Irving Wladawsky-Berger

Agent Explainability: The Foundation for Trust in the Agent Ecosystem

What is Agentic AI? There are multiple answers to this question, not surprisingly since we’re still in the early stages of this complex technology. Wikipedia emphasizes the autonomous nature of agentic AI in its definition: Agentic AI is “a class of artificial intelligence that focuses on autonomous systems that can make decisions and perform tasks without human intervention.”

McKinsey noted that AI is now “moving from thought to action” in a July, 2024 report that explained the difference between generative and agentic AI. “We are beginning an evolution from knowledge-based, gen-AI-powered tools — say, chatbots that answer questions and generate content — to gen AI-enabled agents that use foundation models to execute complex, multistep workflows across a digital world. … Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner.”

Similarly, IBM answers the question “What is Agentic AI” by writing: “Agentic systems provide the flexibility of LLMs, which can generate responses or actions based on nuanced, context-dependent understanding, with the structured, deterministic and reliable features of traditional programming. This approach allows agents to think and do in a more human-like fashion.”

The Long Term Evolution of AI Economics: Automation vs Human Complementarity

The 2025 MIT Sloan CIO Symposium took place on Tuesday, May 20 at the Royal Sonesta hotel overlooking the Charles River, a short walk from the MIT campus. Not surprisingly, AI was the dominant theme in this year’s Symposium, with a number of keynotes and panels on the topic.

Over the past few years, the Symposium’s closing keynote has been given by a prominent MIT faculty member. In 2023, economics professor David Autor talked about “The Impact of AI on Jobs and the Economy,” where he discussed the potential of AI to reshape the nature of human expertise. In 2024, MIT professor emeritus of robotics Rodney Brooks talked about “What Works and Doesn’t Work with AI,” where he explained what he called the “Seven Deadly Sins of Predicting the Future of AI.”

This year, Daron Acemoglu, Institute professor of economics and 2024 Nobel laureate, talked about “The Long Term Evolution of AI Economics.”

The Key Differences Between Analytical and Generative AI

“Since OpenAI announced ChatGPT in November of 2022, many business executives have focused their attention on generative AI,” wrote Babson College professor Tom Davenport, and technology and business strategist Peter High in “How Gen AI and Analytical AI Differ — and When to Use Each,” an article published in the Harvard Business Review (HBR) issue of December, 2024. “This relatively new technology set off a frenzy around AI and caused companies to pay attention to it for the first time. This is a positive development, since the technology is powerful and important, and enables many new business possibilities.”

“However, many companies have been using AI for years with less visibility,” the authors added. “Those that have recently discovered generative AI are at risk of overlooking an older and better-established form of AI, which we’ll call analytical AI. This form of AI is by no means obsolete and is still an important resource for the great majority of companies.”

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

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

The Economic Impact of Generative AI at Work

“The emergence of generative artificial intelligence (AI) has attracted significant attention for its potential economic impact,” wrote economists Erik Brynjolfsson, Danielle Li, and Lindsey Raymond in a recently published article, “Generative AI at Work.” “Although various generative AI tools have performed well in laboratory settings, questions remain about their effectiveness in the real world, where they may encounter unfamiliar problems, face organizational resistance, or provide misleading information.”

Their article studied the impact of generative AI (GenAI) on productivity and worker experience in the customer-service sector, an industry with one of the highest rates of AI adoption. It analyzed survey data from 5,172 customer-support agents working for a Fortune 500 firm that sells business-process software in order to quantify the effect of deploying GenAI at scale in the workplace. The AI system used in the study was designed to monitor customer-service chats and provide the agents with real-time suggestions for how to respond. While the AI system is intended to assist the agents, the agents remain responsible for the overall conversation and are free to ignore or edit the AI's suggestions.

“Computers and software have transformed the economy with their ability to perform certain tasks with far more precision, speed, and consistency than humans,” said the authors. “To be effective, these systems typically require explicit and detailed instructions for how to transform inputs into outputs: a software engineer must program computers. Despite significant advances in traditional computing, many workplace activities, such as writing emails, analyzing data, or creating presentations, are difficult to codify and have therefore defied computerization.”

The State of Open Standards

“Technical standards define guidelines and specifications across products, services, or systems to ensure consistency, compatibility, and quality,” said “The State of Open Standards,” a recent report by “Linux Foundation Research” in its Introduction. “They are fundamental in facilitating trade, ensuring product safety, and enabling interoperability between different technologies and systems. With such an interconnection of objectives, requirements and stakeholders, approaches to create technical standards tend to vary along a continuum of characteristics. The Linux Foundation supports hundreds of projects that work to achieve standardization across various levels, from agreement within a single project’s ecosystem to globally-adopted ISO Standards.”

Open standards have played a central role in fostering innovation, ensuring interoperability across systems and applications, and driving the growth of the digital economy. For example, in the early decades of the IT industry, proprietary systems from different vendors didn’t interoperate with each other. Just sending an e-mail across two different proprietary networks and applications was quite complicated, as was sharing information across such disparate systems.

This all finally changed in the 1990s when the Internet and the World Wide Web brought a badly needed culture of standards to the IT industry. Open network protocols (e.g., TCP/IP), were widely embraced across the marketplace, making it possible to interconnect systems from any vendors. Internet e-mail protocols, — SMTP, MIME, POP, IMAP, — enabled people to easily communicate with anyone on any system. And the Web’s open standards, — HTML, HTTP, URLs, — enabled any PC connected to the Internet to access information on any web server anywhere in the world. Open standards have also played a central role in the explosive growth of open source software and collaborative innovation.

In addition, despite the major technology advances of the past few decades, the lack of open, interoperable standards has been a major obstacle to progress in critically important industry sectors like healthcare systems and global supply chains.

The Future of Agentic AI Ecosystems

Wikipedia defines Agentic AI as “a class of artificial intelligence that focuses on autonomous systems that can make decisions and perform tasks without human intervention.” A recent IBM website, “What is Agentic AI,” adds that: “Agentic systems provide the flexibility of LLMs, which can generate responses or actions based on nuanced, context-dependent understanding, with the structured, deterministic and reliable features of traditional programming.”

“Broadly speaking, agentic systems refer to digital systems that can independently interact in a dynamic world,” explained a McKinsey report published in July of 2024.  “While versions of these software systems have existed for years, the natural-language capabilities of gen AI unveil new possibilities, enabling systems that can plan their actions, use online tools to complete those tasks, collaborate with other agents and people, and learn to improve their performance. Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner. … In short, the technology is moving from thought to action.”

“Billions of dollars of investment by some of the largest firms on the planet are flowing into tools that will make it easy to build autonomous agents,” wrote senior technology executive Eric Broda in “Agentic Mesh: The Future of Generative AI-Enabled Autonomous Agent Ecosystems,” a November 2024 article in the Medium platform. “And if this huge investment, and the recent headlines, are any indication, we will soon have many, many autonomous agents collaborating in a dynamic ecosystem.”

Is AI a Normal Technology?

Hard Fork is a weekly podcast from the NY Times that examines the impact of the latest developments in technology on the economy and society. The podcast is hosted by Kevin Roose, NY Times technology journalist, and Casey Newton, founder of the Platformer newsletter. A few weeks ago the podcast discussed Is AI a ‘Normal' Technology? with Princeton computer science professor Arvind Narayanan, co-author of AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference with Princeton PhD candidate Sashay Kapoor, a book that was published in September of 2024.

Roose and Newton started the podcast by noting that the previous week their guest at the podcast had been AI researcher Daniel Kokotailo, founder and executive director of the AI Futures Project, where they discussed their new article, AI 2027, which predicted that “the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.” Narayanan is highly skeptical of the fast takeoff predictions of AI 2027. In a recent essay also co-authored with Kapoor, “AI as a Normal Technology,” they laid out an alternative vision of AI, “basically one that treats AI not as some looming superintelligence that’s going to go rogue and take over humanity, but as a type of technology like any other, — like electricity, like the internet, like the PC, — that have taken a period of years or even decades to fully diffuse throughout society.”

This is a very important question I’ve been thinking a lot about. Will AI’s impact on economies and societies be similar to those of previous technological revolutions of the past two and a half centuries, — that is, Is AI a normal technology?, — or is AI in a class by itself, destined to exceed the impact of the Industrial Revolution in just one decade?

The 2025 AI Index Report: AI’s Influence Across Society Continues to Transform the Way We Live and Work

The 2025 AI Index Report was released in early April by the Stanford Institute for Human-Centered Artificial Intelligence, — it’s eigth annual analysis of the impact, progress, and trends of AI. Led by an interdisciplinary group of experts from across academia and industry, the AI Index report aims to be the world’s most authoritative source for data and insights about AI.

“What a year 2024 has been for AI,” wrote the report’s co-directors Yolanda Gil and Raymond Perrault. “The recognition of AI’s role in advancing humanity’s knowledge is reflected in Nobel prizes in physics and chemistry, and the Turing award for foundational work in reinforcement learning. The once-formidable Turing Test is no longer considered an ambitious goal, having been surpassed by today’s sophisticated systems. Meanwhile, AI adoption has accelerated at an unprecedented rate, as millions of people are now using AI on a regular basis both for their professional work and leisure activities. As high-performing, low-cost, and openly available models proliferate, AI’s accessibility and impact are set to expand even further.”

“AI is no longer just a story of what’s possible — it’s a story of what’s happening now and how we are collectively shaping the future of humanity,” Gil and Perrault added. At over 450 pages and eight chapters, the 2025 AI Index is recognized globally as one of the most comprehensive and authoritative resources on artificial intelligence, referenced in hundreds of academic papers and used by policymakers and government agencies around the world.

Pathways and Challenges Toward an Open Data Ecosystem

“Our data is everywhere and powering everything,” noted “Pathways to Open Data,” a report by Linux Foundation Research published in March of 2025. “From marketing, to healthcare, to government services, to the emerging phenomenon of programming AI agents, organizations leverage data to be as efficient and effective as possible. However, data is often siloed within entities and any third-party data access requires overcoming significant technical, legal, economic, operational, and cultural obstacles that are multifactorial and at times may seem intractable. The increasing reliance on data calls for an assessment of these obstacles and how organizations can shift toward greater openness and sharing.”

The report defines open data as “data infrastructure that has the technical and legal requirements in place to make the data freely accessible for universal use, reuse, and redistribution.” Open data has its roots in open science and open knowledge in general, where non-personal and non-commercial data is freely published for the purpose of greater innovation, transparency, and collaboration. This culture of openness is strongest in public institutions, where the data collected is considered a public good without profit-generating opportunities, and where transparency of government and public-sector information is encouraged.”

Many governments have developed open data portals to enable access to public information, such as the Public Sector Information Directive launched by the European Union in 2003, and Data.Gov, launched by the Obama administration in May of 2009 with the goal of increasing transparency and accountability through open data. Initially launched with 47 data sets, Data.Gov now includes over 300,000 open data sets.

What Are AI Agents and How Should We Use Them?

After decades of promise and hype, AI has (finally) come of age. The 2010s saw increasingly powerful deep learning systems surpass human levels of performance in tasks like image and speech recognition, skin and breast cancer detection, and winning at championship-level Go. The 2020s saw the advent of generative AI systems, with their impressive ability to create high-quality original content, — e.g., text, images, video, audio or software code, — in response to a a user’s natural language prompts. And now, we’re seeing the emergence of agentic AI.

Wikipedia defines agentic AI as autonomous AI systems “that can make decisions and perform tasks without human intervention.” Agentic AI systems are designed to “autonomously make decisions and act, with the ability to pursue complex goals with limited supervision. It brings together the flexible characteristics of large language models (LLMs) with the accuracy of traditional programming.”

A few weeks ago I posted a blog, Agentic AI: The Evolution of Application Development, mostly based on a McKinsey Digital report, “Why agents are the next frontier of generative AI.” Agentic AI systems are now taking AI to the next level, enabling us to automate processes that can plan and execute their actions with limited human intervention. “In short, the technology is moving from thought to action.”

Informational GPS: Navigating the Complex World of 21st-Century Knowledge

“Until the early 2000s, paper maps did not exist — they were simply called maps” wrote Reid Hoffman and Greg Beato in “Informational GPS,” one of 12 essays in The Digitalist Papers, a roadmap of the possible futures that the AI revolution might produce. Before May of 2000, the Global Positioning System (GPS) was deliberately limited for civilian use due to security concerns. But then access to GPS was made available to everyone as a public good

“In the wake of this policy shift, GPS experienced a Cambrian explosion of innovation.” Improved performance and falling prices for consumer receivers quickly turned GPS into an indispensable part of life in the 21st century. A 2019 study on the Economic Benefits of GPS by the National Institute of Standards and Technology (NIST) estimated that by 2017, GPS had generated roughly $1.4 trillion in location-based services across a variety of industries since being made available for civilian and commercial use.

The term GPS has entered the popular vernacular to mean one’s specific location at a specific point in time. Its breakthrough application has been turn-by-turn navigation, enabling us to move through the physical world with precise, constantly updated information. “At literally every turn, these navigation systems increase individual agency by telling us where we are, what else is nearby, what obstacles might impede our progress, and so much more.”

A major reason why GPS has been so widely adopted is that it offers us individual agency along with broad access to navigation information. GPS not only suggests the best routes for us to follow based on current traffic conditions, but it also offers us the ability to modify our route along the way and quickly readjusts to accommodate our wishes and new position.

Agentic AI: The Evolution of Application Development

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

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

Agentic systems are now taking AI to the next level.

The Future of Work from Home (WFH)

Work from home (WFH) has been around for decades. The share of people working from home three or more days per week was under 1% in 1980, growing modestly with the rise of the Internet to around 2.4% in 2010 and 4% 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.

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

I’ve been tracking the evolution of WFH by following their monthly surveys. 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 started to decline in subsequent months. One year later WFH was around 45%. 

In an April 2021 working paper, “Why Working from Home Will Stick,” Barrero, Bloom, and Davis predicted that the shift to work from home would be one of the biggest legacies of the pandemic. They projected that “American workers will supply about 20 percent of full workdays from home in the post-pandemic economy, four times the pre-COVID level. Desires to work from home part of the week are pervasive across groups defined by age, education, gender, earnings, and family circumstances.” They estimated that higher levels of WFH would boost productivity by about 4.6%, with over half of the productivity gain reflecting the WFH savings in commuting time.

A Planetwide Vision of the Future of Global Supply Chains

“The central purpose of supply chains is to fulfill the needs and wants of humanity — delivering all the food, medicine, energy, apparel, and other worldly goods needed by the eight billion people in the planet,” wrote MIT professor Yossi Sheffi in the preface of his 2023 book, The Magic Conveyor Belt: Supply Chains, A.I. and the Future of Work. “The societal and economic spasms of the early 2020s highlighted the crucial role of world-spanning supply chains in the modern global economy, as well as the growing role of digital technology, including A.I. and automation, in the future economy.”

I’ve been following the evolution of global supply chains for the past three decades. The 1990s ushered a golden age of globalization, when the world seemed to be coming together. Thomas Friedman’s The World is Flat became an international best-seller in 2005, nicely explaining what globalization was all about, including the key forces that contributed to the flattening of the world, from the explosive growth of the Internet to the rise of just-in-time global supply chains.

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

I became convinced that global supply chains would emerge as the killer app for enterprise blockchain applications. Over the following years, I closely tracked the evolution of blockchain-based supply chain systems. I assumed that, much like the Internet in the 1990s and Linux in the 2000s, it was just a matter of time before blockchain technologies would be widely embraced in the business world.

But, as we now know, things didn’t quite turn out as I had expected. “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. So far that hasn’t happened,” wrote the Wall Street Journal in a December 2022 article, “Blockchain Fails to Gain Traction in the Enterprise.”

Business Strategy in the Emerging AI Era

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

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

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

While AI Has Been Eating Computing, Philosophy May Be Ready to Make a Meal of AI

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

Moore’s Law has had quite a run, but like all things based on exponential improvements, it eventually slowed down and flattened out in the 2010s. What will now drive computing in the post-Moore’s-Law era?

Software has been a key driver of computing since the 1980s. In a 2011 essay, “Software is Eating the World,” technologist and investor Marc Andreessen wrote that “we are in the middle of a dramatic and broad technological and economic shift in which software companies are poised to take over large swathes of the economy. More and more major businesses and industries are being run on software and delivered as online services — from movies to agriculture to national defense.” Entrepreneurial companies all over the world have been disrupting established industries, with innovative software-based solutions.

In 2017, Nvidia CEO and co-founder Jensen Huang boldly updated Andreessen’s essay by predicting that “Software Is Eating the World but AI is Going to Eat Software.” In 2024, he further predicted that “the future of coding as a career might already be dead in the water with the imminent prevalence of AI.

“But even as software eats the world and AI gobbles up software, what disrupter appears ready to make a meal of AI?,” asked MIT researchers Michael Schrage and David Kiron in “Philosophy Eats AI,” a recent article in MITSloan Management Review. “The answer is hiding in plain sight. It challenges business and technology leaders alike to rethink their investment in and relationship with artificial intelligence. There is no escaping this disrupter; it infiltrates the training sets and neural nets of every large language model (LLM) worldwide.”

The Impact of AI on Research and Innovation

Postscript: On May 16, 2025, MIT issued a press release explaining the concerns raised about the integrity of the research in the preprint of the paper “Artificial Intelligence, Scientific Discovery, and Product Innovation” that was published on arXiv in November 2024 and referenced in this blog.

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On December 29, the WSJ  published “Will AI Help or Hurt Workers?,” an article based on a research
paper by Aidan Toner-Rodgers, a second year PhD student in MIT’s Economics Department.

One of the reasons the WSJ article caught my attention is that it featured a photo of the MIT graduate student in between two of the world’s top economists whose research I’ve closely followed for years: Daron Acemoglu, — who in October was named a co-receipient of the 2024 Nobel Memorial Prize in Economic Science, and David Autor (along with his dog Shelby) — who was a co-chair of a multi-year, MIT-wide Taskforce on the impact of AI on “The Work of the Future.”

Both professors raved about Toner-Rodger’s research even though they have somewhat different points of view on the impact of AI on workers. Professor Autor is more optimistic, arguing that “AI Could Actually Help Rebuild The Middle Class.” His friend and colleague, professor Acemoglu, worries that AI could actually worsen income inequality and not do all that much for productivity and GDP growth over the coming decade. “For all the talk about artificial intelligence upending the world, its economic effects remain uncertain,” he said in a recent interview. “There is massive investment in AI but little clarity about what it will produce.” But they both agreed that “the research by Toner-Rodgers, 26 years old, is a step toward figuring out what AI might do to the workforce, by examining AI’s effect in the real world.”

Leveraging GenAI for Workforce Productivity: a Scenario Based Analysis

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

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

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

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

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

How Humans and AI Can Complement Each Other in the Workplace

General purpose technologies (GPTs) are the defining technologies of their times. They are engines for growth that can radically transform the economic environment. “Early versions of GPTs often look very different from their mature forms,” wrote MIT’s Isabella Loaiza and Roberto Rigobon in “The EPOCH of AI: Human-Machine Complementarities at Work.” “Only after waves of adjustments and extensive collective tinkering do these technologies evolve into foundational parts of society — and even after long periods of refinement, they continue to evolve.”  

“The process of continuous refinement not only enhances the technology but also uncovers new applications, setting the stage for a myriad of new products, services, and even new GPTs. As a result, these technologies hold the power to create and reshape entire markets while rendering others obsolete, driving profound societal shifts and large-scale resource reallocation. While transformative, long-lasting change is a defining feature of GPTs, the specific direction of that change is likely shaped by the political, social, economic, and institutional structures in place when the technology emerges.”

The Future of Self-Driving Cars and Robotaxis

MIT professor emeritus Rodney Brooks has been posting an annual Predictions Scorecard  in rodneybrooks.com since January 1, 2018, where he predicts future milestones in three technology areas: AI and robotics, self driving cars, and human space travel. He also reviews the actual progress in each of these areas to see how his past predictions have held up. On January 1 he posted his 2025 Predictions Scorecard.

Two weeks ago I discussed Brooks’ predictions on “The Future of AI and Robotics.” I now want to discuss his predictions on The Future of Self-driving Cars and Robotaxis.

Fully autonomous vehicle (AVs) may well be the quintessential symbol of our AI/robotics age. Cars are a major part of our daily lives. Unlike other AI and robotics applications, a self-driven vehicle is a concept that requires little explanation, — something we can all quickly grasp. It wasn’t that long ago that the notion of an AV driving us around while we read or sleep would have felt like the stuff of science fiction. This is no longer the case.

Brooks wrote that “the definition, or common understanding, of what self driving cars really means has changed since my post on predictions seven years ago. At that time self driving cars meant that the cars would drive themselves to wherever they were told to go with no further human control inputs. Now self driving cars means that there is no one in the drivers seat, but there may well be, as is the case in all cases so far deployed, humans monitoring those cars from a remote location, and occasionally sending control inputs to the cars.”

A Roadmap for Long Term Resilience and Innovation in Global Supply Chain Networks

“In the complex and rapidly evolving business landscape and volatile global market conditions, supply chains cannot thrive in isolation,” begins “Building Constellations of Value — Transforming Supply Chains to Drive Collaboration, Innovation, and Long-Term Value,” a recently published report by the Digital Supply Chain Institute (DSCI), an organization focused on the evolution of enterprise supply chains. “Companies are increasingly recognizing the need for collaborative networks — Constellations of Value (CoV) — that bring together diverse stakeholders across a supply chain to tackle shared challenges and unlock new growth opportunities.”

The CoV framework is based on inputs from DSCI members and other global supply chain leaders. “A key insight from this effort was that successful supply chains are no longer defined solely by their internal efficiencies; instead, they must be designed as collaborative networks that drive shared value, resilience, and innovation.” 

I couldn’t agree more.

As I’ve previously written, I first became interested in global supply chains in the 1990s, when the explosive growth of the Internet was driving a new golden age of globalization. In his 2005 international best-seller The World is Flat, Thomas Friedman explained that the rise of global supply chains was one of the key forces that contributed to flattening the world, along with the Internet, open source software, and related technologies.

The Future of AI and Robotics — 2025 Predictions Scorecard

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

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

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

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

How is Open Source Software Being Adopted Across the World?

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

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

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

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

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

Artificial Intelligence and the Future of Work

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

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

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

What Do We Know About the Economics of AI?

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

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

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

The World Ahead in 2025

A few weeks ago, The Economist published “The World Ahead 2025,” its 39th annual issue that examines the trends and events that will likely shape the coming year. Last year, the overriding message from “The World Ahead 2024” was the mind-boggling speed at which life was changing, from the Middle East turmoil and economic uncertainty to the redrawing of the global energy-resources map and the rapid progress in artificial intelligence (AI).

“It seems appropriate that 2025 has been designated the year of quantum science and technology by the United Nations,” wrote World Ahead editor Tom Sandage  in his introduction to this year’s issue. “Because like Schrödinger’s cat, which (in a quantum thought-experiment) was both alive and dead at the same time inside a closed box, 2025 has hovered in a superposition of two very different states, defined by the outcome of America’s election. Now the ballot boxes have been opened, the world knows which 2025 to expect: the one where Donald Trump returns to the White House. With that uncertainty resolved, here are ten themes to watch in the coming year.”

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

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

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

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

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

The Impact of Open Source on the Future of AI

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

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

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

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

The Evolution of Global Supply Chains

“The central purpose of supply chains is to fulfill the needs and wants of humanity — delivering all the food, medicine, energy, apparel, and other worldly goods needed by the eight billion people in the planet,” wrote MIT professor Yossi Sheffi in the preface of his 2023 book, The Magic Conveyor Belt: Supply Chains, A.I. and the Future of Work. “The societal and economic spasms of the early 2020s highlighted the crucial role of world-spanning supply chains in the modern global economy, as well as the growing role of digital technology, including A.I. and automation, in the future economy.”

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

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

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

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

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

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

The Arenas of Today

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

The Breakthroughs AI Needs

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

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

Let me discuss each of these two innovations.

An Open Architecture for Health Data Interoperability

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

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

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

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

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

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

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

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

The 2024 State of Open Source in Financial Services

“For the financial services industry, open source collaboration is a strategically relevant practice” said “The 2024 State of Open Source in Financial Services,” the fourth annual research report by the Fintech Open Source Foundation (FINOS) in partnership with Linux Foundation Research. Over the past four years, this study has aimed to provide concrete industry-wide trends about the state of open source adoption in the financial services industry. “Even in the relatively short time since studying sector trends in open source began, we’ve seen encouraging signals for the financial services industry that there are numerous benefits to realize through increased collaboration at the precompetitive level of technology stacks and that more participants in the sector are coming on board.”

As in prior years, the report is based on empirical evidence, including a global survey, industry data, and insights from interviews with senior finance and IT leaders.

The global survey was conducted in May and June of 2024 and received responses from 249 qualified individuals, — 39% from North America, 28% from Europe, and 29% from Asia-Pacific countries. 58% of respondents worked in financial institutions, and 35% were employed by providers of financial and IT services.

“Altogether, the data and the trends collectively underscore the growing importance of open source in driving innovation, enhancing efficiency, and fostering collaboration. Our findings reveal that financial services organizations are not only consuming open source software at increased levels to achieve a variety of objectives but also contributing back to the community in meaningful ways … For stakeholders from capital markets, retail or private banking, asset management, insurance, or for those fintech leaders delivering innovative solutions for the sector, there’s more reason than ever to participate in open source ecosystems.”

The Top 10 Emerging Technologies of 2024

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

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

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

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

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

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

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

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

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

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

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

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

Will AI Devour Software Engineering (SE)?

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

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

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

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

Digital Twins Are Changing the World

“Digital twins are fast becoming part of everyday life,” said the lead article in the August 31 issue of The Economist. A Digital Twin is essentially a computerized companion to a real-world entity, be it an industrial physical asset like a jet engine, an individual’s health profile, or a highly complex system like a city.

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

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

AI: The Industrial Revolution of Knowledge Work

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

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

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

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

The Three Laws of Robotics

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

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

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

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

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

Is the AI Revolution Already Losing Steam?

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

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

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

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

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

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

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

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

The 2024 State of Talent in the IT Sector

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

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

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

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

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

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

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

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

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

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

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

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

In its early decades, IT helped companies become significantly more productive by automating many of their processes, reengineering their overall operations, and reaching out beyond the boundaries of the enterprise. But, their actual products were largely unaffected. This has all been changing in the 21st century with the rise of the Internet of Things (IoT), Big Data, and Artificial Intelligence, whose combined impact is driving a new era of smart connected products.

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

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

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

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

Language is a Tool for Communication, Not for Thinking

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

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

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

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

Embracing Beneficial Friction for Human-Centered AI

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

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

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