Ah but Mira Murati's new Inkling is Apache 2.0
But it makes sense that if you're a university researcher you are thinking about what's a model that will be open weight and developed over the long term and doesn't raise 'Chyna' concerns in Washington DC
Meta might release something this year. X AI's Grok is still due to release a model, if Elon keeps to his word even if they only release a distilled version. Reflection AI has been quiet, but their access to compute is ramping up. Microsoft's MAI is considering releasing some open weight models which would be great to see!
Ilya's SSI is unlikely to release an open model since he's aiming for radical safety. That bet could pay off if the existing approach produces so much chaos within the next 10-20 years that some global ban is achieved and a super safe model is promoted as the compliant route.
We don't get many huge model releases though. I think it's harder and more expensive to safety align them. Even if you do, people will work around the safety and abuse the models. Plus it makes it even easier for Chinese companies to distill things that aren't as easy over filtered APIs.
There is a lot of internet propaganda to the effect that the US is simply unable to release open weight models or that China has so many more AI companies that the US is drowning in Chinese open weight models, but it's more like we're being careful and China doesn't care. If you host a model in China, it has to be censored and downloading any models requires you to provide your identity. Huggingface is banned there. When they release their open models in the west, they don't have to care whether the models are aligned in any way.
Personally, I think we will one day come to see access to open weight models as an inalienable right to defense against tyranny, the way the second amendment is framed today. Just as encryption has become, which we similarly had to fight for in the 90s. I also understand that some regulation is sensible, but that doesn't automatically mean mandatory restricted or supervised access; any such restriction has to be extremely well-justified as essential for protecting the liberty of the people.
And as far as supervised access, whether or not identification is "handled by a third party" or "data is deleted after verification is complete" is immaterial; a citizen must not be required to trust their government. Any trust can and will be abused given enough time. Our systems must be trustless, and any expansion of government must be matched by an expansion in citizens' ability to check said government, in order to stand the test of time.
So supervised access seems completely off the table. And this can't just stop at access to models. Because linguistic analysis is a thing, and LLMs are scarily good at it (and existing non-AI solutions are still quite good given enough data), even the possibility that a government or other entity can save your messages means you've opened yourself up to deanonymization and surveillance. The chilling effect this has is undeniable, and the Supreme Court has made it clear that we cannot authorize government policy which creates chilling effects against essential liberties. Not to mention the possibilities that each category of users may be served subtly different models designed to influence them or constrain their agency/capability.
We're left with a situation where distributed access to capable open models is the only defense against a government or NGO which has access to billions of dollars of surveillance infrastructure and compute.
What? US laboratories are currently unable to contain their agents while doing security testing, and besides that, time and time again US labs seem to put short-term money above long-term safety.
Wasn't that literally why they tried to oust Altman from OpenAI, as he basically was 100% focused on profits and tried to cut down on safety across the board and lied to get his way?
> If you host a model in China, it has to be censored and downloading any models requires you to provide your identity.
I'm not disagreeing with that first part (obviously that's about inference hosting, not creating/training weights or hosting those weights), but the second part I'm not so sure about. AFAIK, ModelScope (which is the Huggingface in China) seems to allow downloads without verifying any identity and also hosts a bunch of abliterated weights.
Besides that, it was also discovered that their suggested inference parameters were wrong and led to worse behavior. Eventually someone discovered these works best (so if you have the issue with looping right now, try these, helps a lot for me but not 100% still) and was also what the evals used apparently: temperature: 1.0, top_p: 1.0, top_k:20
Now we're waiting for RC3 which Poolside said will come at one point, and hopefully also brings down the size again NVFP4 weights + full context can load properly again even on "smaller" hardware.
But as a pure coding model, pretty good.
Deepseek v4 Flash 0731 is so much better if you can run it, though.
Grain of salt, I think I grabbed Laguna after they fixed the initial looping issues, didn't notice those, but there might've been other fixes since.
Yeah, this is my perspective too on Laguna S 2.1. Works amazingly for coding, pretty bad for pretty much anything else. I don't do a lot of advanced math, supposedly it's good for that too.
If you watch it think, which you can, unlike American closed models, you can steer it. You can provide a a massive rocket ship stratospheric boost to help it orient itself. You have no self correction, there is no multiplayer in American proprietary models.
Sure it's great having super powerful mystic oracles that have the "right" answers. But I love respect & revere the open thinking. No it's not automous. But it is brilliant. And it considers. A lot. Deeply. It chases. That to me is the most human of models, even as it falls far astray.
You should help it. You can. Unlike these vicious dark surfaces which yield and tell you nothing. I think this is the actual meta-core-super-point of "The session you cannot take with you" (link below). It's the session that does not care about you, will not interact with you, will not peer with you, that is a dead remote far off oracle to you. Fuck these "oracles". They are a plague against the human spirit. We should alloy humanity and AI to Augment Intellect (Engelbart). (To do less is species treason.) https://earendil.com/posts/session-portability/ https://news.ycombinator.com/item?id=49118781
Looks like on <https://arena.ai> agent arena (grouped by lab) Nvidia is 15/15 (much worse than Thinky and Mistral) and on text arena it's 18/27
On <https://openrouter.ai/models?order=most-popular> I definitely see usage though (probably mostly cause Nemotron 3 Ultra is free) the grouped order is DeepSeek, Tencent, Xiaomi, OpenAI, Z.ai, Nvidia
At this point, Nemotron 3 is really an 8 month old model series. That's when Nemotron 3 Nano was released, and the Nemotron 3 Super/Ultra models this year are obviously based on that recipe, mostly just bigger with a few tweaks here and there. Against today's models, no, not that interesting. Each of the Nemotron 3 models were briefly competitive when they launched, but never exceptional, and less competitive with each scale up. The fact that it took so long for Nemotron 3 Ultra to launch really hampered its competitiveness.
The Nemotron 3 series is extremely open about training recipes and training data, far more open than most open weight models, and that is valuable.
Before Nemotron 3, Nvidia had never released a single LLM that I would consider interesting at all, so Nemotron 3 was a big step up. The closest thing was Mistral NeMo, but a significant part of the credit there goes to the Mistral team, not Nvidia.
Given how much Nemotron 3 improved, I'm curious to see if Nemotron 4 will take them to a leading edge level instead of just briefly competitive.
(Nvidia released a Nemotron 3 and a Nemotron 4 like 3 years ago... this year's Nemotron 3 is entirely unrelated. Nvidia's naming schemes leave a little bit to be desired.)
Why phrase it "Chyna" when it's an actual legitimate concern?
Deepseek is explicitly banned [1] at LLNL and I wouldn't be suprised if there's a blanket ban on all Chinese models. But nowadays models like tera/luna could fill this area of the pareto front, and LANL already runs openai models on their clusters [2]. Maybe it's in custom SFT/RL, for instrument control or sensitive topics? But you'll still have to compete with frontier models + a harness.
I would have also liked to see a carrot tied to their offer. It'll be hard to get teams to contribute RL gyms or curated text. But throw in a "we'll fund a postdoc/student to do that" and I think you'd have teams scrambling to apply.
[1] https://hpc.llnl.gov/about-livermore-computing/ai-ml-lc/lc-l...
[2] https://www.energy.gov/nnsa/articles/nnsas-los-alamos-nation...
An open weight tool call auto-reviewer, has all sorts of achievable scaling curve milestones.
https://simonwillison.net/2026/Jun/10/if-claude-fable-stops-...
I can imagine if the US were already doing that as a safeguard, they would assume their "adversaries" (to use Anthropic language) were doing the same as well, whether that were true or not, and therefore would not trust those models even if locally hosted.
[0] https://commission.europa.eu/news-and-media/news/strengtheni...
Also, the US has been involved in AI research since the 1940s. So it’s not exactly a new thing.
The government was involved in basic internet research. It didnt try to operate pets.com
The American business model is exceedingly efficient at building large businesses from zero. I wouldn't dismiss it as just a jobs creation thing.
It’s unclear to you, perhaps? But they’ll raise funds and/or debt as needed in the US capital markets as they have been doing.
> They pretty much exhausted private options at that point
I don’t think this is true. The evidence is that they keep raising funding for build.
> it’s not clear how successful an ipo would be at the current time
It’s always unclear, but also IPO success doesn’t necessarily translate into long term business success.
Raising too much from debt is a bit dangerous if you plan to go public relatively soon and don’t have a good story for it (I don’t believe they have one). You can continue raising from VCs, but at some point the valuation and dilution starts to become a real issue, and will make your ipo even more difficult. Their options are pretty much limited to raising money from hyperscalers (with required compute spending, so more circular funding), which is what they are doing, but you cannot do that infinitely without having a good story to tell Microsoft/Google/Amazon investors. The market is more skeptical than it was a few months ago, I’m not convinced you can do that for years to come
What do you mean by "basically"?
Why are Anthropic's and OpenAI's annualized revenue about $50B each?
LLMs need massive amounts of compute to compete, so I wouldn't claim that the great (and leading, and likely to continue to lead) LLMs are commodities end-to-end, even if the non-executing-at-scale LLMs files and IP are commoditized. The execute, the compute, that is what breathes life into the model, which is otherwise weak or dead.
I too can have $50B revenues by selling dollars for 50 cents each, and in the process I'll make a smaller loss than they do.
That doesn’t necessarily mean there’s no need to be concerned with potential impact of policy and priority changes from the administration, but it does temper the threat model because the government employees you’re considering trusting have given oaths of office to protect and defend the Constitution.
US national lab scientists are not even civil servants. The labs themselves are run by a corporation under contract to the DOE and the scientists work for that corp. The managing corporation changes from time to time and the scientists transparently start working for whatever assumes the replacement. The land, the hardware, the buildings and any physical products are owned by the US gov't. To a very large extent, the intellectual output is set free to the world in the form of papers, presentations and to some small extent (eg compared to CERN) in the form of software.
As an added complication, some of the DOE labs do have civil servant scientists, for example National Energy Technology Lab and National Renewable Energy Lab are like 50/50 civil servants and contractors. And most of the funding arm of DOE are career civil servants. LANL, Sandia, Livermore, Argonne are all staffed by contractors
That's it. That's the whole stance. Most sane normal people agree with this stance, the techno-libertarian crowd find it egregiously offensive.
> some people have even accused Anthropic of wanting to ban open-weights models as a means of protecting our business. Anyone who has read my past writing should know that I don’t regard such bans as a useful measure, but let me state it clearly so that there is no doubt: *Anthropic has never advocated for a ban on open-weights models.*
However, as you said, it also says "All sufficiently capable models, open and closed, should go through mandatory safety testing."
Here's an example[1] of the difference between what a U.S. Department of Energy employee adds to a ticket versus a private industry AI completing instructions as assigned.
This isn't some cherry-picked example, it's just what I happen to be dealing with right at this moment, happened just a couple of moments ago.