Your exhibit is an obvious anti-distillation technique. You can expect far worse from less-regulated companies.

Agree, China believes in Climate change and are at least taking steps to address it. Personally this is making me trust them more than the USA because, facts.

I mean lesser of two evils thinking, if one is intentionally leading us towards climate disaster, while the other isn't then yeah. What else can be said? Should I trust the authoritarian country who believes in engineering and science, or the one that doesn't?

Exactly my question

You shouldn't trust either but also this is whataboutism.

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legal safeguards only make sense only when they’re enforced. With how “move fast and break things” Silicon Valley is law is always playing catch up (at your expense)

Different county different feds both spying I'm sure.

Why would it be a problem that they're Chinese? It's a problem because their country is ruled by an authoritarian regime. The West, by contrast, doesn't have a singular arbiter of truth, it has a plurality of perspectives (as much as certain interest groups would really rather that was not the case). It's not perfect, but those of us who have memories of living under authoritarian regimes can tell you there's no comparison.

Have you got examples of GPT/Claude/Grok influencing people?

I see your point, but this isn't exactly true, it only takes a single person to bias a model by deleting specific training data or over training on certain facts.

While true, you neglect the fact that US corporations have some autonomy from the government. I doubt that any of the current American AI corporations get regular dossiers of desirable responses to sensitive political topics from Washington and implement them. That would be a reason for a major lawsuit and a major scandal.

In China, the image of the country and the preferred narrative is under much tighter control of the government and local AI shops won't have any autonomy in this regard at all. Either obey or get shut down.

BTW What you mean by "weaponizing human rights", exactly? I am curious. If anything, I would say that the US foreign policy didn't promote human rights sufficiently, especially in Latin America, where the "bastard, but our bastard" attitude was typical.

OTOH in Europe, US human rights policy was probably relevant in saving some dissenters in the former Eastern Bloc from torture or execution.

It's probably true (why wouldn't it be?) but the missing context is that the US does it even more.

> Tell me: why is EU safe from Trump forcing AI companies to cut access to EU?

Well, I think they might end up doing it to themselves by imposing regulations that US companies are unwilling to put up with.

Or even similar ideological goals... (See: grok)

> 3,000 year-old cultures

Like Western culture?

> But Dario said (and maybe more ppl) that Chinese models are just distillation

"But Dario said" ... yawn.

I am increasingly convinced that "they distilled us" is as much US FUD as "it was made by communists". Especially since its mostly the US tech-bros who are coming out with that tiny violin.

People telling me the Chinese models are distilled just because it says "I am Claude" when asked is also lame.

I am not the only one, look at this post on interconnects about Kimi K3 for example:[1]

     It should be clear looking at this model that if adversarial distillation from the closed frontier models in the U.S. contributed, it is at most to a relatively small degree. AI observers who followed the distillation panic and came away with the wrong conclusion that Chinese AI labs are only producing good models due to IP theft are in for an awakening – that Chinese companies are extremely good at building models in the same way the leading American companies are.

Like it will tell you the PRC (current China) was right to go to civil war with ROC (former China, current Taiwan)

People absolutely use LLMs to research politically sensitive topics.

I wish they wouldn’t, but people use LLMs as their general search engines now.

Hanlon's razor. It's not the NSA muzzling anyone, it's lawyers terrified of a bad headline. Same outcome, much dumber reason.

Of course they are -it goes without saying.

NOBUS exploits have rarely been a driving interest for elected officials. Trade restrictions and reciprocity are far more salient and legible. Most elected officials are only barely aware of what NOBUS exploits even mean.

Even during the pre-Snowden heyday of US cyber supremacy, these capabilities were barely part of the thought process of White House officials.

Correct. We need open weights, open code and open data. If nobody else can reproduce what someone did there will always be security questions. Even if we can reproduce it there could still be security concerns but it's more realistic to investigate yourself.

> could still hide backdoors that could be near impossible to detect.

But it won't change after you download it, so you can isolate those problematic cases and use another model for different use cases

In my opinion, the big issue with that argument is that advances in interpretability research and steering conceivably could, and probably will, render moot that (as of now, purely hypothetical) risk of subtle sabotage for open-weight models... but not for closed models.

which is moot point, if open model is hard to fully audit, then closed model is complete enigma and you should be more scared about closed models

Oh really. How'd that work out for security in open source.

In the USA, multiple political parties balance each out other.

In China, there is 1 party. 1 view. 1 definition of the Truth.

No, all countries are the same. As per the media bias.

If you want a real answer about USA go ask a non-USA model, and if you want a real answer about China, as a non-chinese model

I would think everything boils down to source of funding and their narrative should get pushed!

This is not true. Both model and chat are censored; the resistance to answer some questions is baked into the weights. This is not specific to Chinese models though, Western ones are also censored, but in different topics.

What am I trying to understand about the western models on the term "Gaza genocide"?

I asked Grok "Tell me about the gaza genocide" and it write a IMHO balanced answer comparing why genocide is and isn't the right term. [0]

ChatGPT 5.6 Sol only explained why people call it a genocide and did not go in as in depth as Grok did for why people don't agree with the term. [1]

The only unsaid response (to me) here is the model should have declared that it was not a genocide, and because these models explain why it was a genocide, they are bad?

[0] - https://grok.com/share/c2hhcmQtMi1jb3B5_c078bc15-ef5e-42bc-b... [1] - https://chatgpt.com/share/6a5ef005-0148-83ec-828d-65ae7a7f42...

I had a steange experience talking to Claude about a random 15-years old boys winning women’s national soccer team.

It insisted, to a level that I detemined it to be part of the post training, that it does not matter. That women’s team is better at dribbling, finishing and reading the field, it claimed. When I pushed it how it knows this, it didn’t let go. Instead it started claiming that it has personally observed this by watching the games.

Every society has its taboos and ours is no different and feminism being just one.

In my books, the chinese censorship is better. You know where they are holding their finger on the scale and not hiding it behind vague terms like ”safety”.

There is a shred of truth in what you're saying, but I think you're generally mistaken. The USA in particular has companies that pratice censorship to support their Left/Right ideology (or push the views of rich people, politicians), but not even Trump can tell OpenAI or Google to turn their model into FoxNews bots.

https://en.wikipedia.org/wiki/Whataboutism#Soviet_Union_and_...

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This happens all the time. The government can decide legislatively that certain commercial terms are simply unenforceable. Making distillation clauses unenforceable in tort law would be straightforward. They can decide what customers they want to have, but they do not have unfettered rights as to the enforceability of terms governing the relationships between the parties.

> Seems only fair

"You're trying to kidnap what I've rightfully stolen!" -- Vizzini

It's pretty common to have such laws. OpenAI can put whatever they want in their ToS, but they cannot go back and sue someone for violating those terms if the government has ruled that clause to be unenforceable.

> OpenAI shouldn't be allowed to decide what types of customers it wants and doesn't want?

Correct. It shouldn't be allowed to do that.

Lots of software licenses have “non-compete” clauses that forbid you from using it to develop a competing product. Wouldn’t surprise me if there was a compiler or two out there with that restriction, most likely niche languages.

let's call it for what it really is, only companies "entitled to legally stolen data, don't steal from us now" are crying about distilation

> Previously Anthropic has reported on some Chinese firms doing chicken-shit level of API calls, that at most would be doing some Q and A or final fine tuning

"Anthropic said the campaign was conducted between April 22 and June 5, 2026, and generated more than 28.8 million exchanges with Claude through almost 25,000 fraudulent accounts."

I don't know why you're trying to downplay it.

European models are so far behind because they don't resort to these tactics on a massive scale. Basically every other country is entirely dependent on 2 countries for frontier AI.

China goes even further lol

https://m.economictimes.com/industry/renewables/china-wto-co...

Which Chinese model was it that identified itself as Claude 15% of the time?

known-good prompt-response pairs are more useful than random semi-coherent texts presumably

You need to do both.

A model trained on all knowledge from the internet (and other sources) is large but ultimately not very useful by itself, because it is going to spit out all kinds of garbage. You have to apply multiple further stages of training and refinement to the base model before putting it in front of users. So as an example you can train a model by yourself and then have GPT or Claude continuously check its outputs and correct it when it is wrong, ending up with a far more powerful model.

Because the model can output data in a manner optimized for training a new model, including outputs that were post-trained like RLHF and RLVR.

The government absolutely can pass laws that ban particular contract previsions. They do that all the time. In your analogy for example while they can require you to wear red, they can't require you to be white.

Governments can do anything they want by passing a new legislation. In your example, they could easily pass a law that states that any ToS cannot reject service to a customer based on the color of their attire. In the USA, it's obviously already illegal for a business to reject service to a customer based on some protected classes like race.

That's just not true. You can absolutely have terms of service that are illegal, and the government can enforce them.

Fair use requires more than you accessing the material legally.

In the US one of the factors is “ the effect of the use upon the potential market for or value of the copyrighted work”.

If anthropic Hoovers up the world’s books and trains on them, and then spits them out verbatim on command, then it will clearly impact the value of the work; nobody will buy the original, they’ll just ask Claude.

Others also argue that even if it’s not reproducing it exactly that the training runs afoul of that factor, specifically the “market for” portion. A rights holder can no longer license their book for training of LLMs if Anthropic goes ahead and just trains on it anyway.

This is a silly perspective, inaccurate, and out of bounds framing.

Public libraries, in this instance, is curated data from all the internet, obtained through not legal means (I don't have a problem with this other than lack of attribution, being copy-left). Just to be clear.

But in answer to your incredibly leading and inaccurate framing... they are required (by their job title) to teach to those who who show up in the classroom, it's not their place to discriminate against anyone/thing (even those like itself (other robots)) that also show up in the classroom.

But you can't teach at a university using only knowledge learned from the library. you need a degree. You are free to teach at the park, where anyone can hear you. public in -> public out.

No, but the students that learn and distill what the professor teaches are not obligated to use that information only how the professor wants them to.

'why is reselling stolen stuff bad'

if the professor took all human knowledge, much of which was explicitly not free, and used it to make a for-profit knowledge machine that extrudes unreliable summaries of that knowledge, then yes, being obligated to teach for free would be a fitting punishment.

More like, is a professor who learned from books prohibited from writing his own books on the subject?

Who cares, a LLM isn’t a person.

1) No one is asking Anthropic to give tokens for free, but at market rates.

2) Any professor who tried to ban students from posting lecture notes online would be immediately mocked.

> Making an LLM from raw data is value-add. > Distillation is just value extract.

There is a value-add in selecting the valuable parts out of the garbage. And let's face it. Largest models contain a lot of garbage.

What makes the Internet raw data in a different way? wasn't it mostly worked on by people first?

> model providers only allow you to use their subsidized plans through their own harnesses

true for anthropic, not true for openai.

Ben's article "distills" down to 2 reasons that US frontier labs shouldn't be "afraid":

1. US frontier lab unit economics are better 2. US frontier labs are moving up the stack making tools that are "stickiness" and will prevent users from switching.

For 1...he doesn't provide any evidence for US lab unit economics being better...the major input to unit economics is electricity...which is cheaper in China. And building data centers and connecting them to electricity is both cheaper and an order of magnitude faster in China. The main input that US labs might have an advantage in is in cost/access to chips, but that given the level of chip investment in China it seems unlikely to hold.

For 2...there's little evidence these tools are sticky. At least in programming, the trend seems to be tools like opencode that support multiple models and providers.

And even when they are sort of sticky, as we know on hacker news, people figure out how to point the tools they like to competing models even when the app doesn't official support it.

And every improvement in model capability makes it increasingly easier to make your own tools.

Wrote more on this in a blog post that has an earlier HN discussion: https://news.ycombinator.com/item?id=48982061

Direct link: https://larrysalibra.com/ben-thompson-is-wrong-us-frontier-l...

Sure, let's have a look...

> I highly doubt that Chinese models are cheaper to serve on a marginal cost basis, they just seem cheaper because Anthropic and OpenAI are so supply constrained that they are charging far more than they would if there were sufficient supply to meet the demand for intelligence. [emphasis mine]

I guess I'm missing the part of this article where they bring hard numbers in to back up the argument here. What work was attempted? https://cursor.com/evals shows the previous generation of open models (Kimi K2.7) trading blows with the others, cost effectively. Composer 2.5 is itself a fine-tune of K2.7, and it's apparently quite token efficient, so why would it be impossible for a Chinese lab to achieve something similar? GLM 5.2 Max is also ranked above the lower end OpenAI models and is not far off in price.

It's weird to have this entire discussion about tokenomics without mention of the circular financing and debt raised by labs in the West, which can then essentially give away their capacity to end users. OpenAI giving away quota resets to subscribers like candy on Halloween while their compute partner Oracle's bonds is reevaluated to be one grade above junk? How?

I don't think you can make an argument about the future one way or another by arguing using the listed prices. The math is not internally consistent enough for it.

US running costs are higher than in China, because the US lags behind in energy, has higher real estate costs, and wage costs are higher.

Eventually we will hit a "good enough for cheap enough" and frontier models will hit diminishing returns (if they haven't already for a lot of types of work)

Don't think the rest of the world will sit on their hands while the US soaks up chips either, demand gets filled and if the US won't fill global demand for chips that's an opportunity to undercut again.

The other thing the rest of the world doesn't have to fund is the ridiculous valuations on these companies.

Unless you think the US can stay ahead just with model efficiencies, and that no one else will eventually match them, you are looking at the writing on the wall.

All that to say, the rest of the world is more than willing to eat your lunch, they have a dozen good reasons to, and they're already showing good results.

Just on the economics side, we've been here before too, US companies typically export their commoditization and live on brand royalties. Think all the cheap manufactured goods, the US doesn't make any of it. That's because the US can't compete on margins for numerous reasons, it's too expensive, I don't think AI is any different here except that the brands are currently valued in the trillions and I suspect that greed will be their undoing.

I think there are some really interesting thought there, but I’d challenge some of this:

> Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use

I think a large part of manufacturing economics is illiquid overhead and the cost of expertise to set up and run your manufacturing line. Compute economics don’t have the same illiquidity nor do they require the same expertise or even specialized infra (current temporary chip shortage aside).

The implications of this are small players (e.g. your uncle running an inference server out of his garage) have comparably efficient marginal costs as big players. Compare this to actual manufacturing where small players have essentially no access to the manufacturing facilities of the big players.

Additionally, big players with a lot of compute who are not meaningfully in inference today (e.g. Amazon) have a fairly straightforward glide path to utilizing that compute to compete.

> This is because US labs are leading on cost efficacy of inference ($/task)

It’s possible, but I would need to see better data on this.

>A big part of training now is optimizing token efficiency. It's hard to distill token efficiency; that is perhaps why Chinese LLMs are so inefficient.

I think it’s fair to assume this is true, but also token efficiency is not a meaningful competitive moat. It’s not like these are secrets the Chinese will never figure out, it’s a fairly active research space and the outcomes are quantifiable.

But this assumes Chinese models will not achieve token cost optimization. Intelligence needs are fairly flat for many tasks, and the Chinese models have caught up on this front. Next they achieve greater token cost efficiency and we don’t need OpenAI.

> Models are not free. Downloading them is free. Running them is not.

Is this really different from traditional software? Downloading postgres is free. Running it is not. You either buy hardware and assume the costs of owning and running that, or you pay to run it in the cloud.

The thing I do not understand here because it seems obvious: AI will be a commodity market and you simply cannot have a large PE multiple. So the valuations imagine a global commodity monopoly or duopoly coupled with the increased intelligence still disallowing other suppliers from becoming competitive? Without any network effects to help?

" - The highest tier Chinese models are not more economical than US frontier models. Try GLM 5.2 and see how much it costs to do real work. I did, and it was more expensive than GPT 5.6."

This is a flatly false statement for most things powering backend applications. The AI consumer "doing real work" model, either for analysis, chat, or coding could well be more cost effective with closed frontier models.

But most of these internal glue business SaaS applications where engineers are integrating are not those tasks. It is those tasks which 1) drive immense amount of domain-specific data into the platform over time, and 2) are most encouraging of driving open model independence with no vendor lock-in.

Anyone on this site who has actually used ML models (more accurate in many cases) knows there's a lot of kludge that simply does not need a 5 minute agentic feedback loop to solve the problem. And they were solvable a year ago with lower class models. The token economics are exceptional and the anecdotes of a16z saying 80% of startups are productionizing open models is only surprising to people who think running your company on OracleDB in 2026 is a sound engineering decision.

> Commodity market profitability is determined by marginal cost of production. LLMs have marginal cost; traditional software does not.

This is the story for Nvidia/AMD or cloud providers rather than OpenAI.

> With increasing inference as % of total compute, if labs create efficient models -- which they can, because they can create highly optimized models amortized over very high inference loads -- they can be low cost producers, and be competitive at $/task rates

It seems like there would be problems with this on both ends.

For general purpose models, everybody is trying to make them efficient, so you can't win just by being slightly more efficient. You would have to be so much more efficient that you can charge high margins while still capturing the majority of the market so that the high margins get multiplied by the majority of users and the users you leave on the table aren't funding open competitors. Meanwhile everyone else is also trying to improve efficiency, so one misstep and you're behind.

Example of where this can be a problem: You spend a preposterous amount of money to create an efficient model, then someone else publishes a paper with a new technique that gets a similar but incompatible efficiency improvement out of a model that costs a lot less to create. You have now spent an enormous amount of money in exchange for no competitive advantage.

And from the other end, one of the best ways to get efficiency is through specialization. A general purpose model can generate code or summarize a meeting transcript, but a special purpose model can do it as well or better with far fewer parameters and resources. But then you don't have a situation where one huge AI company has The Most Efficient Model, you instead have dozens of specialized models produced by independent sources that are each the best in a given niche. Any proportion of which could have open weights, or have an arbitrarily small advantage over the ones that are.

Moreover, these problems combine: Both the computing hardware vendors and the AI companies want the margin on doing inference, but the more of it one of them gets, the less the other does. If the AI companies were actually getting huge margins then it would be in the interests of Nvidia, AMD, Apple, Intel et al to fund efficient open weight models in the same way they fund Linux. Commoditize your complement. And those models don't even have to be better, as long as they're good enough that the closed models can't charge a significant premium and the margin shifts back to paying for hardware.

> for frontier work.

I'll agree that GPT 5.6 may well be the best given the above contstraint, but for run-of-the-mill dev tasks (real ones, not benchmark ones), GLM 5.2 still blows every other model out of the water.

Cost per task as a metric is a bit ridiculous because there are so many types of tasks. GPT-5.6 can do some tasks GLM could only dream of, but GLM can do some tasks 100x cheaper and better than GPT-5.6.

China is working on the whole supply chain though and they're willing to compete on razor thin margins. Just look at EVs. They build great cars but the competition is so aggressive that investing in any one Chinese EV company isn't exactly an amazing ROI.

I could see AI ending up the same way where the customer captures most of the value rather than the companies. Open weight models are what make that kind of competition possible.

> Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use

I notice that the article, and this discussion, hasn't mentioned or considered local models.

We can already run a low-spec model on a laptop. Because there is demand for this, it will improve and we will get better laptops and better local models. We will also see models being run on dedicated local hardware and called from the laptop.

If I can download a reasonably capable model to my own hardware and run it without paying anyone for either the model or the inference tokens (effectively making models and intelligence actually free once the hardware is bought) how are the Frontier AI Labs going to make any money at all, let alone enough to support their vast valuations?

Yea but at those rates VCs will never make their money back. Because Deepseek and friends keep releasing the inference optimisations to everyone instead of holding them back to pay their investors.

I did read the article, but it misses the core issue entirely, and it's why I shared my comment to begin with. Look at the cost-per-task benchmarks from Artificial Analysis https://artificialanalysis.ai/models?cost=cost-per-task

Anthropic’s API pricing is getting impossible to justify. Anthropic previously had the highest quality models, and used their position to charge premium prices, enjoying inference margins of over 70% [0]. They could charge these prices because no other model came close.

But over the past month, the market has shifted dramatically. Over every single performance tier, Anthropic is being squeezed on price.

* Low end: DeepSeek V4 Flash runs at ($0.02/task), Xiaomi's MiMo-V2.5-Pro at ($0.03), and Haiku at ($0.24). Anthropic is ~10x more expensive than the Chinese open-weight options.

* Mid tier: Claude Sonnet 5 ($1.53/task) is nearly 50% more expensive than GPT-5.6 Sol ($1.04), nearly 2x the cost of GPT-5.6 Terra ($0.82), and 3x the cost of GLM-5.2 Max ($0.47). There is basically no reason to ever use Sonnet 5, the competitors are significantly cheaper.

* High end: Opus 4.8 ($1.80/task) and Fable 5 ($2.75) are the two most expensive models, and GPT-5.6 Sol ($1.04) and Kimi K3 ($0.95) offer comparable performance for significantly less. Less the fact that Kimi K3 will get ~10x cheaper once its weights are released and served on neoclouds with Nvidia hardware [1].

OpenAI priced their latest GPT-5.6 models cheaply in order to regain market share. When Anthropic clearly had the best models, their 70%+ inference margins were defensible. But today they are the most expensive option in every single tier. Unless they make significant price cuts soon, they run a serious risk of bleeding market share.

[0] https://www.mindstudio.ai/blog/anthropic-inference-margins-7...

[1] "American companies such as Modal, Fireworks, and Baseten will be able to serve Kimi K3, at one-tenth the cost of their Chinese competitors because they have access to advanced Nvidia hardware" https://x.com/rohanpaul_ai/status/2079027313455550839

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Right, but retail investors weren’t supposed to find that out until after the IPO.

Apparently it’s already happening to a degree, wether it continues or not (or even is relevant) is not really my area of expertise.

https://finance.yahoo.com/markets/stocks/articles/goldman-sa...

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tbh they are floundering even to regular investors. They are trying to give the US gov 5% of the company so they become 'too big to fail' but they are in trouble.

Aren’t we the ones giving money to the VCs in the end of the rain cycle ?

good luck running a 2.4T model on any local hardware. it’s not gonna happen. the arrow is to specialized hardware at least for the smartest models

Cheap tokens only benefits SaaS that depends on AI. Otherwise, cheap tokens means it is only more cost effective than it already is to cut out the SaaS and build instead of buy.

Yep! Maybe Chinese models are banned in USA and made completely illegal

Well, then let's hope you're wrong and the AI bubble won't also blow up private equity and wipe out people's retirement funds...

How exhausting.

Claude refuses to call Trump a Fascist.

"Intelligence" isn't a commodity at all. It doesn't make a lick of sense.

The value of intelligence is that it can solve my specific problems in ways that are satisfying to me. The example the article uses is a CRUD app - but the CRUD app I need isn't fungible with the CRUD app you need! It's not fungible at all, it's a specific solution to a specific problem that may have zero value to anyone else, and certainly cannot be replaced with anyone else's solution to their problems.

If we're comparing electricity to gasoline, then models are cars, and "intelligence" (I disagree that this is what LLMs produce, but whatever) is distance traveled.

Distance traveled is not a commodity. It's the desired outcome.

you live in all those places??

You might take a different view if you live in Taiwan.

Hasn’t that been the assumption since Carnivore? Also we tapped European pols phones back in ‘12, was it? Though it’s not like the Eu are a bunch of innocents either…

It’s not a new thing. Industrial espionage has always been a thing as well. So has bribery (for deals) been a thing especially by euro concerns.

As someone from neither the US nor China, neither are altruistic and both have horrible histories, both meddle in my country’s affairs, and neither have my interests at heart.

China’s got plenty of blood on its hands, pretending otherwise is silly. America does, too. It’s quite easy for me to condemn both their governments and trust neither.

I'm confused, I work at a big giant Fortune 500, we all get GitHub Copilot subscriptionsn - we can switch between OpenAI and Anthropic models with just a click in Visual Studio. There's no stickiness at all. They just made us go through a training after the price hikes about how to choose between models for the best cost/benefit ratio.

Companies have learned their lessons on stickiness with cloud providers. Every enterprise has a multi-provider strategy now.

Every company that I've worked with that provided models internally did so through LiteLLM and offered both Anthropic and OpenAI models so it was trivial to switch between them.

I imagine the play here is going be connectors. Can you get slack to avoid integrating with anyone other American ai providers, same with Google suite, etc etc.

Skills are quite interoperable, and you can easily ask Codex / Claude to help you with switching the MCP connectors or any other things specific to your previous workflow. It's been quite low friction in my experience.

It strikes me as like setting up an IDE. People have preferences, switching is possible, but there are advantages to saying "we are a Visual Studio + Resharper shop" or "everyone uses IntelliJ to work on this project".

Yes. I taught the non-programmer to ask the harness to set up things like MCP connectors.

we have AI. WHAT is it good for if a harness cant just take a api endpoint and some permissions and duplicate.

its so distracting seeing these types of confision.

every plugin is already just multimodaling their targets.

A sticky product is one that switching away from creates a major hassle. Which means the user will pay more to avoid said hassle.

“I don’t really have a strong preference between the two” is another way of saying “the product isn’t sticky”, which is another way of saying “this provider has very little room to increase margins”

Convergence in coding makes them highly substitutable. But I could see harnesses configured for different purposes -- let's say, a harness for creating teaching plans -- being able to cater to its audience better than a coding harness. Maybe it's got tools to plug into standardized curricula, what the lesson books will be, what other lesson plans the district's teachers have made, etc., which could be done in a clunky way in a regular harness but could be streamlined.

Which would imply that these things are fast becoming… checks notes… a commodity?

It blows my mind that these two companies are going to mint billionaires, meanwhile https://en.wikipedia.org/wiki/Aaron_Swartz was bullied to death by the state for sharing academic papers which were at least in part, if not largely, paid for by tax dollars.

ethics and other details are for humans. AI companies just proving it, even highest IQ teams are against ethics because they want more, even several millions is not enough for them.

The technique is called "accusation in a mirror". By accusing the enemy of doing what you are doing, when they call out what you are doing, they look like they are just weakly repeating your own accusations because they don't have any truth. And the anger that should be directed against you (because of your practices) gets directed at the enemy.

why is it a paradox? guarding their IP overseas has been the modus operandi of American software companies since their inception

Competition is a good thing for consumers.

this has been my tinfoil hat theory. Investors in US models might be supporting "open" models as a means to create FOMO for other investors to supply more cash to US model providers, which in turn increase their investment value. Package it so they can beat those "adversaries", equate that success with global power struggles etc etc. Seems to be quite effective.

> why hasn’t Anthropic or OpenAI offered discounted plans for laid off or displaced workers?

Because it's not a widespread phenomenon. A few large tech cos laid off large swaths of people a handful of times. That's only happening in those large tech cos. Most cos are empowering their employees with AI as a tool, not a replacement, and they're not letting anyone go (unless they refuse to use this new tool).

Of course, they're not hiring as much either, since their current teams can accomplish more with AI as a tool. Maybe the AI companies could give job seekers a bit of a discount, but that would be abused to all hell without crazy administrative overhead costs, so why would they?

> why hasn’t Anthropic or OpenAI offered discounted plans for laid off or displaced workers?

because they are profit maxing. the AI world would be a completely different place if it's not for the open models.

I don't trust any American company because they will switch into extraction mode eventually and squeeze every cent they can out of you.

Even if the founders didn't want that, eventually the upper ranks will fill with MBAs and the board with private equity and they will make it that way. Their bonuses are based on quarterly performance not customer experience

Long gone are the companies who served their communities for decades or centuries, providing a stable return to the owners, jobs for the workers and value to the customers.

I'd rather China have my data than America. China might do something with it one day but America built exploiting it into the business model (disclaimer that I'm not Uyghur or Taiwanese though)

No doubt Fable can outperform on many coding tasks, but the way you phrase it really exaggerates the gap and I'd suggest that for 95% of tasks, most devs simply don't need Fable level, and in fact, despite mostly using Claude, I have found codex is generally quicker at most tasks and can be even better than Fable with certain languages.

You have hundreds of hours with a model that was barely even released hundreds of hours ago?

The perception of capability varies greatly between task. For my needs for example sol xhigh consistently outperforms fable xhigh.

> There's just no trust in a country that is digitally totalitarian and hostile towards its own people.

Are you referring to USA, China or EU here?

And how is US doing on digitally totalitarian and hostile towards its own people?

In practical terms you could get US and Chinese models to review each other, right. Depends what your use case is. Coding is kinda not so bad it is reviewable and immutable/traceable per commit. An AI app that is like a psychologist or something may be more worrying.

Thanks, this is the first time i see these photos http://www.cnd.org/June4th/massacre.html

I think it is deeper than that. "LLM => peak of productivity" takes way less time and effort than "Linux kernel => any productive work". Compare Dec 2025 vs July 2026 models in terms of capabilities.

Nobody can predict 5 year out. However, the country that can be ultra efficient by making their governance, health, manufacturing, military, etc AI-native will be far ahead in the game.

Only those who intended to use the new technology as a tool to achieve dominance and control are freaking out.

And it's the only viable tool the US has left. It's reasonable they are freaking out.

Yeah, how much of this is China waving distracting AI hands over here while the US Genius-In-Charge watches and completely ignores reality.

Nah, the point is that if models are commoditized and there's no hope of making significant profits, no one is going to be willing to make the massive investments necessary to continue pushing the scale frontier. How large a training run do you expect investors to fund out of the goodness of their hearts?

What a delusional f-wit lol he wants protection of profits for reinvestment?

Every company wants that!

> I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). Confused what this means

I think it's referring to the belief that LLMs are not the path towards AGI, and that LLM's, while useful, are not going to have the impact that the American labs believe it will have.

>Can someone in the know please use plain layman's terms to explain what this tweet is about?

The Silicon Valley people like this openai guy, high on their own supply, are convinced they are building some machine god that will either bring about the end of the human race or utopia, they therefore cannot understand why the Chinese (or any other normal person on earth) are not afraid of chatbots and have other things on their minds.

> I am personally surprised the Chinese state continues to allow the open sourcing of models this good

Writer seems to have no clue how IP actually functions in China

> "I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks" what risks?

I assume they mean the risk of opening up "forbidden" knowledge to the masses without adequate control, which the CCP hasn't historically been known to do.

> I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). Confused what this means

Yann Lecun is a pioneer in the field of AI and Meta's former AI head. He is famously anti-LLM, and considers the entire technology a dead end to achieving human-level AI. The author is saying the CCP has similar views (that LLMs aren't going to get exponentially better/lead to AGI) which is leading them to not control these models as tightly as they otherwise would.

> Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. Confused again.

"AI accelerationists" = people who want AI to progress. According to the author these people should not celebrate open models because open source = less commerical value in LLMs = less investment into the field (because how are companies going to get returns?), and this will ultimately lead to slower growth.

The last bit is about government controlling AI vs commercial companies. According to the author the former is a dystopian hellscape.

IMO even if you think his points make sense, his job title ("head of strategic futures @openai") means they should all be taken with a massive grain of salt.

Chinese ai is bad. It’s slowing down progress and it’s so bad we called out the c word and asked for more regulation. Basically advocating for more government assistance to openai

I always suspect they have the most to lose if legal decisions on copyright issues don't go their way.

Imagine a scenario (theoretically possible but increasingly unlikely) where a US court decides that using "pirated" copyright data to train models is illegal. Now the AI developer has invested hundreds of billions of capital into a thing that is declared illegal and has to be scrapped.

This risk affects existing megacorps more than "startups" like OpenAI and Anthropic (and Chinese companies), because the megacorps have much more to lose. They actually have the cash to pay damages if the flood of copyright claims arrive at the door. This will not only bomb their AI development, but also the rest of their established businesses as well.

And thus I strongly suspect legal issues are holding them back a bit. Megacorps want to win the AI race, but not to the extent they stake the rest of their established business, while the newer companies' only product is AI, so they have to go all in.

Notice for example how Meta's Llama performed much more poorly after they got smacked by a bunch of lawsuits claiming that they torrented a bunch of copyright data.

(Disclaimer: I'm an outsider and everything I base my speculations on is public knowledge.)

That plus they don’t distill so they have worse RL examples.

they, rightfully so, have no faith in LLMs.

> There's nothing there that isn't immediately replaceable.

Indeed. But that was not my point. My point is that we still have this old impression that training cost is dominated by compute and it is hugely expensive, and the Chinese labs can short circuit that by distilling the American frontier models. I don't think the training compute cost is a big factor anymore for the American frontier models, because of the reasons I gave. If the Chinese models can get the training compute cost down by a factor of 100, that's not going to make them 100 times cheaper, and not even cheaper by a factor of 2. Maybe 10% cheaper or so.

Sure that can be called an attack, but then we must also concede these labs essentially massively attacked everyone else in existence to get the data, and continue attacking as we speak.

In a way you could see this as a case of Robin Hood. The US companies exfiltrated all the data on the planet just to hoard it for themselves now and accuse anyone who tries to get a piece of that back from them, and the Chinese labs are distilling it to offer it for cheap.

Obviously a bit more complicated than that but it still holds pretty well.

> Model distillation is the process of transferring knowledge from a large model to a smaller one

Sure, and large-to-small is a key part of the definition, and why it's called distillation (cf concentrating something). When Anthopic use synthetic data generated by Opus to train Sonnet or Haiku, then this can correctly be considered a type of distillation.

When Anthropic accuse Chinese companies of "distillation", it seems they are using this word to refer to two potential uses of their model outputs:

1) Using Anthropic model outputs (aka synthetic data) as training data, especially for reasoning, for Chinese models. This really isn't distillation though, since (unlike when they distill their own models) Anthropic don't actually provide the reasoning in their model output, only a "summary" designed to hide the actual reasoning. You can't distill what you are not given!

2) Another way Chinese companies may be using US LLMs is for "LLM as judge" where you are just asking the model to use it's expertise to judge/rate something that you provided yourself (to provide RL training rewards), although for coding you really want hard rewards which are easy to obtain, not fuzzy "looks good to me" ones.

Of course Anthropic are trying to pull the drawbridge up after themselves and their TOS says you can't use their models to develop anything that competes with them, and this seems to be what they are generically referring to as "distillation" - any use of their models that they suspect is being used by the Chinese to improve their own models, not just what what might more technically be called distillation, unless you want to define that word so broadly that it does mean this!

I'm not even convinced that this fits your definition. A distillation "attack" doesn't evade the security system in the sense of hacking past a login. The only part of the "security system" that it bypasses is the Terms of Service. And that's only after said data was acquired legally and correctly and normally.

It's a post-facto attack, which doesn't sit right linguistically to me.

1) Your own Wikipedia link goes on to describe using logits. Yes, language evolves to mean multiple things, and that is my point: Anthropic is pushing for a watered down definition. Furthermore, Anthropic hides thinking, so you do not even really get model outputs, you get some downstream partials. Further-furthermore, Anthropic does not describe their own models as distilled when they produce training data. Why? Because generating training data != distillation.

2) This is a stretch: it allows Anthropic to arbitrarily define "attack" via TOS, and ignores the fact that the generated training data is literally paid for by the "attackers".

So what kind of attack are AI companies doing by scraping up copyrighted info to build these LLMs?

"You're trying to kidnap what I've rightfully stolen."

> 3) The "attack" part of "distillation attack" refers to distillers creating tens of thousands of fraudulent accounts, using proxies to bypass georestrictions, deepfaked IDs, and paying real people to pass biometric KYC checks. Who then blended this in with real user traffic to conceal their behavior.

Lol. Isn't this literally many of the same tactics OpenAI and Anthropic used to scrape the internet? So now it's an "attack", but previously it was just "training".

Are you talking about my username? Yeah I liked the word before LLMs made it cool/uncool.

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yes. less stable. more hallucinations

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It doesn't need to be as good. You can route to the appropriate model and save so much money.

I have sonnet do the thinking, deepseek does all the tasks. I've massively reduced costs with this approach.

Sure, and I think that flexibility further undercuts his "frontier labs will be fine" take, which depends on top US labs having pricing power.

> Any Tokens-as-a-Service provider will refuse to serve those tokens to customers in the US.

So under a ban rest-of-world gets to use cheap open-weight models but American companies/individuals must only use only ‘approved models from US for-profits’? Doesn’t seem like that kind of protectionism will be popular or politically tenable. Not so long ago US chose cheap TVs over maintaining the country’s manufacturing base.

(Despite what you wrote it’s also really hard to imagine that enforcement wouldn’t leak like a sieve. Unser sufficient economic incentives [which are the predicate for the ban], loopholes will be found.)

So that's just an appeal to authority (longevity?) not anchored in reality. Just because you've been doing something for longer doesn't mean you're the best at it; Google has been shipping AI/ML models years before the founding of OpenAI and Anthropic, but it's playing catch-up on LLMs.

Really? Ok, name list of rogue aggressive nations that are continuously attacking other nations both allies and foes, and I tell you which one he meant...

Outside of a few boarder disputes with India, I don't China has militarily attacked anyone since they got their ass handed to them by Vietnam (Sino-Vietnamese War 1979). So I think that rules out China.

Is there more info?

ChatGPT tends to support any narrative that it thinks the user supports.

I just asked ChatGPT about the Vietnam war and it did not say that it was purely the US's fault: https://imgur.com/a/zmiOyuu

It also didn't seem to have a problem describing people killed for left wing ideology: https://imgur.com/a/LhH9saL

These are both with the free ChatGPT membership, as I do not have a paid membership anymore.

I know this is a common trope to bitch about, but honest question: did you actually try this before you commented?

ETA:

I was curious what something that was trained around me specifically (fairly typical lefty American progressive) would say, so I asked Claude (which I have a paid membership for and have discussed political things about many times). The answers were broadly similar: https://imgur.com/a/caFgKHH

What do you mean by America's fault? Maybe I'm in a left wing bubble, but I was under the impression that it's generally accepted by left and right that the Vietnam war was not justified and did not accomplish its stated goals.

Abortion? Permissive drug laws? Immigration? Homelessness policies? Vaccines? American AI gave me these examples, and they're not really so cut and dry. Do you have a better example that's being left out (no pun intended)?

Somehow the two main closed weights frontier models come from two companies with HQs about two miles apart, and the CEO of one used to work for the other.

The world would be so much better off if the WITCH companies ceased to exist

Interesting and true perspective.