That's a way of separating those artisanal items from machine-made, mass-produced competitors.
But it's not just physical goods that are made more quickly and cheaply by machine. Artificial intelligence is making decisions that would previously be done by humans. Not in 5 or 20 years. Now.
"My general view is that people will always want convenience, but they’re not willing to pay premiums for it," says GoButler CEO Navid Hadzaad. "And then when you think about the human labor behind it, I think that will become a luxury more and more."
GoButler used to be a virtual assistant service, with humans (called heroes) who would carry out tasks for users, like making restaurant reservations, ordering pizzas, or in one memorable instance, drawing horses. It all happened over chat, like an iMessage with a resourceful friend, or say, a butler.
This required actual human employees — most of whom were in expensive cities like Berlin or New York.
But then GoButler's algorithm technology started studying the way users interacted with heroes. All those human-to-human messages formed a database that the algorithm could study, learning how to respond to things users say.
If that sounds familiar, it might be because it's the same technique that Google's AlphaGo program used to master the ancient, inscrutable game of Go and beat a champion human player 4-1.
Instead of keeping the human-enabled GoButler service (which would ultimately have required moving the support operation to a place like the Philippines) Hadzaad decided to double-down on the algorithmic-only offering of doing travel bookings exclusively. No heroes needed.
The idea of intellectual human labor as an unecessary luxury isn't just happening in concierge apps. It's happening on Wall Street, as the work of research analysts — like seeing how news events like the way in Syria affects the prices of oil and currency — is now being handled by clever programming at places like Goldman Sachs.
Whenever a game or a service has a fixed enough rules, the computers will win. That's what Brown University computer scientist Michael L. Littman explained to us after Google's AI victory.
"What we're finding is that any kind of computational challenge that is sufficiently well defined, we can build a machine that can do better," Littman says. "We can build machines that are optimized to that one task, and people are not optimized to one task. Once you narrow the task to playing Go, the machine is going to be better, ultimately."