Irving Wladawsky-Berger

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. 

The Top Ten Emerging Technologies of 2017

Earlier this year, Scientific American, in collaboration with the World Economic Forum, published a special report on The Top Ten Emerging Technologies of 2017.  These technologies, - selected by a global panel of experts, - “are expected to become increasingly commonplace in the next few years,” and are “attracting increased funding or showing other signs of being ready to move to the next level.”

Here are the 10 technologies comprising the 2017 list, along with the reasons cited in the report for their selection:

The MIT 2017 Inclusive Innovation Challenge

The MIT Initiative on the Digital Economy (IDE) was organized in 2013 by Erik Brynjolfsson and Andy McAfee to examine the impact of digital technologies on the world.  Understanding the future of work and jobs is one of the major areas of research being address by IDE.  What will the workforce of the future look like?, Where will jobs come from in the coming years, especially for the workers must impacted by automation?,  How can we accelerate the transformation of institutions, organizations, and human skills to keep up with the quickening pace of digital innovation?

To help come up with breakthrough, real-world answers to these tough questions, IDE launched the MIT Inclusive Innovation Challenge (IIC) last year.  The Challenge aims to identify, celebrate and award prizes to organizations around the world that are developing innovative approaches for improving the economic opportunities of middle- and base-level workers.

Now in its second year, the IIC introduced the winners of its 2017 competition at an event held on October 12 as part of Boston’s annual HUBweek.  Over $1 million was awarded to the winners in four categories: job creation and income growth, skills development and matching, technology access and financial inclusion.  The grand prize winner in each category received $150,000, while the three runners up in each category received $35,000.  The awards were funded with support from from Google.org, The Joyce Foundation, ISN and Joseph Eastin.

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.”

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.

Automation Anxieties - We’ve Been Here Before

“The rise of artificial intelligence is the great story of our time,” notes What to Do When Machines Do Everything in its Preface.  The book was published earlier this year by Malcolm Frank, Paul Roehrig and Ben Bring of Cognizant’s Center for the Future of Work.  “Artificial intelligence has left the laboratory (and the movie lot)…  It’s pervading all the institutions that drive our global economy.… And this is just the beginning.”

The book deals with a number of important questions: When machines do everything, what am I going to do?; Will a robot take my job away?; How are humans going to make a living?; What will my industry look like in 10 years?; Will my children be better off than I am?

Overall, the authors are optimistic about the future, reminding us that we’ve been here before.  Automation anxieties have been with us ever since the 1810s, when the so-called Luddites smashed the new weaving machines that were threatening their textile jobs, and they continued to resurface over the past two centuries right along with advances in technology.

Over 60 years ago, the advent of computers led to a new round of automation fears.  In 1965, Newsweek devoted a special issue to The Challenge of Automation, which it called  “the most controversial economic concept of the age.  Businessmen love it.  Workers fear it.  The government frets and investigates and wonders what to do about it.

In his 1964 Bill creating the National Commission on Technology, Automation and Economic Progress, President Lyndon Johnson said that “Technology is creating both new opportunities and new obligations for us, opportunity for greater productivity and progress - obligation to be sure that no workingman, no family must pay an unjust price for progress.”

Should Your Company Get on the Blockchain Learning Curve Now or Wait?

A few weeks ago I wrote about the current state of blockchain.  In particular, I asked whether blockchain is ready to cross the chasm from its early adopters, - who are already involved with blockchain in one way or another, - to the considerably larger set of mainstream users who may be considering blockchain but are waiting for results and assurances before jumping in.

I explored this question by comparing blockchain today to the Internet in the early-mid 1990s, which is roughly when the Internet made its own transition from early adopters to mainstream users.  My conclusion was that blockchain isn’t quite ready for this important transition.  It’s still in its early adopters stage, - perhaps akin to the Internet in the late1980s.  A lot is going on with blockchain, so much so that last year the World Economic Forum (WEF) named The Blockchain one of its Top Ten Emerging Technologies for 2016.  But much remains to be done in key areas, including standards, applications and governance.  Blockchain has the potential to make our digital infrastructures much more secure, efficient and trustworthy, but it will take time.

The Internet in the early-mid 1990s was clearly more advanced than blockchain is today.  On the other hand, the business environment in 2017 is quite different from the one back then.  The Internet and associated technologies like smartphones and cloud computing have significantly lowered the costs of collaboration and experimentation, enabling innovations to emerge more rapidly.  In addition to providing a good set of lessons for blockchain’s adoption, the Internet is smoothing the way for the development of blockchain platforms, applications and ecosystems.

What should  your company do?  Get on the blockchain learning curve now or wait until the technology is more mature before jumping in?  Become an early adopter of this potentially transformative technology, or run the risk of being left behind by more aggressive competitors?  How should your company decide when and how to best embrace a disruptive new technology like blockchain?

Is Blockchain Ready to “Cross the Chasm”? Lessons from the Internet

Earlier this year, IBM conducted the largest study to date on the state of adoption of blockchain.  The study interviewed almost 3,000 C-Suite executives from over 80 countries and 20 industries to learn about their company’s blockchain plans.  Survey responses where classified into three groups: explorers, investigators and passives.

Explorers are already involved in blockchain pilots and experiments.  They make up an average of 8 percent across all industries, with higher activity in certain industries like financial services.  25 percent of organizations are investigators, - considering but not yet ready to deploy blockchains, and 67 percent are passives, - not considering blockchains so far.

These survey results are not surprising for a technology as new and complex as blockchain.  With such technologies, you typically have a relatively small number of early adopters.  A larger number of followers are waiting to learn from the early adopters’ experiences, while the laggards are not yet sure what the technology is about or if it applies to them.

The Productivity Puzzle

Last year, Foreign Affairs focused its March/April issue on Surviving Slow Growth.  “The first decade of the twenty-first century was a time of unprecedented economic growth,” said the issue’s introductory article.  “The rich world got richer, and the developing world raced ahead: by 2007, the emerging-market growth rate had hit 8.7 percent… Then came the fall… growth has ground to a halt almost everywhere, and economists, investors, and ordinary citizens are starting to confront a grim new reality: the world is stuck in the slow lane and nobody seems to know what to do about it.”

Economic growth has two main components, productivity growth and the growth of the labor force.  Fewer workers is one of the key reasons for our stagnant economic growth.  The labor force is still growing in some developing countries like India, Nigeria and the Philippines, but it’s already shrinking in China, Japan and Germany.  In the US, the labor force is growing very slowly.

Over the coming decades, the labor force is expected to shrink in most parts of the world as fertility rates continue to decline.  The 2016 US fertility rate was the lowest it’s ever been.  Increasing productivity is thus more crucial than ever to promote economic growth.  But, in the US and other advanced economies, productivity growth has significantly slowed down over the past few decades.

On the Governance of Blockchain: Lessons from the Internet

A few weeks ago, the World Economic Forum (WEF) released Realizing the Potential of Blockchain, a report by Don Tapscott and Alex Tapscott, authors of Blockchain Revolution which was published in May, 2016, and founders of the recently launched Blockchain Research Institute.  “The last few decades brought us the internet of information.  We are now witnessing the rise of the internet of value,” wrote the authors in the report’s Executive Summary.  “Like the first generation of the internet, this second generation promises to disrupt business models and transform industries… pulling us into a new era of openness, decentralization and global inclusion.”

“However, this extraordinary technology may be stalled, sidetracked, captured or otherwise suboptimized depending on how all the stakeholders behave in stewarding this set of resources - i.e. how it is governed,” they add in a strong note of caution.  “How we govern the internet of information as a global resource serves as a model for how to govern this new resource: through a multi-stakeholder approach using what we call global governance networks.”

I totally agree.

The Internet and World Wide Web brought a badly needed culture of collaboration and standards to the IT industry.  In the 1980s, it was quite difficult to get different IT systems to talk to each other.  Just sending an e-mail across two different applications from two different vendors was quite a chore, as was sharing information across disparate systems.  Then in the 1990s, the open Internet protocol, - TCP/IP, - was widely embraced by the general IT marketplace, making it possible to interconnect systems and applications from any vendors.  Internet e-mail protocols, - SMTP, MIME, POP, IMAP, - enabled people to easily communicate with anyone on any system.  At the same time,  the Web’s open standards, - HTML, HTTP, URLs enabled any PC connected to the Internet to access information on any web server anywhere in the world.

A similar story played out with Unix.  In the 1980s, Unix became a popular operating system for technical workstations, supercomputers and other kinds of systems.  Every vendor had developed its own version of Unix, - IBM’s AIX, Sun’s Solaris, HP’s HP-UX, and several others, - and they were all somewhat different and incompatible, making it difficult to port applications across these different flavors of Unix.  Attempts to unify UNIX were not successful.  Finally, in the 1990s Linux emerged as a Unix-like operating system that over time was embraced by just about all vendors.

Serious AI Challenges that Require Our Attention

Several weeks ago Vanity Fair published and article by NY Times columnist Maureen Dowd, - Elon Musk’s Billion-Dollar Crusade to Stop the AI Apocalypse.  “Elon Musk is famous for his futuristic gambles, but Silicon Valley’s latest rush to embrace artificial intelligence scares him,” noted Dowd.  “And he thinks you should be frightened too.”

Entrepreneur and inventor Elon Musk is one of a number of world-renowned technologists and scientists who have expressed serious concerns that AI might be an existential threat to humanity, a group that includes Stephen Hawking, Ray Kurzweil and Bill Gates.  But, the vast majority of AI experts do not share their fears.  A few months ago, Stanford University’s One Hundred Year Study of AI project published a report by a panel of experts assessing the current state of AI.  Their overriding finding was that:

“Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to humankind.  No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future.  Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers.  At the same time, many of these developments will spur disruptions in how human labor is augmented or replaced by AI, creating new challenges for the economy and society more broadly.”

Just because experts conclude that, - at least for the foreseeable future, - AI does not pose an imminent threat to humanity, doesn’t mean that such a powerful technology isn’t accompanied by serious challenges that require our attention.

Is Blockchain the Major Next Step in the Evolution of the Internet?

In the Fall of 1995, IBM made the decision to embrace the Internet and make it the centerpiece of its strategic directions.  As the general manager of the newly formed IBM Internet Division, I spent a lot of time thinking how to best articulate the promise of the Internet.  Looking back, I often described that promise using some variation of: The Internet has the potential to transform the economy, society and our personal lives.

It’s taken a a few additional innovations, - e.g., smartphones, social media, cloud computing, big data, IoT, - for its historical impact to be fully evident, but by now, most everyone agrees that the Internet has become one of the most transformative technologies the world has ever seen, - right up there with electricity, telecommunications, cars and airplanes.

About a year ago, the World Economic Forum (WEF) published its annual list of the Top Ten Emerging Technologies for 2016, and named The Blockchain as one of the technologies in the 2016 list.  The WEF report compared the blockchain to the Internet, noting that “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built.  And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

As Don Tapscott and Alex Tapscott wrote in the opening paragraph of their recently published book  Blockchain Revolution, “It appears that once again, the technological genie has been unleashed from its bottle.  Summoned by an unknown person or persons with unclear motives, at an uncertain time in history, the genie is now at our service for another kick of the can - to transform the economic power grid and the old order of human affairs for the better.  If we will it.”

Become a More Productive, Empathetic, Creative Person With the Help of AI-Based Tools

Despite dramatic advances in technology, most of the world’s economies have been stuck in a long period of slow growth and slow productivity.  This is one of the most serious challenges in our 21st century economy.  Opinions abound, but there’s little consensus on its causes, and, nobody seems to know what to do about it, or how long it will likely last, - years or decades. 

In a recent article in the MIT Sloan Management Review, MIT Research Fellow Michael Schrage proposed a provocative and counterintuitive approach for enhancing innovation and productivity through man-machine collaborations.  Schrage’s approach has been more influenced by behavioral economics than by technology or algorithmic advances.  Instead of just asking how can people create more valuable innovation?, why not also ask How can innovation create more valuable people?  Don’t just leverage advanced technologies, - e.g., bots, software agents, and digital assistants, - to automate away a large portion of the workforce, but also focus on enhancing innovation and productivity by leveraging technology to create higher-performance versions of employees.

“Designing and training smarter algorithms may be cheaper and easier than retraining smart people,” wrote Schrage.  “Advocates of autonomous systems and machine learning typically innovate to minimize or marginalize human involvement in business processes.  For them, people are part of the problem, not the solution.  Organizations that take productivity seriously, however, understand that false dichotomies make poor investments: Smarter machines can - and should - be keys to unlocking greater returns from human capital.”

Mobile Payments - a Long, Relentless War of Attrition

Apple Pay was launched in September of 2014.  It was positioned as a relatively easy, secure way to pay for purchases in physical stores as well as online using a variety of supported Apple devices including iPhones, iPads, Macs and Apple Watch. The general reaction to Apple’s announcement was quite positive:  “[F]or now, at least, analysts believe if there is any company to persuade consumers of the mobile wallet’s value, it is Apple.” 

The payments community welcomed Apple with open arms, hoping that Apple could play a major role in bringing together the different players in the fragmented payments ecosystem, much as it had previously done with iTunes for music and the App Store for smartphone apps.  Apple Pay embraced a number of industry standards, including NFC (near-field communications) for contact-less payments; secure element, a dedicated device chip; and network-based tokenization to protect sensitive financial data.  In addition, Apple collaborated with the credit card networks, several banks and a number of merchants in the development and deployment of Apple Pay, as well as publishing the APIs so developers could embed Apple Pay services in their own apps.  

“Say hello to Apple Pay. It’s the new kid on the payments block and, depending on how things unfold, it could be the new Gorilla in the mobile payments ecosystem,” wrote Karen Webster, CEO of Market Platform Dynamics and an expert in digital payments.  But, she also added a prescient note of caution: “As great as this sounds, there are two limitations of Apple Pay right now for consumers and merchants.  And it’s that old chicken and egg issue that gets in the way of every new payments system.”  The chicken and egg issue is that there aren’t enough consumer devices supporting Apple Pay, and there aren’t enough merchants that accept it.  Both, consumers and merchants, have to be convinced to move away from payment systems they know well and feel comfortable with.

Global Trends and Business Strategy

McKinsey recently published an article on the long-term trends that are reshaping the business environment.  The article reminds us that the trend is your friend is an old adage that applies to business strategy as well as to investing.  The ability to anticipate where your industry is moving and to begin to reposition your company ahead of competitors is one of the most important elements of business strategy.  Companies that correctly identify and ride the tailwinds of technology and market trends will generally perform significantly better than companies for whom these same trends become tough headwinds.

But, the full version of the phrase is “The trend is your friend, until the end when it bends.”  Identifying long term trends is easier said than done.   Major trends involve competing complex forces interacting with each other, making it even more difficult to ascertain where things are heading.  And, they will eventually change direction.  It can be just as painful to miss an important trend as to stay with the trend once it starts to bend.

The article discusses nine major global forces organized into three distinct mega-trends: global growth shifts, accelerating industry disruption, and a new societal deal.  Let’s take a closer look at each of them.

The Emerging Data Economy

The Economist’s May 6 issue referred to data in its cover as the world’s most valuable resource.  Its lead article, - Data is giving rise to a new economy, - called it the fuel of the future.  “Data are to this century what oil was to the last one: a driver of growth and change.”  Both oil refineries and data centers fulfill similar roles: “producing crucial feedstocks for the world economy.”  Much of modern life would not exist without the cars, plastics and drugs made possible by oil refineries.  Similarly, data centers “power all kinds of online services and, increasingly, the real world as devices become more and more connected.”

Data has been closely intertwined with our digital technology revolution since the early days of the IT industry.  Data processing was the term then used to describe the applications of IT to automate highly structured business processes, e.g., financial transactions, inventory management, airline reservations.  Over time, increasingly sophisticated applications were developed to better manage key business operations along with their associated data, including enterprise resource planning, customer relationship management and human resources.  Beyond their use in operations, the information generated by these various applications was collected in data warehouses, and a variety of business intelligence tools were used to analyze the data and generate management reports.

These commercial applications dealt mostly with structured information in those early years of computing.  But at the same time, the scientific community was developing tools for managing, analyzing and visualizing the high volumes of much more unstructured data generated by their  experiments and observations.  Physicists, astronomers, biologists, and other scientists and engineers were developing methodologies and architectures for dealing with very large volumes of unstructured data, as well as analytical techniques, like data mining, for discovering patterns and extracting insights from all that data.

Then came the explosive growth of the Internet in the mid-1990s.  Ever since, digital technologies have been permeating just about every nook and cranny of the economy, society and our personal lives.  Data is now being generated by just about everything and everybody around us, including the growing volume of online and offline transactions, web searches, social media interactions, billions of smart mobile devices and 10s of billions of smart IoT sensors.

Not Your Grandfather’s Manufacturing

The February 23 issue of the NY Times Magazine was devoted to the future of work, and in particular, to what it called the new working class.  The Oxford Dictionary defines working class as “The social group consisting of people who are employed for wages, especially in manual or industrial work.”  The working class includes many traditional blue-collar occupations, - factory workers, truck drivers, electricians, plumbers, - but it also now includes an increasing number of white-collar and service jobs.

While the new working class comprises a variety of jobs, most discussions on the topic are generally centered on the deindustrialization of America over the past few decades, as well as on the ensuing decline in both the number and the wages of manufacturing jobs.  “Now when politicians invoke the working class, they are likely to gesture, anachronistically, to an abandoned factory,” notes the issue’s introductory article.

The ongoing transformation of manufacturing jobs was the subject of Learning to Love Our Robot Co-Workers by Kim Tingley.  Her article nicely explained the major changes taking place in US manufacturing, especially the appearance of a new generation of flexible, programmable, moderately priced robots that are designed to safely collaborate with humans, - each doing what they do best.

Robotics is an exciting, fast moving discipline, which is playing an increasingly important role in the future of manufacturing.  All computers are defined by what their brains, - that is, their hardware and software, - are capable of computing and controlling.  Robots are computers that have both a brain and a body.  A robot’s capabilities are defined by what its brains and body can jointly do.

What’s the Value of Blockchain?

A few weeks ago I attended the IBM Blockchain Summit 2017 in New York.  The Summit included a number of talks and panels, and - most important, - presentations of concrete blockchain use cases IBM has been working on in a number of areas, including finance, supply chains and healthcare.  Such real-world use cases are particularly important when introducing a new, complex set of technologies in the marketplace.  They’re the best way of getting on the learning curve for the new technologies as well as of explaining the kinds of problems they help us address.

Blockchain has the potential to transform our economic and social systems, but such a transformation is still many years away.  Like the Internet, blockchain is a foundational technology, whose adoption process is gradual, incremental and steady, unlike the hockey stick adoption we typically associate with disruptive technologies as defined by Clayton Christensen 20 years ago.  Foundational transformations must overcome many barriers, - technological, organizational, governance, political, - which is why they take a long time to play out.

Lest we forget,  TCP/IP, - the communications protocol that defines the Internet to this day, - was first adopted in the mid-1980s.  So were the protocols used in e-mail, - the first widely used Internet application which enabled its user base in government, universities and research institutions to easily communicate with each other.  It wasn’t until the late 1980s that the Internet was first opened up to public, commercial users.  General Internet adoption took off a few years later with the advent of the World Wide Web and the Web browser, and its transformational power wasn’t fully evident until the late 2000s, with the explosive growth of smartphones and the mobile Internet.

The concrete uses cases presented by IBM as it defines its blockchain strategy brought to mind the approach we took in the early days of formulating IBM’s Internet strategy, which I was closely involved with.

Technology, Capitalism and “The American Way of Life”

Much has been written about our digital technology revolution and its impact on economies, societies and individuals.  But few have brought the kind of historical perspective to the subject that helps us better understand our turbulent times like scholar and consultant Carlota Perez in her 2002 book Technological Revolutions and Financial Capital:  The Dynamics of Bubbles and Golden Ages and related publications.

According to Perez, over the past few centuries, we’ve had a technology revolution every 40 to 60 years, starting with the Industrial Revolution in 1771, which was characterized by the emergence of machines, factories and canals.  This was followed by the age of steam, coal, iron and railways starting in 1829; steel, electricity and heavy engineering in 1875; and oil, autos and mass production in 1908.  Our present age of information and communication technologies (ICT), whose starting point she pegs at 1971, is the fifth such major revolution.

Technology revolutions have two distinct periods, each lasting 20 to 30 years.  The installation period is the time of creative destruction, when new technologies emerge from the lab into the marketplace, entrepreneurs start many new businesses based on these new technologies, VCs encourage experimentation with new business models, and the new ventures attract considerable investments and financial speculation.  Inevitably, this all leads to bubbles and crashes like the recent dot-com and subprime mortgage bubbles.

After the crash, comes the deployment period, a time of institutional recomposition.  The now well accepted technologies become the norm.  New paradigms emerge for guiding innovation and for determining winners and losers in the marketplace.  Over time, these new paradigms significantly transform the economy and everything around it, as well as re-shaping social behavior and the institutions of society.

Last year Perez published Capitalism, Technology and a Green Global Golden Age: The Role of History in Helping to Shape the Future.  In this recent article, she argued that we’re going through a crucial moment in history similar to the 1930s, which requires the kind of creative thinking and bold measures for which we justifiably celebrate statesmen like Franklin Delano Roosevelt and John Maynard Keynes.  Let me summarize her argument.

Open Innovation 2.0

I recently read Twelve Principles for Open Innovation 2.0, an article published last year in Nature by Martin Curley.  Curley, - who’s long been involved with innovation in both his business and academic positions, - nicely summarized its evolution over the past several decades, first from closed to open innovation, and now to what he calls open innovation 2.0.  Let me discuss each of these phases of innovation.

Closed Innovation

Closed innovation was the general approach followed by industrial R&D labs during much of the 20th century, - e.g., Bell Labs, Xerox PARC, IBM Research.  Their scientists and engineers came up with a variety of technology breakthroughs and inventions, including the transistor, the laser, the UNIX operating system, the personal computer, DRAM, disk drives, relational data bases and many more.  But, over time we learned that while breakthroughs and inventions are absolutely necessary, they’re not by themselves sufficient for a business to become an innovation leader and achieve competitive advantage in the marketplace.  Examples abound of companies that failed to commercialize the inventions developed in their labs.

As the rate and pace of technology advances accelerated, - most prominently in the IT industry, - the closed innovation model started to fall apart.  The hand-offs and elapsed times to take an invention from the lab to the marketplace were no longer competitive.  Startups did away with the gaps altogether, significantly decreasing the time-to-market for new products and services, and putting huge pressure on those enterprises still operating under the old rules.

Machine Learning and Knowledge Discovery

After decades of promise and hype, artificial intelligence is finally achieving an inflection point of market success.  It’s now seemingly everywhere   In the past few years, the necessary ingredients have come together to propel AI forward beyond the research labs into the marketplace: huge amounts of data; powerful, inexpensive computer technologies; and the advanced algorithms needed to analyze and extract insights from those oceans of data.  This is evidenced by the number of companies embracing AI as a key part of their strategies, the innovative, smart products and services they’re increasingly bringing to market, and the volume of articles being written on the subject.

I’d like to discuss a couple of such articles I recently read, one on the major advances recently achieved in machine learning, and the other explaining the differences between machine learning and knowledge discovery.  

This past December, the NY Times Magazine published a very interesting article, The Great AI Awakening by Gideon Lewis-Kraus.  The article told the story about Google’s adoption of an AI first strategy, and in particular, its use of deep machine learning to dramatically improve Google Translate, one of its more popular online services.  In addition, the article nicely explains the technologies developed by the Google Brain research project to achieve its breakthrough improvements.

The Competitive Value of Data: From Analytics to Machine Learning

A recent article by NY Times reporter Steve Lohr discussed the rising importance of data as a major competitive differentiator.  The article noted that regulators, policy makers and academics in Europe and the US are increasingly concerned that the vast data assets of digital giants could become a competitive barrier to startups and innovation.

Such competitive concerns have long applied to hardware and software platforms in the IT industry.  IBM’s System 360 family of mainframes, announced in 1964, became the premier platform for commercial computing over the following 25 years by developing a 3rd party ecosystem of add-on hardware, software and services.  In 1969, the US Department of Justice launched an antitrust suit against IBM based on its market dominance, which was eventually dropped in 1982 given the changing environment in the IT industry.

In the 1980s and 1990s, the explosive growth of personal computers was largely driven by the Wintel platform based on Microsoft’s Windows operating systems and Intel’s microprocessors, which attracted an even  larger ecosystem of hardware and software developers.  It also attracted the attention of the Justice Department and several states, which sued Microsoft in the 1990s, - a lawsuit that was eventually settled.  More recently, the European Commission has accused Google of unfair competition based on its Android mobile operating system.

The competitive concerns surrounding these various platforms are closely linked to the concept of network effects.  Scale significantly increases a platform’s value.  The more 3rd party applications and services a platform offers, the more users it will attract, helping it then attract more offerings, which in turn brings in more users, which then makes the platform even more valuable… and on and on and on.

AI and Public Policy

Two years ago, Stanford University launched the One Hundred Year Study of AI (AI100), “to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.”  One of its key missions is to convene a Study Panel of experts every five years to assess the then current state of the field, as well as to explore both the technical advances and societal challenges over the next 10 to 15 years. 

The first such Study Panel recently published Artificial Intelligence and Life in 2030, a report that examined the likely impact of AI on a typical North American city by the year 2030.  The report is organized into three main sections: Section I described the key research trends influencing AI’s future; Section II examined the key economic sectors that are most likely to be impacted by AI;  and Section III looked into issues surrounding AI and public policy.  Over the past several weeks I’ve discussed the first and second sections.  I now would like to turn my attention to Section III: Prospects and Recommendations for AI Public Policy.

“Throughout history, humans have both shaped and adapted to new technologies.  This report anticipates that advances in AI technologies will be developed and fielded gradually - not in sudden, unexpected jumps in the techniques themselves - and will build on what exists today, making this adaptation easier… The measure of success for AI applications is the value they create for human lives.  Going forward, the ease with which people use and adapt to AI applications will likewise largely determine their success.”

The report then adds some very important warnings.  “Conversely, since AI applications are susceptible to errors and failures, a mark of their success will be how users perceive and tolerate their shortcomings.  As AI becomes increasingly embedded in daily lives and used for more critical tasks, system mistakes may lead to backlash from users and negatively affect their trust.”

The Internet, Blockchain, and the Evolution of Foundational Innovations

Last year, a panel of global experts convened by the World Economic Forum selected blockchain as one of the Top Ten Emerging Technologies for 2016, based on its potential to fundamentally change the way economies work.  But, how transformative will blockchain turn out to be?  How long is the transformation likely to take?  And how will it compare with the Internet-based transformation of the past few decades?

These questions were nicely addressed in The Truth about Blockchain, a recent Harvard Business Review article by Harvard professors Marco Iansiti and Karim Lakhani.  Their article starts off with one of the best descriptions I’ve seen of the long-term promise of blockchain.

“Contracts, transactions, and the records of them are among the defining structures in our economic, legal, and political systems.  They protect assets and set organizational boundaries.  They establish and verify identities and chronicle events.  They govern interactions among nations, organizations, communities, and individuals.  They guide managerial and social action.  And yet these critical tools and the bureaucracies formed to manage them have not kept up with the economy’s digital transformation.  They’re like a rush-hour gridlock trapping a Formula 1 race car.  In a digital world, the way we regulate and maintain administrative control has to change…”

“With blockchain, we can imagine a world in which contracts are embedded in digital code and stored in transparent, shared databases, where they are protected from deletion, tampering, and revision.  In this world every agreement, every process, every task, and every payment would have a digital record and signature that could be identified, validated, stored, and shared.  Intermediaries like lawyers, brokers, and bankers might no longer be necessary.  Individuals, organizations, machines, and algorithms would freely transact and interact with one another with little friction.”

Securing the Foundations of our Digital Economy

In February of 2016, the President issued an Executive Order establishing the Commission on Enhancing National Cybersecurity within the Department of Commerce.  The Commission was charged with “recommending bold, actionable steps that the government, private sector, and the nation as a whole can take to bolster cybersecurity in today’s digital world, and reporting back by the beginning of December.”

On December 1 the Commission issued its final Report on Securing and Growing the Digital Economy.  The Commission urged the incoming administration to strengthen cybersecurity efforts within its first 100 days.  The report includes 16 major recommendations and related actions that can hopefully serve as a useful framework for significantly enhancing the Nation’s cybersecurity.

The widespread success of the Internet in the 1990s has led to a historical transition from the industrial age of the past two centuries to an economy and society increasingly based on global, digital interactions.  This transition has significantly accelerated over the past decade with the advent of billions of smartphones, tens of billions of IoT devices and huge amounts of data, all now connected via Internet-based broadband networks.

At the same time, Internet threats have been growing.  Large-scale fraud, data breaches, and identity thefts are becoming more common.  Companies are finding that cyber-attacks are costly to prevent and recover from.  As we move from a world of physical interactions and paper documents, to a world primarily governed by digital data and transactions, our existing methods for protecting identities and data are proving inadequate.

The 2016 MIT Inclusive Innovation Awards

Several months ago, MIT’s Initiative on the Digital Economy launched its first annual Inclusive Innovation Competition (IIC).  MIT is one of the world’s top research universities, renowned as well for its entrepreneurial culture.  But the objectives of the IIC are quite different and unique.  Instead of competing on creating the most advanced technologies or compelling startups, the IIC is focused on innovations aimed at improving the economic opportunities of middle- and low-income earners around the world.

Applicants competed for $1 million in prizes to be awarded in four main categories: Skills, - how employees were re-skilled for new types of jobs; Matching, - how qualified people were connected to new types of jobs; Humans + Machines, - how technology is used to augment human labor; and New Models, - how new business models revolutionize labor markets and job opportunities.  In each of these categories, the grand prize winner was awarded $125,000, while each of the four runner-ups received $25,000.  In addition, four additional $25,000 Judges Choice Awards were given to organizations deemed by the judges to be uniquely inventive.

I’m a Fellow of the Initiative on the Digital Economy, and served as one of the judges in the competition.  243 applications were received from around the world, from which we selected 24 finalists.  I personally reviewed around 20 applications, and was really impressed not only by their innovative ideas, but also by their courage and determination as they addresed some of society’s toughest problems.

Becoming a Data-Driven Business: Challenges and Opportunities

A few weeks ago, McKinsey published The age of analytics: Competing in a data-driven world, a comprehensive report on the state of big data, and in particular, on the challenges and opportunities a company faces as it strives to become a data-driven business.

Big data,- including analytics, data science, artificial intelligence and related information-based technologies - are now everywhere.  “Is big data all hype?,” asks McKinsey in the report’s introduction.  “To the contrary: earlier research may have given only a partial view of the ultimate impact… the range of applications and opportunities has grown and will continue to expand.  Given rapid technological advances, the question for companies now is how to integrate new capabilities into their operations and strategies - and position themselves in a world where analytics can upend entire industries.”

It’s very hard to anticipate the consequences of disruptive innovations.  Even harder is predicting when the impact will be felt across the economy and society.  It’s often been said that truly transformative innovations are overhyped in the short term but under-hyped in the long term.  Think of electricity and automobiles, the Internet more recently and now big data.

The Transformative Power of Cloud Computing

I was recently involved in two different meetings, each convened by an IT research organization focused on providing strategic advice to CIOs.  My participation in each of the CIO  meetings was in the form of a fireside chat with a senior analyst of the organization.  Given that I’ve closely followed cloud computing over the years, the state of cloud was one of the main topics we discussed.

For a while now I’ve thought of cloud as a new model of computing in the IT world.  For the IT industry, a new computing model is a very big deal.  In the sixty years or so since there’s been an IT industry, this would be only the third such model, centralized and client-server computing being the two previous ones.  Mainframes and PCs were the defining technologies of the centralized model and client-server models respectively.

The Internet is the defining technology of the cloud computing model.  There are clearly other major technologies around us, - e.g., smartphones, IoT, analytics, AI, - but when you think about it, all of them rely on their connections to cloud-based data, applications, and services for much of their functionality.

Cloud computing has gone through three major stages over the past decade: IT-as-a-service in its initial years, then application innovation in it’s next stage, and it’s now just entering the business transformation stage.  Let me say a few words about the first two before turning to business transformation.

How Will AI Likely Impact How We Work, Live and Play?

A few weeks ago I discussed whether AI is finally reaching a tipping point, mostly based on a recently published report, - Artificial Intelligence and Life in 2030.  The report was developed by a study panel of AI experts convened by the One Hundred Year Study of AI (AI100), an initiative launched at Stanford University in December, 2014 “to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.”  To better understand the future impact of AI on everyday lives, the panel focused the study on the likely influence of AI on a typical North American city by the year 2030.

The report is organized into three main sections.  Section I, - What is Artificial Intelligence?, - describes how researchers and practitioners define AI, as well as the key research trends that will likely influence AI’s future.  Section II looks into AI’s overall impact on various sectors of the economy, while the third Section, examines AI issues related to public policy.

My previous discussion was primarily focused on Section I.  I’d now like to turn my attention to Section II, - AI by Domain.  To help analyze where AI might be heading, the study panel narrowed its explorations to the eight domains most likely to be impacted by AI:

Digital Twin: Bringing the Physical and Digital Worlds Closer Together

A few weeks ago I first learned about a relatively new concept - Digital Twin.  A Digital Twin is essentially a computerized companion to a real-world entity, be it an industrial physical asset like a jet engine, an individual’s health profile, or a highly complex system like a city.  It’s a highly realistic, one-to-one digital model of each such specific physical entity.

Digital Twin helps bring the physical and digital worlds closer to each other.  It’s intertwined with and complementary to the Internet of Things (IoT).  The huge amounts of data now collected by IoT sensors on physical objects, personal devices and smart systems make it possible to represent their near real-time status in their Digital Twin alter-ego.

“The myriad possibilities that arise from the ability to monitor and control things in the physical world electronically have inspired a surge of innovation and enthusiasm,” said a 2015 McKinsey report on the Internet of Things.  Experts estimate that the number of connected things or devices will reach 50 billion by 2020, growing to 100s of billions in the decades ahead.  The economic potential of the smart solutions this makes possible is enormous, possibly reaching several trillion dollars within a decade.

The Evolution of the Firm

A few weeks ago I discussed The Rise of the Global Superstar Company based on a recent special report on the subject by The Economist.  The report noted that the decade-long trend toward increasingly concentrated global firms is somewhat surprising. 

“The rise of the giants is a reversal of recent history…  In the 1980s and 1990s management gurus pointed to the demise of size as big companies seemed to be giving way to a much more entrepreneurial economy.  Giants such as AT&T were broken up and state-owned firms were privatised.  High-tech companies emerged from nowhere.  Peter Drucker, a veteran management thinker, announced that ‘the Fortune 500 [list of the biggest American companies] is over.’ That chimed with the ideas of Ronald Coase, an academic who had argued in ‘The Nature of the Firm’ (1937) that companies make sense only when they can provide the services concerned more cheaply than the market can.”

Professor Coase’s views on the firm changed quite a bit over the years.  In 1937 he published The Nature of the Firm, a seminal article which along with other major contributions earned him the 1991 Nobel Prize in economics.  In the article, Professor Coase provided a simple answer to the question: Why do firms exist?  He explained that, in principle, a firm should be able to find the cheapest, most productive goods and services by contracting them out in an efficient, open marketplace.  However, markets are not perfectly fluid.  Transaction costs are incurred in obtaining goods and services outside the firm, such as searching for the right people, negotiating a contract, coordinating the work, managing intellectual property and so on.  Thus, firms came into being to make it easier and less costly to get work done.

Towards a Trusted Framework for Identity and Data Sharing

I recently participated in a Treasury Identity Forum organized by the US Treasury Department in Washington, DC.  The Forum focused “on the critical role of legal identity for financial inclusion, economic development, and anti-money laundering/counter financing of terrorism (AML/CFT) safeguards, and the development of new technology identification/authentication solutions to help achieve these goals.”  It brought together stakeholders from governments, financial service companies, FinTech startups and technologists to better understand how emerging technologies and legal frameworks can help us develop the required digital identity systems.

I was a member of a panel on how government, business and research communities can collaborate in developing workable identity solutions.  Let me summarize the points I made in my introductory remarks.

From time immemorial, 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.  As the economy and society move toward a world where interactions are primarily governed by digital data and transactions, our existing methods of managing identity and data security are proving inadequate.  Large-scale fraud, identity theft and data breaches are becoming common, and a large fraction of the world’s population lacks the credentials needed to be part of the digital economy.

The Rise of the Global Superstar Company

The September 17 issue of The Economist included a special report on the rise of so called superstar companies.  “Disruption may be the buzzword in boardrooms, but the most striking feature of business today is not the overturning of the established order,” notes The Economist.  “It is the entrenchment of a group of superstar companies at the heart of the global economy.”

This trend toward consolidation and growing size is evidenced by a few worldwide statistics: 10% of public companies generate 80% of all profits; firms with over $1 billion in annual revenues are responsible for 60% of total global revenues; and the rate of mergers and acquisitions is more than twice what it was in the 1990s.  The trend is particularly prominent in the US: in the 20 years from 1994 to 2013, the share of GDP generated by the 100 biggest companies rose from 33% to 46%; the five largest banks now account for 45% of all banking assets, up from 25% in 2000; and new firm formation has been going down since the late 1970s, leading to an overall decline in young- and medium-aged companies over the years.

The current rise of large companies is somewhat unexpected.  Following the Great Depression and WW2, the country welcomed the stability promised by corporate capitalism.  Big, multinational companies dominated most industries, - from GM, Ford and Chrysler in cars to Esso/Exxon, Mobil and Texaco in oil and gas.  It was an era characterized by bureaucratic corporate cultures, 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; telecommunications was deregulated; ATT was broken up; and Silicon Valley became the global hub for innovation, emulated by regions around the world.

Is Design Thinking the “New Liberal Arts”?

Design thinking has become an increasingly popular topic of discussion over the past decade.  It was featured in the September, 2015 issue of the Harvard Business Review with several articles on the subject.  Design is no longer just for physical objects, e.g. cars, bridges, shoes, jewelry, smartphones.  Design thinking is now being applied to abstract entities, - e.g. systems, services, information and organizations, - as well as to devise strategies, manage change and solve complex problems.

The application of design thinking beyond products isn’t new.  Nobel laureate Herbert Simon discussed the concept in his 1969 classic The Sciences of the Artificial.  IDEO, a firm best known for pioneering this expanded view of design, traces its roots back to 1978.  The School of Design in London’s Royal College of Art has long been expanding the boundaries of industrial design.  Stanford’s Institute of Design, - better known as the d.school, - was launched in 2004 as a graduate program that integrates business, the social sciences, the humanities and other disciplines into more traditional engineering and product design.

The d.school’s website nicely explains its design-thinking point of view:  “Students and faculty in engineering, medicine, business, law, the humanities, sciences, and education find their way here to take on the world’s messy problems together.  Human values are at the heart of our collaborative approach… Along the way, our students develop a process for producing creative solutions to even the most complex challenges they tackle…  Our deliberate mash-up of industry, academia and the big world beyond campus is a key to our continuing evolution.”

Has AI (Finally) Reached a Tipping Point?

After many years of promise and hype, AI seems to be finally reaching a tipping point of market acceptance.  “Artificial intelligence is suddenly everywhere… it is proliferating like mad.”  So starts a Vanity Fair article published around two years ago by author and radio host Kurt Andersen.  And, this past June, a panel of global experts convened by the World Economic Forum (WEF) named Artificial Intelligence, - Open AI Ecosystems in particular - as one of its Top Ten Emerging Technologies for 2016 because of its potential to fundamentally change the way markets, business and governments work.  

AI is now being applied to activities that not long ago were viewed as the exclusive domain of humans.  “We’re now accustomed to having conversations with computers: to refill a prescription, make a cable-TV-service appointment, cancel an airline reservation - or, when driving, to silently obey the instructions of the voice from the G.P.S,” wrote Andersen.  The WEF report noted that “over the past several years, several pieces of emerging technology have linked together in ways that make it easier to build far more powerful, human-like digital assistants.”

What will life be like in such an AI-based society?  What impact is it likely to have on jobs, companies and industries?  How might it change our everyday lives?

These questions were addressed in Artificial Intelligence and Life in 2030, a report that was recently published by Stanford University’s One Hundred Year Study of AI (AI100).  AI100 was launched in December, 2014 “to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.”  The core activity of AI100 is to convene a Study Panel every five years to assess the then current state of the field, review AI’s progress in the years preceding the report, and explore the potential advances that lie ahead as well the technical and societal challenges and opportunities these advances might raise.

Reflections on the Development of Implementable, Winning Strategies

I recently read an article by strategy consultant and writer Ken Favaro which nicely explained how to best think about strategy in today’s business environment.  “Many business leaders subscribe to the classic definition of strategy as a set of actions designed to achieve an overall aim,” wrote Favaro in The Trouble with Putting Goals Ahead of Strategy, published in strategy+business in 2015.  “In other words, they believe strategy starts with a goal.  But for companies that have implemented winning strategies, that’s not how it typically happens.”

Goals and strategies serve very different purposes.  Goals, visions and missions are important to paint an exciting picture of the future around which everyone can rally, as well as to help set the general direction of a company.  But being too high level, goals by themselves don’t give you much guidance on how to get things done and what key decisions must be made and prioritized.  “[G]oals tell you very little about the fundamental choices you should make around creating customer and company value.  Such choices are the very essence of your strategy.”

Most winning strategies start with an idea for an innovative new product, service or business model, followed by a plan to bring the idea to market.  Only then should come a big, bold goal, as a way “to crystalize an ambition, motivate the troops, and excite investors.  Unfortunately, strategic planning in most companies gets this sequence exactly reversed  - and when that happens, bad strategies result.”

I totally agree with Favaro, based on my personal experiences leading emerging technology initiatives at IBM, including parallel supercomputing, the Internet, and Linux.  Let me share some of what I learned when working on IBM’s Internet strategy.

Blockchains: the Promise of More Frictionless, Trusted Economies

Why do firms exist?  Ronald Coase, - the eminent British economist and University of Chicago professor, - addressed this question in The Nature of the Firm, - a seminal paper published in 1937 which along with other major achievements earned him the 1991 Nobel Prize in economics.

Professor Coase explained that, in principle, a firm should be able to find the cheapest, most productive, highest quality goods and services by contracting them out in an efficient, open marketplace.  However, markets are not perfectly fluid.  Transaction costs are a kind of friction incurred in obtaining goods and services outside the firm, such as searching for the right supply chain partners, establishing a trusted relationship, negotiating a contract, coordinating the work, managing intellectual property and so on.  Firms came into being to make it easier and less costly to get work done.

A recent IBM report, - Fast forward: Rethinking enterprises, ecosystems and economies with blockchains, - harks back to Coase’s paper to analyze the potential value of blockchains.  The report notes that while transaction costs are lower within firms, “in recent years as enterprises have scaled, the added complexity of operations has grown exponentially while revenue growth has remained linear.  The result?  At a certain point, organizations are faced with diminishing returns.  Blockchains have the potential to eradicate the cost of complexity and ultimately redefine the traditional boundaries of an organization.”

Competing Against “Digital Invaders”

Last year, IBM’s Institute for Business Value conducted a C-suite study aimed at identifying the key disruptive trends that will likely impact companies around the world over the next three to five years, as well as what their senior executives are doing to better prepare their organizations for the expected disruptions.  The study surveyed over 5,200 CEOs, CFOs, CIOs, CMOs and other C-suite executives across 21 industries in over 70 countries, - most of them in face-to-face interviews.  After analyzing the survey data,  - including the use of Watson Analytics to extract inferences from open-ended responses, - IBM published its findings in Redefining Boundaries: Insights from the Global C-suite Study.

To better understand the traits of the most successful enterprises, the IBM study also asked CxOs to rank their companies based on their innovation reputation and their financial performance over the previous three years.  After analyzing their responses, it identified 5% of companies as Torchbearers, the name given to those companies enjoying both a strong innovation reputation and an excellent financial track record.  At the other end of the spectrum, 34% of enterprises were identified as Market Followers or laggards in both innovation and financial performance.

The report organized its findings and recommendations into three main areas:  Preparing for digital invaders; Creating a panoramic perspective; and Be first, be best or be nowhere.  Let me briefly discuss each.

Blockchain Can Reshape Financial Services… But it Will Take Significant Time and Investment

In early August, the World Economic Forum (WEF) published two excellent reports: A Blueprint for Digital Identity, - which I wrote about a few weeks ago, - and The future of financial infrastructure: An ambitious look at how blockchain can reshape financial services, - which I’d now like to discuss.

“Distributed ledger technology (DLT), more commonly called blockchain, has captured the imaginations, and wallets, of the financial services ecosystem,” notes the report, citing a few statistics as evidence: over 90 central banks are engaged in DLT discussions around the world; more than 24 countries have already launched blockchain-based initiatives; 80% of banks predict that they’ll launch blockchain projects by 2017; over 90 financial and technology companies have already joined blockchain consortia; over the past 3 years; more than 2,500 patents have been filed; and $1.4 billion has been invested in blockchain-based startups over the same time span.

In addition, a panel of global experts convened by the WEF selected blockchain as one of the Top Ten Emerging Technologies for 2016 because of its potential to fundamentally change the way markets and governments work, - further evidence that blockchain is reaching a tipping point of market acceptance.

But, significant hurdles must be overcome before the advent of large-scale blockchain infrastructures.  The WEF correctly warns that this will take time.  Not only are there major technology and standards issues to be worked out, but the industry will have to collaborate with governments around the world to develop the appropriate legal frameworks and regulatory environments.

Automation Technologies and the Future of Work

Last year, McKinsey launched a multi-year study to explore the potential impact of automation technologies on jobs, organizations and the future of work.  “Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life?,” its initial article on the study asked, or “Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric?”

Most jobs involve a number of different tasks or activities.  Some of these activities are more amenable to automation than others.  But just because some of the activities have been automated, does not imply that the whole job has disappeared.  To the contrary, automating parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as by enabling them to focus on those aspect of the job that most need their attention.

Given that few jobs or occupations will be entirely automated in the near-mid future, the study focused instead on the kinds of activities within jobs that are more likely to be automated, as well as how those jobs and business processes will then be redefined.  It did so by analyzing the extensive data in O*NET, a web-based application sponsored by the US Department of Labor which includes the most comprehensive information on US occupations.

The study analyzed around 2,000 work activities across 800 different occupations, and grouped them into 18 different capabilities, - 3 of them social in nature; 10 cognitive; and 5 physical, - and then assessed the automation potential of each.  It found that 45% of work activities could be automated using existing, state-of-the-art technologies.

Blockchain - Once Again, the Technological Genie Has Been Unleashed from its Bottle

Skeptics needing further reassurance that the Blockchain is truly reaching a tipping point can rest easier with the recent publication of Don Tapscott’s Blockchain Revolution, co-written with his son Alex.  Don, whom I’ve long known, has a knack for identifying what The Next Big Thing will likely be, and shortly thereafter writing a book to explain it to everybody else.  The Digital Economy, written in the mid 1990s, anticipated the unbundling of vertically integrated companies into business webs, giving rise to the open networked enterprise.  His 2006 Wikinomics predicted the rise of mass collaboration in what became known a few years later as the On Demand Economy.  Blockchain Revolution, his 16th book, now explains “How The Blockchain Will Transform Everything From Banking To Government To Our Identities.”

The book is organized into three main sections.  The first explains the blockchain from two complementary points of view: as a major next step in the evolution of the Internet; and as the architecture underpinning bitcoin, the best known and most widely held digital currency.  The second and longest section describes how blockchain could potentially transform financial services, companies, government, the Internet of Things, and other key areas.  The last section summarizes the major challenges that must be overcome as well as the governance required to fulfill the promise of blockchain.

“It appears that once again, the technological genie has been unleashed from its bottle,” write the authors in their opening paragraph.  “Summoned by an unknown person or persons with unclear motives, at an uncertain time in history, the genie is now at our service for another kick of the can - to transform the economic power grid and the old order of human affairs for the better.  If we will it.”

The Top Ten Emerging Technologies of 2016

For the past several years, the World Economic Forum (WEF) has published an annual list of the Top Ten Emerging Technologies that would likely have the greatest impact on the world in the years to come.  A few weeks ago, the WEF published its 2016 Top Ten list.  The list is compiled by the WEF Meta-Council on Emerging Technologies, a panel of global experts led by Dr. Bernard Meyerson, - IBM Fellow and Chief Innovation Officer.

“Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors,” said Meyerson. “The global community needs to come together and agree on common principles if our society is to reap the benefits and hedge the risks of these technologies.”

The technologies on the list are not new.  They’ve been worked on for many years.  But their selection to the Top Ten List indicates that, in the opinion of the council members, each of these technologies has now reached a maturity and acceptance tipping point where its impact can be meaningfully felt.

Is the Blockchain Now Reaching a Tipping Point?

A few weeks ago, the World Economic Forum (WEF) published its annual list of the Top Ten Emerging Technologies for 2016.  The technologies on the list have been worked on for years.  But their inclusion in the Top Ten List indicates that each has now reached a market acceptance tipping point where its impact can be meaningfully felt.  The Blockchain is one of the technologies in this  year’s list, selected by the WEF panel of global experts because of its emerging potential to fundamentally change the way markets and governments work. 

What does it mean for an infrastructure technology like the blockchain to have reached such a tipping point?  The WEF report compared the blockchain to the Internet, noting that “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built.  And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

I agree with this comparison and find it useful to help us understand how blockchains might evolve over the years. So, I’d like to compare the state of the blockchain in 2016 to the state of the Internet 25 years ago or so.

The Continuing Evolution of Service Science

Service Science emerged in the mid-2000s as an academic discipline aimed at applying technology and science to the service sector, - by far the largest sector of the US economy and of most economies around the world.  Since its inception, IBM has played a major role in the continuing evolution of service science, coining the term Service Science, Management and Engineering (SSME) to highlight the interdisciplinary nature of this emerging discipline.  I was personally involved in these efforts during my last few years at IBM.  Subsequently, it’s continued to be one of the main subjects I’ve talked about in seminars and written about in this blog.

A few weeks ago I had an interesting conversation on the the state of service science with analysts from an IT research organization who were preparing a report on the subject for their clients.  Our discussion led me to reflect on the evolution of service science over the past several years.  I think that we are hearing a bit less about it these days.  But is that because we’ve become tired of the subject and moved on, or because the application of science and technology to services is now so well accepted that it’s no longer a topic of debate?  I very much think it’s the latter.