Irving Wladawsky-Berger

Blockchain and Public Health Solutions

The Blockchain Research Institute (BRI) is a global think tank dedicated to the strategic implications of blockchain technologies to business, government and society.  On March 26, I participated in a virtual roundtable convened by the BRI to discuss the potential use of blockchain technologies for public health solutions.  The roundtable’s findings and recommendations were released in early April in Blockchain Solutions in Pandemics: A Call for Innovation and Transformation in Public Health.

The report identified five key areas where blockchain could be deployed to fight Covid-19 as well as future pandemics: identity, health records and shared data; just-in-time supply chains; sustaining the economy; a rapid response registry for medical professionals; and incentives models to reward responsible behavior.  In each of these areas, the report presented uses cases where blockchain is already being deployed, and recommended a number of blockchain related public health measures that will help better prepare for future pandemics.  Given the broad scope of the report, I will focus my attention on one area in particular: identity, health records and shared data.

Getting Through Highly Uncertain Times - Some Lessons Learned

What does it take to get through highly uncertain times, like the ones we’re in the middle of as we confront the Covid-19 pandemic?  Like most of us, I’ve lived through times of high uncertainty in both my personal and professional lives.  On the personal front, my main observation is that most of us are much more resilient than we realize.  I leave it to psychologists and other experts to offer advice on what it takes to get through such times.

But, when it comes to getting through tough times in our professional life, I’d like to share some lessons learned based on my experiences when IBM, - the company I joined in 1970 after completing my university studies, - almost went out of business in the early 1990s.  Let me describe a few of those lessons.

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 Coming Era of Decision Machines

“Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals,” wrote Harvard professor David Parkes in A Responsibility to Judge Carefully in the Era of Prediction Decision Machines, an essay recently published as part of Harvard’s Digital Initiative.  “Machines need to be able to predict to decide, but decision making requires much more.  Decision making requires bringing together and reconciling multiple points of view.  Decision making requires leadership in advocating and explaining a path forward.  Decision making requires dialogue.”

In April, 2017 I attended a seminar by University of Toronto professor Avi Goldfarb on the economic value of AI.  Goldfarb explained that the best way to assess the impact of a new radical technology is to look at how the technology reduces the cost of a widely used function.  For example, computers are essentially powerful calculators whose cost of digital operations have dramatically decreased over the past several decades.  Over the years, we’ve learned to define all kinds of tasks in terms of digital operations, e.g., financial transactions, word processing, photography.  Similarly, the Internet has drastically reduced the cost of communications and of access to all kinds of information, - including text, pictures, music and videos.

Viewed through this lens, the AI revolution can be viewed as reducing the cost of predictions.  Prediction means anticipating what is likely to happen in the future.  Over the past decade, increasingly powerful and inexpensive computers, advanced machine learning algorithms, and the explosive growth of big data have enabled us to extract insights from all that data and turn them into valuable predictions.

Given the widespread role of predictions in business, government and everyday life, AI is already having a major impact on many human activities.  As was previously the case with arithmetic, communications and access to information, - we will be able to use predictions in all kinds of new applications.  Over time, we’ll discover that lots of tasks can be reframed as prediction problems.

The Current State of Open Innovation

In January, UC Berkeley professor Henry Chesbrough published Open Innovation Results: Going Beyond the Hype and Getting Down to Business, his fourth book on innovation in the last two decades.  Chesbrough is adjunct professor and faculty director of the Garwood Center for Corporate Innovation at UC Berkeley’s Haas School of Business.  He’s credited with coining the term open innovation in his 2003 book of the same title.

Open innovation is the antithesis of the vertically integrated, closed innovation approach followed by industrial R&D labs through much of the 20th century.  As the rate and pace of technological advances accelerated, - most prominently in the IT industry, - the closed innovation model started to fall apart.  The elapsed times to take an invention from lab to market were no longer competitive, especially against a new generation of startups that significantly decreased the time-to-market for new products and services.  Companies realized that they could no longer rely on their own R&D as the only source of innovative ideas.

Moreover, as innovation was now being applied to broader and more complex problems, external collaborations with clients, business partners, universities, open source communities and others became increasingly important.  Given the fast moving, highly competitive environment, firms recognized that there were a lot more capabilities for innovation in the marketplace than they could possibly create on their own, no matter how big and powerful the company.  The dramatic success of the Internet, World Wide Web, Linux and similar initiatives in the 1990s ushered this new era of open, collaborative innovation.

Honest Conversations - The Key to a Winning Transformational Strategy

“Nearly every organization - whether private, social, or governmental - is grappling with huge strategic challenges, often with a need to reimagine its very purpose, identity, strategy, business model, and structure,” writes Harvard emeritus professor Michael Beer in his recently published book Fit to Compete.  “Most of these efforts to transform will fail.  And in most cases, they miss the mark not because the new strategy is flawed, but because the organization can’t carry it out.”

For over three decades, professor Beer and his various collaborators have been analyzing the reasons why so many organizational transformations fail.  In general, the common root cause has been the failure to align the organization’s culture and management processes with the strategic directions they’re after.  The book argues that two interrelated reasons explain these failures.

First, “the whole system of organizing, managing, and leading has to be transformed if organizational behaviors and underlying mindsets are to be changed.”  A transformational strategy that aims to take advantage of new technology and market innovations must involve the whole organization.  But, we often forget that such transformations are truly disruptive, not only to the marketplace but also to your own organization.  You cannot transform a large complex organization through business-as-usual objectives, - e.g., growing revenue and profit, cutting expenses, improving quality, developing better products and services.

Any major company-wide initiative must have the strong and visible support of the CEO and other top executives in the company.  The initiative must be carefully nurtured and protected in its early phases until it’s strong enough to stand on its own.  In any organization, there’s a natural competition for resources between new ventures and the existing lines of business.  Human nature being what it is, managers in the existing units will often feel that any new investments are best given to them to grow their businesses, rather than invested in a new, unproven area. This is one of the many challenges that leaders of the new initiative must skillfully navigate, otherwise the inevitable cultural and political – that is, human – issues will slow them down and potentially kill the new venture.

The Pace of Creative Destruction is Accelerating

“A gale force warning to leaders: at the current churn rate, about half of S&P 500 companies will be replaced over the next ten years,” is one of the key insights from the 2018 Corporate Longevity Forecast.  The forecast is conducted every two years by Innosight, the innovation consulting firm co-founded by renowned Harvard professor Clayton Christiansen (who sadly died from cancer on January 23).  “The 33-year average tenure of companies on the S&P 500 in 1964 narrowed to 24 years by 2016 and is forecast to shrink to just 12 years by 2027.”

Shrinking company lifespans in the S&P 500 is a useful indicator of marketplace turbulence, driven to a large extent by technology disruptions and business model changes.  Every year, companies drop off the list for a variety of reasons.  They may have run into hard times and their market cap size fell below a certain threshold, - currently around $6 billion; they may have been overtaken by faster growing firms; or they could have ceased to exist as an independent company due to a merger, acquisition or private equity deal.

According to the Innosight Forecast report, a few key megatrends are responsible for the market turbulence behind the changing makeup of the S&P 500.

Why Some AI Efforts Succeed While Many Fail

Winning with AI, - a 2019 report based on a survey jointly conducted by the MIT Sloan Management Review and the Boston Consulting Group, - found that 90% of respondents agree that AI represents a business opportunity for their company.  The global survey attracted over 2,500 respondents from 29 industries and 97 countries, and conducted interviews with 17 executives leaders of AI initiatives in large organizations.

The report classified the total survey population into four subgroups based on their understanding of AI tools and concepts and their levels of adoption of AI applications: Pioneers (20%) are leading-edge organizations that both understand and have widely adopted AI; Investigators (30%) understand AI but have not deployed applications beyond the pilot stage; Experimenters (18%) are learning by doing, conducting pilots without a deep understanding of AI; and Passives (32%) have not adopted AI and have little understanding of the technology.

“Many AI initiatives fail,” was the report’s overriding finding.  “Seven out of 10 companies surveyed report minimal or no impact from AI so far.  Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made significant investments in AI.  Even so, this means 40% of organizations making significant investments do not report business gains from AI.”

Innovation and National Security in the 21st Century

“Countries that can harness the current wave of innovation, mitigate its potential disruptions, and capitalize on its transformative power will gain economic and military advantages over potential rivals,” was the top finding of the Innovation and National Security Task Force which was commissioned by the Council of Foreign Relations (CFR) to assess the current state of US technological innovation.  The Task Force noted that leadership in innovation, research and technology since World War II has made the US the most secure and economically prosperous nation on earth.

“Today, this leadership position is at risk,” the Task Force warned.  Federal support and funding for R&D has stagnated over the past two decades.  “Washington has failed to maintain adequate levels of public support and funding for basic science.  Federal investment in R&D as a percentage of GDP peaked at 1.86 percent in 1964 but has declined from a little over 1 percent in 1990 to 0.66 percent in 2016.”

The US has successfully responded to technological competition in the past.  Sputnik was a tipping point in the space race with the Soviet Union during the cold war, leading to, among other things, a significant increase in the number of graduate student fellowships in STEM disciplines, of which I was personally a beneficiary.  Another prominent example is the strong economic competition from Japan in the 1980s, which the US fended off a decade later with our leadership in the Internet and other major digital innovations.

The Internet of Things is Changing the World

“How the world will change as computers spread into everyday objects,” is the title of the lead article in a comprehensive review of the Internet of Things (IoT) in a recent issue of The Economist.  Like many technological advances, IoT has been long in coming.  Ubiquitous or Pervasive Computing dates back to the 1990s, when neither the necessary low-cost devices and wireless networks were anywhere near ready.  “But the transformation is about to go into overdrive, notes The Economist.  “One forecast is that by 2035 the world will have a trillion connected computers, built into everything from food packaging to bridges and clothes.”

IoT promises to bring many benefits, including a whole new generation of innovative products.  We’ve long thought 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.  IoT has been ushering a whole new class of smart connected products.  In addition to mechanical and electrical components, these new products use digital components like microprocessors, sensors, data storage, software, and connectivity in a wide variety of ways.

As the world’s digital and physical infrastructures converge, digital technologies are being designed right into all kinds of consumer and industrial products.  Internet-connected smart doorbells, for example, include motion sensors and video cameras that notify a home owner when someone arrives at the door.  Using a smartphone app, the home owner can watch and talk to the visitor, while a video of their interaction is saved for an added level of security.

“The magic of computers is that they provide in a machine an ability - to calculate, to process information, to decide - that used to be the sole preserve of biological brains,” writes The Economist.  “The IoT foresees a world in which this magic becomes ubiquitous.  Countless tiny chips will be woven into buildings, cities, clothes and human bodies, all linked by the internet… Over the past century electricity has allowed consumers and businesses at least in the rich world, access to a fundamental, universally useful good - energy - when and where they needed it.  The IoT aims to do for information what electricity did for energy.”

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.

Can AI Help Develop and Execute a Competitive Business Strategy?

Strategy For and With AI, a recent article in the MIT Sloan Management Review by David Kiron and Michael Schrage, argues that while formulating a comprehensive strategy for the use of AI technologies is absolutely necessary, it’s not sufficient.  “Creating strategy with AI matters as much - or even more - in terms of exploring and exploiting strategic opportunity.”  The article is based on a survey of over 3,000 executives, managers and analysts from companies in over 100 countries and 20 industries, as well as on interviews with executives and academics.

AI is expected to be the biggest commercial opportunity for companies and industries over the next couple of decades.  Two 2018 reports, one by PwC and one by McKinsey, estimated that AI has the potential to incrementally add around $13-$15 trillion to global economic output by 2030.  The growth will come from productivity gains, - e,g, the continuing automation of routine tasks, and AI-based tools to augment human capabilities, - and from increasingly sophisticated, AI-enhanced products, services, and system-wide applications.

Machine learning advances, like deep learning, have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed.  Machine learning methods for solving problems, - subtle adjustments to the numerical weights that interconnect huge number of artificial neurons, - are radically different and complement the way humans solve problems.  But, while being a very powerful tool, machine learning, and AI in general, lack all-important human qualities like common sense and empathy, so their decisions and suggestions should be carefully reviewed by humans.  People and technology must closely collaborate, with each playing the particular role they are best at.

Every day we can read about the latest AI advances from research labs, startups and large companies.  AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, playing championship-level Go, and the early detection and diagnosis of various forms of cancer.  But, can AI help address broad, open-ended and ambiguous problems like developing and executing a competitive business strategy?

How to Survive and Thrive in a World of Disruption

Looking back upon my long career, it’s frankly sobering how many once powerful IT companies are no longer around or are shadows of their former selves, e,g, Digital, Wang, Sun Microsystems, BlackBerry.  The forces of disruption might have been more powerful in the fast-changing IT industry, but no industry has been immune.  In less than two decades, the global recorded music industry has lost over half its revenues, while the drop in newspaper advertising revenue in the US has been even steeper.  Retail has undergone major changes with the rise of e-commerce, as has telecommunications with the transition to mobile phones.

Why have so many companies been done in by disruptive technological and market changes?  Is it that in spite of all their efforts in strategy formulation, their management was unable to anticipate them and were caught by surprise?  Or it is that, as if actors in a kind of Greek tragedy, they see the changes coming and understand what needs to be done, but are somehow unable to make the needed strategic and cultural transformations?

“Evolutionary fitness of organizations is determined by their ability to effectively respond to environmental changes,” write Leo Tilman and Charles Jacoby in their recently published book Agility: How to Navigate the Unknown and Seize Opportunity in a World of Disruption.  “Extinctions generally follow a familiar pattern.  Environmental signals - both positive and negative - are undetected or ignored.  Leaders fail to recognize the new reality and accept it for what it is.  No viable strategic vision for the new environment is developed.  Instead, a myriad of tactical activities is deployed, without amounting to a coherent strategy.  Existential risks remain hidden until it’s too late.”

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.

Impact of AI on Government - Opportunities and Challenges

A few weeks ago I wrote about Leveraging AI to Transform Government based on a 2018 report by the Partnership for Public Service and the IBM Center for the Business of Government.  The report illustrates how government is using AI by describing four use cases being pursued by different agencies.  In 2019, the Partnership and IBM collaborated on two additional reports, Assessing the Impact of AI on the Work of Government, and Building Trust, Managing Risks.

This first report is focused on the impact of AI on the federal workforce, based on an analysis of the jobs and tasks most susceptible to automation, as well as on roundtables and interviews with over 40 AI experts.  The overriding conclusion is that, over time, AI will have a deep impact on how government works.  More than 80 different federal occupations, employing over 130,000 workers, are likely be significantly transformed, including 30,000 in the Treasury Department, 16,000 in Defense, and 5,000 in Transportation, as well as a variety of occupations in independent agencies like the Securities and Exchange Commission and the Government Publishing Office.

In particular, AI will have a major impact in three key areas: the transformation of the federal workday, the potential for personalized customer service and the increased importance of technical and data skills.  Let me summarize the findings and recommendations in each of these areas.

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.”

Leveraging AI to Transform Government

“AI has the potential to help address many of our country’s pervasive problems and advance our safety, health and well-being,” said The Future Has Begun, a report on the impact of AI on government by the Partnership for Public Service and the IBM Center for the Business of Government.  “That promise has great potential to transform government.”

 “The most challenging problems AI may help us solve - from fighting terrorists to serving vulnerable populations - will involve government.  More immediately, though not less consequentially, AI will change the way public servants do their jobs.  Because of the impact AI could have on how governments ensure our safety and further our well-being, government leaders are likely to play a significant role in dictating future directions for AI.”

To illustrate how government is using AI, the report is focused on four concrete use cases being pursued by different agencies.  In-depth interviews were conducted with 14 people from 10 organizations, including the various government agencies involved, as well as their collaborators in non profits and universities.  Let me briefly summarize each of these four case studies.

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.”

The Blockchain Value Framework

In July, 2019, the World Economic Forum and Accenture released Building Value with Blockchain Technology, a white paper aimed at helping organizations evaluate blockchain’s benefits and build an effective business case.  The white paper is based on a global survey of 550 individuals across 13 industries, including automotive, banking, and retail; interviews with CEOs and public sector leaders; and a detailed analysis of 79 blockchain projects.

Survey participants were asked for the top reasons that led their organizations to invest in blockchain solutions.  Across all industries, the top three priorities were “full traceability of information on the blockchain”; “ability to ensure data has not been tampered with”; and “increased security”.  “New business products or services” ranked last among the options for investing in blockchain, suggesting that at this early stage, improving existing products and services is a higher priority than considering new business opportunities.

As is often the case, fear that the train is leaving the station is another powerful motivator for investing in a new, fast growing technology.  Worldwide spending on blockchains solutions is expected to reach almost $3 billion in 2019 and close to $12.5 billion in 2022.  51% of survey respondents mentioned “missing out on developing new products/services” as their top concern if they don’t invest in blockchain in the near future; 23% mentioned “missing out on efficiency gains”; and 15% were concerned with “missing out on cost savings.”

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.

Despite Their Huge Upside Potential, Why Do Most Platforms Fail?

“For anyone who follows the world of business, it is now common knowledge that the most valuable firms on the planet and the first companies to surpass the trillion-dollar mark in value (albeit temporarily) are platforms,” note Michael Cusumano, Annabelle Gawer, and David Yoffie in their recently published book The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power.  “Moreover, in a recent list of more than two hundred current and former ‘unicorns’ - startups with valuations of $1 billion or more - we estimated that platforms made up between 60 and 70 percent.”

What do we mean by platform?  The book offer a simple definition.  “Platforms, in general, connect individuals and organizations for a common purpose or to share a common resource.”  A platform strategy differs from a product strategy in that it relies on an external ecosystem to generate complementary innovations and/or business transactions.  The effect is much greater potential for innovation and growth than a single product-oriented firm can generate alone.

Platforms are all about network effects.  The more products or services a platform offers, the more users it will attract, helping it then attract more offerings, which in turn brings in more users, which then makes the platform even more valuable.  Moreover, the larger the network, the more data is available to customize offerings to user preferences and better match supply and demand, further increasing the platform’s value.

Using Agile Processes to Develop AI-Based Solutions

In the waterfall development model, a project is broken down into a set of sequential phases, - e.g., conception, analysis, design, construction, testing, deployment, maintenance, - each dependent on the completion of the previous phase.  Such a model is most appropriate for complex engineering projects, like bridges, airplanes and computer hardware.  It’s also quite applicable for mission critical software and infrastructure projects where security, availability, scalability and other ilities are paramount.

But, a serial, orderly approach isn’t appropriate for projects where time-to-market, usability, and flexibility are the highest priorities.  For such projects, an agile development approach is much more suitable.  Key to the agile approach is the involvement of the product’s target end-users early in the process to make sure that the product under development is responsive to their requirements. 

Agile development is evolutionary in nature, based on the early availability of experimental prototypes, user feedback, continuous improvements, and rapid responses to technology, market and user requirements.  It’s not surprising that agile software development took off in the 1990s, when the explosive rise of the Internet brought millions and millions of users online, lots of new user-oriented applications, and short time-to-market cycles in response to fierce competition.

Our increasingly sophisticated smart machines bring with them a whole new set of development challenges.  These AI-based systems are generally software-intensive, user-centric, and data-driven.  Their complexity goes way up the smarter we want the systems to be.  The behavior of an AI system based on machine learning depends on the information we use to train its algorithms rather than on the precise set of software instructions that tell the computer what to do.

AI systems are designed to sense, respond and adapt to a changing environment.  Their development feels sort of like bringing up and sending a teen-ager into the world.  Given that we want them to be able to handle unanticipated situations, they must have a fair degree of flexibility and autonomy.  But, it’s hard to be sure that the system (or the teen-ager) will always behave the way we’d like them to.

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.

The Transformative Power of Blockchain

“Blockchain matters because no business operates in isolation,” says Blockchain for Business, a recently published book by Jai Arun, Jerry Cuomo, and Nitin Gaur.  “Multiple institutions can achieve more together than any single institution can alone.  By implementing business processes that leverage the collective knowledge of the group, processes can be orders of magnitude more cost-efficient.  New processes that were not possible before blockchain, can be created.  This opens new opportunities and creates competitive advantage for many businesses.”

Since if fist came to light over a decade ago as the public, distributed ledger for the Bitcoin cryptocurrency, people have struggled to understand what blockchain is about and what it’s good for.  This is not unusual for potentially transformative technologies in their early stages, as was the case with the Internet in the early-mid 1990s.  The key question is whether blockchain has the potential to become a truly transformative technology over time.  Most everyone agrees.  According to Gartner, the business value-add of blockchain will be more than $176 billion by 2025, and will exceed $3.1 trillion by 2030.

There are two fairly distinct blockchains camps, - one primarily focused on blockchain as the underlying platform for cryptocurrencies, the other on the use of blockchain in the business world.  The cryptocurrency camp, - best characterized by Bitcoin, - is mostly based on public permissionless blockchains, which operate anonymously and require some kind of proof-of-work or proof-of-stake systems.  The second camp, - best characterized by Hyperledger, - is mostly interested in business applications, and is based on the use of private or public permissioned blockchains networks to handle interactions between institutions that are known each other.

From “Buy What We Have” to “Connected Customer Strategies”

Adapting to the ongoing digitization of the economy is one of the key challenges faced by just about every company around the world.  While there are many ways a company can leverage digital technologies to differentiate itself from competitors, two are particularly prominent: streamline operations to develop higher quality products at competitive prices, and provide a superior, cost-effective customer experience through digital channels

The explosive growth of mobile devices means that a company can be engaged with its customers whether they are at home, at work, or shopping in a store.  But, digital customers can be fickle and hard to satisfy.  A successful business will try to make each of its clients feel special by understanding and addressing their unique requirements, and by providing excellent customer service and quickly resolving problems when they come up.  This is hard, which is also why it may very well be the most important way for a business to stand out from its competitors.

“Thanks to new technologies that enable frequent, low-friction, customized digital interactions, companies today are building much deeper ties with customers than ever before,” said a recent Harvard Business Review (HBR) article, The Age of Continuous Connection by Nicolaj Siggelkow and Christian Terwiesch, in its opening paragraph.  “Instead of waiting for customers to come to them, firms are addressing customers’ needs the moment they arise - and sometimes even earlier.  It’s a win-win: Through what we call connected strategies, customers get a dramatically improved experience, and companies boost operational efficiencies and lower costs.”

Most companies engage with their customer fleetingly, using what the authors call the buy what we have model.  In it, companies provide high-quality offerings at competitive prices, but it’s up to customers to identify their needs and seek out the appropriate products or services to meet them.  The key problem with the traditional buy what we have journey is that company and customer are poorly connected throughout.

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.

Is the Digital Revolution At Last Shaking Up Banking?

Digital transformations have generally followed three distinct stages.  First comes the use of IT to improve the productivity and quality of back- and front-end processes.  Distribution is next, leveraging the universal reach and connectivity of the Internet for online transactions and access to information. Finally, the transformation reaches a tipping point when technologies radically change the business model and user experience, leading to a fundamental disruption of the industry.

While such a digital journey is ultimately inevitable, the pace varies widely across industries.  The IT industry has been the most disrupted, - often by its own digital creations in a kind of sorcerer’s apprentice scenario.  Over my long career, I’ve seen many once powerful IT companies done in by technology and market changes, and either disappear altogether or become shadows of their former selves.

Beyond IT, few industries have felt the impact of digital forces like media.  In less than two decades, the global recorded music industry has lost over half its revenues, while the drop in newspaper advertising revenue in the US has been even steeper.  Retail has also undergone major changes with the rise of e-commerce, as has telecommunications with the transition to mobile phones.  How about banking, which has long been a leading user of information technologies?

The May 4 issue of The Economist addressed this question in a special report on Banking and Technology, with several articles on the subject.  “Over the past two decades people across the world have seen digital services transform the economy and their lives,” said its lead article.  “Taxis, films, novels, noodles, doctors and dog-walkers can all be summoned with a tap of a screen.  Giant firms in retailing, carmaking and the media have been humbled by new competitors.  Yet one industry has withstood the tumult: banking.”

But, adds The Economist, “technology is at last shaking up banking.”  Two major forces are driving this shake-up: the dramatic growth of digital payments in China and other Asian countries; and the rise of mobile banking in the West, especially among young digital natives.

The Economic Value of Digital Identity

Identity plays a major role in our everyday life.  It’s the key that determines the particular transactions in which we can rightfully participate as well as the information we’re entitled to access.  Think about going to an office, getting on a plane, logging on to a website or making an online purchase.  We generally don’t pay much attention to the management of our identity credentials unless something goes seriously wrong.

For much of history, our identity systems have been based on face-to-face interactions and on physical documents and processes.  But, the transition to a digital economy requires radically different identity systems.  In a world that’s increasingly governed by digital transactions and data, our existing methods for managing security and privacy are proving inadequate.  Data breaches, large-scale fraud, and identity theft have become more common.  In addition, a significant portion of the world’s population lacks the credentials needed to participate in the digital economy.  Our existing methods for managing digital identities are far from adequate.

A few weeks ago, the McKinsey Global Institute released Digital Identification: A key to inclusive growth, a comprehensive report at over 100 pages that examined the state of digital identities around the world.  The report analyzed the value creation potential of good digital IDs for individuals and institutions, as well as the potential risks and challenges.  McKinsey’s analysis was informed by nearly 100 concrete uses of digital IDs in seven countries: Brazil, China, Ethiopia, India, Nigeria, the UK and the US.

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%, while the earnings of those with a post-college education have risen 80% to 100%.  At the same time, 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.

Blockchain - the Networked Ecosystem is the Business

A few weeks ago I attended two back-to-back blockchain events in Toronto, - the Blockchain Research Institute All-Member Summit followed by the inaugural Blockchain Revolution Global conference.  Both events included a number of excellent talks and panels.  One of the presentations I particularly enjoyed was Scaling Blockchain for the Enterprise: Emerging Business Models, by IBM’s Andrew Martin and Smitha Soman.  Their presentation was based on their recently published report Building your blockchain advantage.

Since it first came to light a decade ago as the public, distributed ledger for the Bitcoin cryptocurrency, people have struggled to understand what blockchain is all about and what it’s truly good for.  This is not unusual for potentially transformative technologies in their early stages, as was the case with the Internet, and is still the case with AI.  The key question is whether blockchain has the potential to become a truly transformative technology over time.  With few exceptions, the answer is positive.

The essence of blockchain, notes the report, is that the unit of competition is the networked ecosystem, no longer a single enterprise.  “As blockchain adoption continues to gather momentum, organizations must approach their blockchain strategies with the same rigor and commitment as any other new and transformative strategies.  They can’t just fall back on prototypes alone.  They need to build a robust business case for blockchain that includes a fair incentive model to attract all the partners required for the success of their networks.”

A Novel Method for Measuring the Value of Free Digital Services

Gross domestic product (GDP) is the basic measure of a country’s overall economic output, based on the market value of all the goods and services the country produces.  Most measures of economic performance used by government officials to inform their policies and decisions are based on GDP figures.  But, concerns have long been raised about the adequacy of GDP-based measurements given the major changes that’ve taken place in economies around the world over the past several decades.

GDP is essentially a measure of production.  While suitable when economies were dominated by the production of physical goods, GDP does not adequately capture the growing share of services and increasingly complex solutions that characterize advanced economies.  Nor does it reflect important economic activity beyond production, such as income, consumption and living standards.

In 2008, a Commission on the Measurement of Economic Performance and Social Progress led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen was convened to look at the limits of GDP as an indicator of economic performance and progress.  “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted.” noted its report.  The Commission recommended complementing classical measures of GDP and economic production with additional measurements that captured people’s well being, as well as factoring in measurements to help reflect the evolution of the economy into the future.

Over the past decade, a new set of concerns have arisen with the rise of the Internet-based digital economy.  How do you measure the value of the increasing amounts of free information goods available over the Internet, including Wikipedia articles, Google maps, Facebook interactions, smartphone apps and You Tube videos?

Once Lumbering, Now Innovative Incumbents Hit Their Stride

Last year, IBM’s Institute for Business Value published Incumbents Strike Back, a global C-suite study based on survey data from over 12,800 executives from 112 countries,  including CEOs, CFOs, CMOs, COOs, CIOs, and CHROs.

“Two decades after the Internet became a platform for transformation, we’re still wondering how it all might turn out,” notes the report.  “The signals aren’t always clear.  Today, winner-take-all organizations are on the rise, but collaborative ecosystems are flourishing as well.  Even in industries where competitive concentration is increasing, innovation hasn’t – as would be expected – flatlined.”

Overall, respondents were evenly divided on whether the key to the future are established markets or new ones.  But they stand in close agreement on two areas:  68% of executives said that their organizations will emphasize customer experience over products, and 63% said that they will continue to expand their networks of business partners.

There was also general agreement on the top external forces that will have the greatest impact in the 2-3 year timeframe: 69% said market factors such as increased competition and changing customer preferences; 63% said technological factors; and 61% said people skills, reflecting the increased importance of talent and ideas.

AI and the Evolution of History

Several weeks ago, I wrote about the event I attended on February 28 to celebrate the launch of MIT’s new Schwarzman College of Computing, - MIT’s strategic response to the rise of artificial intelligence, - a technology that will reshape “geopolitics, our economy, our daily lives and the very definition of work” in the decades to come.  The all-day celebration featured talks and panels on a wide variety of topics, some focused on innovative applications of AI technologies, others on the challenging issues raised by these powerful technologies.

A few weeks later I wrote about one of these challenging issues, the impact of AI on our social interactions, based on the talk by MIT professor Sherry Turkle on Rethinking Friction in Digital Culture, and a related article by Yale professor Nicholas Christakis on How AI Will Rewire US.

I now want to discuss the interview conducted by NY Times columnist Thomas Friedman with former US Secretary of State Dr. Henry Kissinger at the February 28 event.  The interview, - which can be seen in this video, - was based on a June, 2018 article by Dr. Kissinger in The Atlantic, - How the Enlightenment Ends: “Philosophically, intellectually - in every way - human society is unprepared for the rise of artificial intelligence.”

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.

The Top Ten Emerging Technologies of 2018

For the past several years, the World Economic Forum (WEF) has published an annual list of the Top Ten Emerging Technologies that would be potentially disruptive over the next three to five years while also providing significant benefits to economies and societies.  Starting in 2017, the Top Ten list has been chosen in collaboration with Scientific American and published in an issue of the magazine.

The Top Ten Emerging Technologies 2018 was released in September, 2018.  The final list was selected by a steering group of experts from an initial list with more than 50 submissions.  Here are the 10 technologies comprising the 2018 list, along with the reasons cited in the report for their selection:

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.

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.

Fostering an Innovation Culture: Talent, Discipline and Leadership

Innovation has been a hot topic for the past few decades.  Companies all over the world have integrated innovation into their overall strategies and marketing campaigns.  Nations and regions have launched innovation initiatives in an attempt to attract such companies and their accompanying well-paying jobs.  But, as I have learned over my long career, managing innovation initiatives is actually quite hard, much harder than it may at first appear.

I was reminded of this point as I read The Hard Truth About Innovative Cultures, an article in a recent issue of the Harvard Business Review (HBR) by Harvard professor Gary Pisano.  “A culture conducive to innovation is not only good for a company’s bottom line,” writes Pisano.  “It also is something that both leaders and employees value in their organizations.”

Most everyone agrees that such a culture entails five key behaviors: tolerance for failure, willingness to experiment, psychological safety, collaborative, and non-hierarchical. “But despite the fact that innovative cultures are desirable and that most leaders claim to understand what they entail, they are hard to create and sustain.  This is puzzling.  How can practices apparently so universally loved - even fun - be so tricky to implement?”

The reason, answers Pisano, is that these much-liked behaviors are only one side of the coin, which must be counterbalanced with some tougher behaviors.  People love solving problems that seem impossible to everyone else and creating something qualitatively different from anything that came before.  But they will rarely perform at the necessary levels if they’re relaxed and happy.  True innovation is, in fact, not all that much fun.  In order to reach inside yourself and find that something extra needed for true innovation, you have to feel the stress that comes when you know that what you’re doing is absolutely crucial.  In the end, necessity is the mother of true innovation.

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.

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 Rise of Global, Superstar Firms, Sectors and Cities

In the 1990s, the Internet was supposed to usher a much more open, decentralized, democratic economy and society.  Startups with innovative business models where now able to reach customers anywhere, anytime.  Companies, from the largest to the smallest, could now transact with anyone around the world.  Vertically integrated firms became virtual enterprises, increasingly relying on supply chain partners for many of the functions once done in-house.  Experts noted that large firms were no longer necessary and would in fact be at a disadvantage in the emerging digital economy when competing against agile, innovative smaller companies.

Some even predicted that the Internet would lead to the decline of cities.  People could now work and shop from home; be in touch with their friends over e-mail, video calls, and text messaging; and get access to all kinds of information and entertainment online.  Why would anyone choose to live in a crowded, expensive, crime-prone urban area when they could lead a more relaxing, affordable life in an outer suburb or small town?

But, as we well know, it hasn’t quite worked out as expected.  Instead, we’ve seen the rise of the global superstar company, the unicorn startup, and the winner-take-all city.  As it’s turned out, the Internet’s universal reach and connectivity has led to increasingly powerful network effects and to the rise of platform economies.

Beyond Machine Learning: Capturing Cause-and-Effect Relationships

Artificial intelligence is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies.  AI technologies  are approaching or surpassing human levels of performance in vision, speech recognition, language translation, and other human domains. Machine learning advances, like deep learning, have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed.

Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks.  It’s essentially a method for machines to learn from all kinds of data, whether structured or unstructured, that’s loosely modeled on the way a biological brain learns new capabilities.  Each artificial neural unit is connected to many other such units, and the links can be statistically strengthened or decreased based on the data used to train the system.  Each successive layer in a multi-layer network uses the output from the previous layer as input.

Machine learning can be applied to just about any domain of knowledge given our ability to gather valuable data in almost any area of interest.  But, machine learning methods are significantly narrower and more specialized than humans.  There are many tasks for which they’re not effective given the current state-of-the-art.  In an article recently published in Science, professors Erik Brynjolfsson and Tom Mitchell identified the key criteria that help distinguish tasks that are particularly suitable for machine learning from those that are not.  These include:

• Tasks that map well-defined inputs to well-defined outputs, - e.g., labeling images of specific animals, the probability of cancer in medical record, the likelihood of defaulting on a loan application;
• Large data sets exist or can be created containing such input-output pairs, - the bigger the training data sets the more accurate the learning;
• The capability being learned should be relatively static, - If the function changes rapidly, retraining is typically required, including the acquisition of new training data; and
• No need for detailed explanation of how the decision was made, - the methods behind a machine  learning recommendation, - subtle adjustments to the numerical weights that interconnect its huge number of artificial neurons, - are difficult to explain because they’re so different from those used by humans.

Blockchain Beyond the Hype

A recent issue of The Economist included a special report on cryptocurrencies and blockchains.  The Economist’s overall conclusion was that “Bitcoin has been a failure as a means of payment, but thrilling for speculators.”  Its assessment of blockchain was somewhat more positive.  “For blockchains, the jury is still out,… For all the technology’s potential, though, most attempts to use it remain tentative,…  The advantages of blockchains are often oversold.” 

In 2016, blockchain made the list of the World Economic Forum’s Top Ten Emerging Technologies.  That same year, blockchain also made its first appearance in Gartner’s yearly hype cycles.  Even The Economist has been guilty of overselling when it featured blockchain in the cover of its October 31, 2015 issue with the tag line “The Trust Machine: How the technology behind Bitcoin could change the world.” 

Is there too much hype surrounding blockchain?  Absolutely,… but not surprising.  All potentially transformative technologies are oversold in their early stages.  Remember the dot-com bubble of the late 1990s.  Blockchain is still in its early phases of experimentation and adoption.  Much work remains to be done on standards, platforms, interoperability, applications and governance. 

But, does blockchain have the potential to become a truly transformative technology over time?  Yes, said McKinsey in a recent article on the strategic value of blockchain beyond the hype.  The article starts out by acknowledging that all the hype around blockchain makes it difficult to nail down not just blockchain's long term strategic value, but also what it actually is and what it isn’t in the present. To make sure everyone is on the same page, the article first recaps what it calls “the nuts and bolts of blockchain.”  Let me start by summarizing blockchain’s nuts and bolts.

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?