By Russ Banham
Emerging technologies like artificial intelligence (AI), blockchain, 5G, cryptocurrencies, and the Internet of Things (IoT) are improving how we communicate and exchange information. To varying degrees and often in combination, these tools also are transforming the fundamentals of commerce and production.
Yet, these breakthrough technologies tend to stir up worries of jobs lost to machines. The truth, however, is more nuanced. While machines will absorb many laborious tasks, people will be needed to operate these technologies. Hence the increasingly accepted idea of human-machine partnerships and their ability to change the types of work people perform to the betterment of business and the global economy.
This is just one of several important insights into human-machine partnerships and how they will reshape the world’s economy in a report by Dell Technologies and the Institute for the Future, a not-for-profit think tank based in Palo Alto, California. The study, titled “The Future of the Economy,” draws from interviews with twenty global experts across an array of disciplines. According to the report, three pivotal socioeconomic shifts are simultaneously occurring to create a friction-free economy by 2030.
The first shift is toward autonomous commerce, whereby machines will be able to assess the needs of consumers and businesses to deliver products and services on an automatic, cost-effective, low-to-no-human touch basis. For example, an internet-enabled clothes washer will “negotiate” with other appliances in a house to prioritize hot water use at times of day when energy is the least expensive.
When maintenance and repair issues arise, the machine will interact with other machines over the internet to contact service personnel to fix the problem, either online through electronic interventions or via a home visit. The related transactions will be triggered by pre-established smart contracts within secure blockchain platforms, with the payment made in cryptocurrencies.
The second shift cited in the report is toward anticipatory production, whereby automated micro-manufacturing augments and, in some cases, replaces traditional mass production. Once a consumer signals an intent to purchase a product, companies will be able to leverage emerging technologies to rapidly fulfill this demand, in part through “maker” communities with unique capabilities.
A case in point is a maker of 3D-printed components. When a customer signals a demand for a product, this information automatically triggers a smart contract in a blockchain platform, activating the making of the printed part. Since customer demand fluctuates, anticipatory production will leverage technologies to gauge these changes in advance, ensuring a ready-and-steady inexpensive supply from makers when the need arises.
The last shift is toward leapfrog communities. Smaller economies around the world that are unburdened by antiquated legacy systems will be positioned to leap forward, thanks to innovative financial services concepts. For example, distributed ledgers in a blockchain platform can empower the disenfranchised to document their identities to participate more fully in the global economy, using mobile handsets to transfer money and obtain micro-credit. These new ways of accessing capital are designed to improve the lives of people in their local communities.
These shifts are already evident in many cases. Rather than displace workers, they reimagine the nature of work, offering newer forms of craft and service. Nevertheless, every socioeconomic transition—especially one so technologically transformative—has impact well beyond more productive ways of performing a business operation or task.
“Machine learning, for instance, is an amazing technology, and it creates an ability to change the world like nothing we’ve seen before,” says Erik Brynjolfsson, director of the Initiative on the Digital Economy and a professor of management at MIT Sloan School of Business. “While it can be used to create more broadly shared prosperity to solve all sorts of health problems and empower people, it can also be used to concentrate wealth and power, and eliminate privacy. There is no economic law that we automatically share equally.”
To alter such negative outcomes, Brynjolfsson says that we must identify the potential downsides of emergent technologies to make ethical decisions on how these tools can be used to generate positive economic and societal outcomes.
“Real value is created by managers and entrepreneurs who know how to reinvent a factory or reinvent retailing—using some AI but also using people and combining both in new ways,” he explains. “The human and the machine working together can do something neither of them could have done separately. I’ve seen a sea change where most AI researchers now really think a lot about the ethics.”
Preparing for an Extraordinary Future
When asked to weigh in on the three shifts outlined by “The Future of the Economy” report, Brynjolfsson and Karen Harris, managing director of consultancy at Bain’s Macro Trends Group, both agreed these technologies can be enablers of a better future— assuming their challenges are mitigated to reduce negative outcomes.
One such outcome is a substantial reduction in traditional forms of employment. “I agree with the idea of autonomous commerce, where machine learning and internet-enabled sensors collaborate with or without people to provide services and products in the transitional period through 2030, but there are potentially adverse implications, chief among them massive dis-employment,” says Harris.
As an example, Harris points to Amazon’s current employment of warehouse workers, who are likely to be displaced as more efficient, productive and cost-effective robots are developed. “Amazon has been quietly rolling out these robots that handle customer orders, reportedly removing 24 job positions at each facility where they’ve been deployed.”
Certainly, the knock-on effects on people caused by emerging technologies, in addition to the economic impact of higher unemployment, cannot be ignored, particularly as the cost of automation comes down.
“With this as backdrop, the focus needs to be on the development of different types of jobs and places to work,” Harris says. “If businesses fail to provide this employment, then governments may need to intervene—much like the federal government did during the Great Depression with the Works Projects Administration (WPA), which gave jobs to more than 8 million people.”
Both futurists are neither cynics nor Luddites when it comes to the value of technological enhancements. As Harris observes, “Each new technology is built on a previous technology to create incredible upsides.”
Brynjolfsson agrees, commenting that human-machine partnerships have too much going for them to turn back the dial. “We’re moving from human decision-makers to having machines do more and more, either through big data or artificial intelligence,” he says. “But we’re far from general artificial intelligence that can just do everything, and that means we need to have a partnership between humans and machines. They each have their strengths and weaknesses.”
With regard to the weaknesses, he cites the stark possibility of growing wealth disparity. “We can use these powerful tools to create more inequality or more shared prosperity,” says Brynjolfsson. “This realization should be front and center as we apply the tools.”
The notion of shared prosperity also resonates with Harris, especially in relation to local maker communities producing specialty goods on an on-demand basis, given the availability of work provided and the opportunity for independent entrepreneurs to choose where to live.
“Because of the old industrial economy, people flocked to cities that were technically designed for mass production and concentrated labor,” she explains. “The cities eventually became unaffordable for the middle class, who migrated to the suburbs and commuted to the urban core. Now independent makers can live in the urban burbs or exurbs that are sprouting well outside major cities, where homes are more affordable and quality of life considerations like walkable communities are increasing.”
Moreover, unique forms of work are developing to provide an additional means of income in local communities. “A traditional small business like a hardware store can expand its customer base by becoming part of the maker revolution,” says Harris. “Instead of going to a retailer selling a silverware set that is mass-produced in Vietnam, a consumer who needs one or two forks and spoons and doesn’t want to buy an entire set can go to a hardware store with 3D printing capabilities to make the utensils on a customized basis. An entirely new enterprise and market opens up for an old-timey business.”
Harris adds that the shift toward anticipatory production also presents the opportunity for lower-income people to have more children. “Rich people today have more kids than people in the middle and lower classes because of the growing cost of living; our research indicates that birth rates rise as a family’s income begins to exceed $150,000 to $200,000 annually,” she explains. “People at lower income levels don’t want to have more kids than they can afford to provide a decent education and healthcare. The socioeconomic benefits would be more equally shared across families in different income classes.”
Brynjolfsson has a similar perspective of shared socioeconomic benefits, insofar as the value of emerging technologies to foster diversity of thought within companies. Many businesses tend to hire people who think just like those currently employed, “which leads to groupthink,” he says. “That’s where crowd innovation needs to come in.”
He is referring to loosely decentralized but well-functioning crowds of diverse people that collectively bring to the decision-making table a wide variety of skills, experiences and perspectives to solve business problems and ideate new business concepts.
“Diverse ways of thinking often come from people who have diverse experiences—different races, genders, classes, countries, training disciplines, backgrounds,” Brynjolfsson says. “You get people who think outside-the-box. It’s not that they’re experts, it’s that they’re experts in something else.”
The use of machine learning and predictive data analytics can ferret out individuals’ diverse experiences and the skill sets they generate, whereas human recruiters have unconscious biases that limit this possibility. “It’s really hard to get people to be less biased,” Brynjolfsson acknowledges. “While machine learning systems are imperfect, people are way more imperfect. And machine learning systems have the advantage of improving over time.”
Preparing for Tomorrow
Much work still needs to be done within business organizations to set the stage for the profitable outcomes available from human-machine partnerships. “Technology is the tip of the iceberg, but the iceberg is our skills—human capital and organizational capital,” says Brynjolfsson.
He advises businesses to invest first in people and operations before spending capital on technology, “because that’s where the bottlenecks reside,” he says. “If somebody snapped their fingers and said, `Okay, no more technology development for the next 30 years,’ we’d still have lots of work to do reinventing retailing, manufacturing, government, and medicine just using the technologies we have right now.”
Harris has a similar perspective, citing the wisdom socioeconomic implications of a technology as it is under development. “AI, blockchain, robotics, the Internet of Things, they all have this incredible upside, but that doesn’t change the fact that moving too quickly can have adverse impacts like labor dislocation,” she explains. “We need to be realistic that some outcomes will be negative, requiring thoughtful analysis and mitigations.”
From the advent of the wheel to today’s machine learning systems, all technologies are simply tools designed to make work less toilsome and people more productive. As these tools become more sophisticated in the decade ahead, now is the time to evaluate and act upon their complex socioeconomic impacts.