Irving Wladawsky-Berger

A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects.

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  • Managing AI as Both Tool and Teammate

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    Executives have long relied on simple categories to frame how technology fits into organizations: tools automate tasks, people make decisions, and strategy determines how the two work together. But the rise of agentic AI is beginning to blur these familiar distinctions. As noted in “The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI,” a November 2025 report by the MIT Sloan Management Review in collaboration with the Boston Consulting Group, a new class of systems is emerging that does not fit neatly into traditional management frameworks. The study, conducted by Sam Ransbotham, David Kiron, Shervin Khodabandeh, Sesh Iyer, and Amartya Das, explores how organizations are beginning to grapple with this new reality.

    “That framing is no longer sufficient,” the authors wrote. “A new class of systems — agentic AI — complicates these boundaries. These systems can plan, act, and learn on their own. They are not just tools to be operated or assistants waiting for instructions. Increasingly, they behave like autonomous teammates, capable of executing multistep processes and adapting as they go.” Notably, 76% of respondents to their global executive survey said they view agentic AI as more like a coworker than a tool.

    Agentic AI introduces a fundamentally new category: systems that behave partly like software tools and partly like human coworkers. Traditional management logic assumes that technology either substitutes or complements labor, automates or augments tasks, functions as labor or capital, or behaves as a tool or a worker — but not all at once. Organizations now face an unprecedented challenge: managing a single system that requires both human resource approaches and asset management techniques.

    This dual nature creates a set of tensions that traditional management frameworks were never designed to address. “Technology executives focus on technical issues, making pilot, vendor, or infrastructure decisions. IT leaders want predictable, scalable systems with clear technical specifications. Business executives focus on markets, competition, and people. CFOs need investment models with measurable returns and depreciation schedules. HR executives require performance management frameworks and supervision protocols. Business leaders demand both efficiency and adaptability from the same system.” (more…)

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  • How Is AI Shaping the Future of Work?

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    Earlier this year, the National Academies of Sciences (NAS) released “How Is AI Shaping the Future of Work,” a video podcast and accompanying transcript of a conversation between MIT economist David Autor and writer Sara Freuh in Issues in Science and Technology, a quarterly journal jointly published by the NAS and the University of Arizona.

    “For as long as people have speculated about the development of artificial intelligence, they have debated its potential impacts on the labor market,” noted the article in its introduction. “Today, several years into widespread use of large language models, those questions are more urgent, but the answers are less clear. Is AI already taking jobs away? Could human beings flourish in a world in which they no longer have to perform economically valuable work?”

    “As artificial intelligence becomes ever more advanced and capable, it’s being harnessed more frequently in American workplaces, and it’s raising thorny questions about how it will impact the nature and number of human jobs in the future,” Freuh added. “I’m joined today by David Autor, a professor at the Massachusetts Institute of Technology and one of the world’s leading labor economists. He’s known for his research on the effects of globalization, automation, and technological change on the labor market … and he’s written extensively on the subject.”

    I didn’t focus much on economics during my long career as a technologist and business strategist at IBM. But, since becoming affiliated with MIT in 2005, I’ve become increasingly interested in economics — in particular, on the impact of advanced technologies like the internet and AI on jobs and the labor markets. And, I can personally vouch for Freuh’s comments about Professor Autor’s excellent articles, from which I’ve learned so much over the past couple of decades.

    Let me highlight some of the key themes in the conversation between Freuh and Autor. (more…)

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  • What Causes AI Brain Fry?

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    In a recent Harvard Business Review (HBR) article, “When Using AI Leads to ‘Brain Fry,’” participants in a research study conducted by the Boston Consulting Group described experiencing “a mental fog with difficulty focusing, slower decision-making, and headaches” due to excessive use or oversight of AI tools beyond their cognitive capacity. The article’s authors — Julie Bedard, Matthew Kropp, Megan Hsu, Olivia T. Karaman, Jason Hawes, and Gabriella Rosen Kellerman — named this condition “AI brain fry.”

    Their findings are based on a survey of 1,488 full-time U.S.-based workers (48% male, 51% female) at large companies across a variety of industries, roles, and levels. The survey asked about patterns and intensity of AI use, work experiences, and cognitive and emotional effects.

    The authors concluded  that AI brain fry arises not from the technology itself, but from the “excessive use or oversight of AI tools beyond one’s cognitive capacity.”

    “This problem is becoming more common,” the authors wrote. “As enterprises use more multi-agent systems, employees find themselves toggling among more tools. Contrary to the promise of having more time to focus on meaningful work, juggling and multitasking can become defining features of working with AI.” Unsurprisingly, workers are running up against the limits of their cognitive capacity. In recent weeks, online AI users have described increased cognitive load, “saturated” attention, and mental fatigue in social media posts. (more…)

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  • Know Your Agent (KYA): Governing AI in the Enterprise

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    “Agents will be embedded in business processes with direct impact on operations, data, and compliance,” wrote Eric Broda and John Y Miller  in “KYA — Know Your Agent,” a recent article in their Agentic Mesh Substack. Broda is the founder of Broda Group Software, co-founder of The Agentic Mesh Company, and co-author of Agentic Mesh, a book published in February 2026; Miller is an Agentic AI Systems Builder and Podcast Host.

    What Is KYA and How Does It Work?

    In the early days of the digital economy, organizations learned that trust could not be assumed — it had to be engineered. That realization led to the development of frameworks like Know Your Customer (KYC) and Know Your Business (KYB), thus enabling institutions to verify identities, manage risk, and operate at scale in increasingly complex environments. “KYC and KYB exist for a simple reason: when a bank or regulated firm lets someone open an account, move money, or access sensitive services, the firm needs confidence about who is on the other end and what risks they bring.”

    A similar challenge is now emerging, but with a new kind of actor: AI agents. “Agents will be embedded in business processes with direct impact on operations, data, and compliance. This creates three engineering requirements: limit what the agent can do, record what it did, and explain why it did it.”

    In the emerging age of AI, enterprises face a fundamental question: how do you establish trust, accountability, and controls when the “actor” is no longer a person or an organization, but an intangible asset, i.e., software acting with increasing autonomy?

    Know Your Agent (KYA) is one answer to that question. It extends the principles of identity, risk management, and governance from people and institutions to AI agents operating at scale — ensuring that their behavior is transparent, auditable, and aligned with organizational objectives. (more…)

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  • The Economic Value of Open Source Software Contributions

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    “Organizations leveraging open source software (OSS) face a critical strategic question: should they merely consume OSS, or should they contribute upstream?,” asked a February 2026 report, “ROI for Open Source Software Contribution,” in its executive summary. “For years, the answer has been obscured by uncertainty about costs, benefits that are difficult to quantify, and a perception that contribution is primarily altruistic.”

    “In an effort to resolve these ambiguities, the Linux Foundation ran a survey in late 2025 to understand the investments, benefits, and ROI of contributions to open source software projects.” The survey was conducted by Linux Foundation Research (LFR) and sponsored by the Cloud Native Computing Foundation, Toyota, and Intel.

    The economic value of open source software (OSS) as a development paradigm has been extensively studied and quantified over the past few decades. In my opinion, one of the strongest indicators of its success is the impressive growth of the Linux Foundation since its founding in the early 2000s., which now includes roughly 1,500 member companies supporting a wide variety of open source projects.

    In addition, a number of recent studies have quantified the economic value of OSS. For example, “The Value of Open Source Software,” a 2024 article by Manuel Hoffmann, Frank Nagle, and Yanuo Zhou, estimated that the cost of recreating widely used OSS would be around $4.2 billion, depending on assumptions, while the value to companies using OSS ranges from $2.6 trillion to $13.2 trillion.

    To further quantify the benefits that organizations derive from OSS, the Linux Foundation sponsored a study led by UC Berkeley professor Henry Chesbrough, based on a survey of companies that have embraced open source. “The perceived benefits clearly exceed the perceived costs for a strong majority of respondents — 60% to 75%, depending on the specific question,” wrote Chesbrough in “Measuring the Economic Value of Open Source.” “And the ratio of benefits to costs appears to be rising for nearly half of respondents, while only 16% felt that the ratio was declining. This strongly suggests that the value of OSS will continue to increase for most participating organizations.” (more…)

  • There Is Time to Adapt AI If We Use the Time Wisely

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    The January 31, 2026 issue of The Economist included a special focus on the mounting anxiety about the social consequences of AI, with four articles devoted to the subject. “Solving fiendish maths problems, making complex medical diagnoses, conjuring up new software in moments: the feats of generative AI get more impressive by the day,” noted the issue’s lead article, urging readers to “Stop panicking about AI. Start preparing.”

    While the future course of AI is obviously uncertain, there are good reasons to believe that society has time to prepare and adapt. “It takes time for a new technology to diffuse from the cutting edge to the office cubicle,” the article noted. Firms and governments should use this breathing space to help those most at risk of displacement.

    “So far labour markets seem unruffled. Service jobs are most exposed to generative AI, yet in America the number of white-collar jobs has gone up by 3m since ChatGPT was launched, while blue-collar jobs have stayed flat. Employment has risen even in areas that have been keen adopters, such as coding.” One reason for the slow economic impact is that while AI excels at some tasks, it also “confidently spouts nonsense, or struggles to count the number of ‘r’s in ‘strawberry.’” This unpredictability means that companies and workers need time to figure out where, and how, to apply AI effectively.

    “Moreover, business processes don’t change overnight. Electricity was first harnessed commercially in the 1880s, but it took 40–50 years to generate productivity gains on factory floors. Plants had to be redesigned and workflows rethought.” This time, too, companies must think carefully about how to encourage workers to use AI, how to mitigate its shortcomings, and how to deploy it successfully.

    Realizing the potential of a general-purpose technology (GPT) — like steam power, digital computers, and now AI — requires large investments and a fundamental rethinking of how production is organized. It takes considerable time for these technologies and new business models to be widely deployed across economies and for their full benefits to be realized. (more…)

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  • Large Company CEOs Are Leading AI Adoption

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    “Chief executives of some of the world’s largest companies are all-in on artificial intelligence, though many haven’t yet seen meaningful returns on their investments,” said the WSJ in a recent article, “CEOs to Keep Spending on AI, Despite Spotty Returns.” The article is based on the 2026 annual survey of Teneo, a global CEO consulting and advisory firm. “After a year in which trillions of dollars worth of AI investments buoyed global markets and the economy, 68% of CEOs plan to spend even more on AI in 2026,” according to the Teneo survey of more than 350 public-company CEOs.

    “Teneo also surveyed about 400 institutional investors, of which 53% expect that AI initiatives would begin to deliver returns on investments within six months,” the WSJ article added. “That compares to the 84% of CEOs of large companies — those with revenue of $10 billion or more — who believe it will take more than six months.”

    Teneo’s Vision 2026 survey was conducted from mid-October to mid-November, 2025. Let me summarize its key findings.

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  • What Happens When Genius AI Becomes Abundant?

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    “Transformative AI will generate a genius supply shock: abundant, cheap, and fast agents that can outperform human beings across many domains. But society is likely to adapt too slowly to this remarkable but unfamiliar new capability,” wrote University of Toronto professors Ajay Agrawal and Joshua S. Gans in the introduction to their essay, “Transformative AI and the Increase in Returns to Experimentation: Policy Implications.” Their essay, published in Volume 2 of The Digitalist Papers, explores policies “that can help companies, regulators, and individuals learn how to use these powerful new tools and put them to effective use.”

    What Is the Genius Supply Shock?

    The authors cite a few concrete examples of what they mean by a genius supply shock. “Humanity’s Last Exam” is a test developed over the past couple of years by a team of researchers who claim it’s the hardest test ever administered to an AI system. The exam includes roughly 3,000 multiple-choice and short-answer questions designed to test AI systems’ abilities in areas ranging from analytic philosophy to rocket engineering.

    “Unlike traditional AI evaluations, which test for narrow capabilities in isolated tasks, this benchmark simulates the challenge of a PhD qualifying exam merged with a generalist’s oral defense,” wrote Agrawal and Gans. “Questions are long-form and open-ended. To score well, an AI must not only know, but understand. Success requires what we typically associate with our highest-functioning minds: flexible reasoning, conceptual abstraction, and the capacity to transfer knowledge across domains. Until recently, no machine had come close to passing.”

    In 2024, researchers gave the Humanity’s Last Exam to six leading AI models. “All of them failed miserably,” said a NY Times article. The AI model with the highest score got only 8.3 percent correct answers. The percentage of correct answers improved significantly over the next two years, with a few models achieving accuracies above 40 percent, — putting them within striking distance of high-performing postgraduate scientists.

    In their essay, Agrawal and Gans noted that the advances realized in 2025 “were anticipated by, among other experts, Dario Amodei, cofounder and CEO of AI foundation model company Anthropic, who described forthcoming systems as providing a country of geniuses in a datacenter.” Each such genius, Amodei, added, would be “smarter than a Nobel Prize winner across most relevant fields — biology, programming, math, engineering, writing, etc.” These systems do not simply automate routine tasks, he speculated; they synthesize knowledge across domains, propose and critique solutions, and do so at digital scale. Unlike human experts, they are cheap, abundant, and tireless. (more…)

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  • The Economic Impact of Transformative AI

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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…)

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  • The Economic Impact of the Emerging Age of AI

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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…)

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