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.

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

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

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

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

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

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

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

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

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.”

Getting Ready for the 100-Year Life

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

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

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

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.

The Work of the Future Is Ours to Invent

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

The Task Force was commissioned by MIT President Rafael Reif in the spring of 2018 to address one of the most critical questions of the digital economy, - an economy that technology universities like MIT have played a major role in bringing about: as emerging technologies raise aggregate economic output and the wealth of nations, will they also enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity? Led by Autor, Mindell and Reynolds, the task force involved over 20 faculty members from 12 departments at MIT as well as more than 20 graduate students. Its final report was released in November of 2020. In addition, the Task Force published a number of working papers and research briefs.

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

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

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.

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.

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.”

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).”

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.

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.

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 Evolution of Shopping in the Digital Economy

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

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

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

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

The Coming Era of Productivity Growth

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

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

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

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

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

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

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

The Transformation of Logistics in the Retail Industry

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

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

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

What Musical Mindsets Can Teach Us About Business Innovation

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

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

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

Leveraging Cloud Computing to Enhance the Nation’s Cybersecurity

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

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

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

Cloud’s Trillion Dollars 2030 Potential

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

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

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

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

A Biological Framework for the Study of Labor Market Resilience

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

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

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

Business and Technology Predictions for 2021 and Beyond

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

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

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

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

Can We Look Forward to a New Decade of Innovation?

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

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

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

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

Will AI Ever Be Smarter Than a Baby?

I recently listened to a fascinating podcast where NY Times columnist Ezra Klein interviewed Berkeley psychologist 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.”

The Intriguing Nature of Non-Fungible Tokens

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

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

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

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

The Current State of Cloud Computing

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

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

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

The Increasing Influence of Recommendation Systems in Our Everyday Lives

A few years ago, I attended a seminar by University of Toronto professor Avi Goldfarb on the economic value of AI. Goldfarb explained that the best way to assess the impact of a new radical technology is to look at how the technology reduces the cost of a widely used function. Computers, for example, are powerful calculators whose cost of arithmetic and other digital operations have dramatically decreased over the past several decades. As a result, we’ve learned to define all kinds of tasks in terms of digital operations, e.g., financial transactions, inventory management, word processing, photography. Similarly, the Internet and World Wide Web have drastically reduced the cost of communications and of access to all kinds of information, - including numbers, text, pictures, music and videos.

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

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

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

The Current State of AI Adoption

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

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

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

How Can Blockchain Become a Truly Transformative Technology?

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

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

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

New Directions for Government in the Digital Age

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

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

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

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

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

AI Is Mostly About Business Value, Not Technology

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

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

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

Ten IT Predictions for the Post-Pandemic New Normal

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

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

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

Is Work-from-Anywhere Here to Stay?

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

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

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

The Surprising Power of Business Experimentation

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

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

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

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

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

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

The Intangible Asset Revolution

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

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

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

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

Trends That Will Likely Shape a Highly Uncertain Year

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

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

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

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

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

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

How Covid Is Reordering the Global Economy

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

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

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

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

Why Business Transformation Efforts Often Fail

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

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

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

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