Irving Wladawsky-Berger

The Impact of Open Source on the Future of AI

In the early decades of the IT industry, vendors brought to market proprietary networking, communications, and file systems that worked well if used within the same company or with companies using the same vendor’s architecture, but were quite difficult to use otherwise.

The Internet changed all that. The adoption of open internet and email protocols in the 1970s and 1980s by research communities, followed by the rise of web standards in the 1990s, transformed the IT industry, forcing companies to embrace a culture of open standards and collaborative innovation. Similarly, Linux emerged as an open-source operating system in the 90s. Initially adopted by research communities, it eventually gained widespread corporate support. Over time, an increasing number of companies have embraced Linux, contributed to its development, and now support hundreds of open source projects in just about all industries.

Could open source now play a similar transformative role in the evolution of Generative AI (GenAI) technologies and business strategies?

To explore this important question, Linux Foundation Research and its partners conducted a web survey in August and September of 2024. The report, “Shaping the Future of Generative AI: The Impact of Open Source Innovation,” highlights how open source technologies are accelerating GenAI adoption while setting a foundation for the future of AI.

Language is a Tool for Communication, Not for Thinking

I often look to biology and evolution for inspiration when thinking about highly complex systems. So I was very intrigued when several weeks ago I learned about the work being done by Elemental Cognition, a company founded in 2015 by my former IBM colleague David Ferrucci. Ferrucci led the team that developed the Watson computer system, which in 2011 won the television quiz show Jeopardy! against two of the world’s top Jeopardy champions. Let me explain.

Large language models (LLMs), chatbots, and generative AI have introduced technologists like me to the fascinating world of human language and cognition. Last year, for example, I learned the difference between four important linguistic concepts in a 2020 research paper by linguistic professors Emily Bender and Alexander Koller.

• Form is any observable expression of language, whether written, spoken, or signed.
• Communicative intent is the purpose a speaker intends to achieve through language, such as to convey some information or just to socialize.
• Meaning is the relation between the form in which the language is expressed and the communicative intent it’s being used to evoke in the listener or reader.
• Understanding is the listener’s ability to capture the meaning that the speaker intends to convey.

The authors wrote that while the success of LLMs and chatbots on many natural language tasks is very exciting, “these successes sometimes lead to hype in which these models are being described as understanding language or capturing meaning.” However, “a system trained only on form has apriori no way to learn meaning.” Research on language acquisition has found that “human language learning is not only grounded in the physical world around us, but also in interaction with other people in that world. … Human children do not learn meaning from form alone and we should not expect machines to do so either.”

The Current State and Future Trends of Enterprise Blockchain Technologies

“Blockchain is one of the major tech stories of the past decade,” said a December, 2022 McKinsey article, “What is Blockchain.” “Everyone seems to be talking about it — but beneath the surface chatter there’s not always a clear understanding of what blockchain is or how it works. Despite its reputation for impenetrability, the basic idea behind blockchain is pretty simple,” namely: “Blockchain is a technology that enables the secure sharing of information.”

That same month, a WSJ article, “Blockchain Fails to Gain Traction in the Enterprise,” noted that for years, blockchain “has been viewed by some companies as a way to drive industry-transforming projects, among them the tracking of assets through complex supply chains.” But, “So far, that hasn’t happened,” the article added.

I’ve been closely following the evolution of blockchain technologies for a number of years. Between 2016 and 2023, I’ve posted over 50 entries on blockchain and related technologies in my blog. For example, in 2017 I asked Is Blockchain the Major Next Step in the Evolution of the Internet?, based on the fact that the previous year, the World Economic Forum (WEF) had named The Blockchain as one of its Top 10 Emerging Technologies for 2016. In the blog I wrote that “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built. And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

The World Economic Five Year Jobs and Skills Outlook

Earlier this year, the World Economic Forum (WEF) published “The Future of Jobs Report 2023.” The WEF has been publishing Future of Jobs Reports since 2016 to explore how socio-economic and technology trends are likely to shape the evolution of jobs and skills over the following 5 years.

“In 2023, labour-market transformations driven by technological breakthroughs, such as the coming of age of generative artificial intelligence (AI), are being compounded by economic and geopolitical disruptions and growing social and environmental pressures,” wrote WEF Managing Director Saadia Zahidi in the Preface of the 2023 Report. Encompassing almost 300 pages, the report explores how jobs and skills are likely to evolve in the 2023-2027 period. Its trends and predictions are based on a survey of over 800 large companies which collectively employ over 11.3 million workers across 27 industry clusters and 45 economies from all around the world.

Let me discuss a few of the report’s key findings and predictions.

Why Digital Wallets Are Critical to the Evolving Digital Landscape

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

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

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

Getting the Most Out of Cloud Investments

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

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

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

The Current State of Service Science

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

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

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

The Carnegie Cloud Governance Project

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

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

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

Disruptive Forces Necessitate Bold Decisions

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

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

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

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

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

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

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

The Evolving Role of the CIO

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

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

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

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

The Coming Era of Productivity Growth

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

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

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

Leveraging Cloud Computing to Enhance the Nation’s Cybersecurity

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

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

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

Cloud’s Trillion Dollars 2030 Potential

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

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

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

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

Business and Technology Predictions for 2021 and Beyond

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

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

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

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

The Post-Pandemic Outlook for Technology, Jobs, and Skills

“The COVID-19 pandemic-induced lockdowns and related global recession of 2020 have created a highly uncertain outlook for the labour market,” said the World Economic Forum (WEF) in The Future of Jobs Report 2020. The pandemic has caused labor markets to change significantly faster than previously expected. What used to be considered the future of work has already arrived. As McKinsey noted in a May, 2020 article: “we have vaulted five years forward in consumer and business digital adoption in a matter of around eight weeks.”

The WEF report aims to shed light on the post-pandemic outlook for technology adoption, jobs and skills over the next five years, including detailed quantitative profiles on 15 industry sectors and 26 advanced and emerging countries. The report is based on a survey of senior executives from nearly 300 global companies which collectively employ 8 million workers. The survey asked 49 questions on the outlook for their companies’ workforce through 2025, including major trends affecting the labor market, the technologies their companies are adopting, the expected evolution of jobs and skills, their training and reskilling programs, and the near-term impact of the pandemic on their workforce.

“The global shift to a future of work is defined by an ever-expanding cohort of new technologies, by new sectors and markets, by global economic systems that are more interconnected than in any other point in history, and by information that travels fast and spreads wide. … As a new global recession brought on by the COVID-19 health pandemic impacts economies and labour markets, millions of workers have experienced changes which have profoundly transformed their lives within and beyond work, their well-being and their productivity. One of the defining features of these changes is their asymmetric nature - impacting already disadvantaged populations with greater ferocity and velocity.”

The Current State of Cloud Computing

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

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

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

Ten IT Predictions for the Post-Pandemic New Normal

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

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

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

The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

• 20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;
  
• Many projects are in pilots but some are already in production;
• Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
  
• Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
• The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;
• Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
  
• Further AI growth is inevitable.

The Business Value of Augmented Reality

I recently heard a very interesting presentation at the 2019 MIT CIO Symposium, - Why Companies Need an Augmented Reality Strategy, - by Harvard professor Michael Porter and James Heppelmann, CEO of PTC, an industrial software company.  The talk was based on their HBR article A Manager’s Guide to Augmented Reality.

We generally think of products as physical entities built with a combination of mechanical and electrical components, - e.g., appliances, cars, agricultural machines, industrial equipment, - some quite simple and some highly complex.  But, in a 2014 HBR article, Porter and Heppelmann introduced the notion of smart connected products (SCPs), a whole new class of products that, in addition to mechanical and electrical components, use digital components like microprocessors, sensors, data storage, software, and connectivity in a wide variety of ways.  Smart connected products are radically reshaping companies and exposing them to new competitive opportunities and threats, while altering industry boundaries and creating entirely new industries.

Through its first five decades, IT helped companies become significantly more productive, by automating many of their processes, reengineering their overall operations, and reaching out beyond the boundaries of the enterprise.  But, their actual products were largely unaffected.  This is all now changing with the rise of the Internet of Things (IoT), cloud computing, Big Data, AI, and other innovations whose combined impact is driving the new era of smart connected products.

As the world’s digital and physical infrastructures converge, IT is now an integral part of the products themselves. Digital technologies are designed right into the products.  Massive amounts of product-usage data can now be gathered, stored and analyzed by a variety of applications in the product’s cloud counterpart.  The cloud might also include a digital twin of each specific physical product, that is, a highly realistic 3-D digital model that monitors the real-time status of the digital twin’s physical counterpart, - e.g., a jet engine, elevator or turbine, - and helps make important decisions such as the need for preventive maintenance to prevent a product failure.

The Productivity Paradox: Digital Abundance and Scarce Genius

Despite the relentless advances of digital technologies, productivity growth has been declining over the past decade.  Investment and interest rates have remained low, and income has continued to stagnate for the majority of workers in the US and other developed economies. The world seems to be stuck in a period of slow growth and no one is quite sure what’s causing this apparent contradiction.

Opinions abound.  Harvard economist Larry Summers has argued that secular stagnation is the reason behind this unusual situation, caused primarily by a reluctance of companies to invest and of consumers to spend, with the ensuing excessive savings acting as a drag on economic growth.

Some contend, - most prominently Northwestern University economist Robert Gordon, - that over the past few decades there’s been a fundamental decline in innovation and productivity.  Perhaps our current technologies, advanced and exciting as they might be, aren’t as transformative as the technologies from the period between 1870 and 1970, when we experienced high productivity growth and a rising standard of living. 

Demographic change, namely, the declining population and labor force growth around the world, is another potential cause for slow economic growth.  The global labor force grew at an average of 1.8% per year between 1960 and 2005, but since then it’s been growing at just 1.1% per year, and it’s already shrinking in a number of countries, including Japan, Germany and China.

Another possible explanation, - advanced by Erik Brynjolfsson, Daniel Rock, and Chad Syverson in AI and the Modern Productivity Paradox, - is that the paradox is primarily due to a time lag between technology advances and their impact on the economy.  We’ve been living in a time of major transformative technologies, - the Internet, smartphones, IoT, big data, AI - whose deployment and impact on productivity growth are still lagging.

While technologies may advance rapidly, humans and our institutions change slowly.  Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy.  Leading edge firms are already benefiting from these advances, but most are still in the early learning stages.  Translating technological advances into productivity gains requires major transformations in business processes, organization and culture, - and these take time.

Serverless Computing - An Innovative Approach to Software Development

In October of 2008, The Economist published a special report on a then relatively new, rapidly growing IT development, - cloud computing.  In the lead article, technology editor Ludwig Siegele defined cloud computing by first giving a very succinct history of computing:

“In the beginning computers were human. Then they took the shape of metal boxes, filling entire rooms before becoming ever smaller and more widespread.  Now they are evaporating altogether and becoming accessible from anywhere…  Now,… computing is taking on yet another new shape.  It is becoming more centralised again as some of the activity moves into data centres.  But more importantly, it is turning into what has come to be called a ‘cloud’, or collections of clouds.  Computing power will become more and more disembodied and will be consumed where and when it is needed…  It will allow digital technology to penetrate every nook and cranny of the economy and of society…”

I was reminded of Siegele’s decade-old article when recently learning about another, relatively new, IT innovation, - serverless computing, - a name that connotes the increasingly disembodied nature of computing.  I first came across the term in a Forrester report on the Top Emerging Technologies to Watch in 2018.

Serverless is an approach to software development for quickly building and deploying applications as a collection of loosely coupled modules and microservices.  According to Forrester, “Firms report a better software development experience, rapid scaling of services that compose applications, lower costs, and better infrastructure utilization when workloads are variable.  They also spend less time maintaining cloud infrastructure.” 

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. 

Customer Experience - the Key Competitive Differentiator in the Digital Age

About five years ago, McKinsey conducted an online survey of over 850 CEOs and other senior executives to find out how their companies were faring in their implementation of digital technologies and strategies.  The data showed that executives were generally optimistic, but they still had much to do to achieve their digital business objectives.  Organizational alignment and leadership were the critical factors in the success or failure of their digital strategies.

More specifically, the survey asked them about their progress in embracing five major digital trends: big data and advanced analytics, digital engagement of customers, digital engagement of employees and external partners, automation, and digital innovation.  While all five trends were important, executives said that digital customer engagements was their most important competitive differentiator and where they were expecting the largest financial returns.

This is not surprising, even more now than at the time of the survey.  A company can differentiate itself from competitors in one of two key ways: by providing a superior customers experience or by offering the lowest prices.  For companies that prefer the former, digital channels are, far and away, the most cost-effective way of reaching out to their clients.  The explosive growth of mobile devices means that you can be engaged with your customers whether they are at home, at work, in your store or anywhere else.

But, digital customers can be fickle and hard to satisfy.  It’s quite difficult to keep up with their fast changing behaviors and expectations.  New products and services are hitting the market faster than ever, brand loyalty keeps decreasing, and the increased competition continues to shift power from institutions to individuals.

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? 

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.

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.

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.

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.

Computing Beyond Moore’s Law

The March 12 issue of The Economist  includes a special report on the future of computing after the very impressive 50-years run of Moore’s Law.

In his now legendary 1965 paper, Intel co-founder Gordon Moore first made the empirical observation that the number of components in integrated circuits had doubled every year since their invention in 1958, and predicted that the trend would continue for at least ten years, a prediction he subsequently changed to a doubling every two years.  The semi-log graphs associated with Moore’s Law have since become a visual metaphor for the technology revolution unleashed by the exponential improvements of just about all digital components, from processing speeds and storage capacity to networking bandwidth and pixels.

The 4004, Intel’s first commercial microprocessor, was launched in November, 1971.  The 4-bit chip contained 2,300 transistors.  The Intel Skylake, launched in August, 2015, contains 1.75 billion transistors which collective deliver about 400,000 more computing power than the 4004.  Moore’s Law has had quite a run, but like all good things, especially those based on exponential improvements, it must eventually slow down and flatten out.

In its overview article, The Economist reminds us that Moore’s Law was never meant to be a physical law like Newton’s Laws of Motion, but rather “a self-fulfilling prophecy - a triumph of central planning by which the technology industry co-ordinated and synchronised its actions.”  It also reminds us that its demise has been long anticipated: for a while now, the number of people predicting the death of Moore’s Law has also been doubling every two years.

So what happens as the end is now in sight?  I’ve been thinking about this question for a while.  And, and as is often the case when it comes to highly complex systems, I find myself turning to biology as a source of inspiration, in particular, to evolutionary biology.

Anticipating the Future of the IT Industry

How can we best anticipate the future of a complex, fast changing industry like IT?  Which hot technology innovations, - e.g., artificial intelligence, the blockchain, cloud computing, - will end up having a big impact and which are destined to fizzle?  What can we learn from the IT industry’s 60-year history that might help us better prepare for whatever lies ahead?

A major way of anticipating the future of any economic or social entity, - be it a company, industry, university, government agency or city, - is to explore and learn from its history.  While there’s no guarantee that historical patterns will continue to apply going forward, they might well be our most important guides as we peer into an otherwise unpredictable future.

I’ve been involved with computers since the early 1960s, first as a student at the University of Chicago, then in my long career at IBM, and subsequently through my relationship with a number of companies and universities.  I’ve thus had a ringside seat from which to observe the journey the IT industry’s been on since those early days.

Let me share some of my personal impressions of this journey through the lens of three key areas, each of which has played a major role throughout IT’s history, and will continue to do so well into its future: data centers, transaction processing, and data analysis.

The Rise of the Platform Economy

What do we mean by platform?  I particularly like this definition by MIT Professor Michael Cusumano: “A platform or complement strategy differs from a product strategy in that it requires an external ecosystem to generate complementary product or service innovations and build positive feedback between the complements and the platform.  The effect is much greater potential for innovation and growth than a single product-oriented firm can generate alone.”

The importance of platforms is closely linked to the concept of network effects - the more products or services it offers, the more users it will attract.  Scale increases the platform’s value, helping it attract more complementary offerings which in turn brings in more users, which then makes the platform even more valuable… and on and on and on.

Platforms have long played a key role in the IT industry.  IBM’s System 360 family of mainframes, announced in 1964, featured a common hardware architecture and operating system, enabling customers to upgrade their systems with no need to rewrite their applications.  The ecosystem of add-on hardware, software and services that developed around System 360 helped it become the premier platform for commercial computing over the next 25 years.

In the 1980s, the explosive growth of personal computers was largely driven by the emergence of the Wintel platform based on Microsoft’s operating systems and Intel’s microprocessors, which attracted a large ecosystem of hardware and software developers. 

The 1990s saw the commercial success of the Internet and World Wide Web, driving platforms to a whole new level.  Internet-based platforms connected large numbers of PC users to a wide variety of web sites and online applications.  The power of platforms has grown even more dramatically over the past decade, with billions of users now connecting via smart mobile devices to all kinds of cloud-based applications and services.

What’s the current state and growth potential of platform companies?  How many large platforms are currently operating around the world?  What’s their impact on established enterprises?  These are among the questions addressed in in a recent report, The Rise of the Platform Enterprise: A Global Survey led by Peter Evans and Annabelle Gawer and sponsored by the Center for Global Enterprise.  The report is based on a comprehensive survey of the 176 platform companies around the world with an individual valuation exceeding $1 billion.  Their aggregate market value was over $4.3 trillion.

The Continuing Evolution of Cloud Computing

I’ve been closely following cloud computing for a number of years, having posted my first cloud blog in March of 2008.  A few months later I gave a presentation at a  cloud-based conference.  The overall sense at that conference was that something big and profound was emerging in the IT industry.  Cloud computing could well be The Next Big Thing - one of those massive changes that the IT industry goes through from time to time that really shake things up, - like the advent of personal computers in the 1980s and the Internet in the 1990s. 

Cloud has fulfilled those early expectations, having made much progress in a relatively short time.  As I look back at its evolution over the past few years, I think of cloud as having gone through three main, overlapping phases.

Early adopters viewed cloud as computing-on-demand.  Next, cloud introduced a much more disciplined approach to the architecture and management of large IT infrastructures.  And by now, cloud has become accepted as a major transformational force in IT, in business, and in the economy in general.

Let me discuss each of these phases. 

Reflections on Linux and Mainframes

A year ago, McKinsey Quarterly published a special edition, Management: The next 50 year, to commemorate its 50th anniversary.  To illustrate how different the world was 50 years ago, its lead article characterized the 1964 environment with three key events: the Summer Olympics in Tokyo, which was the first global telecast via satellite; the last of the baby-boomers were born that year; and IBM announced the new System/360 family of mainframes.  I was reminded of that third, distant event by IBM’s recent announcement of LinuxONE, its new Linux-only mainframes.

In 1964 I was a second year college student at the University of Chicago.  I was also working part-time at the university’s 2-year old computation center, and still remember attending a presentation on the new System 360 by a visiting IBM executive.  I later went on to graduate school in physics at the university, where I made extensive use of S/360 computers for my thesis research.  In IBM, which I joined after finishing my studies in 1970, major portions of my 37 year career involved mainframe architecture and strategy.

But it wasn’t only my long association with mainframes that this announcement reminded me of.  The Linux side of the announcement also brought to mind some more recent memories.

The Rise of the On-Demand Economy

The Industrial Revolution led to the creation of many large and mid-size companies in which people worked together to produce goods and/or deliver services.  Having a good job meant working for one such company.  These workers, many of them unionized, enjoyed relatively secure, stable jobs and a number of company benefits, including health care insurance, vacations and retirement pensions. 

This corporate capitalism era, - characterized by hierarchically organized institutions, - reached its height in the US in the period following World War 2, when the country craved a sense of stability following the pain and chaos of the Great Depression and the war years.  But those days are long gone.  In the more recent past, these relatively slow moving organizations were not able to keep up with fast changing technologies and markets, nor could individuals assume that being loyal to a company would translate into a secure job with good benefits. 

In its stead, we are seeing the rise of what The Economist called the On-Demand Economy in a recent article.  “Ever since the 1970s,…  Manufacturing jobs have been automated out of existence or outsourced abroad, while big companies have abandoned lifetime employment.  Some 53m American workers already work as freelances.”

The New Era of Smart, Connected Products

For the past few years, some have justifiably questioned whether innovation has been going through a period of stagnation, especially when compared to major 19th and 20th century innovations like electricity, cars and airplanes.  Have we pretty much stopped solving big problems?  “We wanted flying cars - instead we got 140 characters,” is how PayPal cofounder Peter Thiel succinctly expressed this sentiment in a 2011 New Yorker article. 

Others argue that while the nature of innovation is definitely changing as we evolve and adapt to an information-based digital economy, it’s impact is no less transformative.  Last week, for example, I discussed a recent article in Wired by Kevin Kelly on the future of AI.  Kelly fully expects that AI will transform the global economy and civilization in general, much as electricity did more than a century ago.  “Everything that we formerly electrified we will now cognitize,” he said.  “There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ…  This is a big deal, and now it’s here.”  I totally agree.

An equally optimistic view was expressed in another excellent article, - How Smart Connected Products are Transforming Competition, - published in the November issue of the Harvard Business Review by Michael Porter and James Heppelmann. 

We generally think of products as physical entities, - e.g., clothes, light bulbs, appliances, cars, - some quite simple and some much more complex, built using sophisticated mechanical and/or electrical components.  The article introduces a whole new class of smart connected products - “complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways.”

The Evolution of the Internet of Very Smart Things

I recently saw a very interesting presentation, - From Cloud to Fog: The Internet of Things, - by Cisco executive Michael Enescu.  He tells us that the Internet of Things (IoT) is truly taking off, already comprising about 25 billion smart devices at the edges of the Internet, a number that’s expected to more than double over the next decade, and continue to keep growing for the foreseeable future. 

These devices generate massive amounts of data, a lot of which requires real-time actions.  It’s impractical to move all that data to a central cloud for analysis and actions.  Computing and intelligence thus have to move closer to the edge, to both ameliorate the data transport challenges and enable real-time actions as required.  This all leads to what has come to be known as edge computing, - an architectural bridge between the clouds at the center and the IoT devices all around the edges of the Internet.  Cisco calls it fog computing, an allusion to what happens to clouds as they get closer to the ground. 

Cisco defines fog computing as “a highly virtualized platform that provides compute, storage, and networking services between end devices and traditional Cloud Computing Data Centers, typically, but not exclusively located at the edge of network.”  Fog complements cloud with a number of important capabilities, including edge location awareness, low latency in highly constrained connectivity environments, bandwidth and energy optimizations, and nearly unlimited scale. 

Enescu notes that cloud and fog represent two very different IoT paradigms for dealing with huge amounts of data.  In cloud, the data generated by the smart sensors at the edges is transferred and stored in the center, where it’s analyzed, and the appropriate actions then flow back across the network.  With fog on the other hand, the analysis is done in real time at or near the edge devices, from which actions then flow across the network, with only the data to be stored being transferred to the central cloud.

The Complex Nature of Cloud-based Innovation

By now, most will agree that cloud computing is a major transformational force in the world of IT.  But, cloud has not the easiest concept to grasp.  Not that long ago, even people who agreed that something big and profound was going on, were not totally 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,” was the overall conclusion of a June, 2008 conference on The Promise and Reality of Cloud Computing.  Cloud has continued to evolve and advance over the ensuing years.  People are no longer starting their sessions by saying: “let’s define cloud computing,” said an article on a 2013 cloud conference.

I find it helpful to look at cloud along two key dimensions: as a technology to improve IT productivity, and as a platform for enabling business innovation.  A 2012 survey of business and technology executives found that two thirds of respondents viewed cloud as a leading priority for their IT organizations, while one third said that it was a company-wide business priority.  Only one company in six viewed cloud as a way of fostering business innovation.

An increasing number of companies are now deploying cloud-based solutions.  Most are focused on improving the economics of IT.  They are looking to cloud to help them expand their current offerings without major investments in additional IT infrastructure.  Cloud offers financial flexibility, reducing fixed IT costs by shifting from capital to operational, pay-per-use expenses, as well as the ability to easily and economically scale business operations by provisioning IT resources on an as-needed basis.  In addition, cloud can help business users become more agile and keep up with the fast pace of technological and market changes.  Turning to an external cloud service provider is often a faster and less costly way of prototyping and deploying a new application than relying on the internal IT organization.

The Digital Transformation of the Customer Experience

We are in the middle of a kind of digital perfect storm, where several major IT trends, - e.g., mobile, cloud, social, big data, - are each gathering speed while interacting with and amplifying each other.  Every day brings new stories about the transformative impact of digital technologies on companies in just about every industry.  Adapting to the ongoing digitization of the economy, and of society in general, is arguably the most challenging transformation every business is facing.

A few weeks ago, Altimeter, - a research and advisory firm, - released an excellent report on the subject:  Digital Transformation - Why and How Companies are Investing in New Business Models to Lead Digital Customer Experiences.  Rather than looking at the enterprise-wide impact of digitization, the report analyzes how companies are tackling their digital transformation through a more focused lens: the customer experience. 

A company can differentiate itself from competitors in one of two key ways: by providing a superior customer experience or by offering the lowest prices.  For companies that prefer the former, - and want to avoid the relentless low price pressures of the latter, - digital technologies are the best means of engaging with customers and providing them a superior experience at affordable costs.  As the Altimeter report succinctly observes: “Digital transformation represents the quest to understand how disruptive technology affects the customer experience.”

An Old Friend, - the IBM Mainframe, - Turns 50

On April 7, 1964, IBM announced the Systems/360 family of mainframes.  “System/360 represents a sharp departure from concepts of the past in designing and building computers,” said IBM’s then chairman and CEO Thomas J. Watson Jr.  “It is the product of an international effort in IBM’s laboratories and plants and is the first time IBM has redesigned the basic internal architecture of its computers in a decade.  The result will be more computer productivity at lower cost than ever before.  This is the beginning of a new generation - not only of computers - but of their application in business, science and government.” 

In April of 1964 I was a second year college student at the University of Chicago, working part time at the university’s computation center, which used IBM computers.  I still remember attending a presentation on the announcement given by a visiting IBM executive.  Over the next several years I used high-end models of S/360 for the physics calculations I was doing as part of my doctorate studies.  My thesis sponsor, - Chicago professor Clemens Roothaan, one of the early leaders in computational sciences, - was consulting with IBM on the design of future versions of S/360, and I was also involved in some of this work.  This relationship with IBM researchers led to my joining the computer sciences department at IBM’s Watson Research Center once I finished my studies in 1970.   

I was closely associated with mainframes through most of my 37 years in IBM.  I was involved in a number of research initiatives on the future of large systems.  After moving to the large systems products divisions in the mid 1980s, I worked on the evolution of mainframes to CMOS-based microprocessors and parallel architectures.  Later in the 1990s and 2000s I worked closely with the mainframe teams as they supported the Internet, Linux and other emerging initiatives I was involved in.   

Having lived through the near demise of mainframes in the early 1990s, - which would have inevitably led to IBM’s own demise, - their ability to have survived after all these years is truly impressive.  A look at the IT industry over the past several decades will reveal the large number of once great products and companies that are no longer around.  Few computer families could trace their vintage to the 1980s, let alone the 1960s.  There is something pretty unique about the mainframe being not only alive but doing so well after all these years.

Why is the mainframe still around, celebrating its 50th birthday last week?  What enables it to keep reinventing itself while embracing the latest technologies, including the Mobile Internet, Cloud Computing and Big Data?  In a world where product life-cycles are measured in web years, what can we learn from the mainframe’s rather unique longevity?

Data Science: from Half-Baked Ideas to Data-Driven Insights

In a Big Data presentation last year, MIT professor Erik Brynjolfsson pointed out that throughout history new tools beget revolutions.  Scientific revolutions are launched when new tools make possible all kinds of new measurements and observations.

Big data is leading to such a measurement-driven revolution, brought about by the new digital tools all around us, including our mobile phones; searches and web links; social media interactions; payments and transactions; and the myriads of smart sensors keeping track of the physical world.  These digital tools are enabling us to collect massive amounts of information on who we are, what we do and how we interact as individuals, communities and institutions.

The explosive growth of big data is in turn giving rise to data science, one of the most exciting new professions and academic disciplines.  Data science is a mashup of several different fields.  Its data part deals with acquiring, ingesting, transforming, storing and retrieving vast volumes and varieties of information.  Its science part seeks to extract insights from the data by applying tried-and-true scientific methods, that is, empirical and measurable evidence subject to testable explanations and predictions. 

One of the most exciting part of data science is that it can be applied to many domains of knowledge, given our newfound ability to gather valuable data on almost any topic. But, doing so effectively requires domain expertise to identify the important problems to solve in a given area, the kinds of questions we should be asking and the kinds of answers we should be looking for, as well as how to best present whatever insights are discovered so they can be understood by domain practitioners in their own terms.

Innovation Hubs in the Global Digital Economy

Last week I wrote about Cloud, Services and the Transformation of Production, based on Part I of Escape from the Commodity Trap: Will the Production Transformation Sustain Productivity, Growth and Jobs?, a paper by UC Berkeley professor John Zysman.  Part II of the paper deals with the economic and policy implication of this ICT-based production transformation. 

“For the advanced industrial democracies to expand the real income of the citizens and sustain growth in employment and productivity, their economies will have to escape from the commodity trap,” writes Zysman in the paper’s abstract.  “The commodity trap is the price-based competition throughout markets for standard goods and services, which puts pressure on wages and profit margins alike.  Clearly, the way out of this trap is to create distinctive high value added products  - both goods and services.  The emerging transformation of the production of goods and services is dramatically altering what is produced, where, how, and who captures the value.  It creates opportunities and challenges.” 

Early in the Industrial Revolution , the so-called Luddites smashed the new textile machines that were threatening their jobs.  Zysman reminds us that while many of the Luddites lost their jobs, the overall English economy prospered and the average standard of living went up. “It is easier to identify the jobs that are destroyed,. . .  than to imagine the ways new jobs will be created.  Of course, the character and location of those jobs may advantage some communities and disadvantage others.” 

Disruptive innovations, - like the power looms allegedly smashed by protesting Luddites, - have been displacing human labor for the past two centuries.  These periods of creative destruction, - when new technology-based innovations led to painful transitions in once-dominant companies and jobs, - eventually worked themselves out.  Over time, these same disruptive innovations rejuvenated the economy, and led to the creation of new industries and jobs.

Cloud, Services and the Transformation of Production

Ever since cloud computing first appeared in the IT world, people have struggled to define what it is and why it’s so important. “There is a clear consensus that there is no real consensus on what cloud computing is,” said the organizer of a 2008 cloud conference in his closing remarks.  Just about everyone agreed that something big and profound was going on, although we were not totally sure what it was yet. 

Is cloud the 21st century version of time sharing, enabling users to get virtual access to different kinds of IT resources and software capabilities without having to own computers?  Is it a return to centralized computing, driven by the rising complexities, management costs and energy inefficiencies of distributed systems?  Is it the evolution to utility computing, where, as with electricity and water, you get your IT from large service providers and pay for it based on usage? 

Or, as The Economist said in an excellent 2008 article, is cloud the next step in the brief history of computers, as the machines “are evaporating altogether and becoming accessible from anywhere”. . . as computing is becoming “more and more disembodied and will be consumed where and when it is needed.” 

“The rise of the cloud is more than just another platform shift that gets geeks excited,” it presciently added.  “It will undoubtedly transform the information technology (IT) industry, but it will also profoundly change the way people work and companies operate.  It will allow digital technology to penetrate every nook and cranny of the economy and of society, creating some tricky political problems along the way.”

Cloud Computing: Lots of Progress; Lots to Do

In June of 2008 I participated in a conference on The Promise and Reality of Cloud Computing.  “There is a clear consensus that there is no real consensus on what cloud computing is,” said the conference organizer in his closing remarks. There was general agreement that something big and profound was going on out there, although we were not totally sure what it was yet. 

Along with a number of others, I struggled to define what cloud computing was all about.  I finally concluded that the reason we were both excited, but had trouble articulating what we were excited about was because cloud isn’t any one thing.  Rather, it’s a new model of computing, only the third such model in the history of the IT industry, centralized and client-server being the two previous ones.  There is no single dimension around which to define a computing model, which accounts for the variety of definitions. 

Cloud computing has continued to evolve and advance over the ensuing five years, and I think that 2013 will be viewed as the year when cloud truly took off.  An article on last April’s Cloud Connect conference pointed out that people are no longer starting their sessions by saying let’s define cloud computing.  “This is a clear indication that the industry has moved beyond elementary knowledge-gathering and onto the practicalities associated with cloud implementation and rollout.”

A Kind of Digital “Perfect Storm”

Every day brings new stories about the impact of digital technologies on every aspect of the world around us, including their transformative impact on companies in just about every industry.  Some of the articles are about the creation of innovative new technologies and applications, while others are about the creative destruction such innovations leave in their wake.  Lately, the latter kind of stories seem to predominate.

For a while now, companies have been racing to try to keep with the relentless advances of digital technologies.  But, the race keeps getting tougher every year.  We seem to be heading toward a kind of digital perfect storm, where four major IT trends are each gathering speed while interacting with and amplifying each other: mobile devices, cloud computing, social networks and big data and analytics.

These widely discussed technologies are still in the early stages of deployment to a greater or lesser degree. Mobile is furthest along while cloud adoption is taking off.  Data science applications are starting to get deployed, although we are just beginning to appreciate their profound implications.  And while consumer social networks have been thriving for years, their use in business has been slower than expected. 

The disruptive impact of this digital perfect storm is already being felt by technology companies as well as by those in industries whose transformation is already underway.  And perhaps most important, we know that even greater disruptions are in store in the not too distant future.  The resulting creative destruction makes for interesting stories about the changing fortunes of companies as well as their leaders.

Parallel Supercomputing: Past, Present and Future

From the early days of the industry, supercomputers have been pushing the boundaries of IT, identifying the key barriers to overcome and experimenting with technologies and architectures that are then incorporated into the overall IT market a few years later.  While we generally focus on their computational capabilities as measured in FLOPS, - Floating-point Operations Per Second, -  supercomputers have been at the leading edge in a number of additional dimensions, including the storage and analysis of massive amounts of data; very high bandwidth networks; and highly realistic visualizations.

Through the 1960s, 1970s and 1980s, the fastest supercomputers were based on highly specialized, powerful technologies.  But, by the late 1980s, these complex and expensive technologies ran out of gas and parallel computing became the only realistic alternative to scaling up performance.  

Instead of building machines with a small number of very fast and expensive processors, the early parallel supercomputers ganged together 10s, 100s, and over time 1000s of much less powerful and inexpensive CMOS microprocessors, similar to the micros used in the rapidly growing personal computer and workstation industry.  A similar evolution to microprocessor components and parallel architectures took place a few years later in the mainframes used in commercial applications.

The transition to parallel supercomputing was seismic in nature.  Everything changed, from the underlying computer architecture, to the operating systems, programming tools, mathematical methods and applications.  It took considerable research and experimentation to learn to effectively use these new kinds of machines.  Moreover, there were widely different parallel architecture designs, some coming from universities and others from industry.  It wasn’t clear at all which designs worked well for different kinds of applications and would thus be commercially viable.

The Department of Energy (DOE) national labs have long been among the world’s leading users of advanced supercomputers and played a leading role in the transition to parallel architectures.  In 1983, the DOE’s Argonne National Lab established the Advanced Computing Research Facility (ACRF), an experimental parallel computing lab which brought together computer scientists, applied mathematicians and supercomputer users and vendors to learn how to best use this new generation of parallel machines. 

This past May, Argonne convened a Symposium to mark the 30th anniversary of the ACRF.  The Symposium looked both at the progress made in parallel computing over the past 30 years and the major trends for the future.  I attended the Symposium and led a panel on The Impact of Parallel Computing on the World.

The Continuing, Transformative Impact of IT

Last month, the McKinsey Global Institute published Ten IT-enabled business trends for the decade ahead.  As is generally the case with McKinsey, this is a well researched, well written report.  There are few surprises.  Any technology expected to have a transformative impact on business over the next decade has to at least be already in the hands of leading edge users.  Disruptive innovations take time to play out.

“Many trends reflect the growing dominance of the Internet as an enabling technology, as well as a model and metaphor for commercial and social interactions,” says the report in its introduction.  “Twenty years into the Internet revolution, businesses and consumers have come to expect that information is a Google search away, friends and associates are always available on social networking sites, and goods and services (including public goods such as education and government services) can be had instantly from an online vendor anywhere in the world at any time of the day.”

The social matrix, the Internet of all things, big data and advanced analytics, and realizing anything as a services are the report’s top four trends.  SMAC - Social, Mobile, Analytics & Cloud, - has become the new plastics, capturing the future of IT in one word, or rather, one acronym.  Just about everyone agrees that these are foundational technologies, like the Internet twenty years ago, that every business must embrace.

Cloud Computing and the Changing Role of the CIO

Last week I wrote about the 2013 MIT Sloan CIO Symposium which I recently attended.  The Symposium included a number of talks and panels on the key issues facing CIOs, as every business is essentially becoming a digital business.  Big data and cloud were the most prominent transformative technologies discussed at the Symposium.  My remarks last week were focused on big data.  I now want to turn my attention to cloud computing, and its impact as a key driver of the changing role of the CIO.

These days, few question the importance of cloud to individuals, companies of all sizes and the economy in general.  In a just published report by the McKinsey Global Institute, Disruptive technologies: Advances that will transform life, business, and the global economy, cloud is listed as one of 12 technologies with significant potential to drive economic impact and disruption by 2025.  The report said:

“The cloud is enabling the explosive growth of Internet-based services, from search to streaming media to offline storage of personal data (photos, books, music), as well as the background processing capabilities that enable mobile Internet devices to do things like respond to spoken commands to ask for directions.  The cloud can also improve the economics of IT for companies and governments, as well as provide greater flexibility and responsiveness.  Finally, the cloud can enable entirely new business models, including all kinds of pay-as- you-go service models.”

“IT Doesn’t Matter” Ten Years Later

How quickly time passes. . .  It’s already been ten years since the publication of Nicholas Carr’s controversial article IT Doesn’t Matter.  The article appeared in the May 2003 issue of the Harvard Business Review, where Carr was then an editor-at-large.  CIO Magazine marked the 10th anniversary with an interview with Carr and a retrospective story on the reactions to the article by Ann Bednarz, who wrote: 

“The jarring headline of Carr’s May 2003 article, IT Doesn’t Matter, is what many people remember, and it tends to overshadow his more thought-provoking thesis: that companies have overestimated the strategic value of IT, which is becoming ubiquitous and therefore diminishing as a source of competitive differentiation.”  His claims that companies were overspending on IT, because the technology was becoming more commoditized and accessible to everyone “ignited an industry firestorm.” 

In the interview, Carr said that the reaction to the article went way beyond what he expected.  IT vendors were up in arms, because he was essentially telling their customers that the competitive advantage gained by IT investments was shrinking once everyone had access to the same technologies, and that they therefore did not need to be spending much money on the latest hardware and software products.  CIOs and IT analysts had a more mixed reaction, with some disagreeing with Carr’s points, while others saying that they made a lot of sense.

Cloud Computing and Open Architectures

Earlier this month IBM announced that all its cloud services and software will be based on open standards and an open cloud architecture.  Standards and open source software are necessary to help cloud computing achieve the kind of market success enjoyed by the Web, e-mail and other widely used Internet-based services.  IBM’s announcement could in turn influence other IT vendors, application developers and users everywhere to similarly embrace open cloud architectures, much as was the case with IBM’s Web-based e-business strategy in the 1990s, and with its Linux initiative in the 2000s. 

I personally think of cloud computing as the emergence of a new model of computing in the IT industry.  This is important because since the advent of the IT industry over fifty years ago, this is only the third such model, the mainframe-based centralized computing and the PC-based client-server computing being the two previous ones. 

Unlike the previous two models, cloud computing is totally centered around the Internet and related open architectures.  Its most prominent characteristic is its ability to deliver all kinds of IT resources as-a-service over the Internet, e.g., computing, storage, software, platforms, applications and so on.