Stefaan Verhulst

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)”.

Article by Tal Feldman: “Foreign governments have tried to influence the United States since its founding. President George Washington warned in his Farewell Address that “foreign influence is one of the most baneful foes of republican government.” The old methods have certainly not gone out of vogue; just this week Nvidia CEO Jensen Huang admitted to having a romantic relationship with a businesswoman who U.S. officials believe was working for Chinese intelligence. But today, a new type of foreign agent has arrived: Chinese artificial intelligence companies.

Like the foreign agents of old, the models these companies sell may seem harmless—just a cheap tool for American businesses to use. But their makers train them to promote the interests of the Chinese Communist Party (CCP). American businesses are outsourcing more and more critical decisions to AI. If Washington does nothing, those businesses will hand over control to models whose objective is to promote a foreign government.

Although U.S. policymakers now debate banning Chinese AI, the tried-and-true American solution is not censorship but disclosure. In 1938, Congress passed the Foreign Agents Registration Act. Rather than outlaw propaganda, FARA allowed foreign agents to continue spreading it—as long as they registered with the government and disclosed whom they really worked for. Their communications also had to carry a conspicuous label naming their foreign sponsor.

FARA’s wisdom has aged well over the decades. In recent years, the Justice Department has ordered the Russian state television network RT and the Chinese state media organizations Xinhua and CGTNto register as foreign agents and to label their content as such. Those who failed to register as foreign agents, such as Paul Manafort, President Donald Trump’s 2016 campaign chairman, and former U.S. Representative David Rivera of Florida, were sentenced to prison. (Trump pardoned Manafort in 2020.)

Today, rather than banning AI companies controlled by foreign governments, the United States should treat them as foreign agents under FARA. No new statute is needed. Washington should simply enforce the one on the books. Of course, FARA is no silver bullet against all foreign influence, but it fits the shape of this problem. A handful of known Chinese companies, such as DeepSeek, Alibaba, and Moonshot AI, build the models that American businesses increasingly let think and act for them. The Justice Department can reach every one of them.

Democracy is strong enough to survive foreign influence but not invisible foreign influence. And to make sure the world does not subordinate its decision-making to Beijing, Washington and its allies must create an allied AI compact and then spread their own models far and wide…(More)”.

Report by The One Hundred Year Study on Artificial Intelligence (AI100) 2026 Study Panel : “The main techniques that underpin most of today’s AI technologies—neural networks—were developed long before the current boom. But it took the emergence of powerful graphics processing units (GPUs) and the era of big data to make neural networks possible at large scale. Neural networks that leveraged these compute and data resources demonstrated their value for tasks like image recognition more than a decade ago. Since then, much of the field of AI has focused on the pursuit of more capable AI systems by increasing computational scale. The use of ever more powerful computing chips and ever more digital data, together with the capital to put them together, allowed for the creation of progressively bigger and more capable neural network models, and ultimately enabled a qualitatively new class of AI systems. Large language models (LLMs), which burst onto the global stage in November 2022 with the release of ChatGPT by OpenAI, are the highest-profile example of what the scaling era enabled.

In what follows, we use the term foundation model¹ to mean a very large-scale machine-learning model trained on large datasets and built to perform multiple tasks, and frontier model to mean a foundation model that is at the cutting edge of current capability. Frontier LLMs are the best-known foundation models, although foundation models are increasingly being developed in specialized domains such as medicine.² Since 2022, we have seen the rapid advance of generative AI: systems that learn from existing data to create new content, including written text, realistic or stylized artistic images, original songs, and computer programs.

The centrality of scale to modern AI has been apparent for some time, but the past five years have seen scaling taken to an unprecedented degree. The companies building frontier models today invest billions of dollars on infrastructure to develop a new state-of-the-art model. They are willing to do this because past experience has shown that increasing the training budget will lead to a predictable rise in quality—and a competitive advantage in the market…(More)”.

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)”.

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)”.

Book by Steven Strogatz and Alex Townsend: “It’s in every photo you post to Instagram, every Google search you make, and every answer you get back from ChatGPT. It routes our packages and planes, maps social networks, and predicts the weather. This is Big Math, and it is quietly running our world—while also reshaping it in ways we can’t yet comprehend. A toolkit of powerful ideas such as matrices and vectors, networks and optimization, Big Math allows humanity to tackle problems too vast to hold in our heads. Yet the tools themselves are not especially new or mysterious. We created them, and for most of their history we understood them and controlled them. But once we began supercharging them with computers to explore truly enormous systems, things changed.

Now, as celebrated mathematicians Steven Strogatz and Alex Townsend reveal in this dazzling and unsettling book, Big Math is beginning to drift beyond our grasp, even as it delivers greater gifts and power through machine learning and, soon, quantum computing. What happens when Big Math outgrows its creators—when it becomes something we no longer understand, but only witness? And how can we learn to live wisely with tools that grow stronger and stranger every day?..(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)”.