Irving Wladawsky-Berger

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.

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.

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.

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.”

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 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 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.

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.

Building Better Jobs in an Age of Intelligent Machines

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

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

The Public Face of Science - Implications of the Covid Crisis

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

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

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

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

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

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

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

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

May the Network Force Be With You

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

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

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

The Real Business of Blockchain

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

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

The Social Network Is the Computer

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

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

Reflections on the Later Stages of Our Careers

I recently read “Your Professional Decline Is Coming (Much) Sooner Than You Think,” an essay in The Atlantic by social scientist and author Arthur C. Brooks.  Brooks essay is a personal reflection on professional careers.  In particular, the essay explains why in July of 2019 at the age of 55, he concluded that it was time to resign his decades-long position as president of the prestigious American Enterprise Institute (AEI) to join the faculty of the Harvard Kennedy School.  I found Brooks’ essay particularly intriguing as a lens through which to reflect on the later stages of my own career; - when I turned 60 in 2005, I started the transition toward my 2007 retirement from my long IBM career by becoming affiliated with MIT and starting to write my weekly blogs.

The essay references the work of Dean Simonton, professor emeritus of psychology at UC Davis, who’s conducted extensive research on the trajectories of creative careers and is one of the world’s leading experts on the subject.

Over the years, Simonton developed a mathematical model that explains the long term variations in the career trajectories of different individuals, as well as the differences in career trajectories across disciplines and domains.  A few parameters account for most individual differences: the age at which the career starts, the career’s initial creative potential, the rates at which new ideas are generated, and the rate at which the ideas become finished results as publications or products.  This simple model can then predict the trajectory of each individual’s career, including the ages of their first, their best and their final contribution.  Simonton confirmed the validity of his model by analyzing the creative output of the careers of over 2,000 scientists and inventors, as well as the output of musicians, painters, authors and other artists.

“[S]uccess and productivity increase for the first 20 years after the inception of a career, on average,” wrote Brooks summarizing Simonton’s results.  “So if you start a career in earnest at 30, expect to do your best work around 50 and go into decline soon after that.  The specific timing of peak and decline vary somewhat depending on the field.”  Other research has found that the likelihood of a major discovery by scientists and investors increases steadily through their 20s and 30s, and then declines through their 40s, 50s and 60s.  While there are outliers, “the likelihood of producing a major innovation at age 70 is approximately what it was at age 20 - almost nonexistent.”

User-Based Free Innovation

In his 2016 book of the same title, MIT professor Eric von Hippel defines free innovation “as a functionally novel product, service, or process that (1) was developed by consumers at private cost during their unpaid discretionary time (that is, no one paid them to do it) and (2) is not protected by its developers, and so is potentially acquirable by anyone without payment - for free.” The book is available as a free download here.

Free innovation is a tightly constrained subset of commons-based peer production, - also known as open, collaborative innovation.  Participants in a commons-based peer production project can do so for monetary gains, either as individual users or as employees of a collaborating company, as has been the case with firms involved in the continuous development of Linux.  In contrast, while free innovation has potentially important economic impacts, it’s fundamentally not about money.

Free innovation is part of the household sector of national economies, carried out by the members of a household for their own consumption, using their own capital and unpaid labor.  “In just six countries surveyed to date, tens of millions of individuals in the household sector have been found to collectively spend tens of billions of dollars in time and materials per year developing products for their own use,” writes von Hippel.

How Will the Pandemic Reshape Labor Markets?

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

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

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

The AI Factory: A New Kind of Digital Operating Model

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

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

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

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

Why 70 Percent of Digital Transformations Fail

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

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

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

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

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

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

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

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

Reconnecting Society and Reopening the Economy

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

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

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

Are We Becoming a Decadent, Stagnating Society?

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

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

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

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

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

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

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

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

Could AI Reshape the Global Balance of Power?

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

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

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

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

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

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

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

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

How Will Covid-19 Transform Urban Life?

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

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

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

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

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

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

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

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

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

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

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

Building a More Resilient, Data-Driven Economy

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

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

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

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

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

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

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

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

Even When Plans Are Useless, Planning Is Indispensable

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

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

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

What History Tells Us About the Accelerating AI Revolution

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

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

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

An Increasingly Digital “New Normal”

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

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

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

The Urgent Need to Strengthen the Nation’s Resilience

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

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

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

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

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