Why the Automation Boom Could Be Followed by a Bust
You may not be sharing your office with a robot yet, but the next wave of automation has begun. Humanoid service robots, machine learning algorithms and autonomous logistics will replace millions of service workers in the coming decade. Experts are rushing to forecast the likely impact on jobs. But most projections overlook two powerful forces that will combine with automation to reshape the global economy by 2030: rapidly aging populations and rising inequality.
The collision of these three forces sets the stage for a 10- to 15-year economic boom followed by a bust. An aging workforce, advances in automation, and growing income inequality point to an era of rapid and volatile change—and greater economic disruption than we have seen over the past 60 years. In the coming decade extremes are likely to become more extreme.
How would this boom-bust cycle likely play out? As populations age, labor force growth will slow, triggering labor scarcity in a growing number of industries. Faced with labor shortages, companies will accelerate their investment in automation technologies. Our research shows incremental capital investment in automation could reach $8 trillion in the US by 2030. That translates to about $5 trillion in net accumulation to the US capital stock, increasing capital per worker to a net figure nearly 1.5 times higher than today.
The magnitude of the investment in automation in the coming decade is likely to be greater in scale than in previous periods because it will primarily affect the service sector, and it will spread through advanced economies as well as parts of the developing world. An $8 trillion investment boom would result in average annual US growth of about 3% and roughly 60% more economic output in 2030 than in 2015.
Typically, in an investment boom of this kind, supply growth creates the demand for more supply—a virtuous cycle of growth. In the early 2020s, rapid investment in automation would likely offset a little more than half the negative impact of automation on employment, easing the demand constraint on growth and potentially mitigating the immediate displacement of millions of workers. But by the end of the 2020s, automation could eliminate 20% to 25% of current US jobs—40 million workers—hitting middle- to low-income workers the hardest. At the same time, many of the companies that invested heavily in automation will be saddled with assets that are out of step with demand.
That’s the crucial pivot between boom and bust. As the investment wave recedes, it risks leaving in its wake deeply unbalanced economies in which income is concentrated among those most likely to save and invest, not consume. Growth at that point would become deeply demand-constrained, exposing the full magnitude of labor market disruption temporarily hidden from view by the investment boom.
Consumers who have lost their jobs to automation will spend less, putting further downward pressure on demand. By the late 2020s, unemployment and wage pressures may exceed levels following the Great Recession in 2009. Income inequality, having grown steadily for a decade, could approach or exceed historical peaks, choking off economic growth.
The benefits of automation, by contrast, will flow to about 20% of workers—primarily highly compensated, highly skilled workers—as well as to the owners of capital. The growing scarcity of highly-skilled workers may push their incomes even higher relative to less-skilled workers. As a result, automation has the potential to significantly increase income inequality.
The speed of change matters. A large transformation that unfolds at a slower pace allows economies the time to adjust and grow to reabsorb unemployed workers back into the labor force. However, our analysis shows that the automation of the U.S. service sector could eliminate jobs two to three times more rapidly than in previous periods of labor transformation in modern history.
In estimating the dislocation from the next wave of automation, we looked at the peak movement from agriculture to industry and the movement out of manufacturing into services. What differs in those previous transformations is the pace of change. The transition of farm workers into the industrial sector took place over four decades. The automation of manufacturing occurred over a shorter time period—roughly 20 years—but the share of the labor force in manufacturing jobs was relatively small in the U.S. Investment in automation in the 2020s is likely to proceed faster than agricultural automation or manufacturing automation unless other forces act to impede its progress, and it will affect a larger percentage of the total workforce.
Of course, the clear pattern of history is that creating more value with fewer resources has led to rising material wealth and prosperity for centuries. We see no reason to believe that this time will be different—eventually. But the time horizon for our analysis stretches only into the early 2030s. If the automation investment boom turns to bust in that time frame, as we expect, many societies will develop severe imbalances.
The coming decade will test leadership teams profoundly. There is no set formula for managing through significant economic upheaval, but companies can take many practical steps to assess how a vastly changed landscape might affect their business. Resilient organizations that can absorb shocks and change course quickly will have the best chance of thriving in the turbulent 2020s and beyond.