Source: HBR, May 2015
Brynjolfsson and McAfee explain that while digital technologies will help economies grow faster, not everyone will benefit equally—as the latest data already shows. Compared with the Industrial Revolution, digital technologies are more likely to create winner-take-all markets.
once you adjust for inflation, an American household at the 50th percentile of income distribution earns less today than it did in 1998, even after accounting for changes in household size.
… the Great Decoupling. The two halves of the cycle of prosperity are no longer married: Economic abundance, as exemplified by GDP and productivity, has remained on an upward trajectory, but the income and job prospects for typical workers have faltered.
Workers’ prospects are deteriorating in the developing world, too. A recent study by Loukas Karabarbounis and Brent Neiman found that labor’s share of GDP had declined in 42 out of 59 countries, including China, Mexico, and India. The researchers concluded that as advances in information technology caused the price of plants, machinery, and equipment to drop, companies shifted investment away from labor and toward capital.
The net effect has been to decrease the demand for low-skilled information workers while increasing the demand for highly skilled ones. … skill-biased technical change. By definition, it favors people with more education, training, or experience.
What if we were to reframe the situation? What if, rather than asking the traditional question—What tasks currently performed by humans will soon be done more cheaply and rapidly by machines?—we ask a new one: What new feats might people achieve if they had better thinking machines to assist them? Instead of seeing work as a zero-sum game with machines taking an ever greater share, we might see growing possibilities for employment. We could reframe the threat of automation as an opportunity for augmentation.
Brynjolfsson: You could break the Second Machine Age into stages. In stage II-A, humans teach machines what we know painstakingly, step-by-step. That’s how traditional software programming works. Stage II-B is when machines learn on their own, developing knowledge and skills that we can’t even explain. Machine learning techniques have had some success doing that in areas as diverse as understanding speech, detecting fraud, and playing video games.
Is there a third stage?
Brynjolfsson: Maybe. It might be when machines understand emotions and interpersonal reactions, an area where humans still have the edge.
humans are still far superior in three skill areas. One is high-end creativity that generates things like great new business ideas, scientific breakthroughs, novels that grip you, and so on. Technology will only amplify the abilities of people who are good at these things.
The second category is emotion, interpersonal relations, caring, nurturing, coaching, motivating, leading, and so on. Through millions of years of evolution, we’ve gotten good at deciphering other people’s body language…
Brynjolfsson: …and signals, and finishing people’s sentences. Machines are way behind there.
The third is dexterity, mobility.
The intellectually easy thing to do is to look at an existing process and say, How can I have a machine do part of that job? It does take a certain amount of creativity and a little bit of work to do that, and it does create value. However, it takes a lot more creativity to say, How can I have this machine and this human work together to do something never done before and create something that will be more valuable in the marketplace?