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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  • Why AI Can’t Replace Software Engineering

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    AI’s potential threat to programming has been widely debated over the past few years, as evidenced by the growing number of provocative headlines. For example, “The End of Programming” argued that “the end of classical computer science is coming, and most of us are dinosaurs waiting for the meteor to hit.” Other articles have predicted that “ChatGPT Will Replace Programmers Within 10 Years,” while NVIDIA CEO Jensen Huang has suggested that coding may no longer be a viable long-term career because of increasingly capable AI systems.

    In addition, several widely discussed studies have identified programming as one of the occupations most exposed to AI and have documented declining employment among younger workers in software-related jobs. Unsurprisingly, parents of computer science students increasingly worry about their children’s career prospects.

    But these concerns raise a more fundamental question: What exactly is being threatened? Is AI making software engineering obsolete, or is it primarily changing the nature of programming?

    “Social media provide a steady diet of dire warnings that artificial intelligence (AI) will make software engineering (SE) irrelevant or obsolete,” wrote Carnegie Mellon computer scientists Mary Shaw and Eunsuk Kang in their 2024 article “Chill, Y’all: AI Will Not Devour SE.” “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.”

    I have long known professor Shaw since we both served in the President’s Information Technology Advisory Committee (PITAC) in the late 1990s. She’s been writing about software as an engineering discipline, going back to her 1990 article on “Prospects for an Engineering Disciplines of Software.” Over the years, I have closely followed her continuing research on the topic.

    “Software engineering (SE) is a rich, robust discipline that covers software systems from idea through their lifetime,” Shaw and Kang explained in their article. SE is “the branch of computer science that creates practical, cost-effective solutions to computing and information processing problems, by applying the best-systematized knowledge available, developing software systems in the service of mankind.” It “encompasses the full scope of software systems from concept to retirement — a full spectrum of issues from understanding what problem the software should solve through overall design, tradeoff resolution, performance, reliability, sustainability, usability, fitness for purpose, programming of components, composition of components, validation, adherence to policy and standards, and evolution.” (more…)

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  • The 2026 State of Tech Talent

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    The “2026 State of Tech Talent Report” was recently published by Linux Foundation Research. “Over the past several years, the Linux Foundation has surveyed hiring and training stakeholders to capture the state of the technical talent market amid technological shifts and economic changes,” wrote authors Adrienn Lawson, Marco Gerosa, and Anna Hermansen in the report’s executive summary. “This year’s study is based on an online survey fielded in February 2026, which collected responses from 400 participants worldwide and examined the impact of AI, especially generative AI, on the IT talent market.”

    The report’s central conclusion is both clear and timely: despite widespread concerns about AI-driven job displacement, the technology sector is facing a skills crisis rather than a jobs crisis.

    “While AI is a net driver of job creation in IT, with a +31% net hiring effect expected for 2026, organizations are struggling with a major full-stack readiness problem,” wrote the authors. “Security concerns are also the #1 barrier to getting value from new technologies. To counter these challenges, organizations strongly prefer upskilling existing staff over external hiring, preserving institutional knowledge and boosting retention.”

    “There is little doubt that AI is changing the IT job market,” the report added. “Adoption is widespread and organizations across industries and regions are integrating AI into core business functions.” This widespread adoption raises important questions. Where do organizations expect AI to deliver value? Is that value coming at the expense of technical jobs, or is it creating new ones? What skills are required to operationalize AI at scale, and how are organizations developing those capabilities? (more…)

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  • The State of AI in Higher Education: Widespread Use, Unclear Rules

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    “Artificial intelligence is shaping how many college students think about their academic paths,” said a recently published Gallup article, “College Students Weigh AI’s Impact on Majors and Careers.” Forty-two percent of bachelor’s degree students say AI has caused them to give serious thought to changing their major, including 13% who say they have thought about it a great deal. Even more associate degree students — 56% — say AI has prompted them to reconsider their field of study.

    These findings reflect a broader reality: AI is already deeply embedded in higher education, even as many colleges and universities are still struggling to determine how students should use it and how faculty should teach it.

    The Gallup article is based on a web survey conducted in October of 2025 by Gallup and the Lumina Foundation among nearly 4,000 US college students pursuing bachelor’s and associate degrees. The results were published in a joint Lumina-Gallup report on AI in higher education.

    The survey found that 57% of students already use AI daily or weekly for schoolwork, while only 13% say they never use it. Yet more than half of students report that their institution discourages or outright prohibits AI use in coursework, and 52% say that at least some of their classes lack clear guidance on acceptable AI use.

    The findings point to a widening gap between student behavior and institutional policy — one with significant implications for academic integrity, teaching practices, and workforce preparation. (more…)

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  • AI Interpretability: Understanding How AI’s Black-Box Brain Works

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    “When Deep Blue, IBM’s chess-playing supercomputer, beat Garry Kasparov in 1997, computers were still just computers,” noted a recent NY Times Magazine article, “We Don’t Really Know How A.I. Works. That’s a Problem,” by freelance writer Oliver Whang. Deep Blue determined the best next move by simulating and assigning values to board positions up to 12 moves ahead — amounting to billions of positions — using algorithms explicitly programmed by its designers. “There was no mystery around what was going on inside them,” wrote Whang, “even though they were, in a way, intelligent.”

    Fifteen years later, everything changed. In 2012, researchers at the University of Toronto developed AlexNet, a neural-network system that identified objects in images far more accurately than previous approaches. AlexNet’s success transformed AI research and accelerated the adoption of deep neural networks across a wide range of applications.

    But there was a catch. Unlike Deep Blue, neural networks operate largely as black boxes. As these models become larger and more capable, they also become increasingly difficult to understand — even for the researchers building them. These systems represent a new kind of machine intelligence whose internal reasoning processes remain poorly understood, even by the researchers building them.

    This has led to the growing field of AI interpretability, whose goal is to better understand how modern AI systems actually work internally, especially as they are increasingly deployed in high-stakes applications ranging from healthcare and finance to law enforcement and military systems. (more…)

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  • The Evolution of Digital Market in the Age of AI Agents

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    “AI agents — autonomous systems that perceive, reason, and act on behalf of human principals — are poised to transform digital markets by dramatically reducing transaction costs,” wrote Peyman Shahidi, Gili Rusak, Benjamin Manning, Andrey Fradkin, and John Horton in their recent NBER working paper, “The Coasean Singularity? Demand, Supply, and Market Design with AI Agents.”

    The authors argue that the broad adoption of AI agents will have far-reaching economic consequences. While the exact outcomes remain uncertain, the underlying forces are familiar: supply and demand will shape how agents are deployed, and technological change will alter the relative costs of economic activities. To understand these shifts, they turn to a foundational insight from economics — Ronald Coase’s 1937 theory of the firm.

    Why do firms exist? This question has long been central to economic theory, most notably to the theories of Ronald Coase, the eminent British economist and recipient of the 1991 Nobel Prize in economics. In his 1937 seminal paper, “The Nature of the Firm,” Coase explained that in principle, firms could rely entirely on open markets to procure goods and services efficiently. In practice, however, markets are not frictionless. Transaction costs — searching for suppliers, negotiating contracts, coordinating work, and managing intellectual property — make purely market-based coordination costly and inefficient. Firms emerged as a way to reduce these costs and organize economic activity more effectively.

    A firm will expand as long as it is cheaper to perform additional activities internally than to contract them out in the marketplace. But this expansion has limits. As firms grow, they often become more hierarchical and bureaucratic, which can slow decision-making and reduce adaptability. Successful organizations therefore seek an optimal balance between internal production and external sourcing — the classic “make-or-buy” decision. (more…)

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  • The Future We Should Want from the AI Revolution

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    “Transformative AI has revolutionary potential,” wrote Stanford University postdoctoral fellow Gabriel Unger in “Economic Possibilities for Artificial Intelligence,” one of the 21 essays in Volume 2 of The Digitalist Papers — a roadmap of the relentlessly advancing capabilities of the AI revolution.

    Unger adds that the AI revolution has arrived at a particularly significant moment. “America has spent most of the past 50 years in a period of stagnant productivity growth.” Income inequality has risen while trust in institutions — and in each other — has declined. “There is both the economic fact of objectively diminished prospects, and the social fact of rising pessimism in opinion polls about our present and our future.” Our real opportunity, he writes, “is about how we might be able to use a profoundly transformative technology to help rescue ourselves from our decades of increasing dissatisfaction and diminished expectations, in the service of a more promising economic and social life.”

    Unger argues that whether we can fulfill this potential depends critically on answering three broad questions:

    • Can we articulate a compelling shared vision of a future with Transformative AI (TAI) that ordinary people will find exciting and compelling?
    • Can we settle on a theory of economic growth that helps us realize the full economic potential of AI?
    • And can we redesign education and strengthen human connections in the age of Transformative AI?

    “There are plenty of AI optimists and AI pessimists,” notes Unger. The pessimists’ fatalism stems from their belief that the future has already been decided, while the optimists believe that the future remains open. The optimists argue that we should turn our attention “to the unresolved questions most important to a better future from AI.” (more…)

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  • Forecasting the Economic Impact of AI

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    Over the past few years, I’ve written a number of posts on the potential economic impact of artificial intelligence. One theme that has consistently stood out is the wide divergence of views on how transformative that impact will be. While there is broad agreement that AI capabilities are advancing rapidly, there is far less agreement on what those advances will mean for economic growth, jobs, and productivity.

    “Among tech evangelists in Silicon Valley, it has become conventional wisdom that artificial intelligence will rapidly reshape the labor market, for better or worse. Economists, however, have often discussed A.I.’s impact with a skepticism bordering on dismissiveness,” wrote Ben Casselman, chief New York Times economics correspondent, in a recent article, “Economists Once Dismissed the A.I. Job Threat, but Not Anymore.”

    “Recently, however, the message from economists has undergone a subtle change,” he added. “Most still do not see much evidence that A.I. is disrupting the job market. But they are starting to take seriously the possibility that it could someday soon. If it does, they are worried that policymakers are not ready to respond.”

    The article cites a March 2026 working paper, “Forecasting the Economic Effects of AI,” based on a survey of how AI might affect the U.S. economy over the next 5 and 25 years. The survey, conducted from October 2025 to February 2026, tracked the views of 69 leading economists, 52 AI industry and policy experts, 38 highly accurate superforecasters, and 401 members of the general public.

    The survey results were summarized in a Substack essay, “Forecasting the Economic Effects of AI,” by the Forecasting Research Institute (FRI). (more…)

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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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