This special issue of Media-N gathers perspectives on artistic labor in an increasingly automated global economy, focusing on the impact of artificial intelligence and the role of Amazon.com, Inc. in reshaping how work is defined, valued, and performed. Our contributors are artists, curators, scholars, and critics interested in labor value, emerging forms of exploitation and alienation, as well as new possibilities for collective resistance, solidarity, and critique.

Artificial intelligence (AI) promises to transform business processes and productivity across industries and the economy as a whole. But unless businesses rethink how their people can work in partnership with intelligent machines, there's a risk that this promise will quickly pass them by. According to Jim Wilson, Managing Director of Information Technology and Business Research at Accenture Research, the most lasting, impactful performance boost happens when people and AI-powered machines work together to create "collaborative intelligence." Wilson, who conducted research with more than 1,000 global companies to co-author Human Machine: Reimagining Work in the Age of AI with colleague Paul Daugherty, found that if AI is deployed primarily to displace human workers, short-term productivity gains are about as good as it gets. As Elon Musk said, 'Excessive automation at Tesla was a mistake. By contrast, Wilson's research found that enterprises that reimagine work around human and AI collaboration outperform those that focus solely on automation by more than three times -- and often, by more than six times -- in areas like speed, scalability, flexibility, and decision-making within processes.

They further say, as a result, we need to rely on the government to support the population via universal basic income or UBI. Even Bill Gates who has transformed the world through tech innovation argued we should tax robots to slow innovation and use the money to hire people. Virtually all the other top tech billionaires and Warren Buffet seem to agree. But as we mentioned above, the experts seem to be very wrong about so many major predictions. It is actually very difficult to think of predictions that they got right.