Irving Wladawsky-Berger

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 Foundation Model Transparency Index

“Foundation models are the epicenter of artificial intelligence (AI) as AI begins to shape how the economy and society function,” begins “The Foundation Model Transparency Index v1.1 (FMTI v1.1),” a May, 2024 paper by a group of researchers from Stanford, MIT, and Princeton under the auspices of Stanford’s Center for Research on Foundation Models (CRFM). “For such a high-impact technology, transparency is vital to facilitate accountability, competition, and collective understanding. As an illustrative example, the current lack of transparency regarding the data used to build foundation models makes it difficult to assess what copyrighted information is used to train foundation models. Governments around the world are intervening to increase transparency.”

In October of 2023, CRFM introduced the Foundation Model Transparency Index (FMTI), a scoring system designed by a multidisciplinary research team to score the transparency of foundation models in order to promote responsible business practices and greater public accountability. The FMTI features three top-level domains, which in turn aggregate 23 subdomains and 100 transparency indicators:

• Upstream domain — the 32 indicators and 6 subdomains used to build the model, including data size and composition, human labor used, training methods, hardware and energy used, and environmental impacts;
  
• Model domain — the 33 indicators and 8 subdomains that identify the properties and function of the foundation model itself, including size, capabilities, limitations, risks, and trustworthiness; and
  
• Downstream domain — the 35 indicators and 9 subdomains that identify how the model is used, including release process, distribution channels, user interface, data protection, feedback mechanisms, and documentation.
  

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?

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.”

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.

What’s the State of Income Inequality Around the World?

Over the past few decades, technology advances like the smartphone and wireless internet have significantly improved the quality of life of billions around the world. The economic development brought about by globalization led to the Gintic decline of extreme poverty in China, East and South Asia, and Latin America. Globalization was supposed to boost prosperity, as nations were becoming more economically interdependent, but it’s also led to major dislocations in labor markets, benefiting workers in low-cost developing nations while severely impacting workers in the US and other advanced economies. The promise of technological progress and globalization as drivers of growth and prosperity has been a reality for many, but it’s also created winners and losers within nations, leading to rising economic inequality.

“Across the globe, but especially in the wealthy economies of the West, the gap between the rich and the rest has widened year after year and become a chasm, spreading anxiety, stoking resentment, and roiling politics,” wrote economist Branko Milanovic in “The Great Convergence: Global Equality and Its Discontents,” a recent article in Foreign Affairs. “Globalization, the argument goes, may have enriched certain elites, but it hurt many other people, ravaging one-time industrial heartlands and making people susceptible to populist politics.”

“There is much that is true about such narratives — if you look only at each country on its own,” added Milanovic. “Zoom out beyond the level of the nation-state to the entire globe, and the picture looks different. Global inequality refers to “the income disparity between all citizens of the world at a given time, adjusted for the differences in prices between countries. … At that scale, the story of inequality in the twenty-first century is the reverse: the world is growing more equal than it has been for over 100 years.”

Inequality is commonly measured by the Gini coefficient, a statistical measure of income or wealth inequality within a group, a nation, or the whole world that was first developed over 100 hundred years ago. A Gini score of 0 means perfect equality, where all income and wealth are the same for all members of the group, while a score of 100 means maximal inequality, where a single individual has all the income and all others have none.

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.   

The Origins and Content of New Work

“Broadly speaking, many — perhaps most — workplace technologies are designed to save labor,” wrote MIT economist David Autor in his 2015 paper, “Why Are There Still So Many Jobs? The History and Future of Workplace Automation.” “Given that these technologies demonstrably succeed in their labor saving objective and, moreover, that we invent many more labor-saving technologies all the time, should we not be somewhat surprised that technological change hasn’t already wiped out employment for the vast majority of workers?, asked professor Autor.

The answer, he noted, underlines a fundamental economic reality that’s frequently overlooked: “tasks that cannot be substituted by automation are generally complemented by it.” Automation does indeed substitute for labor. However, automation also complements labor, raising economic outputs in ways that often lead to higher demand for workers. Articles often overstate the extent of machine substitution for human labor “and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor.”

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

The advent of automated teller machines (ATMs) in the 1970s is a case in point. By 2010, there were approximately 400,000 ATMs in the US.  But, not only were bank tellers not eliminated, but their numbers actually rose modestly from 500,000 in 1980 to 550,000 in 2010, driven by two major forces. First, as a result of IT advances that  reduced the costs of operating a bank branch, the number of urban bank branches increased significantly by 2010. Second, while most of the routine cash-handling tasks of bank employees were automated by ATM technologies, IT-based innovations enabled a broader range of bank employees to become involved in relationship banking, providing a variety of relationship-based services to customers, including credit cards, personal loans, mortgages, and investment options.  

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 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.”

The Dueling Visions for the Soul of AI

On November 17, OpenAI's board of directors ousted chief executive and co-founder Sam Altman, saying in a statement that “The board no longer has confidence in his ability to continue leading OpenAI.” Then on November 22, after an intense pressure campaign from employees and investors, OpenAI announced that it had reached “an agreement in principle for Sam to return to OpenAI as CEO” with a reconstituted board.

What led to this chaotic governance drama over approximately 107 hours from November 17 to 22?

“Many of the people at the company seem simultaneously motivated by the scientist’s desire to discover, the capitalist’s desire to ship product and the do-gooder’s desire to do this all safely,” wrote NY Times columnist David Brooks In “The Fight for the Soul of A.I.,” where he discussed the conflicting issues that led to OpenAI’s chaotic 4.5 days. “Is this fruitful contradiction sustainable? Can one organization, or one person, maintain the brain of a scientist, the drive of a capitalist and the cautious heart of a regulatory agency?”

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.”

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.”

Winners and Losers in the Emerging Age of AI

“The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone,” wrote Microsoft co-founder Bill Gates in “The Age of AI has begun,” a March, 2023 blog. “It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.”

Over the past two centuries, major transformative technologies like AI have led to both winners and losers. Kodak, Blockbuster, Digital, Wang, and BlackBerry are among the corporate giants who were once leaders in their fields, but were unable to survive the disruptive technologies of the past few decades. Amazon, Alphabet/Google, Microsoft, Facebook/Meta, and Apple have been among the big winners in the internet-based digital economy.

A recent issue of The Economist explored this question. We may all assume that given the continuing pace of technology and business change, no company is safe from disruption. But, in “America’s corporate giants are getting harder to topple,” The Economist questioned these assumptions. “America Inc has experienced surprisingly little competitive disruption during the internet age. Incumbents appear to have become more secure, not less.”

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.

How the Original Promises of the Open Source Movement Now Apply to AI Systems

“AI systems don’t operate like traditional software – they require distinct development processes and rely on specialized and costly resources currently pooled in the hands of a few large tech companies,” wrote David Gray Widder, Meredith Whittaker, and Sarah Myers West in a recent research paper, “Open (for Business): Big Tech, Concentrated Power, and the Political Economy of Open AI” (Open AI). “Even so, many of the original promises of the open source movement, which were made in reference to open source software, are now being projected onto ‘open’ AI. From the promise that open source could democratize software development, to the view that many eyes on open code could ensure it did what it said it did and was free of vulnerabilities, to the perspective that open source levels the playing field, allowing the most innovative to triumph. Open source software did many of these things, to varying degrees. But ‘open’ AI is a different story.”

I’ve been personally involved with open source, open standards, and related initiatives for the past few decades. In the mid-late 1990s, I led IBM’s Internet Division, and let's remember that a major part of the explosive growth of the internet and World Wide Web was due to the widespread availability of open source implementation of their key open standards. In the early 2000s I led IBM’s Linux initiative, which played a leading role in organizing a consortium of companies to support the development of Linux which later became the Linux Foundation (LF). And, for the past two years, I’ve been a member of the Advisory Board of the Linux Foundation Research division.

In trying to understand how the original promises of the open source movement now apply to this new generation of AI systems, I found the Open AI paper quite helpful. Let me summarize some the key points discussed in the paper.

Reconciling a Puzzling Paradox: Slow Productivity Growth Despite Breakneck Technology Advances

“One of the most enduring macroeconomic puzzles of the last several decades is the pervasive slowdown in productivity growth across industrialized nations, despite breakneck advances in information and communication technologies (ICT) and electronics,” wrote economists Daron Acemoglu, David Autor, and Christina Patterson in their recent paper Bottlenecks: Sectoral Imbalance and the US Productivity Paradox.

Over the past three decades, ICT and electronics patents and productivity growth have been moving in starkly different directions. The total number of patents granted between 1990 and 2010 grew from 99,000 to 208,000, with ICT and electronics patents accounting for 80% of the increase. And, while total factor productivity (TFP), — generally considered the primary contributor to GDP growth, —grew by 1.7% in the US between 1997 and 2005, its growth has been minimal, just around 0.4% since 2005.

“How can these facts be reconciled?,” ask the authors. Are we in a “new age of abundance” driven by our increasingly smart machines? Or have we entered “an age of slower growth because the most-impactful technologies have already been developed and exploited?”

The Critical Value of Human-Created Knowledge to the Future of Generative AI

“In early 2021, a Wikipedia editor peered into the future and saw what looked like a funnel cloud on the horizon: the rise of GPT-3, a precursor to the new chatbots from OpenAI,” wrote author and journalist Jon Gertner in “Wikipedia’s Moment of Truth,” a recent NY Times Magazine article. “When this editor — a prolific Wikipedian who goes by the handle Barkeep49 on the site — gave the new technology a try, he could see that it was untrustworthy. The bot would readily mix fictional elements (a false name, a false academic citation) into otherwise factual and coherent answers. But he had no doubts about its potential.”

The article caught my attention because I’ve long been relying on Wikipedia as my go-to website for looking up topics I want to learn more about, as well as using links to Wikipedia articles as references in the weekly blogs I’ve been posting since 2005.

Over the past decade, Wikipedia has become much more than an encyclopedia, — “a kind of factual netting that holds the whole digital world together.” Google, Bing, Siri, Alexa, and other search engines and digital assistants often rely on Wikipedia for the information needed to answer users’ questions. More recently, Wikipedia has been one of the largest sources of the data, — currently estimated at around 3% to 5%, — for training Large Language Models (LLMs) and related chatbots. Wikipedia has played a major role in the digital world because its large amounts of data are free, easily accessible, high quality, and well-curated.

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%.

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.

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.

The Era of AI Is Here: Why the Panic?

“The era of Artificial Intelligence is here, and boy are people freaking out,” wrote technologist, entrepreneur and VC investor Marc Andreessen in the opening sentence of his article, Why AI Will Save the World  posted in his personal website. The article argues that not only will AI not destroy the world, but that it might in fact help save it.

While a computer sciences student at the University of Illinois, Andreessen was the leader of the team that developed the Mosaic browser in the early 1990s, the first easy-to-use graphical web browser that could be ported to a wide range of computers. Soon after its release in 1993, millions started using Mosaic to access the fast growing World Wide Web. After getting his degree in 1993, Andreessen moved to Silicon Valley and became a co-founder of Netscape, one of the first companies focused on bringing the fast growing World Wide Web to the commercial world.

The Mosaic browser and Netscape played a major role in the explosive growth of the internet in the 1990s. To give us a sense of the world-changing transformations we might expect from generative AI, a recent essay in The Economist cited the browser, along with the printing press as two major historical analogues to generative AI. Given his accomplishments, Andreessen is clearly someone whose opinions we should pay attention to when discussing the potential impact of generative AI, large language models, chatbots, and related AI technologies.

His article is organized in a number of sections. The first two sections give a detailed explanation of why he believes that AI can make everything we care about better, and why in contrast to his positive view, so many people are panicking. He then systematically discusses and refutes each of the major AI risks that are causing the fear and panic. He adds that the real risk is not aggressively pursuing AI in the US and the West in general, and proposes a simple plan on what’s to be done. 

Let me summarize a few of the key points in each section.

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 World Is Facing Serious Global Risks

“The first years of this decade have heralded a particularly disruptive period in human history,” wrote the World Economic Forum (WEF) in its 18th annual edition of the “Global Risks Report 2023.”  “The return to a ‘new normal’ following the COVID-19 pandemic was quickly disrupted by the outbreak of war in Ukraine, ushering in a fresh series of crises in food and energy – triggering problems that decades of progress had sought to solve.” As we stand on the edge of a low-growth and low-cooperation era, “the world faces a set of risks that feel both wholly new and eerily familiar.”

The WEF defines global risk as “the possibility of the occurrence of an event or condition which, if it occurs, would negatively impact a significant proportion of global GDP, population or natural resources.”

The Global Risks Report 2023 is based on two complementary surveys: the Global Risks Perception Survey (GRPS), the WEF source of original risk data, based on the the insights and opinions of over 1,200 experts from academia, business, government, the international community and civil society; and the Executive Opinion Survey (EOS) of over 12,000 business leaders in 121 countries to help identify the risks that pose the biggest threat in each of their countries over the next few yeas.

The report presents its key findings along two distinct timeframes. The short term considers global risks which are already unfolding and which are likely to play out over the next two years. The long term considers global risks that that are likely to be most severe over the next ten years. Let me summarize the report’s key findings.

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.

Are Open AI Models Safe?

A recent NY Times article, “In Battle Over A.I., Meta Decides to Give Away Its Crown Jewels,” reported that Meta, — the technology giant that owns Facebook, Instagram, and WhatsApp, — made the decision to open source LLaMA, its state-of-the-art large language model (LLM), making the code available to academics, government researchers and others once they’d been properly vetted. Meta’s actions are quite different from those of Google and OpenAI, its chief AI rivals, who’ve been keeping the software underpinning their AI systems proprietary.

“Driven by its founder and chief executive, Mark Zuckerberg, Meta believes that the smartest thing to do is to share its underlying A.I. engines as a way to spread its influence and ultimately move faster toward the future,” said the article. Meta’s chief AI scientist, NYU professor Yann LeCun noted in a recent interview that the growing secrecy of Google and OpenAI is a “huge mistake,” arguing that consumers and governments will refuse to embrace AI if it’s under the control of a couple of powerful American companies. LeCun, along with Geoffrey Hinton and Yoshua Benjio, received the 2018 Turing Award, — often considered the “Nobel Prize of Computing,” — for their pioneering work on deep learning.

Meta’s open source approach to AI isn’t novel. After all, the infrastructures underlying the Internet and World Wide Web were all built on open source software, as has the widely used Linux operating system. And, in September, 2022, Meta contributed its PyTorch machine learning framework to the Linux Foundation to further accelerate the development and accessibility of the technology.

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.”

AI Need Not Be World-Ending to Be World-Changing.

“How to worry wisely about artificial intelligence” was the overall theme of the April 22 issue of The Economist, with several articles on the subject. “Rapid progress in AI is arousing fear as well as excitement. How worried should you be?,” said the issue’s lead article. “In particular, new ‘large language models’ (LLMs)—the sort that powers Chatgpt, a chatbot made by Openai, a startup—have surprised even their creators with their unexpected talents as they have been scaled up.”

“Proponents of AI argue for its potential to solve big problems by developing new drugs, designing new materials to help fight climate change, or untangling the complexities of fusion power. To others, the fact that AIs’ capabilities are already outrunning their creators’ understanding risks bringing to life the science-fiction disaster scenario of the machine that outsmarts its inventor, often with fatal consequences.”

The Economist issue included an essay, “How AI could change computing, culture and the course of history,” by European business editor Ludwig Siegele in close collaboration with briefings editor Oliver Morton. I found the essay quite interesting, and recognized that it had been written by Siegele, with whom I’ve long been discussing advanced technologies given his previous positions as technology correspondent for The Economist. Let me summarize the essay’s key points.

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.”

The Potential Impact of Large Language Models on Jobs

Throughout the Industrial Revolution there were periodic panics about the impact of automation on jobs, going back to the Luddites, - textile workers who in the 1810s smashed the new machines that were threatening their jobs. But each time those fears arose in the past, technology advances ended up creating more jobs than they destroyed.

Automation fears have understandbly accelerated in recent years, as our increasingly smart machines have been applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans. Over the past decade, powerful AI systems have matched or surpassed human levels of performance in a number of tasks such as image and speech recognition, skin cancer classification, breast cancer detection, and highly complex games like Go. More recently, large language models (LLMs) and chatbots like ChatGPT are taking AI-based automation to a whole new level.

 “OpenAI’s ChatGPT is the latest advance in a steady march of innovations that have offered the potential to transform many occupations and wipe out others, sometimes in tandem,” wrote journalists Lydia DePillis and Steve Lohr in “Tinkering With ChatGPT, Workers Wonder: Will This Take My Job?,” a recent NY Times article. “It is too early to tally the enabled and the endangered, or to gauge the overall impact on labor demand and productivity. But it seems clear that artificial intelligence will impinge on work in different ways than previous waves of technology.” In particular, AI is now “confronting white-collar professionals more directly than ever. It could make them more productive — or obsolete.”

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.