The other implication here is that this is all software improvements and optimization. There might be a lot more wiggle room for improving quality over time. It seems the model and reasoning quality is improving faster than the hardware currently.

The over reasoning that Simon Willison highlights here is a real issue though. I've observed it with some of the OpenAI models as well. They are prone to overthinking and overengineering things.

What I would love is models that figure out their own appropriate reasoning effort given a task. I'm spending too much brain cycles worrying on what model speed, reasoning, and quality settings to pick. It's not just a cost concern it's also a time concern. Wasting a lot of time for simple UI tweaks because the model is set to high or ultra or whatever is counter productive. The last few iterations of frontier models seem to emphasize benchmarks and reasoning effort.

But of course the day to day reality of many developers is that they are trying to solve relatively simple problems compared to e.g. proving some so far unproven theorems, solving some Nobel prize level problems, etc. I'd love my tools to start making sane choices based on what I ask rather than defaulting to "boil the oceans". These tools need some kind of Auto select. Mostly Ultra is overkill and a waste of time and resources. And of course with local models, keeping simple things local is a nice option.

It's nice to have Sol Ultra extra fast as an option in my back pocket. But it's complete overkill 99% of the time. And it's not like most users make good choices here or are even capable of making good, informed choices. The models are more intelligent than the tool UX. Arguably, a local model of very modest size might be able to do better for this specific choice.