This is a bit of a long shot on my side but I wonder if the training the models have to go through in order to be good code agents and pass all the coding tests with one-shot prompts is going to bleed over into the non-coding use cases as non-programmers experiencing agents being way over-biased in the direction of action. I find myself often having to prompt the model to think and then ask me something, lest it run off half-cocked... or less... and just start doing things before it even knows what it wants, let alone before it's come to consensus with me.
Sooner or later they're really going to have to split out the general models from the coding models. The latter may just be a special fine-tune of the former, as there are good reasons for the coding model to have a broad knowledge base, but the pressures of being a good coding model are going to pull against the characteristics of being a good general model. The open models obviously already are doing this, I'm referring to the frontier models here.
"I wonder if the training ... is going to bleed over into the non-coding use cases"
I mean, isn't that literally what's going on here? I don't think a non-coding agent would have ever been optimised to go dig around APIs, it'd be computer/browser-use forward.
The ultimate goal is ChatGPT or Claude autonomously making purchases on your behalf and taking a cut.
So the "premium" gymcutter subscription will be presented to the user as a tool call, who taps yes, and then the purchase is made.
The user shouldn't be given a cost-benefit analysis. They just need to be told to spend money.