Irving Wladawsky-Berger

How is Open Source Software Being Adopted Across the World?

“Open source software (OSS) has become a driving force behind innovation, collaboration, and the democratization of technology,” said the 2024 Global Spotlight Insights Report. The report, published last month by Linux Foundation Research, analyzed regional and industry differences in open source opportunities and challenges and tracked year-over-year trends.

The report’s findings are based on the 2024 Global Spotlight Survey. The Survey’s 48 questions received over 1,250 valid responses on how OSS is being adopted, supported, and sustained across industries and regions around the world. Let me summarize its key findings.

A shift in priorities compared to last year’s Global Spotlight 2023

Confidence in the security of open source software remains robust: 68% of respondents believe that OSS is more secure than closed source software, consistent with the 2023’s results; those that said that OSS is not more secure than proprietary software dropped from 15% in 2023 to 21% in 2024, while those that said that they don’t know or aren’t sure if OSS is more secure increased from 17% in 2023 to 20% in 2024.

In which areas do you think there should be further investment in open source across your geographic region? In 2023 the top priority was government adoption of open source at 40%, followed by better funding of commercial open source startups at 30%, and open source alternatives to technology monopolies at 29%. In 2024, the top priority at 49% is open source alternatives to technology monopolies, followed by government adoption of open source at 40%, and better academic education at 31%. The change reflects the growing concern over reliance on proprietary platforms and the need for vendor-neutral solutions, especially when it comes to artificial intelligence (AI).

What Do We Know About the Economics of AI?

A few weeks ago, MIT News published an article on “What do we know about the economics of AI?” The article is based on the research of MIT Nobel laureate professor Daron Acemoglu, who in October was awarded the 2024 Nobel Memorial Prize in Economic Sciences along with his MIT colleague Simon Johnson and University of Chicago economist James Robinson. “For all the talk about artificial intelligence upending the world, its economic effects remain uncertain,” said the article. “There is massive investment in AI but little clarity about what it will produce.”

Professor Acemoglu has long been conducting research on the impact of technology on the economy. “Artificial intelligence (AI) has captured imaginations. Promises of rapid, even unparalleled, productivity growth as well as new pathways for complementing humans have become commonplace,” he wrote in “The Simple Macroeconomics of AI,” a research paper published in May of 2024.

“AI will have implications for the macroeconomy, productivity, wages and inequality, but all of them are very hard to predict. This has not stopped a series of forecasts over the last year, often centering on the productivity gains that AI will trigger. Some experts believe that truly transformative implications, including artificial general intelligence (AGI) enabling AI to perform essentially all human tasks, could be around the corner. Other forecasters are more grounded, but still predict big effects on output.”

What Will it Take to Get AI Right? A 2050 Thought Experiment

“During this period of astonishing technical progress and public engagement on artificial intelligence, the world has been grappling with how to get AI right — and for good reason,” wrote James Manyika, in “Getting AI Right: A 2050 Thought Experiment.” Manyika is Senior Vice President of Research, Technology and Society at Google and Chairman Emeritus of the McKinsey Global Institute. His essay is part of The Digitalist Papers, a series of 12 essays on the future of the age of AI.

“[T]he world has been grappling with how to get AI right—and for good reason,” noted Manyika. “AI is a foundational and general-purpose technology with the potential to benefit people and society in a range of ways”:

• assist people with everyday tasks, help them access and benefit from the world’s information and knowledge, and pursue their most ambitious, productive, and creative endeavors;
• contribute to economic progress by enabling entrepreneurs, powering small and large businesses, and fueling innovation and productivity;
• accelerate scientific advances in fields ranging from medicine to materials, physics, climate sciences and more; and
• help make progress on many of humanity’s most pressing challenges and opportunities, from food security to health and well-being.

“At the same time there are concerns about AI’s development, deployment, and use, including: robustness, accuracy, bias, and privacy risks; risks from misapplication and misuse; potentially complex impacts on society from jobs to education and democracy, and the possibility of unintended or unforeseen consequences; and challenges of alignment with human preferences and human flourishing as AI becomes more capable. Such concerns, if unaddressed, could create information hazards, lead to safety and security risks, and cause harm.”

The Evolution of Global Supply Chains

“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 needed by the eight billion people in the planet,” wrote MIT professor Yossi Sheffi in the preface of his 2023 book, The Magic Conveyor Belt: Supply Chains, A.I. and the Future of Work. “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.”

Lately, I’ve been thinking a lot about supply chains. Not only are global supply chains among the most complex of man-made systems, but supply chains are nowhere near ready to be transformed because many of the tasks and processes involved in supply chains are still in the early stages of digitalization. As I think about my personal experience in the evolution of the IT industry, the successful collaboration around standards spurred a new era of productivity and growth. We have yet to see a similar catalyst in the supply chain ecosystem.

I’ve been following the evolution of global supply chains for the past three decades. The 1990s ushered in a golden age of globalization, when the world seemed to be coming together. Thomas Friedman’s The World is Flat became an international best-seller in 2005, nicely explaining what globalization was all about, including the key forces that contributed to flattening the world, from the explosive growth of the Internet to the rise of just-in-time global supply chains. The very nature of the global organization was transformed, with IBM CEO Sam Palmisano declaring in a 2006 Foreign Affairs article that “The Globally Integrated Enterprise” was destined to become the corporate model of the future.

My interest in global supply chains increased significantly in the mid 2010s, fueled by my fascination with enterprise blockchain technologies. In 2016, the World Economic Forum (WEF) included blockchain in its annual list of Top Emerging Technologies. The WEF report compared blockchain to the Internet, noting that “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built. And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

What Industries Are Most Likely to Reshape the Global Economy by 2040?

“The next big arenas of competition,” a recently published report by the McKinsey Global Institute (MGI), “identifies and describes a category of industries that could account for much of the future change in the business landscape and transform the world. We call these industries arenas of competition,” said the report.

Arenas of competition are defined by two characteristics: “they capture an outsize share of the economy’s growth, and market share within them changes hands to an outsize degree. The presence of those two attributes indicates that a new competitive game has begun, usually prompted by a new bundle of technologies and business models.” To identify the key arenas of the future, the report looked back at the arenas of today to see how they evolved.

The Arenas of Today

The arenas of today include 12 industries that showed outsize growth between 2005 and 2020. These 12 industries had a compound annual growth rate (CAGR) of 10%, a market capitalization of 16%, and tripled their global GDP share from 3% to 9% over that period. By contrast, non-arenas had only a 4% revenue annual revenue growth and a 6% market cap growth over the same period.

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.”

Who Will Be Proved Right? The Bullish or the Skeptic AI Camp?

A few weeks ago I posted “AI Advances Aren’t Likely to Occur Nearly as Quickly as Many Believe,” a blog focused on a research paper, “The Simple Macroeconomics of AI,” by MIT professor Daron Acemoglu, — a co-recipient of the 2024 Nobel Memorial Prize in Economic Sciences. The blog referenced a  NY Times article, “What if the A.I. Boosters Are Wrong?,” which discussed Acemoglu’s skeptical views that AI will supercharge economic productivity by the end of this decade, as well as “Gen AI: Too Much Spend, Too Little Benefit?, a report by Goldman Sachs Research senior strategist Allison Nathan, which included a number of insightful interviews, including one with Professor Acemoglu.

As I wrote in the blog, Nathan started her interview by asking why his paper argued that the upside to US productivity and GDP growth from GenAI will likely prove more limited that those of other predictions, including some from Goldman market analysts. “Why are you less optimistic on AI’s potential economic impacts?,” she asked him.

Acemoglu replied that the forecast differences are primarily about the timing of AI’s economic impacts rather than about the ultimate promise of the technology. “Generative AI has the potential to fundamentally change the process of scientific discovery, research and development, innovation, new product and material testing, etc. as well as create new products and platforms.” But the economic impact of historically transformative technologies like AI take time to play out.

I’d like to now discuss another recent NYT article, “Will A.I. Be a Bust?,” which was focused on the views of Jim Covello, Goldman Sachs’ head of stock research, one of Wall Street’s leading AI skeptics. “Three months ago, he jolted markets with a research paper that challenged whether businesses would see a sufficient return on what by some estimates could be $1 trillion in A.I. spending in the coming years,” said the article.

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.

AI Advances Aren’t Likely to Occur Nearly as Quickly as Many Believe

“What if the A.I. Boosters Are Wrong?,” asked a recent NY Times article in its title. “A skeptical paper by Daron Acemoglu, a labor economist at M.I.T., has triggered a heated debate over whether artificial intelligence will supercharge productivity,” it added. “The bullish camp has great  hopes for AI,” such as whether the technology will usher the next industrial revolution which could help wipe out poverty around the world. “But if the boosters are wrong, it could be trouble for the developed world, which is in desperate need of a productivity breakthrough as its work force ages and declines.”

(Before continuing with the blog, let me mention that on October 14, 2024, Professor Acemoglu was awarded the 2024 Nobel Memorial Prize in Economic Sciences along with his MIT colleague Simon Johnson, and University of Chicago economist James Robinson.)

“Artificial intelligence (AI) has captured imaginations,” wrote Acemoglu in “The Simple Macroeconomics of AI,” the skeptical paper referenced in the NYT article. “Promises of rapid, even unparalleled, productivity growth as well as new pathways for complementing humans have become commonplace. There is no doubt that recent developments in generative AI and large language models that produce text, information and images — and Shakespearean sonnets — in response to simple user prompts are impressive and even spellbinding.”

“AI will have implications for the macroeconomy, productivity, wages and inequality, but all of them are very hard to predict,” he added. “This has not stopped a series of forecasts over the last year, often centering on the productivity gains that AI will trigger. Some experts believe that truly transformative implications, including artificial general intelligence (AGI) enabling AI to perform essentially all human tasks, could be around the corner.” But, according to Acemoglu, AI will contribute only modest improvements to worker productivity and will add no more than 1 percent to U.S. economic output over the next decade.

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.

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.”

Is the AI Revolution Already Losing Steam?

“It sure seems like the AI hype train is just leaving the station, and we should all hop aboard,” said “The AI Revolution Is Already Losing Steam,” a recent WSJ article by technology columnist Christopher Mims. “But significant disappointment may be on the horizon, both in terms of what AI can do, and the returns it will generate for investors. The rate of improvement for AIs is slowing, and there appear to be fewer applications than originally imagined for even the most capable of them. It is wildly expensive to build and run AI. New, competing AI models are popping up constantly, but it takes a long time for them to have a meaningful impact on how most people actually work.”

As we’ve learned since the advent of the Industrial Revolution, there’s generally been a significant time lag between the broad acceptance of a major new transformative technology and its ensuing impact on industries and economies. After transitioning from the R&D labs to the marketplace, it takes considerable time, — often decades, — for the products, services, and business models based on the new technologies to be widely embraced across economies and societies, and for their full benefits to be realized.

The reason, explained economists  Erik Brynjolfsson, Daniel Rock, and Chad Syverson in “The Productivity J-Curve,” a 2018 NBER working paper, is that while historically transformative technologies have the potential to radically change the economic environment, “realizing that potential requires larger intangible and often unmeasured investments and a fundamental rethinking of the organization of production itself.”

Their paper identifies two phases, investment and harvesting, in the life cycle of a historically transformative technology. In general, these technologies require massive complementary investments, such as business process redesign, co-invention of new products and business models, and the re-skilling of the workforce. Moreover, the more transformative the technologies, the longer it takes for them to reach the harvesting phase when they are widely embraced by companies and industries across the economy. Such an evolution has been has been pithily captured in what’s become known as Amara’s Law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.

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.”

The 2024 AI Index Report: AI’s Influence on Society Has Never Been More Pronounced

Earlier this year, the Stanford Institute for Human-Centered Artificial Intelligence released the 2024 AI Index Report, its seventh annual edition of the impact, progress, and trends in AI. At over 500 pages and 9 chapters, the report aims to be the world’s most authoritative source for data and insights about AI in order to help develop a more thorough understanding of the rapidly advancing field of AI.

Last year’s report concluded that AI had now moved into its era of deployment, with new large-scale models being released every month. While these systems demonstrated capabilities unimagined a decade ago, they were prone to so called hallucinations, that is, they sometimes generate responses which contain false or misleading information presented as facts, as well as being prone to bias and to their use for negative purposes.

“The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced,” said this year’s report. “As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.”

Regulation by Design: Embedding Regulatory Objectives into the Design of AI Systems

In its early 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 has all been changing in the 21st century with the rise of the Internet of Things (IoT), Big Data, and Artificial Intelligence, whose combined impact is driving a new era of smart connected products.

As the world’s digital and physical infrastructures converge, digital technologies are designed right into complex products, — e.g., jet engines, power generators, medical equipment, energy pipelines. Massive amounts of usage data can now be gathered over the internet, stored and analyzed by sophisticated applications to help monitor the product and anticipate potential problems. This is particularly important in industries where safety by design can have a significant economic impact and in some cases, literally save lives. Safety by design features are now in widespread use in all kinds of mechanical, electronic, and other complex physical systems.

What if we could similarly embed regulatory objectives directly into the technical design of AI systems?, asked Robert Mahari in  his keynote on Regulation by Design at the recent 2024 MIT IDE Annual Conference. Mahari is pursuing a joint JD-PhD degree. He received a JD at the Harvard Law School in 2022 and is now a PhD candidate in the MIT Media Lab research group led by professor Alex ‘Sandy’ Pentland.

“Compliance and regulation by design represent a risk-management paradigm that’s uniquely suited for AI,” Mahari said. “Intelligent technology design can proactively prevent failures and risks.”

AI systems are the result of a very complex supply chain, including the choice of the large amounts of data needed to train AI models, and the difficulty of testing, predicting, and explaining the models’ expected behavior. As a result, compliance with regulatory and ethical objectives is very hard for the users of AI-based applications who have little control or understanding of how the AI system works. Instead, as is the case with advanced engineering systems, can an AI system monitor how it’s used, identify high risk sessions, alert its developers and overseers, and thus ensure compliance?

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.

The Emerging Data-Centric AI Era: Where Will All the Data Come From?

The data-centric AI era started about 25 years ago when the explosive growth of the internet led to what’s become known as big data, that is, the availability of huge amounts of digital data, including text, voice, and images. The 2010s saw major innovations in multi-layered deep learning algorithms, followed in the past few years by the advent of foundation models, including generative AI, large language models, and chatbots.

Given the explosive growth of interest in AI, the demand for data keeps increasing. No matter what an organization hopes to achieve, success is impossible without ready access to high-quality data. But, as data becomes increasingly critical for business success, how are enterprises adjusting to the increasing importance of data? Where is all the data needed to train AI models going to come from? And what are the potential legal and ethical issues that companies need to watch out for?

To explore these important questions, I moderated a panel on The Data Provenance Dilemma at the 2024 MIT Sloan CIO Symposium on May 14. “Generative AI models are highly dependent on the quality and quantity of data used in their training. But, many of these models are being trained on vast, diverse, and inconsistently documented datasets that have been raising serious concerns about the legal and ethical risks involved,” notes the brief for the panel in the Symposium agenda. “The panel will discuss the complexities of using large, diverse datasets in the training of generative AI models, the difficulties involved in understanding the provenance of the data being used, and the kinds of tools and standards needed for the responsible use of these powerful models.”

The panel included Mike Mason, chief AI officer of the technology consultancy Thoughtworks; Shayne Longpre, a doctorate candidate at the MIT Media Lab; and Robert Mahari, who’s pursuing a joint JD-PhD degree at the Harvard Law School and MIT’s Media Lab. Let me summarize the key points we discussed in the panel.

Advancing Global Trade through Open-Source, Digital Bill of Lading

“Imagine you’re a cargo owner in the year 1450,” begins “The multi-billion-dollar paper jam: Unlocking trade by digitalizing documentation,” a McKinsey article published in October of 2022. “You hand over your goods to the ship that will carry them across the world, and are presented with a bill of lading — a piece of paper stating what you’re shipping, where it comes from, and where it’s heading. Fast forward to the year 2022: The world has changed dramatically, but the bill of lading remains relatively unchanged. Today, the bill of lading process is still reliant on the physical transfer of paper records and applies to roughly 40 percent of all containerized trade transactions.”

“Current trade documentation spans many documents and processes, and is a manual, time-consuming, and resource-intensive process for all stakeholders. Documentation for a single shipment can require up to 50 sheets of paper that are exchanged with up to 30 different stakeholders. The bill of lading, issued by carriers to acknowledge receipt of cargo from the shipper, is one of the most important trade documents required for shipping.”

According to McKinsey, the bill of lading accounts for between 10 and 30 percent of total trade documentation costs; adopting an electronic bill of lading could save $6.5 billion in direct costs and enable between $30 billion and $40 billion in new global trade volume. “While the banking and aviation industries have implemented digital standards enabling automated trade systems, shipping has not matured far beyond where it was in the 1400s.”

In a recent article, “Advancing Global Trade through Open-Source Electronic Bill of Lading,” the Digital Supply Chain Institute (DSCI) explained the role of bill of lading in global supply chains. “The bill of lading (B/L) functions as an important role in global trade, providing a tangible record of cargo ownership, conditions, and delivery obligations,” according to the article. “While deeply entrenched in trade practices, the traditional paper-based bill of lading has its shortcomings: inefficiencies stemming from manual processes have led to delays, errors, and a higher susceptibility to loss and fraud.”

The Critical Value of Data to Business Success in the Emerging AI Era

“Data has become an increasingly critical component to business success,” said “Modernizing Data with Strategic Purpose,” a recently published report by the technology consultancy Thoughtworks and MIT Technology Review Insights. “The ability to make timely and informed decisions, create tailored customer experiences, and identify new opportunities has enabled the rise of today’s leading digital companies. And with the recent explosion of interest in AI, the demand for data has only increased. No matter what an organization hopes to achieve, success is impossible without ready access to high-quality data. Despite advances in technologies, extracting and transforming enterprise data into a usable asset remains a tremendous challenge for most organizations.”

The report is based on a survey conducted in early 2024 of 350 senior data and technology executives from organizations in the US, the UK, Germany, Singapore, and Australia that earn $500 million or more in annual revenue, with almost half (47%) having revenue of $10 billion or more. Eight industries are represented in the survey: financial services, energy, health care and life sciences, manufacturing, public sector, publishing and media, retail, and travel and transport.

The survey sheds light on how enterprises are adjusting to the increasing importance of data in our emerging data-centric AI era. The data-centric AI era started about 25 years ago when the explosive growth of the internet led to what’s become known as big data, that is, the availability of huge 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). All these data has enabled AI to make great strides in the 2010s with multi-layered deep learning algorithms, and more recently with foundation models, including generative AI (GenAI), large language models, and chatbots.

“As AI pushes data modernization to the top of more organizations’ agendas, it will also become increasingly important to ensure that the data strategy is closely aligned to the broader business strategy and data leaders are able to clearly articulate how data and analytics can help business units achieve their objectives,” notes the report. “This will not only ensure critical buy-in, but also put the organization in the best position to achieve its goals and lay a solid foundation for continuous improvement, evolution, and value creation.”

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.

An Open Source, Standards-Based Approach for the Future of Supply Chains

In 2018 I participated in a blockchain panel at a conference of senior supply chain executives representing a variety of industries and companies. I welcomed the opportunity to learn how these supply chain executives felt about the potential impact of blockchain technologies in complex supply chains.

At the time, I was convinced that blockchain technologies would be a major next step in the evolution of the internet. Two years earlier, for example, the World Economic Forum (WEF) had named The Blockchain as one of the Top Ten Emerging Technologies for 2016 in its annual list. The WEF report compared the blockchain to the internet, noting that “Like the Internet, the blockchain is an open, global infrastructure upon which other technologies and applications can be built.  And like the Internet, it allows people to bypass traditional intermediaries in their dealings with each other, thereby lowering or even eliminating transaction costs.”

I also believed that supply chain applications would emerge as the killer app for blockchain technologies, that is, an IT application whose value is simple to explain, its use is fairly intuitive, and the application turns out to be so useful that it helps propel the success of new technologies or products. For example, spreadsheets and word processing played a major role in the 1980s adoption of personal computers, as did e-mail and the World Wide Web in the 1990s mainstream success of the internet.

But, it hasn’t quite worked as expected, either for blockchain technologies or for their applications in supply chains. “Blockchain, the technology underpinning bitcoin and other cryptocurrencies, for years has been viewed by some companies as a way to drive industry-transforming projects, among them the tracking of assets through complex supply chains,” according to “Blockchain Fails to Gain Traction in the Enterprise,” an article published in the Wall Street Journal (WSJ). “So far that hasn’t happened.”

Health and AI

The March 30, 2024 issue of The Economist includes a special  section on Health and AI, with six articles on the topic. “Artificial intelligence (AI) is generating excitement and hyperbole everywhere, but in the field of health care it has the potential to be transformational,” said the lead article, “The AI doctor will see you … eventually.” “In Europe analysts predict that deploying AI could save hundreds of thousands of lives each year; in America, they say, it could also save money, shaving $200bn-360bn from overall annual medical spending, now $4.5trn a year (or 17% of GDP).”

Healthcare is a system of coupled systems, encompassing medical and pharmaceutical research; the delivery of healthcare to patients by a variety of practitioners, including hospitals, physicians, nurses, and pharmacists; and the insurance companies and governments that pay for healthcare. It’s a highly regulated industry, which creates major barriers for the sort of innovative start-ups that have been the engine of transformation in other sectors. In advanced economies, a handful of companies, — e.g., Cerner, Epic, Athena, — support large healthcare providers with proprietary platforms that don’t interoperate with each other, making it difficult for smaller companies and startups to develop innovative AI tools and applications as has been the case in other industries.

There’s considerable hope that AI could help us better deal with the inherent complexity of the healthcare sector. “AI systems can enhance diagnostic accuracy and disease tracking, improve the prediction of patients’ outcomes and suggest better treatments,” notes The Economist. “It can also boost efficiency in hospitals and surgeries by taking on tasks such as medical transcription and monitoring patients, and by streamlining administration. It may already be speeding the time it takes for new drugs to reach clinical trials. New tools, including generative AI, could supercharge these abilities.”

However, “although AI has been used in health care for many years, integration has been slow and the results have often been mediocre.” Among the major reasons for the slow progress are the concerns about accuracy and trust necessary to protect patients’ safety. “Improving accuracy and reducing bias in AI tools requires them to be trained on large data sets that reflect patients’ full diversity.” But, while there’s lots of available health data, the data is highly fragmented making it difficult to use properly.

An Economic Framework for Understanding AI

“Artificial intelligence (AI) systems touch the lives of virtually every American,” said “An Economic Framework for Understanding Artificial Intelligence,” an overview of the economic potential of AI which was recently released by the White House Council of Economic Advisors (CEA) as part of their “2024 Economic Report of the President.” “In recent years, AI systems have advanced rapidly as recent developments in computing, data availability, and machine learning models have simultaneously come together to produce rapid improvements.”

“Still, much remains unknown,” the CEA added. “While AI’s capabilities will depend in part on the technology itself, its effects will be shaped by economic, regulatory, and social pressures. How society deploys this technology and what technology-specific guardrails are implemented will be critical factors in determining both the breadth and magnitude of its effects.”

“An economic framework, combined with a basic understanding of AI technology, allows us to make predictions about when, how, and why AI may be adopted. While such a framework can also tell us what broader effects AI adoption may have, applying economic insights to an evolving and proliferating technology like AI is especially challenging. However, it is also especially valuable, because decisions made at the onset of a new technology have a greater influence on its eventual impact.”

The AI Economic Framework is a rather long and comprehensive document. Let me briefly discuss a few of the its key points.

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.”

A Deteroriating Global Risk Outlook

“Last year’s Global Risks Report warned of a world that would not easily rebound from continued shocks,” wrote Saadia Zahidi, managing director of the World Economic Forum (WEF), in the Preface of The Global Risks Report 2024. “As 2024 begins, the 19th edition of the report is set against a backdrop of rapidly accelerating technological change and economic uncertainty, as the world is plagued by a duo of dangerous crises: climate and conflict.”

“Underlying geopolitical tensions combined with the eruption of active hostilities in multiple regions is contributing to an unstable global order characterized by polarizing narratives, eroding trust and insecurity,” she added. “At the same time, countries are grappling with the impacts of record-breaking extreme weather, as climate-change adaptation efforts and resources fall short of the type, scale and intensity of climate-related events already taking place.”

The Global Risks Report 2024 is based on two complementary surveys: the Global Risks Perception Survey (GRPS), the WEF’s source of original risk data, which includes the insights and opinions of over 1,500 leaders from academia, business, government, the international community and civil society; and the Executive Opinion Survey (EOS) of over 11,000 leaders from 113 countries who were asked to identify risks that pose the most severe threat to each of their countries over the next two years. In addition, the report includes insights from over 200 risk specialists.

The WEF report analyzed global risks and presented its findings along three distinct timeframes:

• The current term are the risks most likely to present a material crisis on a global scale in 2024.
• The short term considers global risks which are likely to play out over the next two years.
• The long term considers global risks that are likely to be most severe over the next ten years.

The Case for Open Healthcare Systems

As we’ve learned since the advent of the Industrial Revolution, major technological advances have the potential to radically transform a whole industry, but realizing that potential generally requires large intangible investments and a fundamental rethinking the industry being transformed. So, it’s not surprising that there’s much hope and excitement that the advent of AI as a major 21st century technological advance might, over time, help to radically transform our highly complex and expensive healthcare industry. 

Healthcare is a system of coupled systems, encompassing medical and pharmaceutical research; the delivery of healthcare to patients by a variety of practitioners, including hospitals, physicians, nurses, and pharmacists; and the insurance companies and governments that pay for healthcare. Healthcare costs have continued to rise across the world, especially in the US. Total healthcare spending in 2023 was approximately $4.7 trillion, accounting for 18% of US GDP, and is projected to grow at an average of 5.5% per year to over $7 trillion in 2031, roughly 20% of US GDP.

The potential evolution of healthcare brings to mind my personal experience in the information technology (IT) industry for the past 50 years.

The Uncertain Impact of AI on Economies and Societies

“Artificial intelligence (AI) promises to boost productivity and growth, but its impact on economies and societies is uncertain, varying by job roles and sectors, with the potential to amplify disparities,” said “GenAI: Artificial Intelligence and the Future of Work,” a recently published report by the International Monetary Fund (IMF). “As a positive productivity shock, AI will expand economies’ production frontiers and will lead to reallocations between labor and capital while triggering potentially profound changes in many jobs and sectors. AI offers unprecedented opportunities for solving complex problems and improving the accuracy of predictions, enhancing decision-making, boosting economic growth, and improving lives. However, precisely because of its vast and flexible applicability in numerous domains, the implications for economies and societies are uncertain.”

“The impact of AI is also likely to differ significantly across countries at different levels of development or with different economic structures,” adds the IMF report. “Advanced economies, with their mature industries and service-driven economies, typically have a higher concentration of jobs in sectors that require complex cognitive tasks. These economies are therefore both more susceptible to, yet better positioned to benefit from, AI innovations.”

“Conversely, emerging market and developing economies, often still reliant on manual labor and traditional industries, may initially face fewer AI-induced disruptions. However, these economies may also miss out on early AI-driven productivity gains, given their lack of infrastructure and a skilled workforce. Over time, the AI divide could exacerbate existing economic disparities, with advanced economies harnessing AI for competitive advantage while emerging market and developing economies grapple with integrating AI into their growth models.”

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.

The Potential Impact of Open Source Generative AI in the Business World

“Generative AI, commonly referred to as GenAI, stands at the forefront of a technological revolution, profoundly altering diverse sectors by synthesizing vast amounts of data and generating new outputs,” said the “2023 Open Source Generative AI Survey Report,” published by the Linux Foundation (LF) in December of 2023. “From creating intricate artworks and composing music to designing novel pharmaceutical compounds and simulating realistic human language, the potential applications of GenAI are vast and transformative.”

“The open source approach, rooted in principles of transparency, collaboration, and shared innovation, holds transformative potential for the advancement of GenAI technologies. By democratizing access to AI algorithms and datasets, open source initiatives allow a broad and diverse pool of developers to contribute to, refine, and critique GenAI systems. This collective intelligence accelerates the pace of innovation and uncovers and rectifies biases or vulnerabilities that might otherwise go unnoticed in closed development environments.”

To help understand the potential impact of open source GenAI on the market, LF Research and LF AI & Data launched a worldwide survey. The survey explored the current state of GenAI technologies in companies, including models, databases, applications, and frameworks.

A Revolutionary Technology Is Not the Same As a Technological Revolution

“Artificial intelligence is almost certainly revolutionary in the sense that it will spawn new technology platforms, transform or eliminate many industries, and create new ones,” wrote British-Venezuelan economic historian Carlota Perez in a recent article, “What Is AI’s Place in History?” “But it must be understood as belonging to a larger, more mature technological revolution that began a half-century ago.”

 In her influential 2002 book, “Technological Revolutions and Financial Capital,” Perez wrote that technological revolutions have long been engines of growth, rejuvenating and transforming economies and societies. Since the advent of the Industrial Revolution, we’ve had a major technological revolution approximately every 60 years. First was the age of machines and factories starting in Britain in the 1770s. It was followed by the age of steam, coal and iron in the1830s; electricity, steel, and heavy engineering in the 1870s;  automobiles, oil and mass production in the 1900s; and the information and communications technology (ICT) and digital revolution in the 1970.

Technological revolutions encompass two different periods, each lasting 20 to 30 years. The installation period is the time of creative destruction, when novel exciting technologies emerge on the market, entrepreneurs launch many new startups based on these promising technologies, and venture capitalists encourage experimentation with new business models and money-making schemes. Inevitably, these speculative behaviors lead to a financial bubble, which eventually crashes and results in a period of economic stagnation.

After the crash, comes the deployment period, a time of institutional recomposition. The now well accepted technologies become the norm. Infrastructures and industries are better defined and more stable, and production capital drives long-term growth and expansion, spreading and multiplying the successful business models. New paradigms emerge for guiding innovation. Over time, these new paradigms transform the economy and everything around it, as well as re-shaping social behavior and the institutions of society.

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.   

Open Source AI: Opportunities and Challenges

“Since the 1980s, open source has grown from a grassroots movement to a vital driver of technological and societal innovation,” said “Standing Together on Shared Challenges,” a report published by Linux Foundation Research in December of 2023. “The idea of making software source code freely available for anyone to view, modify, and distribute comprehensively transformed the global software industry. But it also served as a powerful new model for collaboration and innovation in other domains.”

The report is based on the Open Source Congress that was convened by LF Research in July of 2023 in Geneva to discuss the key challenges facing the open source community, such as safeguarding critical open source infrastructures and ensuring that new regulations are compatible with open source principles and practices. In addition, the Open Source Congress explored the opportunities for open source collaborations on important new topics, in particular, artificial intelligence. Given my interest in “How the Original Promises of the Open Source Movement Now Apply to AI Systems,” I’ll focus my discussions below on the report’s section on AI.

“Does AI Change Everything? What is Open?” is the theme of the report’s AI module. “For the OSS community, AI presents an array of opportunities and challenges. The community leaders assembled in Geneva discussed the need to align on a definition of open AI, as well as on the challenges created by AI-enabled code generators for licensing, security, and intellectual property. Finally, Congress participants reflected on the broader societal impacts of AI and the role of the open source community in addressing issues such as bias, privacy, and existential threats to humanity.”

Technologies that Will Drive Business Transformation Over the Next Two Years

In early December, Deloitte released “Tech Trends 2024,” its 15th annual report that “chronicles the growing business imperative to balance today’s tactical needs with tomorrow’s strategic ambitions.” The report is focused on six major technology trends that are likely to be critical to business transformation over the next 18-24 months. Let me discuss each of these trends, starting with:

Genie out of the bottle: Generative AI as growth catalyst.

Not surprisingly, generative AI and its potential impact on business and the world is Deloitte’s overriding trend. “Philosophers have long debated whether machines are capable of thought, but generative AI makes the question moot,” said the report. “Regardless of whether it possesses intelligence in the philosophical sense, it does in a practical sense, creating the opportunity for huge productivity and efficiency gains in enterprise settings. Now that machines can behave, comprehend, and narrate like humans, the question becomes how this will impact business and the world broadly.”

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 Coming AI Revolution

“The Coming AI Economic Revolution: Can Artificial Intelligence Reverse the Productivity Slowdown?” was recently published in Foreign Affairs by James Manyika and Michael Spence, two authors I’ve long admired. James Manyika is senior VP of research, technology and society at Google, after serving  as chairman and director of the McKinsey Global Institute from 2009 to 2022. Michael Spence, a co-recipient of the 2001 Nobel Prize in Economics, is professor in economics and business at NYU’s Stern School of Business, and was previously professor of management and dean of the Stanford Graduate School of Business.

“By the beginning of the next decade, the shift to AI could become a leading driver of global prosperity,” wrote the authors. “The prospective gains to the world economy derive from the rapid advances in AI — now further expanded by generative AI, or AI that can create new content, and its potential applications in just about every aspect of human and economic activity. If these innovations can be harnessed, AI could reverse the long-term declines in productivity growth that many advanced economies now face.”

“This economic revolution will not happen on its own,” they added. Unleashing an AI-powered economy will require a new policy framework that fosters AI’s most productive uses and enhance human potential and ingenuity. “These policies must promote technologies that augment human capabilities rather than simply replace them; encourage AI’s widest possible implementation, both within and across different sectors, especially in areas that tend to have lower productivity; and ensure that firms and sectors undergo necessary process and organizational changes and innovations to effectively capitalize on AI’s potential.”

Taking the Pulse of AI in Medicine

“AI explodes: Taking the Pulse of artificial intelligence in medicine,” is the title of the latest issue of the Stanford Medicine Magazine. The issue includes a number of articles that explore the promise of AI in medicine, ranging from What’s driving this tumor, — which discusses the efforts to stymie breast cancer through gene testing and AI, — to Adding ethics to the mix when developing health care AI, — which deals with the potential hazards posed by algorithms developed by AI experts with no formal training in the ethical standards for treating patients.

“Imagine a future where your doctor has an AI medical assistant by their side – distilling, in seconds, a world’s worth of medical research into a personalized treatment plan for you,” wrote Dr. Lloyd Minor, Dean of the Stanford University School of Medicine in the issue’s Letter from the Dean. “What if, at the click of a button, a researcher could design a custom molecule with the potential to treat a previously untreatable disease? With artificial intelligence’s rapid emergence, we are barreling toward this reality. Academic medical centers around the world, including Stanford Medicine, have begun investigating how AI, including large language models such as ChatGPT, can help us improve patient care, reduce clinician workload, better understand complex biological systems and accelerate drug discovery.”

As I discussed in a recent blog, “The Promise of Generative AI in Healthcare,” there’s a lot of excitement that AI might help us address our highly complex healthcare industry. Healthcare is a system of coupled systems, encompassing medical and pharmaceutical research; the delivery of healthcare to patients by a variety of practitioners, including hospitals, physicians, nurses, and pharmacists; and the insurance companies and governments that pay for healthcare.

According to Wikipedia, the healthcare industry comprises over 10% of the GDP of most developed countries. “The per capita expenditure on health and pharmaceuticals in OECD countries has steadily grown from a couple of hundred in the 1970s to an average of US$4'000 per year in current purchasing power parities.” In 2022, US healthcare spending “grew 4.1% to $4.5 trillion in 2022, or $13,493 per person, and accounted for 17.3% of Gross Domestic Product (GDP).”

In addition to its inherent complexity, healthcare has several attributes that make the successful deployment of new technologies significantly more challenging than in other industries. HIPPA, for example, protects the privacy of individuals in the US, but it also restricts the data sharing that’s essential to achieving many of the benefits of generative AI. And because the stakes in healthcare are too high to tolerate flaws that could harm patients, it’s harder to introduce new technologies and applications compared to most other industries.

The Case for an AI-Powered Productivity Boom

“Large language models such as ChatGPT are emerging as powerful tools that not only make workers more productive but also increase the rate of innovation, laying the foundation for a significant acceleration in economic growth,” wrote economists Martin Neal Bailey, Erik Brynjolfsson, and Anton Korinek in “Machines of mind: The case for an AI-powered productivity boom,” a Brookings Institutions report. “However, official statistics will only partially capture the boost in productivity because the output of knowledge workers is difficult to measure. The rapid advances can have great benefits but may also lead to significant risks, so it is crucial to ensure that we steer progress in a direction that benefits all of society.”

The authors noted that generative AI systems are machines of the mind, increasingly capable of generating coherent and contextually appropriate text, images, videos, and audio, in a wide range of information-based tasks. Millions of knowledge workers, — from doctors and lawyers to managers and sales people, — will experience ground-breaking improvements in their productivity within a few years.

The Promise of Generative AI in Healthcare

“Since they became publicly available in late 2022, generative artificial intelligence (genAI) tools such as ChatGPT have elicited enormous enthusiasm, as well as concern, in all sectors of the economy,” said “Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?,” a recent article in the Journal of the American Medical Association (JAMA) by Robert Wachter, — professor and chairman of the Department of Medicine at the University of California, San Francisco (UCSF), and Erik Brynjolfsson, — professor and director of the Digital Economy Lab at Stanford University.

“But the potential impact of genAI in health care seems particularly noteworthy. In a field in which an estimated 30% of the $4.3 trillion spent each year in the US adds little to no value, in which many tens of thousands of people die yearly from preventable mistakes, and in which access to care is fragmented and inequities are commonplace, it is natural to be enthusiastic about the potential for genAI to improve quality, efficiency, equity, and patient experience.”

A few weeks ago I posted an entry in my blog on “AI and the Future of Healthcare Systems,” in which I discussed the huge challenges posed by the inherent complexity of the healthcare sector of the economy. Healthcare is a system of coupled systems, comprised of medical and pharmaceutical research; the delivery of healthcare to patients by a variety of practitioners, including hospitals, physicians, nurses, and pharmacists; and the insurance companies and governments that pay for healthcare. The convoluted interactions of these various  domains, — each of which is quite large and complex in its own right, — is one of the main reasons why healthcare systems are so incredibly complex.

Given the central role of data in AI, electronic health records (EHRs) play a critical role in the development of AI systems capable of dealing with the complex real-world challenges in the healthcare industry. As explained by the Centers for Medicare & Medicaid Services (CMS): “An Electronic Health Record (EHR) is an electronic version of a patients medical history, that is maintained by the provider over time, and may include all of the key administrative clinical data relevant to that persons care under a particular provider, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports.”

The World Ahead 2024: The World Is Changing at Mind-Boggling Speed

In mid-November, The Economist published “The World Ahead 2024,” its 38th annual issue that examines the trends and events that will likely shape the coming year. Last year, the overriding message from “The World Ahead 2023” was that after two years in which the pandemic was the dominating force shaping the immediate future, the war in Ukraine would be the main driver in 2023, causing the world to have to grapple with the conflict's impact on a series of unpredictable issues, incluging geopolitics, security, inflation, energy markets, and China’s post-pandemic path.

“Life comes at you fast,” wrote World Ahead editor Tom Sandage  in his introduction to the  “The World Ahead 2024.” “Whether it’s the upsurge in armed conflict, the redrawing of the global energy-resources map or rapid progress in artificial intelligence (AI), the world is changing at mind-boggling speed. From the situation in the Middle East to the adoption of electric vehicles to the treatment of obesity, things look very different from the way they did just a year or two ago.”

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.

Working With AI’s Human-Like Alien Intelligence

“When OpenAI unleashed its humanlike ChatGPT software on the world last year, one thing was clear: These AI systems are coming for our jobs. But don’t write off the humans just yet.” said “The New Jobs for Humans in the AI Era,” a WSJ article published on October 5. As has been the case with major technologies over the past two centuries, — e.g., steam power, electricity, the internet,  — AI will both threaten and lead to the creation of new jobs. While still in the early stages, “AI is already creating new opportunities,” said the article, mentioning a few of those new opportunities.

I found two of those new jobs particularly intriguing: prompt engineer and AI psychotherapist. Generative AI and large language models (LLM) are a new kind of human-like alien intelligence that even the people building them don’t really understand how they work. Researchers are attempting to unlock the AI black box in hopes of understanding how to best work with these powerful, alien technologies.

Let me discuss each of these two jobs.

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.