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Equity Perspectives

While leading high-tech companies have transformed industries, there is widespread debate over the cumulative impact on employment.


In Brief

  • Major advances in artificial intelligence and robotics have become a major investment theme, as many industries seek to boost productivity and cut costs.
  • Millions of jobs may be lost, but if past technology revolutions are any guide, millions of new jobs, some not even envisioned, are likely to be created.
  • In a competitive market, massive innovation also means lower consumer prices, which in turn leads to a positive “income effect,” enabling consumers to buy more of everything they desire. The big question is whether that will lead to strong job growth in all industries. 


Take a look at the stocks in small-, mid-, and large-cap  indexes, and you’ll find a wide range of companies that have embraced artificial intelligence (A.I.) and robotics in one form or another, with the potential to solve complex business problems, boost productivity, and better compete in a global economy as the Internet enters its next phase.

The industries affected by such innovation run the gamut: from Rustbelt steel companies to Sunbelt chipmakers; from heartland auto companies to hospitals that use A.I. to diagnose and treat cancer; from on-call mobile shopping services to multinational banks looking to improve customer relationships. The list goes on, which explains why Lord Abbett research analysts regularly share insights on how advanced technologies are transforming their respective industries. 

While A.I. may be in its infancy,  many tech companies are gearing up for dramatic growth in implementation of select forms of A.I. (see Chart 1) across broad swaths of the economy. (See Chart 2.)  “We’ve reached a critical inflection point,” said Lord Abbett research analyst Eric Ghernati.

But what about the jobs all this progress has cost? While headline employment numbers have shown considerable improvement in recent years, the number of discouraged workers not actively looking for a job, or part-timers who want something full-time, remains an economic headwind (see Chart 3), although manufacturing production has climbed steadily since 2010 (see Chart 4).

Against that backdrop, a number of studies have predicted millions of workers will lose their jobs to automation, be it machines, software, or cloud computing. Among the Cassandras were Oxford University economists Dr. Carl Frey and Dr. Michael Osborne, who, in 2013, estimated that about 47% of total U.S. employment is at risk from ongoing computerization. In 2014, Gartner Research predicted one in three jobs will be taken over by software, robots, and smart machines by 2025.   

On a more optimistic note, Robert Cohen, a senior fellow at the Economic Strategy Institute, has predicted that as many as 25 million jobs will be created as a new “virtualized infrastructure” gets built out over the next 15 years.  Sure, automation is likely to eliminate many jobs, but Cohen estimates the net gain will still be around 15 million, as cloud computing, Big Data, and the Internet of Things2 create new types of jobs.3

“Major breakthroughs in technology and innovation may cause fear and anxiety, but history shows that new jobs are created in the long run,” said Giulio Martini, Lord Abbett director of Strategic Asset Allocation.  (More on that later.)


Chart 1. Artificial Intelligence Revenue by Segment, 2015–25E
($ in billions)

Source: BofA Merrill Lynch Global Research estimates.


Chart 2. Artificial Intelligence Revenue End-market, 2015

Source: Tractica. 


Chart 3. Unemployment Has Dropped Since 2008, but Many Remain on the Sidelines
Alternative measures of labor underutilization, 1994–2016, seasonally adjusted

Source: U.S. Bureau of Labor Statistics, Current Population Survey,
Note: U-3 = Total unemployed, as a percent of the civilian labor force (official unemployment rate). U-5 = Total unemployed, plus discouraged workers, plus all other persons marginally attached to the labor force, as a percent of the civilian labor force plus all persons marginally attached to the labor force. U-6 = Total unemployed, plus all persons marginally attached to the labor force, plus total employed part time for economic reasons, as a percent of the civilian labor force plus all persons marginally attached to the labor force.
Note: Persons marginally attached to the labor force are those who currently are neither working nor looking for work but indicate that they want and are available for a job and have looked for work sometime in the past 12 months. Discouraged workers, a subset of the marginally attached, have given a job-market related reason for not currently looking for work. Persons employed part time for economic reasons are those who want and are available for full-time work but have had to settle for a part-time schedule.  


Chart 4. Manufacturing Production Has Far Eclipsed Jobs
Manufacturing production versus employment, percentage change since the end of the most recent recession 

Source: Federal Reserve and the U.S. Bureau of Labor Statistics.


Deep Learning Goes Deep
In meetings with various investment professionals in 2015, Ghernati broached a “variant perception” that challenged the prevailing notion that deep learning—a major breakthrough in artificial intelligence—would be an investment case for 2020 at the earliest. On the contrary, Ghernati argued that we were on the cusp of much stronger demand for deep-learning applications than appreciated by consensus, in large part based on availability of broad and comprehensive sets of data, maturity of enabling technology (software, chips, cloud), the deep involvement of tech disruptors with household names, and, most importantly, a strong appetite across a broad spectrum of industries (automotive, media, search, medical, security, tech, finance) to use deep learning to enter new markets and/or build competitive advantage in their existing businesses.

Deep learning—the fastest growing part of machine learning—is used to help solve many big-data problems, such as computer vision. Practical examples include vehicle, pedestrian, and landmark identification for driver assistance (including predictions of where traffic jams might occur); image recognition; speech recognition and translation; natural language processing; and life sciences. The lists of applicable day to day applications keeps growing exponential on our research work.

The vanguard of deep-learning projects involves the training of deep neural networks, an advanced branch of artificial intelligence that simulates the way the brain works. One company is using robots to train deep neural networks to program its robots to grasp random objects by basically improving a robot's hand-to-eye coordination.

Ghernati’s “variant perception” proved prescient. As of October 11, 2016, the stock prices of the companies he identified as well positioned to benefit from deep learning had appreciated meaningfully, including helping the broader Philadelphia Stock Exchange Semiconductor Index4 advance more than 22% in that time frame.

“We’ve witnessed an exponential increase in investor interest around this theme,” Ghernati said, citing a number of major companies (such as Alphabet, IBM,, Microsoft, Apple) that are either pursuing A.I. and deep learning or aggressively integrating such technology into their business models.

Then there’s the automobile industry, which invested heavily in robots for assembly lines and hazardous functions in the 1980s, only to run into some costly fiascos.  Fast forward to the present and you’ll find that all original equipment manufacturers will use some form of deep learning for both semiautonomous driving in the short run and self-driving cars in the long run.

As for trucking, some experts believe the proliferation of self-driving commercial vehicles is all but inevitable, particularly when it comes to long-haul truck driving, where there isn’t much judgment involved and it’s a fairly controlled environment.5

Meantime, there have been all kinds of announcements about how A.I. is going to be leveraged in health care, professional services, financial services, travel, and transportation, among other sectors.

“Deep learning is extracting more and more and more intelligence from everyday data, from everything and everyday businesses, and the potential returns on investment are enormous from the right application(s) that resonate with users,” said Ghernati. “It allows users to do more valuable things by compressing the time it takes to analyze sets of data from months to seconds. What kinds of businesses will emerge from such transformative innovation may be hard to predict. But it probably will be valuable to the global economy.”

As director of Strategic Asset Allocation, Giulio Martini is responsible for directing the portfolio management, research, and trading activities for Lord Abbett’s multi-asset-class strategies. And with more than 30 years of experience investing across the global capital markets, he takes a more sober view of the way artificial intelligence and robotics will affect the labor markets, as follows: 

1) The biggest industrial users of computing power to date have been health care and financial. These also have been two of the most prolific industries for new job creation over the past 20–50 years.

2) The very long-term history of technological progress is that substituting machinery for humans lowers production costs. In a competitive market, this also means lower consumer prices. The lower prices in turn lead to a positive “income effect,” enabling consumers to buy more of everything they desire. That leads to strong job growth in all industries, even if the technological improvement was concentrated in a single industry.

3) Inasmuch as high-income economies have working-age populations that are growing at slower and slower rates, the problem of losing jobs to machines should be less acute over time than it has been up to now. In Japan, for example, the size of the working-age population is going to start falling precipitously. With fewer people to employ, the consequences of job losses to new technologies should be dampened considerably.

4) The real question we have to solve is which demographics and constituencies are affected most, and the same one that arises from trade agreements and environmental regulation.

“The substitution of computing power for human labor is undoubtedly a benefit for society as a whole, in the sense that it will lead to higher real per-capita income,” Martini said. “However, there inevitably will be people who are losers in the medium term. The net benefit comes from the fact that the winners gain enough to make the losers whole, and still have something left over. But if our social contract doesn’t facilitate the transfers [of economic benefits] necessary to achieve that, then we’ll have too many people who are left behind—and the backlash could threaten the undeniable progress that the new technologies offer.”

All of which helps explain why some of the biggest tech companies have formed a partnership among themselves to study and formulate best practices on A.I. technologies, to advance the public’s understanding of A.I., and to serve as an open platform for discussion and engagement about A.I. and its influences on people and society.6


Carl Benedikt Frey and Michael A. Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerization?” Oxford University, September 17, 2013.
2 Spawned by the build-out of wireless networking, cloud computing, and Big Data, along with the proliferation of smartphones, the Internet of things connects everyday objects so they can sense and communicate with other operating systems, allowing them to be monitored and controlled from anywhere.
3 Rick Wartzman, “25 Million New Jobs Coming to America, Thanks to Technology,”, January 15, 2016.
4 The PHLX Semiconductor Sector IndexSM (SOXSM) is a modified market capitalization-weighted index composed of companies primarily involved in the design, distribution, manufacture, and sale of semiconductors.
5 Natalie Kitroeff, “Robots Could Replace 1.7 million American Truckers in the Next Decade,” The Los Angeles Times, September 25, 2016.
6 For more information, go to


The information provided here is for general informational purposes only. It does not constitute a recommendation nor investment advice , and should not be used as the basis for any investment decision. This is not a representation of any securities Lord Abbett purchased or would have purchased or that an investment in any securities of such issuers would be profitable. This article may contain assumptions that are “forward-looking statements,” which are based on certain assumptions of future events. Actual events are difficult to predict and may differ from those assumed. There can be no assurance that forward-looking statements will materialize or that actual re turns or results will not be materially different from

Investing involves risk, including possible loss of principal.
Forecasts and projections are base d on current market conditions and are subject to change without notice. Projections should not be considered a guarantee.

The opinions in the preceding commentary are as of the date of publication and subject to change based on subsequent developments and may not reflect the views of the firm as a whole. This material is not intended to be legal or tax advice and is not to be relied upon as a forecast, or research or investment advice regarding a particular investment or the markets in general, nor is it intended to predict or depict performance of any investment. Investors should not assume that investments in the securities and/or sectors described were or will be profitable. This document is prepared based on information Lord Abbett deems reliable; however, Lord Abbett does not warrant the accuracy or completeness of the information. Investors should consult with a financial advisor prior to making an investment decision.

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