Irving Wladawsky-Berger

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

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

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

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

Is College Still a Good Investment?

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

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

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

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

How Can AI Accelerate the Pace of Scientific Discovery?

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

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

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

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.”

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.

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?”

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 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%.

Productivity Gains of Generative AI for Software Developers

In June of 2023, McKinsey published “The Economic Potential of Generative AI: The Next Productivity Frontier.” Based on its analysis of 63 business use cases, and estimates of its effect on the productivity of the global workforce, McKinsey’s study projected that generative AI could add an additional $6.1 trillion to $7.9 trillion annually, boosting the overall impact of artificial intelligence on the global economy by an annual $17.1 trillion to $25.6 trillion.

The McKinsey study found that while generative AI use cases could have an impact on most enterprise business functions, four in particularly could account for approximately 75% of the total annual value from generative AI use cases: customer operations, marketing and sales, research and development, and software engineering.

Digital technologies, including advanced analytics and machine learning algorithms, have been applied to the first three functions for the past few decades, so it’s easier to understand how GenAI would build on previous work as well as give rise to innovative new advances. But that hasn’t quite been the case with software engineering.

In a recent article, “Quantifying the Productivity Gains of Generative AI for Developers,” IBM Fellow Jerry Cuomo nicely explained the role of generative AI in enhancing the productivity of software developers by examining the potential benefits for developers of varying experience levels, from novice to experts. Over the years, Cuomo has played a major role in a number of major software advances, from IBM’s WebSphere Application Server to open source Hyperledger blockchain technologies. He’s currently CTO in IBM's Consulting business unit where he’s responsible for AI-based automation technologies and strategy.

“The productivity gains enabled by the introduction of generative AI allow for a shift to value and create opportunities for increased innovation, improved quality, enhanced customer experience, and faster time-to-market,” wrote Cuomo. “Specifically, we estimate that generative AI can lead to a 15–20% increase in the number of new products or features developed, a 10–15% reduction in the number of bugs found in production, a 5–10% increase in customer retention and loyalty, and a 10–15% reduction in time-to-market for new products or features.”

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

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

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

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

Why Transformative AI Is So Hard to Achieve

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

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

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

What’s the Likely Long Term Evolution of AI?

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

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

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

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

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

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

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

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

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

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

Risks and Opportunities in an Increasingly Complex AI World

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

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

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

A recording of our panel can be found here.

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.

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

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

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

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

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

The Economic Potential of Generative AI

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

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

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

Machine Learning Has an AI Problem

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

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

Looming Challenges Make US Productivity Growth an Imperative

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

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

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

The 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.”

Its Global Risks Report 2023 is based on two complementary surveys: the Global Risks Perception Survey (GRPS), the source WEF’s 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 Current State of AI: A Tale of Two Worlds

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

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

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

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

Let me summarize the report’s key findings.

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

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

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

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

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

Why Men and Boys Are Falling Behind

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

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

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

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.”

How to Leverage AI for Substantial Business Value

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

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

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

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

The 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.”

Fostering the Development of Interoperable, Open Source Digital Wallets

In September of 2022, the Linux Foundation (LF) announced its intent to launch the Open Wallet Foundation (OWF), — a new collaborative effort to develop open source software that anyone can use to build interoperable wallets supporting a wide range of wallet use cases. After working with a number of enterprises, nonprofits, academic institutions and government entities to organize the effort, the Linux Foundation Europe announced the official formation of the OWF on February 23, 2023.

“The OWF will not publish a wallet itself, nor offer credentials or create new standards,” said the announcement. “Instead, its open source software engine aims to become the core that other organizations and companies leverage to develop their own digital wallets. The wallets will seek feature parity with the best available wallets and interoperability with major cross-border projects such as the EU’s Digital Identity Wallet.”

At the same time, the OWF in partnership with LF Research released a new report, “Why the World Needs an Open Source Digital Wallet Right Now.”

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.


Large Language Models: A Cognitive and Neuroscience Perspective

In 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. These AI breakthroughs have been based on increasingly powerful and inexpensive computing technologies, innovative deep learning (DL) algorithms, and huge amounts of data on almost any subject. More recently, the advent of large language models (LLMs) are taking AI to the next level. And, for many technologists like me, LLMs and their associated chatbots have introduced us to the fascinating world of human language and cognition.

I recently learned the difference between form, communicative intent, meaning, and understanding from “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data,” a 2020 paper by linguistic professors Emily Bender and Alexander Koller. These linguistic concepts helped me understand the authors’ argument that “in contrast to some current hype, meaning cannot be learned from form alone. This means that even large language models such as BERT do not learn meaning; they learn some reflection of meaning into the linguistic form which is very useful in applications.”

A few weeks ago, I came across another interesting paper, “Dissociating Language and Thought in Large Language Models: a Cognitive Perspective,” published in January, 2023 by principal authors linguist Kyle Mahowald and cognitive neuroscientist Anna Ivanova and four additional co-authors. The paper nicely explains how the study of human language, cognition and neuroscience sheds light on the potential capabilities of LLMs and chatbots. Let me briefly discuss what I learned.

“Today’s large language models (LLMs) routinely generate coherent, grammatical and seemingly meaningful paragraphs of text,” said the paper’s abstract. “This achievement has led to speculation that these networks are — or will soon become — thinking machines, capable of performing tasks that require abstract knowledge and reasoning. “Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use: formal linguistic competence, which includes knowledge of rules and patterns of a given language, and functional linguistic competence, a host of cognitive abilities required for language understanding and use in the real world.

The Economic Value of Open Source

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

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

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

Driving Value with the Metaverse in the Enterprise

A killer app is an IT application whose value to individuals and companies is relatively simple to explain, its use is fairly intuitive, and the application turns out to be so useful that it can singlehandedly propel the success of a new product or service. Word processing and spreadsheets, for example, were major factors in the widespread adoption of personal computers in the 1980s.

I got interested in 3D immersive environments in the mid-2000s when IBM launched an exploratory initiative on the topic. As part of these efforts, we conducted a number of experiments on Second Life, a platform that allowed its users to develop a variety of virtual world applications. We built a number of workplace applications, including virtual meetings, and online events, and even built a virtual replica of Beijing’s Forbidden City in partnership with the Palace Museum that was opened to the public. But, despite our high expectations, we couldn’t find any compelling enterprise killer apps, and after about three years, the project was dissolved. In the ensuing years, 3D immersive technologies and applications continued to advance, but mostly focused on video-games and related industries.

The past few years have seen a surge of interest in the metaverse and extended reality. “After desktop computing, the consumer internet and the smartphone boom, the consumer-computing industry is past due its Next Big Thing,” wrote The Economist in  “A reality check for the metaverse is coming,” an article in its 2022 year-end issue. “[A]n internet which is still largely flat — based on two-dimensional text, images and video — is ripe for replacement with one that is three-dimensional and immersive.”

The Three Major Structural Changes in the Global Economy

“To say that the last few years have been economically turbulent would be a colossal understatement,” wrote economist and businessman Mohamed El-Erian in “Not Just Another Recession: Why the Global Economy May Never Be the Same,” a  recently published article in Foreign Affairs. El-Erian, — one of the world’s most influential economic thinkers, — is President of Queens College in Cambridge University, chief economic advisor at Allianz, and former CEO of PIMCO.

“Inflation has surged to its highest level in decades, and a combination of geopolitical tensions, supply chain disruptions, and rising interest rates now threatens to plunge the global economy into recession,” he added. “Yet for the most part, economists and financial analysts have treated these developments as outgrowths of the normal business cycle. But rather than one more turn of the economic wheel, the world may be experiencing major structural and secular changes that will outlast the current business cycle.”

As evidence of these changes, the article cites a series of highly unlikely economic and financial development that have taken place in the past few years. These include the strengthening of autocracies around the world, the political polarization in several liberal democracies, the increasing US turn from free trade to protectionism, and the growing tensions between China and the US.

Are GPT-3 and ChatGPT Truly Intelligent?

Large language models (LLMs) — e.g., BERT, GPT-3, — and chatbots like ChatGPT, are quite impressive. In the coming years, they will likely lead to a number of transformative tools and applications. They may well herald a coming AI revolution. But, how much closer are they getting us to the kind of general intelligence that, for the foreseeable future, only humans have? How can we best sort out their true significance and value from the accompanying hype? Let me discuss how two different articles addressed these questions.

“The success of the large neural language models on many NLP [Natural Language Processing] tasks is exciting” wrote linguistic professors Emiliy Bender and Alexander Koller in their 2020 paper “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data.” “This has led to claims, in both academic and popular publications, that such models understand or comprehend natural language or learn its meaning. From our perspective, these are overclaims caused by a misunderstanding of the relationship between linguistic form and meaning.”

In their paper, Bender and Koller explain why LLMs like GPT-3 are likely to become innovative language tools, — kind of like highly advanced spell checkers or word processors, — while dismissing claims that they have the ability to reason and understand the meaning of the language they’re generating. Their explanations are couched in linguistic concepts which they carefully define: form, communicative intent, meaning, and understanding.

Management Priorities for Highly Turbulent Times

“The COVID-19 pandemic rewrote the rules, and now a new and potent disruption seems to arrive every other day,” said a recent McKinsey article, What matters most? Six priorities for CEOs in turbulent times. “You know the list of issues; we won’t go through them here. Suffice to say that managing complex organizations is much harder today than it was just a few years ago. And the hardest task of all for CEOs is to decide what needs to be done now and what can wait.”

To find out what matters most almost three years into the pandemic, McKinsey spoke with hundreds of leading CEOs around the world. Six priorities featured prominently on their agendas:

• Resilience. Not surprisingly, “resilience is emerging as a vital muscle for companies operating in a world of endless volatility and disruption.”
• Strategic Courage. “The best leaders and companies are ambidextrous: prudent about managing the downside while courageously pursuing the upside.”
• Building New Businesses. “Going for greatness within a company’s industry is one thing. Venturing into an entirely different sector is another, more complicated story.”
• Technology. “Building a new business inevitably means new and better technology.” 
• Net-zero emissions. “[T]he goals of sustainability, economic competitiveness, affordability, and national security dovetail as never before.”
• Rebuild the employee experience. “In the wake (we hope) of the COVID-19 pandemic, CEOs need to find a new plane of engagement.”

Can an AI System Exhibit Commonsense Intelligence?

“One of the fundamental limitations of AI can be characterized as its lack of commonsense intelligence: the ability to reason intuitively about everyday situations and events, which requires rich background knowledge about how the physical and social world works,” wrote University of Washington professor Yejin Choi in “The Curious Case of Commonsense Intelligence,” an essay published in the Spring 2022 issue of Dædalus. “Trivial for humans, acquiring commonsense intelligence has been considered a nearly impossible goal in AI, added Choi.”

What is commonsense intelligence? In a 2020 presentation, Choi defined common sense as “the basic level of practical knowledge and reasoning concerning everyday situations and events that are commonly shared among most people.” Common sense is essential for humans “to live and interact with each other in a reasonable and safe way,” and for an AI system, common sense is essential “to understand humans needs and actions better.” She further explained the difference between intuitive commonsense reasoning and analytical rational reasoning by discussing the pioneering research of Daniel Kahneman, — Princeton Professor Emeritus and recipient of the 2002 Nobel Prize in Economics, — and his long time collaborator Amos Tversky, who died in 1996.

In his excellent 2011 bestseller Thinking, Fast and Slow, Kahneman explained that our mind is composed of two very different systems of thinking, System 1 and System 2.  System 1 is the intuitive, unconscious, fast, and effortless part of the mind. Thoughts come automatically and very quickly to System 1 without us doing anything to make them happen.

Are Business Schools to Blame for Wage Stagnation?

Several weeks ago I listened to a very interesting podcast, Are MBAs to Blame for Wage Stagnation, with Freakonomics Radio host, Stephen Dubner and MIT economist Daron Acemoglu. The podcast discussed a working paper by Acemoglu, Alex He, and Daniel le Maire on the effect of a CEO’s education on the wages of their company’s employees that was recently published in the National Bureau of Economic Research (NBER).

In the podcast, Acemoglu noted that over the last few decades, wages have grown much slower than productivity, — a development that has long puzzled economists. “If you look at the real wages of workers with a high school degree, it increased about 2.5% a year in real terms between1945 and the late 1970s — a remarkable increase,” he said. “Since 1980, it has been declining in real terms every year. So a large fraction of the U.S. population has been becoming poorer and poorer, even as we are building the most amazing technologies, the largest companies humanity has seen, the most modern economy.”

The labor share, — that is, the share of GDP that goes to workers, — declined from 63% in the 1980s to 58% in 2020. And during those same four decades, the share of US firms that were run by a business manager, — that is, an executive with an MBA or some other business degree, — increased from 25% in the 1980s to over 40% in 2020. Were these two trends connected? “Did the professionalization of the managerial class cause the labor share to fall? The answer wasn’t obvious.”

To shed light on this question, Acemoglu and his collaborators analyzed US and Danish data, including firm- and worker-level information. They chose Denmark, in addition to the US, because the country has very high-quality data that makes it possible to do a lot of analysis that would be harder to do with US data alone. Their methodology was very simple. They essentially tracked what happens to the earnings of the same workers when there is a switch in the type of manager leading their company, i.e., from a business to a non-business manager and vice versa.

Is AI (Finally) Coming of Age?

Artificial intelligence first came to light in the mid-1950s as a promising new academic discipline. AI became one of the most exciting areas in computer sciences over the next two decades. But, after years of unfulfilled promises and hype, a couple of so called AI winters of reduced interest and funding set in that nearly killed the field. AI was successfully reborn in the 1990s with a new statistical paradigm based on analyzing large amounts of data with powerful computers and sophisticated algorithms. Now, six decades after the field was founded, AI seems to be finally coming of age.

“2021 saw the globalization and industrialization of AI intensify, while the ethical and regulatory issues of these technologies multiplied,” said the 2022 AI Index report on the progress of AI, which was released in March of 2022 by Stanford’s Institute for Human-Centered Artificial Intelligence (HAI). “2021 was the year that AI went from an emerging technology to a mature technology - we’re no longer dealing with a speculative part of scientific research, but instead something that has real-world impact, both positive and negative,” wrote Jack Clark, co-chair of the AI Index. Multiple factors led to his conclusion, in particular the advent of foundation models like OpenAI’s GPT-3 and Google’s BERT.

In the past decade, increasingly powerful AI systems have matched or surpassed human levels of performance in a number of specific tasks like image and speech recognition. These task-specific deep learning (DL) systems have generally relied on supervised learning, a training method where the data must be carefully labelled, - e.g., cat, not-cat, - thus requiring a big investment in time and money to produce a model that’s narrowly focused on one task and can’t be easily repurposed.

Foundation models have gotten around these DL limitations based on two recent advances, transfer learning and huge scale. Transfer learning takes the knowledge learned from training a specific task and applies it to different but related tasks, - such as using the training in recognizing cars in images and applying it to recognizing trucks and buses. As a result, foundation models can be adapted to many different tasks with relatively small amounts of fine-tuning.

Has Economic Globalization Been a Failure?

“So one big promise of globalization was that countries, as they became more integrated in the global economy, would also modernize on a political dimension,” said Freakonomics Radio host, Stephen Dubner in the introduction to his recent podcast Has Globalization Failed?

Globalization was supposed to boost prosperity and democracy at the same time. The 1990s ushered a golden age of globalization, when the world seemed to be coming together. Nations were becoming more economically interdependent. The internet fostered worldwide communications. The ideological contest between communism and capitalism appeared to be over. Democracy was spreading a set of universal values - freedom, equality, human rights.

“I’m curious how successful or unsuccessful you think that’s been?,” Dubner asked his podcast guest Anthea Roberts, professor at the Australia National University and co-author of the 2021 book Six Faces of Globalization: Who Wins, Who Loses, and Why It Matters.

“One of the things that I think has clearly come out from both Russia and China is that that has not borne fruit in quite the way the United States may have hoped,” answered Roberts. “But part of it may also be that the U.S. may be retelling that story a little bit. It may have been that they wanted to say that it was about democracy, but actually a lot of it was also just about their own economic interests, and now their understanding of their economic interests have changed.”

Why Digital Wallets Are Critical to the Evolving Digital Landscape

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

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

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

Why Are Enterprises Struggling with Blockchain?

“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 a recent article in the Wall Street Journal, “Blockchain Fails to Gain Traction in the Enterprise.”

“So far that hasn’t happened.”

Blockchain technologies first came to light in 2008 as the architecture underpinning bitcoin, the best known, most valuable, and most widely held cryptocurrency. Over the years, blockchain has evolved in two major directions. One continues to focus on blockchain as the underlying platform for bitcoin, as well as for the large number of cryptocurrencies, digital tokens, and other crypto assets that have since been created. The other focuses on blockchain as a distributed data platform for collaborative applications involving multiple institutions, such as supply chains, financial services and healthcare systems. 

Blockchain made its first appearance in Gartner’s yearly hype cycles in 2016. Despite its being at an early and immature stage, many of its proponents believed that enterprise blockchain platforms were just around the corner, and so it quickly rose to the peak of inflated expectations. But as reality set in, expectations started sliding down in 2021, and by 2022 blockchain platforms had reached the bottom of the trough of disillusionment.

As years go, 2022 was a very tough one for blockchains in general. In the crypto space, bitcoin and most other crypto assets have lost a very large chunk of their value, and numerous crypto companies have collapsed, most prominently FTX. In the enterprise, a number of ventures went belly up. we.trade, a blockchain-based trade finance, shut its operations in June after running out of cash. A few months later, the Australian Securities Exchange canceled its much-delayed blockchain-based clearing system. Most prominently, in late November TradeLens announced that it was discontinuing its blockchain-based global trade platform. Other enterprise blockchain projects, like Walmart’s food traceability application, still carry on but their uptake and progress have been slower than anticipated.

Will the Metaverse and AR/VR Headsets Be IT’s Next Big Thing?

“Is it really the next big thing,” asked The Economist in A reality check for the metaverse is coming, another recent article in “The World Ahead 2023”, its year-end issue which I wrote about last week. “After desktop computing, the consumer internet and the smartphone boom, the consumer-computing industry is past due its Next Big Thing,” said the article. “The coming year will see big tech firms doubling down on two related, much-hyped possibilities. One is virtual- (vr) and augmented-reality (ar) headsets; the idea that, having shrunk computers into our pockets, the next step is to strap them to our faces. The other is the metaverse, which holds that an internet which is still largely flat - based on two-dimensional text, images and video - is ripe for replacement with one that is three-dimensional and immersive, experienced as a sort of globe-spanning video game.”

There is little question that the metaverse and AR/VR headsets are important trends to watch out for in the coming years. As The Economist wrote in a November, 2021 article, “as computers have become more capable, the experiences which they generate have become richer. The internet began its life displaying nothing more exciting than white text on a black background.”

The last major advance in user interfaces took place in the 1980s when text interfaces gave way to graphical user Interfaces (GUIS). GUIs were first developed at Xerox PARC in the late 1970s and later popularized by the Apple Macintosh in the ’80s. In the 1990s, GUIs were embraced by just about every PC and user device, and GUI-based Web browsers played a major role in the explosive growth of the internet.