Robotics is a fast-growing industry. A recent report from GlobalData forecasts that it will pass the $500bn mark in 2030, after a decade of double-digit annual growth. That's an impressive figure for an industry that generated global revenue of just $45bn in 2020. Most of the value generated by robotics comes from service robots, a broad category that includes consumer robots, as well as robots used in logistics, healthcare, security, and many other areas of the service sector. However, industrial robots will grow at a faster rate in the 2020s.

But concerns over growing inequality and the lack of opportunity for many in the labor force--serious matters linked to a variety of structural changes in the economy–are well-founded and need to be addressed, four scholars on artificial intelligence and the economy recently told an audience at Stanford Graduate School of Business. That's not to say that artificial intelligence isn't having a profound effect on many areas of the economy. But understanding the link between the two trends is difficult and it's easy to make misleading assumptions about the kinds of jobs that are in danger of becoming obsolete. "Most jobs are more complex than [many people] realize," said Google's chief economist, Hal Varian, during a forum on the future of work, which was sponsored by the Stanford Institute for Human-Centered Artificial Intelligence. Today's workforce is sharply divided by levels of education, and those who have not gone beyond high school are affected the most by long-term changes in the economy, says David Autor, professor of economics at the Massachusetts Institute of Technology.

Led by Fei-Fei Li, the director of the Stanford University artificial intelligence lab and a newly minted Google employee, a team of academics recently explored a new way of tracking socioeconomic trends across the US. Rather than knocking on doors and asking questions, they pulled more than 50 million photos from Google Street View and fed them into neural networks. Simply by identifying the make, model, and year of automobiles appearing in the photos, the researchers said, their tech could accurately estimate the income, race, education, and voting patterns of citizens in particular precincts. If the number of sedans on a short stretch of road exceeded the number pickup trucks, for instance, they found that a city was 88 percent likely to vote for a Democrat during the next presidential election. If pickups exceeded sedans, a city was 82 percent likely vote Republican.