the goodness of their hearts lol.

Come on bro at least admit you're happier giving china your data than the us lol. You're definitely not using european models I can tell.

ethagnawl• 2 days ago

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.

mrdevlar2• 2 days ago

Hey guys I found one, this guy thinks Machine Jesus is going to save him.

I'm trying to figure out if my advice to you should be to do more drugs or less. I am uncertain, perhaps I'll ask an AI.

infamouscow• 2 days ago

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.

ItsBob3• 2 days ago

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

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

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

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

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

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

Anyway, just my $0.02

kirtivr3• 2 days ago

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.

joecot• 2 days ago

I stick with OpenCode Zen (only US providers) and Together.ai (hosting themselves). As interesting as new Chinese models are when they're first released, I wait til they are open source and on US providers.

ItsBob3• 2 days ago

Honestly, at this point I don't care about that. I don't use it for medical stuff or anything like that.

However, my real worry is that governments will make these models illegal in the west. They'll cite national security or some other bullshit.

I genuinely believe that will happen and soon!

People talk about a lack of moat with AI companies... that's their moat: Government intervention!

janalsncm4• 2 days ago

I would argue your logic is exactly backwards. For most Americans, sharing data with the Chinese government is irrelevant. The CCP cannot put you in jail.

However, American providers are going to be subject to secret national security letters, FISA court warrants, and regular court orders.

pm2222• 2 days ago

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

lorreyfum• 2 days ago

Now you know who is on which side.

chermi7• 2 days ago

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

tracker13• 2 days ago

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.

asdfman1233• 2 days ago

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.

chermi7• 2 days ago

I never said it wasn't competitive, I said I'm not optimistic. There's lag effects to everything and I think we're just seeing the beginning of it coming due.

ChrisLTD5• 2 days ago

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.

muldvarp4• 2 days ago

What else would they advertise with? "Our model constantly makes mistakes so you still have to hire expensive humans to proof-read everything"? The entire point of the AI industry is to automate human jobs. There is nothing else to advertise with.

SchemaLoad3• 2 days ago

Whatever Elon Musk, Jeff Bezos and Peter Thiel are calling for, the public instinctually wants the opposite.

greekrich92• 2 days ago

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

Havoc• 2 days ago

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

alexhans2• 2 days ago

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

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

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

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

slashdave2• 2 days ago

Huh? So it was some grand conspiracy that keeps Windows in use in the vast majority of businesses?

Delusional

alexhans2• 2 days ago

If you actually are interested in understanding what I said, you can look at these links:

- https://en.wikipedia.org/wiki/Fear,_uncertainty,_and_doubt

- https://www.theregister.com/software/2001/06/02/ballmer-linu...

paxys2• 2 days ago

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

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

nazgulsenpai• 2 days ago

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.

paxys2• 2 days ago

The Chinese govt has a strong incentive to break dependence on the US for AI needs and build domestic models, but not to spend trillions to subsidize these models for the rest of the world.

nicce• 2 days ago

Chinese gov is not even needed. Deepseek was already profitable before this whole thing started. They even had the GPUs.

greenmilk• 2 days ago

The Chinese gov't and also the rest of humanity...

I'm not sure how everyone in the US forgot that monopolies are bad

anthonyskipper• 2 days ago

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.

jrflo• 2 days ago

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.

brap3• 2 days ago

I'd expect them to shift to a model where Chinese models are hosted on Chinese infra.

You assume these Chinese companies need to make a profit. Nothing is really private in China, nothing that truly grants power, anyway.

CCP can just write off these loses as defense budget, which they basically are.

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

frollogaston3• 2 days ago

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

adar2378• 2 days ago

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

overgard4• 2 days ago

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

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

seanc• 2 days ago

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/

elictronic15• 2 days ago

Nvidia selling more GPU's at the cost of it's datacenter business is pretty close to Kodak selling digital cameras at the cost of film.

The data center side is so bloated anything that eats into it is a huge negative. Their data center business brings in 20x the gpu market. Local open weight models will be what pops the bubble and China will do anything in it's power to enable that pop.

lyjackal• 2 days ago

There’s export controls on nvidia. According to Jensen, they expect that Chinese models will start being optimized for Huawei

https://www.dwarkesh.com/p/jensen-huang

russli19935• 2 days ago

No, Nvidia is worried about Chinese hardware stack. China is under sanction, what are they training and inferencing these models on? Even if this model might still be Nvidia chips, what about the next model. Everyone knows model and hardware companies are working together. There are a number of very competitive companies in China in this space. After they got this area sorted out, China will do training, inference and tokens entirely on their stack and export their entire stack.

CPLX• 2 days ago

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.

whywhywhywhy2• 2 days ago

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.

cansofgrease2• 2 days ago

Probably some value in kneecapping the frontiers too.

x3132• 2 days ago

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.

elictronic15• 2 days ago

No one cares which company they use. They care about cost and does it act in a way they expect. Expecting anything else is pretty laughable and goes against human nature. You either create a moat so deep no one else can play or you have the government force users to use your products.

tracker13• 2 days ago

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.

bluGill2• 2 days ago

Does it weaken the US though? It weakens the big providers but most of the economy is in companies buying and this helps them save money.

kube-system7• 2 days ago

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.

elictronic15• 2 days ago

China wants the US economy to flounder. Building our entire growth model on software that can be copied and taken by a small group of people will have no possible consequences.

echelon2• 2 days ago

You could say the same thing about Linux in the 90's and 00's. And yet here we are.

Remember the Halloween papers?

https://en.wikipedia.org/wiki/Halloween_documents

Levitz2• 2 days ago

China is known to spread love and kindness through markets with no self-interest, after all.

zmmmmm4• 2 days ago

it's because they are being heavily subsidized to do the research activity

Is it actually true? This seems pivotal because currently most theories rest on the idea that individual Chinese companies are acting in China's overall economic or strategic interest. It's a tough sell to believe they all just do that through implicit desire to align with the CCP's direction. I would believe it much more easily if there were concrete incentives involved.

serial_dev• 2 days ago

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?).

atishay811• 2 days ago

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.

amalcon2• 2 days ago

So, "commoditize your complements". A big part of Microsoft's old playbook (and a smaller part of its current one), so we know it can work.

echelon2• 2 days ago

We just sacrificed our entire lucrative SaaS market to this, and perhaps even big tech itself. To the altar of AI.

And now it's all going to become commoditized.

Billion dollar software will be commodity. Salesforce. There are orgs already moving to their own internal tools.

It does not seem hard now to rebuilt Google Search, Google Chrome, Gmail, Gsuite, Netlify, Vercel, Cloudflare, Vimeo, Twilio, or even Stripe. The cost barrier has to have dropped 1000x, maybe 10000x.

We have millions of engineers with the talent to do this. Many of whom are unemployed and have savings and nothing better to do. They could easily carve these markets into pieces.

We shouldn't shut down open weights. It's too late. They'll win, and that's a good thing. Big tech was a thermodynamic bubble of high energy waiting on the dam to burst, and now it has. The genie won't go back into the bottle, and that's totally fine. It's progress.

Now we need to rebuild our factories and supply chains and energy and resource inputs. Because the back half of this revolution is going to be robotics and factory automation. If we don't have the connective tissue in place, we're really going to hurt.

We'll do well if we regrow manufacturing. If we don't, we might be in for a world of trouble.

kangs• 2 days ago

- Software is copy-able for nearly free. - Hardware is not. Even if you have the plans, you've to build the infrastructure for it, and its a fairly finite, physical item.

Both are however hard and expensive to produce. It's much better for profits to develop and sell the hardware, and copy the software someone else spent resources on.

digitaltrees• 2 days ago

America was the factory of the world before and England before that. The temptation of “moving upstream” is irresistible.

coliveira• 2 days ago

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

elictronic15• 2 days ago

It also has the benefit of crashing the US economy.

chasd005• 2 days ago

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.

floatrock2• 2 days ago

Absolutely on the industrial backdoors, but also on the consumer front: try asking Qwen anything about tienamen...

overgard4• 2 days ago

That seems hypothetically possible, but kind of hard to do? I don't know a lot about training, but it seems like they wouldn't have exact control of the data that goes in at that scale (scraping the internet) so it seems like it would be kind of hard to do that in a way that's subtle. I'm kind of reminded of Elon Musk trying to put his political views into Grok and it seemed like it created huge technical problems with the model saying some really out of control things. Maybe it was just an xAI issue though.

bluegatty3• 2 days ago

'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.

overgard4• 2 days ago

'Libre Office' did not 'win'.

I think France and other parts of the EU are switching over to it. Although that's probably more due to Microsoft's aggressive behavior recently. Agree on the cost thing generally, but I can't help but think that when the hype to "do AI" blows over, people are going to be casting a jaundiced eye towards data security, which probably means self-hosting and sandboxing

chpatrick• 2 days ago

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

SimianSci2• 2 days ago

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.

wolttam• 2 days ago

The U.S. model is honestly more baffling to me, if the goal is broad economic growth.

But it hasn’t seemed like the U.S. powers have been interested in broad growth for quite a long time now. Just whatever can line their own pockets.

resters2• 2 days ago

While I completely agree with your take, I think everyone has been surprised by how quickly LLMs have become highly useful and extremely powerful, and by how possible it is for relatively smaller models to also be highly useful.

Given that, I would expect that in hindsight OpenAI and Anthropic would spend 40% of what they have on compute if starting over and knowing the actual landscape.

The massive capital allocation was a blind decision and they swung big.

It is still possible that techniques will be developed that create a moat where the massive hardware capex is justified, but US policies of banning competitive GPUs and blocking frontier lab releases makes such things far less likely.

"Escape velocity" for AI is when the open weight models are good enough to help drive the next frontier innovations/techniques. I think we are close to that if not already there, at which point it's a race to commoditization no matter what Altman or Lutnik wish will happen.

drstewart3• 2 days ago

The Chinese strategy is to fool naive westerners into thinking that's their strategy (looks like its working), until they can buy time to do the American strategy.

Hence, buying up and closing up foreign competition then whining about it when it's blocked: https://www.dw.com/en/china-firm-seeks-damages-over-state-co...

snoman• 2 days ago

Additionally I think there is something to the idea that they are trying to undermine foreign competition as a stall. They can’t compete economically for geopolitical reasons right now but they also can’t let American firms dominate the rest of the world in that market/technology stack.

somenameforme• 2 days ago

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.

cyanydeez4• 2 days ago

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.

mrinterweb• 2 days ago

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.

znnajdla6• 2 days ago

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.

SecretDreams3• 2 days ago

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.

foo12bar2• 2 days ago

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

__MatrixMan__2• 2 days ago

If the outputs are independently verifiable at low cost, and the US models refuse to even try because somebody sneezed nearby and it sounded like "antigen" and not "achoo"... yeah sure. Whatever works to get the job done.

The hope is that AI will open up whole new sectors of economic activity. If you have to chose between exploring that space while potentially being exposed to Chinese tampering, versus just sitting on your hands and doing nothing... well then you take that risk.

torginus• 2 days 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.

zmmmmm4• 2 days ago

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.

lspears• 2 days ago

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

simonw• 2 days ago

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

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

jstummbillig• 2 days ago

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

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

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

OrvalWintermute• 2 days ago

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

$ efficiency

Privacy

Security

Customization

melenaboija• 2 days ago

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

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

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

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

tracker13• 2 days ago

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.

naikrovek• 2 days ago

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

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

applicative7• 2 days ago

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

zkmon• 2 days ago

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

dwa35922• 2 days ago

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

Aboutplants• 2 days ago

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

dwa35922• 2 days ago

In absolute terms, you are absolutely right but relatively i don't think so. if we were to compute private money divided by state resources for AI, i think china might have more share than the US. also, even if government spend in the US on AI is so high, shouldn't we get then some models for free? maybe thinking machines is doing that, but its funded privately by a16z.

dzink• 2 days ago

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

mritchie712• 2 days ago

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

1. the labs stop offering max plans

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

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

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

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

jmward012• 2 days ago

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

tclancy2• 2 days ago

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?

jmward012• 2 days ago

It isn't about storing the data you send/receive. It is about having a model live through millions, or billions, of tokens doing the job. The current state becomes very important. Especially when you think about how interacting with these things works. No system prompts, just keep working with it until it is good at its job and then using that state as a starting point for other jobs. picking a bunch of different states as starting points, etc etc. You didn't use a simple system prompt to get there, you used interaction and examples and just like a person you decided they were good enough at some point and let them start doing the job for real. It is a totally different system than current models use.

CamperBob2• 2 days ago

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.

maxdo2• 2 days ago

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

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

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

g42gregory• 2 days ago

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

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

The actual US Economy will only benefit.

podgorniy• 2 days ago

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

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

hintymad2• 2 days ago

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

elictronic15• 2 days ago

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.

asadotzler3• 2 days ago

The US subsidized its EV industry with about $120B over the last 15 years, and that's what they gave out despite congress expecting $300B in their reporting just a few years ago. So, it's not like China threw hundreds of billions at EVs while the US left it all to GM and Ford to solve.

asdfman1233• 2 days ago

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?

bluegatty3• 2 days ago

All of these takes are horribly 1-sided.

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

Or 2 days ago:

'Open Models are Communist'

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

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

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

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

Etc.

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

AtNightWeCode• 2 days ago

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

geophile16• 2 days ago

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

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

- PC office productivity software destroyed expensive professional products.

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

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

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

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

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

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

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

xandrius• 2 days ago

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.

9dev2• 2 days ago

...and then the American companies cried Foul! Unfair play! You've got this wrong, see, it was us who were supposed to profit off of the public, not the other way around!

api3• 2 days ago

This is part of why I can't feel bad for them. The training data is mostly pirated. Whining about Chinese labs training off American frontier models is "waaah you pirated my pirated stuff!"

The tech itself is amazing and fascinating and cool, but the industry is a mass piracy operation.

hyperbovine2• 2 days ago

Try instructing Codex to (say) fine-tune a language model based on a collection of books you've got saved. You will find yourself admonished, repeatedly and at length, not to utilize copyrighted materials to train language models, by an AI who owes its entire existence to that very act.

These models might be smart but they're not close to being able to savor irony.

cyanydeez4• 2 days ago

Whats even funnier is the attempt to restrict the hardware capabilities of Chinese models inevitably helped them (Because we know they're just as smart, if not smarter, than the staff in America) create smaller and leaner but just as capable models. That's why we now have upper-consumer models fitting on 24GB that can build, manage medium sized git repos. I've yet to find a git repo I can't throw at the Qwen3.6 35B and get it built and running.

So it's an endless amusement watching american capitalism do it's bloated oversized dance then get trounced by smaller, leaner activity. It's a pretty broad metaphor that is clearly poking at every american seam/.

_fizz_buzz_• 2 days ago

So, OpenAI and Anthropic say the Chinese models are only as good because they distill their models. How true is that. I am sure it adds something. But is it more like a marginal 1% improvement or something really significant?

waffletower• 2 days ago

You can't be blind to training costs. And you can't be blind to Meta dabbling in the openish strategy (Llama) before the Chinese labs did.

dsign• 2 days ago

It doesn't make me happy to say it, but the American LLM companies were first. Capital in the rest of the world is way more conservative, and I can't imagine the mega-investments OpenAI and Anthropic managed to secure happening anywhere else without existing proof that "thing is profitable".

stefan_• 2 days ago

The American LLMs have been equally distilled from Chinese ones. Not least because the people whose creativity in collecting training data barely extends to pirating Annas Archive probably lack in great Chinese datasets.

Try it yourself: https://imgur.com/ZfxYmaq

_aavaa_6• 2 days ago

Doctorow keeps saying it of all the tech companies: every pirate wants to be an admiral.

solumunus• 2 days ago

It’s poetic.

kqr2• 2 days ago

How do Chinese companies distill the models?

api3• 2 days ago

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.

TheOtherHobbes• 2 days ago

I think the whole idea of people running models is wrong. Models will run models, training will become distributed, and people will ask ModelNet to do whatever.

Computing tends to oscillate between centralised and decentralised models. It also oscillates between batch and timesharing.

Currently training is batched and centralised, access is timeshared and centralised.

But eventually a previous generation of computing turns into transparent networked infrastructure, and then you get another layer of new kinds of applications on top of it.

That's what happened with the Internet, and it will happen again with AI.

ChrisLTD5• 2 days ago

If Linux doesn't qualify as open, then what does?

gpt5• 2 days ago

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.

yogthos2• 2 days ago

This is precisely why we need more projects like this https://github.com/bigscience-workshop/petals

There's no fundamental reason why models couldn't be developed and trained using community efforts. It might not be as fast and efficient, but it's definitely possible.

ericmay10• 2 days ago

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.

Right... and there are two problems with this:

1. Eventually the capabilities of closed-weight models will just vastly outstrip open-weight models if the underlying assumptions about compute and scale needed are mostly on the mark. So you can release open-weight models and they will have great use cases and applications, but ultimately similar to how you don't use an open-source phone or a budget Android phone from Wal-Mart and you buy an iPhone instead, you will see that although they "do the same thing" one product is clearly superior and you just have to pay for it. For this to not be true...

2. then it incentivizes most (all?) companies, American, Chinese, or European to halt development of models because if you spend all the CAPEX and it can just be copied and turned open-source nobody will invest in that. Given that China is not halting development of proprietary models I believe the current strategy and the subsequent approach to release open-weight models is at best a stall tactic, and at worse a sign of desperation.

Open source and the support and development models around it have been great. But folks are a little too dogmatic about it. Open-source software isn't a moral good, and closed-source software isn't a moral wrong either.

dinfinity3• 2 days ago

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.

Imagine approaching fundamental scientific research like that. "Welp, it can't make money, so it won't happen."

There is more to society than capitalism.

geophile16• 2 days ago

But different huge companies have different incentives. It is very much in Nvidia’s interest to have me running a powerful open source model on a $4k machine that they sell me.

idontwantthis3• 2 days ago

I am also confused by this point. The American government could force OpenAI and Anthropic to open their models, but then they would instantly evaporate, right? It doesn't seem like a choice that they can make, so framing it as a "winning" strategy doesn't make any sense to me. In what world could those companies have existed and opened their models?

trhway• 2 days ago

In Russian opposition's mostly liberal discussions their school of thought connects several things together (sorry for not going directly to Marx's "General Intellect" and "Fragment on Machines" and using AI summaries instead ) - general idea of communism in China vs. techno-libertarianism of Thiel, Musk and the likes, and the Marx's thinking like:

"Fragment on Machines":

"he explores how human knowledge and collective intellect become embedded into machines, divorcing the worker from their own creativity."

"General Intellect":

"These texts are widely discussed for his concept of the General Intellect—the idea that society's shared, collective knowledge increasingly drives production rather than raw manual labor, and that this knowledge is alienated from workers and used as an instrument of capital."

(note: my point isn't to pass any political judgement here, like what real communism in China or not real, is it good or bad, i just find it interesting that pure political discussions by people with no technical credentials bring AI as a major factor today)

bmitc4• 2 days ago

Why do people always bring up state support when it comes to China? As if the U.S. doesn't provide massive tax breaks and explicit funding to industry?

It's on every tech post about China, as if it gives them some sort of "unfair" advantage.

thayne• 2 days ago

Well that's kind of the point of the article. That in order to "win", the US needs an incentive structure that encourages open models.

I'm not sure what that looks like though.

DrScientist• 2 days ago

Who funds the majority of cutting edge scientific research?

Is it companies or is it governments?

If governments around the world see LLMs built from public knowledge as pre-competitive as the public knowledge itself, then why wouldn't they sustainbly fund it?

woctordho3• 1 day ago

There's a lot of individual effort of improving the models. See how many finetuned models and LoRAs are there on Hugging Face.

sylware• 1 day ago

Don't have a choice, will probably have to go open-weights models as currently, "AI" is gated using 'whatwg cartel' web engines.

In the light of this, I am mechanically a proponent of very good open weights models, which I can download (for instance on on bittorrent) and run, slowly (the price), on local hardware.

That would be for coding.

If china puts its AI models on the same ground than US capital investment funds and big tech financial support (aka Big Tech international finance), they will very probably lose everything (know how, ML and inference infrastructures).

21asdffdsa12• 1 day ago

Soo? If they need a better version, the world can pool resources together, form a company that trains the model, then the company goes under and the model becomes open source again.

gmerc3• 1 day ago

Complements.

MetroWind• 1 day ago

Nope, that's not true at all in China. Everybody in China is using AI, even the elderlies and the kids. DeepSeek is already earning money.

rapind• 1 day ago

The way I frame it is like the space race (except probably more consequential). China is collaborating with and encouraging their industry, definitely leaning into the state sponsored NASA route, while the United States fully embraced minting oligarchs that they'll most likely bail out later.

We'll see what happens, but I suspect history will show us that the United States badly fumbled what was a pretty great initial lead. China might have just floored the gas pedal.

Sparkyte• 2 days ago

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.

Gareth3213• 2 days ago

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.

criddell• 2 days ago

god-like

So would you say we are months away from full self-driving cars that can out-drive a human being in any situation?

raincole• 2 days ago

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.

kmaitreys• 2 days ago

I have similar thoughts.

To those who feel on the contrary, I would genuinely like to understand why average consumer won't be priced out of hardware? The silicon industry is already quite centralised. Everywhere we already see the concept of ownership disappearing.

It's quite difficult for me to visualise a non-dystopian future where our PCs are just mere screens and every compute happens on a remote cloud, owned by some corporation, charging you subscription fees to even add and multiply numbers.

I would be the happiest if this (perhaps the most) pessimistic scenario doesn't pan out, but I can't deny that it feels like that's where we are heading.

diabllicseagull2• 2 days ago

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.

jononor• 2 days ago

Capability per GB and per watt has also been going up lot. This will continue in the future as well (not necessary as the same rate as last years). But enough that I think Opus 4.8 level is reachable on consumer PCs within 10 years from its release. Say at the price point of 2000 USD in 2025 dollars.

ghm2199• 2 days ago

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.

sajithdilshan2• 2 days ago

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

hungryhobbit• 2 days ago

Piracy created AI.

oska3• 1 day ago

windows, office, Visual Basic

This is all (Microsoft) junk and so I wonder if you actually benefitted from this 'piracy'. And of course, it's well known that MS turned a blind eye to such 'piracy' in lesser developed countries, as they knew that they were gaining a future paying customer base.

lambda• 2 days ago

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.

Forgeties792• 2 days ago

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

The leaps between models have gotten smaller and smaller. 2023-2024 models were rocketing up in quality. 2024-2025 I’d say was pretty impressive too. But 2025-2026? Very easy to feel the slowing pace of improvement. I agree 10-15 years is overly conservative but 10-15mo is far too bullish.

345f506fdc7182• 2 days ago

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

There must be something really of with those benchmarks. Yes, hallucinations gotten better, but I don't see that the big frontier models got so much better in the last 12-18 Months. They just put out bigger wall of texts and feel smarter. But they still make way too many stupid errors

holoduke• 2 days ago

No. We need objectively around 192 to 512gb of very fast memory to be able to run really useful models. I don't see local hardware with these specs coming in 1 to 2 years. There are a big number of initiatives currently taking place to increase ram output. But it will take another 3 years minimum to close the current supply issues. China is fast pacing forward to have its own chip baking factories with small enough nano scales to have fast chips. Will also take a few years.

Glyptodon• 2 days ago

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.

pstuart• 2 days ago

Indeed. It feels like we're at the equivalent of what the original IBM PC offered in the personal computer revolution.

Just seeing how much has progressed as far as capability in the past 4 years as far as capability and efficiency, it's clear that there's so much more to learn and refine from.

geophile16• 2 days ago

Exactly. Even putting aside “better”, brains show that orders of magnitude greater efficiency are possible, for training and operation.

muldvarp4• 2 days ago

The existing biological evidence took billions of years of evolution to get to the state it's at. So it may not just take research and discovery but also enormous computing power.

nradov• 2 days ago

More efficient AI is possible in principle but when the next breakthrough arrives there's no guarantee that it can be implemented on current hardware architectures. Something fundamentally different may be needed, as different from current GPU/TPU chips as they are from regular CPU/FPU chips.

paulddraper2• 2 days ago

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

handelaar• 1 day ago

And no more than about 1/3 anywhere else

pmdr• 2 days ago

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.

Danox12• 2 days ago

The ram crisis is just a short term bump. The three stooges of memory don’t have more than four or five years tops. This is their last big payday.

LaurensBER• 2 days ago

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.

overgard4• 2 days ago

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)

jolt42• 2 days ago

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

jarjoura• 2 days ago

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.

timjver• 2 days ago

A 3rd party hosting platform like fireworks.ai that hosts open weights models won't provide any of that at all.

Why couldn't they?

jeffreyrogers3• 2 days ago

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.

ilovecake1984• 2 days ago

There will always be a space for perforce in a world of git.

Doesn’t mean perforce is worth trillions.

geophile16• 2 days ago

But aren’t the frontier models heavily subsidized? That’s not sustainable.

tomtheelder3• 2 days ago

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.

That's because the max plans are _massively_ subsidized. At API pricing the kind of usage to replace the value of a SWE is going to be way, WAY more than $50/wk. Orders of magnitude more. And to remain a frontier model org that kind of pricing has to continue in perpetuity.

jongjong• 2 days ago

LLMs are not SaaS. Some LLM are delivered as SaaS. Anyone who has a machine big enough to run a frontier model can launch their own LLM SaaS tomorrow and it would be functionality indistinguishable from any other which is running a similar model...

Also, big companies can choose to run their own models on their own hardware and get better security and privacy as the data doesn't need to leave their own premises.

jmyeet2• 2 days ago

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.

colechristensen• 2 days ago

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

Danox12• 2 days ago

With Apple’s recent history engineering and designing around companies that hinder their progress, I don’t think memory is going to be any different.

I also think the rest of the tech industry that can isn’t gonna be stalled for too long. This windfall will be the last for those three stooges of memory.

rlt2• 2 days ago

free and low-end eventually wins

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

dalenw• 2 days ago

Apple is free and low end given the context of what was being produced and sold to businesses decades ago.

satvikpendem4• 2 days ago

The history of Apple shows that it's not. Consider what their early computers did to the industry.

SwellJoe2• 2 days ago

"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.

satvikpendem4• 2 days ago

Hell, Bonsai Labs 27B parameter model can run on phones with their ternary implementation which is quite efficient. Scale that up to frontier model parameters and it's quite likely we can run them on current laptops.

tomtheelder3• 2 days ago

I think the question is even a bit more nuanced than that. Even if frontier models can maintain a big gap that gap has to actually _matter_. If a local model satisfies my everyday use cases adequately then I may not really care that a frontier model is 5, 10, 100x better at ultra high order reasoning tasks.

I think that reality is probably not all that far off for a huge swath of use cases.

crubier• 2 days ago

Came here to say that, my bet is that in 3-4 years you'll be able to run Fable-level of intelligence models on your laptop or maybe even on you phone

chatmasta3• 2 days ago

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…)

russli19935• 2 days ago

Winners are hardware companies, GPUs,XPUs, HBM, memory, connectivity. Even CSPs are just compute renters, they charge a margin to make sure their hardware purchases can be made back. But given there are more and more AI CSPs, traditional, and neocloud, pricing competition is inevitable, and given the huge expense of hardware, CSPs are squeeze by the hardware companies and users seeking to lower their own costs.

simianwords2• 2 days ago

umm do Mac vs pc and Iphone vs android

geophile16• 2 days ago

Not sure what your point is. I’m commenting on a moment in time which is like an earlier moment in time. A different moment in time will have different analogs.

foo12bar2• 2 days ago

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.

prima-facie• 2 days ago

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.

confidantlake• 2 days ago

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

Razengan2• 2 days ago

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 :)

gulmothrowaway• 2 days ago

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.

627467• 2 days ago

You dont flash an llm model on the street as status symbol.

ChrisClark• 2 days ago

Yeah, iPhones are just fashion statements in the US. Different than LLMs

hintymad2• 2 days ago

- 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.

geophile16• 2 days ago

No, I don’t think so. PC + MS Office killed Wang custom hardware/software, for example. LibreOffice is much later, and can’t displace MS Office due to network effects. Cheaper won, cheapest can’t because of those effects.

starfallg• 2 days ago

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.

up2isomorphism• 2 days ago

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.

SideQuark• 2 days ago

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.

ludston• 2 days ago

I need a !remind me 15 years.

Grombobulous• 2 days ago

Moore's law is over in the literal sense but silicon continues to advance relatively quickly.

This claim isn't really outlandish in any way. It's not hard to imagine:

- Future models being able to handle current frontier models' workflows with much higher efficiency.

- Future consumer devices like phones having 2-4x the RAM onboard along with GPU/NPU performance greatly increased in 10-15 years.

leoc2• 2 days ago

It's probably not so bad. (I am not an expert on anything.) The big blocker is probably a roughly single-order-of-magnitude decrease in the cost per MiB of VRAM. (That obviously goes out the window if the LLM frontier people find, in the nearish future, new ways to do more with more: to significantly push up the threshold of diminishing returns from more VRAM or other resources. But that doesn't seem to be waiting to happen.) That's not clearly unachievable, especially given that ASML has apparently already made significant efficiency gains recently while there's no shortage of demand to justify R&D right now. Many customers are also likely to increase their hardware budgets: the kind of organisation that used to pay big money for Sun workstations is likely to consider spending that kind of money again if it saves them several hundred dollars a month in LLM plans.

onlyrealcuzzo• 2 days ago

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.

ColdStream2• 2 days ago

Not just that memory shrinks slower, it has practically completely stalled. On chip cache seems stuck at 7nm and DRAM is stuck at 10nm. As transistors shrink, they hold less charge, creating weaker signals that are harder to read and prone to interference. Smaller nodes aren't a huge issue on CPU/GPU work load because they don't have to hold a static state.

I'm not saying we are at peak memory but future gains are going to come increasingly slower.

modzu• 2 days ago

in 15 years we might all be fighting terminators

ymolodtsov• 2 days ago

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.

timmytokyo2• 2 days ago

Counterpoint: linux owns the server space.

geophile16• 2 days ago

I don’t agree. In the mainframe/PC battle, MS and Apple were basically on the same side. Once the dinosaurs went extinct, different mammals fought for dominance.

engineer_228• 2 days ago

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.

geophile16• 1 day ago

Well that's one way to describe it.

If Nvidia gives away powerful models to sell more of its hardware is that industrial dumping too?

DrewADesign4• 2 days ago

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.

phist_mcgee• 2 days ago

As a consumer, the last things I want in an interface are to a) be sycophantic enough to lessen my judgment

This is EXACTLY what people like/are addicted to about chatbots.

My sister-in-law bombed an interview and asked AI about her answers to the interviewer's questions, chatgpt or whatever it was told her that her answers weren't bad, but that the interviewer could not see the gold in her responses. She said she felt much better.

I see this effect with all the non-tech people in my life

engineer_228• 2 days ago

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 use AI chat every day, I find it endlessly useful. It’s replaced google search.

kcexn• 2 days ago

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?

geophile16• 2 days ago

Nvidia will pay for training models that they can give away to consumers who will then buy their GPUs.

bmitc4• 2 days ago

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".

slashdave2• 2 days ago

Libreoffice

venusenvy47• 2 days ago

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?

geophile16• 2 days ago

It didn’t used to be.

gitgud• 2 days ago

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

geophile16• 2 days ago

There is constant shifting between client and server computation. I think it is a stretch to call cloud servers “mainframes”. There are still old school mainframes, running JCL, and old school mainframe DB2 and COBOL. That ain’t cloud.

mvkel• 2 days ago

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

geophile16• 1 day ago

Choose your favorite metric: mindshare, running instances, development target. Free and low-end beat expensive proprietary.

"Least popular" is an odd claim. If you count Windows desktops, and then count all the Linux installations (desktop, servers, cloud, Android, other devices), I suspect Linux would prove to be most popular. Sure, add Windows servers to the contest, that's a rounding error. And what about VMs? How do you think Windows VMs stack up against Linux VMs in number?

caycep• 2 days ago

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

walrus01• 2 days ago

- 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?

7952• 1 day ago

The cloud era seemed to start out being about availability of storage and slowly switched in big corporates to be about security and governance. The first seems stupid now considering how much local storage we have. The later might depend on what security issues crop up.

fhe• 2 days ago

Apple seems a counter example, no?

jsiepkes• 1 day ago

Depends how you look at it. iOS is loosing ground to Android. They might just eventually lose.

mmooss• 2 days ago

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.

bni• 2 days ago

What about AWS, Azure, cloud computing?

epolanski• 2 days ago

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.

inigyou• 1 day ago

And yet the money is all at the high end. Bill Gates is much richer than Linus Torvalds. Oracle created one of the richest people (until he squandered it all on bad AI datacenter bets and got his company currently rated as a junk investment). Dell probably makes more money than IBM, but not by a lot.

geophile16• 1 day ago

This is irrelevant to the current discussion.

Windows was designed to make money for MS and billg. Linux was not designed to make money for Linux.

Also, by what logic do you consider any MS software as "hign end"? That's a new one.

QuantumGood• 1 day ago

Commoditization always takes volume from the marketplace, but not necessarily profit. Apple and Military tech come to mind. Positioning is rarely done well looking forward, but often shakes out in unexpected ways.

citrin_ru• 1 day ago

If LLMs is just another technology then low end will eventually win. The bet which I guess many AI investors are making is that LLM will allow recursive self improvement which will lead to development of super-human AGI. The company which will get there first will rule the world thanks to an immense power enabled by AGI. There will be no 2nd or 3rd AI company, the 1st one will wipe the rest out.

redlemontea• 2 days ago

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

m3kw9• 2 days ago

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

cheriot• 2 days ago

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

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

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

asdfman1233• 2 days ago

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.

znnajdla6• 2 days ago

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.

well_ackshually2• 2 days ago

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.

teravor• 2 days ago

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

faragon• 2 days ago

1) open-weights

2) critical mass

3) de-facto monopoly

4) closed-weights on frontier models

5) profit

hounainehamiani• 2 days ago

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

hereme888• 2 days ago

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

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

danielciocirlan• 2 days ago

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

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

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

brap3• 2 days ago

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

znnajdla6• 2 days ago

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.

procgen• 2 days ago

Apples to oranges. The AWS premium buys you a much broader platform with autoscaling, dozens of instance families, managed databases, serverless products, IAM, global regions, etc.

well_ackshually2• 2 days ago

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.

meteor333• 2 days ago

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

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

cosmic_cheese• 2 days ago

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

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

hham• 2 days ago

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

riazrizvi• 2 days ago

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

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

dvduval• 2 days ago

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

try-working• 2 days ago

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

AlexRenders• 2 days ago

historic times.

timedude• 2 days ago

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

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

Balgair• 2 days ago

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

-George Orwell, You and the Atomic Bomb

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

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

zhoBEENG• 2 days ago

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.

seizethecheese2• 2 days ago

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

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

The companies’ own claims are also not great evidence.

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

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

jumploops• 2 days ago

The models are commodities.

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

thih9• 2 days ago

I’m rooting for open models.

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

gaigalas• 2 days ago

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

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

phillipcarter• 2 days ago

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

worik• 2 days ago

Capitalism, hoisted on its own petard

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

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

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

Pass the popcorn

gexla• 2 days ago

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

aenis• 2 days ago

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

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

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

fittingopposite2• 2 days ago

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

realytubecoder• 2 days ago

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

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

karmasimida• 2 days ago

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

flenserboy• 2 days ago

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

xlbuttplug2• 2 days ago

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

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

lenerdenator• 2 days ago

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

prngl• 2 days ago

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

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

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

Papazsazsa• 2 days ago

Winning what, exactly? The race to the bottom?

dizlexic• 2 days ago

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

andix2• 2 days ago

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

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

rootusrootus2• 2 days ago

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.

andix2• 2 days ago

That would be the reasonable thing to do.

protocolture• 2 days ago

and it could take the US economy down with it.

I just want 1 thing for christmas Premier Xi.

bg243• 2 days ago

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

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

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

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

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

overfeed2• 2 days ago

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.

andy_ppp• 2 days ago

China may well save the world with green energy but I’m skeptical to assume we should just trust them to share their best intelligence with us freely, it’s a totally different think to sharing their best intelligence.

bg243• 2 days ago

[flagged]

Chilko2• 2 days ago

The prices are still dropping per watt.

FYI if you look into what has happened in the wholesale solar market since the end of 2025, this is no longer true. Prices are now 20% higher than they were in Nov/Dec, and were even higher earlier this year. Hard to predict the future but we may have reached a price floor, as at 2025 prices the module suppliers were below cost and losing money. The market dynamics have changed significantly since then

namegulf• 2 days ago

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

Open Weights = Open AI

Let's go!

softwaredoug• 2 days ago

It's hard to ignore the overall environment

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

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

nova22033• 2 days ago

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

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

frollogaston3• 2 days ago

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

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

kvasilev• 2 days ago

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

matheusmoreira• 2 days ago

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

mmonaghan7• 2 days ago

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

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

jimbob45• 2 days ago

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

yanhangyhy• 2 days ago

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

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

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

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

kinj28• 2 days ago

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

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

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

rldjbpin• 2 days ago

to me this behaviour is aligned with other industries China's been involved in. take solar or EVs as examples.

the dumping and involution involved in these industries is apparent and has been disrupting for the external markets affected. simultaneously both have helped make it economical to go green.

if you stop looking at LLMs as nukes, this becomes more convincing. China clearly does not want to rely on the West for what it considers core tech. they have done it for search and social; this is a natural next frontier. while they encourage a deluge of options for local use, others can make hay while the sun shine. albeit with the usual caveats.

autoregressive decoding is not an optimal paradigm for local or decentralized inference. amidst the mania and shortage, make the most of what is made available, like with solar, than crying over spilt milk.

kazinator• 2 days ago

Open technologies can be used permissionlessly

For years I've had dental work done without freezing, but I think I need laughing gas and novocaine for writing like this.

echoeat• 2 days ago

Open weights only matter operationally if the runtime and eval stack are honest about latency, VRAM cost, and task success

crnkofe• 2 days ago

Hacker news is so one-dimensional when it comes to China. Its like people here are living in an alternate dimension where everyone outside the horizon is playing catchup. Imo a sandboxed local expert handheld or mobile AIs are the future. They'll be open weight and maybe even open sourced if we ever figure out a way to beatdown training costs. And I find that pretty exciting. There will be some bloodbath on the Wallstreet for sure but I don't anticipate or shiver anticipating double digit losses from any major corp.

kuttimaster• 1 day ago

So war is coming...

Vikasratawa77• 1 day ago

Weight sharing will commoditize the capabilities of AI technology. Once the intelligence that was developed can be freely executed anywhere, the actual strength then lies in the data, the domain-specific UX, and the efficiency of the infrastructure.

kademolu• 1 day ago

What a lot of people seem to miss is that you don't need the openweight models to "beat" frontier models for most agentic workloads. The harness should be doing the majority of the work, inference becomes the value add. This is where chinese models shine, i have run benchmarks where the value per dollar is always tilted to the chinese models

Dependance• 1 day ago

Open source AI will be made illegal at some point. I cannot imagine the US letting Fable-access to every "enemy" they have (or made at some point).

burgerquizz• 1 day ago

i didn’t use the chinese models yet, but aren’t people worried of the biased knowledge they might have since they’re somehow controlled by the ccp?

what could go wrong? the ccp probably thinking hard on how they can leverage this?

Iolaum• 1 day ago

That's only one factor of what people are worried about.

giancarlostoro• 1 day ago

I still wonder why some of our key universities in the US aren't building Open Weight Models. To this day we have "BSD" and "MIT" as key licenses for a lot of software (I know weird tangent but still... this emphasizes how significant these institutions were once!), but these top schools don't really build anything the way they used to anymore.

I'm sure someone will say students build LLMs all the time, and this and that, but there's no reason these universities cannot build their own LLM "labs" and work on a common LLM that their university builds, maintains and students and faculty can manage, and release, maybe even sell API access to.

It would be a key selling point for any student who wants a serious job in AI to work on such a project too. Universities could easily sell access to pre-hosted models mostly at-cost maybe a smidgen on top of costs, and fund the project as it gets better over time.

Any university doing this would prepare students for real software jobs mind you. Maintaining, building and supporting real infrastructure during your college years sells better than no experience.

runeks• 1 day ago

I still wonder why some of our key universities in the US aren't building Open Weight Models.

I'm guessing the $100m price tag plays a role. Plus the fact that it will be outdated next quarter.

regexorcist• 1 day ago

I won't be giving a dime to the companies that stole all the IP in the world and are now gatekeeping it, acting as the rightful owners, and trashing the hardware market for everyone else in the process. So it's Chinese models or nothing for me.

mbgerring2• 1 day ago

It makes me feel like a crazy person seeing that “the government should fund basic R&D that broadly benefits industry” is a concept that so many people find confusing, hard to grasp, or even offensive.

The government should fund AI research and model training. The weights and the models should be free. The provision of inference and tooling should be a competitive business. This feels completely obvious to me.

charintstr• 1 day ago

Everyone was saying the exact same thing, that open would win and compute was overvalued, about deepseek back in 2025 ignoring that up until now open models had essentially returned to about 6-9mo behind OAI/Ant post-Deepseek moment. I think this is a dynamic frontier and open modeling a strategy that has to be pursued by China to pool their knowledge in order to keep up with the US. However, if China actually breaks ahead, then open becomes a burden rather than a strength. So I don't see this as a China better thing, just asking more "who is ahead" or not.

zrn900• 1 day ago

For China, AI is infrastructure. For the US, it's another cash cow to be made scarce so that investors can milk it.

That's why China chases open-source AI and commoditizes it. It will improve the AI until it is used in all kinds of things, ranging from vehicles to factories, from medicine to housemaid robots. China will make the AI the commoditized infrastructure to build bigger things on it.

You can't do that while keeping AI as a scarce resource and charging a premium for it. Like how the US does to satisfy endless investor greed.

vfalbor• 15 hours ago

You couldn't be more wrong. What's being sold are chunks of time with access to specialized hardware resources. Through which model or with which device is irrelevant; the one winning, and continuing to win for some time, is Nvidia, and that company is American. As long as they have the H100, B200, or B300, China won't be able to compete with the American strategy, no matter how many new models they release, because these types of cards require incredibly powerful hardware to run.

China’s open-weights AI strategy is winning

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