My position is typically to focus on getting things to work rather than making noise about what needs to be done, but enough people have messaged me in last 24 hours that felt I should write some brief thoughts. The tl;dr is I completely agree with the sentiment but think no-one aside from Pluralis is earnestly moving towards a realistic solution. The reality is without some mechanism to pool resources for the creation of the models - with hardware that can't be taken away - the situation remains exactly the same, with a faint hope that Nvidia pours enough money into various challengers that the open <> closed model gap stays narrower for longer. I see very few scenarios where an individual group can pull ahead to the frontier (and even if they do, they will be subject to the same dystopian regulation the labs are now speedrunning towards). It must be a community driven effort to achieve this and hence we require a substrate to pool compute, data, and expertise effectively. This looks like p2p/distributed/multiparty training using hardware that has already been purchased to do local AI, thats sitting idle half the time, is physically controlled by individuals and can't be taken away. Repurposing this hardware for creation of the models and not just consumption is absolutely critical. I don't view most of the other things in this list as meaningfully moving the needle towards a good outcome. RL post-trains as a service.. ok great you made it easier for enterprise clients to use open models. Inference providers serving openweights... ok you host models and make money I don't see what you've changed. Open harnesses... my concern was never that I'd be locked out of the harness layer. Local inference... I don't really care about qwen3.8 27b running at 200 tok/s I want deepseek v4 pro running locally, and I want a better model than deepseek v4 pro. I wanna own and impact the process not the output. We don't have this yet because its hugely technically challenging - it requires solving fundamental research problems around low-bandwidth training and a re-write of most layers of the training stack. This is what we've been doing with extreme urgency for the last year and what we will continue to do until its working. Once such a system exists however, we will look back and find it strange that private companies ever felt they would be able to monopolize and control something this fundamental.