I get what you mean but the GPU comparison isn't the best here, I think. Money-is-no-object-I-want-the-best approach is questionable, definitely. But no one can argue that an old Nvidia card is objectively better for e.g. 4k gaming than a 4090 if you don't mind the wattage. You can just measure it.

With LLMs the problem is more complex, it's people getting used to how a model works and to the ecosystem. Sure, you can make all your skills harness-agnostic and deal with Anthropic's stubborn refusal to adopt the common naming/directory structure. But most people don't. So then you end up with something closer to the ancient Android vs iOS discussion. Can you prove, in isolation, that iOS is more energy efficient, the hardware is faster? Yeah. But that won't speak to someone who has been on Android for 10 years and would have to migrate and get used to iOS to experience that, first.

I've noticed myself how I get used to common failure modes of particular models in my projects. GPT5.5 tends to create some checks/booleans I don't need, it heavily overcorrects on error handling, etc. While Claude 4.7/4.8 doesn't do those as often but gets derailed on our E2E test suite, forgets to run linting despite guidance. So even assuming fully harness-agnostic working setup, a new LLM model with its own quirks can be a lot friction for heavy users who might be used to Claude specifically and all their skills/guidance pre-address common failure modes.

E.g. I might be a Prius owner, then you gift me an objectively better, more efficient, safer, newer, same-size, physical knobs car ...and I might still swear by my Prius! I'm used to how it turns, how it feels, I can repair some issues myself. Isn't that a normal reaction then?