Machines of Loving Grace (12 page)

There was mounting evidence that the impact of technology was not just a hollowing out but a “dumbing down” of the workforce.
In some cases specific high-prestige professions began to show the impact of automation based on the falling costs of information and communications technologies, such as new global computer networks.
Moreover, for the first time artificial intelligence software was beginning to have a meaningful impact on certain highly skilled jobs, like $400-per-hour lawyers and $175-per-hour paralegals.
As the field of AI once again gathered momentum beginning in 2000, new applications of artificial intelligence techniques based on natural language understanding, such as “e-discovery,” or the automated processing of the relevance of legal documents required to disclose in litigation, emerged.
The software would soon go beyond just finding specific keywords in email.
E-discovery software evolved quickly, so that it became possible to scan millions of documents electronically and recognize underlying concepts and even find so-called smoking guns—that is, evidence of illegal or improper behavior.

In part, the software had become essential as litigation against corporations routinely involved the review of millions of documents for relevance.
Comparative studies showed that the machines could do as well or better than humans in analyzing and classifying documents.
“From a legal staffing viewpoint, it means that a lot of people who used to be allocated to conduct document review are no longer able to be billed out,” said Bill Herr, who as a lawyer at a major chemical company used to muster auditoriums of lawyers to read documents and correspondence for weeks on end.
“People get bored, people get headaches.
Computers don’t.”
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O
bserving the impact of technologies such as e-discovery software, which is now dramatically eliminating the jobs of lawyers, led Martin Ford, an independent Silicon Valley engineer who owned a small software firm, to self-publish
The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future
at the end of 2009.
Ford had come to believe that the impact of information technology on the job market was moving much more quickly than was generally understood.
With a professional understanding of software technologies, he was also deeply pessimistic.
For a while he stood alone, much in the tradition of Rifkin’s 1995
The End of Work,
but as the recession dragged on and mainstream economists continued to have trouble explaining the absence of job growth, he was soon joined by an insurgency of technologists and economists warning that technological disruption was happening full force.

In 2011, two MIT Sloan School economists, Erik Brynjolfsson and Andrew McAfee, self-published an extended essay titled “Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy.”
Their basic theme was as follows: “Digital technologies change rapidly, but organizations and skills aren’t keeping pace.
As a result, millions of people are being left behind.
Their incomes and jobs are being destroyed, leaving them worse off .
.
.
than before the digital revolution.”
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The “Race Against the Machine” essay was passed around samizdat-style over the Internet and was instrumental in reigniting the debate over automation.
The basic theme of the discussion was around the notion that this time—because of the acceleration of computing technologies in the workplace—there would be no Keynesian solution in which the economy created new job categories.

Like Martin Ford, Brynjolfsson and McAfee chronicled a growing array of technological applications that were redefining the workplace, or seemed poised on the brink of doing
so.
Of the wave of new critiques, David Autor’s thesis was perhaps the most compelling.
However, even he began to hedge in 2014, based on a report that indicated a growing “deskilling” of the U.S.
workforce and a declining demand for jobs that required cognitive skills.
He worried that the effect was creating a downward ramp.
The consequence, argued Paul Beaudry, David A.
Green, and Ben Sand in a National Bureau of Economic Research (NBER) working paper, was that higher-skilled workers tended to push lower-skilled workers out of the workforce.
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Although they have no clear evidence directly related to the deployment of particular types of technologies, the analysis of the consequences for the top of the workforce is chilling.
They reported: “Many researchers have documented a strong, ongoing increase in the demand for skills in the decades leading up to 2000.
In this paper, we document a decline in that demand in the years since 2000, even as the supply of high education workers continues to grow.
We go on to show that, in response to this demand reversal, high-skilled workers have moved down the occupational ladder and have begun to perform jobs traditionally performed by lower-skilled workers.”
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Yet despite fears of a “job apocalypse” based on machines that can see, hear, speak, and touch, once again the workforce has not behaved as if there will be a complete collapse precipitated by technological advance in the immediate future.
Indeed, in the decade from 2003 to 2013, the size of the U.S.
workforce increased by more than 5 percent, from 131.4 million to 138.3 million—although, to be sure, this was a period during which the population grew by more than 9 percent.

If not complete collapse, the slowing growth rate suggested a more turbulent and complex reality.
One possibility is that rather than a pure deskilling, the changes observed may represent a broader “skill mismatch,” an interpretation that is more consistent with Keynesean expectations.
For example, a recent McKinsey report on the future of work showed that between 2001 and 2009, jobs related to transactions and production
both declined, but more than 4.8 million white-collar jobs were created relating to interactions and problem-solving.
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What is clear is that both blue-collar and white-collar jobs involving routinized tasks are at risk.
The
Financial Times
reported in 2013 that between 2007 and 2012 the U.S.
workforce gained 387,000 managers while losing almost two million clerical jobs.
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This is an artifact of what is popularly described as the Web 2.0 era of the Internet.
The second generation of commercial Internet applications brought the emergence of a series of software protocols and product suites that simplified the integration of business functions.
Companies such as IBM, HP, SAP, PeopleSoft, and Oracle, helped corporations to relatively quickly automate repetitive business functions.
The consequence has been a dramatic loss of clerical jobs.

However, even within the world of clerical labor there are subtleties that suggest that predictions of automation and job destruction across the board are unlikely to prove valid.
The case of bank tellers and the advent of automated teller machines is a particularly good example of the complex relationship between automation technologies, computer networks, and workforce dynamics.
In 2011, while discussing the economy, Barack Obama used this same example: “There are some structural issues with our economy where a lot of businesses have learned to become much more efficient with a lot fewer workers.
You see it when you go to a bank and you use an ATM; you don’t go to a bank teller.
Or you go to the airport, and you’re using a kiosk instead of checking in at the gate.”
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This touched off a political kerfuffle about the impact of automation.
The reality is that despite the rise of ATMs, bank tellers have not gone away.
In 2004 Charles Fishman reported in
Fast Company
that in 1985, relatively early in the deployment of ATMs, there were about 60,000 ATMs and 485,000 bank tellers; in 2002 that number had increased to 352,000 ATMs and 527,000 bank tellers.
In 2011 the
Economist
cited 600,500 bank tellers in 2008, while the Bureau of Labor Statistics
was projecting that number would grow to 638,000 by 2018.
Furthermore the
Economist
pointed out that there were an additional 152,900 “computer, automated teller, and office machine repairers” in 2008.
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Focusing on ATMs in isolation doesn’t begin to touch the complexity of the way in which automated systems are weaving their way into the economy.

Bureau of Labor Statistics data reveal that the real transformation has been in the “back office,” which in 1972 made up 70 percent of the banking workforce: “First, the automation of a major customer service task reduced the number of employees per location to 75% of what it was.
Second, the [ATM] machines did not replace the highly visible customer-facing bank tellers, but instead eliminated thousands of less-visible clerical jobs.”
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The impact of back-office automation in banking is difficult to estimate precisely, because the BLS changed the way it recorded clerk jobs in banking in 1982.
However, it is indisputable that banking clerks’ jobs have continued to vanish.

Looking forward, the consequences of new computing technology on bank tellers might anticipate the impact of driverless delivery vehicles.
Even if the technology can be perfected—and that is still to be determined, because delivery involves complex and diverse contact with human business and residential customers—the “last mile” delivery personnel will be hard to replace.

Despite the challenges of separating the impact of the recession from the implementation of new technologies, increasingly the connection between new automation technologies and rapid economic change has been used to imply that a collapse of the U.S.
workforce—or at least a prolonged period of dislocation—might be in the offing.
Brynjolfsson and McAfee argue for the possibility in a much expanded book-length version of “Race Against the Machine,” entitled
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
.
Similar sentiments are offered by Jaron Lanier, a well-known computer scientist now at Microsoft Research, in the book
Who
Owns the Future?
Both books draw a direct link between the rise of Instagram, the Internet photo-sharing service acquired by Facebook for $1 billion in 2012, and the decline of Kodak, the iconic photographic firm that declared bankruptcy that year.
“A team of just fifteen people at Instagram created a simple app that over 130 million customers use to share some sixteen billion photos (and counting),” wrote Brynjolfsson and McAfee.
“But companies like Instagram and Facebook employ a tiny fraction of the people that were needed at Kodak.
Nonetheless, Facebook has a market value several times greater than Kodak ever did and has created at least seven billionaires so far, each of whom has a net worth ten times greater than [Kodak founder] George Eastman did.”
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Lanier makes the same point about Kodak’s woes even more directly: “They even invented the first digital camera.
But today Kodak is bankrupt, and the new face of digital photography has become Instagram.
When Instagram was sold to Facebook for a billion dollars in 2012, it employed only thirteen people.
Where did all those jobs disappear to?
And what happened to the wealth that those middle-class jobs created?”
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The flaw in their arguments is that they mask the actual jobs equation and ignore the reality of Kodak’s financial turmoil.
First, even if Instagram did actually kill Kodak—it didn’t—the jobs equation is much more complex than the cited 13 versus 145,000 disparity.
Services like Instagram didn’t spring up in isolation, but were made possible after the Internet had reached a level of maturity that had by then created millions of mostly high-quality new jobs.
That point was made clearly by Tim O’Reilly, the book publisher and conference organizer: “Think about it for a minute.
Was it really Instagram that replaced Kodak?
Wasn’t it actually Apple, Samsung, and the other smartphone makers who have replaced the camera?
And aren’t there network providers, data centers, and equipment suppliers who provide the replacement for the film that Kodak once sold?
Apple has 72,000 employees (up from
10,000 in 2002).
Samsung has 270,000 employees.
Comcast has 126,000.
And so on.”
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And even O’Reilly’s point doesn’t begin to capture the positive economic impact of the Internet.
A 2011 McKinsey study reported that globally the Internet created 2.6 new jobs for every job lost, and that it had been responsible for 21 percent of GDP growth in the five previous years in developed countries.
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The other challenge for the Kodak versus Instagram argument is that while Kodak suffered during the shift to digital technologies, its archrival FujiFilm somehow managed to prosper through the transition to digital.
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