Stefaan Verhulst

Article by Stefaan Verhulst and Claudia Chwalisz: “The race to build the infrastructure of artificial intelligence is accelerating. Across the world, fields, industrial parks, and suburban edges are being transformed into data centers — vast, warehouse-like facilities that power everything from cloud storage to large language models.

For technology companies, this expansion is claimed to be essential. For the communities where these facilities are built, it is becoming increasingly contentious.

Recent reporting in The New York Times and elsewhere has captured the growing unease. Residents are questioning the scale of water consumption required to cool servers, the strain on local energy grids, and the transformation of landscapes once defined by entirely different economic and environmental logics. In many cases, the promised benefits — jobs, investment, growth — feel limited when set against the demands these facilities place on shared resources.

What is emerging is not simply a series of local disputes. It is a broader challenge of legitimacy.

There is a concept for this, though it predates the digital economy. In the 1990s, mining and energy companies (often called extractive industries) began to recognize that regulatory approval was no longer sufficient to ensure that projects could proceed smoothly. Communities could — and did — push back against developments that were fully legal but widely perceived as unfair or harmful. The term that emerged to describe what was missing was “a social license to operate”.

A social license is not granted by governments. It is conferred, informally but powerfully, by the people who live with the consequences of a project. It depends on trust, on transparency, and on a sense that the balance between costs and benefits is acceptable. Crucially, it is not static. It can be strengthened over time — or withdrawn.

Data centers are now encountering this reality…(More)”.

White Paper by the Siegel Family Endowment: “We’re living in an era of unprecedented information abundance, yet still struggling to generate real insight. The issue isn’t a lack of data, but a lack of well-formed questions. The way we frame problems—and who gets to frame them—shapes everything that follows.

Better Questions, Better Insights introduces the emerging science of questions: a more rigorous approach to defining, testing, and refining the inquiries that guide our work.

At Siegel Family Endowment, this approach has shaped an inquiry-driven model of philanthropy—one that moves beyond linear solutions toward deeper systems change.

This paper offers a practical framework for embedding inquiry into decision-making, helping organizations move from information to insight—and from insight to impact…

This paper is an invitation. A look under the hood at how we’ve approached inquiry in our own work, and a starting point for shared exploration.

As the complexity of societal challenges grows, our approaches must evolve with it. That means embracing a more rigorous practice of curiosity—asking better questions, together—and expanding who gets to ask them.

If we can do that, we have an opportunity to modernize and democratize philanthropy in ways that better meet this moment…(More)”.

Report by the World Economic Forum: “Agentic artificial intelligence (AI) is driving a fundamental shift in capability, allowing systems to autonomously execute end-to-end, multi-step workflows. This technological progress is poised to transform how governments operate and serve citizens. However, without a strategic, evidence-based grasp of where agentic AI can deliver the greatest public value – balancing high potential with manageable complexity – governments risk investing in the wrong places, undermining confidence in the technology and launching pilots that fail to scale…(More)”.

Initiative by Hugging Face: “Many of the deepest challenges in advancing AI for scientific discovery are not purely technical—they are social and organizational. Progress is often limited not by algorithms or computational power, but by how effectively we coordinate efforts, share resources, and collaborate across disciplinary boundaries. Hugging Science brings together a global community of researchers, developers, and practitioners committed to accelerating breakthroughs in physics, biology, chemistry, neuroscience, and beyond through open collaboration.

Our vision is grounded in the argument presented in our position paper: democratizing AI for science requires treating it as a collective social project where equitable participation and sustainable collaboration are prerequisites for technical progress.

What We Do

• Launch collaborative challenges and open problem calls to identify and mobilize collective effort around upstream computational bottlenecks with broad applicability across scientific domains—such as efficient PDE solvers, multi-scale coupling, and high-dimensional sampling—rather than fragmenting resources across narrow, domain-specific applications
• Build open toolkits, benchmarks, and workflows that address data fragmentation through standardized formats and shared evaluation metrics, making it easier for researchers at institutions of all resource levels to collaborate and build on each other’s work
• Support cross-disciplinary exchange and education by creating resources that bridge the communication gap between domain scientists who prioritize mechanistic understanding and ML researchers who focus on predictive performance, enabling more effective collaboration
• Nurture a community that values contributions to data curation, infrastructure development, and educational resources alongside algorithmic innovation—recognizing that datasets and infrastructure often have far greater long-term impact than individual models
• Learn together through open discussion of both technical advances and the social and institutional barriers that constrain progress, working to align incentives and build sustainable practices for scientific AI..(More)”.

Blog by Michael Hallsworth: “Last week, I was in San Francisco for the HumanX conference. Listening to people there pushed me to ask a question that’s been bouncing around in my head with increasing insistency:

What’s the psychological impact of being the human in the loop?

I feel like this issue is a time bomb that could destroy current plans of how AI will be governed. If you listen to any AI policy conversation for more than a few minutes, you’re likely to hear the phrase “human-in-the-loop” (HITL). It’s a catch-all term that provides reassurance and allow us carry on with the technical discussion. Like in the workplace, if we just keep the right people “in the loop,” all will be well.

The idea evokes an image of a capable, watchful person who will intervene expertly if the system goes wrong. Whole governance frameworks are built on top of this comforting picture. For example, Article 14 of the EU AI Act tries to put a set of requirements on humans to “prevent or minimise the risks to health, safety or fundamental rights”.

But the Act says nothing about whether these humans will have the skills, attention, or motivation to perform this oversight. Or, even if they can, for how long. Or what the experience would be like.

In other words, we’re not thinking enough about what it actually feels like to be the human in the loop.

I find that gap increasingly hard to ignore because billions (?) of humans-in-the-loop may soon face two contrasting problems that we’ve been neglecting:

• Verification burdens caused by too much cognitive stimulus;1
• Vigilance atrophy caused by too little stimulus.

The tricky thing is that these two risks can affect the same person on the same day. Moreover, they call for almost opposite responses. Even trickier! Here I suggest how we should start tackling this problem…(More)”.

Paper by Colleen Thouez and Raphaela Schweiger: “Municipal leadership has become increasingly central to addressing global challenges such as war-related displacement, migration governance, and climate change, reflecting a broader shift toward polycentric and networked forms of multilateralism. This study examines how cities have expanded their international roles over the past decade, responding to governance gaps with pragmatic, people-centred action. Using a qualitative, theory-informed comparative case study design, it draws on three original case studies grounded in direct practitioner experience: European municipal cooperation supporting Ukraine during war; city engagement in shaping the Global Compact for Migration; and municipal leadership in advancing climate action and the emerging climate mobility agenda. Across these cases, the analysis identifies consistent patterns of multi-scalar municipal agency, including decentralized humanitarian action, norm-setting in international negotiations, and innovations in multilevel climate governance. Cities leverage transnational networks—such as the Mayors Migration Council and the C40 Cities Climate Leadership Group—to amplify political influence, exchange solutions, and secure resources, even as fiscal pressures and political polarization increasingly constrain local capacity. It concludes that cities are becoming important actors in shaping global governance, yet their effectiveness depends on institutionalized representation, enhanced fiscal autonomy, and stronger protections for local leaders. Embedding municipalities more fully within evolving multilateral architectures can better align global commitments with local implementation and improve the resilience and legitimacy of international policy coordination..(More)”.

Book by Gustavo Moreira Maia: “… the first comprehensive examination of how states can transition from reactive bureaucracies to anticipatory public services. Written for policymakers, practitioners, and scholars, it combines theoretical frameworks with practical implementation guidance.

The book argues that expanding state informational capacity creates both an opportunity and an obligation to redesign public services around citizen life events rather than administrative processes.

“The principal limitation of contemporary digital government does not lie in the absence of technological tools, but in the persistence of an action model structured to respond after the request, and not to recognize the context.”

Through detailed analysis of global examples and original case studies, the book maps the conditions under which proactive government becomes possible, desirable, and democratically legitimate…(More)“.

Book by Josh Tyrangiel: “AI is often framed as a force of radical transformation, either catapulting us into a utopian future or dragging us toward existential ruin. But this book tells a different story. It’s not about high-profile tech CEOs who want to use AI to “break shit,” but about a bunch of smart pragmatists using AI to make the world better.

Josh Tyrangiel’s journey into AI began with a late-night YouTube video featuring General Gustave Perna, the retired four-star general who orchestrated the distribution of Covid vaccines during Operation Warp Speed. Perna’s success—and the end of the pandemic—depended on AI’s practical ability to synthesize and standardize vast amounts of logistical data. AI wasn’t the hero of the story—it was the tool that helped real people get things done.

This book follows those people, who make up a kind of AI counterculture. It explores AI’s quiet revolution in government services, medicine, education, and human connection—places where it’s being used to amplify human judgment rather than replace it. It tells the stories of teachers, doctors, and bureaucrats who often stumbled into AI as a means to solve specific, tangible problems, often with no prior software expertise.

While the loudest voices in AI debate doomsday scenarios and trillion-dollar market opportunities, this book focuses on those working in the messy, incremental, but deeply impactful space of AI practice. However, there is one big caveat—success is not guaranteed. Change is hard. Institutions move slowly. But even in failure there are lessons for everyone who’s interested in using AI—carefully, thoughtfully—to build a better world today…(More)”.

Essay by Henry Farrell & Cosma Rohilla Shalizi: “…. LLMs create social relations between their users and the authors of the text in their training corpora. With the right access to the model and the corpus, one can trace the connections from system output back to individual source texts and their authors (Grosse et al., 2023). These social relations are mechanically mediated, giving users the illusion that they are interacting with just the machine and not an assemblage of people. But mediated social relationships and their illusions are a common fact of modern life. The social relations created by LLMs in turn cut across, and interact with, other social relations, including those shaped by other social technologies.

Our goal here is to clear a common space where the social sciences and computer science and engineering can discuss the social consequences of AI. We draw heavily on the ideas of Simon (1996), who saw AI, political science, administration, economics, computer science, and cognitive psychology as so many branches of the “sciences of the artificial,” studying how human beings create “artifacts” that model, and act on, their environment. From this perspective, AI models are another means of “complex information processing” (Newell and Simon, 1956). As Simon emphasizes, such systems encompass both information technologies, as studied and built by computer scientists and engineers, and social information systems such as markets, bureaucracy, and, although Simon himself does not stress this, democracy (Lindblom, 1965). All such systems process information by reducing complex realities into more tractable ‘coarse-grainings’ or abstractions that (hopefully) capture important features of the data. Producing coarse-grainings is not all that large-scale social institutions do, but it is quite important. Economic, administrative, and political coordination simply cannot work at scale if complex social relationships are not compressed into visible, tractable representations…(More)”.

Book by Turi Munthe: “Our opinions – whether we believe in God or in ghosts, our views on sex or animal rights or immigration, our basic sense of what’s good or fair – are shaped by a breathtaking web of hidden forces. The age-old idea that our views are forged by reason and evidence alone is wrong: we are influenced by everything from the quirks of distant history, through the geology of where we grew up, to the lines of our genetic code.

This astounding book takes us through culture, biology, geography, history, psychology and much more to uncover the hidden DNA of our opinions. It reveals:

• why the descendants of rice farmers have different values to the descendants of grain farmers
• how our physical appearance shapes the way we see the world – and why conventionally attractive people tend to support the free market
• why liberals think pineapple should go on pizza, and why conservatives prefer smooth peanut butter to crunchy
• why hot and humid countries favour authoritarian leaders, and drought-prone ones prefer authoritarian gods

Packed with extraordinary stories and counterintuitive discoveries, Why We Think What We Think asks a fundamental question of ourselves. If we are predisposed to our beliefs, how can we escape the bounds of our own perspective? The answer lies in disagreement. Argument is how we reason, how we think our way to a better world. To thrive, as individuals and societies, we need the other side…(More)”.