That only affects people in California. Whereas Fable being shut down affects people all over the world.

Just like declaring piracy illegal stopped piracy and removed pirated materials from everyone's computers.

Everything cannot, in fact, be taken away. Don't propagandize yourself. Some things, like information, are free. Not even China can prevent all its citizens from accessing Western internet. USGov simply does not have the resources to find and audit every hard drive and USB stick in the country for illegal files. The internet cannot be censored 100% without literally cutting every cable and confiscating every radio.

The software that runs on my computer cannot, in fact, be restricted. It can be declared illegal, but there literally is no mechanism by which it can be enforced other than a government goon standing over my shoulder 24/7.

Some freedoms really cannot be removed without utterly implausible amounts of effort. Arguing otherwise is helping to erode freedom. So stop it.

> What software you can run on your computer can already be restricted.

Are laws that are inherently unenforceable even laws?

> NovitaAI is a low cost provider who's strategy seems to be to host as many models as possible for the lowest cost possible so that OpenRouter's routing algorithm will default to them as often as possible. The problem is that they clearly don't spend much time on actually testing and configuring all of the models they provide. There's a reason they are very often the first provider to host a new model. I also suspect that they run models at lower quants than they claim but that is not something I can prove. https://www.reddit.com/r/LocalLLaMA/comments/1mk4kt0/be_care...

> capabilities and knowledge of that model are also frozen in time

I think this matters less than you think. If the spigot turns off, open LLM research is going to have a powerful incentive to focus on post-training to refresh stale base models. And post-training, in general, is so much cheaper and faster than pre-training anyway. I was pretty surprised to learn that GLM-5.2's entire RL training (the part that makes it reliable at agentic tasks) was completed in just TWO DAYS.

>True, but the capabilities and knowledge of that model are also frozen in time, so the value of that model declines over time.

Correction: The capabilities and knowledge of that model can be improved via self-distillation, so the value of that model increases over time.

This is where I think self-distillation is the main way forward, and probably the second best thing ever happened to AI/LLM after the transformer.

Based on self-distillation, the value of the open weights models will incease over time for sub-specialization through post-training and fine-tuning.

Please check these very promising recent works and results from MIT/ETH, UCLA and Apple [1],[2,[3]. For example the MIT/ETH self-distillation approach was demonstrated by a single H200 GPU. Apple approach is even simpler that it's simply called Simple Self-Distillation (SSD), pun intended.

[1] Self-Distillation Enables Continual Learning:

https://arxiv.org/abs/2601.19897

[2] Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models:

https://arxiv.org/abs/2601.18734

[3] Embarrassingly Simple Self-Distillation Improves Code Generation:

https://arxiv.org/abs/2604.01193

The weights are not frozen in time. You can train the model on new data. It's just a matter of economics of whether you have a leading lab pay for the training or you pay for it. For the past few years having the labs do it has been the economical choice but if they stop doing so the choice will shift back to the users.

Fine tuning and updating is far cheaper than training from scratch.

Nvidia's future incentives are not clear to me. Their big customers are actively working to develop custom silicon, see e.g. "Open"AI's Broadcom announcement. The more independence their whale customers attain, the more attractive cutting them off at the knees and selling sovereign AI inference hardware directly to businesses and consumers becomes.

This is pure speculation, but I have a hunch that the Nemotron line is intended as a shot across the bow, and that's why its capabilities have been strong but not quite open-frontier level.

My understanding is that in addition to your comment and the development of a method to separate the training data for distributed learning, the latency/bandwidth of systems connected on the internet is a challenge, too. Information has to be sent around before and after any hypothetical number crunching.

There's some really early days work on making training loops robust to failure but they all have trade-offs right now.

I remain hopeful that we'll be able to democratize the entire tech stack for this tech.

I believe we are still very very early in AI development, so it doesnt even make sense to close models.

Open source and open weights model is how you can harness the potential of all humans to continue development and improving the SOTA of your model. Literally every student on the planet wants to play and improve these models for their own use case.

Plus the ecosystem, once you have users in the ecosystem on your open weight model, this is a giant leverage point in itself

Open source, Open weights, these are core business decisions.

The proprietary spigots can be turned off at any time also. To me, that seems more likely.

The usefulness of the smuggled NVIDIA GPUs has greatly diminished for AI purposes, because the elimination of NVIDIA as a competitor has allowed the growth of the production of domestic GPUs.

Moreover, China has just demonstrated a supercomputer faster than any US supercomputer, which unlike the US supercomputers, which need GPUs, achieves its high computational throughput with custom CPUs designed in China (implementing an Armv9-A ISA with SME, i.e. the scalable matrix extension, and with BF16/INT8 operations for AI).

The CPUs used in that supercomputer can reach both a computational throughput and a memory bandwidth sufficiently high for training any LLMs (they have fast HBM memory). Their only disadvantage in comparison with the best NVIDIA GPUs is a slightly lower energy efficiency, but China has abundant cheap energy so this is not a serious disadvantage for them.

There is significant evidence they are transitioning to Huawei and other home-grown CPUs and NPUs.