Stefaan Verhulst
Paper by Zeynep Engin et al: “The digital substrate of states — data, algorithms, infrastructure, platforms, applications — is being governed without adequate conceptual foundations. The ability and legitimacy required to govern this substrate, and to govern with it, are simultaneously misaligned, contested, and structurally absent. We introduce digital statecraft as the organising concept for this emerging field, arguing that ‘digital’ reconstitutes the statecraft question rather than merely extending its domain. The concept operates on two dimensions – statecraft over digital systems, concerning the authority and capacity of the state in relation to the digital substrate itself, and statecraft with digital systems, concerning the deployment of algorithmic tools as instruments of governing authority. And it rests on two foundational requirements, technical coherence and legitimate authority, that are genuinely in tension. We derive ten principles of digital statecraft from these foundations, each naming a condition whose absence produces an identifiable and structural governance failure: public interest first, human-machine complementarity, governability by design, systemic coherence, hybrid institutions, adaptive governance, human centricity and civic agency, accountable and traceable authority, judgment across time, and the non-delegable core. This article takes the state as the starting point, the institutional form that developed historically in response to the problem of effective and legitimate public governance, and the only current candidate for which the full set of legitimacy conditions is institutionally available. But the digital statecraft programme holds open a deeper question than just whether states can reform themselves: governing well in the algorithmic age may require rethinking the boundaries, scale, and affiliative basis of statehood itself…(More)”.
Article by Emanuel Maiberg: “As AI companies search for more training data to improve their models, one company is offering old, printed books as an ideal source because they are guaranteed to be free of the very AI slop AI companies are producing. “The world’s best AI training data is sitting on a shelf,” ISBNdb, a company that produces what it claims is “the world’s largest book database,” and that offers high-volume book acquisition services for AI companies, says on its site. “Books represent curated, peer-reviewed, domain-specific human knowledge, structured in a way no web crawl can replicate. Dense, edited, authoritative.” In one article on its site, ISBNdb explains that printed books published before 2022 are ideal for AI training data because they don’t include AI generated text. As the article correctly notes, much of the data that AI companies can scrape from the internet today is likely to include AI generated text, which could result in “model collapse,” a process by which AI models that are trained on AI generated data results in worse models that are more prone to errors. The article also notes that book authors who object to their writing being scraped for training purposes can now easily poison AI models by producing writing designed to manipulate and sabotage the resulting AI models. As AI companies search for more training data to improve their models, one company is offering old, printed books as an ideal source because they are guaranteed to be free of the very AI slop AI companies are producing…(More)”
Article by Leya Mohsin: “…Successful moonshots involve both a clear what and a clear how. If you think about prior moonshot efforts, there are probably a select few that jump to mind: the Apollo Program, the Manhattan Project, and the Human Genome Project are the most notable efforts. What’s something these all have in common?: A “moon”, a goal that anyone can clearly define. This is a critical element of successful moonshots.
But other similar efforts have been tried before to less avail. As an example, consider the Human Brain Project. This project had a clear goal: to simulate the entire human brain on a computer. It concluded in 2023 after 10 years and €1 billion spent. The effort did lead to significant advances in neuroscience, but it failed to accomplish its stated goal. From the jump, scientists were highly critical of this initiative because they did not believe the requisite technology existed, or was even close to existing. In other words, the how was unclear.
This is common for many such initiatives. For example, when the Human Genome Project was launched, we did not have the technology capable of meeting the mission. The project was highly criticized by many in the research community because the traditional method of DNA sequencing could not possibly be scaled to meet the goal. They needed a new way to sequence DNA faster, with longer reads, and for less money. Therefore, the first goal was to drive investment in the development of technology that could answer the how question.
A successful moonshot is one where it’s clear that we are on the precipice of achieving a once-unimaginable goal, if only we make a significant investment toward that end. Think of the original moonshot: when JFK announced this initiative, we had seen a man orbit the Earth. And we knew that putting a man on the moon was challenging, but far from impossible – we just needed to devote the appropriate time, resources, and organization around that goal…(More)”.
Article by Anne-Laure Le Cunff: “More than 60 percent of Google searches in the United States now end without the user clicking on a link. We type a question, read an artificial-intelligence-generated summary of the results and leave with our answer.
Google is hardly alone. Claude, ChatGPT and upstart competitors like Perplexity do roughly the same thing: They take a question and swiftly return an answer, compressing what used to be a meandering journey through the internet into an immediate arrival at your destination. The explorative phase of searches — clicking through links, stumbling onto unexpected pages, following a reference that leads to somewhere unplanned — is disappearing.
For anyone who publishes on the internet, this is a troubling development, since it lowers website traffic and makes protecting and profiting from your intellectual property more difficult. But you might think it is good news for internet users. Could there be anything wrong with getting a reliable answer more quickly?
There is. By shortening the time between asking a question and getting an answer, these tools are actually undermining curiosity — and paradoxically threatening our ability to understand the world.
About a decade ago, I worked at Google. When I was there, we often measured the value of internet content based on factors that indicated user engagement, like clicks and scroll depth. The metric Google seemed to reward — people exploring — is precisely what its A.I. products are now designed to eliminate.
I left Google to study neuroscience, and what I found in the research literature helps explain why the A.I. summary poses a danger to learning. Curiosity, it turns out, is not just an individual’s desire to find out discrete facts; it’s also a feature of our biology designed to help us learn more broadly. And it requires a specific condition: a gap between what you want to know and what you find out.
Researchers have found that people in a state of curiosity, while waiting for an answer to an intriguing question, remember unrelated information they encounter during that time far better than they otherwise would. In that study, the researchers also placed those people in brain scanners. They found that waiting for an answer activates reward circuits in the brain and readies the hippocampus to help create memories. Similar findings have been reported by other researchers in studies involving infants, older children and adults.
In short, curiosity puts the entire brain into a mode of heightened receptivity — not just for the specific thing you want to know but also for everything around it. Curiosity opens a window, and while the window is open, learning deepens across the board…(More)”.
Essay by Audrey Tang: “When people ask whether AI threatens humanism, I often feel the question arrives a little late. The deeper threat came earlier, when digital systems learned to sort attention at industrial scale. Long before generative models could write essays, compose songs or simulate conversation, many of us had already been trained to behave like components inside ranking systems: always visible, always reactive, always measurable. By the time synthetic fluency arrived, a quieter transformation was underway. We were becoming easier to score.
This is why I do not think the seminal question of this century is whether machines will become more human. The question is whether humans will still be allowed to remain more than something that machines can easily evaluate.
I have seen both possibilities. On one hand, a language model can help me enter a conversation I otherwise could not have had. Before meeting a Japanese thinker, whose newest work I could not read in the original, I used AI to build a working vocabulary across our different philosophical traditions. The system did not replace the encounter. It made the encounter possible. Once the conversation began, the model’s importance began to fade. It had done its work well precisely because it no longer needed to be at the center.
On the other hand, we have all experienced systems that do the opposite. They do not deepen understanding. They train people to live at the tempo of the feed, to compress themselves into whatever is most legible to the platform, and to mistake constant reaction for participation. The issue is not that AI will talk like us. The issue is that institutions will reward us for talking like machines.
Our scarcity is not content. It is mutual comprehension. A humane technological future will not be secured by sentimentality, nor by a nostalgic defense of every task that software can now accelerate. It will depend on whether we can design tools, institutions and norms that protect what is distinctively human in public life: the capacity to interpret one another across differences, to make commitments that bind us over time, to revise ourselves without humiliation, to hold power answerable and to care for consequences that no benchmark can fully capture…(More)”.
Essay by E. Glen Weyl, James Evans and Chris White: “…Technology capable of enlarging human freedom, abundance and collective intelligence is being deployed into labor markets, public institutions and information systems that were already creaking before AI arrived. Left unaddressed, that mismatch produces three foreseeable failure modes.
- Productivity without prosperity. As the economists Erik Brynjolfsson, Daniel Rock and Chad Syverson illustrate in a 2021 article, general-purpose technologies create broad value only after heavy complementary investment in new processes, skills and organizational redesign. When those complements lag, work is reorganized faster than people can retrain, bargain or move, and the gains fail to translate into security, mobility or broader participation.
- Execution without verification. As the economists Christian Catalini, Xiang Hui and Jane Wu argue in a recent working paper, once the cost of producing text, images and code collapses, the scarce input becomes the human bandwidth to validate outputs, establish provenance and ensure accountability. Decisions grow easier to automate than to justify, and algorithmic systems without accountability erode sacred values and the social fabric.
- Capacity without constraint. AI can make states and large organizations far more capable, but as influential intellectuals and policy leaders in AI, Justin B. Bullock, Samuel Hammond and Sèb Krier note in a chapter published last year in “The Art of Digital Governance,” the same tools lower monitoring costs and concentrate discretion, pushing bureaucracies toward centralized control unless contestability, appeal and civil-liberties safeguards advance in tandem.
None of these is inevitable. Each is a consequence of institutional neglect, which raises the question of why some societies, in earlier periods of upheaval, adapted far more successfully than others…(More)”.
Article by the StanfordReport: “Bureaucracy moves slowly, and decades of outdated provisions clogging the bylaws of cities and states only make it worse. Sometimes dubbed “policy sludge,” such obsolete processes and stale reporting requirements can trap civil servants in red tape and cause program dysfunction.
Scholars at the Stanford Institute for Human-Centered AI (HAI) and Stanford RegLab are working with cities and states across the country to find and cut out this sludge. They developed an AI tool to examine 500 million words of state statutes across all 50 U.S. states to reveal common patterns in the sludge, which they detail in a new paper, “The Abundance of Reports and Incapacity of States,” forthcoming in the Yale Journal on Regulation. RegLab also partnered with the states of New York, California, and Maryland to help these governments start paring these provisions from their books to speed the pace of government.
“This is a direct example of how AI can benefit millions of people,” says Daniel E. Ho, faculty director of the RegLab, associate director at HAI, and a Stanford Law School professor. “These tools can clean up outdated requirements, reduce burdens on civil servants, and enable government to serve regular people who don’t want to face delays in filing for a new business, building a house, or securing a license. We hope that more states and cities will adopt this approach to clear clutter.”
Based on this work, New York Gov. Kathy Hochul issued an Executive Order this month, directing state agencies to carry out a “regulatory reset” to remove outdated requirements, burdensome fees, and obsolete and unnecessary reports and commissions. “People have lost confidence in their state to be able to deliver the way they want it to,” Hochul said in a recent New York Times story about the order. “We have to shake it all up – be creative, and rethink government on a daily basis.”..(More)”.
Editorial by Rachel A. Ankeny: “When we hear talk of ‘open science’, we often focus on the idea of what it means to make scientific knowledge and associated infrastructures more accessible, transparent, and collaborative, wondering for whom scientific results or methods are now more readily available (and whom not), and paying particular attention to problems associated with equity, justice, and fairness in open science and its practices. But to fully explore the ‘opening up’ of the processes and outputs of scientific research, it is equally important to ask how open science-related practices have changed contemporary science in perhaps unanticipated or unexpected ways. In short, how has open science affected what we consider to be or to count as ‘science’ and the practices associated with it?
Practices in scientific fields have always varied and evolved over time due to the interactions of diverse factors that go far beyond the epistemic and methodological content of science, as previously argued in our repertoires framework (Ankeny and Leonelli 2016) and as explored by many philosophers of science in practice. Thomas Gieryn’s use of ‘boundary work’ (Gieryn 1983, 1999) to capture the discursive strategies as well as the material and procedural tools which scientists use to separate legitimate science from other practices, and the fluidities of these boundaries, is more aligned with the questions that I wish to explore than are the factors highlighted in traditional, analytic debates over demarcation. If, as Gieryn describes it, the qualities ascribed to science change depending on the activities with which it is compared, it is important to pose the question of what expectations have come to be associated with ‘good’ scientific practices in the early twenty-first century and how these have been shaped by the ‘opening up’ of science. To put it another way: how can explorations of open science shed light on evolving understandings of what science is, who practices it, and what makes activities ‘scientific’? And as a result, have these understandings changed in ways that rely on fewer unwarranted presuppositions about what counts as ‘science’ and fostered greater equity? Note that I favour discourse related to ‘equity’ here rather than ‘democratisation’: the latter, with its focus on making scientific research, data, results, and so on widely accessible via open access to publications and data, is arguably critical and perhaps even necessary, but not sufficient, for opening up science on a deeper, epistemic level. However, openness is always context dependent and its instantiation complex in any real-life field or case (see e.g. Levin and Leonelli 2017). Furthermore, there is no ‘one-size-fits-all’ solution for achieving equity in these domains (Bezuidenhout 2025) as well illustrated by the papers in this special issue…(More)”.
Article by Hetan Shah: “…Given AI’s exponentially growing ability to solve well-defined, data-rich problems, it is plausible that employers will place more of a premium on the skills the humanities offer, such as critical and creative thinking. Anthropic’s co-founder Daniela Amodei recently said: “I actually think studying the humanities is going to be more important than ever . . . this idea that there are things that make us uniquely human — understanding ourselves, understanding history, understanding what makes us tick — I think that will always be really, really important.”
It would be ironic if universities cut these subjects just as demand turned a corner — switching from literature to AI-prompt engineering may turn out to have been a poor trade. And it would be bad for society if we made these subjects the preserve of the wealthy. Our research at the British Academy shows emerging regional “cold spots” where it is no longer possible to study particular humanities courses at a reasonable distance from home. This is increasingly important as nearly a third of prospective students — often the poorest — intend to stay at home while they study.
Cutbacks in universities are also affecting humanities research, where the UK is world-leading, which contributes in a broad way to building a thoughtful and prosperous society. Historians can guide us on why our policymaking is the way it is, so we can try to learn from the past. Philosophers are thinking about responsible use of AI in multiple contexts, including the creation and preservation of artworks. Academics feed into the development of video games such as the globally successful Hellblade, which had a reader in Scandinavian history as its historical adviser to provide an accurate account of Viking society and beliefs.
You might think these research cutbacks in universities are the exercise of market forces, with the strongest departments surviving. But it is more complicated than that because of the complex cross-subsidies within higher education. Financial instability is largely driven by the drop in high-paying international students and the erosion in the value of tuition fees by inflation over the past decade. Caught up in the crossfire of this instability and through no fault of their own, some of the top-ranked humanities research departments are closing, while our analysis shows the number of early-career academics in the humanities has fallen by 20 per cent over the past decade…(More)”.
Book by Kent Anderson and Joy Moore: “Scientific evidence affects policies, business, health outcomes, and economies worldwide. But is that scientific claim you just read reliable? Or nonsense? More and more often, it’s unreliable. The global system managing scientific claims has been hacked by bad ideas, big money, and bad incentives, and is being flooded by sketchy papers.
How the Internet Disrupted Science reveals the untold story of how science has been corrupted by digital information, academic and professional incentives, strange political ideologies, and big money interests. In this explosive expose, Kent Anderson and Joy Moore uncover how notions from the Big Tech world such as ‘move fast and break things’ and ‘information wants to be free’ have corrupted a scientific endeavor that once prided itself on truth-seeking, accountability, and transparency. Soon after that, scientific publishers were abdicating their responsibilities to practitioners and the public, while organized crime rings and conspiracy theorists took over.
In How the Internet Disrupted Science, two experts who witnessed this shift firsthand throughout their decades of experience in scientific publishing share a sprawling, endlessly fascinating tale decades in the making— one that is more relevant with each passing day, as we face new outbreaks, uncertainty around what information we can trust, a gutted scientific infrastructure, and concerns about centralized information in Large Language Models and AI systems. There is a way out of this mess, but only if we return to the self-correcting practices and core values that made science a reliable engine of progress for more than 500 years…(More)”.