The Techno-Optimists: What, Me, Worry?

By Alberto Moel, Vice President Strategy and Partnerships, Veo Robotics


In our previous blog post on the macroeconomic impact of automation we went off on a bit of a pop culture and high culture tangent before getting to the crux of the matter, that the automation debate has many angles and is far from settled. In order to put some semblance of order to the situation, we proposed a 2x2 matrix:

  • Techno-optimists believe machines will replace humans, but also that the displaced humans will find alternate and meaningful employment as a result.
  • Techno-pessimists acknowledge the advance of robots, automation, and AI, but do not expect the impact on labor productivity or employment to be meaningful enough to make a difference.
  • Traditionalists believe that automation technologies will eventually have a material impact in some specific applications and verticals, leading to slow and steady unemployment in those areas.
  • Dystopians expect generalized AI penetration and rapid productivity growth, resulting in persistent mass unemployment and a permanent underclass.

In this post we’ll dissect the techno-optimist position with a fine scalpel.

Andy McAfee and Erik Brynjolfsson are two good examples of mainstream techno-optimism. Their book, "The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies," is cautiously optimistic about the impact of technology evolution on employment and income inequality.

Like McAfee and Brynjolfsson, many techno-optimists point to historical evidence that technological disruption in the past has overall been beneficial to both productivity growth and employment:

  • Automatic passenger elevators: The Robotics Business Review a while back evocatively provided this example: "In 1958, a wave rose and crested over Manhattan where 200,000 people went to work each day as elevator operators. Within ten years, nearly all had lost their jobs to automation. Today, no one scans the help wanted adverts for employment as an elevator operator. Robots now perform those jobs: tirelessly whisking millions up and down buildings everywhere, some up to 700 feet per minute, and even warning exiting passengers to watch their step." But clearly those 200,000 people found alternative and meaningful employment as doormen, taxi drivers, or perhaps elevator repairmen or we would have heard about it by now.
  • Smartphones: About 3 billion people own smartphones. We can assume that they are all better off now than when they didn’t have one. Sure, parents may fret about the screen time children enjoy and studies may moan about their impact on attention spans, sleep, and social behaviors, but now we can all communicate with each other and access troves of information instantly. Not many are worse off because of the smartphone. Factory workers who produced feature phones (or pagers, before phones) were re-deployed to smartphone manufacturing. Postal workers found new work pushing ecommerce packets (although some 200,000 US postal workers did lose their jobs in 2003-2013). We could count the employees of Nokia, Palm, BlackBerry, and the likes, perhaps, among the worse off, but even they benefited in their personal lives from the rise of smartphones. Camera manufacturing and a few other segments are worse off, but overall there’s a very high net benefit.

Some traditional economists, while in line with the techno-optimist position, believe that viewing displaced workers as "excess" is falling for the classic "lump of labor" fallacy, where there is only so much work to go around. This argument goes as far back as the 18th century to the work of French economist Jean-Baptiste Say, who coined "Say's Law," which is sometimes glibly summarized as "supply creates its own demand." In other words, labor-saving or productivity-enhancing technological change generates savings for somebody, be it lower costs for the manufacturer or lower prices for the end consumer. This value or money surplus then gets passed on to be spent elsewhere, creating jobs for the displaced workers, or perhaps for others, but with a net positive welfare effect.1

Other economists have looked at how automation not only replaces labor, but also complements it, increasing labor productivity and total welfare generation. The leading economist in this line of reasoning is MIT's David Autor, who states:2

Most work processes draw upon a multifaceted set of inputs: labor and capital; brains and brawn; creativity and rote repetition; technical mastery and intuitive judgment; perspiration and inspiration; adherence to rules and judicious application discretion. Typically, these inputs each play essential roles; that is, improvements in one do not obviate the need for the other.

Another interesting example that combines both the effect of Say's Law and the complementarity of automation on labor is James Bessen's work on the relationship between technology and occupations.3 Using US government data, Bessen studies the impact of automation and computers on 317 occupations between 1980 and 2013. He finds that employment grows significantly faster in occupations with higher levels of automation and computerization.

His best example is that of bank ATMs, which proliferated in the late 1990s. One would expect that these ATMs would simply replace bank tellers, but the opposite actually happened:

The ATM is sometimes taken as a paradigmatic case of technology substituting for workers; the ATM took over cash handling tasks. Yet the number of full-time equivalent bank tellers has grown since ATMs were widely deployed during the late 1990s and early 2000s. Indeed, since 2000, the number of full-time equivalent bank tellers has increased 2.0% per annum, substantially faster than the entire labor force.

Why didn't employment fall? Because the ATM allowed banks to operate branch offices at lower costs. This prompted them to open many more branches, offsetting the erstwhile loss in teller jobs. At the same time, teller skills changed. Non-routine marketing and interpersonal skills became more valuable while routine cash handling became less important.

Bessen finds that the average number of employees per branch dropped from 20 in 1988 to 13 in 2004. This lowered the cost of branch openings, and bank branches increased 43% over the same period. He cites other interesting examples where the adoption of technology in an occupation actually increased labor demand for that same occupation:

  • Barcode scanners have reduced cashiers’ checkout times by 18-19%, but the number of cashiers has grown since scanners were widely adopted during the 1980s;
  • Since the late 1990s, electronic document discovery software for legal proceedings has grown into a billion-dollar business, taking over work previously done by paralegals, but the number of paralegals has grown strongly; and
  • The share of the workforce in manufacturing grew from 12% in 1820 to 26% by 1920 despite, you know, the Industrial Revolution.

Overall, although the jury is still (well) out, the techno-optimists are sanguine about the impact of automation and believe that, in the end, all will be well. So do the techno-pessimists, but for different reasons, which we’ll explore in our next post.

1 A good treatment of this economic concept and its relevance to the current era can be found in The Economist's Ryan Avent's book, "The Wealth of Humans."

2 Autor, D., Polanyi's paradox and the shape of employment growth, NBER Working Paper 20485, 2014.

3 Bessen, J., How computer automation affects occupations: Technology, jobs, and skills, Law and Economics Working Paper, Boston University School of Law, 2015.