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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  • The Democratization of (Artificial) Intelligence

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    “New technologies don’t emerge in a vacuum,” wrote Sarah Friar, Chief Financial Officer of OpenAI in “The Democratizing of Intelligence,” one of 21 essays in The Digitalist Papers Volume 2, — a roadmap of the relentlessly advancing capabilities of the AI revolution. “In the beginning, they often reflect and amplify the inequalities of their time — access is shaped by where you live, what you earn, and the systems around you. Then, broader diffusion takes hold. That is what we have seen in the past, with previous waves of technological progress. And that is what we see with AI today.”

    Friar referenced a recent study that tracked the use of ChatGPT over the three years since its launch in November, 2022. As has generally been the case with new technologies, the majority of early adopters were young, highly educated males in living in high-income countries. But by mid-2025, the story had started to change. ChatGPT had already been adopted by 800 million active users, which is roughly 10% of the world’s adult population. The gender gap has narrowed dramatically, and the growth rate is higher in lower-income countries. While there is steady growth in work-related usage, — which  is more common for educated users in higher paid professional occupations, the growth has been even faster in non-work related activities, which grew from 53% to more than 70% of all usage.

    “The data suggest that AI is being democratized faster than previous technologies, with early disparities in geography and gender closing at a remarkable pace,” wrote Friar. Her essay summarized the key changes in the use of ChatGPT over the past three years:

    • Growth in low- and middle-income countries outpaced rich countries by more than four to one over the past year.
    • As of mid-2025, internet-enabled penetration in countries at the 25th percentile of GDP per capita matched adoption in high-income economies.
    • The gender gap closed: users with typically feminine first names slightly outnumbered those with typically masculine names.
    • While all activity grew, non-work usage grew faster than work-related ones, climbing from 45% of traffic in July 2024 to more than 60% by mid-2025.
    • Nearly 80% of conversations involve practical guidance, information seeking, or writing, indicating that people are mostly trying to get something done.
    • Education stands out —10% of all messages, and nearly 40% of practical-guidance queries, are tied to tutoring or teaching.
    • Among work tasks, writing leads the way, with editing and improving existing text outpacing new content creation by two to one, demonstrating that people are using ChatGPT as a tool to augment their work, not do it for them.

    So, will AI democratize intelligence or will it lead to a new kind of AI divide? (more…)

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  • The 2026 AI Index Report

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    A few weeks ago, the Stanford Institute for Human-Centered Artificial Intelligence (HAI) released the 2026 AI Index Report, its ninth 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 offers one of the most comprehensive, data-driven views of the continuing evolution of artificial intelligence.

    “A year ago, this report documented AI’s arrival as a mainstream force,” wrote the report’s co-directors Yolanda Gil and Raymond Perrault. “This year’s data shows what happens after arrival.”

    “This is a technology that has reached mass adoption faster than the personal computer or the internet. Generative AI hit nearly 53% population-level adoption within three years. Leading AI companies are reaching meaningful revenue scale in a fraction of the time it took previous technology generations, and global corporate investment more than doubled in 2025. Organizational adoption rose to 88%, and early estimates suggest the consumer value of generative AI has grown substantially within a year.”

    “The data does not point in a single direction,” they added. “It reveals a field that is scaling faster than the systems around it can adapt.” (more…)

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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.

    (more…)

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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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