Irving Wladawsky-Berger

The Rise of Global, Superstar Firms, Sectors and Cities

In the 1990s, the Internet was supposed to usher a much more open, decentralized, democratic economy and society.  Startups with innovative business models where now able to reach customers anywhere, anytime.  Companies, from the largest to the smallest, could now transact with anyone around the world.  Vertically integrated firms became virtual enterprises, increasingly relying on supply chain partners for many of the functions once done in-house.  Experts noted that large firms were no longer necessary and would in fact be at a disadvantage in the emerging digital economy when competing against agile, innovative smaller companies.

Some even predicted that the Internet would lead to the decline of cities.  People could now work and shop from home; be in touch with their friends over e-mail, video calls, and text messaging; and get access to all kinds of information and entertainment online.  Why would anyone choose to live in a crowded, expensive, crime-prone urban area when they could lead a more relaxing, affordable life in an outer suburb or small town?

But, as we well know, it hasn’t quite worked out as expected.  Instead, we’ve seen the rise of the global superstar company, the unicorn startup, and the winner-take-all city.  As it’s turned out, the Internet’s universal reach and connectivity has led to increasingly powerful network effects and to the rise of platform economies.

Beyond Machine Learning: Capturing Cause-and-Effect Relationships

Artificial intelligence is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies.  AI technologies  are approaching or surpassing human levels of performance in vision, speech recognition, language translation, and other human domains. Machine learning 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.  It’s essentially a method for machines to learn from all kinds of data, whether structured or unstructured, that’s loosely modeled on the way a biological brain learns new capabilities.  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.  Each successive layer in a multi-layer network uses the output from the previous layer as input.

Machine learning can be applied to just about any domain of knowledge given our ability to gather valuable data in almost any area of interest.  But, machine learning methods are significantly narrower and more specialized than humans.  There are many tasks for which they’re not effective given the current state-of-the-art.  In an article recently published in Science, professors Erik Brynjolfsson and Tom Mitchell identified the key criteria that help distinguish tasks that are particularly suitable for machine learning from those that are not.  These include:

• Tasks that map well-defined inputs to well-defined outputs, - e.g., labeling images of specific animals, the probability of cancer in medical record, the likelihood of defaulting on a loan application;
• Large data sets exist or can be created containing such input-output pairs, - the bigger the training data sets the more accurate the learning;
• The capability being learned should be relatively static, - If the function changes rapidly, retraining is typically required, including the acquisition of new training data; and
• No need for detailed explanation of how the decision was made, - the methods behind a machine  learning recommendation, - subtle adjustments to the numerical weights that interconnect its huge number of artificial neurons, - are difficult to explain because they’re so different from those used by humans.

Blockchain Beyond the Hype

A recent issue of The Economist included a special report on cryptocurrencies and blockchains.  The Economist’s overall conclusion was that “Bitcoin has been a failure as a means of payment, but thrilling for speculators.”  Its assessment of blockchain was somewhat more positive.  “For blockchains, the jury is still out,… For all the technology’s potential, though, most attempts to use it remain tentative,…  The advantages of blockchains are often oversold.” 

In 2016, blockchain made the list of the World Economic Forum’s Top Ten Emerging Technologies.  That same year, blockchain also made its first appearance in Gartner’s yearly hype cycles.  Even The Economist has been guilty of overselling when it featured blockchain in the cover of its October 31, 2015 issue with the tag line “The Trust Machine: How the technology behind Bitcoin could change the world.” 

Is there too much hype surrounding blockchain?  Absolutely,… but not surprising.  All potentially transformative technologies are oversold in their early stages.  Remember the dot-com bubble of the late 1990s.  Blockchain is still in its early phases of experimentation and adoption.  Much work remains to be done on standards, platforms, interoperability, applications and governance. 

But, does blockchain have the potential to become a truly transformative technology over time?  Yes, said McKinsey in a recent article on the strategic value of blockchain beyond the hype.  The article starts out by acknowledging that all the hype around blockchain makes it difficult to nail down not just blockchain's long term strategic value, but also what it actually is and what it isn’t in the present. To make sure everyone is on the same page, the article first recaps what it calls “the nuts and bolts of blockchain.”  Let me start by summarizing blockchain’s nuts and bolts.

Redefining Work - Leveraging Human Capabilities in a Future of Expanding Automation

How will labor markets evolve in our 21st century digital economy?  What’s the likely future of jobs, given that our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans?  How will AI, robotics and other advanced technologies transform the very nature of work?

Over the past few years, a number of papers, reports and books have addressed these very important questions.  They generally conclude that AI will have a major impact on jobs and the very nature of work.  For the most part,  they view AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention.  Overall, few jobs will be entirely automated, but automation will likely transform the vast majority of occupations. 

A recent McKinsey report noted while there will likely be enough work to maintain full employment by 2030, the transition will be very challenging, “on a scale not seen since the transition of the labor force out of agriculture in the early 1900s in the United States and Europe, and more recently in in China.”  The report estimated that “up to 375 million workers, or 14 percent of the global workforce, may need to change occupations - and virtually all workers may need to adapt to work alongside machines in new ways.”

Given these predictions about the changing nature of work, what should companies do?  How should firms prepare for a brave new world where we can expect major economic dislocations along with the creation of new jobs, new business models and whole new industries, and where many people will be working alongside smart machines in whole new ways?

Blockchain’s Potential for Environmental Applications

In mid-September, the World Economic Forum (WEF) released Building Block(chain)s for a Better Planet, a report that examined how blockchain technologies could be harnessed to address serious environmental issues, better manage our shared global environment and help drive sustainable growth and value creation.  The report outlines some of the world’s most-pressing environmental challenges and highlights eight blockchain-based game changers that could lead to transformative solutions to these pressing problems.

The WEF report includes a short blockchain tutorial.  Blockchain is essentially “a new, decentralized and global computational infrastructure that could transform many existing processes in business, governance and society.”  Blockchain technologies promise to significantly improve the efficiency and security of business transactions and data sharing among participants in a global ecosystem, such as supply chains, financial services, and similar complex applications involving multiple institutions.

But, the hype surrounding blockchain, can tempt solution designers to try to use it for applications that it’s not suitable for.  The report recommends that designers first consider three key questions:

• Will blockchain solve your actual problem; is a distributed architecture vital to your solution?
• Have you built the right ecosystem of stakeholders; do they understand and trust how blockchain is being used?
• Can you acceptably manage the downside risks or unintended consequences, e.g., scalability, performance, implementation risks?

A Biological Approach to Management

Darwinian principles seem to apply in business almost as much as in biology.  After analyzing the longevity of more than 30,000 public US firms over a 50-year span, Martin Reeves, Simon Levin, and Daichi Ueda noted in a 2016 HBR article, The Biology of Corporate Survival, that companies are disappearing faster than ever before.  “Public companies have a one in three chance of being delisted in the next five years, whether because of bankruptcy, liquidation, M&A, or other causes.  That’s six times the delisting rate of companies 40 years ago… Neither scale nor experience guards against an early demise.”

Biological systems have long been an inspiration in the study of complex systems.  In their HBR article, the authors argued that companies are not just like biological species, but in some important respects they’re actually identical to biological species.  Companies and biological systems are both what’s known as complex adaptive systems, that is, systems in which a perfect understanding of their individual components does not automatically lead to a perfect understanding of their overall system behavior.

Companies are an example of sociotechnical systems, that is, systems that have to deal with  complex technologies and infrastructures, and the even more complex issues associated with human and organizational behaviors.  Other examples include cities, government agencies, industries and economies.  The dynamic nature of their technology and human components, as well as their intricate interrelationships renders such systems increasingly unpredictable and accounts for their emergent behavior and unintended consequences.

It's All About the Business Model, - Not the Technology

In Reinvent Your Business Model, his recently published book, Mark Johnson argues that digital transformation and business model innovation are not the same thing.  New technology alone, - no matter how transformative, - is not enough to propel a business into the future.  The business model wrapped around the technology is the key to its success or failure.  Johnson is Senior Partner in Innosight, the strategy consulting firm he cofounded with Harvard Business School professor Clayton Christensen in 2000.  He’s been conducting research on business model innovation for over a decade, and in 2010 published Seizing the White Space.

Business model innovation has long been the domain of disruptive startups looking to compete against established companies by changing the rules of the game, - and, hopefully, creating new markets and reshaping entire industries.  But, it’s no longer enough for established companies to just roll out improved products and services based on their once-reliable business models.

“Building a great business and operating it well no longer guarantees you’ll be around in a hundred years, or even twenty,” notes Johnson.  “In 1965, the average length of time a company remained on the S&P 500 was thirty-three years.  By 1990, it had dropped to twenty years; in 2012, it was just eighteen.  Based on the 2017 churn rate, it is forecast that half of the S&P 500 will be replaced over the next ten years.”

Examples abound.  In the 1970s, Xerox PARC famously developed, - but didn’t commercialize, - some of the key innovations of the PC era, including the graphical user interface, the mouse and local area networks.  In 2010, Blockbuster filed for bankruptcy, a victim of Netflix’s new business models.  Done in by Apple’s iPhone and Android-based smartphones, the Blackberry, - once the undisputed leader in its market, - fell into a death spiral from which it never recovered. 

Cryptocurrencies and Blockchains - How Are They Doing After 10 Years?

Bitcoin was created about a decade ago, with the release of the original design paper, Bitcoin: A Peer-to-Peer Electronic Cash System, under the pen name of Satoshi Nakamoto.  Bitcoin aimed to become a decentralized cryptocurrency and digital payment system that would enable the secure exchange of digital money without the need for central government authorities or financial intermediaries.  It introduced the blockchain, - which most everyone agrees is a truly brilliant architecture built on decades-old fundamental research in cryptography, distributed data, distributed computing, game theory and other advanced technologies.

How are they doing after 10 years?  The September 1 issue of The Economist includes a special report on cryptocurrencies and blockchains, with nine articles on the subject. The report’s overview article doesn’t mince words in articulating its overall assessment: “Bitcoin and other cryptocurrencies are useless.  For blockchains, the jury is still out.”

“Bitcoin has been a failure as a means of payment, but thrilling for speculators,” it further added.  Its price has been subject to wild fluctuations over the past two years.  It rose from around $600 in September, 2016, to $6,000 in September, 2017, reaching a peak of over $19,000 in December, 2017, before falling to around $7,000 earlier this year.  It’s been hovering between $6,500 and $7,500 for the past few months.  Other cryptocurrencies have experienced similar price fluctuations.  “That has made a few people very rich (just 100 accounts own 19% of all existing bitcoin), encouraged others to play for quick gains and left some nursing substantial losses.”

Solow Paradox 2.0: The Lagging Impact of Technology Advances on Productivity Growth

Fewer workers is one of the key reasons for our stagnant economic growth.  Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline, especially in advanced and emerging economies.  The US labor force is growing very slowly while fertility rates keep hitting record lows.

Our aging economies are thus dependent on productivity gains to drive long-term economic growth, future prosperity and higher standards of living.  Which is why few topics have generated as much concern among economists and policy makers as the sharp decline in productivity growth in the US and other advanced economies over the past decade, - despite accelerating technology advances.

“Labor productivity growth is near historic lows in the United States and much of Western Europe,” noted Solving the Productivity Puzzle, a report published earlier this year by the McKinsey Global Institute (MGI).  The report analyzed the productivity-growth declines across a sample of seven countries, - France, Germany, Italy, Spain, Sweden, the United Kingdom, and the United States, - which represent about 65% of the GDP of advanced economies.  Their average productivity growth in the 5 years between 2000 and 2004 was 2.4%.  A decade later, - in the 5 year period between 2010 and 2014, - their average productivity growth had declined to .5%.  While starting to pick up recently, productivity growth remains at or below 1 percent in many of the countries in the study, still quite low relative to historical levels.

Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Social physics first emerged over 200 years ago as an attempt to understand society and human behavior using laws similar to those of the physical sciences.  But, it wasn’t until the past two decades that we finally had enough data, powerful computers and sophisticated mathematical algorithms to develop quantitative theories of human social interactions.  We were now able to reliably predict how large groups of people make decisions by analyzing how information and ideas flow from person to person.

“The engine that drives social physics is big data: the newly ubiquitous digital data now available about all aspects of human life,” wrote MIT professor Alex (Sandy) Pentland in his 2014 book Social Physics: How Good Ideas Spread - The Lessons from a New Science.  “Social physics functions by analyzing patterns of human experience and idea exchange within the digital bread crumbs we all leave behind as we move through the world, - call records, credit card transactions, and GPS location fixes, among others.  These data tell the story of everyday life by recording what each of us has chosen to do… Who we actually are is more accurately determined by where we spend our time and which things we buy, not just by what we say we do.”

The book was the result of over a decade’s worth of research in the Human Dynamics group at MIT’s Media Lab.  Pentland, along with his graduate students and research associates, partnered with a variety of companies to obtain and analyze real-world data, properly anonymized to protect the privacy of the individuals whose behavior was reflected in the data.  They eventually discovered that all event-data representing human activity contain a special set of social activity patterns regardless of what the data is about.  These patterns are common across all human activities and demographics, and can be used to detect emerging behavioral trends before they can be observed by any other technique. 

Deep Learning: Is it Approaching a Wall?

Deep learning and related machine learning advances 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.  In just the past two years, Google’s deep-learning-based AlphaGo defeated the world’s top Go players, surprising most AI experts who thought that it would take another 5 to 10 years to achieve such a milestone.  Similarly, when Google switched to its new deep learning AI system in late 2016, it achieved an overnight improvement in the quality of its machine translations roughly equal to the total gains that the previous program had accrued over its 10 year lifetime.

As is typically the case with major technology achievements, - e.g., the dot-com bubble, - deep learning has quickly climbed to the top of Gartner’s hype cycle, where all the excitement and publicity accompanying new, promising technologies often leads to inflated expectations, followed by disillusionment if the technology fails to deliver.  AI may be particularly prone to such hype cycles, as the notion of machines achieving or surpassing human levels of intelligence leads to feelings of wonder as well as fear.  Over the past several decades, AI has gone through a few such hype cycles, including the so-called AI winter in the 1980s that nearly killed the field.

In a recent article, Deep Learning: A Critical Appraisal, author and NYU professor Gary Marcus offers a serious assessment of deep learning.  He argues that, despite its considerable achievements over the past 5 years, deep learning may well be approaching a wall, an opinion apparently shared by University of Toronto professor Geoffrey Hinton, the so-called Godfather of Deep Learning.  

The Myth of the Washed-Up, Middle-Age Entrepreneur

Steve Jobs, Bill Gates, and Mark Zuckerberg were in their early 20s; Sergei Brin and Larry Page were 25; and Jeff Bezos was just 30 when they founded their world leading high-tech companies.  Their companies are truly in a class by themselves, even having their own, somewhat frightful acronym - FAMGA.  Their founders’ ages are not only evidence that young people can be highly successful entrepreneurs, but it’s led to the belief that they’re uniquely capable of coming up with big, transformational ideas.

“Young people are just smarter,” Zuckerberg told the audience of a 2007 VC conference, adding that successful start-ups should only employ young people with technical expertise.  In 2010, PayPal’s co-founder Peter Thiel created the Thiel Fellowships, a program that provides $100,000 over two years to would-be entrepreneurs 23 and under so they can drop out of school to work on their ideas.  “The stereotype of an entrepreneur is a college drop-out,” said a recent Economist article.

“Silicon Valley has become one of the most ageist places in America,” noted a 2014 New Republic article on The Brutal Ageism of Tech, where years of experience are considered obsolete.  “People under 35 are the people who make change happen,” said VC Vinod Khosla at a 2011 conference.  “People over 45 basically die in terms of new ideas” because they “keep falling back on old habits.”  Entrepreneur and VC Paul Graham said in a 2013 interview that investors tend to be biased against older founders.  “The cutoff in investors’ heads is 32… after 32, they start to be a little skeptical.”

These various statements remind me of the 1960s counterculture saying don’t trust anyone over 30.  But, are they justified?  Are young entrepreneurs more likely to produce high-growth firms?  Can middle-age founders in their 40s be successful? 

AI - the Creation of a Human-Centric Engineering Discipline

AI is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies, including applications that not long ago were viewed as the exclusive domain of humans.  Over the past few years, the necessary ingredients have finally come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms.

Machine learning  and related algorithms like deep learning, have played a major role in AI’s recent achievements.  Machine learning gives computers the ability to learn by ingesting and analyzing large amounts of data instead of being explicitly programmed.  It’s enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to difficult AI problems like language translation, natural language processing, and playing championship-level Go.

Over the past several decades, AI has been besieged by rounds of hype which over-promised, under-delivered, and nearly killed the field.  Once more, a recent Gartner article positioned machine and deep learning at the top of their hype cycle, - when all the excitement, publicity and promising potential often leads to a peak of inflated expectations, risking falling into the trough of disillusionment if the technology fails to deliver.  Now that AI is finally reaching a tipping point of market acceptance, it’s particularly important to be cautious and not repeat past mistakes.

In Artificial Intelligence - The Revolution Hasn’t Happened Yet, UC Berkeley professor Michael I. Jordan aims to inject such a note of caution.  “AI has now become the mantra of our current era…  The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us  -  enthralling us and frightening us in equal measure.  And, unfortunately, it distracts us…  Whether or not we come to understand intelligence any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life.”

The Identity Challenge: On the Internet, Nobody Knows You're a Dog

I recently read Better Identity in America, a report by the Better Identity Coalition, an organization launched earlier this year to focus on promoting the development and adoption of solutions for identity verification and authentication.  The report outlines a policy agenda for improving the privacy and security of digital systems and help combat identity fraud.

How bad is the problem?  Data breaches, large-scale fraud, and identity theft are becoming more common.  In 2017, there were 16.7 million victims of identity fraud in the US, causing a loss of $16.8 billion; there was a 44.7% increase in US data breaches between 2016 and 2017 and a 30% rise in online shopping fraud; 179 million personal information records were exposed to data breaches, 69% of which were identity theft incidents.

The report notes that “the ability to offer high-value transactions and services online is being tested more than ever, due in large part to the challenges of proving identity online. The lack of an easy, secure, reliable way for entities to verify identities or attributes of people they are dealing with online creates friction in commerce, leads to increased fraud and theft, degrades privacy, and hinders the availability of many services online.”

As the report reminds us, the extent of this challenge was famously captured back in 1993 by Peter Steiner’s New Yorker cartoon with the caption On the Internet, nobody knows you’re a dog.  25 year later, the cartoon still perfectly describes the identity challenge.  If anything, the challenge is even more serious in 2018, given that the volume and variety of online transaction services are greater than ever before.

An Agile, Incremental Approach to Business Process Automation

I’ve long believed that customer self-service was key to the success of e-business, - the IBM Internet strategy I was closely involved with in the mid-late 1990s.  It was quite revolutionary how easy it was to now do for yourself so many ordinary activities that previously required a phone call during office hours or a trip to a store or office.

Moreover, such e-business applications were relatively easy to develop.  By integrating their existing transaction and data base applications with a web front-end, - a strategy we succinctly described as Web + IT, - any business could now be in touch with its customers, employees, suppliers and partners at any time of the day or night, no matter where they were.  Companies were able to engage in their core activities in a more productive way by web-enabling their back-end systems, that is, linking their web front-ends to the presentation services of their back-end applications.

I was reminded of IBM’s e-business strategy when recently reading about Robotic Process Automation (RPA), a technology for automating business processes based on emulating the manual actions of a human at a keyboard.  RPA aims to improve the operational efficiency of office and service workers by automating tedious, repetitive, tasks, such as those associated with widely used horizontal processes in HR, Finance & Accounting and IT services.

RPA enables process automation at a fraction of the cost and time of classic software development.  Rather than automating a process by redesigning the overall back-end application, RPA interfaces with the back-end system by performing the same actions that a human does via the application’s user interface.  RPAs thus creates kind of software robots, or bots for short, that work alongside the humans.

The Emergence of a Blockchain-Based Token Economy

Blockchain Revolution, published in May of 2016 by Don Tapscott and Alex Tapscott, was one of the first books that explained the promise of blockchain technologies to the general public.  Its central argument is that for nearly four decades, the Internet has been great for reducing the costs of searching, collaborating, and exchanging information.  But, it has serious limitations for business and economic activity.  “Doing business on the Internet requires a leap of faith,” the book noted.  The Internet was designed to move information, but it’s lacked the necessary trust, security and privacy safeguards to move assets of value.  With blockchain, we’re now seeing the emergence of such an Internet of value.  “Now for the first time ever we have a native digital medium for value, through which we can manage, store, and transfer any asset.”

An updated edition of Blockchain Revolution was published this past June.  A lot has happened with blockchain in the intervening two years.  The updated edition explains some of these recent developments in a new preface, including cryptoassets, permissioned networks, identity and supply chains.  I’ll focus my comments on the emergence of a blockchain-based token economy, driven by the explosive growth in the value and variety of cryptoassets.

When the original book was published, the entire cryptoasset market had a value of $9 billion, - mostly dominated by the value of Bitcoin at $7 billions, while Ether, the Ethereum cryptocurreny, had reached around $1 billion.  The new edition estimates that as of March, 2018, the cryptoasset market was around $400 billion in size, a value subject to rapid fluctuations.  In addition, a wide variety of cryptoassets have been developed over the past two years. 

What Machine Learning Can and Cannot Do

Artificial intelligence is rapidly becoming one of the most important technologies of our era.  Every day we can read about the latest AI advances from startups and large companies.  Over the past few years, the necessary ingredients have come together to take AI across the threshold: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms, especially machine learning.  Machine learning has enabled AI to get around one of its biggest obstacles, - the so-called Polanyi’s paradox.

Explicit knowledge is formal, codified, and can be readily explained to people and captured in a computer program.  But, tacit knowledge, a concept first introduced in the 1950s by scientist and philosopher Michael Polanyi, is the kind of knowledge we’re often not aware we have, and is therefore difficult to transfer to another person, let alone capture in a computer program.

“We can know more than we can tell,” said Polanyi in what’s become known as Polanyi’s paradox.  This common sense phrase succinctly captures the fact that we tacitly know a lot about the way the world works, yet aren’t able to explicitly describe this knowledge.  Tacit knowledge is best transmitted through personal interactions and practical experiences.  Everyday examples include speaking a language, riding a bike, and easily recognizing many different people, animals and objects.

Machine learning, and related advances like deep learning, have enabled computers to acquire tacit knowledge by being trained with lots and lots of sample inputs, thus learning by analyzing large amounts of data instead of being explicitly programmed.  Machine learning methods are now being applied to vision, speech recognition, language translation, and other capabilities that not long ago seemed impossible but are now approaching or surpassing human levels of performance in a number of domains.

Are Innovation and R&D Yielding Decreasing Returns?

Given the pace of technological change, we tend to think of our age as the most innovative ever.  But over the past several years, a number of economists have argued that increasing R&D efforts are yielding decreasing returns.

Has the ideas machine broken down?, asked The Economist  in a January, 2012 article that examined the growing concerns that we may be in a long-term period of slow innovation despite our rapidly advancing technologies.  “With the pace of technological change making heads spin, we tend to think of our age as the most innovative ever,” said The Economist.  “We have smartphones and supercomputers, big data and nanotechnologies, gene therapy and stem-cell transplants. Governments, universities and firms together spend around $1.4 trillion a year on R&D, more than ever before.”  But, perhaps these don’t quite compare with modern sanitation, electricity, cars, planes, the telephone, and antibiotics.  These innovations, first developed in the late 19th and early 20th century, have long been transforming the lives of billions.

In a September, 2012 paper, Northwestern University economist Robert Gordon questioned the generally accepted assumption that economic growth is a continuous process that will persist forever.  He wrote that the slow growth we’ve been experiencing in the US and other advanced economies isn’t cyclical, but rather evidence that long-term economic growth may be grinding to a halt.

The rapid growth and rising per-capita incomes we experienced at the height of the Industrial Revolution, - between 1870 and 1970, - may have been a unique episode in human history.  Since the 1970s, US productivity and income growth have dipped sharply except for an Internet-driven productivity boost between1996 and 2004.  Innovation may be hitting a wall of diminishing returns.  There was little growth before 1800, and there might conceivable be little growth in the future.

A similar pessimistic view was expressed by George Mason University economist Tyler Cowen in his 2011 book The Great Stagnation.  According to Cowen, over the past two centuries the US economy has enjoyed lots of low-hanging fruit, including a vast, resource rich land, waves of immigrant labor, access to education and the technological advances of the Industrial Revolution.  But, Cowen believes that we are at a technological plateau, and wonders whether long term growth is still possible because the supply of low-hanging economic fruit is nearly exhausted.

Getting on the AI Learning Curve: A Pragmatic, Incremental Approach

After decades of promise and hype, AI is now seemingly everywhere.  Over the past several years, the necessary ingredients have come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms including machine learning.

While AI is likely to become one of the most important technologies of our era, we’re still in the early stages of deployment, especially outside leading-edge technology companies.  But, as AI continues its rapid progress, it’s not too early to ask a few important questions: What is AI’s overall value to the economy? What are the biggest application opportunities?; and, what are AI’s most serious challenges and limitations?

To address these questions, McKinsey recently published a discussion paper on the marketplace potential of AI.  The paper is particularly focused on machine learning and related technologies, and is based on a detail analysis of more that 400 use cases across 19 industries and 9 business functions.

“Two-thirds of the opportunities to use AI are in improving the performance of existing analytical use cases,” is the paper’s overriding finding.  This is a very interesting insight.  AI is now being successfully applied to tasks that not long ago were viewed as the exclusive domain of humans, - machine translation, natural language processing, defeating the world’s top Go players, - but only 16% of the use cases studied by McKinsey are greenfield cases, where only machine learning techniques can be used.  For the vast majority of the use cases, 69%, the key value of machine learning is to improve performance beyond that provided by traditional analytic techniques.  And, in the remaining 15% of cases, machine learning provided limited additional performance over existing analytical methods. 

Harnessing the Collective Intelligence of Humans and Machines

For over a decade, MIT professor Thomas Malone has been conducting pioneering research on the collective intelligence of groups.  Malone is the founding director of the MIT Center for Collective Intelligence, and has authored and co-authored a number of seminal articles on the subject.  A few weeks ago, Malone published Superminds: The Surprising Power of People and Computers Thinking Together, - a book that nicely explains the concept of collective intelligence and how our increasingly capable machines are now complementing the intelligence of groups of people.  The book illustrates these concepts with a wide variety of examples and case studies.

He defines a supermind as “a group of individuals acting in ways that seem intelligent.”  A supermind has measurable properties just as individual do.  It can have a life of its own beyond those of its individuals constituents.  And, as Malone points out, throughout history, groups of people working together as superminds have been responsible for the vast majority of human achievements. 

From Horseless Carriages to Driverless Vehicles

Autonomous vehicles (AVs) may well be the quintessential symbol of our AI/robotics age.  Cars are a major part of our daily lives.  A self-driven car is a concept that requires little explanation, something we can all quickly grasp.  It wasn’t that long ago that the notion on an AV driving us around while we read or sleep would have felt like the stuff of science fiction.  Having experimental AVs coursing through public roads in Silicon Valley, Pittsburgh and Phoenix is concrete evidence that our smart machines are achieving human-like intelligence, raising a number of important questions:  how long before AVs are all around us?; how will they impact our lives?; what unintended consequences might we have to deal with?; and what should be done to ensure that they arrive as safely and smoothly as possible?

These are among the questions addressed in the March 1 issue of The Economist, which includes a comprehensive special report on autonomous vehicles with seven articles on the subject.  The special report starts out with the assumption that whatever technological hurdles lie in their way will be eventually overcome.  But there are wider economic, social and public policy issues to be explored, starting with: what can we learn from the transition to horseless carriages in the 20th century that can now be applied to the transition to driverless cars?

The Promise and Current State of Blockchain

I recently participated in a blockchain panel at a conference of senior supply chain executives from a variety of companies.  I welcomed the opportunity to learn how these real-world executives feel about blockchain, as well as how to best communicate the promise and current state of this emerging and potentially transformative technology.  Let me share a few of my observations.

Before our panel started, the executives were asked how they viewed blockchain.  In general, they felt that there was too much hype surrounding blockchain, were skeptical about its value to supply chain systems, but expressed a wait-and-see attitude.  They were then asked how many were conducting blockchain prototypes in their companies.  The majority raised their hands. 

I thought that their views and subsequent questions and comments during the panel succinctly captures the current state of blockchain in the business world: skeptical, turned off by the hype, but hedging their bets by getting on the learning curve with prototypes and experimentation.

The moderator first asked each panelist to address the audience’s skepticism about blockchain.  What, in our opinion, is the key value of blockchain?  I said that I look at the value of blockchain through two complementary lenses: enhancing the security of the Internet, and reducing the inefficiencies and overheads in applications, like supply chains, involving multiple institutions.

The Growing Importance of Resilience in an Increasinsgly Turbulent World

In March of 2017, Northeastern University launched a new interdisciplinary initiative, the Global Resilience Institute (GRI).  GRI is a university-wide research and educational effort to advance the resilience of individuals, communities, economies and societies around the world by strengthening their capacity to adapt to an increasingly turbulent world.  Its key mission is to develop and deploy practical tools, applications and skills to help effectively respond to human-made and naturally-occurring disruptions and disasters.

GRI’s Founding Director is Northeastern professor Stephen Flynn.  In his Director’s Welcome web page, Flynn writes that “Building resilience within and across multiple levels, from individual to societal, requires a comprehensive effort that matches the complexity of the increasingly interdependent systems and networks we all rely on… Our aim is to serve as both a channel and a catalyst for experts in industry, academia, and government to collaborate on solving the world’s most pressing resilience challenges.  These include both slowly emerging disruptions as well as shocks and sudden disasters.”

Most everyone agrees that dealing with our increasingly turbulent, complex world us requires significant enhancements to the resilience of our societies and systems.  But, five critical barriers stand in the way:

• Widespread risk illiteracy and a limited understanding of the new dependencies and interdependencies that pervade our more connected lives;
• Inadequate designs for embedding resilience into systems, networks, and infrastructure at multiple levels;
• Pervasive economic disincentives for investing in proven resilience measures;
• Inadequate governance frameworks and policy guidance to foster resilience; and
• A lack of adequate training and education to support the development and implementation of comprehensive capabilities for advancing resilience.

A Framework for the Safe Management of Digital Identities and Data

Identity plays a major role in everyday life.  Think about going to an office, getting on a plane, logging to a website or making an online purchase.  Identity is the key that determines the particular transactions in which we can rightfully participate as well as the information we’re entitled to access.   But, we generally don’t pay much attention to the management of our identity credentials unless something goes seriously wrong.

For much of history, our identity systems have been based on face-to-face interactions and on physical documents and processes.  But, the transition to a digital economy requires radically different identity systems.  In a world that’s increasingly governed by digital transactions and data, our existing methods for managing security and privacy are proving inadequate.  Data breaches, large-scale fraud, and identity theft are becoming more common.  In addition, a significant portion of the world’s population lacks the credentials needed to participate in the digital economy.  Our existing methods for managing digital identities are far from adequate.

As explained in A Blueprint for Digital Identity, - a 2016 report by the World Economic Forum, - identity is essentially a collection of information or attributes associated with a specific individual.  These attributes fall into three main categories:  inherent - attributes intrinsic to an individual, - e.g., age, height, date of birth, fingerprints, color of eyes, retinal scans;  assigned - attributes attached to but not intrinsic to the individual - e.g., e-mail address, telephone numbers, social security, drivers license, passport number; and accumulated - attributes gathered or developed over time - e.g., health records, job history, home addresses, schools attended.

While mostly associated with individuals, identities can also be assigned to legal entities like corporations, partnerships and trusts; to physical entities like cars, buildings, smartphones and IoT devices; and to digital entities like patents, software programs and data sets.

Data attributes are generally siloed within different private and public sector institutions, each using its data for its own purposes.  But to reach a higher level of privacy and security, we need to establish trusted data ecosystems, which requires the exchange and sharing of data across a variety of institutions.  The more data sources a trusted ecosystem has access to, the higher the probability of detecting fraud and identity theft while reducing false positives.  In addition, an ecosystem with a variety of data sources can help foster economic inclusiveness by certifying the identities and credit worthiness of poor people with no banking affiliation.

However, safeguarding the data used to validate identities creates security and privacy issues of its own.  It’s unsafe to gather all the needed attributes within one institution or central data location, making it a target for data breaches.  But, it’s also highly unfeasible, as few institutions will let their critical data out of their premises.

Human + Machine: The Impact of AI on Business Transformation

Last week I wrote about the impact of blockchain technologies on business transformation.  I now want to turn my attention to the more immediate impact of AI on business, based on Human + Machine: Reimagining Work in the Age of AI, a recently published book by Accenture executives Paul Daugherty and Jim Wilson.

The book reminds us that technology has long been transforming business.  Electricity and standardized processes ushered the first wave of business transformations about 100 years ago.  Manufacturing was decomposed into a set of processes and reassembled into the assembly line, leading to the age of mass production.

Several decades later, IT and process automation brought us the second wave of business transformation.  Mainframes and data processing software enabled the automation of back-office processes in the 1960s and 1970s.  Front-office automation followed in the 1980s with the advent of PCs and client-server systems.  A decade later, advances in networking and ERP software led to  business process reengineering and management. 

“Now, the third wave involves adaptive processes,” write Daugherty and Wilson.  “This third era, which builds on the previous two, will be more dramatic than the earlier revolutions enabled by assembly lines and digital computers, and will usher in entirely new, innovative ways of doing business… This adaptive capability is being driven by real-time data rather than by an a priori sequence of steps.   The paradox is that although these processes are not standardized or routine, they can repeatedly deliver better outcomes.”

Blockchain and Business Process Transformation

One can look at the transformational impact of IT on companies and industries through a variety of lenses, - e.g., products and services, revenue and profit, sales and support, market strategy.  One of the most important such lenses is the impact of IT on business processes, that is, on how work is actually done across the various functions of an organization. 

Examining the impact of IT over the past several decades through a process point of view, one can identify three distinct phases: first came process automation in the early years of IT, followed around the 1990s by enterprise-wide process reengineering and management.  The third phase, is now starting, focused on the processes and transactions that determine how institutions interact with each other around the world.  Let me discuss each of these phases. 

The MIT Intelligence Quest

How does human intelligence work, in biological as well as in engineering terms? And how can we use such an understanding of human intelligence to build wiser and more useful machines?  On February 1, MIT launched the Intelligence Quest (MIT IQ), - an initiative aimed at addressing these big questions by advancing the science and engineering of both human and machine intelligence.  MIT IQ aims “to discover the foundations of human intelligence and drive the development of technological tools that can positively influence virtually every aspect of society.”

MIT has been deeply involved in artificial intelligence since the field’s inception in the 1950s.  MIT professors  John McCarthy and Marvin Minsky were among the founders and most prominent leaders of the new discipline.  AI was one of the most exciting areas in computer sciences in the 1960s and 1970s.  Many of the AI leaders in those days were convinced that a machine as intelligent as a human being would be developed within a couple of decades.  They were trying to do so by somehow programming the machines to exhibit intelligent behavior, even though to this day we have little idea what intelligence is about, let alone how to translate intelligence into a set of instructions to be executed by a machine.  Eventually, all these early AI approaches met with disappointment and were abandoned in the 1980s.  After years of unfulfilled promises, a so called AI winter of reduced interest and funding set in that nearly killed the field.

AI was reborn in the 1990s when it adopted a more applied, engineering-oriented paradigm.  The new AI paradigm enabled computers to acquire intelligent capabilities by ingesting and analyzing large amounts of data using powerful computers and sophisticated algorithms.  Instead of trying to explicitly program intelligence, this new approach was based on feeding lots and lots of data to the machine, and then letting the algorithms discover patterns and extract insights from all that data.

Such a data-driven, machine learning approach produced something akin to intelligence or knowledge.  Moreover, unlike the explicit programming-based approaches, the statistical-based ones scaled very nicely.  The more data you had, the more powerful the supercomputers, the more sophisticated the algorithms, the better the results.  Machine learning and related advances like deep learning, have played a major role in AI’s recent achievements. 

Blockchain Killer-Apps

A killer app is an IT application whose value is simple to explain, its use is fairly intuitive, and the application turns out to be so useful that it helps propel the success of a product or service.  Spreadsheets and word processing were major factors in the 1980s adoption of personal computers, for example.  A decade later, e-mail, and the World Wide Web played crucial roles in the mainstream success of the Internet.  While there are a number of strong candidates for smartphone killer-apps, navigation and music are among the most widely used.

Blockchain has been in the news lately, but beyond knowing that it has something to do with payments and digital currencies, most people don’t know what blockchain is or why they should care.  A major part of the reason is that we still don’t have the kind of easy-to-explain blockchain killer-apps that propelled the Internet forward.

Blockchain has yet to cross the chasm from technology enthusiasts and visionaries to the wider marketplace that’s more interested in business value and applications.  There’s considerable research on blockchain technologies, platforms and applications as well as market experimentation in a number of industries - roughly where the Internet was in the mid-late 1980s: full of promise but still confined to a niche audience.

In addition, outside of digital currencies, -a somewhat exotic topic of interest to a relatively small audience, - blockchain applications are primarily aimed at institutions.  And, given that blockchain is all about the creation, exchange and management of valuable assets, its applications are significantly more complex to understand and explain than Internet applications.

The management of information is quite different from the management of transactions.  The latter, especially for transactions dealing with valuable or sensitive assets, requires deep contractual negotiations among companies and jurisdictional negotiations among governments.  Moreover, since blockchain is inherently multi-institutional in nature, its applications involve close collaboration among companies, governments and other entities.

A Framework for Building AI Capabilities

After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance.  AI is seemingly everywhere.  Every day we can read about the latest AI advances and applications from startups and  large companies.  AI was the star of the 2018 Consumer Electronic Show earlier this year in Las Vegas.

But, despite its market acceptance, a recent McKinsey report found that AI adoption is still at an early, experimental stage, especially outside the tech sector.  Based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors, the report found that 20 percent of respondents had adopted AI at scale in a core part of their business, 40 percent were partial adopters or experimenters, while another 40 percent were still waiting to take their first steps.

The report adds that the gap between the early AI adopters and everyone else is growing.  While many companies have yet to be convinced of AI’s benefits, leading edge firms are charging ahead.  Companies need to start experimenting with AI and get on the learning curve, or they risk falling further behind. 

AI will likely become the most important technology of our era as it’s improved upon over time, but we’re still in the early stages of deployment.  It’s only been in the last few years that complementary innovations, especially machine learning have taken AI from the lab to early marketplace adopters.  And, history shows that even after technologies start crossing over into mainstream markets, it takes considerable time, - often decades, - for the new technologies and business models to be widely embraced by companies and industries across the economy.

Blockchain and the Promise of an Open, Decentralized Internet

The January 21 issue of the NY Times Magazine included a very good article, Beyond the Bitcoin Bubble, by science writer Steven Johnson.  His article aims to explain what blockchain, cryptocurrencies and related technologies are all about as well as their potential impact on the economy and society.  These are not only complex but esoteric subjects, as nicely illustrated in this funny video.  While blockchain and Bitcoin have been frequently in the news lately, beyond knowing that they have something to do with money, most people don’t really know what they are or why they should care.

This is not surprising.  In their early years, it’s hard for most people to appreciate the potential of a transformative technology.  There’s generally a considerable time-lag between the time we start reading articles about their exciting potential and their adoption by companies and industries across the marketplace.  This is particularly the case for general purpose technologies, like electricity, the Internet and blockchain, which are the most potentially transformative due to their widespread use.   Their deployment time-lags are longer because attaining their full benefits requires a number of complementary co-inventions and investments, including additional technologies, applications, processes, business models, and regulatory policies.

Human Beings Need a Story

It wasn’t that long ago that we didn’t have much use for probabilities in our daily life, - outside of weather reports, election predictions, baseball and financial markets.  But that’s all changed with the growing datafication of the economy and society.  Probabilities have been playing an increasing role in our work and personal life given our newfound ability to quantify just about anything.  In all kinds of everyday situations, - from medical diagnoses to financial decisions, -  we now have to accept the fact that it’s impossible to predict what will actually happen.  Instead, we have to get used to living in a complex world of uncertainties and probabilities.  We have to learn how to deal with the very messy world of big data, and how to best apply our learning to make good decisions and predictions.

Physics went through such a transition about 100 years ago.  In the deterministic world of classical mechanics, there’s always a real truth.  The same objects, subject to the same forces, will always yield the same results.  Elegant mathematical models can be used to make perfect predictions within the accuracy of their human-scale measurements.  Early in the 19th century, French mathematician and scientist Pierre-Simon Laplace observed that if we knew the precise state of the universe as represented by the position and speed of every one of its particles, classical mechanics would enable us to calculate all past and future states of the universe.

But, this predictable world began to fall apart in the early 20th century.  19th century classical physics was replaced by a kind of 20th century magical mystery tour, ruled by the new principles of quantum mechanics and relativity.   Classical mechanics could not explain the counter-intuitive and seemingly absurd behavior of energy and matter at atomic and cosmological scales.

At these scales, the world is intrinsically unpredictable.  Instead of a deterministic world, we now have a world based on probabilities.  You cannot predict all the future states of an electron, for example, based on its present state.  You can map out its behavior, but only as probability distributions of all the possible states it could be at.  Moreover, the Heisenberg uncertainty principle tells you that it’s impossible to know the exact state of a particle.  You cannot simultaneously determine its exact position and velocity with any great degree of accuracy no matter how good your measurement tools are.

Will There Be Enough Work in the Future?

Will there be enough work in the future?  Opinions are fairly divided between those who believe that technology advances will reduce human jobs, and those who believe that technology advances will produce as many jobs as they displace.  It’s easier to predict the jobs that will be automated away by technology, but much more difficult to predict the new jobs that these same technologies will create.  In the end, we don’t really know.

In December of 2017, the McKinsey Global Institute published Jobs Lost, Jobs Gained: Workforce Transition in a Time of Automation, a report that directly addresses this question.  The McKinsey study examined in great detail the work that’s likely to be displaced by automation through 2030, as well as the jobs that are likely to be created over the same period.  It analyzed data from 46 countries comprising almost 90 percent of global GDP, focusing particularly on six countries: China, Germany, India, Japan, Mexico and the US.  For each of this six countries, the study modeled the potential for employment changes in more than 800 occupations based on different scenarios for the pace of automation adoption and for future labor demand.

The report’s overall conclusion is that a growing technology-based economy will create a significant number of new occupations, - as has been the case in the past, - which will more than offset declines in occupations displaced by automation.  However, “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”

The Challenges of Executing a Transformational Strategy

The 2018 MIT CIO Symposium will take place on May 23.  This year’s Symposium will be focused on the challenges of executing a digital transformation strategy.   It’s no longer enough to have formulated a digital strategy for the business.  As a number of recent reports have indicated, leading edge companies are pulling ahead of everyone else by pushing the boundaries of digitization.  And, having a strong digital foundation gives those companies a leg up in embracing data analytics, AI, blockchain, and other advanced technologies, further widening their advantage.  “The time for merely thinking digital has passed; the future belongs to the doers,” notes the Symposium’s website.

Why do so many organizations struggle to implement their well formulated strategies, especially transformational strategies like the transition to digital, that encompass new technologies, business models and management practices?  This is a question I’ve long been thinking about, having lived through IBM’s near-death experience in the early1990s.

Is it that in spite of all their efforts in strategy formulation, their management was unable to anticipate them and were caught by surprise?  Or it is that, as if actors in a kind of Greek tragedy, they saw the changes coming and understood what had to be done, but were somehow unable to execute their strategies?

MIT’s Donald Sull has been exploring these questions for over 20 years.  As this 2015 HBR article notes, we know a lot more about strategy than about translating strategy into results.  “Books and articles on strategy outnumber those on execution by an order of magnitude…  A recent survey of more than 400 global CEOs found that executional excellence was the number one challenge facing corporate leaders in Asia, Europe, and the United States, heading a list of some 80 issues, including innovation, geopolitical instability, and top-line growth.  We also know that execution is difficult.  Studies have found that two-thirds to three-quarters of large organizations struggle to implement their strategies.” 

Cloud Computing and the Supply Chain of Services

In 2008 I gave a talk at a conference on The Promise and Reality of Cloud Computing.  In his closing remarks, the conference organizer noted that most everyone had agreed that something big and profound was going on, but they weren’t quite sure what it was they were excited about.  “There is a clear consensus that there is no real consensus on what cloud computing is,” he said.

At the time, most people associated cloud with utility computing and IT-as-a-Service.  Technology writer Nicholas Carr was also a speaker at the conference.  In his talk, based on his then recently published book The Big Switch, Carr nicely framed the historical shift to cloud computing, comparing the evolution of IT to that of power plants.  In the early days, companies usually generated their own power with steam engines and dynamos.  But with the rise of highly sophisticated, professionally run electric utilities, companies stopped generating their own power and plugged into the newly built electric grid. 

IT was then undergoing a similar transformation, as cloud service providers were starting to offer IT-as-a-Service with near unlimited scalability at very attractive prices, based on a flexible pay-as-you-go delivery model.  Early cloud adopters were mostly focused on improving the economics of IT, giving them the ability to launch new offerings or expand their current ones without major investments in additional IT infrastructure while paying for whatever cloud resources they actually used.

Cloud continued to evolve and advance over the ensuing years.  As an article noted about a 2013 cloud conference: “There wasn’t a single speaker who started off a session by saying, ‘Let's define cloud computing.’  That gets tiresome when seen in session after session, year after year, so its absence is gratefully received. This is a clear indication that the industry has moved beyond elementary knowledge-gathering and onto the practicalities associated with cloud implementation and rollout.”  Still, debates continued on the true definition of cloud computing.  Do private and hybrid clouds qualify or only public ones? 

AI and the Productivity Paradox

AI is being successfully applied to tasks that not long ago were viewed as the exclusive domain of humans, matching or surpassing human level performance in more and more domains.  But, at the same time, productivity growth has significantly declined over the past decade, and income has continued to stagnate for the majority of Americans.  This puzzling contradiction is addressed in Artificial Intelligences and the Modern Productivity Paradox, a working paper recently published  by the National Bureau of Economic Research (NBER).

The paper’s authors, - MIT professor Erik Brynjolfsson, MIT PhD candidate Daniel Rock, and University of Chicago professor Chad Syverson, - note that “aggregate labor productivity growth in the U.S. averaged only 1.3% per year from 2005 to 2016, less than half of the 2.8% annual growth rate sustained from 1995 to 2004…  What’s more, real median income has stagnated since the late 1990s and non-economic measures of well-being, like life expectancy, have fallen for some groups.”

This productivity puzzle isn’t confined to AI.  “Few topics in economics today in most large economies generate as much debate as the productivity puzzle,” said McKinsey in a March, 2017 report.  “In  the United States, productivity growth has declined sharply since 2004 yet digital technology has been widely apparent during this period…  The answer to this puzzle holds the key to future prosperity because now more than ever our economy depends on productivity improvements for long-term economic growth.  Economists have proposed competing explanations for declining productivity growth and so far have failed to reach a consensus.” 

Blockchain and the Music Industry: Turning Pennies into Dollars

Just about every industry has been significantly transformed in the past few decades.  But few have been as disrupted as the music industry.  Everything seems to be changing at once, from the way content is produced and delivered, to the sources of revenue and profits.  Digital technologies, - the Internet, smartphones, cloud computing, … - have literally turned dollars into pennies.  Now, blockchain and related technologies may once more play a major role in the music industry, - this time helping to turn those pennies back into dollars.

We’re truly surrounded by music as never before, - in a wide variety of styles; in physical and digital formats; over the Internet, satellite, and broadcasts; in mobile devices and home music systems.  But, the shift from physical to digital, and then from downloads to streaming have wreaked havoc on the business of music.  US retail revenues of recorded music were close to $14 billion in 1998 before starting their decline.  According to the Recording Industry Association of America (RIAA), revenues fell from roughly $12 billion in 2006 to around $7 billion in 2010.  They stayed flat at $7 billion though 2015, starting to increase in 2016 mostly due to a growth in paid streaming music subscriptions.  Revenues are expected to be around $8 billion in 2017.

Turning dollars into pennies succinctly captures the music industry’s precipitous revenue declines.  As this article noted, “During the heyday of the 80s, the music industry was rolling in so much money it could afford to leave some on the table… which it often did…  There were so many dollars coming in, they didn’t bother paying attention to the pennies.  Well, as we all know those days are gone.  And after a decade of losses from its traditional revenue stream (sales), we’ve seen the music industry forced to look for revenue in new places and in different ways.  In the absence of dollars, the pennies now matter.”

As a result, the music industry now finds itself trying to figure out how to keep track of all those pennies in the hope of turning them back into dollars.  In the first half of 2017, streaming services accounted for 62% of the total market, with digital downloads and physical sales accounting for 19% and 16% respectively, said the RIAA.  But streaming pays royalties in fractions of pennies, so legislation, new business models and technological innovations are needed to make sure that all the necessary data are efficiently collected so the various artists involved in a song are paid.

The Rise of Winner-Take-All Urbanism

Earlier this year, Richard Florida published Why America’s Richest Cities Keep Getting Richer in The Atlantic.  Florida is a leading urban studies author and researcher, professor at the University of Toronto and Director of Cities at its Martin Prosperity Institute.

“The most important and innovative industries and the most talented, most ambitious, and wealthiest people are converging as never before in a relative handful of leading superstar cities that are knowledge and tech hubs,” wrote Florida.  “This small group of elite places forge ever forward, while most others struggle, stagnate, or fall behind.  This process is one I like to call winner-take-all urbanism.”

Winner-take-all urbanism is another manifestation of the winner-take-all economics of the past few decades, in which a relatively small number of players reap a very large share of the rewards, upending whole industries with their outsized returns.

AI and The Future of Work

Last month I attended AI and the Future of Work, a conference hosted by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and its Initiative on the Digital Economy (IDE).  The two-day agenda included over 20 keynotes and panels on AI-related topics, and around 60 speakers and panelists from academia and business.

New technologies have been replacing workers and transforming economies over the past two centuries.  But, over time, these same technologies led to the creation of whole new industries and new jobs.  While the technologies of the industrial economy helped to make up for our physical limitations, the technologies of the digital economy are now enhancing our cognitive capabilities.  They’re being increasingly applied to activities that not long ago were viewed as the exclusive domain of humans.  Will the AI revolution play out like past technology revolutions, - short term disruptions followed by long-term benefits, - or will this time be different?

Conference participants generally agreed that AI will have a major impact on jobs and the very nature of work.  But, for the most part, they viewed AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention.  Overall, a small percentage of jobs will be fully automated, while many more will be significantly transformed.

Conference participants also generally agreed that the more advanced AI-based transformations will not happen rapidly, but are likely decades away.  Much progress has been recently made in the ability to extract features from all the data we now have access to, as well as in machine learning algorithms that give computers the ability to learn by ingesting large amounts of data instead of being explicitly programmed.  While such statistical pattern recognition approaches can be applied to many tasks, they’re no substitute for model formation, the main approach used by humans, - from toddlers to physicists, - to understand how the world works.  We’re a long way from the development of AIs that truly learn and reason like people. 

The Emergence of Industry 4.0

Over the past few years, Industry 4.0, - aka the 4th Industrial Revolution, - has been the subject of several articles, studies, and surveys, as well as a book published earlier this year by Klaus Schwab, founder and executive chairman of the World Economic Forum (WEF).  They all attempt to describe and put a name to the disruptive changes taking place all around us within the context of the past 250 years.

“Industrial revolutions are momentous events,” said A Strategist Guide to Industry 4.0, - a 2016 article in strategy+business.  “By most reckonings, there have been only three.”  The First Industrial Revolution, - starting in the last third of the 18th century, - introduced new tools and manufacturing processes based on steam and water power, ushering the transition from hand-made goods to mechanized, machine-based production.  The Second starting a century later, brought us steel, cars, chemicals, petroleum, electricity, the telephone and radio while creating the age of mass production.  The Third, - following World War II - saw the advent of computers, digital technologies, the IT industry, and the automation of process in just about all industries.

There’s general agreement that the 4th Industrial Revolution is primarily driven by a fusion of once separate technologies that when joined together are integrating the physical and digital worlds.  But, there’s a spectrum of opinions as to the scope of its impact, some arguing that Industry 4.0 applies primarily to manufacturing technologies and industries, while others argue that it’s profoundly transforming our economies and societies.

A Historical Perspective on the Digital Economy

A few months ago, the magazine strategy+business (s+b) published a roundtable discussion between s+b editor-in-chief Art Kleiner, economic historian Carlota Perez, and PwC UK partner Leo Johnson.  The discussion was centered on Perez’s theory of technology revolutions and economic cycles.  In her 2002 influential book, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages and in several other publications, Perez framed the digital economy of the past few decades from a historical perspective.  If we examine the long term historical big picture, patterns emerge which can help guide our understanding and planning for the future. 

According to Perez, since the onset of the Industrial Revolution we’ve had 5 major technology-driven economic cycles, each one lasting roughly 50-60 years.  First was the age of machines, factories and canals starting in 1771.  This was followed by the age of steam, railways, iron and coal, starting in 1829; steel, electricity and heavy engineering in 1875; oil, automobiles, and mass production in 1908; and our present ICT-based digital age starting in 1971.

Each economic cycle is composed of two very different periods, each lasting roughly 20 - 30 years.  The installation period is the time of creative destruction, when new technologies emerge from the labs into the marketplace, entrepreneurs start many new businesses based on the new technologies, VCs encourage business model experimentation, and the new ventures attract considerable investments and financial speculation.  Inevitably this all leads to a financial bubble, which eventually bursts in spectacular fashion leading to a time of crisis.

After the crash, comes the deployment period, which Perez views as a time of economic transformation and institutional recomposition.  The now well accepted technologies and business paradigms become the norm; infrastructures and industries start getting better defined and more stable; and production capital drives long-term growth and expansion by spreading and multiplying the successful business models.

Human-AI Decision Systems

I recently read The Human Strategy, a very interesting article on Human-AI decision systems by MIT Media Lab professor Sandy Pentland.  Pentland is the faculty director of MIT Connection Science, - with which I’m associated as a Fellow, - as well as director of the Human Dynamics Lab.  In 2014 he published Social Physics: How Social Networks Can Make Us Smarter.

“Perhaps the most critical function of any organization or society is its decision systems,” wrote Pentland.  “In modern societies, decision systems provide a leader the ability to make informed and timely decisions, supported by a complex enterprise of distributed information and communication systems that provide situational awareness.  Traditionally, decision systems have been confined to individual physical domains, such as logistics, physical plant, and human resources, and more recently virtual domains such as cyber, by both policy and technology, resulting in challenges with the integration of information across disparate domains.”

But, despite the increasingly complex decisions that organizations are called upon to make, decision-making remains human-intensive and anecdotal.  Few organizations have applied social network analysis to help them scale the size and expertise of the decision-making group.  Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. 

Where is Technology Taking the Economy

I recently read Where is Technology Taking the Economy?, an article in McKinsey Quarterly by W. Brian Arthur, - External Professor at the Santa Fe Institute , Visiting Researcher at PARC, and former faculty member at Stanford University.

According to Arthur, the digital revolution has morphed through three distinct eras over the past several decades.  The first era, in the 1970s and 1980s, brought us Moore’s law and the dramatic advances in semiconductor technologies.  From mainframe and supercomputers to PCs and workstation, IT was now being used in a wide variety of applications, from financial services and oil exploration, to computer-aided design and office systems.  “The economy for the first time had serious computational assistance.”

Then came the second era in the 1990s and 2000s, which enabled us to link computers together, share information, and connect digital processes.  “Everything suddenly was in conversation with everything else,” giving rise to “the virtual economy of interconnected machines, software, and processes…, where physical actions now could be executed digitally.”

We’re now in the third era, which began roughly in the 2010s.  It’s brought us smartphones, ubiquitous sensors, IoT devices and oceans and oceans of data.  Powerful computers and intelligent algorithms are enabling us to make sense of all that data by searching for patterns and doing  something with the results, including computer vision, natural-language processing, language translation, face recognition and digital assistants.

A Novel Approach for Measuring the True Impact of the Digital Economy

Gross domestic product (GDP), the basic measure of a country’s overall economic output, is generally used by governments to inform their policies and decisions.  “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted,” noted a 2009 Commission convened to look at the adequacy of GDP as an indicator of economic performance, - which was led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen.

GDP is essentially a measure of production.  While suitable when economies were dominated by the production of physical goods, GDP doesn’t adequately capture the growing share and variety of services and the development of increasingly complex solutions in our 21st century digital economy.

Digital Spillover, a recent report co-developed by Huawei and Oxford Economics, explores how to better define and measure the true impact of the digital economy.  It argues that the scope of the digital economy is expanding and proposes a novel way of measuring its impact.

“A truly digital economy is one in which businesses from across the industrial spectrum are investing in digital and making the most productive use of it,” notes the report.  “The mechanisms by which this is happening are complex and evolving.  Over and above the direct productivity boost that companies enjoy from digital technologies, a more profound chain of indirect benefits also takes place, as the impact spills over within a firm, to its competitors, and throughout its supply chain.  These digital spillover effects materialize through numerous channels, and are integral to understanding the role digital technologies play in the economy.”

The Evolution Toward Digital Supply Chains

Two years ago, a group of leading CEOs from around the world met in China to discuss the major issues facing their companies in the global marketplace.  The meeting was organized by the Center for Global Enterprise (CGE), - the nonprofit research institution founded by former IBM Chairman and CEO Sam Palmisano to study the contemporary corporation, globalization, economic trends, and their impact on society.  The CEOs at the meeting in China concluded that their highest research priority was the evolution toward Digital Supply Chains (DSC), and its potential to transform organizations and the conduct of business around the world.  In response, the Digital Supply Chain Institute (DSCI) was launched a few months later.

In October of 2016, the DSCI, in partnership with CREATe.org  published its first research paper, Digital Supply Chains: A Frontside Flip.  The paper explains the key differences between traditional and digital supply chains, and provides practical advice to help companies prepare for the digital supply chains of the future

The traditional supply chain has evolved over the years as a crucial process used by many companies for the production, handling, and/or distribution of their products or services.  Traditional supply chains generally comprise five separate back-office functions: transport, warehousing, purchasing, marketing and finance.  Their key objective has been to improve the efficiency of getting goods and services from suppliers to customers, through a series of intermediate steps including manufacturing, distributors and retailers.  But, these supply chains were quite fragmented, with limited interactions and information sharing among their various functions and steps.

Over the past couple of decades, the explosive growth of the Internet has made it much easier for companies to transact with each other around the world.  The connectivity and universal reach of the Internet has enabled companies to integrate and better coordinate all their various processes, as well as to go beyond the boundaries of the firm and develop highly sophisticated global supply chains.  Vertically integrated firms have evolved into virtual enterprises, increasingly relying on supply chain partners for many of the manufacturing and services functions once done in-house.

The DSCI paper argues that it’s time to focus on the transformation of the whole supply chain process, not just in further increases in efficiency.  Their traditional linear nature is increasingly misaligned with the more networked nature of today’s global production.

Gender Diversity, Empathy and Technology

What’s the value of gender diversity in technology-based disciplines?  Is gender diversity an asset for technology-based companies?  Are men inherently better than women at technology-based jobs?

These are questions that have long been discussed.  Lots of progress has been made in addressing them.  But they recently resurfaced following an internal memo by Google engineer James Damore that criticized the company’s diversity initiatives, for which he was subsequently fired by Google.

Let me attempt to explore these questions based on relatively recent academic research in three key areas: gender-based behavioral differences; how to create smarter working groups; and the changing skills requirements in the digital economy.

The True State of Globalization

The July-August issue of the Harvard Business Review features an article by NYU and IESE professor Pankaj Ghemawat, - Globalization in the Age of Trump: Protectionism Will Change How Companies Do Business - But Not in the Ways You Think.  Ghemawat has long been conducting research on globalization, and has written extensively on the subject.  His latest book, The Laws of Globalization, was published in October of 2016.

“Business leaders are scrambling to adjust to a world few imagined possible just a year ago,” notes the article’s opening sentence.  “The myth of a borderless world has come crashing down.  Traditional pillars of open markets - the United States and the UK - are wobbling, and China is positioning itself as globalization’s staunchest defender.  In June 2016, the Brexit vote stunned the European Union, and the news coverage about globalization turned increasingly negative in the U.S. as the presidential election campaign progressed.”

What should companies do?  Should they pack up and return home?  Is this the end of globalization, only a dozen years after Thomas Friedman’s The World is Flat became a prominent international best-seller?  “Not according to my research,” says Ghemawat. 

Blockchain and the Future of the Firm

Last week I wrote about Machine, Platform, Crowd: Harnessing Our Digital Future, a recently published book by MIT’s Andy McAfee and Erik Brynjolfsson.  The book is organized into three main sections: Mind and Machine, Product and Platform, and Core and Crowd, each based on  a business trend that’s significantly reshaping the business world.  I based my discussion on the Mind and Machine section, which I found to be an excellent explanation of recent advances in artificial intelligence.

I now want to focus my attention on the provocative questions raised in the book’s latter chapters.  “In this era of powerful new technologies, do we still need companies?”  How are firms likely to evolve given the continuing advances of Internet-based technologies, as well as emerging blockchain-based technologies such as distributed ledgers and smart contracts?  “Are companies passé” in an increasingly decentralized economy?

Following the Great Depression and WW2, the country welcomed the stability promised by corporate capitalism.  Big, multinational companies dominated most industries, - GM, Ford and Chrysler in cars, Citibank, American Express and Morgan Stanley in finance, and Esso/Exxon, Mobil and Texaco in oil and gas.  It was an era characterized by bureaucratic corporate cultures, - not unlike military hierarchies - focused on organizational power and orderly prosperity.

This all started to change a few decades later with the advent of a more innovative, fast moving entrepreneurial economy.  The 1980s saw the rise of young, high-tech companies, - e.g., Microsoft, Apple, Oracle, Sun Microsystems, - and Silicon Valley became the global hub for innovation, emulated by regions around the world.  Turtlenecks and jeans replaced blue suits, white shirts, and ties.

The advent of the Internet and e-commerce pushed all these trends into hyperdrive in the 1990s.  The Internet made it much easier for companies to transact with each other around the world.  Vertically integrated firms evolved into virtual enterprises, increasingly relying on supply chain partners for many of the functions once done in-house.  Management experts noted that large firms were no longer necessary and would in fact be at a disadvantage in the Internet era when competing against agile, innovative smaller companies.

Minds and Machines

A few months ago, Andy McAfee and Erik Brynjolfsson published Machine, Platform, Crowd: Harnessing Our Digital Future, - their third book on the impact of the 21st century digital revolution on the economy and society, - following the publication of The Second Machine Age in 2014 and Race Against the Machine in 2011.  Brynjolfsson and McAfee are professor and research scientist respectively at MIT’s Sloan School of Management, as well as co-Directors of MIT’s Initiative on the Digital Economy.

The book is organized into three sections, each focused on a major trend that’s reshaping the business world: the rapidly expanding capabilities of machines; the emergence of large, asset-light platform companies; and the ability to now leverage the knowledge, expertise and enthusiasm of the crowd.  These three trends are combining into a triple revolution, causing companies to rethink the balance between minds and machines; between products and platforms ; and between the core and the crowd.

I cannot possible do justice to all three trends in one blog, so let me summarize the key themes of the Mind and Machine section, which I found to be an excellent explanation of the current state of AI.

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

AI is now seemingly everywhere.  In the past few years, the necessary ingredients have come together to propel AI beyond the research labs into the marketplace: powerful, inexpensive computer technologies; advanced algorithms and models; and most important, oceans and oceans of data.

“Artificial intelligence is getting ready for business, but are businesses ready for AI?,” asks McKinsey in a recently published report - Artificial Intelligence: the Next Digital Frontier.  “AI adoption outside of the tech sector is at an early, often experimental stage,” is the report’s succinct answer.  “Few firms have deployed it at scale.”

The report is based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors.  Only 20 percent of respondents had adopted AI at scale in a core part of their business.  40 percent were partial adopters or experimenters, while another 40 percent were essentially contemplators.

AI encompasses a broad rage of technologies and application.  It’s often viewed as the leading edge of IT.  As soon as AI is successfully applied to a problem, the problem is no longer a part of AI.  The McKinsey report is focused on five technologies being increasingly deployed in business, that most everyone agrees are part of AI: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.

Platforms, Blockchains, and the Evolution of Trust

“In primitive economies, people traded mostly with members of their village and community,” wrote David Brooks in a June, 2014 NY Times OpEd, - The Evolution of Trust.  “Trust was face to face.  Then, in the mass economy we’ve been used to, people bought from large and stable corporate brands, whose behavior was made more reliable by government regulation.  But now there is a new trust calculus, powered by both social and economic forces.”

Brooks’ article focused on the rise of the on-demand economy, - aka, the collaborative, sharing, peer-to-peer economy, - and in particular on the surprising success of Airbnb.  Rooms for rent in boarding houses and child and pet care services are nothing new.  What’s new is the impact of technology, platforms and blockchain in particular, on the growing on-demand economy.  Companies are being disrupted as consumers are now able to deal with each other bypassing traditional hotels and taxi services.  All kinds of on-demand products and services are now coming to market.

The on-demand economy wouldn’t be possible without the mobile devices and platforms that enable peer-to-peer transactions among individuals any time and place; the digital payment systems that reliably and securely broker the transactions between buyers and sellers; and the social reputation systems, where people rank buyers and sellers, - a critical requirement for the smooth functioning of collaborative markets like Airbnb.  In the digital economy, our online reputations follow us everywhere, whether we are the renters or the ones renting, the service providers or the service consumers.