Irving Wladawsky-Berger

The Economic Potential of the Internet of Things

In June of 2015, the McKinsey Global Institute published a report on the Internet of Things (IoT). The report analyzed the long term economic potential of IoT by examining over one hundred use cases and the IoT-based solutions in which they were generally deployed such as factories, homes, offices, and cities. It then estimated that the economic potential of IoT by 2025 would be approximately $3.9 trillion to $11.1 trillion per year.

The estimate was based on the likely evolution of technology and the rate of adoption of IoT solutions between 2015 and 2025, as well as economic and demographic trends. The estimated range was so wide because there were so many unknowns at that early stage in IoT’s development, including the costs of technology, the rate of development and deployment of these highly complex solutions, their level of acceptance by consumers and workers, and the policies and regulations enacted by governments.

Six years later McKinsey published a new report, The Internet of Things: Catching up to an accelerating opportunity, which updated the 2015 analysis. The recent report evaluated how much of its projected value in the earlier report had been captured and estimated the IoT’s economic potential through 2030, as well as the key headwinds slowing its progress and the key tailwinds propelling it forward.

“While the potential economic value from the IoT is large and growing, capturing this value has proved challenging,” said the recent report. “Our latest research shows that the total value captured in 2020 ($1.6 trillion) is at the lower end of the range of the scenarios we mapped out in 2015. We have updated our estimates for 2025 and beyond by adjusting for current conditions, and we have developed scenarios that account for the range of various uncertainties. In aggregate, both the low- and high-end scenarios are lower than the original 2015 estimates: around $2.8 trillion to $6.3 trillion in potential IoT economic value in 2025, compared with around $3.9 trillion to $11.1 trillion from the 2015 work.”

The Powerful Human Forces Driving the End of Globalization

“I can remember a time  - about a quarter-century ago - when the world seemed to be coming together,” wrote NY Times columnist David Brooks in a recent essay, Globalization Is Over. The Global Culture Wars Have Begun. “The great Cold War contest between communism and capitalism appeared to be over. Democracy was still spreading. Nations were becoming more economically interdependent. The internet seemed ready to foster worldwide communications. It seemed as if there would be a global convergence around a set of universal values - freedom, equality, personal dignity, pluralism, human rights.”

“We called this process of convergence globalization,” he added. “It was, first of all, an economic and a technological process - about growing trade and investment between nations and the spread of technologies that put, say, Wikipedia instantly at our fingertips.”

The 1990s ushered a golden age of globalization, when the world seemed indeed to be coming together. Thomas Friedman’s The World is Flat became an international best-seller in 2005 by nicely explaining what globalization was all about, including the key forces that contributed to flattening the world, - from the collapse of the Berlin Wall in November of 1989 and the Netscape IPO in August of 1995, to the rise of outsourcing, offshoring and global supply chains.

But, ever since the 2008 global financial crisis, globalization and global trade started to slow down. “Globalisation has slowed from light speed to a snail’s pace in the past decade for several reasons,” wrote The Economist in a 2019 article. “The cost of moving goods has stopped falling. Multinational firms have found that global sprawl burns money and that local rivals often eat them alive. Activity is shifting towards services, which are harder to sell across borders: scissors can be exported in 20ft-containers, hair stylists cannot.”

Disruptive Forces Necessitate Bold Decisions

In January of 2021 I attended Predictions 21, an online event organized by Forrester Research.  “Faced with the pandemic, firms did things that once seemed impossible - sometimes overnight,” said Forrester last year, adding that “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.”

Earlier this year I attended Predictions 22, and was particularly curious to see how things had changed in the intervening year. “Disruptive Forces Necessitate Bold Decisions,” was the overriding message in this year’s event guide.  “The old ways of working no longer work. The future is up for grabs. Leading firms will use the crucibles of 2020 and 2021 to forge a path to an agile, creative, and resilient tomorrow.”

Let me summarize some of Forrester’s key predictions in three areas: technology, customer experience, and industry trends.

Web3 - Safeguarding Our Identity and Personal Data in the Digital World

Transformative technologies are generally accompanied by a mixture of excitement and confusion in their early years. Something important is going on out there, although there’s no consensus on what it is yet. A major reason for the lack of consensus is that there’s no single dimension around which to define an emerging technology or business 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 are in complete disagreement.

This was the case with the advent of the commercial internet in the early 1990s. A lot was starting to happen around the internet, but we weren’t sure where things were heading. It was pretty clear that a communications revolution was underway: after all, the internet was fundamentally a network of networks, and email was one of its earliest and most popular applications. It was also an information revolution: anyone with a browser, a PC, and an internet connection could now access all kinds of content in the new World Wide Web. And, above all, it promised to be an economic revolution: the internet ushered a historical transition to a new kind of digital economy, including many innovative e-business applications.

Over the past few decades, the term internet has come to encompass a number of related technologies including broadband networks, mobile devices, social media, cloud computing, e-commerce platforms, big data, AI, and more. More recently, we’ve seen the emergence of a new set of technologies and business models that are once more generating excitement, confusion, and multiple opinions on what they’re all about: Web3.

Last week I wrote about web3, referencing two recent books: the just-published Digital Asset Revolution by Alex Tapscott, and the soon-to-be-published Think Blockchain by Jerry Cuomo.

I wrote that web3 aims to usher a more open, entrepreneurial internet and digital economy by replacing today’s corporate mega-platforms with blockchain-based decentralized networks. Web3 would thus give creators, developers and users a way to monetize their contributions, involve them in the governance and decision-making of the platforms supporting their work, and give individuals more privacy and control over their data.

I now want to discuss another perspective on web3 based on The Emerging New Economy: Causes and Consequences of Web 3.0, a recent Stanford seminar by Alex (Sandy) Pentland, MIT professor and faculty director of the MIT Connection Science Research initiative, - with which I’ve long been affiliated as a Connection Science Fellow.

The Impressive Scope of the Linux Foundation in the 21st Century Digital Economy

Last week, the Linux Foundation held its North America Open Source Summit in Austin. The week-long summit included a large number of breakout sessions as well as several keynotes. Open Source Summit Europe will take place in Dublin in September and Open Source Summit Japan in Yokohama in December.

I’ve been closely involved with open, collaborative innovation and open source communities since the 1990s. In particular, I was asked to lead a new Linux initiative that IBM launched in January of 2000 to embrace Linux across all the company’s products and services.

At the time, Linux had already been embraced by the research, internet, and supercomputing communities, but many in the commercial marketplace were perplexed by IBM’s decision. Over the next few years, we spent quite a bit of effort explaining to the business community why we were supporting Linux, which included a number of Linux commercials like this one with Muhammad Ali that ran in the 2006 Super Bowl. IBM also had to fight off a multi-billion dollar lawsuit for alleged intellectual property violations in its contributions to the development of Linux. Nevertheless, by the late 2000s, Linux had crossed the chasm to mainstream adoption, having been embraced by a large number of companies around the world.

In 2000, IBM, along with HP, Intel, and several other companies formed a consortium to support the continued development of Linux, and founded a new non-profit organization, the Open Source Development Labs (OSDL). In 2007, OSDL merged with the Free Standards Group (FSG) and became the Linux Foundation (LF). In 2011, the LF marked the 20th anniversary of Linux at its annual LinuxCon North America conference. I had the privilege of giving one of the keynotes at the conference in Vancouver, where I recounted my personal involvement with Linux and open source.

How to Build Organizational Resilience to Cyberattacks

I became quite interested in cybersecurity around a year ago, given the growing threats of cyberattacks by criminal groups and adversarial governments. I then joined CAMS, MIT’s interdisciplinary cybersecurity consortium and started attending its online weekly seminars. A few weeks ago I attended a CAMS seminar on cyber resilience by Manuel Hepfer, a research affiliate at Oxford University and a research analyst at ISTARI, a cyber risk management company.

What is cyber resilience? While cybersecurity is the practice of protecting critical systems and sensitive information from digital attacks, cyber resilience is the ability to prepare for, withstand, and recover rapidly from any major disruption, whether an intentional cyberattack or a natural disaster.

The seminar was based on a study by Hepfer and colleagues of the 2017 NotPetya ransomware attacks, a series of powerful cyberattacks which caused over $10 billion in global economic damage across a number of industries. Their study was primarily focused on three global companies that were the subject of NotPetya attacks: a logistics company with over 60,000 employees, a manufacturing one with over 20,000 employees, and a third in professional services with over 5,000 employees.

The study conducted around 50 interviews with senior executives across the three companies, including CEOs, CFOs, CIOs, and CISOs. In addition, it reviewed internal documents, presentations, press releases, newspaper coverage, and audio and video files related to events before, during, and after the attack. Their study was published in two articles in the MIT Sloan Management Review, the first in 2020, the second in 2022.

Can We Trust Our Impressive AI Language Models?

One of the key findings of the 2022 AI Index Report was that large language models (LLMs) are setting records on technical benchmarks thanks to advances in deep neural networks and computational power that allows them to be trained using huge amounts of data. LLMs are now surpassing human baselines in a number of complex language tasks, including English language understanding, text summarization, natural language inference, and machine translation.

A.I. Is Mastering Language. Should We Trust What It Says?, a recent NY Times Magazine article by science writer Steven Johnson, took a close look at one such LLM, the Generative Pre-Trained Transformer 3, generally referred to as GPT-3. GPT-3 was created by the AI research company OpenAI. It’s been trained with over 700 gigabytes of data from across the web, along with a large collection of text from digitized books. “Since GPT-3’s release, the internet has been awash with examples of the software’s eerie facility with language - along with its blind spots and foibles and other more sinister tendencies,” said Johnson.

“So far, the experiments with large language models have been mostly that: experiments probing the model for signs of true intelligence, exploring its creative uses, exposing its biases. But the ultimate commercial potential is enormous. If the existing trajectory continues, software like GPT-3 could revolutionize how we search for information in the next few years.” Instead of typing a few keywords into Google and getting back a long list of links that might have the answer, you’d ask GPT-3 what you’re looking for in English and get back a well-written, accurate answer. “Customer service could be utterly transformed: Any company with a product that currently requires a human tech-support team might be able to train an L.L.M. to replace them.”

A Human-Compatible Safe, Controllable AI

“Even in its present state, the technology of artificial intelligence raises many concerns as it transitions from research into widespread use,” wrote UC Berkeley professor Stuart Russell in a recent essay, If We Succeed. “These concerns include potential misuses such as cybercrime, surveillance, disinformation, and political manipulation; the exacerbation of inequality and of many forms of bias in society; the creation and deployment of lethal autonomous weapons; and the usurpation of human roles in the economy and in social relationships.”

“My concern here, however, is with the potential consequences of success in creating general-purpose AI: that is, systems capable of quickly learning to perform at a high level in any task environment where humans (or collections of humans) can perform well.” Decades ago, Alan Turing expressed a similar concern. While answering a question during a lecture in 1951, Turing said: “It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. … At some stage therefore we should have to expect the machines to take control.” Over the past decade, a number of public figures like Stephen Hawking and Nick Bostrom have argued that a superintelligent AI could become difficult or impossible for humans to control, and might even pose an existential threat to humanity.

Until recently, we didn’t have to worry about the consequences of such superhuman AIs because AI applications weren’t all that smart and were mostly confined to research labs. But, powerful AI systems have now matched or surpassed human levels of performance in a number of application- specific tasks such as image and speech recognition, skin cancer classification, breast cancer detection, and complex games like Jeopardy and Go. While these application-specific breakthroughs, - generally referred to as soft, or narrow AI, - were once viewed as the exclusive domain of humans, AI still lacks the deeper, general-purpose human intelligence that’s long been measured in IQ tests.

The 2022 Work Trend Index: How to Get Hybrid Right

“The people returning to the office are not the same as those who left in March 2020,” wrote Jared Spataro, Microsoft Corporate VP for Modern Work, in a recent Harvard Business Review (HBR) article, 5 Key Trends Leaders Need to Understand to Get Hybrid Right. The article summarized the findings of Microsoft’s 2022 Work Trend Index, a report based on various sources of information, including a survey of 31,000 people in 31 countries, labor trends from LinkedIn, and trillions of signals from Microsoft 365 suite of productivity apps.

“The data shows that the past two years left an indelible mark on the psyche of workers, altering their expectations and fundamentally shifting the way work gets done,” said Spataro. “This is a pivotal moment, where leadership matters more than ever. Those who embrace a new mindset and shift cultural norms will best position their people and their business for long-term success. Adapting to the influx of change in this business environment is no easy feat.”

“After many false starts, organizations are finally making the shift into a true hybrid work model. Like every other turning point over the past two years, there’s no shortage of perspectives on what this next phase will look like.” However, there’s no going back to the way work once was. Adapting to the many changes in this business environment is a challenge now facing every leader. To navigate the transition to hybrid work, leaders need to understand five key trends.

How Can You Trust the Predictions of a Large Machine Learning Model?

Artificial intelligence has emerged as the defining technology of our era, as transformative over time as the steam engine, electricity, computers, and the Internet. AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, and other human domains. Machine learning (ML) advances, like deep learning, have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed.

Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks. Each artificial neural unit is connected to many other such units, and the links can be statistically strengthened or decreased based on the data used to train the system. But such statistical methods are not equally suitable for all tasks. Tasks that are particularly suitable for machine learning, exhibit several key criteria, such as the availability of large data sets of well-defined input-output pairs for training ML classifiers, - e.g., carefully labeled cat, not-cat pictures for cat recognition classifiers, and english-french document pairs for machine translation algorithms.

The methods behind a machine learning prediction, - subtle adjustments to the numerical weights that interconnect its huge number of artificial neurons, - are very difficult to explain because they’re so different from the methods used by humans. The bigger the training data set, the more accurate the prediction, but the more difficult it will be to provide a detailed, understandable explanation to a human of how the prediction was made.

A few weeks ago I attended an online seminar, - How Can You Trust Machine Learning? by Stanford professor Carlos Guestrin, - on the difficulty of understanding machine learning predictions. Guestrin’s seminar was based on a 2016 article he co-authored with Marco Tulio Ribeiro and Sameer Singh , “Why Should I Trust You?” Explaining the Predictions of Any Classifier.

The 2022 AI Index Report: AI’s Emerging Real-World Impact

On March 16, Stanford’s Institute for Human-Centered Artificial Intelligence (HAI) released the 2022 AI Index report, its fifth annual study on the impact and progress of AI. The comprehensive study was led by an interdisciplinary group of experts from across academia and industry, and highlights the rapid rate of technical and marketplace AI advances, as well as the growing ethical and regulatory concerns associated with AI.

“2021 was the year that AI went from an emerging technology to a mature technology - we’re no longer dealing with a speculative part of scientific research, but instead something that has real-world impact, both positive and negative,” said Jack Clark, co-chair of the AI Index. “This year’s AI Index tells us that AI is being integrated into the economy and the effects of it are beginning to go global across research, deployment, and even funding.” Two weeks ago I attended an online HAI seminar where Clark gave this excellent presentation on the AI Index.

The report is organized into five chapters: Technical Performance, AI Ethics, R&D, Economy and Education, and Policy and Governance. Let me summarize the key findings of each chapter.

The Tech Trends that Will Most Reshape Companies Over the Next Decade

“In the next decade, we’ll experience more progress than in the past 100 years combined, as technology reshapes health and materials sciences, energy, transportation, and a wide range of other industries and domains,” notes a recent McKinsey study on the The top trends in tech. “The implications for corporations are broad.”

“These trends may not represent the coolest, most bleeding-edge technologies. But they’re the ones drawing the most venture money, producing the most patent filings, and generating the biggest implications for how and where to compete and the capabilities you need to accelerate performance.”

“Unifying and underlying them all is the combinatorial effect of massively faster computation propelling new convergences between technologies; startling breakthroughs in health and materials sciences; an array of new product and service functionalities; and a strong foundation for the reinvention of companies, markets, industries, and sectors.”

The Widening Gap Between Technological and Institutional Change

A few weeks ago I read The Exponential Age Will Transform Economics Forever, an article in Wired by Azeem Azhar based on his recently published book The Exponential Age. Azhar’s central thesis is that the chasm between what technology allows us to do and what our institutions are prepared to handle has been rapidly widening. New technologies are being invented and scaled at an ever-faster pace. But our institutions, - including our economic systems, political organizations and social norms, - are changing much more slowly. While technological advances follow an exponential curve, institutional adaptation follow a straight, incremental line.

The gap between technological and institutional change is nothing new. Ever since the advent of the Industrial Revolution, there’s been a significant time lag between the emergence of a transformative technology and its ensuing impact on economies and societies. Even after reaching a tipping point of market acceptance, it takes considerable time, - often decades, - for their benefits to be fully realized.

In The Productivity J-Curve, a 2018 NBER paper, Erik Brynjolfsson, Daniel Rock, and Chad Syverson  explained that general purpose, transformative technologies, - such as the steam engine, electricity, and semiconductors, - “are the defining technologies of their times and can radically change the economic environment. They have great potential from the outset, but realizing that potential requires larger intangible and often unmeasured investments and a fundamental rethinking of the organization of production itself.”

The life cycle of historically transformative technologies consists of two district phases, investment and harvesting. Since the technologies are general purpose in nature, they require massive complementary investments, including business process redesign, co-invention of new products and business models, and the re-skilling of the workforce. Moreover, the more transformative the technologies, the longer it takes for them to reach the harvesting phase when they are widely embraced by individuals, companies and industries across the economy.

The Impact of Open Source on the EU Economy

In the 1990s, open source was generally viewed as experimental approach to software development mostly used by research and academic communities on emerging projects like the Internet and Linux. Open source has since evolved to become a well-accepted model of economic production across just about all private- and public-sector communities around the world. But, while widely used, what’s been the economic impact of open source on nations and firms? This question was addressed in a recent study on the impact of open source on the European economy.

“The increasing relevance of Open Source (OS) during the last two decades requires an update of an in-depth analysis of its current role, position and its potential for the European economy,” said the European Commission (EC) in its report on The impact of open source software and hardware on technological independence, competitiveness and innovation in the EU economy.

“Whereas Open Source Software (OSS) has become mainstream across all sectors of the software industry during the past 20 years, Open Source Hardware (OSH) is still in an emerging phase. However, the business ecosystem for OSH is developing.”

The EC conducted a comprehensive analysis of the commercial uses, costs, and benefits of OSS and OSH. “On the basis of this information, the study assesses the potential for the European Union (EU) to achieve its policy goals (including economic growth, greater competitiveness, innovation, and job creation) through the use, promotion and support of OSS and OSH.”

The Rise and Fall of Nation-States

Several weeks ago I attended The End of Nation-States, by technology executive and consultant Tomás Pueyo, - part of the Stanford Digital Economy Lab seminar series. In May of 2021, Pueyo launched Unchartered Territories, a newsletter he describes as aiming to explore the unchartered territories of a fast changing world “to know how we can prepare for them.”

His seminar discussed the role of information technologies in the rise of nation-states throughout history, and how information technologies are likely to lead to the end of nation-states in the coming decades. Let me summarize Pueyo’s key arguments based on his talk and two related newsletters.

The Rise of Nation-States

In Internet and Blockchain Will Kill Nation-States, published in August of 2021, Pueyo explained how the printing press led to the emergence of nation-states in the 16th century.

The feudal system was the basic structure of society between the 9th and 15th centuries in medieval Europe. Feudalism was based on the relationship between a landed-aristocracy, - e.g., kings, dukes, counts, - and the vassals under their protection who vowed them allegiance. Power in the feudal system was hyperlocal and widely distributed across different landowners. Communication was difficult across different localities, since they each generally spoke different dialects or altogether different languages.

The Catholic Church was the biggest power in medieval Europe. The root of its power was the Church's well organized hierarchy of priests, bishops, archbishops, cardinals, and the Pope, all of which were able to communicate with each other far better than everyone else, - aristocrats and vassals alike.

Getting Ready for the 100-Year Life

“In the United States, demographers predict that as many as half of today’s 5-year-olds can expect to live to the age of 100,” said The New Map of Life, a recent report from the Stanford Center on Longevity. The   cross-disciplinary Stanford Center was founded in 2007 to conduct research on the key issues associated with increased longevity, - from cognitive health to physical well-being and financial security, - with the aim to make long lives healthy and rewarding.

While the 100-year life may be here, we’re not ready. “By the middle of this century, this once unattainable milestone may become the norm for newborns, continuing a remarkable trend that saw human life expectancies double between 1900 and 2000, and still rising in this century, despite the grievous toll of the Covid-19 pandemic,” notes the report. “Longevity is one of the greatest achievements in human history, brought about by reductions in infant mortality, advances in sanitation and medicine, public education, and rising standards of living. Yet the change came about so quickly that the social institutions, economic policies, and social norms that evolved when people lived for half as long are no longer up to the task.”

According to the Stanford Center, there’s a clear distinction between aging and longevity. Aging is the biological process of getting older - the accumulation of changes in a human being over time. Longevity is “the measure of long life,” - the ways to enhance the quality of a long life “so that people experience a sense of belonging, purpose, and worth at all ages and stages.”

The Promise & Peril of Human-Like Artificial Intelligence

In his 1950 seminal paper, Computing Machinery and Intelligence, Alan Turing proposed what’s famously known as the Turing test, - a test of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. If a human at a keyboard couldn’t tell whether they were interacting with a machine or a human, the machine is considered to have passed the Turing test. “Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers and entrepreneurs,” wrote Erik Brynjolfsson, - Stanford professor and Director of the Stanford Digital Economy Lab, - in a recent article, The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence.

“The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds. But not all types of AI are human-like - in fact, many of the most powerful systems are very different from humans - and an excessive focus on developing and deploying HLAI can lead us into a trap. … On the one hand, it is a path to unprecedented wealth, increased leisure, robust intelligence, and even a better understanding of ourselves. On the other hand, if HLAI leads machines to automate rather than augment human labor, it creates the risk of concentrating wealth and power. And with that concentration comes the peril of being trapped in an equilibrium where those without power have no way to improve their outcomes, a situation I call the Turing Trap.”

Over the past decade, powerful AI systems have matched or surpassed human levels of performance in a number of tasks such as image and speech recognition, applications like skin cancer classification and breast cancer detection, and complex games like Jeopardy and Go. These AI breakthroughs are generally referred to as soft, narrow or specialized AI, inspired by, but not aiming to mimic the human brain. They’ve been generally based on machine learning, that is, on the analysis of vast amounts of information using powerful computers and sophisticated algorithms, whose results exhibit qualities that we associate with human intelligence.

The Work of the Future Is Ours to Invent

The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, by David Autor, David Mindell and Elisabeth Reynolds was published last week. The new book is based on the multiyear MIT Task Force on The Work of the Future that they jointly co-chaired.

The Task Force was commissioned by MIT President Rafael Reif in the spring of 2018 to address one of the most critical questions of the digital economy, - an economy that technology universities like MIT have played a major role in bringing about: 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? Led by Autor, Mindell and Reynolds, the task force involved over 20 faculty members from 12 departments at MIT as well as more than 20 graduate students. Its final report was released in November of 2020. In addition, the Task Force published a number of working papers and research briefs.

“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 Task Force overarching conclusion. But, it argued that with better policies in place, more people could enjoy good careers even as new technologies transform the very nature of work.

Let me summarize the Task Force key findings which the book builds on:

Is It Possible to Establish Norms for Responsible State Behavior in Cyberspace?

In mid-December, the Council on Foreign Relations sponsored a virtual roundtable with Joseph Nye, - former dean of Harvard’s Kennedy School of Government, - to discuss his recent Foreign Affairs article The End of Anarchy?: How to Build a New Digital Order. Professor Nye has long been regarded as one of America’s preeminent strategic thinkers and political scientists. In the 1970s he chaired the National Security Council Group on Nonproliferation of Nuclear Weapons, and over the past decade he’s brought his expertise to the study of conflict and deterrence in cyberspace.

Cybersecurity is an increasingly important aspect of the of US national security strategy, including global trade and the protection of our critical infrastructures. In June of 2021, 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 July editorial, the NY times said 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.”

At an MIT conference in February of 2019, former US Secretary of State Henry Kissinger was asked if we need cybersecurity control agreements with Russia, China and other nations similar to the nuclear arms control agreements that he spent so much time negotiating during the Cold War. Dr. Kissinger replied that for arms control to be effective, the two sides needed to share information and agree to inspections. But such mechanisms are harder to apply in the digital world, because the transparency that was essential for arms control would be very hard to establish for cyber threats. In addition, while controls of physical arms are relatively explicable and negotiable, the variety and speed of cyber attacks make it much harder to develop adequate control agreements.

What’s the State of Our Digital Technology Revolution?

I first met Carlota Perez in the mid-2000s when she gave a seminar at IBM based on her 2002 bestseller Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages. At the time, we had been living through a series of major changes, - the explosive growth of the Internet, the advent of the digital economy, the dot-com bubble, and the bursting of the bubble. In her excellent presentation Perez explained these turbulent times by positioning them within the historical perspective she wrote about in her book.

If you look at the historical big picture, patterns begin to emerge which serve as a good guide for understanding the past and thinking about the future. Since the onset of the Industrial Revolution we’ve had 5 major technological revolutions, each one lasting roughly 40 to 60 years. First was the age of machines, factories and canals starting in 1771. This was followed by the age of steam, railways, iron and coal, starting in 1829; steel, electricity and heavy engineering in 1875; oil, automobiles, and mass production in 1908, and our present information technology and telecommunications (ICT) digital age starting in 1971.

Technology revolutions are engines of growth, ushering new paradigms for innovation, rejuvenating and transforming the economy, and re-shaping social behavior and the institutions of society. To better understand the dynamics of a technology revolution, we should split it into two different periods, each lasting 20 to 30 years.

The World Ahead 2022: Adjusting to New Realities

A few weeks ago The Economist published The World  Ahead 2022, its 36th annual look at the economic, political, social and cultural trends that will likely shape the coming year. “If 2021 was the year the world turned the tide against the pandemic, 2022 will be dominated by the need to adjust to new realities, both in areas reshaped by the crisis (the new world of work, the future of travel) and as deeper trends reassert themselves (the rise of China, accelerating climate change),” wrote the issue’s editor Tom Sandage.

Here are the top ten major trends in The World Ahead 2022:

A Winning Strategy for the Post-Pandemic “New Normal”

“A winning digital strategy requires new twists to familiar moves,” said a recent McKinsey article, Strategy for a digital world. “Competitive differentiation, now more than ever, emerges from superior digital capabilities and technology endowment, more agile delivery, and a progressively more tech-savvy C-suite.”

According to the latest McKinsey Global Survey on digital strategy, the pandemic has sped up the adoption of digital technologies by several years. While the imperative for a strategic approach to technology is universal, the survey found that some companies are already leading the pack because they have better overall technology capabilities, talent, leadership, and resources, and their superior technology endowment is linked to better economic outcomes.

The pandemic has accelerated the pace of change of the leading technology companies as well as the industry leaders who’ve been investing heavily in new digital-enabled strategies and business models, widening the gap between these top economic performers and all other companies. At the same time, the survey showed that many of the organizations that are being left behind could be missing opportunities to catch up by investing in the areas of their business most at risk of digital advances.

Traditional legacy companies should revisit the classic strategies that may have worked well for them in decades past but that must now be updated given the accelerated pace of digital disruption. The McKinsey article offers a road map for so doing based on five major changes.

The Evolution of Enterprise Architecture in an Increasingly Digital World

“What does it take for traditional companies to create value with digital technology?,” asked a McKinsey article published in November of 2018. Based on its research, the article suggested that “successful digital reinventors - digital natives and digitally transformed incumbents - employ a range of approaches, such as investing boldly and adopting cutting-edge technologies at scale.” However, the article warned, such efforts can run into serious difficulties. “In our experience, a push to launch more digital applications can make a company’s technology landscape increasingly complex and difficult to manage, to the point that it impedes transformation programs.”

A few months ago, I wrote about the evolving role of the CIO, based on a survey of over 500 CIOs and related technology leaders. Almost all of the surveyed CIOs agreed that their responsibilities will become more strategic in the coming years, uniting their company’s business and technology strategies and managing increasingly complex and difficult digital transformations.

This strategic role of the CIO is in turn driving the evolution of their enterprise-architecture (EA) teams. Traditionally, enterprise architects have been responsible for translating business needs into IT requirements. A major part of their job is making sure that their company’s IT systems work together to  enable and support the company’s overall digital strategy. And increasingly, EA teams have the primary responsibility for managing the technological complexity inherent in digital transformations.

How to Effectively Manage a Company’s Digital Transformation

The adoption of new digital technologies in response to the pandemic has reached levels that weren’t expected for many year, - and those changes are likely be long lasting. A few months ago, for example, a McKinsey survey of global executives found that the pandemic has accelerated the digitization of their customer and supply-chain interactions, their internal operation, and the share of digitally-enabled products and services in their portfolios by roughly three to four years.

“Faced with the pandemic, firms did things that once seemed impossible - sometimes overnight,” noted Forrester Research in an online event that took place earlier this year. “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.”

But, what should the vast majority of less digitally savvy companies do to embrace the changes they were forced to make in record time to deal with the immediate crisis? How to Speed Up Your Digital Transformation, a recent Harvard Business Review article by Benjamin Mueller and Jens Lauterbach, provides some answers to this question.

“The pandemic has given many organizations an unexpected crash course in digitalization,” wrote the authors. “While much progress has been made - from hardware and infrastructure to updated work processes and a rejuvenated corporate culture - many organizations are confronting the question of how to integrate fragmented and often makeshift digitalization efforts in a way that’s sustainable. … This is particularly pertinent for small and medium-sized organizations that need to be more targeted in their efforts and may not have the resources to engage in the ‘fail fast’ approach often heralded by the larger poster children of the digitalization movement.”

How Has Working from Home Worked Out So Far?

“COVID-19 triggered a mass social experiment in working from home (WFH),” said economists Jose Maria Barrero, Nicholas Bloom, and Stephen J. Davis in Why Working from Home Will Stick, a paper published in April of 2021 by the National Bureau of Economics Research (NBER). “Americans, for example, supplied roughly half of paid workhours from home between April and December 2020, as compared to five percent before the pandemic. This seismic shift in working arrangements has attracted no shortage of opinions about whether WFH will stick.”

Working from home (WFH) has been around for decades, modestly growing in the 1990s with the rise of the Internet. The share of WFH three or more days per week was under 1% in 1980, 2.4% in 2010, and 4.0% in 2018. Then came Covid-19, forcing tens of millions around the world to work from home and triggering a mass workplace experiment that broke through the technological and cultural barriers that had prevented its adoption in the past.

To investigate whether WFH will stick, the authors of the NBER paper devised a survey that asked questions about working arrangements and personal experiences with WFH during the pandemic, as well as worker preferences and employer plans after the pandemic ends. The survey was conducted monthly between May 2020 and March 2021, by which time it had collected 28,597 valid responses from 20-64 years old US workers, of which 43.8% were female. The typical respondent was 40 to 50 years old, with one to three years of college, who earned $40 to $50 thousand in 2019.

Let me discuss a few of the survey’s questions and findings.

The Complex Interplay Between Cybersecurity and Regulatory Compliance

Cybersecurity threats have significantly increased since March of 2020 when much of the economy was forced online to help us cope with the Covid crisis, including a number of high profile attacks by international criminal groups and adversarial governments. This past June, FBI Director Christopher Wray compared the danger of ransomware attacks on US firms by Russian criminal groups to the 9/11 terrorist attacks. When Biden and Putin met in Geneva a few weeks later, cyberweapons control was at the top of the agenda, a spot previously occupied by the control of nuclear weapons.

It’s been clear for a while that in a world increasingly governed by digital data and transactions, our existing cybersecurity methods have been far from adequate. To learn more about this very important area, earlier this year I joined  CAMS, MIT’s interdisciplinary cybersecurity initiative, and started attending its research seminars.

At a recent seminar, I heard a very interesting presentation on Compliance and Cybersecurity by CAMS research affiliate Angelica Marotta. Her seminar was based on Convergence and divergence of regulatory compliance and cybersecurity, a recent paper she co-authored with MIT professor Stuart Madnick.

Marotta’s opening slide included succinct definitions of cybersecurity and compliance that highlight their different objectives:

• Cybersecurity is responsible for the “internal procedures established by a company to improve the security of its operations.”
• Compliance is responsible for the “external regulations established by governments or industries groups that must be met.”

Today, in order to be successful, every organization needs to be both compliant and cyber secure. But, the extent to which compliance helps or hinder security for an organization continues to be a very important research question.

The Supply Chain Economy - A New Categorization of the US Economy

A few weeks ago I attended The Supply Chain Economy: Understanding Innovation in Services, a virtual seminar sponsored by the Council on Foreign Relations with economists Mercedes Delgado and Karen Mills discussing their recent paper A New Categorization of the U.S. Economy.

The debate about the drivers of innovation and job creation has long been centered on manufacturing versus services. The predominant view has been that manufacturing drives good wages, economic growth, and innovation as measured by its large share of patents, while services provide lower-wage jobs, less innovation and significantly fewer patents.

But Delgado and Mills argue that categorizing the economy in terms of manufacturing versus services is no longer meaningful. Instead, they’ve proposed an alternative framework for understanding the drivers of innovation and economic performance that’s focused on the suppliers of both goods and services: the supply chain economy.

“A long academic and policy debate has focused on the role of the manufacturing capacity of a country in its economic and innovative performance,” wrote the authors. “This question has become even more relevant as the U.S. economy has shown a large decline in manufacturing employment in recent decades, in part due to increased import competition. In this debate, the predominant view is that a country's manufacturing capacity drives innovation because of externalities associated with the production of intermediate goods (e.g., machine tools, automation equipment, and semiconductors) that improve the efficiency of the innovation process. Most prior work on innovation focused on a narrow view of suppliers as producers of intermediate goods. However, in today's economy suppliers increasingly produce services (e.g., enterprise software).”

A Pandemic Is Fundamentally an Information Problem

A few weeks ago I attended a very interesting online seminar, Economics in the Age of Covid-19, by University of Toronto professor Joshua Gans. Over the past 18 months, professor Gans has been conducting research and writing extensively on the impact of Covid-19, including a number of articles, a newsletter, and two books. His key thesis is that a pandemic is fundamentally an information problem. If you know that someone you interact with is potentially infectious, you can take actions to limit the interactions. However, if you have to guess whether a person is infectious, you’re taking a risk. Not only can you become infected, but you might also pass that infection on to others.

“The difference between perfect knowledge and no knowledge is what causes an infectious disease to have an impact on social and economic interactions,” wrote Gans in The Pandemics Information Gap, first published in April of 2020, followed by an expanded second edition in November of 2020. “With perfect knowledge, some people get sick, they are isolated, and life is (for most of us) essentially unchanged. … perfect knowledge allows you to avoid all infected people. No knowledge makes it near certain that you will encounter at least one infected person.” Furthermore, when we don’t know who is infected, we have to act as if everyone is infected, which then leads to major economic and social disruptions, including near-empty offices and city centers, decreased travel and leisure activities, and learning from home instead of in school.

In his seminar, Gans cited the 2002 SARS outbreak in China, Hong Kong, and Taiwan and the 2015 MERS outbreak in South Korea as examples of pandemics that were quickly contained. With SARS and MERS, people only became infectious when they developed a fever, cough, and other easily identifiable flu-like symptoms. Anyone suspected of being infected could thus be quickly isolated before infecting many others. This made it possible to contain the SARS and MERS viruses within a few short months, and to suppress them completely a few months later.

The 2020 COVID-19 outbreak was different. About a third of the people infected with the virus were asymptomatic carriers who didn’t develop noticeable symptoms but were still capable of infecting others. Of the people who showed symptoms, around 80% were only mild to moderate. And, there was generally a delay of several days between the time a person first became infected and the appearance of the first symptoms. In other words, the information problem was relatively straightforward with SARS and MERS, which made them easier to manage and contain, whereas managing the information problem with COVID-19 has been much, much harder.

Rethinking Digital Strategy for the Post-Pandemic Era

“One year into the COVID-19 crisis, our newest McKinsey Global Survey on digital strategy indicates that the pandemic has increased the pace of business and that technology capabilities will be critical to companies’ COVID-19 exit strategies as well as to what comes next,” said a recent McKinsey report, The new digital edge: Rethinking strategy for the postpandemic era. The survey was conducted online in January of 2021, and received responses from over 1,100 C-level executives, business unit heads and senior managers from different industries, functions, company sizes and regions.

The survey’s overriding finding is that the pandemic has sped up the adoption of digital technologies by several years. “The imperative for a strategic approach to technology is universal, yet some companies are already leading the pack; their responses show that better overall technology capabilities, talent, leadership, and resources (what we call a company’s technology endowment) are linked to better economic outcomes. At the same time, the results confirm that many organizations could be missing opportunities to invest in the areas of their business models that are most at risk of digital disruption.”

The technology endowments of the top-decile economic performers include cloud adoption and infrastructure, both public and private; a common source of data across the organization; a modern architecture for technology elements; sufficient cybersecurity to mitigate current risks and threats; prioritization of tech resources to the most strategically important efforts; increased investment in talent; filling key technology roles with high-quality individuals; increased R&D spending; and creation of new partnership.

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.

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 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.

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.”

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.”

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 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.