Irving Wladawsky-Berger

Why Transformative AI Is So Hard to Achieve

“Humans have a good track record of innovation,” wrote Arjun Ramani and Zhengdong Wang in an essay which systematically explains “Why transformative artificial intelligence is really, really hard to achieve.” “The mechanization of agriculture, steam engines, electricity, modern medicine, computers, and the internet — these technologies radically changed the world. Still, the trend growth rate of GDP per capita in the world's frontier economy has never exceeded three percent per year.”

“Yet many people are optimistic that artificial intelligence is up to the job. AI is different from prior technologies, they say, because it is generally capable — able to perform a much wider range of tasks than previous technologies, including the process of innovation itself. Some think it could lead to a Moore’s Law for everything, or even risks on a par with those of pandemics and nuclear war.”

The essay consists of three sections: the transformational potential of AI is constrained by its hardest problems; major technical hurdles remain; and social and economic hurdles may limit AI’s impact. Let me summarize a few of the key arguments in each section.

What’s the Likely Long Term Evolution of AI?

Since the advent of the Industrial Revolution, general purpose technologies (GPTs) have been the defining technologies of their times. Their ability to support a large variety of applications can, over time, radically transform economies, and social institutions. GPTs have great potential from the outset, but realizing their potential takes large tangible and intangible investments and a fundamental rethinking of firms and industries, including new processes, management structures, business models, and worker training. As a result, realizing the potential of a GPT takes considerable time, often decades. Electricity, the internal combustion engine, computers, and the internet are all examples of historically transformative GPTs.

For example, after the introduction of electric power in the early 1880s, it took companies about four decades to figure out how to restructure their factories to harness electric power with manufacturing innovations like the assembly line. It took even longer to develop highly popular electric household products like refrigerators, toasters, washing machines, and air conditioners.

The internet’s precursor, ARPANET,  was initially developed by the US Department of Defense in the late 1960s for military and research applications. It wasn’t until the 1990s that the internet was opened up to companies and consumers in the wider commercial marketplace, becoming the most significant platform for innovation the world has ever since, and ushering a new 21st century digital economy.

Similarly, artificial intelligence first came to light in the mid-1950s as a promising new academic discipline that aimed to develop intelligent machines capable of handling human-like tasks like natural language and playing chess. AI became one of the most exciting areas in computer sciences in the 1960s, ’70s, and ’80s, but after years of unfulfilled promises and hype, a so called AI winter of reduced interest and funding that nearly killed the field set in everywhere.

The Evolution of Global Supply Chains in the 21st Century Digital Economy

“The societal and economic spasms of the early 2020s highlighted the crucial role of world-spanning supply chains in the modern global economy, as well as the growing role of digital technology, including A.I. and automation, in the future economy,” wrote MIT professor Yossi Sheffi in the Preface of his new book The Magic Conveyor Belt: Supply Chains, A.I. and the Future of Work. In the Preface, Sheffi succinctly stated that “The central purpose of supply chains is to fulfill the needs and wants of humanity — delivering all the food, medicine, energy, apparel, and other worldly goods need by the eight billion people in the planet.”

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

“The golden age of globalisation, in 1990-2010, was something to behold,” wrote The Economist in a January, 2019 article.  “Commerce soared as the cost of shifting goods in ships and planes fell, phone calls got cheaper, tariffs were cut and the financial system liberalised.” But, ever since the 2008 global financial crisis, globalization and global trade started to slow down. “Globalisation has slowed from light speed to a snail’s pace in the past decade for several reasons,” said The Economist.

Three major shocks have further reshaped globalization: the increasing trade wars and tariffs of the past five years, especially between the US and China; the disruptive impact of Covid-19 on global supply chains; and more recently, the war in Ukraine, which threatens to further decouple the world’s economy into a Western and a Chinese trading bloc. Mohamed El-Erian, one of the world’s most influential economic thinkers, said in a recent podcast interview, that the rewiring of global supply chains is one of the major drivers of major structural changes in the global economy.

Fostering the Development of Interoperable, Open Source Digital Wallets

In September of 2022, the Linux Foundation (LF) announced its intent to launch the Open Wallet Foundation (OWF), — a new collaborative effort to develop open source software that anyone can use to build interoperable wallets supporting a wide range of wallet use cases. After working with a number of enterprises, nonprofits, academic institutions and government entities to organize the effort, the Linux Foundation Europe announced the official formation of the OWF on February 23, 2023.

“The OWF will not publish a wallet itself, nor offer credentials or create new standards,” said the announcement. “Instead, its open source software engine aims to become the core that other organizations and companies leverage to develop their own digital wallets. The wallets will seek feature parity with the best available wallets and interoperability with major cross-border projects such as the EU’s Digital Identity Wallet.”

At the same time, the OWF in partnership with LF Research released a new report, “Why the World Needs an Open Source Digital Wallet Right Now.”

The Economic Value of Open Source

The supply side of open source software (OSS), — that is, the reasons for joining and contributing to open source communities, — has been extensively studied and quantified. For example, the Linux Foundation (LF) has over 1,500 members, and is home to nearly 1,000 open source projects from over 15,000 contributing organizations. The LF estimates that over 50 million lines of code are added weekly to its sponsored projects, which comprise a significant percentage of the world’s mission-critical infrastructures, a figure that’s significantly higher if we include the contributions of other open source communities like the Apache Software Foundation.

However, we know much less about the demand side of OSS. Despite the wide adoption of OSS by firms and industries around the world, we don’t really understand, nor are we able to quantify its overall economic value to the many organizations that rely on open source. What benefits do these organizations derive from OSS? What costs  do they incur from using or contributing to OSS? And, if not able to use OSS in a project, what would have been the next-best alternatives, and how much would they have cost?

To get answers to these and other important OSS questions, the LF sponsored a study led by UC Berkeley professor Henry Chesbrough, — whose fourth book on open innovation, Open Innovation Results, was published in 2020. The key findings and conclusions from the study are discussed in a White Paper, “Measuring the Economic Value of Open Source.”

Driving Value with the Metaverse in the Enterprise

A killer app is an IT application whose value to individuals and companies is relatively simple to explain, its use is fairly intuitive, and the application turns out to be so useful that it can singlehandedly propel the success of a new product or service. Word processing and spreadsheets, for example, were major factors in the widespread adoption of personal computers in the 1980s.

I got interested in 3D immersive environments in the mid-2000s when IBM launched an exploratory initiative on the topic. As part of these efforts, we conducted a number of experiments on Second Life, a platform that allowed its users to develop a variety of virtual world applications. We built a number of workplace applications, including virtual meetings, and online events, and even built a virtual replica of Beijing’s Forbidden City in partnership with the Palace Museum that was opened to the public. But, despite our high expectations, we couldn’t find any compelling enterprise killer apps, and after about three years, the project was dissolved. In the ensuing years, 3D immersive technologies and applications continued to advance, but mostly focused on video-games and related industries.

The past few years have seen a surge of interest in the metaverse and extended reality. “After desktop computing, the consumer internet and the smartphone boom, the consumer-computing industry is past due its Next Big Thing,” wrote The Economist in  “A reality check for the metaverse is coming,” an article in its 2022 year-end issue. “[A]n internet which is still largely flat — based on two-dimensional text, images and video — is ripe for replacement with one that is three-dimensional and immersive.”

Is AI (Finally) Coming of Age?

Artificial intelligence first came to light in the mid-1950s as a promising new academic discipline. AI became one of the most exciting areas in computer sciences over the next two decades. But, after years of unfulfilled promises and hype, a couple of so called AI winters of reduced interest and funding set in that nearly killed the field. AI was successfully reborn in the 1990s with a new statistical paradigm based on analyzing large amounts of data with powerful computers and sophisticated algorithms. Now, six decades after the field was founded, AI seems to be finally coming of age.

“2021 saw the globalization and industrialization of AI intensify, while the ethical and regulatory issues of these technologies multiplied,” said the 2022 AI Index report on the progress of AI, which was released in March of 2022 by Stanford’s Institute for Human-Centered Artificial Intelligence (HAI). “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,” wrote Jack Clark, co-chair of the AI Index. Multiple factors led to his conclusion, in particular the advent of foundation models like OpenAI’s GPT-3 and Google’s BERT.

In the past decade, increasingly powerful AI systems have matched or surpassed human levels of performance in a number of specific tasks like image and speech recognition. These task-specific deep learning (DL) systems have generally relied on supervised learning, a training method where the data must be carefully labelled, - e.g., cat, not-cat, - thus requiring a big investment in time and money to produce a model that’s narrowly focused on one task and can’t be easily repurposed.

Foundation models have gotten around these DL limitations based on two recent advances, transfer learning and huge scale. Transfer learning takes the knowledge learned from training a specific task and applies it to different but related tasks, - such as using the training in recognizing cars in images and applying it to recognizing trucks and buses. As a result, foundation models can be adapted to many different tasks with relatively small amounts of fine-tuning.

Why Digital Wallets Are Critical to the Evolving Digital Landscape

A few months ago, the Linux Foundation (LF) announced its intent to launch the Open Wallet Foundation (OWF) in early 2023. The OWF aims to develop open source software, key building blocks, and best practices that anyone can use to build multi-platform, interoperable, secure, and privacy-protecting digital wallets that will support a wide variety of applications from identity credential to payments.

I’ve been really intrigued by digital wallets, a fairly complex and poorly understood subject. To learn more, I read “The Current and Future State of Digital Wallets,” a comprehensive report on digital wallets published in April of 2019 by technologist and entrepreneur Darrell O’Donnell. The report was sponsored by a consortium of Canadian companies that recognized that digital wallets were a major requirement to be able to properly manage our digital identities, — arguably the toughest challenge in the continuing evolution of the internet and the digital economy.

“Technologies are converging and provide immense potential for shifting how we can improve every aspect of our lives,” wrote O’Donnell in the report’s Executive Summary. “Underneath all of that technology lies the internet, which has a flaw that could have been fatal – until recently it has lacked a built-in digital identity. Hacks to get around this missing piece have resulted in huge security gaps and abusive digital relationships.”

Why Are Enterprises Struggling with Blockchain?

“Blockchain, the technology underpinning bitcoin and other cryptocurrencies, for years has been viewed by some companies as a way to drive industry-transforming projects, among them the tracking of assets through complex supply chains,” according to a recent article in the Wall Street Journal, “Blockchain Fails to Gain Traction in the Enterprise.”

“So far that hasn’t happened.”

Blockchain technologies first came to light in 2008 as the architecture underpinning bitcoin, the best known, most valuable, and most widely held cryptocurrency. Over the years, blockchain has evolved in two major directions. One continues to focus on blockchain as the underlying platform for bitcoin, as well as for the large number of cryptocurrencies, digital tokens, and other crypto assets that have since been created. The other focuses on blockchain as a distributed data platform for collaborative applications involving multiple institutions, such as supply chains, financial services and healthcare systems. 

Blockchain made its first appearance in Gartner’s yearly hype cycles in 2016. Despite its being at an early and immature stage, many of its proponents believed that enterprise blockchain platforms were just around the corner, and so it quickly rose to the peak of inflated expectations. But as reality set in, expectations started sliding down in 2021, and by 2022 blockchain platforms had reached the bottom of the trough of disillusionment.

As years go, 2022 was a very tough one for blockchains in general. In the crypto space, bitcoin and most other crypto assets have lost a very large chunk of their value, and numerous crypto companies have collapsed, most prominently FTX. In the enterprise, a number of ventures went belly up. we.trade, a blockchain-based trade finance, shut its operations in June after running out of cash. A few months later, the Australian Securities Exchange canceled its much-delayed blockchain-based clearing system. Most prominently, in late November TradeLens announced that it was discontinuing its blockchain-based global trade platform. Other enterprise blockchain projects, like Walmart’s food traceability application, still carry on but their uptake and progress have been slower than anticipated.

Will the Metaverse and AR/VR Headsets Be IT’s Next Big Thing?

“Is it really the next big thing,” asked The Economist in A reality check for the metaverse is coming, another recent article in “The World Ahead 2023”, its year-end issue which I wrote about last week. “After desktop computing, the consumer internet and the smartphone boom, the consumer-computing industry is past due its Next Big Thing,” said the article. “The coming year will see big tech firms doubling down on two related, much-hyped possibilities. One is virtual- (vr) and augmented-reality (ar) headsets; the idea that, having shrunk computers into our pockets, the next step is to strap them to our faces. The other is the metaverse, which holds that an internet which is still largely flat - based on two-dimensional text, images and video - is ripe for replacement with one that is three-dimensional and immersive, experienced as a sort of globe-spanning video game.”

There is little question that the metaverse and AR/VR headsets are important trends to watch out for in the coming years. As The Economist wrote in a November, 2021 article, “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.”

The last major advance in user interfaces took place in the 1980s when text interfaces gave way to graphical user Interfaces (GUIS). GUIs were first developed at Xerox PARC in the late 1970s and later popularized by the Apple Macintosh in the ’80s. In the 1990s, GUIs were embraced by just about every PC and user device, and GUI-based Web browsers played a major role in the explosive growth of the internet.

Getting the Most Out of Cloud Investments

“Over the past decade, two major trends have emerged concurrently,” said Five Keys To Innovating Faster With Cloud, a recent report by Forrester Research. “First, there has been a growing need for rapid innovation and adaptivity as businesses respond to customer-driven trends. At the same time, cloud platforms have advanced rapidly in their capabilities and ease of deployment. Leveraging cloud has become the fastest way to modernize your tech stack and deliver the innovation and adaptivity needed in a customer-obsessed strategy.”

Cloud emerged in the late 2000s as a new model of computing, - the third in the history of the IT industry after the mainframe-based centralized computing and the PC-based client-server models. The internet is the defining technology of the cloud model.

Cloud has gone through three major stages over the past fifteen years. First came infrastructure-as-a-service, offering near unlimited scalability at very attractive prices. Then came software-as-a-service, offering a faster and less costly way of prototyping and deploying innovative applications with advanced tools like containers, Kubernetes, and microservices. Cloud computing has now entered the third stage, - a major engine of business transformation that’s helping companies adapt to the digitalization of the economy, - a digitalization which has significantly accelerated since the advent of Covid-19 in March of 2020.

The Current State of Service Science

On October 17, the National Academy of Engineering (NAE) conducted an online forum on Service Systems Engineering in the Era of Human-Centered AI. “With AI advances poised to drive service system productivity and quality - similar to the way previous generations of technology revolutionized agriculture and manufacturing productivity and quality - it is time to take stock for industry-academic-and-government stakeholders on this important topic,” wrote the NAE in its website.

The agenda included an opening keynote by retired IBM executive Nick Donofrio. It was followed by four panels on various aspects of service systems, and concluded with an open discussion of the way forward. I was a member of the panel on Evolving Engineering Education. In my prepared remarks, I reflected on the current state of service science and related sociotechnical systems. Let me share my remarks, slightly edited for clarity.

Service Science, Management and Engineering (SSME) is an initiative launched in IBM’s Almaden Research Lab in the early 2000s in partnership with a number of universities as an interdisciplinary field of study aimed at applying science, technology and innovation to the service sector of the economy.  The service sector is the largest in most economies around the world. At the time, services already accounted for over 70% of GDP and jobs in advanced economies, as well as an increasing portion of the revenues of many companies, including close to 60% of IBM’s revenues.

Culture: The Software of the Mind

Several weeks ago I listened to a very interesting Freakonomics podcast hosted by Stephen Dubner. “According to a decades-long research project, the U.S. is not only the most individualistic country on earth; we’re also high on indulgence, short-term thinking, and masculinity,” said Dubner. The podcast included comments from a number of social scientists but was particularly focused on the work of Gert Jan Hofstede, social sciences professor  at Wageningen University in the Netherlands, whose research is focused on artificial sociality, - the study of human social behavior based on computational models. He's continued to lead a major research project on national cultures started more than 50 years ago by his father Geert Hofstede who passed away in 2020.

“Every person carries within him- or herself patterns of thinking, feeling, and potential acting that were learned throughout the person’s lifetime,” wrote the Hofstedes in their 2011 book Cultures and Organizations: Software of the Mind, coauthored with Michael Minkov. “Much of it was acquired in early childhood, because at that time a person is most susceptible to learning and assimilating. As soon as certain patterns of thinking, feeling, and acting have established themselves within a person’s mind, he or she must unlearn these patterns before being able to learn something different, and unlearning is more difficult than learning for the first time.”

The thinking patterns that guide our behaviors can be thought of as mental programs, a kind of software of the mind. “This does not mean, of course, that people are programmed the way computers are,” said the authors. “A person’s behavior is only partially predetermined by his or her mental programs: he or she has a basic ability to deviate from them and to react in ways that are new, creative, destructive, or unexpected.” Culture, the software of the mind, “only indicates what reactions are likely and understandable, given one’s past.”

In the podcast, Gert Jan Hofstede discussed the power of culture: “It is what we got fed with our mother’s milk and the porridge that our dad gave us. … If you’re part of a society, you’re like one drop in the Mississippi River. You may decide to go another way, but that doesn’t make the river change. So we’re all constraining one another through our collective culture.”

The Disruptive Economics of AI

In 2017, 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 potentially transformative technology is to look at how the technology reduces the cost of a widely used function. Computers, for example, have dramatically reduced the cost of digital operations like arithmetic by several orders of magnitude. 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 has reduced the cost of communications and the Web has reduced the cost of access to information, which has led to a huge increase in applications based on communications and information, like music and video streaming, and digital media.

Viewed through this lens, AI is essentially a prediction technology, and its economic impact is to reduce the cost and expand the number and variety of applications that rely on predictions. A key finding of Stanford’s 2022 AI Index report was that AI is becoming much more affordable and higher performing, leading to the widespread commercial adoption of AI-based applications. “Since 2018, the cost to train an image classification system has decreased by 63.6%, while training times have improved by 94.4%.,” said the report.

Goldfarb and his U of T colleagues Ajay Agrawal, and Joshua Gans published their research in the 2018 book Prediction Machines: The Simple Economics of Artificial Intelligence. And this week, they just published Power and Prediction: The Disruptive Economics of Artificial Intelligence.

“When we published Prediction Machines in 2018, we thought we had said all we needed to on the economics of AI,” wrote the authors in the Preface of their new book. “We were wrong. We laid out a framework for the economics of AI in that book, which remains useful today. However, the Prediction Machines framework only told part of the story, the point solutions part. In the years since, we discovered that another key part of the AI story had yet to be told, the systems part. We tell that story here.”

The AI Maturity Framework

I recently attended a seminar, The Art of AI Maturity, by Accenture executives Philippe Roussiere and Praveen Tanguturi as part of MIT’s Initiative on the Digital Economy (IDE) lunch seminar series. The seminar was based on their recently published article The Art of AI Maturity: Advancing from Practice to Performance.

“Today, so much of what we take for granted in our daily lives stems from machine learning,” wrote the authors in the article’s executive summary. “Every time you use a wayfinding app to get from point A to point B, use dictation to convert speech to text, or unlock your phone using face ID ... you're relying on AI. And companies across industries are also relying on - and investing in - AI to drive logistics, improve customer service, increase efficiency, empower employees and so much more.”

To determine the true state of AI maturity in the marketplace, Accenture conducted a survey in August and September of 2021 of over 1,600 C-suite executives at nearly 2,000 of the world’s largest companies across 16 industries with headquarters in 15 countries. In addition, they interviewed 25 CEOs, Chief Data Officers and Chief Analytics Officers, as well as a number of Accenture AI experts, and developed over 40 case studies on AI transformation.

The Importance of Open Source Digital Wallets to the Future of the Internet

On September 13, the Linux Foundation (LF) announced its intent to form the Open Wallet Foundation (OWF) at its Open Source Summit Europe in Dublin. The OWF aims to develop open source software, key building blocks, and best practices that anyone can use as a starting point to build multi-platform, highly secure, privacy-protecting digital wallets. In addition, the OWF will advocate for the wide adoption of open source wallets that can be used to support a wide variety of applications, from payments to digital identities.

The LF was originally founded in 2000 as the Open Source Development Labs to help set the standards for the Linux operating system and support its continued development and commercial adoption. It grew over the years and assumed its current name, Linux Foundation, in 2007. Over the past decade, the LF has undergone a major expansion beyond its original Linux mission. It now has over 1,260 company members and supports hundreds of open source projects. Some of the projects are focused on technology horizontals, - e.g., AI, blockchain, security, cloud, - and others on industry verticals, including energy, automative, government, and health care.

Let me attempt to explain the importance of this announcement by addressing three key questions: what is a digital wallet?; what are digital identities?; and why do we need standard-based, open source digital wallets?

The 2022 State of AI in the Enterprise

“Rapidly transforming, but not fully transformed - this is our overarching conclusion on the market, based on the fourth edition of our State of AI in the Enterprise global survey,” said Becoming an AI-fueled organization, the fourth survey conducted by Deloitte since 2017 to assess the adoption of AI across enterprises. “Very few organizations can claim to be completely AI-fueled, but a significant and growing percentage are starting to display the behaviors that can get them there.”

AI is increasingly viewed by workers as a trusted assistant. “Within just the last 18 months, AI capabilities have advanced considerably, maturing from what was often experienced as a bothersome critic - telling workers what to do or pointing out their mistakes - to more frequently serving as a copilot, independently executing on insights and trends surfaced through the power and speed of cloud- based data hosting and computation.”

These conclusions are similar to those of Stanford’s 2022 AI Index report, which found that AI was becoming more affordable and higher performing, with lower training costs and faster training times across a number of AI tasks including recommendation engines, image classification, object detection, and language processing. This has led to the widespread commercial adoption and increased real-world impact of AI systems.

The Carnegie Cloud Governance Project

Two years ago, the Carnegie Endowment launched the Cloud Governance Project, a multi-year study on the governance challenges associated with cloud computing. “This project recognizes that the cloud offers huge benefits for individuals, organizations, and national economies through greater IT convenience, flexibility, and cost savings,” said the project’s website. “However, the risks of a major disruption affecting cloud services will invite regulation by governments at the local, national, and international levels. Moreover, as the world grows increasingly dependent on the cloud, other aspects of the technology – related to consumer protection, sustainability, inclusiveness, and human rights – will also attract scrutiny and regulation to protect or advance public interests.”

I’ve been closely following cloud computing since it first emerged in the late 2000s. In 2008 I gave a talk at a conference on The Promise and Reality of Cloud Computing. Most everyone at the conference agreed that something profound and exciting was going on, but there was no real consensus on what cloud computing was. 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, only the third such model in the history of computing. The mainframe-based centralized computing model first appeared in the 1960s. Then came the PC-based client-server model in the 1980s. The more recent internet-based cloud computing model emerged in the late 2000s.

Over the past fifteen years, cloud has gone through three major stages. First came infrastructure-as-a-service, offering near unlimited scalability at very attractive prices. Then came software-as-a-service, offering a faster and less costly way of prototyping and deploying innovative applications leveraging advanced tools like containers, Kubernetes, and microservices. Cloud computing has now become a major engine of business transformation, helping companies adapt to the accelerated digitalization of the economy, especially since the advent of Covid-19 in March of 2020.

A Framework for Understanding the Power of Platforms

This past July, MIT’s Initiative on the Digital Economy held its 10th annual Platform Strategy Summit. The hybrid Summit included a number of panels and keynotes throughout the day, a few of which I attended online. In particular, I’d like to discuss the very interesting presentation by Boston University professor Marshall Van Allstyne as part of a panel on emerging trends in which he raised and answered five fundamental questions about platforms:

• Structure: Why do platforms have such high market caps but so few assets or employees?
• Strategy: Why does traditional product strategy fail in platform markets?
• Regulation: Why does traditional antitrust fail in platform markets?
• Fake News: Why is misinformation such a hard problem?
• Can Decentralized Autonomous Organizations dethrone platforms?

In his presentation, Van Alstyne, aimed to articulate a single framework that will help answer all five questions, that is, a consistent way of thinking about platforms based on the research that he and his colleagues have been conducting over the past decade. Let me summarize Van Alstyne’s presentation by first describing his unifying framework, followed by how the framework applies to each of the five key questions.

Web3: The Promise of a Blockchain-Based Internet of Value

On June 30, MIT hosted the Imagination in Action Web3 Summit, an event jointly organized by MIT Connection Science, Forbes, and Link Ventures. The Summit brought together almost 600 developers, entrepreneurs, investors, and academics to discuss the present state and potential evolution of Web3. The agenda featured a number of panels and talks. Speakers included Alex (Sandy) Pentland, - MIT professor and Connection Science Director, Michael Federle, - CEO of Forbes, John Werner, - Managing Director at Link Ventures, and Esther Dyson, - investor, journalist, and philanthropist.

The MIT Summit is the first in a series of Imagination in Action conferences. The next one will take place on October 6 in San Francisco, followed by one on January 17 at Davos, and back to MIT in June of 2023. The summits aim “to take us from reflections on the last major social and economic transformation precipitated by the arrival of the Internet era, to the economics to build and the governance to sustain Web3,” as well as to envision the possibilities of a Web3 future and to try to discern hype from reality in these early days of a potential new internet era.

I wasn’t able to attend the Summit in person, but I watched the recordings of a number of the sessions, where I heard a variety of opinions on what Web3 is about. This is not surprising. In their early years, major new technologies are generally accompanied by a mixture of excitement, speculation and confusion, as people sort out what the technology might be about and how it’s likely to evolve. Something important is going on out there, but it takes time and marketplace experience to sort things out.

I’ve seen this play out a few times throughout my long career in the IT industry, especially in the early 1990s as the internet was transitioning from a DARPA-sponsored project in the research community to an initiative that was starting to take the broader marketplace by storm. A lot was happening around the internet, but it wasn’t at all clear where things were heading, and in particular what the implications would be to the world of business. Let me share some of the lessons learned in the 1990s while formulating IBM’s internet strategy in our newly formed Internet Division.

The Economic Potential of the Internet of Things

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

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

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

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

Disruptive Forces Necessitate Bold Decisions

In January of 2021 I attended Predictions 21, an online event organized by Forrester Research.  “Faced with the pandemic, firms did things that once seemed impossible - sometimes overnight,” said Forrester last year, adding that “2021 will be the year that every company - not just the 15% of firms that were already digitally savvy - doubles down on technology-fueled experiences, operations, products, and ecosystems.”

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

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

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

Transformative technologies are generally accompanied by a mixture of excitement and confusion in their early years. Something important is going on out there, although there’s no consensus on what it is yet. A major reason for the lack of consensus is that there’s no single dimension around which to define an emerging technology or business model. It’s like the fable of the blind men and the elephant. Each one touches a different part of the elephant. They then compare notes on what they felt, and learn that they are in complete disagreement.

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

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

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

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

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

The Potential Impact of Web3 on the Internet and the Digital Economy

A few weeks ago, the Blockchain Research Institute (BRI) announced that its annual global conference would be renamed Web3 Blockchain World, and that the Enterprise Blockchain Awards would now be called the Web3 & Blockchain Transformation Awards.

I’ve been affiliated with BRI for a number of years as a research contributor and as chair of its blockchain awards committee. I was somewhat concerned with the name change, because, as is generally true of new technologies in their early stages, there are multiple views of what web3 is all about. Critics view web3 as little more than hype, a rebranding effort to shed some of the cultural and political baggage of crypto, while supporters believe that web3 represents the future of the internet, upending traditional gatekeepers and ushering a middleman-free digital economy.

Over the following weeks I learned from BRI founder Don Tapscott his reasoning behind the decision that BRI should embrace web3 in its research and events. I read Think Blockchain, a book by IBM Fellow and VP of Blockchain Technologies Jerry Cuomo that will be published later this month. I read the section on Web3 and the New Internet of Value in Digital Asset Revolution, a book by BRI co-founder Alex Tapscott that was just published earlier this week. And I looked at a number of articles from both supporters and critics of web3.

In the end, I became convinced that web3 is now where the commercial internet was in the early 1990s, cloud computing in the late 2000s, and blockchain in the mid 2010s: a very promising set of technologies and applications in their early years. While, at this stage it’s not yet clear how it will evolve, it’s time to start paying serious attention to web3 and try to influence its directions. Let me briefly share my views of web3.

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

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

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

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

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

Can We Trust Our Impressive AI Language Models?

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

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

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

A Human-Compatible Safe, Controllable AI

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

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

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

The Digital Divide in How We Use the Internet

A few weeks ago I attended an MIT seminar by Esteban Moro, professor at Universidad Carlos III in Madrid and visiting professor at MIT. Moro’s talk discussed the socio-economic digital divide based on how we use the internet, focused on two key questions: how profound is the digital usage gap in our society?; and can we use mobile phone traffic to infer socioeconomic status? His seminar was based on a recently published article in the Journal of the Royal Society Interface co-authored with four collaborators, - News or social media? Socio-economic divide of mobile service consumption.

“Inequality is a central societal problem, especially within rapidly expanding urban areas,” wrote the authors. “While it is a crucial driver for economic growth, the progressive clusterization of workers, industries, companies and services in cities has a tremendous cost in terms of segregation and discrimination. This cost is not only economic: in the same city, different areas can have a 10- to 15-year imbalance in life expectancy and highly divergent education levels, with little chances of social mobility.”

“The design and successful implementation of policies to alleviate these problems require fine-grained, frequently updated information about income, education or inequality across metropolitan areas,” they added. “However, most data sources employed today, such as population censuses or surveys, suffer from sparsity in population coverage or infrequent updating, hence they do not allow the swift evolution that urban societies experience nowadays to be followed. Thus, the traditional ways of understanding cities tend to explain what happened 5 years earlier rather than nowcasting or even predicting urban transformations.”

In his seminar, Moro referred to the socio-economic divide based on how we use the internet as the second digital divide, in contrast to the original digital divide which was based on access to the internet.

The 2022 Work Trend Index: How to Get Hybrid Right

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

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

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

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

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

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

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

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

Beyond GDP: A Framework for Measuring Economic Progress

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

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

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

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 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.”

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.”

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.

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.

Is the Metaverse the Next Major Phase of the Internet?

“Don’t mock the metaverse,” said The Economist in a November 2021 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.

The Evolution of Enterprise Architecture in an Increasingly Digital World

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

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

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

How to Effectively Manage a Company’s Digital Transformation

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

“Faced with the pandemic, firms did things that once seemed impossible - sometimes overnight,” noted Forrester Research in an online event that took place earlier this year. “Under the relentless pressure of new customer realities, the future came into focus: The value of your company depends on how customer-obsessed, resilient, creative, and adaptive you are in jumping to the next growth curve in your industry. … 2021 will be the year that every company - not just the 15% of firms that were already digitally savvy - doubles down on technology-fueled experiences, operations, products, and ecosystems.”

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

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

Impact of Covid-19 on Job Automation

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

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

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

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

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

The Complex Interplay Between Cybersecurity and Regulatory Compliance

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

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

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

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

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

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

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

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

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

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

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

Realizing the Economic Promise of Predictive Analytics

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

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

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

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

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

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

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

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

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 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.

The Emergence of Central Bank Digital Currencies

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

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

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

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