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AI, Automation and the U.S. Economy

January 16, 2017


On December 20, the White House released Artificial Intelligence, Automation, and the Economy, a new report that investigates how AI will likely transform job markets over time, and recommends policy responses to address AI’s impact on the US economy.  This report is further evidence that AI is now reaching the kind of market impact that requires well thought out strategies by both business and government.

AI research has been around since the early days of IT – having gone through ups and downs over the past several decades.  But it’s recent advances based on machine learning and related data-driven technologies are still relatively new, making it hard to predict where AI will be heading over time.

Harder still is predicting AI’s longer term economic and societal impact.  Think of anticipating the impact of the emerging Internet revolution back in the mid 1990s, before the advent of e-commerce and social media, let alone smartphones, IoT and cloud computing in the 2000s.  That’s roughly where we are with AI today.

But, there’s one big difference.  The Internet generated a fair degree of hype, especially in the dot-com era of the late 1990s, but the overriding feelings were ones of hope and excitement.  Not so with AI.  Along with admiration for its considerable accomplishments – from assisting in the treatment of rare forms of cancer to self-driving cars – there are serious fears that AI advances will lead to massive job losses, economic dislocations and social unrest.


Automation anxieties are hardly new, but each time those fears arose in the past, in the long run technology innovations ended up creating more jobs than they destroyed.  Such fears have understandably accelerated in recent years.   For the past few decades, jobs and wages have been declining for many workers, driven by advances in technology as well as by globalization.  But the concerns surrounding AI’s long term impact may well be in a class by themselves.  Like no other technology, AI forces us to explore the very boundaries between machines and humans.

“Accelerating artificial intelligence (AI) capabilities will enable automation of some tasks that have long required human labor,” notes the White House report in its opening paragraph.  “These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans.  Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place.”

How strongly will AI disrupt the US workforce?  Is this time different from past technological disruptions?  How long will it take to arrive?  It’s very hard to come up with precise answers to these questions.  But the report recommends that policymakers should be prepared to deal with AI’s likely impacts – supporting AI for its potential benefits while addressing those disruptions that might affect the livelihoods of millions.

Technology and Productivity Growth

 As has been the case with past technology innovations, in the long run AI should make the economy more efficient and lead to productivity growth and higher standards of living.  Such an AI productivity boost is particularly important, given that productivity has significantly slowed down over the past decade in the US and other advanced economies.

How can AI boost productivity?  I particularly like Kevin Kelly’s comparison to the advent of electricity a century ago in a 2014 article in Wired:  AI will likely evolve as a kind of “cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off…  Everything that we formerly electrified we will now cognitize.”  Like any other tool, this “utilitarian AI will also augment us individually as people (deepening our memory, speeding our recognition) and collectively as a species.  There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ.  In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI.”

Uneven Impact 

  It’s very hard to predict which jobs will be most affected by AI-driven automation. Like IT, AI is a collection of technologies with varying impact on different tasks.

Recent research suggests that the effects of AI in the short- and medium-term will be similar to those of IT, – lower skilled workers face the biggest threats from AI-based automation, while higher-skilled workers stand to benefit most from the new kinds of jobs that AI might create.  New AI-based jobs fall into four main categories:

  • Engagement.  Tasks that cannot be substituted by automation are generally complemented by it, often leading to higher demand for workers.  “Many industry professionals refer to a large swath of AI technologies as Augmented Intelligence, stressing the technology’s role as assisting and expanding the productivity of individuals rather than replacing human work.”
  • Development.  We can expect a great need for highly-skilled software developers and engineers to put AI to practical use across multiple industries.  Given the central role of data in AI applications, there will be increased demand for data scientists, and other data-oriented jobs.
  • Supervision.  There will also be a growing number of jobs to monitor, license, maintain and repair AI systems and applications.  “The capacity for AI-enabled machines to learn is one of the most exciting aspects of the technology, but it may also require supervision to ensure that AI does not diverge from originally intended uses.”
  • Response to Paradigm Shifts.  AI innovations will likely require major changes in the surrounding environment.  Self-driving vehicles, for example, will lead to new careers in transportation engineering and urban planning.  Similarly, the increased use of robotics will be accompanied by major changes in manufacturing systems.

Continue reading the full blog on Medium, here.



This blog was first posted on Jan. 10, 2017, here.