Irving Wladawsky-Berger

The Intriguing Nature of Non-Fungible Tokens

The blockchain first came to light in 2008 as the digital ledger for bitcoin transactions. The blockchain creator’s original vision was limited to enabling peer-to-peer bitcoin transactions with no need for a bank or government agency to certify the validity of the transactions. But, like the Internet, electricity and other transformative technologies, blockchain soon transcended its original objectives.

Over the years, blockchain technologies have evolved along two major lines. One continues to focus on blockchain as the underlying platform for bitcoin, as well as a wide variety of cryptoassets, such as digital tokens and cryptocurrencies. The other focuses on the use of blockchain in the business world, - a kind of Internet 2.0. The cryptocurrency camp is based mostly on public permissionless blockchains, which anyone can join and require some kind of proof-of-work or proof-of-stake systems. The business camp, - best characterized by Hyperledger, - is based on private or public permissioned blockchain networks to support transactions among institutions that need not know nor trust each other.

Given my professional history with the Internet and e-business, I’ve been almost exclusively focused on the business camp for two major reasons. First, blockchain technologies can help us enhance the security of Internet transactions and data, by developing a layer with the required standard services and their open source implementations for secure communication, storage and data access. And second, blockchain technologies can significantly improve the efficiency, resilience, and management of supply chains, financial services, and other complex global applications involving multiple institutions across multiple countries.

I have not paid much attention to bitcoin or other cryptoassets. But recently, I’ve become quite intrigued by non-fungible tokens (NFTs). To me, NFTs are further evidence that, as with the Internet of Things (IoT), we’re increasingly living in a hybrid physical-digital world. Let me explain.

The Increasing Economic Value of Digital Capital

A firm’s financial statement comprises two distinct types of assets. Tangible assets are mostly physical in nature, and include vehicles, land, plants, equipment, and furniture, as well as financial assets like stocks, bonds, account receivables, and cash which have a concrete contractual value. On the other hand, intangible assets aren’t physical. They include patents, copyrights, trademarks, goodwill, brand value, human capital, R&D, software, and data. Intangibles do have a monetary value since they represent future potential revenue, but they’re more difficult to value because, unlike tangibles, their costs and market values are hard to determine.

The economic value of intangible assets has truly exploded over the past several decades. According to a 2019 research report , the overall value of the S&P 500 in 1975 was $715 billion, of which 17% was intangible. By 1995, intangibles had risen to 68% of a $4.6 trillion S&P value. The value of intangibles continued to climb in our 21st century digital economy, reaching 84% of the $25 trillion overall S&P value in 2018. The reason for this fast rising valuations is that intangible capabilities are a major source of a company’s long-term competitive strengths, and are generally quite complex to build and replicate. Yet, conventional accounting treats these capabilities not as company investments but as expenses, which means that their funding isn’t reflected as capital on balance sheets.

“For digitally-focused firms, investments in the intangible assets needed to realize value from new technologies – like cumulative investment in skills training, new decision-making structures within the firm, management practices, and software customization – often account for significantly greater total costs than the technologies themselves,” notes Digital Capital and Superstar Firms, an NBER working paper published in December, 2020.

The paper is focused on the digital capital that’s required to create value out of a company’s investments in hardware, software, and other IT assets that are recorded as tangible capital investments on a company’s financial statement. It introduces a set of methods for quantifying the value of digital capital investments, as well as their impact on a company’s long term productivity and growth.

The Current State of Cloud Computing

In June, 2008 I gave a talk at a conference on The Promise and Reality of Cloud Computing. In his closing remarks, the conference organizer noted that most everyone had agreed that something big and profound was going on, but they weren’t quite sure what it was they were excited about. “There is a clear consensus that there is no real consensus on what cloud computing is,” he said. A few months later, The Economist published a special report on cloud computing with several articles on the subject. In the lead article, technology editor Ludwig Siegele started out his definition of cloud computing by first giving a very succinct history of computing:

“In the beginning computers were human. Then they took the shape of metal boxes, filling entire rooms before becoming ever smaller and more widespread. Now they are evaporating altogether and becoming accessible from anywhere. Computing has constantly changed shape and location - mainly as a result of new technology, but often also because of shifts in demand. Now, … it is turning into what has come to be called a ‘cloud’, or collections of clouds. Computing power will become more and more disembodied and will be consumed where and when it is needed.”

A major reasons for both the excitement and lack of consensus about cloud is that we were basically seeing the emergence of a new model of computing in the IT world. In the sixty years or so since there’s been an IT industry, cloud is only the third such model. First came centralized computing in the early ’60s and ’70s, followed by the distributed client-server model in the 1980s. It’s been difficult to come up with a simple definition of cloud computing, because there’s no single dimension around which to define a computing model. It’s like the fable of the blind men and the elephant. Each one touches a different part of the elephant. They then compare notes on what they felt, and learn that they’re in complete disagreement.

The Increasing Influence of Recommendation Systems in Our Everyday Lives

A few years ago, 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. Computers, for example, are powerful calculators whose cost of arithmetic and other digital operations have dramatically decreased over the past several decades. As a result, we’ve learned to define all kinds of tasks in terms of digital operations, e.g., financial transactions, inventory management, word processing, photography. Similarly, the Internet and World Wide Web have drastically reduced the cost of communications and of access to all kinds of information, - including numbers, text, pictures, music and videos.

Viewed through this lens, the data and AI revolution can be viewed as reducing the cost of predictions. Predictions mean anticipating what is likely to happen in the future. Over the past decade, increasingly powerful computers, advanced machine learning algorithms, and the explosive growth of big data have enabled us to extract insights from the data and turn them into valuable predictions. As was previously the case with digital operations, communications and access to information, - we’re now able to reframe all kinds of applications as prediction problems. A major such family of applications are recommendation engines or recommender systems, which Wikipedia defines as “a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ a user would give to an item.”

“The essential function of recommender systems is mathematically predicting personal preference,” writes MIT visiting scholar Michael Schrage in his recently published book Recommendation Engines. But, “recommendation engines are far more intriguing and important than their definitions might suggest,” as they not only predict but also shape their users’ preferences. Throughout the book, Schrage explores the human yearning to get good, practical and actionable advice, -  whether from the gods, astrology or self-help books, - and the evolution of recommendations into our ubiquitous algorithm-based technologies to the point where they’ve almost become an extension of our brains. “More people around the world are becoming more reliant - even dependent - upon recommendation engines to better advise, inform, and inspire them.”

“Recommenders increasingly influence how individuals spend their time, money, and energy to get more from life. That explains why global organizations ranging from Alibaba to Netflix to Spotify to Amazon to Google invest so heavily in them.” These powerful platforms are driven by their huge economies of scale, - which are largely based on their highly personalized recommendations. The larger the network, the more data is available for recommending highly personalized offerings. That’s what network effects are all about: 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, generates more data, and makes the platform even more valuable

How Can Blockchain Become a Truly Transformative Technology?

I first became interested in blockchain technologies when in 2016 the World Economic Forum (WEF) named The Blockchain in its annual list of Top Ten Emerging Technologies citing its potential to fundamentally change the way markets and governments work. The WEF noted that “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built. And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

The blockchain first came to light in 2008 as the architecture underpinning bitcoin, the best known and most widely held digital currency. The blockchain’s original vision was limited to enabling bitcoin users to transact directly with each other with no need for a bank or government agency to certify the validity of the transactions. But, like the Internet, electricity and other transformative technologies, blockchain has transcended its original objectives. Over the years, blockchains, - and the more encompassing distributed ledger technologies (DLT), - have developed a following of their own as distributed data base architectures with the ability to handle trust-less transactions where no parties need to know nor trust each other for transactions to complete.

Could blockchain/DLT become truly transformative technologies? And if so, what will it take?

New Directions for Government in the Digital Age

Over the past few decades, economies and societies around the world have been going through historical transformations as they transition from the industrial age of the past two centuries to a 21st century digital age. Companies - especially large, global corporations - have leveraged digital innovations to improve their productivity and competitiveness and adapt better to these powerful technology and market changes.

In 2010, the Technology CEO Council released a report recommending that governments embrace the information technologies (IT) and organizational best practices that the private sector was successfully applying: “By harnessing major technological shifts and adopting best business practices, we can not only make our government far more productive, but also foster greater innovation in areas ranging from healthcare to education and energy - innovation that will generate economic growth and job creation.”

But governments have continued to lag the private sector, and risk falling further behind as the pandemic accelerated the digital transformations that are helping companies cope with the crisis. “We’ve seen two years’  worth of digital transformation in two months,” as companies adapt to stay open for business “in a world of remote everything,” said Microsoft CEO Satya Nadella in April 2020. A month later, McKinsey & Company added, “We have vaulted five years forward in consumer and business digital adoption in a matter of around eight weeks.”

The authors of a recently published report, “New Directions for Government in the Second Era of the Digital Age,” argue that governments should exploit this fast-moving era of technology in their own operations, for the overall benefit of their nations. The 120-page report was authored by Don Tapscott, Kirsten Sandberg, and Anthony Williams from the Blockchain Research Institute (BRI), in collaboration with the Chamber of Digital Commerce and experts from several countries. The BRI is a global think tank dedicated to the strategic implications of blockchain and related technologies to business, government and society. I am a research contributor at the BRI.

The report aims to assist government leaders around the world, but is particularly focused on the opportunities and challenges facing the new US administration.

AI Is Mostly About Business Value, Not Technology

A few months ago, Babson College professor Tom Davenport convened a virtual meeting of chief data and analytic officers (CDAO) from a variety of industries to discuss how to best achieve a return on investments (ROI) in AI. “We wanted to learn to what degree these senior leaders shared our perspective that AI faces an important economic return issue, and what, if anything, their companies were doing to address it.”  Many participants said that a decent ROI remains a critical issue for AI projects.

Davenport has long been analyzing how companies should build up their AI capabilities to achieve their business objectives.  For example, in a 2018 Harvard Business Review article he co-authored, Davenport advised companies to build their AI capabilities through the lens of business opportunities, rather than technology.  As has been generally the case with new technologies, highly ambitious, multi-year moon shot projects are less likely to be successful than low-hanging-fruit projects.  Business process automation is one of the least expensive and easiest capability to implement, since companies have long been engaged with enhancing and automating their business processes.

The following year, Davenport talked about the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy  His talk was based on recent surveys by Deloitte of executives in US-based companies who were involved in AI projects.  The surveys found that 20-30% of enterprises were early adopters, having implemented at least one AI prototype or production application; most of the projects were in pilots with relatively few already in production; simple projects prevailed over more ambitious and complex ones; and implementation, integration, data issues and talent topped the list of challenges faced by these early adopters.

Ten IT Predictions for the Post-Pandemic New Normal

“Spurred on by the world reaction to the disruptions caused by the global pandemic in 2020, the global economy remains on its way to its digital destiny, as most products and services are based on a digital delivery model or require digital augmentation to remain competitive,” said a recently published IDC study on the key trends that will shape the IT industry over the next five years.

IDC’s overriding conclusion is that the Covid-19 pandemic will significantly accelerate the digitalization of the global economy  “The majority, 65%, of global GDP will be digitalized by 2022, driving $6.8 trillion of IT spending from 2020 to 2023.”

The study highlights ten predictions that will drive the IT industry in the post-pandemic new normal, including the challenges faced by CIOs and IT professional as they manage the implications to their institutions over the next five years.  Let me summarize IDC’s top ten predictions.

Is Work-from-Anywhere Here to Stay?

Remote work has been around for a few decades, but took off in the mid-late 1990s with the explosive growth of the Internet.  There were predictions that the Internet would lead to the decline of cities, because technology was making location less relevant to our work and personal lives.  Why would anyone choose to live in an expensive, stressful metropolitan area and endure a long daily commute?  However, instead of declining, superstar cities continued to attract talented, ambitious knowledge workers, and to generate the greatest levels of economy activity and innovation.

But, even before the pandemic, “a movement was brewing within knowledge-work organizations,” wrote Harvard professor Prithwiray (Raj) Choudhury in a recent Harvard Business Review article, Our Work-from-Anywhere Future.  “Personal technology and digital connectivity had advanced so far and so fast that people had begun to ask, ‘Do we really need to be together, in an office, to do our work?’”

“We got our answer during the pandemic lockdowns.  We learned that a great many of us don’t in fact need to be colocated with colleagues on-site to do our jobs.  Individuals, teams, entire workforces, can perform well while being entirely distributed - and they have.  So now we face new questions: Are all-remote or majority-remote organizations the future of knowledge work?  Is work from anywhere (WFA) here to stay?”

The Surprising Power of Business Experimentation

We’ve long associated innovation breakthroughs with science and technology coming out of R&D labs, e.g., the transistor, penicillin, DNA sequencing, TCP/IP protocols, and so on.   Such major lab-based breakthroughs are at one end of the innovation spectrum.  At the other end are market-facing innovations, whose purpose is to create appealing and intuitive user experiences, new business models, and compelling market-based strategies.

Lab-based innovations were generally born when scientists, mathematicians or engineers developed new theories, technologies, algorithms or programs in an R&D lab.  Over time, often years, the innovations found their way to the marketplace.  Since technology and markets advanced at a relatively slow pace, there was little pressure to reduce the transition times from lab to market.  This was the prevailing innovation model through most of the 20th century.

It all started to change in the 1980s as the rate and pace of technology advances significantly accelerated.  The hand-offs and elapsed times to take an innovation from lab to market were no longer competitive, especially with products based on fast changing digital technologies.  Start-up companies significantly shortened the time-to-market for new products and services, putting huge pressure on companies still operating under the old rules.

These competitive pressures, were further exacerbated by the explosive growth of the Internet in the 1990s, as I personally learned when becoming general manager of the newly established IBM Internet Division in December of 1995.  A lot was starting to happen around the Internet, but it was not clear where things were heading, and in particular what the implications would be to the world of business.  With the Internet, there was no one technology or product you could work on in the labs that would make you a success in the marketplace.  This time around, the strategy itself had to come from the marketplace, not the labs.

Embracing the Internet turned out to be much more than a technology change.  It had a very big impact on the overall culture of IBM and many other companies, paving the way for a more outside-in approach to innovation based on continuous marketplace experimentation. 

In his 2020 book Experimentation Works: The Surprising Power of Business Experiments, Harvard professor Stefan Thomke explains the major changes that’ve been taking place in innovation and experimentation over the past 20 - 25 years.

The Intangible Asset Revolution

“Take all the physical assets owned by all the companies in the S&P 500, all the cars and office buildings and factories and merchandise, then sell them all at cost in one giant sale, and they would generate a net sum that doesn’t even come out to 20% of the index’s $28 trillion value,” said Epic S&P 500 Rally Is Powered by Assets You Can’t See or Touch, an article in Bloomberg published on October 21, 2020, -  a day when intangible assets made up more than 84% of the S&P value.

“The rise of intangibles helps explain why many American workers have recently had it so rough, with wages stagnating and benefits disappearing,” adds the article.  And, given our increasingly digital post-pandemic new normal, we can expect the share of intangibles to go higher, “a source of deep concern for those who worry about things like employment and inequality.”

Intangible assets are easier to define as the opposite of tangible assets.  Tangible assets are generally physical in nature, including vehicles, land, plants, equipment, and furniture, but they also include financial assets like stocks, bonds, account receivables, and cash which have a concrete contractual value.   On the other hand, intangible assets are neither physical nor have a concretely specified financial value.  Intangibles include patents, copyrights, trademarks, goodwill, brand value, human capital, R&D, software, and data.  Despite having no physical existence, intangibles have a monetary value since they represent potential revenue, but that value must be established based on accounting principles.  And, unlike tangible assets, intangibles are difficult to value and insure.

According to a 2019 research report by the Ponemon Institute, the value of intangible assets has truly exploded over the past several decades, right along with our increasingly digital economy.  In 1975, the overall value of the S&P 500 was $715 billion, of which 17% was intangible.  In 1985, out of a total value of $1.5 trillion, 32%, or around one third was intangible.  By 1995, the percentages had switched, with intangibles now being 68% of $4.6 trillion.  Intangible values continued to climb to 80% out of $11.6 trillion in 2005; and to 84% of $25 trillion in 2018.

How Covid Is Reordering the Global Economy

The October 10 issue of The Economist includes a special report on the impact of covid-19 on the global economy, with eight article on the subject.  “The pandemic has caused the world’s economies to diverge,” notes the issue’s overview article.  “But its long-term impact will be even more far-reaching… huge gaps between the performance of countries are opening up - which could yet recast the world’s economic order.”

According to the OECD, whereas China’s economy is expected to be 10% larger by the end of 2021 than it was in pre-pandemic 2019, the US economy will be essentially the same size, while the economies of the Euro area, the UK, Japan and South Korea will see a decline by the end of 2021 compared to their pre-pandemic levels.

“The covid-19 pandemic will accelerate change in the world economy,” says The Economist.  It’s becoming increasingly clear that “the pandemic will lead to immense structural changes in the global economy: less globalized, more digitized, and less equal.”  Supply chains will bring production closer to home to cut risks.  Office workers will continue to work from their homes for at least part of the week.  Lower paid urban service workers will face unemployment and need to find new jobs.  The gulf between Wall Street and Main Street will widen.  “The challenge for democratic governments will be to adapt to all these changes while maintaining popular consent for their policies and for free markets.”

Let me discuss the three key areas where The Economist expects long-lasting changes: globalization, digitization and inequality.

What Is Objective Reality?

I learned about The Constitution of Knowledge, - a 2018 National Affairs essay by author and journalist Jonathan Rauch, - from a recent OpEd by NY Times columnist David Brooks.  In his “remarkable essay,” wrote Brooks, “Rauch pointed out that every society has an epistemic regime, a marketplace of ideas where people collectively hammer out what’s real.  In democratic, nontheocratic societies, this regime is a decentralized ecosystem of academics, clergy members, teachers, journalists and others who disagree about a lot but agree on a shared system of rules for weighing evidence and building knowledge.”

Rauch’s essay asks a very important and timely question: what is objective reality?  “In everyday vernacular, reality often refers to the world out there: things as they really are, independent of human perception and error,” he wrote.  “Reality also often describes those things that we feel certain about, things that we believe no amount of wishful thinking could change.  But, of course, humans have no direct access to an objective world independent of our minds and senses, and subjective certainty is in no way a guarantee of truth.”

The distinction between subjective opinion and objective facts is nicely captured in this widely used quote: People Are Entitled To Their Own Opinions But Not To Their Own Facts.  It’s the essence of any discussion about objective reality.  However, the distinction between opinion and facts has long been rather porous in totalitarian regimes, as well as in highly polarized periods, - like our own.

On top of which, we now have the impact of social media.  “Over the last decade, we’ve doused our kindling fire of human interaction with high-­octane gasoline,” wrote MIT professor Sinan Aral in his recently published book The Hype Machine.  “We’ve constructed an expansive, multifaceted machine that spans the globe and conducts the flow of information, opinions, and behaviors through society.”  We now have a global social media ecosystems that’s been designed to stimulate and manipulate the human psyche, and to persuade us that there’s no such thing as objective facts and objective reality.

Why Business Transformation Efforts Often Fail

A 2019 study found that the average digitization level across all industry sectors was only around 25% of their ultimate potential.  But, the pandemic has now made the case for accelerating the digital transformations firms and economies were forced to make to help them cope with the crisis.  But, how successful are such major transformation likely to be?

A few months ago I received an email from the Harvard Business Review recommending a classic article from its 50 Best Selling collection that could shed light on this important and timely question, - Leading Change: Why Transformation Efforts Fail, originally published in 1995 by (now emeritus) Harvard professor John Kotter.

“Over the past decade, I have watched more than 100 companies try to remake themselves into significantly better competitors,” wrote Kotter.  These included large and small organizations, US and non-US based, some that were on their knees and some that were doing quite well.  “But, in almost every case, the basic goal has been the same: to make fundamental changes in how business is conducted in order to help cope with a new, more challenging market environment.”

The 1985-1995 decade referenced by Kotter was also marked by major technology changes, - e.g., the explosive growth of PCs, the client-server model of computing, the advent of the Internet and World Wide Web; as well as major economic changes including the rise of global supply chains, outsourcing and offshoring.

How to Emerge Stronger in the Post-Pandemic New Normal

“What now?,” asked a recent McKinsey article on the implications of Covid for business.  “Over the past six months, they have reorganized supply chains, set up remote operations, and made tough financial decisions.  But without a COVID-19 vaccine yet available, not much feels different, and the summer pause hasn’t done much to relieve fatigue.”

“One priority, then, is to reenergize the organization - to act rather than react.  Even as the COVID-19 crisis continues to create a world of uncertainty, the goal must be to rebuild for the longer term.  Companies that are strong and resilient will be better placed to survive and prosper.  Those are qualities that can’t be taken for granted; they need to be cultivated.” 

But, how can a company, - regardless of size or industry, - formulate a rebuilding strategy for the longer term in such an uncertain environment?  As former president Dwight Eisenhower famously said when he was the overall Commander of Allied Forces in WWII: “In preparing for battle I have always found that plans are useless but planning is indispensable.”  In other words, rather than doing so on-the-fly, planning during highly uncertain times requires that companies thoroughly map out their rebuilding strategy, and quickly and flexibly react to the continuing changing circumstances.

Are There Limits to the Predictability of Elections?

The elegant mathematical models of classical mechanics depict a world in which objects exhibit deterministic behaviors. These models make perfect predictions within the accuracy of their human-scale measurements.

But, once you start dealing with atoms, molecules and exotic subatomic particles, you find yourself in a very different  world, one with somewhat counter-intuitive behaviors governed by the laws of quantum mechanics.  The orderly, predictable models of classical physics have now given way to wave functions, uncertainty principles, quantum tunneling and wave-particle dualities.

But, the world of the very small is not the only one with non-deterministic behaviors.  So are highly complex systems, especially those systems whose components and interrelationships are themselves quite complex.  This is the case with social systems, which are based on individuals, groups, and institutions.  It’s quite a challenge to make accurate predictions in such systems due to the the dynamic nature of human behaviors.  Terms, like emergence, long tails, and butterfly effects - every bit as fanciful as quarks, charm and strangeness, - are part of the social systems lexicon.

Which brings us to the 2020 US election. “The polls were wrong again, and much of America wants to know why,” wrote NY Times journalist David Leonhardt in a recent article. “This is a disaster for the polling industry and for media outlets and analysts that package and interpret the polls for public consumption, such as FiveThirtyEight, The New York Times’ Upshot, and The Economist’s election unit,” said David Graham  in The Atlantic.

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.

The Public Face of Science - Implications of the Covid Crisis

The Public Face of Science was launched four years ago by the American Academy of Arts and Sciences to better understand the complex relationship between scientists and the American public.  The initiative has now published three reports.

The first, Perceptions of Science in America, was published in 2018.  It found that a majority of Americans have expressed a great deal of confidence in the scientific community, a confidence which has remained stable over the past thirty years.  But, it also found that confidence in science varied based on demographics, including age, race, educational attainment, regional location, political affiliation and other characteristics.  The report recommended additional research to better understand why certain topics were particularly controversial, especially climate change, vaccine safety, and genetically modified foods.

It was followed by Encountering Science in America, which was published in 2019.  This second report explored the diverse and expanding range of opportunities for people to learn about science outside the classroom, including visits to science centers and museums, general news sources, online information, social media, and entertainment.  It concluded that such a complex landscape calls for a multifaceted approach to the public face of science, and recommended additional research to better understand how effective communications and engagements shape the public’s interest in their understanding and support of science.

Priorities for the Future, the third and final report, was published in August of 2020.  While the report doesn’t directly address the implications of Covid-19 to the Public Face of Science initiative, it does so in an accompanying document which highlights the critical role played by science in ensuring the well-being of individuals and society during the pandemic.  “[T]he experience with COVID-19 reinforces the need for continuing thoughtful work to address public access to reliable scientific content and to enhance the public’s capacity to identify and reject misinformation and disinformation (intentionally false information).”

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.

After Years of Promise and Hype, Is AI Once More Failing to Deliver?

The June 13 issue of The Economist included an in-depth look at the limits of AI, with seven articles on the subject.  “There is no question that AI - or, to be precise, machine learning, one of its sub-fields - has made much progress,” notes The Economist in the issue’s overview article.  “Computers have become dramatically better at many things they previously struggled with…  Yet lately doubts have been creeping in about whether today’s AI technology is really as world-changing as it seems.  It is running up against limits of one kind or another, and has failed to deliver on some of its proponents’ more grandiose promises.”

Transformative technologies, - remember the dot-com bubble, - are prone to hype cycles, when all the excitement and publicity accompanying their early achievements often lead to inflated expectations, followed by disillusionment if the technology fails to deliver.  But AI is in a class by itself, as the notion of machines achieving or surpassing human levels of intelligence has led to feelings of both wonder and fear over the past several decades.

The article reminds us that AI has gone through two such major hype cycles since the field began in the mid-1950s.  Early achievements, - like beating humans at checkers and proving logic theorems, - led researchers to conclude that machines would achieve human-level intelligence within a couple of decades.  This early optimism collapsed leading to the first so-called AI winter from 1974-1980.  The field was revived in the 1980s with the advent of commercial expert systems and Japan’s Fifth Generation project, but it didn’t last long, leading to the second AI winter from 1987-1993.

May the Network Force Be With You

May the Force Be With You has become part of popular culture since the words were first uttered in the 1977 Star Wars film.  As we know, The Force is a source of power for those who, like the Jedi, feel its flow.  “The network force is similar,” wrote Silicon Valley network expert and entrepreneur James Currier in Your Life is Driven by Network Effects.  “You don’t always see it, but it is exerting itself on you.  It wants something from you. Your network force proactively guides you down a path.”

“When you start to see that dialogue between you and your network, the push-pull, you see it everywhere.  The chaos of the world diminishes a bit and becomes more understandable and predictable. And you understand more why things are the way they are and why they stay that way.  Hopefully, it will also give you insights as to where you can push to change things that should be changed, not just about you, but about your company, your city, and your world.”

Over the past few decades, network forces have been influencing many aspects of business, the economy, and our social interactions.  The Internet’s universal reach and connectivity have led to increasingly powerful network effects, such as, for example, platform economies.

The Real Business of Blockchain

“Companies in industries as diverse as finance, sports, health care, retail, oil and gas, and pharma are engaging in a wave of blockchain experiments,” write Gartner analysts David Furlonger and Christophe Uzureau in their recently published book The Real Business of Blockchain.  “Many see it as the solution for bringing trust and transparency to digital environments.” Blockchain has indeed been capturing the imagination of business.  According to a Gartner Research Forecast, the business value-add of blockchain is expected to exceed $3 trillion by 2030, over half that amount by 2025.  But, in plain English, what does this all mean?

“It means you can theoretically do business with an unknown partner located anywhere on the planet and trade any asset at any transaction size and not need a lawyer, a bank, an insurance company, or any other intermediary making sure both of you follow through on what you’ve promised to do,” write the book’s authors.  “Such a solution vastly expands the range of assets that a business could trade.  The arrangement also greatly increases who or what a business could directly trade with, without needing a third party (which would take a piece of the value).”

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.”

The AI Factory: A New Kind of Digital Operating Model

“Whether you’re leading a digital start-up or working to revamp a traditional enterprise, it’s essential to understand the revolutionary impact AI has on operations, strategy, and competition,” wrote Harvard professors Marco Iansiti and Karim Lakhani in “Competing in the Age of AI”, a recently published article in the Harvard Business Review (HBR).  Earlier this year, they also published a book of the same title, which expands on the ideas in the article and illustrates them with a number of concrete use cases.

The age of AI is being ushered by the emergence of a new kind of digital firm.  Rather than just relying on traditional business processes operated by its workers, these firms are leveraging software and data-driven algorithms to eliminate traditional constraints and transform the rules of competition.  Managers and engineers are responsible for the design of the new AI-based operational systems, but the system then runs the operations pretty much on its own.

“At the core of the new firm is a decision factory - what we call the AI factory,” note the authors.  “[T]he AI factory treats decision-making as a science.  Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows…  As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur.”

The Industrial Revolution transformed the economy by developing a scalable, repeatable approach to manufacturing.  The AI factory is now driving another fundamental transformation by industrializing the data gathering, decision making, and overall digital operations of 21st century firms.

Why 70 Percent of Digital Transformations Fail

“Companies either transform or die in industrial revolutions,” writes former P&G executive Tony Saldanha in his recently published book Why Digital Transformations Fail.  “Digital transformation is our current generation’s attempt to transform in the face of the Fourth Industrial Revolution.  However, the sad truth is that 70 percent of all digital transformations still fail today… a shocking number, given the extremely high stakes,” he adds.

What accounts for such a high failure rate?  An inadequate strategy; competitive pressures; technology; talent; culture? While all these factors are important, “The surprising answer to why digital transformations fail is a lack of discipline in defining and executing the right steps for digital transformations to take off and stay ahead,” notes Saldanha, adding that “It is possible to apply the proven checklist methodology from the airline and medical fields to improve the 70 percent failure rate.”

In a 2015 article, Klaus Schwab, - founder and executive chairman of the World Economic Forum, - positioned the Fourth Industrial Revolution within the historical context of three previous industrial revolutions.  The First, - starting in the late 18th century, - was driven by new innovations like the steam engine and marked the evolution from an agrarian to an industrial economy.  The Second, - a century later, - was driven by steel, electricity, the internal combustion engine and mass production techniques.  The Third, - starting in the 1960s, - saw the advent of digital computers, IT, the Internet, and the automation of process in just about all industries.  The Fourth, - driven by increasingly pervasive and inexpensive digital technologies, - is now blurring the lines between the digital, physical, and biological worlds, leading to widespread economic and social disruption.

Saldanha defines digital transformation as “The migration of enterprises and societies from the Third to the Fourth Industrial Revolution era.  For companies, this means having digital technology become the backbone of new products and services, new ways of operation, and new business models.”

“Companies either transform or die in industrial revolutions,” writes former P&G executive Tony Saldanha in his recently published book Why Digital Transformations Fail.  “Digital transformation is our current generation’s attempt to transform in the face of the Fourth Industrial Revolution.  However, the sad truth is that 70 percent of all digital transformations still fail today… a shocking number, given the extremely high stakes,” he adds.

What accounts for such a high failure rate?  An inadequate strategy; competitive pressures; technology; talent; culture? While all these factors are important, “The surprising answer to why digital transformations fail is a lack of discipline in defining and executing the right steps for digital transformations to take off and stay ahead,” notes Saldanha, adding that “It is possible to apply the proven checklist methodology from the airline and medical fields to improve the 70 percent failure rate.”

In a 2015 article, Klaus Schwab, - founder and executive chairman of the World Economic Forum, - positioned the Fourth Industrial Revolution within the historical context of three previous industrial revolutions.  The First, - starting in the late 18th century, - was driven by new innovations like the steam engine and marked the evolution from an agrarian to an industrial economy.  The Second, - a century later, - was driven by steel, electricity, the internal combustion engine and mass production techniques.  The Third, - starting in the 1960s, - saw the advent of digital computers, IT, the Internet, and the automation of process in just about all industries.  The Fourth, - driven by increasingly pervasive and inexpensive digital technologies, - is now blurring the lines between the digital, physical, and biological worlds, leading to widespread economic and social disruption.

Saldanha defines digital transformation as “The migration of enterprises and societies from the Third to the Fourth Industrial Revolution era.  For companies, this means having digital technology become the backbone of new products and services, new ways of operation, and new business models.”

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 Transformational Impact of Blockchain-based Supply Chains: Nine Real-World Use Cases

In May 2016, Don Tapscott published Blockchain Revolution, his 16th book, this one co-written with son Alex.  Don, whom I’ve known for over 25 years, has an uncanny knack for identifying The Next Big Thing and explaining it to everybody else through his books, articles, and speeches.  Don and Alex have each developed a follow-on blockchain book.  Earlier this year, Alex published Financial Services Revolution, while a few weeks ago Don published Supply Chain Revolution, which I will now discuss.

Supply Chain Revolution consists of nine chapters, edited with a foreword by Don.  He opens his foreword by noting, “When we first started working on this book, the coronavirus (COVID-19) hadn’t yet made headlines…  As I’ve become fond of saying, ‘If you’ve got it, a supply chain brought it to you.’  I suppose that, during these times, I could add, ‘If you can’t get it, a supply chain let you down.’”

Each chapter details how blockchain is revolutionizing a different supply chain, from general use cases like global trade to more specific ones like food traceability and manufacturing.  Each was written by some of the world’s top experts on these blockchain applications.

For a few years now, I have argued that supply chain applications will likely emerge as the earliest blockchain killer apps.  Given that blockchain first came to light in 2008 as the architecture underpinning bitcoin and other cryptocurrencies, many thought that its killer-apps would be in the financial services industry.  That might well be the case in the future, but given its underlying complexity, transforming the global financial ecosystem will take considerable time and investment.  Supply chain infrastructures and processes are significantly less complex than those in financial services, healthcare, and other industries, which is why several supply chain use cases have already been brought to market over the past few years.

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?

Could AI Reshape the Global Balance of Power?

The National Security Commission on Artificial Intelligence (NSCAI) was established by the US Congress in Fiscal Year 2019 to “advance the development of artificial intelligence, machine learning, and associated technologies by the United States to comprehensively address the national security and defense needs of the United States.”  A few months ago, the 15 member Commission, - led by former Google CEO Eric Schmidt  and former deputy secretary of defense Robert Work, - released its interim report. The final report is due to be released in March, 2021.  It remains to be seen how the Covid-19 pandemic will impact the final report’s recommendations and release date.

“The convergence of the artificial intelligence revolution and the reemergence of great power competition… threaten the United States’ role as the world’s engine of innovation and American military superiority,” said the interim report in its opening statements.  “How the United States adopts AI will have profound ramifications for our immediate security, economic well-being, and position in the world…  We are concerned that America’s role as the world’s leading innovator is threatened… We know strategic competitors are investing in research and application.  It is only reasonable to conclude that AI-enabled capabilities could be used to threaten our critical infrastructure, amplify disinformation campaigns, and wage war.”

Like the Internet, - let’s remember that ARPANET was started in the late 1960s by the US Department of Defense, - AI is usable for military and civilian purposes.  But, - also like the Internet, - AI is a general purpose technology that can be used for a wide variety of applications, many of them beneficial, but others potentially threatening.

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.”

How Will Covid-19 Transform Urban Life?

In Why America’s Richest Cities Keep Getting Richer, a 2017 article in The Atlantic, urban studies professor and author Richard Florida wrote: “The most important and innovative industries and the most talented, most ambitious, and wealthiest people are converging as never before in a relative handful of leading superstar cities that are knowledge and tech hubs.  This small group of elite places forge ever forward, while most others struggle, stagnate, or fall behind…  They are not just the places where the most ambitious and most talented people want to be - they are where such people feel they need to be.”

Knowledge-intensive industries have long been centered in cities and surrounding metropolitan areas, where they’ve had the most access to a college educated, high skill workforce.  But, - from Milan to New York, - high density, globally connected urban areas have been ground zero for the spread of Covid-19.  Are cities now at risk of losing the superstar status they’ve enjoyed over the past several decades?

This is not the first time we’ve asked this question in the recent past.  In the mid-late 1990s, some predicted that the Internet would lead to the decline of cities, because it would enable people to work and shop from home, be in touch with their friends over e-mail, and get access to news and entertainment online.   But, instead of declining, megacities have continued to generate the greatest levels of innovation and good jobs, and thus attract a disproportionate share of the world’s talent.

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.

The Post-Pandemic Enterprise: What Will It Be Like?

In the near term, nations have been mostly focused on surviving the massive disruptions caused by Covid-19.  But, what about the longer term?  What will the post-pandemic new normal be like for societies and economies?  More specifically, how will the pandemic impact the world of business?  How can enterprises leverage the crisis to accelerate their journey toward what most everyone agrees will be an even faster changing future?  As Rahm Emanuel famously said in a 2008 WSJ interview: “You never want a serious crisis to go to waste.”

“The future is not what we thought it would be only a few short months ago,” said a recent McKinsey article, From thinking about the next normal to making it work.  “As businesses step into the post-coronavirus future, they need to find a balance between what worked before and what needs to happen to succeed in the next normal.”  The article offers 21 concrete recommendations for succeeding in the next normal, organized into seven major areas.  Let me summarize a few of these recommendations.

Building a More Resilient, Data-Driven Economy

“With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and government,” wrote MIT professor Alex “Sandy Pentland in the first chapter of Building the New Economy.  Pentland cites two major historical examples.  In the early 20th century, the rise of mass manufacturing led to the creation of regulations governing working conditions and pay, health rules for mass-produced foods, and laws to prevent monopolies that stifled competition.  Then in mid-century, the decades following WWII saw greater access to higher education, racial and gender progress, and increased support for scientific research and medical advances.

“Today we face two simultaneous disruptions: one is the COVID-19 pandemic and resulting economic shock, and the second is the rise of pervasive digital data, crypto systems, and artificial intelligence (AI).”  The two disruptions are highly interrelated.  Digital technologies have been deployed to fight the global pandemic as well as helping us stay connected as we practice social distancing.

But, these same technologies are also increasing the already high levels of disinformation and fraud, threatening privacy rights with well-meaning public health technology measures, and likely to impact employment as institutions accelerate their embrace of digitization, AI and automation.  “These problems highlight the need to reinvent the ways data and AI are used in all of societies’ civic and government systems, both to guarantee that future pandemics can be handled better and to reinvigorate the economy but also to spread the economic benefits throughout society.”

High resilience in the face of an uncertain, changing environment is the essence of evolution and natural selection.  Building a new post-pandemic economy requires the creation of more agile, resilient systems and institutions to help us better adapt to an unpredictable future and withstand turbulent events like Covid-19.  In a resilient economy, power and decision-making should be distributed among a diverse set of stakeholders rather than concentrated in a few hands.

Even When Plans Are Useless, Planning Is Indispensable

“As scientists race to develop a cure for the coronavirus, businesses are trying to assess the impact of the outbreak on their own enterprises,” wrote MIT professor Yossi Sheffi in a February 18 article in the Wall Street Journal.  “Just as scientists are confronting an unknown enemy, corporate executives are largely working blind because the coronavirus could cause supply-chain disruptions that are unlike anything we have seen in the past 70 years.”

Sheffi is Director of the MIT Center for Transportation and Logistics.  He’s written extensively on the critical need for resilience in global enterprises and their supply chains, - including The Power of Resilience and The Resilient Enterprise, - so they can better react to major unexpected events.  Covid-19 is the kind of massively disruptive event he had in mind when he wrote those books.

While learning from historical precedents is always a good idea, recent supply chain disruptions - the 2003 outbreak of SARS in Asia, the 2011 Fukushima nuclear disaster, or the 2011 Thailand floods, - were very different from our current pandemic.  Those events were much more localized, lasted a relatively short time, and they mostly impacted supply, not demand.  The impact of Covid-19 is much bigger, affecting consumer demand as well as supply chains all over the world, and likely to last quite a bit longer.  “Today’s supply chains are global and more complex than they were in 2003,” with factories all over the world affected by lockdowns and quarantines.  Apple, for example, works with suppliers in 43 countries.

An Increasingly Digital “New Normal”

About a dozen years ago, I attended a conference where I was interviewed on stage by then WSJ technology columnist Walter Mossberg.  He started out by asking me about IBM’s near-death experience in the early 1990s, and the subsequent technical and cultural transformation that made it possible for IBM to reinvent itself and survive, while many other IT companies didn’t make it.  In particular, we discussed whether a successful company transformation must be preceded by the kind of major crisis IBM went through.  When it comes to humans, the ancient Greeks certainly thought so, as reflected in their tragedies.

Transformative change is very difficult for established companies.  Over the years, they’ve amassed valuable assets and extensive organizations.  Already consumed with managing their existing operations, they may look at transformative change as more of a threat or distraction than an opportunity, - unless they’re in dire straits and have no other choice, as was the case with IBM.

A serious crisis is an opportunity for companies to focus on the actions needed to survive in a fast changing environment.  As Rahm Emanuel famously said in a 2008 interview with the WSJ: “You never want a serious crisis to go to waste.”  In that spirit, how should companies take advantage of the Covid-19 crisis, - arguably the biggest shock the world has experienced since WWII, - to accelerate their journey toward what most everyone agrees will be an increasingly digital future?

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.

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.

Dealing with a Highly Unpredictable Future

The high degree of uncertainty surrounding the Covid-19 pandemic is one of its most frustrating qualities.  How long will the pandemic last?  When will normal life return?  When will a vaccine be available?  Not only don’t we have definitive answers, we don’t even know how to best frame these critical questions.  The answers we get from the most trusted of experts feel too vague, - their explanations full of caveats and probabilities, - as they should.  How can we start making sense of such an unpredictable future?

It wasn’t all that long ago that we didn’t have much use for probabilities in our daily life, - outside of weather reports, election predictions and baseball.  But that’s all been changing in our complex economy and society.   Probabilities have been playing an increasing role in our work and personal lives given our newfound ability to quantify just about anything.  In all kinds of everyday situations, - from medical diagnoses to financial decisions, - we’ve had to learn to live and make decisions in the messy, uncertain world of probabilities.

Probabilities are inherently hard to grasp, - especially for an individual event like the weather or an election.  “People understand that if they roll a die 100 times, they will get some 1’s,” wrote David Leonhardt in a 2017 NY Times column.  “But when they see a probability for one event, they tend to think: Is this going to happen or not?  They then effectively round to 0 or to 100 percent…  And when the unlikely happens, people scream: The probabilities were wrong!”

Measuring the Effectiveness of Social Distancing Policies

One of the most interesting projects in the MIT Connection Science group I’m affiliated with is The Atlas of Inequality.  The project, - led by visiting professor Esteban Moro and professor Alex ‘Sandy’ Pentland - uses aggregated anonymous geolocation data from digital devices to estimate where different groups of people in US cities spend their time.  Over time, the project will be expanded to cities around the world.  The data show the significant income inequality among people in those cities, not just by neighborhoods as you’d expect, but also in the restaurants, stores and other places beyond their neighborhoods which they visit every day.

A couple of weeks ago, the Atlas research group started to apply their geolocation data and methods to analyze the effectiveness of the social distancing policies adopted in the New York metropolitan area in response to the Covid-19 pandemic.  These policies include school closures, bans on non-essential gatherings, limiting restaurants to take-out orders, and strict stay-in-place measures.   There’s no way to empirically measure the impact of these social distancing measures in real time on the spread of Covid-19.  You can only measure their impact retrospectively, or simulate what might happen in the future based on past data.

However, you can empirically address a key set of important question: how have these social distancing policies changed mobility and social behavior?; how does social distancing behavior vary across the New York metropolitan area?; how does the behavior vary across the variety of demographic groups in the NY metro area?; and overall, how well are people following these social distance measures?

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 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.

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.

Can Democracy and Free Markets Survive in the Coming Age of AI?

Can technology plan economies and destroy democracy? is the provocative title of an essay in the end-of-2019 issue of The Economist.  “The success of market- and semi-market-based economies… has made the notion of a planned economy seem like a thing of the past.”  But, given all the data that can now be gathered by billions of smartphones and 10s of billions of sensors, - with many more to come, - could our increasingly intelligent and powerful AI systems “replace the autonomous choices on which the market is based?… And if technology can outperform the invisible hand in the economy, might it be able to do the same at the ballot box when it comes to politics?” 

As we’ve already seen, AI applications “can provide not just data on what people want and will tolerate, but also the means to manipulate those desires.  When such means are available to actors within or outside a state the struggle to gain, or for that matter retain, a democracy which reflects a genuine popular will might become even harder than it is.”  Could this all mean that free-market economics and liberal democracy will become obsolete over the coming century?

These questions were often raised and repeatedly tested throughout the 20th century, as various governments attempted to redesign their economies and societies in accord with what were believed to be scientific laws.  But, most such schemes, especially those carried out by authoritarian states, ended up as complete failures.  And some went tragically awry, - like Stalin’s collectivization of agriculture in the Soviet Union and Mao’s Great Leap Forward in China, - both of which brought death and disruption to millions.

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.