Stefaan Verhulst
Article by By Philip Ball: “…Economic growth at a rate of 1–2% annually is the norm for industrialized nations today. But such growth rates did not happen in pre-industrial times, despite technological innovations such as the windmill and the printing press.
Mokyr showed that the key difference between now and then was what he calls “useful knowledge”, or innovations based on scientific understanding1. One example is the advances during the Industrial Revolution, beginning in the eighteenth century, when improvements in steam engines could be made systematically rather than by trial and error.
Aghion and Howitt, for their part, clarified the market mechanisms behind sustained growth. In 1992, they presented a model showing how competition between companies selling new products allows innovations to enter the marketplace and displaces older products: a process they called creative destruction2.
Underlying growth, in other words, is a steady churn of businesses and products. The researchers showed how companies invest in research and development (R&D) to improve their chances of finding a new product, and predicted the optimal level of such investment…
According to Ufuk Akcigit, an economist at the University of Chicago in Illinois, Aghion and Howitt highlight an important aspect of economic growth, which is that spending on R&D does not by itself guarantee higher rates of growth: “Unless we replace inefficient firms from the economy, we cannot make space for newcomers with new ideas and better technologies.”
“When a new entrepreneur emerges, they have every incentive to come up with a radical new technology,” Akcigit says. “As soon as they become an incumbent, their incentive vanishes” and they no longer invest in R&D to drive innovation.
Thus, because companies cannot expect to remain at the forefront of innovation indefinitely, the incentive for investing in R&D coming from market forces alone declines as a company’s market share grows. To guarantee the societal benefits of constant innovation, the model suggests that it is in society’s interests for the state to subsidize R&D, so long as the return is not merely incremental improvements.
The work of all three laureates also acknowledges the complex social consequences of growth. In the early days of the Industrial Revolution, there were concerns about how mechanization would cause unemployment among manual workers — a worry echoed today with the increasing use of AI in place of human labour. But Mokyr showed that, in fact, early mechanization led to the creation of jobs.
Creative destruction, meanwhile, leads to companies failing and jobs being lost. Aghion and Howitt emphasized that society needs safety nets and constructive negotiation of conflicts to navigate such problems.
Their model “recognizes the messiness and complexity of how innovation happens in real economies”, says Coyle. “The idea that a country’s productivity level increases by companies going bust and new ones coming in is a difficult sell, but the evidence that that’s part of the mechanism is pretty strong.”…(More)”.
Article by Stefaan G. Verhulst: “…For years, public interest advocates and other defenders of freedom on the Internet used “open” as a rallying cry. Open data. Open science. Open government. The idea was simple and noble: Knowledge should be shared freely, accessibly, and transparently to empower citizens, accelerate discovery, and improve governance
For a time, this vision made sense, even if it was imperfectly implemented. But as with many well-intentioned revolutions, openness has more recently been weaponised. What began as a movement to democratise knowledge has instead become justification for a new kind of extraction — this time not of oil or minerals, but of meaning. This phenomenon has become especially evident with the rise of generative AI, which relies on its voracious appetite for public data to train its models and refine its predictions. In the process, the very datasets, research repositories, and public web archives that were designed to serve the public interest have been harvested to train the large language models now controlled by a few corporations in a handful of countries.
The situation is dire but it is not hopeless. In what follows, we describe the problem in greater detail, outline the insufficiency of current mechanisms, and then discuss some possible mitigating responses…(More)”.
Editorial to Special Collection by Matteo Fontana, Martina Belmonte, Claudio Bosco Damien Jusselme, Alina Menocal Peters, Umberto Minora, Anna Rosinska and Stefaan Verhulst: “The escalating complexity of global migration patterns renders evident the limitation of traditional reactive governance approaches and the urgent need for anticipatory and forward-thinking strategies. This Special Collection, “Anticipatory Methods in Migration Policy: Forecasting, Foresight, and Other Forward-Looking Methods in Migration Policymaking,” groups scholarly works and practitioners’ contributions dedicated to the state-of-the-art of anticipatory approaches. It showcases significant methodological evolutions, highlighting innovations from advanced quantitative forecasting using Machine Learning to predict displacement, irregular border crossings, and asylum trends, to rich, in-depth insights generated through qualitative foresight, participatory scenario building, and hybrid methodologies that integrate diverse knowledge forms. The contributions collectively emphasize the power of methodological pluralism, address a spectrum of migration drivers, including conflict and climate change, and critically examine the opportunities, ethical imperatives, and governance challenges associated with novel data sources, such as mobile phone data. By focusing on translating predictive insights and foresight into actionable policies and humanitarian action, this collection aims to advance both academic discourse and provide tangible guidance for policymakers and practitioners. It underscores the importance of navigating inherent uncertainties and strengthening ethical frameworks to ensure that innovations in anticipatory migration policy enhance preparedness, resource allocation, and uphold human dignity in an era of increasing global migration…(More)”.
Article by Matt Jancer: “About a year ago, AI began outpacing human writers on the internet. For every one article written by a real-life, blood-bag of a meat puppet, slightly more than one was written by a machine. Don’t get all twisted up about “slightly more than one” article; it’s fractions, my friends.
The news was broken when Graphite published a study showing that AI-written articles surpassed human-written articles by a small margin in November 2024.
“We find that in November 2024, the quantity of AI-generated articles being published on the web surpassed the quantity of human-written articles,” reads the report.
“We observe significant growth in AI-generated articles coinciding with the launch of ChatGPT in November 2022. After only 12 months, AI-generated articles accounted for nearly half (39%) of articles published. The raw data for this evaluation is available here.”
The authors randomly selected 65,000 English-language articles using Common Crawl. The criteria for the articles reviewed were that they were at least 100 words long and were published between January 2020 and May 2025. To determine whether they were AI-written, the authors used Surfer’s AI detector.
According to the study’s authors, most of these AI-written articles, though, don’t appear in either Google or ChatGPT. “We do not evaluate whether AI-generated articles are viewed in proportion by real users, but we suspect that they are not.” The authors didn’t speculate on why they weren’t, or for what purposes a largely invisible, AI-written article might be created…(More)”.
Article by By Darryl Jones: “…We are in Brisbane for the biannual Australasian Ornithological Congress. Some of the biggest names in bird science and conservation have turned up to this session, eager to hear about the latest developments in eBird, the flagship program of the Cornell Lab of Ornithology, located in Ithaca, New York state.
Everyone uses eBird….“You know what I find astonishing about this data?” Wood continues. “It’s that the so-called experts, the professional researchers and consultants and full-time birders, people like us, provided a trivial proportion of all this data. What is genuinely exciting is that almost all of it was submitted by ordinary birders dedicating their time to recording birds wherever they are and submitting them. People like this.”
The map vanishes and a video starts. It’s a TikTok “story”, so we are told. Many of us would not know TikTok from Instagram. But the people on the screen certainly do!
A rapidly changing gallery of young people appears. Moving to the beat of the soundtrack, they talk enthusiastically about bird identification. With staccato editing and pulsating music, kids as young as ten rave about the Merlin app, how to use it and what makes it so cool. (Merlin allows anyone to identify any bird around the world on their phone by using guide-book-type identifying features as well as the species calls.)
These are kids! Rapping about bird ID! And giving advice on how to get your ID right! It is a stark example of how much the image of birding has changed…(More)”.
Article by By Andrew Deck and Hanaa’ Tameez: “…News outlets’ homepages are vital historical records, providing a real-time view into what a newsroom deems the most important stories of the moment. From a homepage — headlines, word choice, story placement — readers get a sense of a newsroom’s editorial priorities and how they change over time. If homepages aren’t saved, records of those changes are lost.
The Wayback Machine, an initiative from the nonprofit Internet Archive, has been archiving the webpages of news outlets — alongside millions of other websites — for nearly three decades. Earlier this month, it announced that it will soon archive its trillionth web page. The Internet Archive has long stressed the importance of archiving homepages, particularly to fact-check politicians’ claims. In 2018, for instance, when Donald Trump accused Google of failing to promote his State of the Union address on its homepage, Google used the Wayback Machine’s archive of its homepage to disprove the statement.
“[Google’s] job isn’t to make copies of the homepage every 10 minutes,” Mark Graham, the director of the Wayback Machine, said at the time. “Ours is.”
But a Nieman Lab analysis shows that the Wayback Machine’s snapshots of news outlets’ homepages have plummeted in recent months. Between January 1 and May 15, 2025, the Wayback Machine shows a total of 1.2 million snapshots collected from 100 major news sites’ homepages. Between May 17 and October 1, 2025, it shows 148,628 snapshots from those same 100 sites — a decline of 87%. (You can see our data here.)..(More)”.
Article by Felix Gille and Federica Zavattaro: “Trust is essential for successful digital health initiatives. It does not emerge spontaneously but demands deliberate, targeted efforts. A four-step approach (understand context, identify levers, implement trust indicators and refine actions) supports practical implementation. With a comprehensive understanding of user trust and traits of trustworthy initiatives, it is important to shift from abstraction to practical action using a stepwise method that delivers tangible benefits and keeps trust from remaining theoretical.
Key Points
- Trust is vital for the acceptance of digital health initiatives and the broader digital transformation of health systems.
- Frameworks define principles that support the development and implementation of trustworthy digital health initiatives.
- Trust performance indicators enable ongoing evaluation and improvement of initiatives.
- Building trust demands proactivity, leadership, resources, system thinking and continuous action…(More)”.
White paper by The Impact Licensing Initiative (ILI):”… has introduced impact licensing as a strategic tool to maximise the societal and environmental value of research, innovation, and technology. Through this approach, ILI has successfully enabled the sustainable transfer of five technologies from universities and companies to impact-driven ventures, fostering a robust pipeline of innovations across diverse sectors such as clean energy, sustainable agriculture, healthcare, artificial intelligence, and education. This whitepaper, aimed at investment audiences and policymakers, outlines the basics of impact licensing and based on early applications, demonstrates how its adoption can accelerate positive change in addressing societal challenges…(More)”.
Report by Epoch: “What will happen if AI scaling persists to 2030? We are releasing a report that examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We further examine the future AI capabilities this scaling will enable, particularly in scientific R&D, which is a focus for leading AI developers. We argue that AI scaling is likely to continue through 2030, despite requiring unprecedented infrastructure, and will deliver transformative capabilities across science and beyond.
Scaling is likely to continue until 2030: On current trends, frontier AI models in 2030 will require investments of hundreds of billions of dollars, and gigawatts of electrical power. Although these are daunting challenges, they are surmountable. Such investments will be justified if AI can generate corresponding economic returns by increasing productivity. If AI lab revenues keep growing at their current rate, they would generate returns that justify hundred-billion-dollar investments in scaling.
Scaling will lead to valuable AI capabilities: By 2030, AI will be able to implement complex scientific software from natural language, assist mathematicians formalising proof sketches, and answer open-ended questions about biology protocols. All of these examples are taken from existing AI benchmarks showing progress, where simple extrapolation suggests they will be solved by 2030. We expect AI capabilities will be transformative across several scientific fields, although it may take longer than 2030 to see them deployed to full effect…(More)”.
Paper by Jed Sundwall: “The COVID-19 pandemic revealed a striking lack of global data coordination among public institutions. While national governments and the World Health Organization struggled to collect and share timely information, a small team at Johns Hopkins University built a public dashboard that became the world’s most trusted source of COVID-19 data. Their work showed that planetary-scale data infrastructure can emerge quickly when practitioners act, even without formal authority. It also exposed a deeper truth: we cannot build global coordination on data without shared standards, yet it is unclear who gets to define what those standards might be.
This paper examines how the World Wide Web has created the conditions for useful data standards to emerge in the absence of any clear authority. It begins from the premise that standards are not just technical artefacts but the “connective tissue” that enables collaboration across institutions. They extend language itself, allowing people and systems to describe the world in compatible terms. Using the history of the web, the paper traces how small, loosely organised groups have repeatedly developed standards that now underpin global information exchange…(More)”.