Each day we read about amazing technology breakthroughs, particularly when it comes to artificial intelligence (AI). But if AI is so great, why are these breathtaking technological achievements not matched with soaring productivity and economic growth? Or, to paraphrase an old jibe: If the economy is so smart, why aren’t we all rich?
After all, we live among astonishing examples of potentially transformative new technologies that could greatly increase productivity and economic welfare. As noted in the 2014 book, “The Second Machine Age ,” leaps in AI, machine learning and, more recently in areas such as image recognition, abound. Investments in AI-related companies surged to over $5 billion in 2016.
At the same time, measured productivity growth over the past decade has slowed to half of its level in the decade preceding the slowdown. The sluggishness is also widespread, occurring throughout the U.S., the U.K. and other Organization for Economic Cooperation and Development (OECD) nations, among others.
In the U.S., the Congressional Budget Office reduced its 10-year forecast for average annual labor productivity growth from 1.8 percent in 2016 to 1.5 percent in 2017. Although modest, that drop implies GDP will be considerably smaller 10 years from now than it would be in a more optimistic scenario — a difference equivalent to almost $600 billion in 2017.
When we studied the topic in depth, however, we drew a surprising and significant conclusion: There is no inherent inconsistency between forward-looking technological optimism and backward-looking disappointment. Both can simultaneously exist.
The nature of transformational technology
Indeed, there are good conceptual reasons to expect them to simultaneously exist when the economy undergoes the kind of restructuring associated with transformative technologies. Corporate wealth and the measurers of historical economic performance show the greatest disagreement precisely during times of technological change. Our evidence suggests that the economy is in such a period now. Economic value lags technological advances.
To be clear, we are optimistic about the ultimate productivity growth fueled by AI and complementary technologies. The real issue is it that it takes time to implement changes in processes, skills and organizational structure to fully harness AI’s potential as a general-purpose technology (GPT). Previous GPTs include the steam engine, electricity, the internal combustion engine and computers.
In other words, as important as specific applications of AI may be, the broader economic effects of AI, machine learning and associated new technologies stem from their characteristics as GPTs: They are pervasive, improved over time and able to spawn complementary innovations. […]