Why should people be able to tell the difference vs the outcome of those products, unless someone (investors?) is specifically just buying into the AI hype itself? (in which case, this is nothing different from when everything was "social")

I'm not saying people should be able to tell the difference, nor am I discounting the value of the final outcome. Just that pitching an app as ML-driven, when it is barely 1% ML-driven is deceptive and mis-informs the public.

Consider taking the side mirror from a Ferrari, putting it on a Ford Escort, and then pitching the Ford Escort as a race-car. The Ford Escort may be a great family car, but it is not a sports car. People who don't know the difference might come to think of it as a sports car, and might even question the wisdom of spending money on a Ferrari.

I get (and perhaps even agree) to the sentiment, logically at least, but realistically all marketing is deceptive. It's pragmatically more useful to understand the how's and why's of that and play along.

If that 1% is essential to the purpose of the app, it seems reasonable no matter what % of the code of the app it represents.

Going along with my racecar example...the side view mirrors are essential...so does that make the Ford Escort a racecar?

The analogy doesn't work, because "racecar" isn't a technology used in the side mirrors.

Differentiation.

Most off the shelf ML tools are not that powerful - and they don't make a difference in how you differentiate as a business. If you have a ML team and know how to build models from scratch with novel data, then you can iterate on your model and expand, something you can't do if you don't own the model.

You should show your differentiation by differentiating. But in many cases the ML differentiation isn't the same as differentiating the value prop of the product.

I got tensorflow to analyze blutooth signals off a wireless controller.

talk about dreamland autoaim

you know, something like thumbs as a correlation