Irving Wladawsky-Berger

Agentic AI: The Evolution of Application Development

I’ve been following the evolution of AI since the 1970s, especially the more recent era of data-centric AI systems based on highly sophisticated models trained with large amounts of information and powerful computer technologies. We were wowed when in 1997 Deep Blue won a celebrated chess match against then reigning champion Gary Kasparov, — one of the earliest, most concrete grand challenges of AI. The 2010s saw increasingly powerful deep learning AI systems surpass human levels of performance in a number of tasks  like image and speech recognition, skin and breast cancer detection, and playing championship-level Go.

More recently, the impressive ability of large language models (LLMs) and chatbots to interact with us and generate cogent, articulate sentences has given us the illusion that we’re dealing with a well educated, intelligent human, rather than with a sophisticated stochastic parrot that’s been trained with huge amounts of human language but has no human-like understanding of the ideas underlying the sentences it’s putting together.

Agentic systems are now taking AI to the next level.

Business Strategy in the Emerging AI Era

“AI is changing the cost and availability of expertise, and that will fundamentally alter how businesses organize and compete,” said “Strategy in an Era of Abundant Expertise,” a Harvard Business Rreview (HBR) article by Harvard Business School professor Karim R. Lakhani and Microsoft corporate strategists Bobby Yerramilli-Rao, John Corwin, and Yang Li. At its most basic level, a business can be thought of as a bundle of expertise organized to accomplish specific tasks for competitive advantage. The evolution of this expertise defines the evolution of the business.

The article defines expertise as “a combination of deep theoretical knowledge and practical know-how in a specific domain.” Expertise can take many forms, such as deep subject matter knowledge in a specific field, — like manufacturing, software engineering, or a medical specialty, — and the skills needed to successfully run a business, — e.g., marketing, sales, operations, finance. “Companies create  value by applying their expertise efficiently at scale to solve problems for their customers. Typically they possess it in a variety of areas, but most differentiate themselves through their unique proficiency in just a handful of activities that are fundamental to how they create competitive advantage.”

While critical to a company’s success, keeping up with technological advances can be quite challenging for two key reasons. First, expertise is constantly expanding, making it difficult to stay abreast of advances and insights in a competitive global marketplace. And second, the cost of accessing that expertise is constantly falling, lowering the barrier for new, fast moving competitors. The interplay between these two forces, — “the increasing amount of expertise required to create value and the decreasing cost of accessing that expertise — shapes companies and affects the scope of their operations.”

Leveraging GenAI for Workforce Productivity: a Scenario Based Analysis

“Generative artificial intelligence (GenAI) has the potential to drive significant improvements in workforce productivity at the level of tasks, organizations and economies,” said a recent report “Leveraging Generative AI for Job Augmentation and Workforce Productivity: Scenarios, Case Studies and a Framework for Action,” by the World Economic Forum in collaboration with PwC. “Delivering those gains depends, among other things, on the deployment of GenAI to augment jobs, i.e. to partially perform tasks in such a way that technology effectively supports or enhances human capabilities through human-machine collaboration.”

“With such a fast-moving technology, it is hard to predict how even the relatively near-term future will play out,” the report added. “To help think through the possibilities, it is useful to think in terms of scenarios.” When the present resembles the past, we can calculate the probability of future outcomes by analyzing past historical data. But, the situation is very different when trying to formulate strategy in the face of high uncertainty because when situations lack analogies to the past, we have trouble envisioning how the future will play out.

The object of scenario planning is to assemble all known facts, trends, and other information and use them to create a number of alternative stories rich in detail, typically three or four, about plausible future outcomes. Military strategists first developed scenario planning in the 1950s to help them envision the impact of a nuclear war between the US and the Soviet Union, - something that was clearly way beyond anyone’s experience.

Scenario planning has continued to be used by military and government policy strategists. For example, every four years since 1997, the US National Intelligence Council has been publishing a Global Trends report outlining the key trends that will shape the world over the following twenty years. The reports illustrate how the future might play out by developing different scenarios, typically three or four, each embodying the key issues, trends, decisions and uncertainties that will shape the global landscape twenty years into the future.

In the business world, scenario planning has become a valuable tool for strategic thinking over the mid- to long-term planning horizon. Alternative technology, market and political environments can be described in a few discrete scenarios while organizing the richness and range of possibilities into plausible future outcomes.

The Future of AI and Robotics — 2025 Predictions Scorecard

On January 1, MIT professor emeritus Rodney Brooks published his 2025 Predictions Scorecard. A member of  MIT’s faculty since 1984, Brooks was director of the MIT AI Lab from 1997 to 2003, and the founding director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) from 2003 until 2007. He’s also been a robotics entrepreneur, having started a number of companies, including iRobot, Rethink Robotics, and Robust.AI.

Brooks has posted a Predictions Scorecard every year since 2018, where he makes predictions about  future milestones in three technology areas that he closely follows: robotics, AI, and machines learning; self driving cars; and human space travel. He also reviews the actual progress in each of these areas to see how his past predictions have held up, and promises “to review them at the start of the year every year until 2050 (right after my 95th birthday), thirty two years in total” in order to hold himself accountable for those predictions. “How right or wrong was I?”

In his 2023 Predictions Scorecard, Brooks explained that he makes his predictions because he’s seen “an immense amount of hype about these three topics, and the general press and public drawing conclusions about all sorts of things they feared (e.g., truck driving jobs about to disappear, all manual labor of humans about to disappear) or desired (e.g., safe roads about to come into existence, a safe haven for humans on Mars about to start developing) being imminent. My predictions, with dates attached to them, were meant to slow down those expectations, and inject some reality into what I saw as irrational exuberance.”

Let me summarize what he wrote in his 2025 Predictions Scorecard in the area I most closely follow:

Artificial Intelligence and the Future of Work

In 2022 Congress requested a study by the National Academies on the current and future impact of AI on the US workforce. The report, “Artificial Intelligence and the Future of Work,” was released in November of 2024. The three year study was conducted by a Committee of experts from universities and private sector institutions co-chaired by Stanford professor Erik Brynjolfsson and CMU professor Tom Mitchell.

“Today, the speed of technological progress is reshaping not just the tools but also the fabric of the workforce and societal structures,” said the report in its Introduction chapter. “AI has emerged as a general-purpose technology with sweeping implications that demand immediate attention and thoughtful analysis. AI stands out among general-purpose technologies owing to its core attribute — a focus on intelligence. This arguably makes it the most general of all general-purpose technologies.”

“The trajectories that Al-enabled futures might take can lead to outcomes of profound benefit or significant disruption. The goal of this report is thus twofold: to responsibly inform about the current state and capabilities of Al as they relate to the workforce and to offer insights that prepare us for the challenges ahead and opportunities that will arise. It also considers how Al is likely to augment human labor, reshape job markets, and influence workforce dynamics.”

The Breakthroughs AI Needs

A recent issue of The Economist featured a technology focus on “The breakthrough AI needs” with eight articles on the topic. “Two years after ChatGPT took the world by storm, generative artificial intelligence seems to have hit a roadblock,” said the issue’s lead article. “The energy costs of building and using bigger models are spiralling, and breakthroughs are getting harder. Fortunately, researchers and entrepreneurs are racing for ways around the constraints. Their ingenuity will not just transform AI. It will determine which firms prevail, whether investors win, and which country holds sway over the technology.”

This is frightening for investors who’ve bet big on AI, but there is no reason to panic, said the article. “Plenty of other technologies have faced limits and gone on to prosper thanks to human ingenuity,” it added. “Already, developments in AI are showing how constraints can stimulate creativity.” In particular, The Economist mentions two such major innovations: the development of chips with the special purpose architectures needed to train and run AI models as fast and energy efficient as possible, and the development of smaller, more specialized domain specific models that consume a lot less energy than the very large models that rely on brute force computational power.

Let me discuss each of these two innovations.

An Open Architecture for Health Data Interoperability

I’ve been a member of the Linux Foundation (LF) Research Advisory Board for the past three years. At a September, 2023 meeting of the Advisory Board, we discussed that one of the few industry sectors in which the LF doesn’t have projects is healthcare, — despite the size and importance of healthcare around the world. The LF had launched open source healthcare initiatives in the past, but they all eventually failed.

A paper on Digital Health in the 21st Century published in June of 2022 by the US National Academy of Medicine noted that “Revolutionary advances in digital health are transforming health, medicine, and biomedical science, and redefining and re-engineering the tools needed to create a healthier future,” including artificial intelligence, cloud computing, and the increased adoption of electronic health records (EHRs). 

So, perhaps it’s time to try again, and LF Research made the decision to launch a healthcare study, led by Anna Hermansen, Researcher and Ecosystem manager in LF Research, to understand the obstacles for open source adoption and interoperable data standards in the healthcare sector. A year later, in October of 2024, Hermansen released the study’s findings and recommendations in a report, “An Open Architecture for Health Data Interoperability: How Open Source Can Help the Healthcare Sector Overcome the Information Dark Ages.”

The Top 10 Emerging Technologies of 2024

Since 2011, the World Economic Forum (WEF) has published an annual list of the Top Ten Emerging Technologies that are likely to be disruptive within three to five years. The Top 10 Emerging Technologies of 2024 was released by the WEF in June. The report is based on the insights of over 300 world-leading academics and researchers from the WEF’s Network of Global Future Councils and University & Research Network, and from the Frontiers network of over 2,000 chief editors from top global institutions.

A survey was distributed to members of these various networks asking them to propose technologies for the 2024 Top 10 list. The survey received over 300 valid technology nominations from 29 countries. After an initial screening, the Top 10 Emerging Technologies Steering Group was presented with a curated list of 70 technologies from which the final ten were selected based on four criteria:

• Novelty: The technology is emerging and at an early stage of development but is not yet widely used.
• Applicability: The technology is potentially of significant use and benefit to societies and economies.
• Depth: The technology is attracting increased development, investment, and excitement.
• Power: The technology is potentially game-changing to established ways and industries.

Large Language Models (LLMs): Mistaking Engineering Achievements for Human Linguistic Competence

“Mistaking the impressive engineering achievements of LLMs for the mastering of human language, language understanding, and linguistic acts has dire implications for various forms of social participation, human agency, justice and policies surrounding them,” wrote cognitive scientists Abeba Birhane and Marek McGann in a recent paper, “Large models of what? Mistaking engineering achievements for human linguistic agency.” “Hyperbolic claims surrounding LLMs often (mis)uses terms that are naturally applied to the experiences, capabilities, and characteristics of human beings.”

LLMs impressive abilities to respond to, and generate natural language are based on their training using massive language data sets generally sourced from the World Wide Web. The training involves breaking down text or speech into tokens, typically a few characters in length, to develop a statistical model of language. Powerful statistical techniques and lots of computational power are then used to analyze the relationship between billions of tokens in order to generate grammatically valid sequences of token concatenations in response to a question or prompt.

Birhane and McGann note that “the processing of datasets and the generation of output are engineering problems, word prediction or sequence extension grounded in the underlying distribution of previously processed text. The generated text need not necessarily adhere to ‘facts’ in the real world,” which is why LLMs are prone to hallucinations, a response generated by its algorithms that, while statistically and grammatically correct, is actually false or misleading real-world information.

Will AI Devour Software Engineering (SE)?

“Social media provide a steady diet of dire warnings that artificial intelligence (AI) will make software engineering (SE) irrelevant or obsolete,” wrote CMU computer scientists Eunsuk Kang and Mary Shaw in their September, 2024 paper, “tl;dr: Chill, y’all: AI Will Not Devour SE.” [tl;dr: a summary of a longer text]. “To the contrary, the engineering discipline of software is rich and robust; it encompasses the full scope of software design, development, deployment, and practical use; and it has regularly assimilated radical new offerings from AI.”

“Current AI innovations such as machine learning, large language models (LLMs) and generative AI will offer new opportunities to extend the models and methods of SE. They may automate some routine development processes, and they will bring new kinds of components and architectures. If we’re fortunate they may force SE to rethink what we mean by correctness and reliability. They will not, however, render SE irrelevant.”

“What’s with the cries of alarm about AI’s threats to SE?,” asked Kang and Shaw. Social media is full of “angst about the imminent demise of SE at the hands of AI.” Their paper referenced a few recent articles about AI’s potential threat to SE: “The End of Programming,”which noted that “The end of classical computer science is coming, and most of us are dinosaurs waiting for the meteor to hit”; “ChatGPT Will Replace Programmers Within 10 Years”; and “NVIDIA CEO says the future of coding as a career might already be dead in the water with the imminent prevalence of AI.”

“It seems that either AI systems are so different from ‘regular’ software systems that SE knowledge has become obsolete or irrelevant, or else AI will soon take over programming, and by extension software development.” I found this last comment very telling, because, as I read related articles, — like “The rise — and fall — of the software developer” and “Tech Jobs Have Dried Up—and Aren’t Coming Back Soon,” it became clear that software engineering is not a well understood discipline and is often confused and used interchangeably with computer programming and software development, important tasks whose scope is significantly more limited than software engineering.

Digital Twins Are Changing the World

“Digital twins are fast becoming part of everyday life,” said the lead article in the August 31 issue of The Economist. 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.

“Digital twins began as basic computer models of physical objects and systems,” explained The Economist. “As computers have become more powerful, twins have become more sophisticated. Complex design and modelling software means many physical objects initially take shape in the virtual world. Small sensors, capable of measuring all sorts of things, feed twins with real-time data, ensuring that they mirror their physical counterparts.”

Digital Twins help bring the physical and digital worlds closer to each other. Digital technologies, internet connectivity, and other innovations are designed right into the products, creating a new era of smart connected products. Massive amounts of product-usage data can now be gathered, stored and analyzed by applications in the product’s cloud counterpart. The use of AI takes all this much further, allowing virtual models to both simulate and optimize activities in the real world.

AI: The Industrial Revolution of Knowledge Work

“AI is the Industrial Revolution of knowledge work, transforming how all workers can apply information, create content, and deliver results at speed and scale,” said the “2024 AI Jobs Barometer,” a report from the professional services firm PwC. With the AI Jobs Barometer, PwC set out to find empirical evidence of the impact of AI on jobs.

PwC analyzed over half a billion job ads from 15 advanced economies, including the US, Canada, Singapore, Australia, New Zealand, and 10 Western Europe countries, as it looked for concrete evidence of AI’s impact on jobs, skills, wages, and productivity. It tracked the growth of jobs that demand specialist AI skills across countries and sectors to figure out the degree of AI penetration.

Overall, PwC found that AI penetration is accelerating and that workers with strong AI skills command significant wage premiums. Moreover, AI’s impact isn’t limited to only those workers who have specialist AI skills.

“Many, if not most, workers who use AI tools in their work do not have or need these specialist skills. For example, a limited number of workers with specialist AI skills may design an AI system or tool for a company that is then used by hundreds or thousands of the company’s customer service agents, analysts, or lawyers — none of whom have specialist AI skills.” In fact, one of the major features of generative AI applications is that they can typically be operated using simple everyday language with no technical skills required.

The Three Laws of Robotics

Earlier this year, MIT professor emeritus Rodney Brooks gave the closing keynote at the 2024 MIT Sloan CIO Symposium. Professor Brooks was director of the MIT AI Lab from 1997 to 2003, and was the founding director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) from 2003 until 2007. A robotics entrepreneur, he’s founded of a number of companies, including iRobot, Rethink Robotics, and Robust.AI.

Given that AI was the overall theme of this year’s MIT Symposium, Brooks focused his keynote on “What Works and Doesn't Work with AI.” He reminded the audience that “AI has been on the verge of changing everything” ever since the field was founded in the 1950s, and explained why so many AI predictions have turned out so wrong.

I wrote about his excellent presentation in “Artificial Intelligence: Realistic Expectations vs. Irrational Exuberance,” and concluded the blog with what Brooks called “My Three Laws of Artificial Intelligence”:

• When an AI system performs a task, human observers immediately estimate its general competence in areas that seem related. Usually that estimate is wildly overinflated.
• Most successful AI deployments have a human somewhere in the loop (perhaps the person they are helping) and their intelligence smooths the edges.
• Without carefully boxing in how an AI system is deployed there is always a long tail of special cases that take decades to discover and fix. Paradoxically all those fixes are AI-complete themselves.

At the end of July, Brooks posted two new essays in his website. One was “Rodney Brooks’ Three Laws of Artificial Intelligence,” where he elaborated on his Three Laws of AI. I’d like to now discuss his second essay, “Rodney Brooks’ Three Laws of Robotics.”

The Geek Way: A Better Way to Run a 21st Century Company

“Have you recently heard the CEO or other senior executive of a company say something like, we’re a technology company now?” asked Andrew McAfee ealier this year in his keynote at the 2024 MIT IDE Annual Conference. McAfee is a Principal Research Scientist at the MIT Sloan School of Management, and co-founder and co-director of MIT’s Initiative on the Digital Economy. “And in particular, when you heard that person say that, did you think to yourself that’s really not a technology company at all?”

He explained that what CEOs are trying to convey when they say that they run a technology company is that their company needs to be managed very differently now. The pressure to innovate, remain agile and execute at the very high level that technology companies have felt for the past two decades has actually been getting more intense and now applies to their own companies, regardless of the industry they are in.

McAfee’s keynote is based on his November, 2023 best seller The Geek Way: The Radical Mindset That Drives Extraordinary Results. The book’s thesis is succinctly articulated in its introduction: “A bunch of geeks have figured out a better way to run a company. As a result, they’re taking over the economy. And they’re just getting started.”

The 2024 State of Talent in the IT Sector

A few months ago the Linux Foundation (LF) published the “2024 State of Tech Talent Report,” a comprehensive analysis of talent management practices in the IT sector. The study is based on a web survey conducted by the LF and its partners earlier this year. The survey reached out to individuals responsible for hiring and training IT professionals around the world to better understand the current state of talent acquisition, retention, and management.

The survey analyzed data from 418 respondents carefully chosen to eliminates sample bias and ensure high data quality. Respondents came from companies of different sizes across a variety of vertical industries: 17% worked in organizations with less that 250 employees, 57% worked in organizations from 250 to 4,999 employees, and 26% in organizations with more than 5,000 employees.

 Respondents included IT vendors, service provides, nonprofits, academic institutions, and government organizations from around the world: 30% came from organizations headquartered in the US and Canada, 19% from Japan, 18% from Western Europe, 8% from India, 8% from other Asian countries, 4% from Eastern Europe, and 4% from Latin America.

The Rise of the Chief Data Officer (CDO) in the AI Era

After decades of promise and hype, artificial intelligence has finally become the defining technology of our era. Over the past few decades, the necessary ingredients have come together to propel AI beyond universities and research labs into the broader marketplace: powerful, inexpensive computer technologies; advanced algorithms, models, and systems; and, most important, huge amounts of all kinds of data.

The data-centric AI era started about 25 years ago when the explosive growth of the internet led to the emergence of what became known as big data, that is, very large amounts of digital data, including text, voice, and images. Digital data has continued to grow exponentially, and is now estimated to have reached around 150 zettabytes (10²¹ bytes).

“Collecting data, transforming it, and putting it to use is essential to remain competitive today,” wrote Thomas Davenport, Randy Bean, and Richard Wang in “CDO Agenda 2024: Navigating Data and Generative AI Frontiers,” a report sponsored by AWS for Data. “Virtually every organization is actively pursuing the goal of becoming more data-centric, enabling them to swiftly uncover and respond to valuable insights. With generative AI, the power and potential of organizational data is bigger than ever before. Organizations often appoint chief data officers (CDOs) to lead them toward data-driven success.”

Embracing Beneficial Friction for Human-Centered AI

Psychological studies have shown that our judgement is affected by the many decisions we make every day and the friction-based paint points of making these decisions. So, not surprisingly, we look to technology and algorithms to help us make our decisions as frictionless as possible, said Renee Gosline in her keynote at the 2024 MIT IDE Annual Conference. Gosline is a Research Scientist and Senior Lecturer at the MIT Sloan School of Management, and the head of the Human-First AI group at MIT's Initiative on The Digital Economy. “But when AI is involved, sometime we need to add beneficial friction. Sometimes it’s useful for us to engage System 2,” she added, referencing the pioneering behavioral research on human judgment and decision-making by Daniel Kahneman, for which he was awarded the 2002 Nobel Prize in Economics.

In his 2011 bestseller Thinking, Fast and Slow, Kahneman wrote that our mind is composed of two very different systems for reasoning and making decisions, which he called System 1 and System 2.  System 1 is the intuitive, fast and emotional part of our mind. Thoughts come automatically and very quickly to System 1, without us doing anything to make them happen. System 2 is the slower, logical, more deliberate part of the mind. It’s where we make complex decisions by evaluating and choosing among multiple options.

Gosline’s human-first AI research group aims to “add beneficial friction in AI-mediated systems to ensure that humans are in the loop,” as well as to reduce bad friction. When a choice is being made, “we might want to remove friction so that we can make the best choice with AI’s help. But there may be circumstances when we want to add friction to reduce harm.”

How Does the Linux Foundation Work and What Does It Do?

I have long been involved with the Linux Foundation (LF). In 2000, a consortium of companies, including IBM, HP, and Intel, founded the Open Source Development Labs (OSDL), a non-profit organization to support the continued development of Linux and promote its use in enterprise computing. A few years later, in 2007, the OSDL merged with the Free Standards Group to become the Linux Foundation. At the time, I was leading the Linux initiative that IBM had launched in January of 2000 to embrace Linux across all the company’s products and services. In 2011, when the LF marked the 20th anniversary of Linux at its annual Linux conference, I had the privilege of giving a keynote presentation where I recounted my personal involvement with Linux and open source.

Over the past decade, the LF has undergone  a major expansion, as open source became widely accepted as a paradigm for innovation across a full suite of technologies, proving its utility beyond the collaborative development of the Linux operating system. Its impact has been felt across just about all industry sectors in our fast growing digital economy. Consequently, its annual conferences were rebranded Open Source Summits to be more representative of the LF’s expanded open source mission beyond Linux. In April of 2021, the LF announced the formation of Linux Foundation Research to broaden the understanding of open source projects and to explore new opportunities for open source initiatives. Later that year, Hilary Carter, LF’s then VP of Research, invited me to become a member of the LF Research Advisory Board.

I am truly impressed by the current scope of the Linux Foundation. The LF now includes over 1,400 company members, supports over 1000 open source projects in a variety of vertical and horizontal industry sectors, holds over 70 events around the world every year, and provides training and certification programs to equip developers with essential skills, not just for coding, but to support the management and best practices the projects themselves. The mission of the LF was quite easy to explain when it was mostly focused on Linux, but it’s quite a bit more complicated now.

LF’s Executive Director Jim Zemlin recently addressed the difficulty of explaining what the LF does and how it works in his June 13 weekly newsletter to the LF board and team members. Zemlin wrote that beyond the technical community, many don’t understand much about how the LF operates, such as the important role of the LF’s company members in sponsoring open source projects, and that generally, the key maintainers of LF’s largest projects are not volunteers but full-time employees of member companies.

Artificial Intelligence: Realistic Expectations vs. Irrational Exuberance

The 2024 MIT Sloan CIO Symposium took place on Tuesday, May 14 at the Royal Sonesta, a hotel overlooking the Charles River a short walk from the MIT campus in Cambridge, MA. Not surprisingly, AI was the dominant theme in this year’s Symposium, with a number of keynotes and panels on the topic. In addition, a pre-event program was added on the day before the Symposium, which included a number of more informal roundtable discussions on various aspects of AI, such as legal risks in AI deployment, AI as a driver for productivity, and human’s role in AI-augmented workplaces.

The Symposium’s closing keynote, What Works and Doesn't Work with AI, was delivered by MIT professor emeritus Rodney Brooks. Professor Brooks was director of the MIT AI Lab from 1997 to 2003, and was then the founding director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) from 2003 until 2007. A robotics entrepreneur, he’s founded of a number of companies, including iRobot, Rethink Robotics, and Robust.AI.

“There is a lot of AI and robotics hype right now,” said the blurb describing Brooks’ keynote in the Symposium agenda. “Amongst it are a few important innovations that may help many businesses. The trick is to figure out what is close to deployable and what is off in an uncertain distant future. This keynote will talk both about the traps all of us fall in to evaluating AI claims and demonstrations, and what to look for specifically which will get you to a solid understanding of the time frame for if and when technology will be ready to deploy.”

Let me summarize his keynote.

Who Still Works from Home?

Remote work, — aka work from home (WFH), — has been around for decades, but took off in the mid-late 1990s with the explosive growth of the internet. Some even predicted that the internet would lead to the decline of cities, because it would enable people to work, be in touch with friends and colleagues, and shop from home. Why would anyone choose to live in an expensive and crowded metropolitan area, when they could live in a more affordable, less stressful, potentially healthier location? Instead of declining, superstar cities continued to generate the greatest levels of innovation and good jobs, thus attracting a disproportionate share of the world’s talent.

But, everything changed when the COVID-19 pandemic spread worldwide in early 2020. “The coronavirus crisis is forcing white-collar America to reconsider nearly every aspect of office life,” wrote a June, 2020 NY Times Magazine article. “Some practices now seem to be wastes of time, happily discarded; others seem to be unexpectedly crucial, and impossible to replicate online. For workers wondering right now if they’re ever going back to the office, the most honest answer is that even if they do, the office might never be the same.”

The article cited the experience of Accenture, which at the time had around 500,000 employees in more than 200 cities in 120 countries. Before the pandemic, no more than 10% worked remotely on any given day. But, by the middle of March, nearly all were asked to work from home. Employees adapted quickly, said Accenture’s CTO. The volume of video calls went up by a factor of six while audio calls tripled. Despite having to switch from face-to-face to audio and video interactions, overall productivity actually went up as measured by several metrics.”

The Promise of Digital Health in the 21st Century

In 2021 the Linux Foundation (LF) announced the formation of Linux Foundation Research, a new organization to broaden the understanding of open source ecosystems and to explore opportunities for new open source initiatives. Given my long involvement with Linux, the Linux Foundation and open source communities in general, I was invited to become a member of the newly created LF Research Advisory Board (RAB).

At a September, 2023 meeting of the Advisory Board, we discussed that one of the few industry sectors in which the LF does not have projects is healthcare, despite its size and importance around the world. The LF had launched healthcare initiatives in the past, but they all eventually failed. At that meeting, LF Research launched a study to understand what we should be doing in the healthcare sector. I’m involved in the study, and for the past several months, I’ve been learning about the opportunities of digital technologies in healthcare systems by reading a number of articles and talking to key experts.

“The Promise of Digital Health: Then, Now, and the Future,” a discussion paper published in June of 2022 by the US National Academy of Medicine is one of the articles that I found most instructive. The paper is based on the contributions of 25 member of the National Academy of Medicine (NAM), including two of the experts I’ve recently spoken with, John Halamka and Peter Lee.

Could AI Reshape the Value and Nature of Human Expertise?

“A recent Gallup poll found that 75% of U.S. adults believe AI will lead to fewer jobs,” wrote MIT economist David Autor in a recent article, “AI Could Actually Help Rebuild The Middle Class.” “But this fear is misplaced. The industrialized world is awash in jobs, and it’s going to stay that way. Four years after the Covid pandemic’s onset, the U.S. unemployment rate has fallen back to its pre-Covid nadir while total employment has risen to nearly three million above its pre-Covid peak.”

“Due to plummeting birth rates and a cratering labor force, a comparable labor shortage is unfolding across the industrialized world (including in China),” he added. “This is not a prediction, it’s a demographic fact. All the people who will turn 30 in the year 2053 have already been born and we cannot make more of them. Barring a massive change in immigration policy, the U.S. and other rich countries will run out of workers before we run out of jobs.”

In his article, Autor argues that AI has the potential to transform the labor market by reshaping the nature of human expertise. AI offers us the opportunity to extend the value of human expertise by enabling “a larger set of workers equipped with the necessary foundational training to perform higher-stakes decision-making tasks currently arrogated to elite experts, such as doctors, lawyers, software engineers and college professors.”

How to Diversify Admissions to Elite Private Colleges

“After the Covid pandemic made it difficult for high school students to take the SAT and ACT, dozens of selective colleges dropped their requirement that applicants do so,” wrote David Leonhardt in “The Misguided War on the SAT,” a recent NY Times article. “Colleges described the move as temporary, but nearly all have since stuck to a test-optional policy. It reflects a backlash against standardized tests that began long before the pandemic, and many people have hailed the change as a victory for equity in higher education.”

“Now, though, a growing number of experts and university administrators wonder whether the switch has been a mistake,” added Leonhardt. “Research has increasingly shown that standardized test scores contain real information, helping to predict college grades, chances of graduation and post-college success. Test scores are more reliable than high school grades, partly because of grade inflation in recent years.”

Leonhardt’s article is primarily based on two recent studies published by Opportunity Insights, a not-for-profit organization based at Harvard that conducts data-driven scientific research on how to improve upward mobility. Each of the studies analyzed the value of SAT and ACT standardized test scores in predicting academic success at the 12 so-called Ivy-Plus colleges, — the eight Ivy League colleges plus Chicago, Duke, MIT, and Stanford.

While using different data and analytical methodologies, the overall conclusion of each of these studies was similar: standardized SAT and ACT scores are highly predictive of academic success at the Ivy-Plus colleges regardless of the students’ socioeconomic backgrounds. In addition, the second study also found that standardized SAT and ACT scores are highly predictive of post-college career success, such as attendance at top graduate schools, high earnings, and employment at prestigious firms. Let me discuss each of these studies.

The Digital Trust Framework: How to Implement New Technologies and Services Worthy of Trust

“Digital trust is a necessity in a world where digital technologies support and mediate virtually all economic transactions, social connections and institutions,” said Earning Digital Trust: Decision-Making for Trustworthy Technologies, a report published by the World Economic Forum (WEF) in November of 2022. The report defined digital trust as “the expectation by individuals that digital technologies and services –- and the organizations providing them –- will protect all stakeholders’ interests and uphold societal expectations and values.”

But, the report noted that “trust is significantly eroding on a global scale. In order to reverse this trend, leaders and organizations creating and implementing new technologies and digital services must make decisions that are worthy of trust.” To address this challenge, the WEF launched the Digital Trust Initiative in 2021. The Initiative convened an interdisciplinary group of over 60 experts and leaders in privacy, cybersecurity, law, technology ethics, and other fields. The group was asked to establish a consensus among key stakeholders on the meaning of digital trust, as well as to come up with measurable steps to make better, more trustworthy decisions regarding new technologies and digital services.   

How Can Companies Figure Out How to Best Apply Generative AI?

“Business leaders are struggling to understand how seriously they should take the latest phenomenon in the world of artificial intelligence: generative AI,” wrote Andrew McAfee, Daniel Rock, and Erik Brynjolfsson in their recent Harvard Business Review (HBR) article “How to Capitalize on Generative AI.”  

“On one hand, it has already displayed a breathtaking ability to create new content such as music, speech, text, images, and video and is currently used, for instance, to write software, to transcribe physicians’ interactions with their patients, and to allow people to converse with a customer-relationship-management system. On the other hand, it is far from perfect: It sometimes produces distorted or entirely fabricated output and can be oblivious to privacy and copyright concerns.”

“Is generative AI’s importance overblown? Are its risks worth the potential rewards? How can companies figure out where best to apply it?,” the authors asked. To illustrate how to best answer these questions, the article references a recent research paper by Brynjolfsson and two collaborators, Generative AI at Work, which shows that there are ways to both reap the benefits of generative AI and contain its risks.

Is College Still a Good Investment?

In 2014, NY Times journalist David Leonhardt published a column with the provocative title: “Is College Worth It?” Despite the mounting debts incurred by many students to obtain a post-secondary education, “The decision not to attend college for fear that it’s a bad deal is among the most economically irrational decisions anybody could make in 2014. The much-discussed cost of college doesn’t change this fact.” According to a recent paper, said Leonhardt, “Over the long run, college is cheaper than free. Not going to college will cost you about half a million dollars.”

But, over the past few years, an increasing number of articles have questioned whether college is still a good investment. “Was your degree really worth it?,” is one such article, published earlier this year in The Economist. “That question once seemed a no-brainer,” said the article. “For decades young adults in rich countries have flocked to higher education. Governments have touted college as a boon for social mobility and economic growth. Yet as fees rise and graduate earnings stagnate, disillusionment is growing.”

The Economist cited a recent poll published in the Wall Street Journal (WSJ) that found that a majority of Americans, 56%, think that a four-year degree is no longer worth the time and money, a marked reversal from the 53% of Americans who were bullish on college just a decade ago.

“Skepticism is strongest among people ages 18-34, and people with college degrees are among those whose opinions have soured the most, portending a profound shift for higher education in the years ahead,” wrote the WSJ. “Enrollment in U.S. colleges declined by about 15% over the last decade while the growth in alternative credentials, including apprenticeships, increased sharply. In 2017, doubt over the value of a college degree was greatest among men, Republicans and people living in rural areas. That disaffection preceded a widening gender gap in higher education as hundreds of thousands of men left college during the pandemic.”

How Can AI Accelerate the Pace of Scientific Discovery?

A recent issue of The Economist included a special focus on “How AI Can Revolutionize Science,” with three articles on the topic. “Debate about artificial intelligence (AI) tends to focus on its potential dangers: algorithmic bias and discrimination, the mass destruction of jobs and even, some say, the extinction of humanity,” noted the issue’s lead article. “As some observers fret about these dystopian scenarios, however, others are focusing on the potential rewards. AI could, they claim, help humanity solve some of its biggest and thorniest problems. And, they say, AI will do this in a very specific way: by radically accelerating the pace of scientific discovery, especially in areas such as medicine, climate science and green technology.”

“Could they be right?,” asked the The Economist, reminding us that in the 1990s we had high hopes that the internet would reduce inequality and eradicate nationalism by transforming the world into a connected global village. Instead, internet-based social media platforms have been blamed for fueling political and ethnic polarization around the world.

“But the mechanism by which AI will supposedly solve the world’s problems has a stronger historical basis, because there have been several periods in history when new approaches and new tools did indeed help bring about bursts of world-changing scientific discovery and innovation.”

Could Generative AI End Up Reducing the Growing Inequality in Labor Markets?

Over the past three decades, high skill jobs requiring expert problem solving and complex communication skills significantly expanded, with the earnings of the college educated workers needed to fill such jobs rising steadily. Conversely, opportunities and wages declined for less educated middle skill blue-collar manufacturing jobs and white-collar administrative jobs whose careers have been upended by automation.

“But the new kind of automation — artificial intelligence systems called large language models, like ChatGPT and Google’s Bard — is changing that,” noted a recent NY Times article, “In Reversal Because of A.I., Office Jobs Are Now More at Risk.” “These tools can rapidly process and synthesize information and generate new content. The jobs most exposed to automation now are office jobs, those that require more cognitive skills, creativity and high levels of education. The workers affected are likelier to be highly paid, and slightly likelier to be women, a variety of research has found.”

The NYT article referenced two that analyzed the occupations most exposed to AI, one from the Pew Research Center, the other from Goldman Sachs Research.

AI and the Future of Healthcare Systems

In 2009 I participated in a panel at an MIT Symposium on complex systems where I gave a talk on complex social organizations. The panel included talks on financial, energy, and healthcare systems by three eminent leaders in their fields. I was particularly impressed by the talk given by Denis Cortese, who at the time was the president and CEO of the Mayo Clinic, and who after retiring in 2010, joined Arizona State University as Foundation Professor and director of its Center for Healthcare Delivery and Policy.

Dr. Cortese explained that healthcare is a system of coupled systems. Healthcare is composed of three interconnected domains: knowledge, — the domain of medical research, where new ideas, inventions and medical approaches are developed; care delivery, — the domain of physicians and hospitals where patients are treated; and payers, — the domain of insurance and governments that pay for the delivery of healthcare. Each of these domains is quite large and complex in its own right, but their convoluted interactions is one of the main reasons why healthcare systems are so incredibly complex.

Could AI now help us better deal with the inherent complexity of healthcare systems? After decades of promises and hype, AI has finally become the defining technology of our era. Over the past two decades, the necessary ingredients have come together to propel AI beyond universities and research labs into the broader marketplace: powerful, inexpensive computer technologies; advanced algorithms and models; and huge amounts of all kinds data. Data has been the key element in the major AI advances over the past 20 years, including big data and data science in the 2000s, machine and deep learning in the 2010s, and Large Language Models (LLMs) and generative AI in the past few years.

In trying to understand the potential use of AI in healthcare systems, I came across the work of John Halamka. Dr. Halamka is president of the Mayo Clinic Platform, an organization that aims to drive innovations in diagnosis, treatment, and operational improvements in healthcare systems. He’s also a prolific writer, having authored a number of articles and books on the subject.

Jobs Expected to Grow and Jobs Expected to Decline Through 2030

“The COVID-19 pandemic disrupted labor markets globally during 2020,” said a February 2021 McKinsey report on “The future of work after COVID-19.” “The short-term consequences were sudden and often severe: Millions of people were furloughed or lost jobs, and others rapidly adjusted to working from home as offices closed. Many other workers were deemed essential and continued to work in hospitals and grocery stores, on garbage trucks and in warehouses, yet under new protocols to reduce the spread of the novel coronavirus.”

Two and a half years later, a recently published report by the McKinsey Global Institute (MGI), “Generative AI and the future of work in America,” paints a radically different picture: “the US job market has come roaring back from its sudden drop. The nature of work has changed as many workers have stuck with remote or hybrid models and employers have sped up their adoption of automation technologies. More recently, the accelerated development of generative AI, with its advanced natural language capabilities, has extended the possibilities for automation to a much wider set of occupations.”

The US labor market is now undergoing a rapid evolution in the way people work and the work people do. “Some 8.6 million occupational shifts took place from 2019 through 2022. Now even more change is in store. We expect an additional 12 million occupational shifts by 2030.”

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.

Answers from Gartner Experts to Key Enterprise Questions about Generative AI

“Worldwide IT spending is projected to total $4.7 trillion in 2023, an increase of 4.3% from 2022,” said Gartner in its July 19 forecast of IT spending. “As CIOs continue to lose the competition for IT talent, they are shifting spending to technologies that enable automation and efficiency to drive growth at scale with fewer employees.” The forecast adds that “While generative artificial intelligence (AI) is top of mind for many business and IT leaders, it is not yet significantly impacting IT spending levels. In the longer-term, generative AI will primarily be incorporated into enterprises through existing spending.”

The forecast includes a link to an excellent article, “Gartner Experts Answer the Top Generative AI Questions for Your Enterprise.” The overall assessment of the Gartner experts is that “Generative AI isn’t just a technology or a business case — it is a key part of a society in which people and machines work together.” Let me summarize their answers to some key questions.

What is Generative AI? In answer to a question or prompt, generative AI can create an impressive variety of content based on the characteristics of its training data, including images, videos, music, speech, text, software code and product designs. Generative AI is based on foundation models, - the name coined by the Stanford Institute for Human-Centered Artificial Intelligence to large machine learning models that are trained on vast amounts of data, and can thus be adapted to a wide range of tasks.

Will Work from Home Stick?

Working from home (WFH) has been around for decades. The share of WFH three or more days per week was under 1% in 1980, growing modestly with the rise of the internet to 2.4% in 2010 and 4.0% in 2018. Then came Covid-19, forcing tens of millions around the world to work from home and triggering a mass workplace experiment that broke through the technological and cultural barriers that had prevented WFH adoption in the past.

Economists Jose Maria Barrero (Instituto Tecnológico Autónomo de México), Nicholas Bloom (Stanford University), and Stephen J. Davis (University of Chicago) have been tracking the evolution of WFH since May of 2020 by conducting monthly surveys. Their surveys ask a number of questions about working arrangements with WFH during the pandemic, as well as worker preferences and employer plans after the pandemic ends. Typical respondent are 40 to 50 years old, have one to three years of college, and earned $40 to $50 thousand in 2019.

One of their first surveys found that the percentage of paid full days worked from home once COVID hit in April of 2020 was 61.4%, a huge increase from their 4.8% WFH estimate just before COVID. The percentage then went down in subsequent months. One year later, in April of 2021, WFH was around 45%.

What Small Children Can Do that AI Doesn’t Know How to Do (Yet)

“It’s natural to ask whether large language models like LaMDA (short for Language Model Dialogue Application) or GPT-3 are really smart — or just double-talk artists in the tradition of the great old comedian Prof. Irwin Corey, ‘The World’s Greatest Authority,’ wrote UC Berkeley cognitive and developmental psychologist Alison Gopnik in ‘What AI Still Doesn’t Know How to Do,’ a July, 2022 WSJ essay. “But I think that’s the wrong question. These models are neither truly intelligent agents nor deceptively dumb. Intelligence and agency are the wrong categories for understanding them.”

“Instead, these AI systems are what we might call cultural technologies, like writing, print, libraries, internet search engines or even language itself. They are new techniques for passing on information from one group of people to another. Asking whether GPT-3 or LaMDA is intelligent or knows about the world is like asking whether the University of California’s library is intelligent or whether a Google search knows the answer to your questions. But cultural technologies can be extremely powerful — for good or ill.”

A recent article in The Economist, “How AI could change computing, culture and the course of history,” made a similar point. That article aimed to give us a sense of the world-changing transformations we might expect from generative AI by discussion two of of the most transformative cultural technologies of the past few hundred years: the printing press and the web browser.

Why Transformative AI Is So Hard to Achieve

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

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

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

What’s the Likely Long Term Evolution of AI?

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

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

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

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

How to Improve Enterprise Project Management As We Enter the Age of AI?

“Quietly but powerfully, projects have displaced operations as the economic engine of our times,” wrote Antonio Nieto-Rodriguez in “The Project Economy Has Arrived,” a December, 2021 article in the Harvard Business Review (HBR). Nieto-Rodriguez is a Fellow and past global chair of the Project Management Institute and the author of the Harvard Business Review Project Management Handbook: How to Launch, Lead, and Sponsor Successful Projects, on which the HBR article is based.

While that shift has been a long time coming, we still have a ways to go, he added. “According to the research firm the Standish Group, around 35% of the projects undertaken worldwide are successful. Given that we’re talking about tens of trillions of dollars, and the labor of millions of employees, that’s a mind-blowing number. It tells us that we’re not only wasting 65% of the time and money that we’ve invested in our projects but also forfeiting trillions of dollars of new value for organizations and society at large.”

A recent McKinsey global survey on the state of AI in the enterprise found that AI adoption has more than doubled over the past five years. Given that the survey was conducted in 2022, before the November 30 release of ChatGPT propelled AI to a whole new level of interest, we can expect a major increase in enterprise AI projects in the coming years. Nieto-Rodriguez's HBR article is thus particularly relevant as we enter the age of AI. 

“What exactly is a project?,” he asked in the article. While project is a word we all commonly use, the article points out that the word means different things to different people. “That’s a problem. As projects drive more and more of the value that organizations create, everyone needs to have a common understanding of what projects and project management are”:

“Projects involve a series of planned activities designed to generate a deliverable (a product, a service, an event). These activities — which can be anything from a grand strategic initiative to a small program of change — are limited in time. They have a clear start and end; they require an investment, in the form of capital and human resources; and they are designed to create predetermined forms of value, impact, and benefits. Every project has elements that are unique. That’s key: Each contains something that has not been done before.”

Risks and Opportunities in an Increasingly Complex AI World

On July 13 I participated in an online panel on Board Governance: Risks and Opportunities in a Complex World, sponsored by eCornell, Cornell University’s executive education unit. The panel was organized and moderated by LizAnn Eisen, acting professor of the practice at the Cornell Law School, and former corporate partner at Cravath.

In addition to me, panel members included Bill Priestap, former assistant director of the FBI, head of the FBI Counterintelligence Division and now founder of the risk consulting startup Trenchcoat Advisors; Noah Phillips, former commissioner at the FTC and now anti-trust partner at Cravath; and David Kappos, former director of the USPTO and now IP partner at Cravath.

The panelist discussed a range of topics that board members should pay attention to when considering risks and opportunities. Each of us foced on our respective areas of expertise. I discussed the potential business impact of artificial intelligence; Phillips focused on regulatory issues that are arising as AI is used in an increasing number of business applications, like the need for consumer protection; Kappos discussed the extensive IP and legal implications posed by AI; and Priestap focused on the challenges posed by an increasingly dangerous global geopolitical environment.

A recording of our panel can be found here.

Data-Centric AI: the Systematic Engineering of Data to Build AI Systems

After decades of promise and hype, artificial intelligence has finally become the defining technology of our era. Over the past few decades, the necessary ingredients have come together to propel AI beyond universities and research labs into the broader marketplace: powerful, inexpensive computer technologies; advanced algorithms, models, and systems; and huge amounts of all kinds data.

Like the steam engine, electricity, computers, and the internet, AI will have a historically transformative impact across economies and societies. But, over the past two centuries we’ve learned that historically transformative technologies have great potential from the outset, but realizing that potential requires major complementary investments including business process redesign; innovative new products, applications and business models; and the re-skilling of the workforce; as well as a fundamental rethinking of organizations, industries, economies, and societal institutions.

In the early 1990s, for example, it was clear that something big and exciting was taking place with the advent of the internet and World Wide Web, but it wasn’t at all clear where things were heading, how to sort out the growing hype from reality, and the implication to industries, companies and jobs. As was the case with the dot-com bubble of the 1990s, there’s now a combination of rapid technology adoption, new standards-based open source infrastructures, exciting new applications, — some of which will turn out to be truly innovative and some rather silly, — and a speculative frenzy that is likely to end with the bursting of the current AI bubble.

However, while sharing many attributes with previous historically transformative technologies, AI is in a class by itself, especially when it comes to the kinds of concerns, fears and uncertainties it’s been generating.

The Economic Potential of Generative AI

In September of 2018, McKinsey published a report on the impact of AI on the world economy. The report’s overriding finding was that AI has the potential to incrementally add 16 percent or up to $15.4 trillion by 2030 to annual global economic output, — an average contribution to productivity growth of about 1.2 percent. McKinsey’s 2018 report was published before the advent of generative AI and related technologies lik large language models (LLMs) and chatbots like ChatGPT that have been taking AI to a whole new level in the past few years.

In June of 2023, McKinsey published, “The Economic Potential of Generative AI: The Next Productivity Frontier.” This new research study aims to take a first look at generative AI’s economic impact over and above the projections in its 2018 report. The 2023 report study evaluated the potential economic impact of generative AI by identifying 63 concrete use cases of generative AI that would lead to measurable outcomes. In addition, the study estimated how generative AI would affect the productivity of the global workforce by analyzing its potential impact on each of more that 2,100 detailed work activities across 850 occupations.

Not surprisingly given that generative AI is still in its early stages, the new report explains that “While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation.”

Machine Learning Has an AI Problem

“Machine learning has an AI problem,” wrote author Eric Siegel in a recent Harvard Business Review (HBR) article, “The AI Hype Cycle is Distracting Companies.” “With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. This begins by correctly naming such projects: Call them ML, not AI. “Including all ML initiatives under the AI umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. For most ML projects, the term AI goes entirely too far — it alludes to human-level capabilities.”

Siegel’s article brought to mind similar concerns many of us had in the 1990s given all the frenzy and hype surrounding the then fast growing Internet which eventually led to what became known as the dot-com bubble. Let me explain.

Looming Challenges Make US Productivity Growth an Imperative

“Labor productivity growth has been the engine of US economic power and prosperity since World War II, adding 2.2 percent annually to economic growth and contributing mightily to a 1.7 percent annual gain in real incomes,” said a recent report by the Mckinsey Global Institute, “Rekindling US productivity for a new era.” But since 2005, “US labor productivity has grown at a lackluster 1.4 percent. At the same time, real wages have slowed and workforce participation has declined.” Most OECD countries have seen similar declines in labor productivity growth over the past 15 years.

Regaining the historical 2.2 percent annual productivity growth rate would add $10 trillion to US GDP between now and 2030, — the equivalent of $15,000 per household, the report added. “Workforce shortages, debt, inflation, and the cost of the energy transition are powerful headwinds. All will be easier to confront with higher productivity.”

Rising labor productivity has not translated into broad increases in income over the past four decades. Between 1948 and 1970, productivity growth averaged 2.8% annually while incomes grew in lock-step at 3.0%. But then came the so-called great divergence, when real compensation lagged productivity growth.

The 2023 MIT Sloan CIO Symposium

The 2023 MIT Sloan CIO Symposium took place on May 15 and 16 at the Royal Sonesta Hotel, overlooking the Charles River, and a short walk from the MIT campus in Cambridge, MA.  Livestream tickets were also offered to help better engage the Symposium’s worldwide audience.

This year’s overall theme was “Driving Digital Resilience in a Turbulent World.” It was a very appropriate theme given that business and economies are still bouncing back from the major disruptions brought about by the COVID-19 pandemic, trying to understand and adapt to transformative technology advances like Generative AI, and coping with the rising volume of cybersecurity threats in an increasingly dangerous world.

2023 marked the 20th anniversary of the MIT CIO Symposium. I’ve been involved with the MIT Symposium for most of those 20 years, having first participated in the 2009 Symposium where I moderated a panel on The Virtual Organization. In those earlier years, CIOs and other senior business executives from around the world came to MIT to explore how leading-edge academic research on information technologies could help address the challenges faced by their companies in the 21st century, Internet-based digital economy.

The Current State of AI: A Tale of Two Worlds

What’s the current state of AI? I’ve been closely following AI over the past several years, and based on everything I’ve read and written about in my blog, the question brings to mind Charles Dickens’ A Tale of Two Cities.

We have in fact a tale of two very different AI worlds. Ever since OpenAI released ChatGPT a few months ago and encouraged the general public to try it, we’ve seen article after article about its emergent unexpected capabilities and potential existential risks. But, at the same time, most recent surveys of the state of AI in the enterprise have concluded that the majority of surveyed companies are still in the early stages of deployment and have not achieved significant outcomes.

For example, Stanford’s recently published 2023 AI Index Report found that industry was now leading the development of state-of-the-art large language models like GPT-4 and chatbots like ChatGPT. But at the same time, the Stanford report references the most recent McKinsey global survey on AI which found that while leading edge firms continue to pull ahead, the number of companies adopting AI has plateaued.

The global survey, “The State of AI in 2022 — and a Half Decade in Review,” marks the fifth consecutive year in which McKinsey has analyzed AI’s impact on companies around the world. The 2022 report is based on a survey of almost 1,500 firms from a variety of regions, industries, and company sizes. Only about half of respondents said that their organization have adopted AI in at least one function.

Let me summarize the report’s key findings.

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

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

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

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

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

Why Men and Boys Are Falling Behind

A few weeks ago, Richard Reeves was Ezra Klein’s guest in his NY Times podcast “The Men — and Boys — Are Not Alright.” Reeves is a writer and Senior Fellow at the Brookings Institution, where he’s been studying inequality, poverty, social mobility, and family policy. His book, — Of Boys and Men: Why the Modern Male Is Struggling, Why It Matters, and What to Do about It, published in September of 2022, — is based on his research on the growing gender gaps in education and employment.

“We’re used to thinking about gender inequality as a story of insufficient progress for women and girls,” wrote Klein in the podcast’s introduction. “There’s a good reason for that: Men have dominated human societies for centuries, and myriad inequalities — from the gender pay gap to the dearth of female politicians and chief executives — persist to this day.”

“But Reeves’ core argument is that there’s no way to fully understand inequality in America today without understanding the ways that men and boys — particularly those from disadvantaged backgrounds — are falling behind. And they’re falling behind in ways that are tough on families, in ways that are tough on marriages, ways that are tough on children. And it gets much, much worse when you go down the income ladder.”

The 2023 AI Index Report: AI Has Moved Into Its Era of Deployment

The 2023 AI Index Report was released in early April by the Stanford Institute for Human-Centered Artificial Intelligence, it’s sixth annual analysis of the impact, progress, and trends of AI. Led by an interdisciplinary group of experts from across academia and industry, the report aims to be the world’s most authoritative source for data and insights about AI.

Last year’s report concluded that 2021 was the year that AI went from an emerging technology to a mature technology, no longer just a subject of speculative lab research but something with real-world economic impact, both positive and negative.

“AI has moved into its era of deployment; throughout 2022 and the beginning of 2023, new large-scale AI models have been released every month,” wrote the report’s co-directors Jack Clark and Ray Perrault about the 2023 report. “Policymakers are talking about AI more than ever before. Industry leaders that have integrated AI into their businesses are seeing tangible cost and revenue benefits. The number of AI publications and collaborations continues to increase. And the public is forming sharper opinions about AI and which elements they like or dislike.”

How to Leverage AI for Substantial Business Value

“AI initiatives at many organizations are too small and too tentative,” wrote Babson professor Tom Davenport and Deloitte principal consultant  Nitin Mittal in “Stop Tinkering with AI,” a recently published HBR article. The article is adapted from their book All-in on AI: How Smart Companies Win Big with Artificial Intelligence which was published earlier this year.

AI technologies have significantly advanced over the past few years. But, while leading edge firms are placing AI at the center of their business strategies, a number of recent surveys continue to show that the majority of enterprises are still in the early stages of AI experimentation and deployment and risk being left further behind. For example, the latest Deloitte survey on the “State of AI in the Enterprise” reached out to over 2,800 executives from advanced economies and found that 28% of respondents were deploying AI at scale and achieving high outcomes, but 46% were still in the early stages of deployment with no significant outcomes.

In another recent survey, Accenture reached out to over 1,600 C-suite executives of the world’s largest companies and found that only 12% had the strategic and operational AI capabilities needed to achieve superior growth, while the majority of firms, 63%, were still at the experimenting stage and had only average AI capabilities.

This is not surprising. As we’ve learned over the past two centuries, even after achieving broad acceptance, it takes considerable time, - often decades, - for transformative technologies like AI to be widely embraced across firms and economies. These technologies have great potential from the outset, but realizing their potential requires major complementary developments including business process redesign, innovative products, and services, new business models and strategies, the re-skilling of the workforce, and a fundamental rethinking of the overall organization.

The Widening Earnings Gap Between College and High School Graduates

“In one of the richest nations on earth, the path to prosperity has narrowed significantly in recent decades — especially for those without a college education,” wrote the NY Times in its January 28 editorial, “See Workers as Workers, Not as a College Credential.” “More than 62 percent of Americans ages 25 and up do not hold bachelor’s degrees, and the earnings gap between those with a college education and those without one has never been wider,” it said. “That’s happening even as the cost of college spirals upward, putting it out of reach for many. This has fueled anxiety, bitterness and a sense of alienation among the millions who see themselves as shut out of an economy that does not value them.”

The editorial cites data from the Federal Reserve Bank of NY showing that the wage gap between recent college and high school graduates hit a record high in 2021. In 1990 the median annual earnings of recent college graduates ages 22 to 27 with a full-time job was $48,900 for those with a bachelor’s degree and $35,300 for those with a high school diploma, a difference of $13,600. But in 2021, the median annual earnings for similar recent college graduates was $52,000 and $30,000 for those with a high school diploma, a difference of $22,000. Not only was the difference in earnings over 60% higher in 2021 than in 1990, but the median earnings for those with a high school diploma has decreased by 15% in the intervening three decades.

This is a stark example of the so-called great divergence of the past four decades. In the 30 years between 1948 and 1978, US productivity as measured by output per hour rose by 108%, an annual growth rate of 2.4%, while the average compensation of production workers increased in lock-step by 95%. But, between 1978 and 2016, productivity rose by 66%, an annual growth rate of 1.3%, while the median compensation of workers increased by only 9%. In other words, while the U.S. witnessed major technological innovations and productivity gains in the past four decades, the wages of workers in the middle have barely changed, with most of the wage benefits of higher productivity going to high earners. Other advanced economies have experienced similar economic, technological and global forces but their workers have fared much better.


The Economic Value of Open Source

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

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

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