Stefaan Verhulst

Substack by Stefaan Verhulst: “We live in a paradoxical era. On the one hand, data is growing exponentially, computational modeling and artificial intelligence are advancing at breathtaking speed, and knowledge is deeper and more specialized than ever before. Yet on the other hand, rates of transformative discovery appear to be slowing rather than accelerating across important domains, and crises of replication and generalizability seem to be intensifying. Across the world, research outputs remain stubbornly misaligned with the problems societies—and people—describe as most important to them. In short, we are living through an era of both epistemic abundance and epistemic crisis.

Several explanations are offered for this apparent gap. Misinformation is said to be eroding the foundations of shared knowledge. Cultural polarization is often cited as a barrier to the collaborative inquiry and pursuit of objectivity on which science depends. Outdated incentive structures—publish or perish, funding biases, information overload—are also mentioned. Each of these no doubt plays a role. But an equally important source of the problem lies further upstream, at a stage of knowledge production that receives surprisingly little systematic attention: the questions we ask.

Across sectors and fields, many of society’s most significant shortcomings today stem not from insufficient data or inadequate analytical tools but rather from the framing of inquiry itself. Scientific agendas are shaped by questions that are too narrow and overly method-driven. Policy is constrained by electoral cycles and available metrics. Even the private sector, for all its celebrated dynamism, asks questions bounded by existing business models and short-term returns.

The consequences of such misdirected inquiry are not abstract. It is now well established that women’s health remained systematically underfunded and understudied for decades–not because the tools to investigate it were lacking, but because the questions that shaped biomedical research agendas were framed around male bodies and male norms. Likewise, pandemic preparedness in the pre-COVID era was repeatedly optimized for speed of vaccine development while neglecting questions about distribution, trust, information, and misinformation. In both cases, the answers developed by experts were technically competent. The questions they asked were wrong, or at least incomplete.

All these shortcomings acquire extraordinary new urgency in the age of artificial intelligence. As AI systems become increasingly powerful “answering machines,” capable of generating immediate, plausible-sounding responses to almost any query, the bottleneck in knowledge creation is shifting: from generating answers to formulating the questions worth answering in the first place. The cheaper answers become, the more valuable questions are.

For all the sophistication of our analytical tools, for all the power of our new technologies, and for all our acute and pressing social needs, we still have no science of questions: no systematic infrastructure for studying how questions emerge, how they can be improved, or how they shape the knowledge that societies produce. This essay argues that we need such a science of questions. It traces the intellectual roots of a question-centered approach to knowledge (Section I), diagnoses the institutional failures that perpetuate what we call a “question deficit” (II), examines the politics of who gets to frame inquiry (III), and outlines the key elements of a science of questions (IV)—one made both possible and urgent by the rise of AI…(More)”.

Article by Deepak Bhargava and Felicia Wong: “Liberal democracy is in crisis in the United States, and the roots of that crisis go far deeper than any single politician or political party. Almost 70 percent of Americans do not believe our democracy is working well. Governing elites and the institutions they run have lost credibility. Pervasive and justified anger has led many to embrace dangerous alternatives or to sit out the democratic process altogether. Ours is an anti-system age.

But surprisingly, many policymakers and civil society leaders seem to believe that upcoming elections will allow for a return to “normal,” and enable them to work more or less as they always have. Legislative advocates are now sharpening their campaigns, jockeying over which issues—health care, voting rights, housing, child and elder care, and so on—should take priority. Political and communications consultants are parsing which policies might address voters’ focus on the affordability crisis. Some problems are new, as recognized by experts puzzling out how to rebuild federal agencies decimated by the Department of Government Efficiency (DOGE). But overall, the approach to governance that seeks to make relatively modest change within a broken system has come roaring back. Even recent ideological debates feel like the rehashing of decades-old arguments. We have seen this movie before, and we know how it ends. The gravitational, habitual pull toward familiar ways of working is very strong.

The hard truth is that in the absence of a dramatically different approach to governing, authoritarianism and oligarchy will become even more entrenched, and far harder to uproot. Both have deep roots in the country’s history. These forces will not go away no matter what the next elections bring. Another even more devastating authoritarian breakthrough is the almost predictable result unless future governance makes a break from the past. The stakes are very high…(More)”.

Report by the Federation of American Scientists (FAS): “Data center development has outpaced most local governments’ capacity to respond to it, as evidenced by the increasing amount of moratoria legislation to give time for policy development and conversations with communities.¹ As data center applications ramp, so have requests for policy guidance by local governments. Broadly, we are starting to see nascent best practices, understanding more clearly the knowledge gaps and research needs, and building a community of practice across industries. This report provides new information for local government policy makers, including property value impacts and a robust collection and analysis of community benefit agreements. 

Over the course of 2026, the Federation of American Scientists (FAS) has conducted primary research, including interviews in the field, issued public records requests for difficult-to-find agreements, and created a series of trackers on new ordinances, state legislation, and moratoriums. This report serves as a landscape assessment and toolbox from which local governments can negotiate an informed position when it comes to the levers available to them and includes a first-of-its kind analysis of ten executed agreements between local governments and data center developers.

This report includes three sections:

Ordinances Analysis: provides a brief overview of what local governments are already doing through a landscape scan of 42 local ordinances and 12 state actions.

Data Center Property Values: provides a sampling of data for taxable value over time across six jurisdictions.

Community Benefits Agreements (CBA) – Review and Comparison: provides a comparative review of ten CBAs, identifying common requirements, what is offered as a benefit to the local government and to the developer, and what policy considerations are included within the agreement…(More)”.

Essay by Dennis M. Hogan: “Imagine a large online lecture class taught by a tenured professor with the assistance of AI tutors. The professor writes and delivers her lectures once; they are infinitely re-playable as long as the course content does not change. Meanwhile, AI agents can design assessments, deliver course material beyond lectures, hold students’ hands through the completion of the assignments, and even evaluate students’ work, eliminating the need for human grading. The professor can simply review the AI’s work and enter the grades, though even this is essentially a formality—there’s nothing stopping the AI agent from entering the grades with the registrar directly. What I have just described is a class that costs almost nothing in labor, especially after the professor recorded the lectures in a previous semester. A really ambitious university might assign a single grad student or teaching assistant to the class just to oversee the automated processes, but even this isn’t strictly necessary. The machinery runs on its own.

If you, like me, are attached to the old model of education, which presupposes that teachers, in delivering instruction, enter into personal relationships with students, you might find this scenario depressing. After all, if no one is really teaching, is anyone really learning? If, on the other hand, you are a college administrator desperately trying to align your instructional obligations to your budgetary limitations, you might find such a model enticing. As a student, you might find it alienating, but then again, if you are already used to Zoom school and distance learning, it might not feel all that different. And perhaps the course doesn’t matter to you all that much. You’re just trying to get a requirement or a prerequisite out of the way. This class is not going to change your life. 

Much of the discourse around generative AI in education revolves, fairly or unfairly, around the ways that students can use the technology to do their work for them. AI is frequently connected to what commentators have called a “literacy crisis”—a longstanding decline in Americans’ levels of reading fluency affecting adults as well as children. While some early childhood educators and policy experts are optimistic about the potential for AI tools to improve childhood reading acquisition alongside traditional classroom instruction and reading with adults at home, many college instructors have been more skeptical, suggesting that students who rely on AI tools lack the motivation to absorb written texts and do not build the skills to understand them effectively. But if you listen to its boosters in academia and industry, AI is much more than a cognitive shortcut or a text-generating machine: It is a technology that is rapidly threatening to transform the entire economy, as companies make massive bets on its potential to increase productivity and even solve previously insoluble problems such as cancer and climate change. It could even amount to a new industrial revolution. So far, the promises of AI have been slow to materialize: Although the technology offers applications for tasks like coding, there remains an abiding sense of confusion about whether—and how—to integrate AI products into other lines of work. In fact, among the general public, AI skepticism abounds. This probably has something to do with the fact that AI boosters tell us, repeatedly, that AI is going to put many of us out of work…(More)”.

Chapter by Melisa Ross & Azucena Morán: “Democratic innovations allow for the direct involvement of lay citizens, for instance, in climate governance. Latin America, where these approaches have multiplied, offers a compelling context to examine to what extent they help face the interrelated challenges that characterise the Anthropocene, and the resulting planetary condition. We discuss three types of democratic innovations, based on intermediation, deliberation, and mobilisation. They aim to manage the conditions for life, seek the consent of affected communities for the continuation of the planetary condition, or counter its causes and effects. We argue that the limits and ambiguities of these innovations are better understood through historically grounded challenges to the concept of the Anthropocene. In particular, the Plantationocene foregrounds colonial and extractive histories that constrain political action. Democratic innovations alone cannot undo systemic trajectories of exploitation and inequality; historicised and politically anchored lenses such as the Plantationocene better shed light on how they also operate as counter-hegemonic struggles for justice, sovereignty, and habitability…(More)“.

Open Access Book edited by Sahana Udupa and Peter Hervik: “…provides a groundbreaking collection of anthropological research on artificial intelligence (AI), examining how anthropology can help to comprehend and critique its development. Evaluating the limits, hopes and fears of AI, leading experts explore its influence as a sociocultural phenomenon rather than a discrete technological system.

Through an ethnographic lens, the Handbook analyzes human–machine entanglements as they have emerged in AI companions, healthcare, automated policing, warfare, conversational chatbots and fact-checking. Chapters assume theoretical perspectives to scrutinize AI power and how AI evolves on the ground in ethnographic locations and case studies from across the globe. Assessing the current state of AI, the collection examines its implications for the future, in areas ranging from climate change to politics…(More)”.

Paper by Donald Moynihan: “The second Trump administration saw the Department of Government Efficiency (DOGE), a group of outsider technologists given extraordinary power to pursue a massive downsizing of the federal government. They also displaced an alternative approach: the civic tech movement. Civic tech for government applies technology, data, and human-centered design to improve policy implementation, fueled by prosocial motivation, promising to increase state capacity in a way that centers attention on public service clients. This article explains the origins of civic tech for government in the US setting, its core values, and evolution. The movement grew in prominence and influence from 2014 to 2024, making steady inroads into government but never gaining sufficient power to implement profound change. The conflicting origins, philosophy, and power between civic tech and DOGE offer contrasting visions for how tech can reshape the American administrative state…(More)”.

Paper by Marco Marini, Jim Tebrake, and Andinet Woldemichael: “This paper presents a methodology for downscaling official national and subnational macroeconomic data into high-resolution grids using spatial machine learning techniques. Traditional macroeconomic data are highly aggregated and obscure the spatial distributions needed to understand and quantify local economic activity, impacts of physical risks, and infrastructure gaps. Our hierarchical approach integrates official macroeconomic accounts with Earth observation predictors, such as nighttime lights, built-up areas, land cover, and gridded population, to deliver high-resolution, gridded estimates that remain fully consistent with official subnational and national totals. The empirical application focuses on constructing experimental gridded GDP by ten-sector industry for Canada and the United States. The methodology complements and enhances official statistics by adding spatial granularity through open geospatial datasets and can be extended to generate gridded capital stock estimates…(More)”.

Initiative by the University of Chicago: “Papers have been the de facto unit of knowledge in science. But science is not static and a paper is not meant to be the final destination, especially in empirical research. A paper may propose a novel way to look at the world and offer a snapshot of what it finds. That snapshot is far from the whole story.

The world keeps changing after a paper is published. New data, new models, and new methods give us a chance to return to its questions: does the pattern persist, does the effect size remain the same, and what has changed? As AI agents become more capable, we have an opportunity to make revisiting important work a routine part of science.

Today, we are introducing Living Science, a project to keep influential research alive by revisiting its findings as new data, models, and methods become available. We are starting with economics, focusing on studies whose data sources are regularly updated. The goal is to not assess whether the paper was correct at the time of publishing but to provide resources and venues for continued discussions of influential research.

We use SAI’s replication agent to attempt to reproduce each paper’s key findings, documenting what matches and what does not, before extending the analysis to newer data where possible. The AI agent will first look for public replication packages if available and then identify relevant data sources for extending the analysis. As a result, we may not use exactly the same dataset and setup. The exercise is closer to writing a follow-up paper than strict replication. We sanity-check the results and reach out to the original authors for feedback. We also invite the community to identify issues and post comments, helping us improve the analyses over time…(More)”.

Report by the WEF: “As AI, clean energy, advanced manufacturing and infrastructure renewal accelerate, there is strong demand for the practical, hands-on skills needed to build, operate, maintain, repair and produce the goods and systems that economies depend on.

This third instalment of the New Economy Skills series examines how these maker skills are becoming a strategic advantage for economies and businesses. Drawing on unique data and insights, the report analyses global supply and demand trends. It finds that the maker skills pipeline is not yet prepared for the scale, pace or complexity of current and future demand. The report outlines how to address three key challenges: adapting to changing skill requirements, attracting more people into maker careers, and ensuring that maker skills are easier to assess, recognize and credential…(More)”.