[maym86]

There is a difference between labeling training data and just using humans to do the work. Some things cannot be achieved yet even with lots of labeled training data but companies are pretending they have solved hard ML problems at a high level of performance when the technology and research aren't there yet.

[pacavaca]

But why should it matter for customers who does the job? I mean, if you don't tell anybody, and pretend it's 100% AI then it's bad, but if it "will eventually become AI", and your investors and everybody interested in the technical details know how it's actually done, then what's wrong? A true "AI" should be able to pass a Turing test, so for the customer, it should be indistinguishable, and shouldn't matter.

Of course, the privacy concerns are there, but then again, if it's a real "AI", then it may be worse for the computer to read your data than for a random low paid worker ;)

[maym86]

Because the business costs don't scale well unless they can invent technology to remove the humans and for harder ML problems that currently isn't possible. The business is only based on being able to sell they hype of "AI" to investors.

[TeMPOraL]

Ask yourself the reverse question - if they already have a working, useful service that's human-powered, why are they lying by saying it's done by AI? Answer: because they're trying to get things they're not entitled to - like better funding, better sales, more attention. In other words, they're trying to cheat other people out of their money or time.