In the US, for instance, the various regions of the country were in the language of economists. The trend reversed in the 1980s after the onslaught of digital technologies. Many manufacturing and retail jobs were eliminated by automation. There were new, well-paying tech jobs in a few cities.
San Francisco, San Jose, Boston, and Seattle had the most tech jobs by the year 2019. San Francisco and San Jose are the only two cities in the United States that account for more than 25% of the assets and capabilities of new artificial intelligence.
The geographical disparity in wealth will continue to increase because of the dominance of a few cities in the invention and commercialization of artificial intelligence. This will foster political and social unrest, but it could hold back the kinds of artificial intelligence technologies needed for regional economies to grow.
Part of the solution could be to loosen Big Tech's hold on defining the agenda. Increased federal funding for research will be needed. Muro and others have suggested hefty federal funding to help create US regional innovation centers.
A more immediate response is to think of artificial intelligence as a way to expand opportunities in sectors that different parts of the country care about, like health care, education, and manufacturing.
Artificial intelligence and robotics researchers often try to get a machine to do a task that is easy for people but difficult for the technology. It could be making a bed or an espresso. Or driving a vehicle. Seeing a robot act as a barista is amazing. The people who develop and deploy these technologies don't think about the impact on jobs and labor markets.
An economist at the University of Virginia says that the tens of billions of dollars that have gone into building self-driving cars will inevitably have a negative effect on labor markets once they are deployed. He wondered if those billions had been invested in tools that would be more likely to expand labor opportunities.
When applying for funding at places like the US National Science Foundation, no one asks how it will affect labor markets.
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The Partnership on Artificial Intelligence in San Francisco is working on ways to get scientists to rethink how they measure success. That is, artificial intelligence scientists grade their programs based on how well a person can identify an object.
The direction of the research has been driven by benchmarking.
She has begun working to create some benchmarks for the performance of human-machine collaborations. She and her team at Partnership for Artificial Intelligence are writing a user guide for developers of artificial intelligence who have no background in economics to help them understand how workers might be affected by the research they are doing.
It is about changing the narrative away from one where artificial intelligence is given a blank ticket to disrupt and then it is up to the society and government to deal with it. She says that every artificial intelligence firm has some kind of answer about bias and ethics, but they aren't there for labor impacts.
The digital transition has been accelerated by the Pandemic. Businesses are replacing workers with automation. Digital technologies have the potential to expand our abilities. They've given us research tools to help create new vaccines and provided a viable way for many to work from home.
It will be worth watching to see if this leads to more damage to good jobs and more inequality. He says that will mean making decisions about the technologies we create and invest in.
The Turing Trap: The Promise and Peril of Human-Like Artificial Intelligence will be published in the spring of 2022.
The wrong kind of artificial intelligence? The future of labour demand and artificial intelligence are discussed in the Cambridge Journal of Regions, Economy and Society.
Cogs and Monsters: What Economics Is, and What It Should Be Diane Coyle
Princeton University Press
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