I'm a strong proponent of Open Source (TM) but I disagree with this take.
The weights are the useful artifact here. You can modify them, fine tune them and do what you want with them.
Unlike binary software there is nothing limiting that.
It is also useful to have access to the training recipes and to some extent the data. But I'm of the opinion that learning on something is not copyright infringement, so there are many circumstances where distributing the raw training data will not be possible.
For me this is like Open Office: it is open source, and largely inspired by and learned from Microsoft Office. But they don't need to distribute MS Office for Open Office to be Open Source.
In addition there are models that meet the criteria you appear to propose. The AllenAI models are a good example.
The analogy falls apart very quickly. Without the training data, your modifications amount to virtually nothing compared to what these "versions" are, and the idea that you can maintain and improve on these models without the continual support of the company that owns the training data AND harnesses AND in general build instructions is not very credible. This is why it's not rare that they "dump" old versions as freeware but at some point switch to not distributing them, and mostly get away with it. As this is really not open, and the threat of an effective fork is therefore non-existent, the pressure for any one who has released freeware models to "go SaaS" is too high.
While if "Open Office" switches to a more problematic license at some point, the existing source has all you need for an organization to support the project without regard to the original company (this has happened already!). If Qwen decides to stop distributing models for download, you're basically stuck, _even_ if you have unlimited resources, it's not clear how the released weights help you; your best bet is to start almost from scratch. This has also happened...
These models are not "Open" by any definition of the word. It is just freely redistributable. You can justify yourself in whatever way you want re a cowboy approach to copyright, but this doesn't change the fact that this is not open, and has almost none of the benefits of open, and therefore it is a huge abuse of the word "Open".
Ironically about the only thing that is copyrightable here is the sum of the training data (possibly) _AND_ the software used to build the model (most definitely). The model itself most likely isn't (databases are not copyrightable), which makes it even more pointless to abuse the word "open" for it. All the value is in the former two.
When kubernetes was released there were very few people who could run it, and even less that could run it usefully.
Right now there a few people who can run a 1T model at home, even less who can run a 5T model and probably single digits who can run a 10T model.
But if an open source 10T model was available you can be sure people would find new ways to quantize it, new ways to configure hardware and and new ways to think about problems that would make it useful.
1T+ models (Deepseek v4, Kimi K2.6 etc) are available as open weights now, and for ~$5000-$10000 you can run them usefully at home. 2 years ago no on was contemplating that.
$250K to run a 10T model might be possible now. There are many companies that will pay that, and that will push the tools and techniques downwards for the rest of us.
They have spoken publicly about how they want open models banned (they call them Chinese models).
They might not want this specific action, but they do want regulation on their own terms. That really is regulatory capture.
> Nobody is doing this intentionally. Have you not paid attention to how quickly idiot stuff gets found out
They don't think is is "idiot stuff" - they are doing it openly and shouting to everyone who will listen! Read Dario's latest essay[1]:
> Many policymakers are showing increased openness to taking action, and it's been encouraging to see our peers come around to the same positions we've been advocating for over the past few years.
[snip]
> Thus, in 2025, Anthropic supported transparency legislation, helping to pass SB 53 in California, RAISE in NY, SB 315 in Illinois (in early 2026), and advocating for a transparency standard at the federal level.
[snip]
> It is time to go beyond transparency to more serious and binding regulation of AI.
> I am grateful to see the Trump administration’s Executive Order move incrementally towards a greater role for government in AI, though Anthropic’s proposal recommends even further action.
> The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks.
I'm not sure why you think they don't want to be "found out"!
It's difficult to predict this administrations actions, but given it included employees that has to be a huge risk for Google, where Deepmind is based in London.
Cohere (Canada) and Mistral (France) are going to get a lot of interest.
Maybe a year ago I’d agree but the gap has grown. I also pay for Cursor which is based on Kimi and there is no comparison for complex code gen vs Fable. It mostly succeeds well at small rapid fire stuff which is the only reason I pay for it (plus the IDE DX). But any heavy feature planning and prototyping I use Claude.
I predict they will all be mostly the same in 5+ yrs but coding is serious work and companies aren’t going to pay for almost good.
I use both Opus and Fable on tasks that are well beyond "things that would take a human 3 hours"
It fails all the time - as in it ends up doing something I want to change.
But this doesn't actually matter - if it takes 3 or 4 iterations on something that would have taken me a week it might be a day of human work, but it's still 5 times better than doing it by hand.
This seems like the obvious correct frame of mind with which to approach these tools. If it works for three hours on a task that would have taken me three work weeks, and 20% of the time it gets the task wrong, then I can just ask it to do it again with adjusted instructions. It will be much more likely to get it right the same time, and I’m still ahead of where I would have been by 14 days and 2 hours.
I don't really agree with their point here, but there are plenty of people in the AI community whose views are aligned with Anthropic's. That doesn't make them shills.
It's actually important those views are put forward.
A place like LessWrong has the opposite problem - there is no one there who questions the "safety narrative" so the discussion swings more and more towards the extreme end of that spectrum.
I think it's a big mistake to conflate the cyber (and bio) refusals with the LLM development refusals.
I can sympathize with the argument for the cyber refusals - especially as a temporary measure - especially if Mythos is available to those trying to defend against vulnerabilities.
The LLM development nerfing (and now refusals) is very different though. Anthropic has even said it isn't just for safety reasons:
> Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
It's at least partially an anti-competitive measure.
The closest analogy is putting measures in a compiler to stop it being able to build other compilers.
Another analogy is priesthoods with secret religious knowledge that "only they are qualified to know".
The Anthropic refusal description is even more direct.
“The request could assist the development of competing AI models, which is restricted under Anthropic's commercial terms. Benign machine learning work can also trigger this category.”
As we’ve seen with Fable, Mythos is more of a hype myth to justify the data retention and restrictions they added. Otherwise it’s just an incremental update of Opus. I can’t really say upgrade because the restrictions makes it a downgrade
It wasn't ok - it's been shown that the data released could individually identify people in releases before the 2010 Census.
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