Stefaan Verhulst
Report by Laura Maher and Stefaan Verhulst: “Any of the defining challenges of our time, from climate change and democratic resilience to public health, AI governance, and systemic inequity, defy linear solutions. They are interconnected, constantly evolving, and often contested in how they are understood and framed.
Yet philanthropy, despite its ambition to address these challenges, remains largely organized around linear theories of change, predefined deliverables, and evaluation frameworks which often assume that problems can be sufficiently understood in advance and progress measured against predetermined outcomes. While essential for accountability, these approaches can leave little room for questioning assumptions, exploring uncertainty, or adapting as new insights emerge.
This tension becomes particularly striking in the age of AI. As our capacity to generate “answers” expands exponentially, the real constraint may no longer be access to information or solutions, but our ability to determine which questions matter, whose perspectives inform them, and when they need to change.
Questions are not neutral. They shape what gets funded, which communities are heard, what evidence is collected, and which possibilities are explored. They are agenda-setting devices that influence not only how problems are addressed, but which problems receive attention in the first place.
For philanthropy, this suggests a shift: from treating questions as a preliminary step toward funding answers to recognizing inquiry as an essential investment in its own right. Supporting the capacity to formulate, test, and revisit questions could make philanthropy more responsive to complexity, more inclusive in setting priorities, and ultimately more effective in generating lasting change…(More)”.
White Paper by Diane Coyle, Luca Gamberi, David Nguyen, and Julia Schmidt: “The purpose of this white paper is to contribute to the development of a shared agenda on the economics of data. Interest in data has increased in recent years, accelerated by the arrival of generative and now agentic AI. It is clear that data (in the form of digital records), in a fundamental sense, fuels the creation and distribution of economic value in today’s economy. Yet there remain significant gaps in understanding the economics of data, from the perspectives of businesses, public organisations, policymakers and official statisticians. This paper builds on a workshop held in Cambridge on 16 September 2026, with 40 senior participants from across multiple sectors and perspectives. The agenda that has emerged from the discussions covered the fundamental economics of data, business decision-making, market structure and public value, as well as measurement questions. This paper ends with a proposed agenda for research and policy, but this is a starting point. As it makes clear, many open questions remain…(More)”.
Essay by Malloy Owen: “The tension between mass and elite agency is one of the oldest themes in political history and philosophy and a special preoccupation in such authors as Thucydides, Livy, and Machiavelli. Yet the last of these authors hit on something profound when he argued that “the disunion of the plebs and the Roman Senate made that republic free and powerful,” to quote the title of a chapter in his Discourses. In that chapter, Machiavelli, a paragon of elite humanist learning and a notorious admirer of princely virtue, argues that the Roman people’s continual assertion of its “ambition” against the designs of the oligarchic senatorial class was the cause of “good laws” that gave rise to “good education,” which in turn generated the heroic “examples of virtue,” bold and vital acts of refounding and renewal, to which his theory of republics affords such an important role. The very chafing of popular agency against elite agency was a stimulus to excellence for the masses and the oligarchs alike.
The coming AI regime promises to end such productive tumult by ending agency—just as liberal technocracy did, only more thoroughly. Cars will drive themselves, the laws will be administered in an orderly and rational manner, and no one will be asked to exercise responsibility or to undergo the long suffering required to bring a new idea into the world. But this is no way for human beings to live. If we accept Machiavelli’s political anthropology, it is not even a good way for political orders to survive. If you don’t allow the people to “vent their ambition” from time to time, he says, you cannot call on them to help you do something great.
In other words, a threat to mass agency is a threat to elite agency, at least in its noblest, most public-spirited, and most truly ambitious forms. A passive and enervated public will not produce brilliant minds and masterful personalities; a disintegrated political society will offer no scope for the exercise of the virtues.
At this point, it seems beyond doubt that machines will acquire the ability to do most of the work human beings do in the coming years, if not the coming decades. The question that remains open is the political one: What kind of life will we live together when most forms of our labor have been rendered obsolete? Silicon Valley shares the rest of the country’s fear that the rise of the machines will entail the collapse of human possibility. Unlike the rest of us, however, the tech elite stands a chance of escaping the end of generative natality and becoming the last human agents, the masters of the machines. In a moment of universal anxiety, as the walls seem to be closing in on all of us, they are understandably tempted to make this their aim…(More)”.
Video by Ollie Ballinger and Zach Toombs: “The U.S. government is trying to censor your view from space.
Ollie Ballinger, an open-source researcher and lecturer, uses satellite imagery to monitor conflicts around the world. But he says pressure from the government has prompted private companies to restrict access to images of places it may not want the public to see.
Why would companies listen to these requests? “Any pressure from the U.S. government carries a lot of weight for these companies,” Mr. Ballinger says, “because the American government is their biggest customer.”
In the Opinion video above, he reveals how these restrictions are limiting a vital tool for accountability and public safety and warns of a future in which governments have the power to control what the public can see and, ultimately, what it can know…(More)”.
Report by Australian Human Rights Commission: “…finds synthetic media has become a low-cost and highly scalable tool for influencing public debate. It can be used to discredit political candidates, mislead voters and undermine confidence in elections. Its speed and reach make harmful content difficult to identify and stop before damage is done.
When people can no longer trust what they see and hear online, it becomes harder to make informed decisions, participate in public discussions and hold institutions to account.
‘Democracy depends on people having access to reliable information and being able to participate in public life free from manipulation and intimidation,’ Commissioner Finlay said.
‘Synthetic media can distort public debate and erode the shared understanding of facts that democratic decision-making relies on.’
The report also highlights how such content can amplify existing prejudice and discrimination. Women, LGBTIQA+ people, culturally and linguistically diverse communities and others have been targeted through harassment, humiliation and disinformation campaigns.
As synthetic media becomes easier to produce and distribute, its influence is increasingly shaped by online platforms and algorithms that determine what content people see…(More)”.
Article by Julian Norris: “A lot of organizational systems thinking has become overly attached to its own representational devices. Causal loop diagrams, systems maps, stakeholder networks, iceberg models, adaptive cycles, and so on. These are useful instruments, but they can become reified. People start treating the map as the territory, and the analytical representation as an adequate substitute for the experience of the phenomenon.
When we ‘map’ or otherwise describe complex reality, we use language, symbols and metaphors. We might begin with mathematical representation, but as soon as we start using words like ‘order’, ‘instability’, ‘complexity’ or ‘attractor’ – they enter a domain of social description and start to carry all kinds of additional cultural meaning and linguistic associations.
And every map rests on a mapping platform that is much more than just a background grid. It’s a crystallized worldview. It decides what counts as a unit, which directions matter, which cycles are primary. Such platforms tend to encode our understanding of the natural world and the nature of our relationship with it. They make some questions answerable and others un-askable. For example, as David Krakauer reminds us, highly symmetrical platforms don’t just struggle to represent complexity – they struggle to detect it in the first place.
If every map is a worldview, we need a way to think about how maps make meaning in the first place. Not just what they show, but how they help us see at all. For that I’m going to lean on the work of anthropologist Roy Wagner and use his ideas as a handrail.
Wagner spent years working with the Daribi people in Papua New Guinea and in The Invention of Culture (1975) and Symbols That Stand for Themselves (1986) he elaborated a theory of metaphor. Most of us were taught that a metaphor is decoration – a colourful way of saying something that could be said plainly. Wagner argued the reverse. A metaphor – he preferred the word trope – is a form of perception.
When you use the classic iceberg model to describe some organizational dynamic, you aren’t just describing the phenomenon. You’re collapsing two worlds into a single image, and the image lets you see something you couldn’t see before. Wagner called a good trope “too definite for words.” It isn’t vague. It’s more precise than any paraphrase, which is why the iceberg needs no explanation in a room. People just get it.
I find three of Wagner’s ideas useful to hold here.
Tropes come in orders. A first-order trope is a single image: the iceberg, the tree. A second-order trope is metaphor spread out – a sequence in which each image transforms the one before. Wagner called this process obviation. It’s a subtle process of contextualization and transformation in which the earlier image doesn’t simply disappear; its meaning is altered by the subsequent image. A third-order trope draws in the perceiver…the image through which we see ourselves seeing…(More)”.
Article by Samuel Greengard: “The generosity of strangers has long powered online communities. Spaces like Stack Overflow, GitHub, curl, OpenSSL, Wikipedia, and OpenStreetMap have a rich history of volunteers sharing knowledge and maintaining code, encyclopedias, and maps.
Now, AI is having a say. Trained on content from these sites, it is changing how volunteers contribute and how users consume content. Stack Overflow, for example, has seen monthly questions fall from a peak of roughly 200,000 in 2014 to about 1,400 in July 2026. Meanwhile, a deluge of contributions—often fake and misleading—has overrun these projects.
This has created unease about the future of online knowledge sharing. “We are starting to replace social communities and their underlying processes with a piece of software,” said Gordon Burtch, Allen and Kelli Questrom Professor in Information Systems at Boston University. “As fewer people go to the community with questions, the incentive for experts to be there falls in turn.”
He isn’t the only one sounding alarms. “There is a huge fear, with growing evidence, that AI companies are kicking the legs out from underneath the table they are eating at,” said Benjamin Mako Hill, an associate professor of communication at the University of Washington and a longtime contributor to peer-production projects.
The stakes extend beyond site traffic and human connections. Fundamental questions arise about the quality of AI-provided information, who owns and profits from it, and what role humans play in vetting it. The cycle can also be recursive: models trained on public knowledge inhibit the production of new content. The question now is: will these communities evolve or disappear?..(More)”.
Article by Barbara Wheeler: “…As the Trump administration has taken down or altered hundreds of federal datasets that don’t align with their political or ideological priorities, it has become clear that the old order of our federal data system has effectively collapsed. The data systems that state and local government agencies, researchers, and community organizations once considered trusted, fixed features of the landscape are no longer reliable, have been weaponized against communities, and in some cases no longer exist.
The ongoing federal data collapse now threatens one of the nation’s most essential data sources—the US Census. Conducted every 10 years, census data is used to allocate federal funding to communities, determine political representation in Congress, and inform programs related to housing, transportation, emergency preparedness, and more. But in September 2026, the Trump administration proposed unprecedented changes to the US Census, including a prohibition on collecting data related to race, ethnicity, and sexual orientation, as well as the exclusion of many noncitizen residents. These changes, if adopted, would infuse discrimination, exclusion, and inaccuracy into a critical national data source. Excluding noncitizens would also likely violate the 14th Amendment’s requirement that census results include “the whole number of persons in each State.”
While the destruction of so many federal data sources must be grieved, it also reveals a moment ripe for reimagination. We are living in a Phoenix moment: a volatile, high-potential period for transformation where we can create new and better approaches to federal data. This moment offers us an opportunity to change, and ultimately improve, how our country produces, protects, and uses civic data—information a society gathers about itself to influence governance and determine where resources should flow. Like the mythical bird of the same name, we must first contend with the burning ashes of the old data order and assess the cost of its limitations and its loss. Only then can we rise anew from the ashes with better approaches to federal data—approaches that are more inclusive, more insightful, and more actionable for communities across the nation…(More)”.
Paper by Terence Chau et al: “Program impact estimates that are not statistically significant are often treated as providing little guidance for policy choice, even when economically important effects remain plausible. In such cases, the decision-relevant task is not simply to test whether the effect differs from zero, but to estimate the probability distribution of programmatic effects. The importance of going beyond hypothesis testing is especially clear when evaluations are underpowered relative to the effect needed for benefits to exceed costs. We illustrate this point using Chicago’s contract for ShotSpotter, an acoustical gunshot detection system that reduces police response times and may therefore have improved survival among shooting victims. Using a geographic RD design, we estimate that ShotSpotter reduced the shooting fatality rate by 2.5 percentage points, equivalent to roughly 49 fewer deaths per year. Although the estimate is not statistically significant, its implied benefit far exceeds the cost of the service. To have enough statistical power under the hypothesis testing approach to detect an effect of this size would require 87 years’ worth of Chicago shooting data. We instead implement a decision-relevant perspective using Bayesian analysis, treating the life-saving effect as a random variable. We find positive expected net benefits, with posterior probabilities that benefits exceed costs ranging from 70% under a flat prior and a $0.5 million value of statistical life to 99.6% under a no-harm prior and a $13.6 million value. The results illustrate how reliance on statistical significance can discard decision-relevant evidence when economically meaningful effects are difficult to detect precisely…(More)”.
Paper by Paul Keller: “…Our proposal starts from the observation that individual licensing cannot resolve this. A licensing market, no matter how well it functions, cannot reach most of the content on which AI models are trained. Where licensing does happen, it happens between the largest AI developers and the largest rightholders. It leaves out the long tail of individual creators and small rightholders, and it leaves out smaller AI developers. The way out is to stop trying to reconcile these objectives transaction by transaction, and to do it collectively instead…
The obligation is triggered by the commercial supply, on the EU market, of access to AI models trained on publicly available content. It applies wherever the company is established and wherever the model was trained. Companies pay a percentage of the EU revenue from the supply of model access into a single EU-level fund. In exchange, training on publicly available content is no longer subject to individual reservations under Article 4 of the CDSM Directive.
The remuneration takes the form of a statutory remuneration claim that cannot be waived and is subject to mandatory collective management. Research, and the development and release of models under open-source licences, fall outside the obligation; it arises only when such a model is commercially supplied.
Rightholders keep control, but they exercise it through access rather than through reservations. Content behind paywalls or other access controls stays outside the mechanism and remains available for licensing. Uses at the point of inference, such as retrieval-augmented generation and grounding, are unaffected by the proposal.
The fund splits the proceeds between two destinations. The larger part goes to creators and rightholders through collective management organisations, with a guaranteed share reserved for authors and performers. A significant, fixed share goes to the stewards of publicly available content: cultural heritage institutions such as libraries, archives and museums, open knowledge projects and public service media…(More)”.