The point is that open weights turns puts inference on the open market, so if your model is actually good and providers want to serve it, it will drive costs down and inference speeds up. Like Cerebras running Qwen 3 235B Instruct at 1.4k tps for cheaper than Claude Haiku (let that tps number sink in for a second. For reference, Claude Opus runs ~30-40 tps, Claude Haiku at ~60. Several orders of magnitude difference). As a company developing models, it means you can't easily capture the inference margins even though I believe you get a small kickback from the providers.
So I understand why they wouldn't want to go open weight, but on the other hand, open weight wins you popularity/sentiment if the model is any good, researchers (both academic and other labs) working on your stuff, etc etc. Local-first usage is only part of the story here. My guess is Qwen 3.5 was successful enough that now they want to start reaping the profits. Unfortunately most of Qwen 3.5's success is because it's heavily (and successfully!) optimized for extremely long-context usage on heavily constrained VRAM (i.e. local) systems, as a result of its DeltaNet attention layers.
It is partly the medium's fault. A lot of the sins of CD/digital mastering wont fly on vinyl because there's physical constraints around what you can literally press into the record groove.
I've had a Framework 13 for several years now, so I'm excited to see this kind of thing start to happen. Praying the next one out is a GPU/tensor workload unit so I'm not stuck at home on my desktop when I want to mess around with local AI models...
I see a lot of debate about whether AI is "conscious" or has "consciousness", with people talking past each other without a firm grasp on their own stances, so I made a quick quiz to help you locate where you stand.