Irving Wladawsky-Berger

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The Economic Impact of Transformative AI

February 25, 2026

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The Digitalist Papers is a series of essays that aim to offer insights on the “possible futures that the AI revolution might produce.” The first volume, released in September 2024, included twelve essays that explored the intersection of AI and democracy in America. Volume 2, released in December 2025, includes 21 essays that examined the economics of transformative AI.

Volume 2 was edited by Daniel Susskind, Erik Brynjolfsson, Anton Korinek, Alex Pentland, and Ajay Agrawal. “AI capabilities have been relentlessly improving across a variety of benchmarks. And in November 2022, a new chapter in this story began,” they wrote in the Volume 2 Introduction. While Volume 1 explored the implications of AI for democracy, “this second volume turns to its economic dimensions, focusing on how transformative AI might alter production, work, and prosperity itself. It brings together leading economists, technologists, philosophers, and others to explore the key economic challenges and opportunities of ‘transformative AI,’ or TAI. Most significantly, though, it is also focused on how we might respond to whatever lies ahead.”

“In what follows, we define ‘TAI’ as advanced AI systems that will usher in an economic transformation comparable to the Industrial Revolution, but on a much shorter timescale,” they wrote. “They exhibit intelligence surpassing the most capable humans across multiple fields, capable of performing not only routine cognitive work, but also complex tasks like solving mathematical theorems, creating novel works, or directing experiments autonomously.”

AI’s technological progress is often described using terms like artificial general intelligence (AGI) and artificial superintelligence (ASI) — that is, AI systems that will eventually match or surpass human capabilities across virtually all cognitive tasks. Transformative AI (TAI), by contrast, reflects a growing consensus in policy circles that even if AI does not fully reach human-level cognition, it could still have an impact on society comparable to the agricultural or industrial revolutions. (more…)

Artificial Intelligence, Economic Issues, Future of Work, Innovation, Management and Leadership, Political Issues, Society and Culture
The Economic Impact of the Emerging Age of AI

February 18, 2026

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A few weeks ago, New York Times columnist David Leonhardt hosted an online conversation about the impact that AI is already having on employment and how large a transition society may be facing with three prominent economists: MIT’s David Autor, the University of Virginia’s Anton Korinek, and Yale University’s Natasha Sarin. I found their discussion quite interesting at multiple levels. Let me summarize some of their key points.

The Near-Term Impact of AI on Jobs

“Before we look toward the future, let’s talk about the present,” Leonhardt began. “There is debate among economists about whether A.I. has already led to a meaningful amount of job loss. What do you each think?”

“The evidence is inconclusive,” said Professor Autor. Some widely discussed findings suggest that entry-level employment for young workers has declined in AI-exposed occupations such as software development and customer service. However, other recent business-cycle factors — such as tariffs and interest rates — may also be influencing hiring trends. “That said,” he added, “there’s every reason to believe that advancing A.I. will fundamentally change hiring and skill requirements across much of the economy. In many cases, I think we’ll see fewer people doing this work, and those who do it will be more expert, solving the thorny problems that A.I. currently cannot solve on its own.”

Professor Sarin also noted that the evidence is inconclusive. That “despite all the vibes and anecdotes you hear about A.I. labor market displacement, there just isn’t evidence in the data that this has happened in a meaningful way so far. … We don’t find differences in employment in the last few years between the occupations most exposed to A.I. and those least exposed.” That, she noted, should not be surprising. “It’s been only three years since the mass introduction of this technology, and it takes firms — and all of us — time to understand how to deploy it in ways that are truly transformative.”

Professor Korinek offered a different lens. While employment data may be ambiguous, he argued, investment data is not. “The leading A.I. labs aren’t making hundred-billion-dollar bets because they expect A.I. to have minor effects on the labor market. They are betting on achieving artificial general intelligence (A.G.I.), which could substitute for human labor across much of the economy.” He also mentioned that few people work at these labs relative to the scale of investment. “The employment effects we are looking for may simply be lagging indicators of a transformation that’s already locked in by the capital being deployed.” (more…)

Artificial Intelligence, Economic Issues, Future of Work, Innovation, Management and Leadership, Society and Culture, Technology and Strategy
Remote-First Organizations: Practices that Drive Talent, Trust, and Performance

February 12, 2026

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“The COVID-19 pandemic forced organizations to reimagine work in ways few had previously considered,” said “Remote-First Organizations: Practices that Drive Talent, Trust, and Performance,” a recently published report by the Institute for Global Responsibility (i4cp) in its Introduction. “What began as an emergency response has since become a long-term operating model for many. Remote and flexible work models are no longer fringe benefits or stopgap solutions — they are strategic choices shaping the future of work.”

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 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. I’ve been keeping up with 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 started to decline in subsequent months. One year later WFH was around 45%. About two years later, the June 2023 survey found that the percentage of paid full days worked from home was around 28%. For the past three years, the percentage of WFH days for all workers has fluctuated around 2.5 days. And, according to their latest survey, around 61% of all full-time employees were full-time onsite, 13% were fully remote; and 26% were in a hybrid arrangement. (more…)

Economic Issues, Future of Work, Political Issues, Society and Culture
AI Foundation Models and Traditional Supercomputing

February 6, 2026

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“There is significant interest in the development and application of foundation models for scientific discovery,” said Foundation Models for Scientific Discovery and Innovation,” a recent report from the National Academies. “Foundation models possess the capacity to generate outputs or findings and discern patterns within extensive data sets with data volumes that are considered overwhelming for classical modes of inquiry. Efforts are under way to use these models to accelerate various aspects of scientific work flows (including streamlining literature reviews, planning experiments, data analysis, and code development) and generating novel findings and hypotheses that can then spur further research directions. However, significant challenges remain in the effective use of these models in scientific applications, including issues with flawed or limited training data and limited verification, validation, and uncertainty quantification capabilities.”

High performance computing has been a major part of my education and subsequent Pdcareer. In the late 1960s I was doing atomic and molecular calculations as a PhD physics student at the University of Chicago. Then in the early 1990s, I was the general manager of IBM’s new Scalable Powerparallel (SP) family of parallel supercomputers.

The advances of supercomputers over the past several decades have been remarkable. The machines I used as a graduate student in the 1960s probably had a peak performance of a few million floating point calculations per second (megaflops). Every year since 1993, the TOP500 project has been publishing a list of the 500 most powerful supercomputers in the world. In the latest such list, the fastest supercomputer surpassed 1.8 billion billion floating point calculation per second (exaflops).

AI is now taking high performance computing to a whole new level of capabilities. A September, 2023 issue of The Economist, “How AI Can Revolutionize Science,” included a number of articles on the impact of AI on scientific discovery. “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.” (more…)

Artificial Intelligence, Complex Systems, Data Science and Big Data, Economic Issues, Innovation, Supercomputing, Technology and Strategy
How Should University Students Be Taught the Proper Use of GenAI

January 28, 2026

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“Even as some instructors remain fervently opposed to chatbots, other writing and English professors are trying to improve them,” observed a recent New York Times article, “AI Is Coming to Class.” At the heart of the article is a debate now unfolding across higher education: whether— and how — university students should be taught to properly use generative AI.

The article illustrates this debate through the first-year writing program at Barnard College, which generally bans generative AI tools such as ChatGPT, Claude, and Gemini — systems that can readily draft paragraphs, conduct research, and compose essays. The program’s policy warns students that AI tools are “often factually wrong” and “deeply problematic,” perpetuating misogyny as well as racial and cultural biases.

Yet the program has made an exception for Benjamin Breyer, a senior lecturer in Barnard’s English Department, who is determined to see whether AI can supplement, rather than short-circuit, students’ efforts to learn academic writing. In doing so, Breyer represents a growing group of faculty who are experimenting with how AI might be used constructively — even as many of their colleagues remain firmly opposed.

I am particularly interested in this debate because it strongly echoes my own early experiences with computers as a physics student in the 1960s — a time when the legitimacy of using machines as intellectual tools was also very much in question. Let me explain. (more…)

Artificial Intelligence, Economic Issues, Education and Talent, Future of Work, Society and Culture, Uncategorized
Is a College Education Still Worth It?

January 21, 2026

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“Americans have grown sour on one of the longtime key ingredients of the American dream,” said a recent NBC News article. “Almost two-thirds of registered voters say that a four-year college degree isn’t worth the cost, according to a new NBC News poll, a dramatic decline over the last decade. Just 33% agree a four-year college degree is ‘worth the cost because people have a better chance to get a good job and earn more money over their lifetime,’ while 63% agree more with the concept that it’s ‘not worth the cost because people often graduate without specific job skills and with a large amount of debt to pay off.’”

The poll is based on interviews with roughly 1,000 registered voters, — 70% via cell phones and 30% via text. 48% of the interview subjects were male, 52% female. 25% were high school graduates, 34% had some college or vocational training beyond high school, 21% had college degrees, and 17% had graduate or professional degrees. About 30% were between 18 and 39 years old, 35% between 40 and 59, 25% between 60 and 74, and 10% over 75 years old.

In 2013, 53% of adults surveyed said that a college degree was worth the costs compared to the 33% that said so in 2025. “The eye-popping shift over the last 12 years comes against the backdrop of several major trends shaping the job market and the education world, from exploding college tuition prices to rapid changes in the modern economy — which seems once again poised for radical transformation alongside advances in AI.”

As has been true for years, data from the US Bureau of Labor Statistics (BLS) has continuously showed that those with advanced degrees earn more and have lower unemployment rates than those with lower levels of education. (more…)

Artificial Intelligence, Complex Systems, Economic Issues, Education and Talent, Future of Work, Innovation, Management and Leadership, Political Issues, Society and Culture
Some Facts about the Recent Impact of AI on Employment

January 14, 2026

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“The proliferation of generative artificial intelligence (AI) has sparked a global debate about its potential impact on the labor market,” said a recent article, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” by Stanford Digital Economy Lab (SDEL) Director Erik Brynjolfsson, Postdoctoral Fellow Bharat Chandar, and Research Scientist Ruyu Chen in its Introduction. “This discourse, across academia, public policy, business, and popular media, spans utopian predictions of enhanced productivity, dystopian fears of widespread job displacement, and skeptical views that AI will have minimal effects on employment or productivity.”

Over the past two centuries, there’ve been periodic fears about the impact of technology-based automation on jobs. In the 1810s, for example, the so-called Luddites smashed the new machines that were threatening their textile jobs. But each time those fears arose in the past, technology advances ended up creating more jobs over the ensuing decades than they destroyed.

Automation anxieties have understandably accelerated in recent years, as AI-based innovations are now being applied to activities requiring cognitive capabilities that not long ago were viewed as the exclusive domain of humans. The concerns surrounding AI’s long term impact on jobs may well be in a class by themselves.

There’s a broad consensus that AI will have a major impact on jobs and the very nature of work, but it’s much less clear what that impact will be. Will AI play out like past technology innovations, — highly disruptive in the near term, but ultimately leading to the creation of new jobs, whole new industries, and a rising standard of living? Or will this time be different, as AI-based innovations end up replacing a large portion of the workforce, — leading to mass unemployment, economic dislocations and social unrest? (more…)

Artificial Intelligence, Economic Issues, Future of Work, Management and Leadership, Political Issues, Society and Culture, Technology and Strategy, Uncategorized
Embodied Artificial Intelligence

January 7, 2026

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“The term Embodied AI is having its moment in the sun right now,” wrote MIT professor emeritus Rodney Brooks in a recent post, “Alan Turing on Embodied Intelligence.” Brooks was a former director of the MIT AI Lab, and the founding director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). In addition, he has founded a number of robotics companies, including iRobot, Rethink Robotics, and Robust.AI.

Brooks has spent much of his career advancing what he calls embodied intelligence — the idea that true intelligence emerges from interaction with the physical world. His post revisited a paper he had written in May 2011, “The Case for Embodied Intelligence,” originally intended for a 2012 conference marking the centenary of Alan Turing’s birth. He included the paper in full, noting that its central ideas — long overshadowed by disembodied, symbolic approaches to AI — are once again attracting serious attention.

Alan Turing—the renowned English mathematician, computer scientist, and cryptologist—published “Computing Machinery and Intelligence” in 1950, one of the most influential papers in the history of AI. It posed the famous question, “Can machines think?” and introduced the Imitation Game, now known as the Turing test.

Less well known is Turing’s 1948 paper “Intelligent Machinery,” in which he distinguished between two kinds of intelligence: embodied and disembodied. Although written earlier, it was not published for a general audience until 1968, fourteen years after Turing’s death. (more…)

Artificial Intelligence, Complex Systems, Innovation, Society and Culture, Technology and Strategy
The World Ahead 2026

December 31, 2025

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A few weeks ago, The Economist published “The World Ahead 2026,” its 40th annual issue examining the trends and events likely to shape the coming year.

Two years ago, the overriding message of “The World Ahead 2024” was the mind-boggling speed at which life was changing—from turmoil in the Middle East and economic uncertainty to the redrawing of the global energy map and the rapid advance of artificial intelligence (AI).

Last year, the dominant theme of “The World Ahead 2025” was uncertainty. As World Ahead editor Tom Sandage wrote in his introduction, “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.”

We are now living in Donald Trump’s world, Sandage writes in the introduction to “The World Ahead 2026.” “The disruptor-in-chief was the biggest factor shaping global affairs in 2025, and that will be the case for as long as he remains in the White House. His norm-shattering approach has caused turmoil in some areas (as in trade) but has also delivered diplomatic results (as in Gaza) and forced necessary change (as with European defence spending). As the Trumpnado spins on in 2026, here are ten trends and themes to watch in the coming year.” (more…)

Artificial Intelligence, Economic Issues, Open Source, Political Issues, Society and Culture, Technology and Strategy
The Post-Training Domestication of ChatGPT

December 27, 2025

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When OpenAI released the artificial intelligence chatbot ChatGPT in late 2022, expectations inside the company were modest. Earlier consumer-facing language models had drawn little interest—or even backlash. Instead, ChatGPT became perhaps the most successful consumer product in history. As a recent New York Times article, “The Race to Build the World’s Best Friend,” reported, in just over three years ChatGPT amassed roughly 800 million weekly active users, stunning both its creators and the broader tech industry.

A major part of ChatGPT’s success is due to the generative pre-trained transformer technology—the GPT in its name—that is used to train OpenAI’s large language model (LLM) with massive amounts of natural language data, enabling it to interact with humans in English and other languages it was trained on.

“But this is only part of the story — and, as OpenAI discovered, perhaps not even the most important part,” noted the article. “In its raw state, the output of GPTs can be off-putting and bizarre. It is only after a second, post-training phase that AI is fit for human interaction. While the engines that power ChatGPT are undeniably impressive, what has made the product succeed is not its capabilities. It is ChatGPT’s personality.”

OpenAI engineers discovered what proved to be a crucial insight: people preferred an AI chatbot that was fine-tuned for human interaction. OpenAI then hired a large number of human evaluators to train the chatbot to generate more human-friendly responses instead of highly accurate but cold, fact-filled ones. Without such post-training, AI could not reliably interact with humans—an insight that proved critical to the success of AI chatbots. (more…)

Artificial Intelligence, Complex Systems, Data Science and Big Data, Economic Issues, Innovation, Management and Leadership, Society and Culture, Technology and Strategy