I've written quite a bit about automation/artificial intelligence/computerization in previous threads and believe that it is looming challenge to which we are not paying enough attention. Below is a fairly balanced and brief article that helps to inform understanding.
The robot revolution hasn't started yet.
BY EDOARDO CAMPANELLA | AUGUST 9, 2018, 5:18 PM
It has been 21 years since IBM’s Deep Blue supercomputer checkmated chess champion Garry Kasparov, marking a historic moment in the development of artificial intelligence technologies. Since then, artificial intelligence has invaded everyday objects, such as cell phones, cars, fridges, and televisions. But the world economy seems to have little to show for the proliferation of smartness. Among advanced economies, productivity growthis slower now than at any time in the past five decades. National GDPs and standards of living, meanwhile, have been relatively stagnant for years.
This situation poses something of a riddle: Previous waves of technical innovation have come with rising productivity and, in turn, leaps forward in economic growth and well-being. For example, once electricity became widespread in the United States in the 20th century, labor productivity started growing at an annual rate of 4 percent—almost four times higher than the current rate.
There are two schools of thought about today’s productivity puzzle. On the one hand are techno-pessimists, such as Northwestern University professor Robert Gordon, who believe that today’s technologies are the issue. The six innovations that powered economic growth from 1870 to 1970—electricity, urban sanitation, chemicals, pharmaceuticals, the internal combustion engine, and modern communications technologies—the thinking goes, were simply more transformative than, say, Siri.
On the other hand are techno-optimists who counter that today’s innovations—cloud computing, big data, and the “internet of things,” which are at the heart of the artificial intelligence revolution—are, indeed, transformative and that their benefits are already being enjoyed by firms and consumers around the world. The problem, scholars such as British economists Jonathan Haskel and Stian Westlake argue, is that national accounting statistics simply cannot capture those benefits. The concept of GDP first emerged in the 1930s to measure economies that were primarily devoted to the production of tangible goods. Intangible goods and services, by contrast, increasingly dominate today’s economies. If GDP figures properly tallied the intangible economy, the argument goes, then productivity growth would look much better.
There is some truth in both theories; certainly, electricity changed the structure of work and home life in ways that Google Home has not. It is likewise true that GDP does not count free online services such as Google, Facebook, and YouTube that massively contribute to the well-being of consumers. But there might be a third, more straightforward, solution to the productivity riddle—one that even reconciles the other two. Simply put, the latest revolution is not showing up in national statistics because it has not yet really begun.
Simply put, the latest revolution is not showing up in national statistics because it has not yet really begun.
In reality, it takes a considerable amount of time for firms to make good use of new technologies, especially general-purpose technologies, as economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson showed in a working paper for the National Bureau of Economic Research. In fact, it is only after a sufficient stock of the new technology and complementary innovations (both tangible and intangible) are built up that a technological revolution shows up in the numbers. And that usually takes a quarter-century years at least.
General-purpose technologies, as the economists Boyan Jovanovic and Peter Rousseau have written, are innovations that are pervasive, improve over time, and spawn further innovation. They have spurred economic revolutions since the 19th century. The steam engine drove the first wave of industrialization in the 1890s to 1920s; electricity powered the second wave in the 1890s to 1930s; and information technologies brought the third, which started in the 1970s and culminated with the explosion of the in
But many artificial intelligence projects are still in the research and development phase. That means that there are a lot of intangible investments(such as software, databases, design, training, and so on) related to this sector, but not goods that national accounts would capture. To see how intangibles are becoming dominant even in traditional sectors, look at the car industry. Software content in vehicles rose from 7 percent of a vehicle’s value in 2000 to 10 percent of a vehicle’s value in 2010. That figure is expected to jump to 30 percent by 2030. Statistical offices are working hard to update the way they build their national accounts, but until radical accounting reforms will not be adopted productivity might remain (apparently) stagnant, even if new technologies become truly widespread and a real boon to the economy.
To be sure, all the ferment in the artificial intelligence sector has probably led to a mismatch between expectations and reality. The Organisation for Economic Co-operation and Development has reported that new technologies developed at the global technological frontier are spreading across countries faster than ever, but that they are taking more time to be adopted by a mass of firms within any given economy. Many small companies are still struggling with the third industrial revolution; artificial intelligence is certainly not a priority for them. And for a while, such adoption will be an economic drain. Companies must invest money, time, and managerial attention to digital assets and capabilities. In many cases, they must duplicate costs to experiment with new processes and models while still preserving their traditional procedures. Take autonomous cars, for example. Even though they are not yet commercially available, they already absorb a lot of resources and attention.
But be patient. If history is any guide, the payoff from artificial intelligence will come at some point—probably not before 2030. So, until then, use the time to learn skills robots will not yet be able to master.
The robot revolution hasn't started yet.
BY EDOARDO CAMPANELLA | AUGUST 9, 2018, 5:18 PM
It has been 21 years since IBM’s Deep Blue supercomputer checkmated chess champion Garry Kasparov, marking a historic moment in the development of artificial intelligence technologies. Since then, artificial intelligence has invaded everyday objects, such as cell phones, cars, fridges, and televisions. But the world economy seems to have little to show for the proliferation of smartness. Among advanced economies, productivity growthis slower now than at any time in the past five decades. National GDPs and standards of living, meanwhile, have been relatively stagnant for years.
This situation poses something of a riddle: Previous waves of technical innovation have come with rising productivity and, in turn, leaps forward in economic growth and well-being. For example, once electricity became widespread in the United States in the 20th century, labor productivity started growing at an annual rate of 4 percent—almost four times higher than the current rate.
There are two schools of thought about today’s productivity puzzle. On the one hand are techno-pessimists, such as Northwestern University professor Robert Gordon, who believe that today’s technologies are the issue. The six innovations that powered economic growth from 1870 to 1970—electricity, urban sanitation, chemicals, pharmaceuticals, the internal combustion engine, and modern communications technologies—the thinking goes, were simply more transformative than, say, Siri.
On the other hand are techno-optimists who counter that today’s innovations—cloud computing, big data, and the “internet of things,” which are at the heart of the artificial intelligence revolution—are, indeed, transformative and that their benefits are already being enjoyed by firms and consumers around the world. The problem, scholars such as British economists Jonathan Haskel and Stian Westlake argue, is that national accounting statistics simply cannot capture those benefits. The concept of GDP first emerged in the 1930s to measure economies that were primarily devoted to the production of tangible goods. Intangible goods and services, by contrast, increasingly dominate today’s economies. If GDP figures properly tallied the intangible economy, the argument goes, then productivity growth would look much better.
There is some truth in both theories; certainly, electricity changed the structure of work and home life in ways that Google Home has not. It is likewise true that GDP does not count free online services such as Google, Facebook, and YouTube that massively contribute to the well-being of consumers. But there might be a third, more straightforward, solution to the productivity riddle—one that even reconciles the other two. Simply put, the latest revolution is not showing up in national statistics because it has not yet really begun.
Simply put, the latest revolution is not showing up in national statistics because it has not yet really begun.
In reality, it takes a considerable amount of time for firms to make good use of new technologies, especially general-purpose technologies, as economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson showed in a working paper for the National Bureau of Economic Research. In fact, it is only after a sufficient stock of the new technology and complementary innovations (both tangible and intangible) are built up that a technological revolution shows up in the numbers. And that usually takes a quarter-century years at least.
General-purpose technologies, as the economists Boyan Jovanovic and Peter Rousseau have written, are innovations that are pervasive, improve over time, and spawn further innovation. They have spurred economic revolutions since the 19th century. The steam engine drove the first wave of industrialization in the 1890s to 1920s; electricity powered the second wave in the 1890s to 1930s; and information technologies brought the third, which started in the 1970s and culminated with the explosion of the in
But many artificial intelligence projects are still in the research and development phase. That means that there are a lot of intangible investments(such as software, databases, design, training, and so on) related to this sector, but not goods that national accounts would capture. To see how intangibles are becoming dominant even in traditional sectors, look at the car industry. Software content in vehicles rose from 7 percent of a vehicle’s value in 2000 to 10 percent of a vehicle’s value in 2010. That figure is expected to jump to 30 percent by 2030. Statistical offices are working hard to update the way they build their national accounts, but until radical accounting reforms will not be adopted productivity might remain (apparently) stagnant, even if new technologies become truly widespread and a real boon to the economy.
To be sure, all the ferment in the artificial intelligence sector has probably led to a mismatch between expectations and reality. The Organisation for Economic Co-operation and Development has reported that new technologies developed at the global technological frontier are spreading across countries faster than ever, but that they are taking more time to be adopted by a mass of firms within any given economy. Many small companies are still struggling with the third industrial revolution; artificial intelligence is certainly not a priority for them. And for a while, such adoption will be an economic drain. Companies must invest money, time, and managerial attention to digital assets and capabilities. In many cases, they must duplicate costs to experiment with new processes and models while still preserving their traditional procedures. Take autonomous cars, for example. Even though they are not yet commercially available, they already absorb a lot of resources and attention.
But be patient. If history is any guide, the payoff from artificial intelligence will come at some point—probably not before 2030. So, until then, use the time to learn skills robots will not yet be able to master.
