Irving Wladawsky-Berger

Building Better Jobs in an Age of Intelligent Machines

On November 18, the MIT Task Force on the Work of the Future released its final report, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines.  The Task Force was commissioned in the spring of 2018 by MIT President Rafael Reif to address what may well be the most critical question of the digital economy: as emerging technologies raise aggregate economic output and the wealth of nations, will they also enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity?  An interim report was released in September, 2019.

“Amidst a technological ecosystem delivering rising productivity, and an economy generating plenty of jobs (at least until the COVID-19 crisis), we found a labor market in which the fruits are so unequally distributed, so skewed towards the top, that the majority of workers have tasted only a tiny morsel of a vast harvest,” is the Task Force overriding conclusion.  Yet, it argues that with better policies in place, more people could enjoy good careers even as new technologies transform the very nature of work.

AI Technologies Are Fundamentally Changing How Work Gets Done

After decades of promise and hype, artificial intelligence is finally becoming one of the most important technologies of our era.  AI technologies, like machine learning, are clearly having a major impact on the very nature of work.  But, how can we best quantify their impact on the actual evolution of jobs?

The best approach for exploring the relationship between technology and jobs is to look at the individual tasks that comprise a job.  Most jobs involve a number of tasks.  Some of these tasks are relatively routine and based on well-understood rules, while others require judgement, social skills and other human capabilities.  The more routine and rules-based the task, the more amenable it is to automation. 

However just because some of its component tasks have been automated, does not imply that the whole job has disappeared.  To the contrary, automating the more routine parts of a job will often increase the productivity and quality of workers by complementing their skills with technologies and tools, thus enabling them to focus on those aspect of the job that most need their attention.  While automation  substitutes for labor, automation also complements labor, increasing productivity and other economic outputs in ways that often raise the demand for and earnings of workers.

“The emergence of artificial intelligence (AI) and machine learning (ML) poses a new set of opportunities - and challenges - for work and workers,” said The Future of Work: How New Technologies Are Transforming Tasks, a research report released last Fall by the MIT-IBM Watson AI Lab.  “The tasks that can be done by machine learning are much broader in scope than previous generations of technology have made possible.  The expanded scope will change the value employers place on tasks, and the types of skills most in demand.”

America’s Obsession with Economic Efficiency

The belief that efficiency is fundamental to competitive advantage has turned management into a science, whose objective is the elimination of waste, - whether of time, materials, or capital, - wrote Roger Martin in “The High Price of Efficiency,” a January 2019 article in the Harvard Business Review.  Martin is professor and former dean of the Rotman School of Management at the University of Toronto, as well as a prolific writer.

“Why would we not want managers to strive for an ever-more-efficient use of resources?,” asked Martin.  Of course we do.  But, an excessive focus on efficiency can produce startlingly negative effects.  To counterbalance such potential negative effects, companies should pay just as much attention to a less appreciated source of competitive advantage: resilience,  - “the ability to recover from difficulties - to spring back into shape after a shock,” he presciently added a year before the advent of Covid-19.

In his recently published book, When More Is Not Better: Overcoming America's Obsession with Economic Efficiency, Martin expanded on his HBR article, arguing that an excessive focus on efficiency is not only detrimental to business but constitutes a serious threat to America’s democratic capitalism.  “Throughout the first nearly two and a half centuries of America’s existence as a sovereign state, most citizens experienced an advance in their economic status in the overwhelming majority of those years.  Based on that trend, Americans have, unsurprisingly, used their votes throughout the years to support and perpetuate capitalism as America’s economic system.  But that consistent economic advance has stalled, and has been for a longer period than ever before in American history.”

The book is based on a six-year project on the future of America’s democratic capitalism by the Martin Prosperity Institute.  The project conducted in-depth interviews with a wide variety of Americans to understand what they thought about the directions of the country.  It excluded people in the top 10% of the income distributions, focusing instead on the vast majority of the populations, which includes people with household incomes ranging from $25,000 to $110,000 with a median of $75,000.

The Social Network Is the Computer

“Human beings have always been a social species,” writes MIT professor Sinan Aral in the opening paragraph of The Hype Machine, his recently published book.  “We’ve been communicating, cooperating, and coordinating with one another since we were hunting and gathering.”  Our increasingly complex social interactions have been the critical factor in the exponential increase of human cranial capacity over the past few million years.  “But today something is different.  Over the last decade, we’ve doused our kindling fire of human interaction with high-­octane gasoline.  We’ve constructed an expansive, multifaceted machine that spans the globe and conducts the flow of information, opinions, and behaviors through society.”

Professor Aral has been studying social media since its beginning in the early 2000s, when it was driven by the idealistic vision of connecting the world and providing free access to information.  The Hype Machine is what he now calls our global social media ecosystem.  This ecosystem has been designed to stimulate and manipulate the human psyche, “to draw us in and persuade us to change how we shop, vote, and exercise, and even who we love.”  His book nicely explains how the social media industrial complex works, how it affects us, and what we can do to help achieve its original vision while avoiding its later perils.  Let me discuss a few of its key points.

How Will the Pandemic Reshape Labor Markets?

“The COVID crisis appears poised to reshape labor markets along at least four axes: telepresence, urban de-densification, employment concentration in large firms, and general automation forcing,” wrote MIT’s David Autor and Elisabeth Reynolds in a July essay on The Nature of Work After the COVID Crisis.   “Although these changes will have long-run efficiency benefits, they will exacerbate economic pain in the short and medium terms for the least economically secure workers in our economy, particularly those in the rapidly growing but never-highly-paid personal services sector.”

Autor and Reynolds are cochairs of the ongoing MIT Work of the Future task force, whose interim report was published in the (pre-Covid) Fall of 2019.  The report’s overriding conclusion was that the likelihood that AI and automation will wipe out major workforce sectors in the near future was exaggerated.  However, there were very important reasons for concern, in particular, the rising polarization of employment and wage distribution over the past few decades which has disproportionately benefited high-skilled professionals. Our most critical challenge, said the report, isn’t necessarily a lack of jobs, but the low quality of jobs and viable careers available to many workers, particularly those without college degrees.

“Despite our concerns about the distributional consequences of advancing technologies, until the COVID crisis began, we were sanguine about the prospects for ongoing employment growth, even in the face of lackluster wage growth,” said the authors in their July essay.  “In the fall of 2019 we wrote, ‘We anticipate that in the next two decades industrialized countries will have more job openings than workers to fill them, and that robotics and automation will play an increasingly crucial role in closing these gaps’. The COVID crisis has upended our confidence in that prediction - not merely because COVID has generated mass unemployment in the short term, which it has, but also because the postcrisis trajectory now worries us.”

Reconnecting Society and Reopening the Economy

A few months ago, MIT Connection Science held it’s annual Sponsors’ Meeting online.  The virtual meeting included a number of very interesting presentations.  Given its continuing importance, I want to focus my discussion on “Reconnecting Society After a Pandemic,” a talk given by visiting professor Esteban Moro. 

Moro started his presentation by showing a slide from this article.  The slide illustrates that if we had done nothing, or just relied on soft mitigation and herd immunity to contain the spread of the Covid-19 virus, the number of cases and deaths would have gone up very rapidly, - overwhelming healthcare systems around the world.  Instead, many national and state governments adopted two distinct sets of actions, the hammer and the dance.

The hammer is the somewhat draconian social distancing policies aimed at containing the spread of the virus, especially in regions like the New York metro area where the number of cases were escalating at a very rapid pace.  The dance starts once the spread of the virus has been brought under reasonable control, - albeit at the price of large economic costs and societal disruption.  The dance is the longer term effort to keep the virus contained until there’s an effective vaccine while reopening the economy and bringing life as close to normal as possible.

Are We Becoming a Decadent, Stagnating Society?

Earlier this year I read a very interesting essay, “The Age of Decadence”, by NY Times columnist Ross Douthat.  The essay is adapted from his recently published book The Decadent Society - How We Became the Victims of Our Own Success.  This long essay covers a lot of ground, from technology and innovation to politics and religion.  The essay was published in early February, before Covid-19 spread across the US.  I’ll discuss the original essay, but I do wonder how it would have been modified to reflect the impact of the pandemic.

“The real story of the West in the 21st century is one of stalemate and stagnation,” wrote Douthat.  “Everyone knows that we live in a time of constant acceleration, of vertiginous change, of transformation or looming disaster everywhere you look.  Partisans are girding for civil war, robots are coming for our jobs, and the news feels like a multicar pileup every time you fire up Twitter.  Our pessimists see crises everywhere; our optimists insist that we’re just anxious because the world is changing faster than our primitive ape-brains can process.” 

“But what if the feeling of acceleration is an illusion, conjured by our expectations of perpetual progress and exaggerated by the distorting filter of the internet?,” he asked.  What if we really inhabit an era in which repetition is more the norm than invention; in which new developments in science and technology consistently undercover; in which we’re comfortably aging, “no longer optimistic about the future… [while] growing old unhappily together.”  What if  “Our civilization has entered into decadence.”

The Long-Term Impact of Early-Career Setbacks

Fail fast, fail often is one of Silicon Valley’s best known mantras.  Is there something to this belief that if you’re not failing fast and often you may be stuck in a stagnating endeavor?  After all, social scientists have their own Matthew Effect, - i.e., the rich get richer and the famous get more famous, - as evidenced in our age of rising economic inequality and superstar dynamics.

In early March, - just before Covid-19 brought normal life to a screeching halt, - I attended a very interesting seminar by Northwestern professor Dashun Wang.  Over the past several years, professor Wang, along with various collaborators, has been investigating the impact of early-career setbacks on the future careers of scientists.

“Setbacks are an integral part of a scientific career, yet little is known about their long-term effects,” notes a recent article he co-authored in Nature Communications.  “Despite the ubiquitous nature of failures, it remains unclear if a setback in an early career may augment or hamper an individual’s future career impact.”  The Matthew Effect would suggest that early-career success is an important determinant of a successful scientific career.  On the other hand, early setbacks could well be a marker for future achievement, teaching young scientists, entrepreneurs, artists, etc, valuable lessons that they wouldn’t have otherwise learned, i.e., what doesn’t kill you makes you stronger.  Which is the most likely scenario?

Scenario Planning - Formulating Strategy in the Face of High Uncertainty

“How can we formulate strategy in the face of uncertainty?,” asks Learning from the Future, a recent Harvard Business Review article by Peter Scoblic, in its opening sentence.  Answering this question has never felt more urgent as leaders prepare for the future.  “Even before the Covid-19 crisis, rapid technological change, growing economic interdependence, and mounting political instability had conspired to make the future increasingly murky.”  Uncertainty was already all-encompassing.

“And then the pandemic hit.”

When the present resembles the past, we can calculate the probability of future outcomes by analyzing past historical data.  That’s how insurance companies accurately predict the life expectancy of different people based on age, gender, general health, behavioral traits like smoking and drinking, etc.  But the situation is very different when the present doesn’t resemble the past.  “Uncertainty stems from our inability to compare the present to anything we’ve previously experienced.  When situations lack analogies to the past, we have trouble envisioning how they will play out in the future.”

Military strategists first developed scenario planning in the 1950s to help them envision the impact of a nuclear war between the US and the Soviet Union, - something that was clearly way beyond anyone’s experience.  The object of scenario planning is to assemble all known facts, trends, and other information and use them to create a number of alternative stories rich in detail, typically three or four, about plausible future outcomes along with the development paths and key events that led to such outcomes.  Altogether, the scenarios should span the scope of possible futures, although the actual future will likely include events from each scenario.

Remote Work is Surprisingly Productive, But For Many… Something Is Missing

What If Working From Home Goes on … Forever?, asks science and technology journalist Clive Thompson in the title of his June 9 NY Times Magazine article.  “The coronavirus crisis is forcing white-collar America to reconsider nearly every aspect of office life.  Some practices now seem to be wastes of time, happily discarded; others seem to be unexpectedly crucial, and impossible to replicate online.  For workers wondering right now if they’re ever going back to the office, the most honest answer is this: Even if they do, the office might never be the same.”

A recent survey found that of the 56% of respondents employed pre-Covid-19, half were working from home, - 35% having recently switched to working from home, while another 15% were already doing so pre-Covid; 37% continued to commute to work, and 10% had been recently laid-off or furloughed.  The survey was  based on two separate national samples of US data, - one which gathered 25,000 responses in early April, and the second another 25,000 responses in early May.

Thompson’s article cites the experience of Accenture, which has around 500,000 employees in more than 200 cities in 120 countries.  Before the pandemic, no more than 10% worked remotely on any given day.  But, by the middle of March, nearly all were asked to work from home.  Employees adapted quickly, said Accenture’s CTO.  The volume of video calls went up by a factor of six while those of audio calls tripled.  Despite having to switch from face-to-face to audio and video interactions, overall productivity actually went up as measured by several metrics.

It’s difficult to judge whether the surge in remote work will last.  “Home life in a lockdown is much harder than usual,” notes Thompson.  “Many workers who live alone are experiencing enforced isolation as an emotional grind… Nearly every parent I spoke to had their fingers crossed that schools and day care would reopen in the fall - at which point remote work might become an option they could choose, as opposed to one they were forced to endure.  Assuming that such a day does arrive, it’s possible that quite a few may elect to continue working outside the office.”

Post-Pandemic, Is the World Likely to Become Significantly Less Flat?

“The golden age of globalisation, in 1990-2010, was something to behold,” wrote The Economist in a January, 2019 article.  “Commerce soared as the cost of shifting goods in ships and planes fell, phone calls got cheaper, tariffs were cut and the financial system liberalised.”

Thomas Friedman’s The World is Flat became an international best-seller in 2005 by nicely explaining what this new golden age of globalization was all about, including the key forces that contributed to flattening the world, - from the explosive growth of the Internet, open source software and related technologies; to the rise of outsourcing, offshoring and global supply chains.

In 2011 I attended a conference where Friedman was one of the speakers.  He said that we’ve transitioned from a connected to an increasingly hyperconnected global economy since he wrote The World is Flat in 2004.  Many of the companies and technologies that were part of every day conversations in 2011, - Facebook, Twitter, LinkedIn, smartphones, cloud computing, big data, broadband wireless Internet, - weren’t mentioned in his book because they had not yet been born or were still in their infancy.

The very nature of the global firm was transformed during these same decades.  In The Globally Integrated Enterprise, a 2006 Foreign Affairs article, then IBM CEO Sam Palmisano argued that the modern industrial corporation was now in its third stage of evolution in the way it conducted business around the world.

Why “The Techlash” Is a Threat to Growth and Progress

A few months ago, the Information Technology and Innovation Foundation (ITIF) published A Policymaker’s Guide to the “Techlash” - What It Is and Why It’s a Threat to Growth and Progress.  “Does information technology (IT) solve problems and make our lives easier, allowing us to do more with less?,” asks the ITIF report.  “Or does it introduce additional complexity to our lives, isolate us from each other, threaten privacy, destroy jobs, and generate an array of other harms?”

It wasn’t all that long ago that digital technologies and Big Tech were largely seen as catalysts for positive change: the Internet had become a global platform for collaborative innovation; social media was a liberating force, helping democratic uprisings like the Arab Spring; and the smartphone was transforming the lives of people all over the world.

But, the 2010s saw the emergence of what’s come to be called the techlash.  The Oxford English Dictionary (OED) defines techlash as “a strong and widespread negative reaction to the growing power and influence of large technology companies, particularly those based in Silicon Valley.”  The term seems to have originated in a November, 2013 article in The Economist, which said that tech elites are turning out to be some of the most ruthless capitalists around, and “will join bankers and oilmen in public demonology.”  By 2018, techlash was one of eight shortlisted entries for OED Word of the Year.

What History Tells Us About the Accelerating AI Revolution

A few weeks before our lives were turned upside down by Covid-19, I read Technology at Work v4.0, the fourth report in the Technology at Work Series developed by Citigroup in collaboration with Oxford University.  The report includes an excellent chapter on What History Tells Us About the Coming AI Revolution by Oxford professor Carl Benedikt Frey based on his 2019 book The Technology Trap.

Recent AI advances have “sparked much excitement…  yet despite this, most ordinary people don’t feel particularly optimistic about the future,” wrote Frey.  For example, a 2017 Pew Research survey found that three quarters of Americans expressed serious concerns about AI and automation, and just over a third believe that their children will be better off financially than they were.

But, in fact, serious concerns about the impact of technology are part of a historical pattern.  “Many of the trends we see today, such as the disappearance of middle-income jobs, stagnant wages and growing inequality were also features of the Industrial Revolution, which began in Britain around 1750.  This was a period of unprecedented technological change but also one of turbulent social and political change.”

The Urgent Need to Strengthen the Nation’s Resilience

Following World War II and the onset of the Cold War with the Soviet Union, the US undertook a series of measures to strengthen the resilience of the country.  These included a significant expansion of government support for scientific research in universities and R&D labs, leading to product innovations from the private sector and better weapons from the defense industry, which contributed to making the US the most prosperous and secure nation in the world.

Another important measure was the expansion of educational opportunities, like the GI Bill which offered the nation’s 16 million WWII veterans payments of tuition and living expenses to attend high school, college or vocational/technical schools.  By 1956, over 2.2 million had used the GI Bill to attend college or university, and 5.8 million had used it for some kind of job training program.

In the 1950s the government enacted the National Interstate and Defense Highways Act, which led to the construction of over 48,000 miles of the Interstate Highway System at a total price estimated at over $500 billion, - the largest public work in America .  The Act had a dual purpose: facilitating the economic growth of the country as well as supporting the country’s defense during a conventional or nuclear war should it be necessary.

And, last but far from least, in the late 1960s the government launched ARPANET, the digital infrastructure that eventually became the Internet.  ARPANET was supported by the Department of Defense to significantly enhance the resilience of the country following a nuclear attack by enabling computers to communicate with each other using a flexible packet-switching digital network.

But, this post-war focus on resilience has significantly waned over the past few decades.  Let me explain.

The 2019 Artificial Intelligence Index

AI has emerged as the defining technology of our era, as transformative over time as the steam engine, electricity, and the Internet.  While still in its early stages, AI is already reshaping the economy, society, our personal lives and the very nature of work, with much more to come as the technology and applications mature, including the scientific research necessary to anticipate and fight future pandemics.  But, what’s the current state of AI and where is it heading?

To help address these important questions, the annual Artificial Intelligence Index was created under the auspices of the Stanford Institute for Human-Centered Artificial Intelligence, - guided by a Steering Committee of experts from academia and industry, and in collaboration with a number of sponsoring partners and data contributors.  The AI Index aims to “provide unbiased, rigorous, and comprehensive data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI.”

Now in its third year, the 2019 AI Index Report was released this past December.  So was the associated 2019 AI Index Data, which includes three times as many data sets as the 2018 edition.  The report  comprises nine chapters.  Let me summarize a few of its highlights.

The Long-Term Future of Work and Education: Three Potential Scenarios

“Experts differ widely in their predictions about how technological innovation will change the labor market, but they all see a need for changes in education,” write British professors Ewart Keep and Phillip Brown in  a recently published article, Rethinking the Race Between Education and Technology.  While experts don’t generally agree on much, they’re pretty much of one mind when it comes to the growing importance of skills and education in our 21st century digital economy.

Every past technological transformation ultimately led to more jobs, higher living standards and economic growth.  But, as a number of recent studies concluded, to ensure that this will indeed be the case, our emerging knowledge economy should be accompanied by the expansion of educational opportunities for everyone.

While noting that this is the most likely scenario for the next 10 - 15 years, Keep and Brown also consider two potential longer-term scenarios.  Perhaps AI will lead to even more pervasive and fundamental transformations in the nature of work, making it difficult for even those with a college or higher education to find a good job.  Beyond that, some have suggested that in the more distant future we might see an even more radical, science-fiction-like transformation: the end of work as we’ve long known it.

The authors argue that considering such a spectrum of possibilities will help us better prepare for what’s essentially an unpredictable future.  In that spirit, their paper discusses three different labor market scenarios: labor scarcity, job scarcity, and the end of work.

The State of AI Adoption - High Performers Show the Way

For the past few years, the McKinsey Global Institute has been conducting a yearly survey to assess the state of AI adoption.  Its 2017 survey of over 3,000 AI-aware executive found that outside the technology sector, AI adoption was at an early, often experimental stage.  Only 20% of respondents used any AI-related technology in a core part to their business.  A common theme throughout the report was that the same players who were leaders in the earlier waves of digitization and analytics were the early leaders in the AI wave.

The McKinsey 2018 survey garnered responses from over 2,000 participants from a wide range of company sizes across 10 industry sectors.  Overall, the 2018 survey found that while the business world had begun to adopt AI, few companies had in place the foundational building blocks that would help them to generate value from AI at scale.  30% were conducting AI pilots.  Nearly half, 47%, had embedded at least one AI capability in their standard business processes, but only 21% reported using AI across multiple business functions.  AI investments were still quite small, with almost 60% of respondents saying that less that one-tenth of their digital budgets went toward AI.

The most recent Global AI Survey was published in November of 2019.  McKinsey garnered responses from over 2,300 participants, nearly 80% of whose companies were using AI in some capacity.  Overall,  the survey showed that “Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce.”  AI is becoming more mainstream, with 58% of respondents using AI in at least one function, - a nearly 25% increase over the previous year; and 30% reported using AI across multiple business units, - a nearly 50% increase.

One of the survey’s major findings is that a small share of high performers, around 3% of companies using AI, are already achieving outsize business results.   High performing companies are much further along in scaling the use of AI in their organizations, with an average of 11 different use cases compared an average of 3 among the other companies.

The Puzzling Economic Impact of Transformative Technologies

“General purpose technologies (GPTs) are engines for growth,…” wrote Erik Brynjolfsson, Daniel Rock, and Chad Syverson in The Productivity J-Curve, a working paper recently published by the National Bureau of Economic Research (NBER).  “These are the defining technologies of their times and can radically change the economic environment.  They have great potential from the outset, but realizing that potential requires larger intangible and often unmeasured investments and a fundamental rethinking of the organization of production itself.”

As we’ve learned over the past two centuries, there’s generally been a significant time lag between the broad acceptance of a major new transformative technology and its ensuing impact on companies, governments and other institutions.  Even after reaching a tipping point of market acceptance, it takes considerable time, - often decades, - for these new technologies and business models to be widely embraced across economies and societies, - and for their full benefits to be realized.

In her 2002 influential book, Technological Revolutions and Financial Capital, economic historian Carlota Perez wrote that since the advent of the Industrial Revolution, we’ve had a major technological revolution every 60 years or so.  First was the age of machines and factories in the latter part of the 18th century.  This was followed by the age of steam, coal and iron in the early to mid 19th century; electricity and steel around the 1870s-1880s; and automobiles, oil and mass production in the early decades of the 20th century.  Then came the computer and communications revolution in the latter part of the 20th century, ushering the transition from the industrial economy of the past two centuries to our present digital economy.

According to Perez, the economic transformations accompanying these technologies are composed of two distinct periods, each lasting roughly 20 to 30 years.  First comes the installation period when the new technologies emerge into the marketplace, entrepreneurs launch many new startups, and venture capitalists encourage experimentation with new business models. This is then followed by the deployment period, when the now well accepted technologies and business models become the norm, leading to long-term economic and productivity growth.

The Increasing Demand for Hybrid, “T-Shaped” Workers

A recent article in The Atlantic used the USS Gabrielle Giffords to illustrate the important changes taking place in the US Navy, - and in the world of work in general.  After discussing various features of its advanced design, the article noted that the ship’s most futuristic aspect is its crew.  “It was designed to operate with a mere 40 souls on board - one-fifth the number aboard comparably sized legacy ships and a far cry from the 350 aboard a World War II destroyer.  The small size of the crew means that each sailor must be like the ship itself: a jack of many trades and not, as 240 years of tradition have prescribed, a master of just one,” noted the article.  Due to increasingly sophisticated technologies and the high cost of personnel, the USS Gabrielle Giffords is among a new class of Navy ships that’s turned away from specialists in favor of so-called hybrid sailors who are able to handle multiple tasks and rapidly acquire new skills.

The replacement of highly specialized workers with problem-solving generalists who are able to handle and quickly learn new tasks has been taking place across a wide variety of  occupations and industries.  Increased productivity and lower costs, i.e., doing more with less, are some of the reasons for this change.  But, the increased complexity of products, applications and systems, and the unanticipated problems that often follow are other important reasons.

The concept of hybrid or T-shaped workers was first introduced around 25 years ago as a metaphor to describe the kind of individuals that the Navy and many other organizations are looking for.  The vertical stroke in the letter T represents a depth of skills and expertise in one or more specific fields; the horizontal bar implies broad multidisciplinary and social skills, as well as the ability to collaborate with experts across disciplines to jointly solve complex problems.

How to Transform a “Big, Old” Company into an Agile Digital Business

“As consumers, we take digital technologies for granted…  We don’t wonder why these things are possible; we simply expect them,” said Designed for Digital a recently published book by Jeanne Ross, Cynthia Beath, and Martin Mocker in its Preface.  But, “the adoption of digital technologies in business doesn’t come as naturally,” - especially if you’re working in a big, old company.  “Inserting digital technologies to enhance operations and create new value propositions has proven extremely challenging.”

Previous research has uncovered this puzzling digital adoption discrepancy between individuals and organizations.  While in our personal lives we’ve enthusiastically embraced digital technology advances, many corporations and institutions still struggle to do so.  At a conference several years ago I learned about a so-called Sunday-night/Monday-morning syndrome, the name given to the frustration of  employees of large companies who, at home on Sunday night, have access to the latest devices and applications, but at work on Monday morning, have to use the more primitive devices and applications that their IT departments support.

A quarter century into the digital age, - as a recent McKinsey study found, - the digital maturity of traditional incumbent firms is still relatively low, - under 25% of their overall potential.  Big, established companies, in particular, have been slow to embrace digital organizational practices, - only 26% of worldwide sales are made through digital channels, 30% of internal operations have been digitally automated, and 25% of supply chains interactions have been digitized.

“If digital technologies are consistently making our consumer lives easier, why aren’t they making it easier to succeed in business?,” asks Designed for Digital.  “Why are business leaders anxious about digital disruption rather than marveling about how quickly they can provide new, exciting, and ever-improving digital solutions to their customers?”  Based on five years of research at nearly 200 large companies, - including executive interviews, surveys of business leaders, and in-depth case studies, - the authors overriding conclusion is that “Big, old companies are simply not designed for digital.”

The MIT Work of the Future Report

“The world now stands on the cusp of a technological revolution in artificial intelligence and robotics that may prove as transformative for economic growth and human potential as were electrification, mass production, and electronic telecommunications in their eras,” said the MIT Task Force on the Work of the Future in its recently released interim report.  The final report will be issued after conducting additional research over the next year.

The Task Force was convened in the spring of 2018 by MIT President Rafael Reif to address what may well be the most critical question of the digital economy: as emerging technologies raise aggregate economic output and the wealth of nations, will they also enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity?

The report’s overriding conclusion is that the likelihood that AI and automation will wipe out major workforce sectors in the near future is exaggerated.  However, we’ve already seen important reasons for concern, especially the rising polarization of employment and wage distribution over the past few decades, which has disproportionately benefited high-skilled professionals while reducing opportunities for mid- and low-skilled workers.

“[A] critical challenge is not necessarily a lack of jobs, but the low quality of many jobs and the resulting lack of viable careers for many people, particularly workers without college degrees.  With this in mind, the work of the future can be shaped beneficially by new policies, renewed support for labor, and reformed institutions, not just new technologies. Broadly, the task force concludes, capitalism in the U.S. must address the interests of workers as well as shareholders.”

How We Learned to Prosper Using Fewer Resources

“We have finally learned how to tread more lightly on our planet.  It’s about time,” writes Andrew McAfee in the Introduction to his new book, More from Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources - and What Happens Next.   McAfee is Co-Director of MIT’s Initiative on the Digital Economy and a Principal Research Scientist at the MIT Sloan School of Management, as well as a prolific writer of books and articles.

“For just about all of human history our prosperity has been tightly coupled to our ability to take resources from the earth,” he adds.  “So as we became more numerous and prosperous, we inevitably took more: more minerals, more fossil fuels, more land for crops, more trees, more water, and so on.  But not anymore.  In recent years we’ve seen a different pattern emerge: the pattern of more from less.”

What led to the emergence of a more-from-less pattern?  The book provides a provocative answer to this question.  The most important forces responsible for the change are technological progress and capitalism.

How to Support the Widespread Adoption of AI

“Artificial intelligence is reshaping business - though not at the blistering pace many assume,” notes Building the AI-Powered Organization, an article in Harvard Business Review by McKinsey partners Tim Fountaine, Brian McCarthy, and Tamim Saleh in its opening sentence.  A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years.  Two recent reports by McKinsey and PwC respectively concluded that AI has the potential to incrementally increase global economic output between now and 2030 by an additional $13 to $15 trillion.

“Yet, despite the promise of AI, many organizations’ efforts with it are falling short,” adds the HBR article.  Surveys with thousands of executives, showed that most firms are only using AI in ad hoc pilots or applying it to a single business process.  Only 8% of firms are engaged in practices that support widespread adoption.

Why the slow progress?  “At the highest level, it’s a reflection of a failure to rewire the organization.  In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers…  at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.”

To support the widespread adoption of AI, companies must make three fundamental shifts.

The Future of Work in the US: A Tale of Multiple Americas

The US economy looks good by almost any measure.  Unemployment is at historical low levels, forcing employers to raise wages and become more aggressive about hiring and training workers.  There are more job openings than unemployed people to fill them.  Inflation remains low.  A number of recent studies have concluded that over the next decade, our technology-based economy will create a significant number of new occupations which will more than offset declines in occupations displaced by automation.  Not only is the US economy doing well in the present, but despite our increasingly smart machines, automation fears appear to be unfounded.

Why then are so many Americans so anxious about the future?  Why do they feel left behind despite our growing economy?  Why is the country so polarized?

A recent McKinsey report, The Future of Work in America: People and place, today and tomorrow, sheds light on these very important questions.  For the past several years, McKinsey has been conducting research on the impact of automation technologies on the future of work at a national level.  This new report is radically different, based on a detail analysis of more than 3,000 US counties and over 300 cities.

Overall, the study found that while in aggregate the country is doing well, there are actually multiple Americas, each with widely different economic patterns and on sharply different trajectories, some of which are doing quite well and face an exciting future, while others are not in good shape and risk falling further behind.

The report is organized into five main sections, and includes a number of tables, graphs and maps.  Let me summarize the key findings of each of the sections.

The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

• 20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;
  
• Many projects are in pilots but some are already in production;
• Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
  
• Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
• The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;
• Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
  
• Further AI growth is inevitable.

The Digitalization of the American Workforce

“Over the past half century, wave after wave of digital innovation has ensured that digitalization - the diffusion of digital technologies into nearly every business and workplace and pocket - has been remaking the U.S. economy and the world of work,” said the Brookings Institution in Digitalization and the American Workforce.  The digitalization of everything has both increased the potential of individuals and societies while contributing to widespread anxiety about its impact on jobs and economic inequalities.  “And yet, for all of the evidence that big changes are underway, surprisingly little data exists to track the spread of digital adoption.”

The Brookings report quantified the spread of digitalization by analyzing the changes in the digital content of 545 occupations between 2002 and 2016 based on O*NET, the most comprehensive data on US occupations sponsored by the US Department of Labor.  O*NET includes highly detailed, task-level information on hundreds of occupations, and includes quantitative measures of knowledge requirements, skills, tools, education, training and work activities. In addition, the report collected information on the occupations’ particular industries, pay, growth rates, geographical locations and distribution across educational and demographic groups.

Each of the 545 occupations, - which encompassed 90% of the US workforce, - was assigned a digital score between 0 and 100 based on the required digital skills.  High digital occupations are those with scores over 60.  They include software developers - with a score of 91; computer system analysts - 79; and financial managers, - 61.  Medium digital occupations scored between 33 and 60, and include lawyers - 58; automative service technicians - 55; office clerks - 55; and registered nurses - 55.  Low digital occupations have a score below 33, and include security guards - 31; restaurant cooks, - 18; construction laborers - 17; and personal care aides - 14.

The (Updated) Purpose of the US Corporation

In September of 1997, the Business Roundtable (BRT), - an association of CEOs of major US companies, - issued a Statement on Corporate Governance which argued that “the paramount duty of management and of boards of directors is to the corporation’s stockholders; the interests of other stakeholders are relevant as a derivative of the duty to stockholders.”

Last week, the BRT released an updated statement on the Purpose of a Corporation which moves away from it’s previous commitment to shareholder primacy to now emphasize a “Commitment to All Stakeholders” and to “An Economy that Serves All Americans.”  This new statement, which was signed by almost 200 CEOs, now places shareholder interests on the same level as the interests of all its other stakeholders, including customers, employees, suppliers, and communities.

The recent BRT statement has received mostly positive coverage. “Top CEOs Say Companies Have Obligations to Society.  Business Roundtable urges firms to take into account employees, customers and community,” said the WSJ in its article’s headline.  Speaking of the change, Fortune noted that over the past decade “Capitalism, at least the kind practiced by large global corporations, was under assault from all sides, and CEOs were getting the message loud and clear…  Capitalism, it seemed, was desperately in need of a modifier.”  The only note of discord I came across was the response of the Council of Institutional Investors, which expressed concerns that the BRT statement “undercuts notions of managerial accountability to shareholders.”

I applaud the BRT statement.  “This is great,” I thought when first reading it, followed by “what took you so long?”  Given the suffering brought upon so many Americans by the 2008 global financial crisis, - the worst since the Great Depression, - restoring trust in business and in our capitalist economic system should have been a top priority for corporate leaders.  To help appreciate why it’s taken the BRT over a decade to affirm that companies have obligations to society, let’s take a look at the prevalent economic and political context of the past 50 years.

A Quarter Century into the Digital Age

The digital age was born around 25 years ago with the public release of the Netscape browser in 1994.  The browser made it much, much easier for the average person to access information over the Internet, sparking the explosive growth of users, websites and online applications.  This was a truly historical point, marking the transition from the industrial economy of the past couple of centuries to a new kind of digital economy.   Ever since those days, the digital age has continued to advance driven by a stream of new technologies, and business models, from smartphones and cloud to big data and machine learning.

A few months ago, the McKinsey Global Institute published Twenty-five Years of Digitization, a report offering ten insights to help business leaders navigate this still relatively new digital world.  Let me first list the ten insights, and then briefly comment on a few of them.

• Large economic potential is linked to digitization - and much of it is yet to be captured;
• Digital superstars are rising far beyond the US big four and China’s big three;
• Digital natives are calling the shots;
• Digital changes everything - even industry boundaries;
• Agile is the new way to compete;
• Playing the platform economy is an in-the-money option;
• Self-cannibalization and innovation are a necessity for digital reinvention;
• Going after the right M&A is key;
• Effective management of digital transformation is vital - but challenging; and
  
• Leveraging, and transitioning from, digital to new frontier technologies is an imperative.

The Challenges of Automation in a Fast Changing Economy

“Technological innovation should be embraced,” notes Automation and a Changing Economy, a recently published report by The Aspen Institute.  “Automation has been a largely positive economic and social force, and looking forward,   automation will be necessary to feed, house, and raise the living standards of a growing and aging population.” 

But, “While it is important to note that automation’s overall, long-run effect on the economy has been positive - more jobs, more growth, higher living standards - this does not negate the disruptive impact of automation on individuals and communities, which results from displacements, changing skill needs, and income inequality.”

The report is divided into two sections: The Case for Action, which explores how automation impacts the economic security and opportunity of American workers, and Policies for Shared Prosperity, which outlines a policy agenda for addressing automation’s challenges and opportunities.  Let me summarize the key conclusions and recommendations in each of the report’s two sections.

Free-Market, Free-Trade Capitalism at a Crossroads

The global economy has undergone considerable change over the past few decades.  Results have been mixed.  On the positive side, the digital revolution has significantly improved the quality of life of billions around the world.  Technology advances like the smartphone and wireless Internet, and the economic development brought about by globalization have led to the dramatic decline of extreme poverty around the world.  However, these benefits have been accompanied by major dislocations in labor markets, benefiting workers in low-cost developing nations while severely impacting workers in advanced economies.

The promise of technological progress and globalization as drivers of growth and prosperity has been a reality for many, but it’s also created winners and losers within nations, leading to rising economic inequality and fueling the rise of populism in countries around the world.  What’s going on?  I believe that part of the answer to this very important question can be found by exploring the evolution of free-market, free-trade capitalism over the past couple of centuries.

Adam Smith, the 18th century Scottish economist and philosopher, is the father of free market, free trade capitalism, and one of the major figures of The Enlightenment.  In the The Wealth of Nations, considered the first modern work of economics, he explained that in a free market, an individual pursuing his own self-interests tends to also promote the good of his community as a whole, and that the free market, while appearing chaotic and unrestrained, is actually guided to produce the right results by a so-called invisible hand.

The past 200 years have seen many variants of capitalism.  They’re all generally based on the private ownership of the means of production, their operation for profit, and the accumulation of capital.  Neoclassical economics, first formulated in the 19th century, led to a laissez-faire model of capitalism, based on the premise that given all relevant information people will make rational decisions, markets on their own will achieve the right results, and government interventions, - e.g., regulations, subsidies, tariffs, - are almost always a bad idea.

Faith in neoclassical economies was shattered in the 1930s by the Great Depression, giving rise to Keynesian economics, named for British economist John Maynard Keynes.  His pragmatic, mixed model of capitalism, based on a predominantly private sector economy but with an appropriate role for the public sector, dominated economic thinking for the next four decades.

The Business Value of Augmented Reality

I recently heard a very interesting presentation at the 2019 MIT CIO Symposium, - Why Companies Need an Augmented Reality Strategy, - by Harvard professor Michael Porter and James Heppelmann, CEO of PTC, an industrial software company.  The talk was based on their HBR article A Manager’s Guide to Augmented Reality.

We generally think of products as physical entities built with a combination of mechanical and electrical components, - e.g., appliances, cars, agricultural machines, industrial equipment, - some quite simple and some highly complex.  But, in a 2014 HBR article, Porter and Heppelmann introduced the notion of smart connected products (SCPs), a whole new class of products that, in addition to mechanical and electrical components, use digital components like microprocessors, sensors, data storage, software, and connectivity in a wide variety of ways.  Smart connected products are radically reshaping companies and exposing them to new competitive opportunities and threats, while altering industry boundaries and creating entirely new industries.

Through its first five decades, IT helped companies become significantly more productive, by automating many of their processes, reengineering their overall operations, and reaching out beyond the boundaries of the enterprise.  But, their actual products were largely unaffected.  This is all now changing with the rise of the Internet of Things (IoT), cloud computing, Big Data, AI, and other innovations whose combined impact is driving the new era of smart connected products.

As the world’s digital and physical infrastructures converge, IT is now an integral part of the products themselves. Digital technologies are designed right into the products.  Massive amounts of product-usage data can now be gathered, stored and analyzed by a variety of applications in the product’s cloud counterpart.  The cloud might also include a digital twin of each specific physical product, that is, a highly realistic 3-D digital model that monitors the real-time status of the digital twin’s physical counterpart, - e.g., a jet engine, elevator or turbine, - and helps make important decisions such as the need for preventive maintenance to prevent a product failure.

The Evolution of Work in Urban Labor Markets

A few weeks ago I heard a very interesting keynote by MIT economist David Autor at the annual conference of MIT’s Initiative on the Digital Economy.  The talk was based on his recently published paper Work of the Past, Work of the Future.

Professor Autor started his talk by noting that over the last four decades, the US and other industrial economies have seen a remarkable rise in wage inequality by educational attainment, with the earnings of the most-educated increasing, and the earnings of the least-educated falling in real terms.  Since the 1980s, the earnings of those with a four year college degree have risen by 40% to 60%, the earnings of those with a post-college education have risen 80% to 100%, and the earnings of those with a high school education or less have fallen among men and barely changed among women.

At the same time, the supply of educated workers in the US has vastly increased.  From the 1960s to the present, the share of the labor force with college or post-college degrees rose from 12% to 39%,  while the share of those with some college rose from 13% to 28%.  But the share of those with a high school degree or less fell from 75 to 33%.  Based on the law of supply-demand, one would expect that the wages of college-educated workers would have fallen in recent years because there are so many of them, while the wages of those with a high school education or less would have risen because they are a significantly smaller share of the labor force.  But, as we well know, this has been far from the case.

Autor addressed this seeming paradox in his keynote by analyzing a number of potential explanations.  Eroding union penetration, falling minimum wages, and the rise of China, India and other low labor-cost countries have certainly contributed to the wage erosion of non-college workers.  But his main focus was the changing structure of work in the US and other advanced economies, as well as how these changes have unfolded across different geographic regions, from low density rural areas to high density cities.

The Puzzling Personal Productivity Paradox

The past 10-15 years have seen a number of technology advances, from smartphones to machine learning.  Yet, despite these impressive advances, for most of this period economies around the world have been stuck in an era of slow productivity growth.  Opinions abound, but in the end, there’s no consensus on the causes of this apparent productivity paradox, on how long the slowdown will likely last, or on what to do about it.

Some economists have argued that secular stagnation is the reason behind this unusual situation, caused primarily by a reluctance of companies to invest and of consumers to spend.  Others feel that aging populations around the world, accompanied by slow or declining labor forces are major factors.  A few contend that there’s been a fundamental decline in innovation and productivity over the past few decades, compared to the period between 1870 and 1970.

Yet others argue that there have always been time lags between technology advances and their impact on economic growth.  While technologies may advance rapidly, humans and institutions change slowly.  And, the more transformative the technologies, - as is currently the case, - the longer it takes for their impact to be felt across economies and societies.

But, companies and industries are not the only ones trying to adjust to our fast changing digital economy.  Individuals are also experiencing their own version of the productivity paradox.  Personal computers, along with applications like word processing and spreadsheets introduced a whole new category of personal productivity tools in the 1980s. A decade later, the Internet and World Wide Web enabled people to easily communicate with each other, and gave them access to all kinds of information and applications.

The Third Wave of the Digital Economy - Promises and Challenges

“The next wave of digital innovation is coming.  Countries can welcome it, prepare for it, and ride it to new heights of innovation and prosperity, or they can ignore the changing tide and miss the wave,” writes Robert Atkinson in The Task Ahead of Us.  Atkinson is founder and president of the Information Technology and Innovation Foundation (ITIF), a think tank focused on science and technology policy.

We’re now entering the third wave of the digital economy, says Atkinson.  The first was based on personal computing, the Internet, Web 1.0, and e-commerce.  The second brought us Web 2.0, big data, smartphones and cloud computing.  The emerging third wave promises to be significantly more connected, - including higher bandwidth and a wide variety of devices; more automated, - with more work being done by machines while integrating the physical and digital worlds; and more intelligent, - leveraging huge volumes of data and advanced algorithms to help us understand and deal with our increasingly complex world.

“Building and adopting the new connected, automated, and intelligent technology system will lead to enormous benefits globally, not least of which will be robust rates of productivity growth and improvements in living standards.  Moreover, these technologies will help address pressing global challenges related to the environment, public health, and transportation, among others.”

We’re in the early stages of this third wave.  5G, IoT, robotics, AI and other promising technologies are being embraced by early marketplace adopters, but their full-scale impact is still 5 to 10 years away.  We’re in a period not unlike the late 1980s, when it was clear that IT was on the brink of a major transition, but the Internet revolution didn’t arrive until the mid-1990s.

What Might Work Look Like in 2030?

A number of recent studies have taken a close look at the the future of work over the next 10 to 15 years.  For example, a December, 2017 report by McKinsey examined which jobs will likely be displaced by automation through 2030, as well as which jobs are likely to be created over the same period, based on data from 46 countries.

McKinsey’s overall conclusion was that a growing technology-based economy will create a significant number of new occupations which will more than offset declines in occupations displaced by automation.  However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”

Another report, The Changing Nature of Work, - published by the World Bank in October, 2018, - concluded that our problem is not that there won’t be enough work in the future.  The key problem is that, in many countries, the workforce isn’t prepared for a fast unfolding future. Neglecting investments in human capital, - i.e., the sum total of a population’s health, skills, knowledge, and experience, - will dramatically weaken a country’s competitiveness.

Let me now discuss Eight Futures of Work Scenarios and their Implications, a white paper published in January, 2018 by the World Economic Forum (WEF) in collaboration with the Boston Consulting Group (BCG).  The paper considered eight distinct scenarios on what work might be like by 2030, based on different combinations of three of the most impactful and uncertain variables affecting the future of work: the rate of technological change; the evolution of learning in the workforce; and the magnitude of talent mobility across geographies.

AI and our Social Interactions

I recently wrote about the event I attended on February 28 to celebrate the launch of MIT’s Schwarzman College of Computing.  This new interdisciplinary College is MIT’s response to the rise of artificial intelligence, - a profoundly powerful technology that will likely reshape our economy, society and personal lives in the decades to come.  But, attaining AI’s broad potential will require not only a continuing slew of technological innovations, but equally important research on the challenges our society must get ready for, including ethical issues, workplace disruptions, and human interactions.

The event included keynotes and panels on a wide variety of topics. One of the talks I found most interesting was Rethinking Friction in Digital Culture by Sherry Turkle, MIT Professor of the Social Studies of Science and Technology.

In her many writings and seminars, - including her 2011 best-seller Alone Together, -  Professor Turkle has long been warning about the impact of technology on our social interactions.  “Most of us here today were introduced to the idea of friction free as a really good thing,” she said at the start of her keynote.  “It’s an aesthetic of engineering efficiency, so why shouldn’t it be a really good thing?”  This idea that technical things should be friction free and smooth easily bleeds into other domains. “Efficiency becomes aspirational - in politics, in business, in education, and in our thinking about relationships.”

She cited a few examples of the kinds of friction free interactions she’s come across in her research on technology and social relationships.  People of all ages have told her that they prefer to text rather than talk, - even when interacting with a colleague in the next cubicle or office.  People have said to her that they prefer to text their spouse rather than have a face-to-face conversation.  Friction free, she explained, “is usually tied up with a hope for greater efficiency and less vulnerability.”

Globalization in Transition

I recently read two interesting reports on the state of globalization in 2019, one by The Economist, the second by the McKinsey Global Institute.  They both agree on the facts, - globalization and global trade have been undergoing considerable changes since the 2008 global financial crisis.  But, they differ in their explanations of the reasons for the changes.

“The golden age of globalisation, in 1990-2010, was something to behold,” notes The Economist in its overview article, The steam has gone out of globalization.  “Commerce soared as the cost of shifting goods in ships and planes fell, phone calls got cheaper, tariffs were cut and the financial system liberalised.”

But now, we’ve transitioned to a so-called Slobalisation era.  “Globalisation has slowed from light speed to a snail’s pace in the past decade for several reasons.  The cost of moving goods has stopped falling.  Multinational firms have found that global sprawl burns money and that local rivals often eat them alive.  Activity is shifting towards services, which are harder to sell across borders: scissors can be exported in 20ft-containers, hair stylists cannot.  And Chinese manufacturing has become more self-reliant, so needs to import fewer parts.”  Finally, we have the well-publicized trade wars and tariffs of the past two years.  All in all, the “rules of commerce are being rewritten around the world.”

The Productivity Paradox: Digital Abundance and Scarce Genius

Despite the relentless advances of digital technologies, productivity growth has been declining over the past decade.  Investment and interest rates have remained low, and income has continued to stagnate for the majority of workers in the US and other developed economies. The world seems to be stuck in a period of slow growth and no one is quite sure what’s causing this apparent contradiction.

Opinions abound.  Harvard economist Larry Summers has argued that secular stagnation is the reason behind this unusual situation, caused primarily by a reluctance of companies to invest and of consumers to spend, with the ensuing excessive savings acting as a drag on economic growth.

Some contend, - most prominently Northwestern University economist Robert Gordon, - that over the past few decades there’s been a fundamental decline in innovation and productivity.  Perhaps our current technologies, advanced and exciting as they might be, aren’t as transformative as the technologies from the period between 1870 and 1970, when we experienced high productivity growth and a rising standard of living. 

Demographic change, namely, the declining population and labor force growth around the world, is another potential cause for slow economic growth.  The global labor force grew at an average of 1.8% per year between 1960 and 2005, but since then it’s been growing at just 1.1% per year, and it’s already shrinking in a number of countries, including Japan, Germany and China.

Another possible explanation, - advanced by Erik Brynjolfsson, Daniel Rock, and Chad Syverson in AI and the Modern Productivity Paradox, - is that the paradox is primarily due to a time lag between technology advances and their impact on the economy.  We’ve been living in a time of major transformative technologies, - the Internet, smartphones, IoT, big data, AI - whose deployment and impact on productivity growth are still lagging.

While technologies may advance rapidly, humans and our institutions change slowly.  Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy.  Leading edge firms are already benefiting from these advances, but most are still in the early learning stages.  Translating technological advances into productivity gains requires major transformations in business processes, organization and culture, - and these take time.

Impact of an Aging Population on Economic Growth and Automation

The world has been stuck in an era of slow economic growth over the past decade.  Top economists and policy makers have proposed a number of explanations for the economic slowdown, but in the end, there’s no consensus on the reasons, on how long the slowdown will likely last, or on what to do about it.

Economic growth has two main components, productivity growth and the growth of the labor force.  One potential theory for our stagnant economic growth is demographics, namely, the rapidly declining population and labor force growth around the world.

The current world’s population is 7.7 billion.  The UN Population Division estimates that global population will reach 9.8 billion with a growth rate of 0.5% in 2050, and will peak around 2100 at roughly 11.2 billion with a growth rate of 0.1%.  Other projections estimate that the population will grow slower, peak at 9.4 billion around 2070 and then decline to around 9 billion in 2100.  Some project that after reaching a peak of 9 billion, the world’s population will decline back to today’s levels, - around 7 billion, - by 2100.

Japan and a few other countries are already experiencing population declines.  The US population is currently around 330 million, and is projected to reach 390 million in 2050 and 450 million in 2100.  While the US birthrate hit a historic low in 2017, the population has continued to grow due to immigration.  The US growth rate is 0.71% including immigration and 0.43% without.

The global labor force grew at an average of 1.8% per year between 1960 and 2005, but since then it’s been growing at just 1.1% per year.  The labor force is still growing in some developing countries like India, Nigeria and the Philippines, but it’s already shrinking in China, Japan and Germany.  In the US, the labor force is growing very slowly, - 0.5 percent per year over the past decade, compared with 1.7 percent from 1960 to 2005.  Given the continuing decline in fertility rates in most parts of the world, the global labor force is expected to slip further in the coming decades.

The New, Interdisciplinary, MIT Schwarzman College of Computing

A few months ago, MIT president Rafael Reif announced the launch of the Stephen A. Schwarzman College of Computing in an e-mail to the MIT community.  “This new College is our strategic response to a global phenomenon – the ubiquity of computing and the rise of AI…” wrote President Reif.  “To state the obvious, AI in particular is reshaping geopolitics, our economy, our daily lives and the very definition of work.  It is rapidly enabling new research in every discipline and new solutions to daunting problems.  At the same time, it is creating ethical strains and human consequences our society is not yet equipped to control or withstand.”

The new College of Computing is made possible by a $350 million foundational gift from Stephen Schwarzman, - chairman, CEO and co-founder of Blackstone, one of the world’s leading investment firms.  The gift is part of a  $1 billion commitment to reshape MIT to help it better address the continuing advances in technology in our 21st century digital economy, as well as their many applications , - both the exciting opportunities and the difficult challenges.

The College is envisioned as an interdisciplinary hub across all five of MIT’s existing schools: Engineering; Science; Humanities, Arts, and Social Sciences; Architecture and Planning; and the Sloan School of Management.  This MIT News announcement succinctly summarized the key objectives of the new College:

• reorient MIT to bring the power of computing and AI to all fields of study at MIT, allowing the future of computing and AI to be shaped by insights from all other disciplines

• create 50 new faculty positions that will be located both within the College and jointly with other departments across MIT - nearly doubling MIT’s academic capability in computing and AI

• give MIT’s five schools a shared structure for collaborative education, research, and innovation in computing and AI

• educate students in every discipline to responsibly use and develop AI and computing technologies to help make a better world; and

• transform education and research in public policy and ethical considerations relevant to computing and AI.

Despite All the Bad News that Surround Us… Is the World Actually Getting Better?

The World is Getting Quietly, Relentlessly Better is the title of a recent WSJ article by its chief economics commentator Greg Ip.  “If you spent 2018 mainlining misery about global warming, inequality, toxic politics or other anxieties, I’m here to break your addiction with some good news: The world got better last year, and it is going to get even better this year,” writes Ip in his opening paragraph.  “Poverty around the world is plummeting; half the world is now middle class; and illiteracy, disease and deadly violence are receding.  These things don’t make headlines because they are gradual, relentless and unsurprising.”

Ip’s article is mostly based on data insights from Our World in Data, a comprehensive website that quantifies the evolution of our global living conditions over the past few centuries.  Our World in Data (OWID) is an initiative of the Oxford Martin Programme on Global Development, led by economist Max Roser at the University of Oxford.

OWID analyzes longitudinal data on how the world has been changing over decades and centuries, as well as explaining the causes and consequences of those changes in its accompanying articles. It makes extensive use of visual aids, including interactive graphs and maps, to present its analyses and insights.  Its content is organized into 15 main sections:  population, health, food, energy, environment, technology, growth & inequality, work & life, public sector, global connections, war & peace, politics, violence & rights, education, media and culture, -  each of which further includes a number of subsections.

Let me summarize just a few of the key trends in the OWID website.

The Business Value of Resilience

The Industrial Revolution was “as much about process innovations that reduced waste and increased productivity as it was about the application of new technologies,” wrote Roger Martin in a recent Harvard Business Review (HBR) article, - The High Price of Efficiency.  Martin is professor and former dean of the Rotman School of Management at the University of Toronto, as well as an accomplished writer.

The belief that efficiency is fundamental to competitive advantage has turned management into a science, - taught in every business school on the planet, - whose objective is the elimination of waste, - whether of time, materials, or capital.

“Why would we not want managers to strive for an ever-more-efficient use of resources?,” asks Martin.  Of course we do.  But, an excessive focus on efficiency can produce startlingly negative effects.  To counterbalance such potential negative effects, companies should pay just as much attention to a less appreciated source of competitive advantage: resilience,  - “the ability to recover from difficulties - to spring back into shape after a shock.”

“Think of the difference between being adapted to an existing environment (which is what efficiency delivers) and being adaptable to changes in the environment.  Resilient systems are typically characterized by the very features - diversity and redundancy, or slack - that efficiency seeks to destroy.”

The Current State of AI Adoption

AI is seemingly everywhere.  In the past few years, the necessary ingredients have finally come together to propel AI beyond early adopters to a broader marketplace: powerful, inexpensive computer technologies; advanced algorithms; and huge amounts of data on almost any subject.  Newspapers and magazines are full of articles about the latest advances in machine learning and related AI technologies.

Two recent reports concluded that, over the next few decades, AI will be the biggest commercial opportunity for companies and nations.  AI advances have the potential to increase global GDP by up to 14% between now and 2030, the equivalent of an additional $14-15 trillion contribution to the world’s economy, and an annual average contribution to productivity growth of about 1.2 percent.

Over time, AI could become a transformative, general purpose technology like the steam engine, electricity, and the Internet. AI marketplace adoption will likely follow a typical S curve pattern, - that is, a relatively slow start in the early years, followed by a steep acceleration as the technology matures and firms learn how to best leverage AI for business value.

To get a better sense of the current state of AI adoption, McKinsey recently conducted a global online survey on the topic, garnering responses from over 2,000 participants across 10 industry sectors, 8 business functions and a wide range of regions and company sizes.  The survey asked about their progress in deploying nine major AI capabilities, including machine learning, computer vision, natural language text and speech processing, and robotic process automation.

The True Value of a Good Education: Adaptability to a Changing Environment

In his 1945 seminal report, Science The Endless Frontier, presidential science advisor Vannevar Bush laid out the blueprint for R&D in post-war America: “New knowledge can be obtained only through basic scientific research” conducted in universities and research labs, which is then applied to develop new products by the private sector and new and improved weapons by the defense sector.  The report was quite influential and led to the considerable expansion of university research, much of it supported by the National Science Foundation (NSF), the National Institutes of Heath (NIH) and other US federal agencies.

A number of large industrial companies also embraced these recommendations, and launched or expanded corporate research labs, such as ATT’s Bell Labs, GE Research, Xerox PARC and IBM Research.  Their job was to push the frontiers of knowledge by conducting both basic and applied research, which would hopefully lead to innovative technologies and products.

A doctoral degree or PhD has long been a key requirement for most research positions, whether in universities, or corporate and government labs.  Thus, since the post-war expansion of research, the number of PhDs has been steadily rising.  NSF data shows that the number of PhD recipients per year from US universities has more than quintupled, - from under 10,000 in 1958 to almost 55,000 in 2017.  Over three quarter of the degrees awarded in 2017 were in STEM disciplines: 23% in Life Sciences, 18% in Engineering, 17% in Social Sciences, 11% in Physical Sciences, and 7% in Math and Computer Sciences. Beyond STEM, 10% of 2017 PhDs were in the Humanities and Arts, 9% in Education, and 5% in various other disciplines.

Data from the US Bureau of Labor Statistics (BLS) shows the value of a PhD education.  According to 2017 BLS data, recipients of doctoral degrees had among the lowest unemployment rates at 1.5%, compared with 2.2% of those with master’s degrees, 2.5% with bachelor degrees and an overall unemployment rate of 3.6%.  Holders of doctoral degrees were also among the best paid, with weekly median earnings of $1,743, slightly below holders of professional degrees, - e.g. physicians, dentists, lawyers, - at $1,836, and higher than the $1,401 for master’s degrees and $1,173 for bachelor’s.

Given these positive figures, one would expect near universal agreement on the value of obtaining a PhD.  But, in fact, this is not the case.  A number of recent articles have been questioning whether a PhD is worth it.  Let’s take a closer look at their arguments.

Will Humans Be Better Off in an Increasingly AI-Based Future?

After decades of promise and hype, artificial intelligence is finally becoming one of the most important technologies of our era.  As AI continues to both spread and advance, will it enhance human capacities or will it lessen human autonomy and agency?  By 2030, will most people be better or worse off than they’re today?

To shed light on these very important questions, the Pew Research Center canvassed AI experts this past summer, including technologists developers, and business and policy leaders.  It received almost 1,000 responses, which formed the basis for its recently published report Artificial Intelligence and the Future of Humans.

Overall, 63% of respondents were optimistic, predicting that most individuals will mostly be better off, while 37% thought otherwise.  But optimistic or not, most experts expressed concerns about the long-term impact of AI on “the essential elements of being human.”

The survey further asked the experts to explain their hopes and fears, to sketch out how they envision human-machine interactions over the coming decade, and what actions should be taken to assure the best possible future. Many answered, and their detailed comments are organized into the report’s three main sections: concerns; suggested solutions; and expectations for the future.  Let me briefly discuss the key points in each section.

The World Bank’s Human Capital Index

Last week I wrote about a recently published World Bank report, The Changing Nature of Work.  The report highlights the increasing importance of investing in human capital by both the public and private sectors.  All around the world, the demand for workers who are good at complex problem-solving, interactions with clients and colleagues, and able to adapt to new tools and technologies is rising, while the demand for less advanced skills that can be replaced by technology is declining.  Neglecting investments in human capital, it warned, can dramatically weaken a country’s competitiveness.

“The world is healthier and more educated than ever…,” notes the World Bank in a companion report, The Human Capital Project.  “But a large and unfinished agenda remains.  Life expectancy in the developing world still lags far behind that of rich countries…  Worldwide, more than 260 million children and youth are not in school. Meanwhile, nearly 60 percent of primary school children in developing countries fail to achieve minimum proficiency in learning.”

Why should countries invest in human capital?  Can early health care and education prepare children to succeed and prosper as adults in a rapidly changing world?  What are the barriers to nurturing human capital and how can countries overcome them?  To address these important questions, the World Bank introduced the Human Capital Index (HCI).  The HCI is designed to quantify the amount of human capital that a child born today can expect to attain by age 18 given the prevailing health and education conditions in the country where the child lives.  It will be updated periodically and refined as data measures improve.  This video nicely explains the HCI and what it measures. 

Human Capital and the Changing Nature of Work

People have long feared that machines are coming for our jobs.  Throughout the Industrial Revolution there were periodic panics about the impact of automation on work, going back to the so-called Luddites, - textile workers who in the 1810s smashed the new machines that were threatening their jobs.

Automation anxieties have understandably accelerated in recent years, as our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans.  But on balance, such fears appear to be unfounded, noted the World Bank in a comprehensive recent report on The Changing Nature of Work.  Our problem is not that there won’t be enough work in the future.  Our key problem is that, in many countries, the workforce isn’t prepared for our fast unfolding future.

In a recent article in Foreign Affairs, World Bank Group President Jim Yong Kim warned that the world is facing a Human Capital Gap.  “Governments in pursuit of economic growth love to invest in physical capital - new roads, beautiful bridges, gleaming airports, and other infrastructure.  But they are typically far less interested in investing in human capital, which is the sum total of a population’s health, skills, knowledge, experience, and habits.  That’s a mistake, because neglecting investments in human capital can dramatically weaken a country’s competitiveness in a rapidly changing world, one in which economies need ever-increasing amounts of talent to sustain growth.” 

Redefining Work - Leveraging Human Capabilities in a Future of Expanding Automation

How will labor markets evolve in our 21st century digital economy?  What’s the likely future of jobs, given that our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans?  How will AI, robotics and other advanced technologies transform the very nature of work?

Over the past few years, a number of papers, reports and books have addressed these very important questions.  They generally conclude that AI will have a major impact on jobs and the very nature of work.  For the most part,  they view AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention.  Overall, few jobs will be entirely automated, but automation will likely transform the vast majority of occupations. 

A recent McKinsey report noted while there will likely be enough work to maintain full employment by 2030, the transition will be very challenging, “on a scale not seen since the transition of the labor force out of agriculture in the early 1900s in the United States and Europe, and more recently in in China.”  The report estimated that “up to 375 million workers, or 14 percent of the global workforce, may need to change occupations - and virtually all workers may need to adapt to work alongside machines in new ways.”

Given these predictions about the changing nature of work, what should companies do?  How should firms prepare for a brave new world where we can expect major economic dislocations along with the creation of new jobs, new business models and whole new industries, and where many people will be working alongside smart machines in whole new ways?

The Jobs Outlook for 2022

A few weeks ago, the World Economic Forum (WEF) released The Future of Jobs Report 2018.  The report takes an in-depth look at the world of work in what the WEF calls the Fourth Industrial Revolution. The report is based on a survey of over 300 chief human resource officers and top strategy executives from large global companies across 12 industry sectors.  These companies collectively represent more than 15 million employees, and conduct business in 20 developed and emerging economies which collectively account for about 70% of global GDP.

First introduced at its 2016 annual Davos Forum, the WEF positions the Fourth Industrial Revolution within the historical context of three previous industrial revolutions.  The First, - in the last third of the 18th century, - ushered the transition from hand-made goods to mechanized, machine-based production based on based on steam and water power.  The Second, - a century later, - revolved around steel, railroads, cars, chemicals, petroleum, electricity, the telephone and radio, leading to the age of mass production.  The Third, - starting in the 1960s, - saw the advent of digital technologies, computers, the IT industry, and the automation of process in just about all industries.

“Now a Fourth Industrial Revolution is building on the Third…” wrote WEF founder and chairman Klaus Schwab in a December, 2015 Foreign Affairs article.  “The possibilities of billions of people connected by mobile devices, with unprecedented processing power, storage capacity, and access to knowledge, are unlimited.  And these possibilities will be multiplied by emerging technology breakthroughs in fields such as artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing.”

The Future of Jobs 2018 examines the potential of these advanced technologies to eliminate and create new jobs, as well as to improve the current workplace and prepare people for emerging jobs. Rather than looking at the future of work over the longer term, the report chose to examine the 2018-2022 period, which is within the planning horizon of the companies and executives that participated in the survey.

The 2018 MIT Inclusive Innovation Challenge

In 2016, MIT’s Initiative on the Digital Economy launched its first annual Inclusive Innovation Challenge (IIC).  The key objective of the IIC is to help create greater shared prosperity around the world by awarding over one million dollars in prizes each year to to entrepreneurs that are leveraging technology advances to reinvent the future of work.

“The IIC believes that Inclusive Innovation is an economic and moral imperative, and that the key question of our era isn’t what technology is going to do to our economy and society, but what we will do with technology.  By identifying and promoting the powerful global community of future of work visionaries, the IIC proactively accelerates the technology-driven solutions enabling greater economic opportunity for working people around the world facing the challenge of rapidly advancing digital progress.”

Entrepreneurs from around the globe compete for awards in four categories: income growth and job creation, skills development and opportunity matching, technology access, and financial inclusion. Approximately 1,500 organizations registered to compete this year, and 500 judges reviewed their applications.   

In 2018, rather than having one global competition as in the previous two years, there were five separate regional competitions, - North America, Latin America, Europe, Africa, and Asia, - each region selecting its own category winners.  A Grand Prize winner was then selected in each category from among the regional winners and each awarded $250,000.  The winners were announced at a gala event that took place in MIT on November 8.