That’s too strong of an assertion.
Local models aren’t deterministically equivalent in capabilities to foundation models. Home computers are turing complete; just like a mainframe. They are just slower. Often not slower enough to matter.
Local models aren’t deterministically equivalent in capabilities to foundation models. Home computers are turing complete; just like a mainframe. They are just slower. Often not slower enough to matter.
Maybe if you ask them that question, but if you show them two products, they'll definitely prefer the faster one. 30 seconds is a long time to watch a progress bar.
People definitely aren't going to accept more expensive + slower ...
You don't think the commercials of Google's AI photo features aren't going to have an impact on Apple users of their phones can do a worse version of that feature and it takes longer?
You could run a pretty good home server on $50 of gear and yet we never saw any real adoption of OwnCloud/NextCloud style products as an alternative to Google Drive/Photos or Apple Cloud.
Why should LLM/Transformers be any different? Especially when you need a proper expensive GPU to run them instead of a Raspberry Pi?
On-device AI is going to be important, I think. It doesn't have to take the form of a chatbot UI to be useful.
I think there’s a sweet spot currently with munging your data blindly on the server so that your client device battery still lasts all day.
Meanwhile Apple and others push on with making client side models more efficient so that eventually the server costs and complexities go away.
If asked to choose between photo editing done within 3s using cloud provider vs an average of 30s using local compute, most consumers will choose the former without hesitation.
Most users' usage is also going to fall nicely in the free tier of a typical freemium pricing model, like ChatGPT today.
People who talk endlessly about local inference have no idea about user workflows and usability.