Irving Wladawsky-Berger

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.

Beyond GDP: A Framework for Measuring Economic Progress

“What is meant by economic progress, and how should it be measured?,” asked economists Diane Coyle and Leonard Nakamura in a recent paper, Time Use and Household-Centric Measurement of Welfare in the Digital Economy. “The conventional answer is growth in real GDP over time or compared across countries, a monetary measure adjusted for the general rate of increase in prices. However, there is increasing interest in developing an alternative understanding of economic progress, particularly in the context of digitalization of the economy and the consequent significant changes Internet use is bringing about in production and household activity.”

Gross Domestic Product (GDP) became the accepted international measure of economic progress in the 1940s. It was a good measure for an industrial economy dominated by the production of physical goods, but GDP doesn’t reflect important economic activity beyond production, such as income, consumption and quality of life, nor does it capture measures of economic welfare or utility, that is, the extent to which a service or good satisfies an individual’s wants and needs. In addition, GDP doesn’t include the value of the increasing amounts of free information goods now available in the Internet-based digital economy, including email, texts, social media, maps, apps and videos.

In their paper, Coyle and Nakamura propose an alternative approach for measuring economic progress based on how much time people spend on different daily activities - e.g., paid work, household tasks, leisure, and consumption - combined with measures of well-being while engaged in these different activities. “In an economy that is four-fifths services rather than goods, with time to consume therefore inherent in the majority of economic activity, the utility of the different uses of time seems key to understanding economic welfare as well as productivity.”

Beatlemania: Why Are Success and Failure So Unpredictable?

“Why did the Beatles become a worldwide sensation? Why do some cultural products succeed and others fail? Why are some musicians, poets, and novelists, unsuccessful or unknown in their lifetimes, iconic figures decades or generation after their deaths? Why are success and failure so unpredictable?,” these are among the questions explored in Beatlemania, a draft paper by Harvard professor Cass Sunstein that will be published later this year in the inaugural issue of The Journal of Beatles Studies.

“On one view, the simplest and most general explanation is best, and it points to quality, appropriately measured: success is a result of quality, and the Beatles succeeded because of the sheer quality of their music,” wrote Sunstein. “On another view, social influences are critical: timely enthusiasm or timely indifference can make the difference for all, including the Beatles, leading extraordinary books, movies, and songs to fail even if they are indistinguishable in quality from those that succeed.”

In 1961 the Beatles  were an obscure English rock band from Liverpool, with no manager and modest prospects. They tried to release a debut single, Love Me Do, but every record label they approached rejected them and the band came close to splitting. In January of 1962 Brian Epstein became their manager. Epstein had no experience in managing artists, but he liked their music and sense of humor. He eventually persuaded EMI producer George Martin to audition the band in June of 1962, who despite feeling that they were “a rather raw group” with “not very good songs,” agreed to sign them to a recording contract.

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.

What Is Web3, and Could It Usher a New Economic System?

Bitcoin was introduced in 2008 with the release of Bitcoin: A Peer-to-Peer Electronic Cash System, which explained how to design a decentralized cryptocurrency and digital payment system without the need for central banks or trusted intermediaries. Blockchain, the digital ledger for managing and certifying the validity of bitcoin transactions, was introduced in the same paper.

Over the years blockchain has transcended its original objectives, and has evolved in two major directions. One continues to focus on blockchain as the underlying platform for bitcoin, but it’s also become the platform for the large number of cryptocurrencies, digital tokens, and other cryptoassets that have since been created. The other is focused on the use of blockchain as a trusted distributed data base for private and public sector applications involving multiple institutions, such as supply chains, financial services and healthcare. The cryptocurrency camp is based on public permissionless blockchains, which anyone can join and require some kind of proof-of-work or proof-of-stake systems. The multi-institution camp is based primarily on private permissioned blockchains where participation is restricted to the institutions transacting with each other.

Given my professional interests, I’m particularly interested in the use of blockchains in business and public sector applications for two major reasons. First, I consider blockchain as a major next step in the continuing evolution of the Internet, helping us enhance the security of Internet transactions and data by developing a layer with the standard encrypted services for secure communication, storage and data access. And, longer term, blockchain technologies can significantly improve the efficiency, resilience, and management of complex global applications involving multiple institutions.

But, while I’ve been somewhat skeptical of the crypto camp, a number of intriguing crypto-related topics have recently captured my attention, including Non-Fungible Tokens (NFTs), Decentralized Finance (DeFi), and especially Web3. These various topics were nicely explained by technology columnist Kevin Roose in The Latecomer’s Guide to Crypto, an article in the March 20 SundayBusiness section of the NY Times.

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.

How Do Digital Infrastructures Increase a Firm’s Value?

“In the information age, the value of a firm rests fundamentally on how it stores, shares and processes information,” wrote Seth Benzell, Jonathan Hersh, and Marshall Van Alstyne in a recent paper, How APIs Create Growth by Inverting the Firm. “Digital infrastructure is therefore central to a firm’s success.”

But, how do digital infrastructures increase a firm’s value? One method is to make more efficient use of the firms internal capital by modularizing, reconfiguring and reusing the firms IT resources. A second method, used in platform businesses, is to leverage external capital by giving access to the firm’s resources to third parties, and capturing a share of the resulting surplus. Both methods require the implementation of Application Programming Interfaces (APIs), whether for only for internal use by the firm’s developers or exposed externally to the firm’s platform partners. Both methods also feature a modular architecture of remixable resources. “Modularity combines the advantages of standardization typically associated with high volume processes together with the advantages of customization typically associated with bespoke processes.”

“An API is a set of routines, protocols, and tools that standardizes building software applications compatible with an associated program or database. APIs fundamentally are code that control access to information. … They govern the type and format of calls or communications that any application can make of another associated program. The answering program is agnostic about the source of the call, yet can require access permission, and the calling program need not know anything about the internal workings of the answering program. … When designing an API, the architect decides how much of the computer system or data to expose to which users. Being accessible on the web, these API endpoints act as a constant conduit to business processes that the firm itself specifies.”

How Does AI Improve Human Decision-Making?

“Artificial Intelligence (AI) is the newest general-purpose technology (GPT),” wrote Sukwoong Choia, Namil Kim, Junsik Kim, and Hyo Kang in a recent paper, How Does AI Improve  Human Decision-Making? Evidence from the AI-Powered Go Program. “One remarkable characteristic of AI is its ability to provide humans with high-quality predictions at relatively low cost and to automate a wide range of predictions. AI has already outperformed human professionals in many domains - including strategic gameplay, medical diagnosis, and new drug development.” 

“Furthermore, as a GPT, the domains where AI outperforms humans are expanding at a fast pace. The rapid development and adoption of AI thus raise an interesting yet pressing question about how AI affects human tasks in these various domains. Studies of AI and human capital, for instance, have examined the performance gap between humans and AI, highlighting AI’s potential for replacing jobs.” However, we should keep in mind that new technologies have been replacing workers and transforming economies for over two centuries. But, over time, these same technologies led to the creation of whole new industries and new jobs, increasing productivity and economic output, raising earnings, and augmenting the demand for labor.

Workers can increase their productivity and performance when assisted in their jobs by AI-based tools. But beyond its assistant role, AI can play a major instructional role by training human professionals to make better decisions. Recent technology advances have significantly improved the quality and reduced the cost of AI-based predictions.

The paper raises a number of important questions about the use of AI to improve human decisions making. Can we show quantitative evidence that AI-based predictions actually improve the quality of human decisions? If so, by what mechanisms are AI-based human decisions improved? And, do the improvements vary between depending on ones attitude toward AI-based decisions?

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.

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.

A Blockchain-based Framework for Safeguarding the Integrity of Real-World Assets

Blockchains first came to light in 2008 as the architecture underpinning bitcoin, the best known and most widely held cryptocurrency. It’s a truly brilliant architecture built on decades-old fundamental research in cryptography, distributed data, game theory and other advanced technologies. The blockchain’s original vision was limited to enabling bitcoin users to transact directly with each other with no need for a financial institution or government agency to certify the validity of the transactions. But, like the Internet and other transformative technologies, blockchain has now transcended its original objectives.

Blockchains are a kind of distributed ledger technologies (DLT), which also include non-blockchain DLTs. Over the past decade, an increasing number of people, including me, consider blockchains and DLTs as major next steps in the evolution of the Internet. In 2016 the World Economic Forum (WEF) named The Blockchain in its annual list of Top Ten Emerging Technologies citing its potential to fundamentally change the way markets and governments work. “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built,” said the WEF. “And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

The Internet is a general purpose network platform that unlocked huge innovations and economic value by significantly lowering the cost of connections and supporting a large variety of applications. A major reason for its ability to support so many different kinds of applications is that the Internet’s foundations, its TCP-IP layer, has stuck to its basic data-transport mission, i.e., just moving bits around.  It has no idea what the bits mean or what they’re trying to accomplish.

The design decisions that shaped the Internet back in the mid-late 1980s didn’t optimize for security and privacy, or for the ability to authenticate transactions between two or more parties. That’s all the responsibility of the applications running on top of the TCP/IP layer, and they each generally do it their own way, sometimes not at all. Not surprisingly, the lack of standards for security, privacy and transactional integrity have been the biggest challenges facing the Internet in our fast growing digital economy.

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.

A New Measurement Framework for the Digital Economy

Several weeks ago I heard a very interesting keynote presentation by Cambridge professor Diane Coyle at the annual workshop of the Stanford Digital Economy Lab, - What Don’t We Know About Measuring the Digital Economy? Professor Coyle is also a research associate of the UK Economics Statistics Centre of Excellence. In 2017 she was a recipient of the Indigo Prize on how to measure economic activity in the digital economy for her essay Making the Future Count, co-authored with Benjamin Mitra-Kahn.

“GDP captures only market transactions at the price of exchange, and not the welfare gains, externalities, environment, distribution of wealth or innovation which occurs in an economy,” wrote the authors. “Hence almost since its creation in the 1940s it has been criticised for its inability to capture economic welfare. Now changes in the economy, being restructured by digital technology and paying the price for unsustainable growth, make the case for a new measurement framework more pressing than ever. GDP was never an ideal measure of economic welfare and its suitability has been decreasing.”

Gross Domestic Product (GDP) became the accepted international measure of economies in the1940s. While being a good measure for the 20th century industrial economy, GDP is a flawed measure for the 21st century economy. It was suitable when the economy was dominated by the production of physical goods, but GDP doesn’t adequately capture the growing share of services and other intangible assets that now characterize advanced economies. Nor does it reflect important economic activity beyond production, such as income, consumption and living standards.

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:

New Ways of Understanding Human Behavior

“It is often said that no person is an island and it takes a village to raise a child, but psychology has largely lacked the scientific evidence to quantify and characterize these aphorisms,” wrote MIT professor Alex (Sandy) Pentland in Contextualizing Human Psychology, - an article published in the August, 2020 issue of Technology, Mind and Behavior, a journal of the American Psychological Association. “As a result, experimental focus is usually on more easily quantifiable individual traits and behaviors.”

“In the last decade, however, digital data from online interactions, cell phones, and credit cards have allowed us to precisely quantify large-scale social behavior at a very fine level of detail. The little data breadcrumbs that we leave behind as we move around in the world are enabling new ways of understanding human behavior, giving rise to the emerging discipline of computational social science. “[T]hese new tools can help relate individual traits to the surrounding social context and thus better explain life outcomes and societal characteristics.” 

The article illustrated the advances and challenges of data-driven predictions by discussing a 2017 mass collaboration experiment. The experiment asked each of 160 academic teams to predict six life outcomes, - such as a child’s grade point average and whether a family would be evicted from their home, - by analyzing development data of over 4.200 at-risk children from the Fragile Families and Child Wellbeing Study. These data had been collected by interviewing primary caregivers over more than 15 years, as well as other assessments including early childhood education and the children’s scores on a variety of standard tests. Additional information was provided on the parents, including medical, employment, and incarceration histories, religion, and child care practices. Almost 13,000 measurements were made for each child and their family.

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.

Is the Metaverse the Next Major Phase of the Internet?

“Don’t mock the metaverse,” said The Economist in a November 20 article. “There are good reasons to take the metaverse seriously. One is historical: as computers have become more capable, the experiences which they generate have become richer. The internet began its life displaying nothing more exciting than white text on a black background. Flat images were added in the 1990s. Video came to dominate in the 2010s. On that reading, a move into three dimensions is a logical consequence of the steady growth in computing power.”

“The metaverse is already here. It’s just not evenly distributed,” the article added. “The video-game industry - the only type of entertainment fully exposed to the compounding power of Moore’s law - has been selling virtual worlds for years.” Online games like World of Warcraft, Fortnite, and Roblox have hundreds of millions of users, and their gaming companies have valuations in the tens of billions, - evidence that immersive virtual worlds can be popular and profitable. “It is hard to argue that an idea will never catch on when, for millions of people, it already has.”

3-D virtual environments have had limited success beyond the video-game industry. But this could change. Leading technology companies have announced plans to expand their use in business, education, social media, and other so-called serious applications. On October 28, Mark Zuckerberg announced his commitment to a metaverse strategy by rebranding Facebook as Meta Platforms. A week or so later, Satya Nadella shared Microsoft’s metaverse plans, starting with enabling users of Mesh for Microsoft Teams to interact and collaborate in 3-D immersive workspaces.

Technology’s Role in Society: It’s Complicated

“Welcome to the future - not 2021, as you might have been expecting, but 2025, or even 2030, depending on whom you ask,” said The Economist in a  November, 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.”

For years, companies and governments found all kinds of reasons for not embracing work from home, virtual meetings, telemedicine, online learning, and other digital applications. But, the pandemic forced us to accelerate the digital transformations of the economy and society to help us cope with the crisis. And, not only have these digital applications worked remarkably well, but they offer a number of important benefits, like not waiting for a straightforward doctor diagnosis in a room full of sick people, and not having to travel for hours to participate in a 45 minute meeting.

Post-pandemic, how much will things snap back?, asked The Economist. “Clearly the world is not going to return to its pre-pandemic state…  some new behaviours will stick, but not all, and the result will be somewhere in the middle. Exactly where will have enormous implications: for transport patterns, property prices and the layout of cities, among other things.”

The societal implications of technological transformations are difficult to anticipate, given the rapid changes we’ve been going through in the last few decades, let alone in the last two years. This topic was directly addressed in Technology’s Role in Society: It’s Complicated, the Fall 2021 issue of Footnotes, a publication of the American Sociological Association. The issue includes nine essays on the various ways technology impacts out lives. “As technology continues to evolve at breakneck speed, we find ourselves constantly adapting and keeping up with the latest advancements,” said its Introduction article.

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.

Impact of Covid-19 on Job Automation

“Why are there still so many jobs?,” asked MIT economist David Autor in a 2015 article on the history of workplace automation. Given that technologies have been automating human work for the past couple of centuries,“should we not be somewhat surprised that technological change hasn’t already wiped out employment for the vast majority of workers?”

As Autor explained in the article, the answer is based on an economic reality that’s frequently overlooked. “Automation does indeed substitute for labor - as it is typically intended to do. However, automation also complements labor, raises output in ways that lead to higher demand for labor, and interacts with adjustments in labor supply. … journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor.”

Professor Autor was co-chair of MIT’s Work of the Future Task Force, which was launched in the spring of 2018 to better understand the impact AI and automation on jobs. The Task Force released an interim report in September of 2019. Its overriding conclusion was that the likelihood that AI and automation would wipe out major workforce sectors in the near future was exaggerated.

However, it raised important reasons for concern, especially the rising polarization of employment and wage distribution over the past few decades, which has disproportionately benefited high-skilled professionals while reducing opportunities for mid- and low-skilled workers. The report emphasized that “at a time of historic income inequality, a critical challenge is not necessarily a lack of jobs, but the low quality of many jobs and the resulting lack of viable careers for many people, particularly workers without college degrees.”

The Covid-19 pandemic came a few months later, pretty much turning our world upside down.

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.

Why Do We Work So Damn Much?

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

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

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

Revitalizing US Manufacturing

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

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

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

Understanding the Impact of Cybersecurity on International Trade

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

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

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

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

The Evolving Role of the CIO

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

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

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

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

The New Joint Cyber Defense Collaborative

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

In the 1950s the government enacted the National Interstate and Defense Highways Act, which led to the construction of the Interstate Highway System. The Act had a dual purpose: facilitating the economic growth of the country as well as supporting the country’s defense during a conventional or nuclear war should it be necessary.

And, last but far from least, in the late 1960s the Department of Defense launched ARPANET, the digital infrastructure that eventually became the Internet. ARPANET was designed as a flexible digital network that would enable computers to continue to communicate with individuals and with each other following a military attack.

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

The Evolution of Shopping in the Digital Economy

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

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

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

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

The Post-Pandemic Future of Work: An Optimistic Outlook

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

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

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

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

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

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

Most jobs involve a number of tasks or processes. Some of these tasks are more routine in nature, while others require judgement, social skills and other human capabilities. The more routine, rules-based the task, the more amenable it is to automation. But just because some of the tasks have been automated, does not imply that the whole job has disappeared. To the contrary, automating the more routine parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as enabling them to focus on those aspect of the job that most need their attention.

The Transformation of Logistics in the Retail Industry

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

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

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

What Might the World Be Like in 2040?

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

Key Recurring Themes

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

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

Leveraging Cloud Computing to Enhance the Nation’s Cybersecurity

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

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

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

Cloud’s Trillion Dollars 2030 Potential

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

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

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

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

The Emergence of Central Bank Digital Currencies

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

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

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

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

A Biological Framework for the Study of Labor Market Resilience

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

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

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

Business and Technology Predictions for 2021 and Beyond

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

The past year has been truly chaotic. How can firms formulate their strategies for the next few years in such an uncertain environment? But, as former US president Dwight Eisenhower famously said when he was Commander of Allied Forces in WWII: “In preparing for battle I have always found that plans are useless but planning is indispensable.” In other words, you must carefully assess the various directions in a highly uncertain future while being ready to flexibly adjust to changing circumstances.

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

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

The Urgent Need for Wide-Scale Upskilling

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

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

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

Will AI Ever Be Smarter Than a Baby?

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

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

What do we mean by intelligence?

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

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

A New Contract with the Middle Class

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

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

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

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

The Intriguing Nature of Non-Fungible Tokens

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

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

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

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

The Increasing Economic Value of Digital Capital

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

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

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

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