Irving Wladawsky-Berger

Realizing the Economic Promise of Predictive Analytics

Wikipedia defines predictive analytics as a set of statistical techniques, - such as data mining, business analytics, and machine learning, - “that analyze current and historical facts to make predictions about future or otherwise unknown events.” Increasingly powerful and inexpensive computing technologies, new algorithms and models, and huge amounts of data on almost any subject have led to major advances in predictive analytics over the past two decades.

“Worldwide revenue for ‘big data’ and business analytics solutions is forecasted to reach $274.3 billion by 2022,” wrote economists Erik Brynjolfsson, Wang Jin, and Kristina McElheran in The Power of Prediction, a research article published earlier this year. In principle, such widespread use of predictive analytics should have a positive impact of the performance of firms. “However, these investments have yet to yield productivity gains in the aggregate,” said the authors. “At the firm level, managers struggle to close the gap between the promise of predictive analytics and its performance. These concerns have been difficult to tackle empirically due to the rate of technological change and, ironically, a dearth of data.”

To address these concerns, the authors launched a research study in collaboration with the US Census Bureau to collect information on the use of predictive analytics in a representative sample of the US manufacturing industry, - an industry that’s historically been an early adopter of leading-edge technologies.

Why Our Judgements Are Often Flawed and What to Do About It

A few weeks ago I listened to a very interesting Freakonomics podcast hosted by University of Chicago economist Steven Levitt. In the podcast, Why Our Judgment is Flawed — and What to Do About It, Levitt interviewed Daniel Kahneman about his recent book, Noise: A Flow in Human Judgement, co-authored with Olivier Sibony and Cass Sunstein. Kahneman is Professor of Psychology and Public Affairs Emeritus at Princeton University. In 2002, he was awarded the Nobel Prize in Economics “for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty.”

Thinking, Fast and Slow, Kahneman’s 2011 bestseller, was about the major discoveries by psychologists and cognitive scientists that have led to our current understanding of judgement and decision-making over the past several decades. Up to the 1970s, the prevailing view among social scientists was that people are generally rational and in control of the way they think and make decisions. It was thought that people only departed from rational behaviors because powerful emotions like fear, hatred or love distorted their judgement.

These assumptions were challenged by the pioneering research of Kahneman and his long time collaborator Amos Tversky, who died in 1996. In a series of experiments, they demonstrated that human behavior often deviated from the predictions of the previous rational models, and that these deviations were due to the machinery of cognition, that is, to the biases and mental shortcuts or heuristics that we use for making everyday decisions, rather than to our emotional state.

Kahneman has been studying biases and how they lead to errors in judgement for most of his career. But a few years ago, he encountered another type of error, noise, that he hadn’t thought about earlier. Noise is “the sort of ignored stepsister of bias… the complement of bias. And in terms of the standard way of measuring accuracy and measuring error, bias and noise are completely independent of each other. … bias is really the average error. And noise is the variability of error, the standard deviation of errors.

Why Do We Work So Damn Much?

A few weeks ago, I listened to a very interesting podcast, Why Do We Work So Damn Much?, where podcast host, NY Times columnist Ezra Klein, interviewed anthropologist James Suzman. Suzman has devoted almost thirty years to studying and writing about the Ju’hoansi and other bushmen from the Kalahari Basin, who are among the world’s few remaining hunter-gatherer societies. He recently published Work: A Deep History from the Stone Age to the Age of Robots, a book about his research.

Modern humans emerged in Africa between 200,000 and 300,000 years ago. Our homo sapiens ancestors were hunter-gatherers for most of those years, collecting wild plants and hunting wild animals. Starting around 12,000 years ago, the agricultural revolution introduced the domestication of plants and animals, leading many hunter-gatherer groups to establish agricultural communities and villages.

The vast majority of hunter-gatherers disappeared a long time ago, but a few groups remain in isolated sections of Africa, Australia, the Amazon rainforest and the Arctic. Anthropologists have been studying these remaining hunter-gatherers to learn how they’ve been able to survive so much longer than other human groups, as well as to understand the behaviors and cultures that modern humans may have inherited from our closest ancestors.

Revitalizing US Manufacturing

“The COVID-19 pandemic has underscored manufacturing’s role in providing products that are critical to health, safety, national security, and the continuity of multiple industries,” said McKinsey in a recent discussion paper, Building a More Competitive US Manufacturing Sector. “It has also revealed the extent to which global supply chains are exposed to shocks and disruptions. All of this has occurred at a moment when new technologies, process innovations, and demand growth are reshaping the sector worldwide. The United States can seize on these developments to make its own manufacturing sector more competitive.”

Manufacturing jobs helped build the US middle class after WWII. In the 1950s, it accounted for roughly 30% of employment and close to 30% of GDP. But manufacturing has significantly declined in the intervening decades, now comprising only 8% of the workforce and 11% of GDP. Manufacturing growth has particularly slowed down over the past 30 years, from 4.9% in the 1990s to 1.4% in each of the past two decades.

Despite its dramatic decline, manufacturing continues to have a disproportionate impact on the US economy, accounting for 35% of productivity growth, 55% of patents, 70% of R&D spending, and 60% of exports. According to McKinsey, these outsize economic contributions uniquely position manufacturing to strengthen the US recovery over the long term.

Understanding the Impact of Cybersecurity on International Trade

The explosive success of the Internet in the 1990s led to a historical transition from the industrial age of the past two centuries to an economy and society increasingly based on global, digital interactions.  This transition has continued to advance over the past two decade with the advent of billions of smartphones, hundreds of billions of IoT devices, a wide variety of online applications and mobile apps, and huge amounts of data, all connected via Internet-based broadband networks.

Then came Covid-19. A recent McKinsey survey found that the pandemic has accelerated the overall adoption of digital technologies and applications by three to seven years in just a few months.

At the same time, cybersecurity threats have been growing. Large-scale fraud, data breaches, and identity thefts have become far more common. As we moved from a world of physical interactions and paper documents, to a world primarily governed by digital data and transactions, our existing cybersecurity methods have been far from adequate.

More recently, international cyberthreats have escalated, with a growing number of high profile attacks by criminal groups and adversarial governments. Cybersecurity is now invoked by governments as a major aspect of national security, as they focus on protecting their critical infrastructures and the overall wellbeing of their nations. In early June, for example, FBI Director Christopher Wray compared the danger of ransomware attacks on US firms by Russian criminal groups to the September 11 terrorist attacks. And, in a recent editorial, the NY times editorial board argued that ransomware attacks have emerged as “a formidable potential threat to national security,” given “their ability to seriously disrupt economies and to breach strategically critical enterprises or agencies,” urging governments that “It is a war that needs to be fought, and won.”

The Evolving Role of the CIO

“CIOs and other technology leaders have led heroic work to help their companies stay in business during the pandemic,” said a recent report, Pilots, co-pilots, and engineers: Digital transformation insights from CIOs for CIOS. “The question now is where will the focus of CIOs and their organizations be in the future?” To shed light on this question, the professional services firm Genpact surveyed 500 CIOs and technology leaders in partnership with the MIT Sloan CIO Symposium and Babson College professor Tom Davenport. Their findings were presented at the 2021 MIT Digital Symposium.

“We found a surprising level of commonality in terms of the technologies on which they are focused, the infrastructure they are attempting to build, and their view of expanding responsibilities,” was the report’s overriding finding. “High proportions of CIOs, for example, are focused on artificial intelligence (AI) and advanced analytics, moving to the cloud, and architecture modernization. There is, however, a lack of consensus on a critical issue: the role of the CIO in business transformation.”

I’ve had a window into the evolution of the CIO role over the past decade through my involvement with the MIT CIO Symposium. In the past, the primary responsibility of many CIOs was operating their companies’ IT infrastructures while keeping costs down. Given their role, they often reported to the COO or the CFO. But increasingly, CIOs have been given responsibility for their companies’ overall digital strategy working closely with senior executives across the business.

“The role continues to evolve,” notes the Genpact report. “Almost all CIOs expect their roles to expand. Almost all report to or interact with the CEO regularly. And many are exploiting the cloud, advanced analytics, and automation. The big future bet that unites all CIOs is artificial intelligence (AI). And to access new talent and skill sets, many are exploiting their external ecosystem partners. All of which bodes well for the CIO’s future transformational activities. In addition to flexing their technology muscles, many CIOs are still building their transformation muscles, which must include strength in change management and business knowledge.”

The New Joint Cyber Defense Collaborative

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 nation. 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.

In the 1950s the government enacted the National Interstate and Defense Highways Act, which led to the construction of the Interstate Highway System. 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 Department of Defense launched ARPANET, the digital infrastructure that eventually became the Internet. ARPANET was designed as a flexible digital network that would enable computers to continue to communicate with individuals and with each other following a military attack.

Fortunately, we never had to test the Internet’s ability to keep the US going following an attack or invasion. But, who would have thought that 50 years after the launch of ARPANET, it would be a global pandemic that’s been testing the Internet’s ability to fulfill its original objective of keeping nations and economies going during arguably the biggest shock the world has experienced since WWII.

The Evolution of Shopping in the Digital Economy

Earlier this year, The Economist published a special report on The Future of Shopping, with nine articles on the subject. In its overview article, The Economist argued that “a new species of shopper is emerging: less centered on America, more intent on ensuring that what they buy reflects what they believe, and technologically dexterous. This latest incarnation of the global consumer looks likely to change how capitalism works - for the better.”

Three major changes are transforming the world of retailing. First, Western Europe and North America are no longer the biggest retail markets; nor do they epitomize the leading edge of retail as has long been the case, with innovations like the department store, the mall, catalogue shopping, and e-commerce. Shoppers are increasingly Asian. “Last year China and America were almost neck-and-neck as the world’s biggest retail markets. China’s two biggest online marketplaces, Alibaba’s Taobao and Tmall, both do more third-party business than Amazon.” And Asia’s shoppers are now at the frontier of retail, with innovations like livestreaming e-commerce.

A second change is that, beyond low prices, younger shoppers are increasingly influenced by their social, ethical, and political values when making buying decisions. They’re selecting  which brands to engage with based on their social values like environmental credentials and workplace environment. Well managed firms work hard to establish the kind of positive reputation that attracts people to want to be associated with the brand and its products and services. This is particularly important in our Internet age, which enables a company’s actions to be instantly communicated to all. “One way that capitalism adapts to society’s changing preferences is through government regulation and laws, which voters influence, at least in democracies. But the dynamic response of companies to the signals that consumers send is a force for change, too,” and the reason firms spend considerable time, energy and funds nurturing their brands.

The final big change is the increasingly digitalization of the economy. The explosive growth of mobile devices means that a company can be engaged with its customers not only when they’re shopping online, but wherever they are, - at home, at work, or in a store. “More accurate and voluminous data about shopping patterns are breaking down the decades-long relationship between mass consumption and mass production. In its place is a more varied world in which the shopper can decide whether to buy online or in store, whether to shop via platforms or from individual brands, and whether to accept targeted ads or not.”

The Coming Era of Productivity Growth

“The last 15 years have been tough times for many Americans, but there are now encouraging signs of a turnaround,” wrote economists Erik Brynjolfsson and Georgios Petropoulos in The Coming Productivity Boom, a recent opinion article in the MIT Technology Review. “Productivity growth, a key driver for higher living standards, averaged only 1.3% since 2006, less than half the rate of the previous decade. But on June 3, the US Bureau of Labor Statistics reported that US labour productivity increased by 5.4% in the first quarter of 2021. What’s better, there’s reason to believe that this is not just a blip, but rather a harbinger of better times ahead: a productivity surge that will match or surpass the boom times of the 1990s.”

After growing at an average annual rate of around 2.8% between 1947 and 1973, US productivity has significantly slowed down, except for the Internet-driven productivity boost between 1996 and 2004. Despite the relentless advances of digital technologies over the past 15 years, - from smartphones and broadband wireless to cloud computing and machine learning, - productivity has only grown at an anemic 1.3%, between 2006 and 2019. Most OECD countries have seen similar slowdowns.

What accounts for this puzzling so-called productivity paradox and when might it finally end? Over the past several years, Brynjolfsson and his various collaborators have explored this question, first at MIT where he was faculty director of the Initiative on the Digital Economy, and since 2020 at Stanford, where he’s Director of the Stanford Digital Economy Lab. Brynjolfsson discussed alternative explanations for the paradox at a recent MIT conference.

The Post-Pandemic Future of Work: An Optimistic Outlook

“The COVID-19 pandemic-induced lockdowns and related global recession of 2020 have created a highly uncertain outlook for the labour market … millions of workers have experienced changes which have profoundly transformed their lives within and beyond work, their well-being and their productivity,” said the World Economic Forum (WEF) in The Future of Jobs Report 2020, published in October, 2020. “Comparing the impact of the Global Financial Crisis of 2008 on individuals with lower education levels to the impact of the COVID-19 crisis, the impact today is far more significant and more likely to deepen existing inequalities.”

The Economist  was considerably more optimistic in a special report on The Future of Work in its April 10 issue, which included several articles on the subject. “A jobs rebound, shifting politics and technological change could bring a golden age for labour in rich countries,” said the overview article. “It might seem premature to predict a wonderful world of work only a year on from a labour-market catastrophe. But America is showing how rapidly jobs can come back as the virus recedes. In the spring of 2020 the country’s unemployment rate was nearly 15%. Now it is already just 6% after a year containing five of the ten best months for hiring in history.”

The key reason for The Economist’s optimistic outlook is that as labor markets recovers, two deeper shifts are unfolding: a political environment which is becoming friendlier to workers than it’s been in decades; and an accelerated digital economy which promises to bring about faster productivity growth. Let me briefly summarize each of these two shifts.

The Three Types of AI: Artificial, Authentic, and Augmented Intelligence

“The history of work - particularly since the Industrial Revolution - is the history of people outsourcing their labor to machines,” notes a recent article in the Harvard Business Review, - AI Should Augment Human Intelligence, Not Replace It, - by National University of Singapore professor David De Cremer and chess grandmaster Garry Kasparov. “While that began with rote, repetitive physical tasks like weaving, machines have evolved to the point where they can now do what we might think of as complex cognitive work, such as math equations, recognizing language and speech, and writing. Machines thus seem ready to replicate the work of our minds, and not just our bodies.”

Throughout the Industrial Revolution there were periodic panics about the impact of automation on jobs, going back to the so-called Luddites, - textile workers who in the 1810s smashed the new machines that were threatening their jobs. Automation does indeed substitute for labor. However, automation also complements labor, raising economic outputs in ways that often lead to more long-run employment, not less.

Most jobs involve a number of tasks or processes. Some of these tasks are more routine in nature, while others require judgement, social skills and other human capabilities. The more routine, rules-based the task, the more amenable it is to automation. But just because some of the 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 machines and computers, as well as enabling them to focus on those aspect of the job that most need their attention.

The Transformation of Logistics in the Retail Industry

In the spring of 2018, MIT launched the Work of the Future Task Force to understand the impact of our increasingly intelligent machines on the future of work, and how to best harness these technological innovations for social benefit. The MIT-wide task force released its findings and recommendations in a November, 2020 report, - Building Better Jobs in an Age of Intelligent Machines.

In addition to the task force report, the Work of the Future initiative has published a number of working papers and research briefs on related topics. I’d like to now discuss one of those briefs, The Future of Work in Logistics by Arshia Mehta and Frank Levy, which explored the transformation of the retail industry since the advent of e-commerce in the 1990s. The brief is a very interesting case study of the evolution of the retail industry over the past few decades.

“Twenty years ago, U.S. distribution networks were built to deliver products in bulk to retail stores,” wrote the authors. “Today, large parts of distribution networks are built to deliver individual items to home residences. The shift has been driven by technology, working through e-commerce, and recently reinforced by the COVID-19 pandemic.”

What Musical Mindsets Can Teach Us About Business Innovation

In their recently published book, Two Beats Ahead: What Musical Minds Teach Us About Innovation, Panos Panay and R. Michael Hendrix argue that “the mindsets developed by musicians make them good entrepreneurs.” Panay is currently Senior VP for Global Strategy and Innovation at the Berklee College of Music. On June 22, he was appointed Co-President of the Recording Academy effective August, 2021. Hendrix is a Partner and Global Design Director at IDEO.

“[T]he creative mindset is more than a unique approach to entrepreneurship, more than drawing upon the improvisation of jazz or practicing until our fingers bleed,” write Panay and Hendricks. “As musicians, we believe that there is something instructive here for our work as entrepreneurs and business leaders. Musicians know how to create moments that break patterns, fill gaps, capture our attention, and inspire not only because of skills they have developed at a keyboard or a microphone, but because they have honed their ability to listen.”

The book is based on interviews with musicians, songwriters and producers who’ve succeeded as both performers and entrepreneurs. The authors interviewed a number of musicians, including Justin Timberlake, Pharrell Williams, Imogen Heap, T Bone Burnett, and Desmond Child. In addition, they surveyed the careers of Björk, Dr. Dre, Jimmy Iovine, Gloria and Emilio Estefan, and others who’ve had a major impact on the music industry as creative artists and business leaders.

What Might the World Be Like in 2040?

The mission of the National Intelligence Council (NIC) is to lead intelligence integration and long-term strategic thinking across a wide variety of sources, including the US Intelligence Community, policy makers, and experts in academia and the private sector. Every four years, the NIC develops a Global Trends report that examines the key trends and uncertainties that will shape the global environment during the next two decades. This is done to provide the incoming administration with a framework for long-range strategic policy assessment. Global Trends 2040 - A More Contested World (GT2040), the latest NIC report, was published in March, 2021, a few weeks after President Joe Biden’s inauguration. Let me briefly discuss.

Key Recurring Themes

“During the past year, the COVID-19 pandemic has reminded the world of its fragility and demonstrated the inherent risks of high levels of interdependence,” notes the report in its introduction.  Not surprisingly, given that we’ve been living through the most significant global disruption since WW2, GT2040 paints a rather dark picture of what lies ahead.

“In coming years and decades, the world will face more intense and cascading global challenges ranging from disease to climate change to the disruptions from new technologies and financial crises. These challenges will repeatedly test the resilience and adaptability of communities, states, and the international system, often exceeding the capacity of existing systems and models. This looming disequilibrium between existing and future challenges and the ability of institutions and systems to respond is likely to grow and produce greater contestation at every level.” As a NY Times editorial observed, “Experts in Washington who have read these reports said they do not recall a gloomier one.”

Leveraging Cloud Computing to Enhance the Nation’s Cybersecurity

There’s clearly an urgent need to significantly improve the security and resilience of IT systems and applications in light of the growing threat of cyberattacks, which FBI director Christopher Wray recently compared to addressing the challenge of global terrorism following the 9/11 attack. Earlier this week, the NY Times published this front page article - Once, Superpower Summits Were About Nukes. Now, It’s Cyberweapons, that said: “The rising tempo and sophistication of recent attacks on American infrastructure - from gasoline pipelines running up the East Coast, to plants providing a quarter of America’s beef, to the operations of hospitals and the internet itself - has revealed a set of vulnerabilities no president can ignore.” On May 12, President Biden issued an Executive Order to Improve the Nation’s Cybersecurity, a welcome and very important step.

Cloud-based technologies and Cloud Service Provides (CSPs) can play a major role in improving the nation’s cybersecurity. Over the years, CSPs have invested billions in cloud security, hired thousands of top cyber experts, and developed an array of new tools and methods. CIOs generally agree that the security on cloud platforms is superior to that of in-house data centers, a major reason why the top IDC prediction for the post-pandemic new normal was that “By the end of 2021, based on lessons learned, 80% of enterprises will put a mechanism in place to shift to cloud-centric infrastructure and applications twice as fast as before the pandemic.”

“Cloud computing drives innovation and productivity across the economy, just as the electric grid did a century ago - yet it is more capable and dynamic.” This is the overriding message of Secrets From Cloud Computing’s First Stage: An Action Agenda for Government and Industry, a report by Bill Whyman that was recently published by the Information Technology & Innovation Foundation (ITIF). As someone who’s long been following and writing about cloud computing, I found the report quite instructive. It includes a very good introduction to cloud computing, its economic impact and market dynamics, and the areas where cloud particularly excels. But, it also addresses the key challenges that cloud must overcome to fully achieve its benefits, as well as the need for a policy agenda for cloud computing, - a very important area that hasn’t received as much focus.

Cloud’s Trillion Dollars 2030 Potential

“Thanks in part to cloud, Moderna was able to deliver the first clinical batch of its vaccine candidate (mRNA-1273) to the US National Institute of Health for phase one trials just 42 days after the initial sequencing of the virus,” wrote the authors of a recent McKinsey article, - Cloud’s trillion dollar prize is up for grabs. When the COVID-19 pandemic hit, “The company was well positioned to quickly design research experiments and to harness its automated laboratory and manufacturing processes and enhanced drug-discovery pipeline.”

Over the past year, a number of articles have pointed out that, in response to the pandemic, digital adoption by business and consumers has already reached levels that weren’t expected for many years. “More companies are starting to see the real benefits of cloud, which has been long heralded as a catalyst for innovation and digital transformation, thanks to its ability to increase development speed and provide near-limitless scale,” adds the McKinsey article. “While Moderna’s success illustrates the business opportunities that cloud makes possible, it only scratches the surface of the potential value at stake. A detailed review of cloud cost-optimization levers and value-oriented business use cases foresees more than $1 trillion in run-rate EBITDA [an accounting measure of a company’s overall financial performance] across Fortune 500 companies as up for grabs in 2030.”

To quantify cloud’s 2030 trillion dollar potential, - an estimate with a range of $0.7 to $1.2 trillion, - McKinsey analyzed the impact of cloud on worldwide IT spending based on independent surveys of over 1,000 organizations. As part of its research, it assessed over 700 use cases across 19 industries to help them forecast the revenues and financial performance of Fortune 500 companies by 2030.

The research looked at cloud adoption along three dimensions, - rejuvenate, innovate, and pioneer. It estimated the specific drivers of value across the first two dimensions and the likely growth opportunities for the third. Let me summarize the article’s key findings.

The Emergence of Central Bank Digital Currencies

Bitcoin was created in October of 2008 with the release of Bitcoin: A Peer-to-Peer Electronic Cash System, the original design paper which also introduced the blockchain architecture. A decade later, The Economist published a detailed evaluation of Bitcoin which succinctly concluded that “Bitcoin and other cryptocurrencies are useless.”

“Bitcoin, the first and still the most popular cryptocurrency, began life as a techno-anarchist project to create an online version of cash, a way for people to transact without the possibility of interference from malicious governments or banks,” it further argued. “A decade on, it is barely used for its intended purpose. Users must wrestle with complicated software and give up all the consumer protections they are used to. Few vendors accept it. Security is poor. Other cryptocurrencies are used even less.”

But last month, the May 8 issue of The Economist reached a very different conclusion in its assessment of central bank digital currencies (CBDCs), - i.e., e-dollars, e-yuans, or e-euros, - which it called “The digital currencies that matter.”

“Bitcoin has gone from being an obsession of anarchists to a $1trn asset class that many fund managers insist belongs in any balanced portfolio. … Yet, as our special report explains, the least noticed disruption on the frontier between technology and finance may end up as the most revolutionary: the creation of government digital currencies, which typically aim to let people deposit funds directly with a central bank, bypassing conventional lenders. These govcoins are a new incarnation of money. They promise to make finance work better but also to shift power from individuals to the state, alter geopolitics and change how capital is allocated. They are to be treated with optimism, and humility.”

A Biological Framework for the Study of Labor Market Resilience

I recently read Universal Resilience Patterns in Labor Markets, - an article published earlier this year in Nature Communications by members of MIT Connection Science and the Max Planck Institute for Human Development. The article analyzed the resilience of labor markets in US cities using a framework inspired by biological ecosystems.

Biological principles have long been an inspiration for the study of sociotechnical systems, - that is, systems that deal not only with complex technologies and infrastructures, but also with the even more complex issues associated with human and organizational behaviors, such as firms, industries, economies, and cities. For example, a 2016 Harvard Business Review article, The Biology of Corporate Survival, applied evolutionary biology principles to analyze why businesses are disappearing faster than ever before.

“[T]he typical study of urban labor at equilibrium obfuscates responses to out- of-equilibrium disruptions,” notes the Nature article. “Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience.”

Business and Technology Predictions for 2021 and Beyond

Earlier this year, I attended Predictions 21, an online event organized by Forrester Research to help companies understand the key business and technology dynamics that will shape 2021 and beyond. “Faced with the pandemic, firms did things that once seemed impossible - sometimes overnight,” said the accompanying event guide. “Under the relentless pressure of new customer realities, the future came into focus: The value of your company depends on how customer-obsessed, resilient, creative, and adaptive you are in jumping to the next growth curve in your industry. … 2021 will be the year that every company - not just the 15% of firms that were already digitally savvy - doubles down on technology-fueled experiences, operations, products, and ecosystems.”

The past year has been truly chaotic. How can firms formulate their strategies for the next few years in such an uncertain environment? But, as former US president Dwight Eisenhower famously said when he was 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, you must carefully assess the various directions in a highly uncertain future while being ready to flexibly adjust to changing circumstances.

Much has been written about the post-pandemic new normal. There are multiple opinions on a number of areas, such as cities, globalization, supply chains, and labor markets. But, just about everyone predicts an increasingly digital post-pandemic new normal. “The COVID-19 crisis seemingly provides a sudden glimpse into a future world, one in which digital has become central to every interaction,” said McKinsey in an April, 2020 article. “The adoption of new technological behaviours in response to the pandemic, from video-conferencing to online shopping, means usage has already reached levels that were not expected for many more years,” noted The Economist several months later.

In addition to the online webinar, Predictions 21 includes a number of related articles and videos. Let me summarize a few of Forrester’s key predictions.

Can We Look Forward to a New Decade of Innovation?

Can we  look forward to a new decade of innovation, asked The Economist in its January 16 issue. “The 2010s were marked by pessimism about innovation,” notes the issue’s overview article. “Productivity growth was lackluster and the most popular new inventions, the smartphone and social media, did not seem to help much… Promising technologies stalled, including self-driving cars, making Silicon Valley’s evangelists look naive.”

“Today a dawn of technological optimism is breaking,” adds the article. Some giddily predict a new decade of progress, - a kind-of 21st century roaring Twenties. While some of this optimism may be overblown, “there is a realistic possibility of a new era of innovation that could lift living standards, especially if governments help new technologies to flourish.”

There are historical precedents for such optimism. The Roaring Twenties was a decade of economic growth and widespread prosperity, as the country recovered from the dual devastations of World World I and the Spanish flu pandemic. Technological advances like electrification, consumer appliances, the mass production of cars, and the advent of commercial aviation transformed the US into the richest country in the world.

Then following World War II and the onset of the Cold War, the federal government significantly expanded its support of R&D in the private and public sectors, making the US the undisputed leader in science, engineering, medicine and other disciplines. The blueprint for R&D in post-war America was laid out by presidential science advisor Vannevar Bush in his 1945 report, Science The Endless Frontier: “New knowledge can be obtained only through basic scientific research” conducted in universities and research labs, which is then applied to develop new products by the private sector and new and improved weapons by the defense sector.

The Urgent Need for Wide-Scale Upskilling

Few topics are as important as the future of work in our 21st century digital economy. In the spring of 2018 MIT President Rafael Reif commissioned a task force on the Work of the Future to address whether our rapidly advancing technologies will not only raise economic output and the wealth of nations, but enable people to attain higher living standards and better working conditions. After working across MIT for over two and a half years, the task force released its final report in November, 2020.

“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,” was the overriding conclusion of the task fore. But, it further argued that with better policies in place, more people could enjoy good careers even as new technologies transform the very nature of work.

Similar conclusions were reached by Upskilling for Share Prosperity, a report published in January, 2021 by the World Economic Forum (WEF) in collaboration with PwC. “Millions of people are already being left behind because of volatile market conditions, the effects of COVID-19, or because they work in industries that are being replaced by new sectors,” said the report… There is an enormous opportunity to reconfigure the world of work at this critical juncture and embark on an upskilling revolution that will give people across the world the ability to participate fully in the future of work, whatever that might be.”

Will AI Ever Be Smarter Than a Baby?

I recently listened to a fascinating podcast where NY Times columnist Ezra Klein interviewed Berkeley psychologist Aliston Gopney. Professor Gopney is best known for her research in cognitive science, particularly the study of children’s learning and development. She’s written extensively on the developmental phases of the human brain from babies to adults.

Gopnik, a member of the Berkeley AI Research group, has also been exploring the differences between human and machine intelligence, more specifically, what babies can teach us about AI. She’s long argued that babies and young children are smarter than we might think. In some ways, they’re even smarter than adults, let alone way smarter than the most advanced AIs.  

What do we mean by intelligence?

In 1994 the Wall Street Journal published the Mainstream Science on Intelligence, an article that included a definition that was agreed to by 52 leading academic researchers in fields associated with intelligence:

“Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings - ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do.”

A New Contract with the Middle Class

“The U.S. is a middle-class nation,” says the opening sentence of A New Contract with the Middle Class, an essay by Richard Reeves and Isabel Sawhill that was recently published by the Brookings Institution. “Since our nation’s founding, the American Dream has always been based on an implicit understanding - a contract if you will - between individuals willing to work and contribute, and a society willing to support those in need and to break down the barriers in front of them.” The essay is part of the Future of the Middle Class initiative launched by Brookings in November, 2018.

Why focus on the middle class? The reason, notes the essay, is that this is how the majority of Americans define themselves. “An aristocratic leisure class and a welfare-dependent underclass are equally unappealing to most Americans.” It’s why paid work is seen as so important. Americans want a society where everybody has the chance to “make something of themselves.” Joining the middle class is the very core of the American Dream, - the promise that anyone can get ahead and achieve success and prosperity through talent and hard work.

Who is in the middle class? While there are multiple definitions, most are based on income, partly because data on income are widely available, and it’s easier to come up with concrete, quantifiable definitions. But also because income tends be highly correlated with other attributes of social class, such as educational level, job status, consumer preferences, behaviors, aspirations, and cultural tastes.

The essay defines middle class as those in the middle 60% of household income distribution. “To be middle class, a household of three would have an income between $40,000 and $154,000,” - neither poor nor prosperous. “The middle class, by our definition, is diverse: 59 percent white, 12 percent Black, 18 percent Hispanic, and 6 percent Asian. Within a few decades, whites will make up the minority of middle-class families.”

The Post-Pandemic Outlook for Technology, Jobs, and Skills

“The COVID-19 pandemic-induced lockdowns and related global recession of 2020 have created a highly uncertain outlook for the labour market,” said the World Economic Forum (WEF) in The Future of Jobs Report 2020. The pandemic has caused labor markets to change significantly faster than previously expected. What used to be considered the future of work has already arrived. As McKinsey noted in a May, 2020 article: “we have vaulted five years forward in consumer and business digital adoption in a matter of around eight weeks.”

The WEF report aims to shed light on the post-pandemic outlook for technology adoption, jobs and skills over the next five years, including detailed quantitative profiles on 15 industry sectors and 26 advanced and emerging countries. The report is based on a survey of senior executives from nearly 300 global companies which collectively employ 8 million workers. The survey asked 49 questions on the outlook for their companies’ workforce through 2025, including major trends affecting the labor market, the technologies their companies are adopting, the expected evolution of jobs and skills, their training and reskilling programs, and the near-term impact of the pandemic on their workforce.

“The global shift to a future of work is defined by an ever-expanding cohort of new technologies, by new sectors and markets, by global economic systems that are more interconnected than in any other point in history, and by information that travels fast and spreads wide. … As a new global recession brought on by the COVID-19 health pandemic impacts economies and labour markets, millions of workers have experienced changes which have profoundly transformed their lives within and beyond work, their well-being and their productivity. One of the defining features of these changes is their asymmetric nature - impacting already disadvantaged populations with greater ferocity and velocity.”

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

The Changing Nature of the Liberal Arts in the Digital Economy

What is meant by the liberal arts? Here is a succinct dictionary definition: “The academic course of instruction intended to provide general knowledge and usually comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.” And, here is a more thorough explanation: “A liberal arts education is by nature broad and diverse, rather than narrow and specialized…  [it] is not intended to train you for a specific job, though it does prepare you for the world of work by providing you with an invaluable set of employability skills, including the ability to think for yourself, the skills to communicate effectively, and the capacity for lifelong learning.”

Over the past few decades, business, engineering, and other professions not previously associated with the liberal arts have embraced a number of its attributes. A few years ago, for example, I came across an article about the efforts of Roger Martin to transform business education. At the time, Martin was the Dean of the Rotman School of Management at the University of Toronto. He had long been advocating “that students needed to learn how to think critically and creatively every bit as much as they needed to learn finance or accounting. More specifically, they needed to learn how to approach problems from many perspectives and to combine various approaches to find innovative solutions.” Such a transformation would require business schools to move into territory more traditionally associated with the liberal arts.

Similarly, in a 2006 report, the National Academy of Engineering called for reforming engineering education. “New graduates were technically well prepared but lacked the professional skills for success in a competitive, innovative, global marketplace. Employers complained that new hires had poor communication and teamwork skills and did not appreciate the social and nontechnical influences on engineering solutions and quality processes.”

A few years ago, USC’s Annenberg School of Communications and Journalism conducted a study to better understand the key competencies companies were looking for. Future leaders, the study found, must be strong in quantitative, technical and business skills. But to advance in their careers, they also need to be good strategic thinkers and must have strong social and communications skills. Companies were looking for so called T-shaped professionals who combine deep quantitative, technical, problem solving hard skills with broad multidisciplinary, communications, and social soft skills that enable them to collaborate with experts in other fields.

The Current State of AI Adoption

For the past few years, McKinsey has been conducting a yearly online survey to help assess the state of AI adoption. The 2019 survey garnered responses from over 2,300 companies, and found that AI was becoming more mainstream, with nearly 80% of the responding companies using AI in some capacity. Most of these companies saw measurable benefits from their AI deployment, with 63% reporting year-over-year (YOY) revenue increase, while 44% reported YOY cost savings. However, much work was still necessary to scale the use of AI across the company, manage the risks, and retrain their workforce.

The most recent survey, took place in June of 2020 and received responses from almost 2,400 participants from different regions, industries, company sizes and functions. The State of AI in 2020, published in November, found that revenue increases had become a bit more common (66%) compared to the previous year, while cost decreases were now somewhat less common (40%). The biggest year-over-year increases were found in the strategy and finance functions (73% vs 59%), risk management (68% vs 57%), manufacturing (71% vs 61%), and supply-chain management (72% vs 63%). The biggest YOY cost decreases were found in strategy and finance (32% vs 50%), risk management (38% vs 54%), manufacturing (50% vs 64%), human resources (43% vs 55%), and product and/or service development (21% vs 29%).

What accounts for these (relatively small) differences between the two most recent surveys? “What we’ve said in the past about ‘following the money’ to find where AI adds value in organizations still holds true,” explained the report. “And, overall, many companies focused on growth in 2019 …; for that reason, it’s likely that we saw more companies driving revenues with AI rather than decreasing their costs - not because AI can’t effectively reduce costs.”

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.

Trends That Will Likely Shape a Highly Uncertain Year

A few weeks ago The Economist published The World in 2021, it’s 35th annual look at the economic, political, social and cultural trends that will likely shape the coming year.  Overall, 2021 will be a year of unusual uncertainty given the still ongoing pandemic, the uneven economic recovery, and the turbulent political environment in the US, Britain, and a number of other countries.  “The great prize on offer is the chance of bringing the coronavirus pandemic under control,” wrote the issue’s editor Tom Sandage.  “But in the meantime risks abound, to health, economic vitality and social stability.”

The World in 2021 highlights ten specific trends.  Let me focus my discussion on three of them.

The Evolution of American Capitalism - Tweaks, Reforms, or Wholesale Change?

“American capitalism has evolved time and again, and we may be poised for another such shift,” said a recent McKinsey article, Rethinking the future of American capitalism, by James Manyika, Gary Pinkus, and Monique Tuin.  “Capitalism has contributed to significant gains in economic growth and prosperity throughout its history.  But at a time of growing public discontent about rising inequality, heightened competition from economies with different models, and existential threats including from climate change, capitalism in its current form - and American capitalism in particular - may face its most serious test.”

Adam Smith, the 18th century Scottish philosopher and economist, is generally considered the father of free-market, free-trade capitalism.  Smith is most famous for The Wealth of Nations, published in 1776 and generally regarded as the first work of modern economics.  He’s also well known for his famous metaphor of the invisible hand - “the free market, while appearing chaotic and unrestrained, is actually guided to produce the right results by this so-called invisible hand.”

Smith believed in a balanced approach to capitalism.  On one side is the fierce competition and self-interest inherent in open, free markets.  On the other, - as he wrote in The Theory of Moral Sentiments, - is the sympathy and community support that’s required to achieve beneficial results in well functioning, stable societies.

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.

What Is the Social Responsibility of Business?

On September 13, 1970, the NY Times Magazine published an essay by University of Chicago economist and 1976 Nobel Prize recipient Milton Friedman - “The Social Responsibility of Business is to Increase its Profits.”  Friedman’s essay turned out to be one of the most influential ever published in the world of business.  To mark its 50th anniversary, the NY Times reached out to 22 business leaders and top economists and asked their opinion on what Friedman got right and wrong in his essay.

“Friedman, who died in 2006 at the age of 94, was no mere economist; he was a kind of celebrity,” noted Andrew Ross Sorkin, NY Times financial columnist and editor.  “He became a regular on the talk-show circuit.  PBS even gave him a 10-part series. His economic theories, among the most consequential of the 20th century, still hold sway over large parts of corporate America, maybe none more so than this 1970 manifesto on corporate governance.”

“In a free-enterprise, private-property system, a corporate executive is an employee of the owners of the business,” wrote Friedman in his 1970 essay.  “He has direct responsibility to his employers.  That responsibility is to conduct the business in accordance with their desires, which generally will be to make as much money as possible while conforming to the basic rules of the society, both those embodied in law and those embodied in ethical custom.”  Business concerns beyond making profit, - such as “promoting desirable social ends,” or “providing employment, eliminating discrimination, avoiding pollution and whatever else,” - amounted to “preaching pure and unadulterated socialism.”

To better appreciate the importance of Friedman’s essay, let’s take a brief look at the historical context around the time it was published.

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.