Prompt engineering and using an expensive general model in order to prove your market, and then putting in the resources to develop a smaller(cheaper) specialized model seems like a good idea?
It’s like how we’ve seen basically all gadgets meld into the smart phone. People don’t have Garmin’s and beepers and clock radios anymore (or dedicated phones!). It’s all on the screen that fits in your pocket. Any would-be gadget is now just an app
I don’t think that’s the expectation set by “everyone else” in the AI space, even if it arguably is for OpenAI (which has always, at least publicly, had something of a focus on eventual omnicapable superintelligence.) I think Google Antigravity is evidence of this: there’s a main, user selected coding model, but regardless of which coding model is used, there are specialized models used for browser interaction and image generation. While more and more capabilities are at least tolerably supported by the big general purpose models, the range of specialized models seems to be increasing rather than decreasing, and seems likely that, for conplex efforts, combining a general purpose model with a set of focussed, task-specific models will be a useful approach for the forseeable future.
But cloud services run in... the cloud. It's as big as you need it to be. My cloud service can have as many backing services as I want. I can switch them whenever I want. Consumers don't care.
"One model that can do everything for you" is a nice story for the hyper scalers because only companies of their size can pull that off. But I don't think the smartphone analogy holds. The convenience in that world is for the the developers of user-facing apps. Maybe some will want to use an everything model. But plenty will try something specialized. I expect the winner to be determined by which performs better. Developers aren't constrained by size or number of pockets.
Foundational models are not great for many specific tasks. Assuming that one architecture will eventually work for everything is like saying that x86/amd64/ARM will be all we ever need for processors.
It seems right now like there is a tradeoff between creativity and factuality, with creative models being good at writing and chatting, and factuality models being good at engineering and math.
It why we are getting these specific -code models.