5 Ways Cooperatives Can Shape the Future of AI


Article by Trebor Scholz and Stefano Tortorici: “Today, AI development is controlled by a small cadre of firms. Companies like OpenAI, Alphabet, Amazon, Meta, and Microsoft dominate through vast computational resources, massive proprietary datasets, deep pools of technical talent, extractive data practices, low-cost labor, and capital that enables continuous experimentation and rapid deployment. Even open-source challengers like DeepSeek run on vast computational muscle and industrial training pipelines.

This domination brings problems: privacy violation and cost-minimizing labor strategies, high environmental costs from data centers, and evident biases in models that can reinforce discrimination in hiring, healthcare, credit scoring, policing, and beyond. These problems tend to affect the people who are already too often left out. AI’s opaque algorithms don’t just sidestep democratic control and transparency—they shape who gets heard, who’s watched, and who’s quietly pushed aside.

Yet, as companies consider using this technology, it can seem that there are few other options. As such, it can seem that they are locked into these compromises.

A different model is taking shape, however, with little fanfare, but with real potential. AI cooperatives—organizations developing or governing AI technologies based on cooperative principles—offer a promising alternative. The cooperative movement, with its global footprint and diversity of models, has been successful from banking and agriculture to insurance and manufacturing. Cooperatives enterprises, which are owned and governed by their members, have long managed infrastructure for the public good.

A handful of AI cooperatives offer early examples of how democratic governance and shared ownership could shape more accountable and community-centered uses of the technology. Most are large agricultural cooperatives that are putting AI to use in their day-to-day operations, such as IFFCO’s DRONAI program (AI for fertilization), FrieslandCampina (dairy quality control), and Fonterra (milk production analytics). Cooperatives must urgently organize to challenge AI’s dominance or remain on the sidelines of critical political and technological developments.​

There is undeniably potential here, for both existing cooperatives and companies that might want to partner with them. The $589 billion drop in Nvidia’s market cap DeepSeek triggered shows how quickly open-source innovation can shift the landscape. But for cooperative AI labs to do more than signal intent, they need public infrastructure, civic partnerships, and serious backing…(More)”.

Trends in AI Supercomputers


Paper by Konstantin F. Pilz, James Sanders, Robi Rahman, and Lennart Heim: “Frontier AI development relies on powerful AI supercomputers, yet analysis of these systems is limited. We create a dataset of 500 AI supercomputers from 2019 to 2025 and analyze key trends in performance, power needs, hardware cost, ownership, and global distribution. We find that the computational performance of AI supercomputers has doubled every nine months, while hardware acquisition cost and power needs both doubled every year. The leading system in March 2025, xAI’s Colossus, used 200,000 AI chips, had a hardware cost of $7B, and required 300 MW of power, as much as 250,000 households. As AI supercomputers evolved from tools for science to industrial machines, companies rapidly expanded their share of total AI supercomputer performance, while the share of governments and academia diminished. Globally, the United States accounts for about 75% of total performance in our dataset, with China in second place at 15%. If the observed trends continue, the leading AI supercomputer in 2030 will achieve 2×1022 16-bit FLOP/s, use two million AI chips, have a hardware cost of $200 billion, and require 9 GW of power. Our analysis provides visibility into the AI supercomputer landscape, allowing policymakers to assess key AI trends like resource needs, ownership, and national competitiveness…(More)”.

Digital Methods: A Short Introduction


Book by Tommaso Venturini and Richard Rogers: “In a direct and accessible way, the authors provide hands-on advice to equip readers with the knowledge they need to understand which digital methods are best suited to their research goals and how to use them. Cutting through theoretical and technical complications, they focus on the different practices associated with digital methods to skillfully provide a quick-start guide to the art of querying, prompting, API calling, scraping, mining, wrangling, visualizing, crawling, plotting networks, and scripting. While embracing the capacity of digital methods to rekindle sociological imagination, this book also delves into their limits and biases and reveals the hard labor of digital fieldwork. The book also touches upon the epistemic and political consequences of these methods, but with the purpose of providing practical advice for their usage…(More)”.

Intended, afforded, and experienced serendipity: overcoming the paradox of artificial serendipity


Paper by Annelien Smets: “Designing for serendipity in information technologies presents significant challenges for both scholars and practitioners. This paper presents a theoretical model of serendipity that aims to address this challenge by providing a structured framework for understanding and designing for serendipity. The model delineates between intended, afforded, and experienced serendipity, recognizing the role of design intents and the subjective nature of experiencing serendipity. Central to the model is the recognition that there is no single definition nor a unique operationalization of serendipity, emphasizing the need for a nuanced approach to its conceptualization and design. By delineating between the intentions of designers, the characteristics of the system, and the experiences of end-users, the model offers a pathway to resolve the paradox of artificial serendipity and provides actionable guidelines to design for serendipity in information technologies. However, it also emphasizes the importance of establishing ‘guardrails’ to guide the design process and mitigate potential negative unintended consequences. The model aims to lay ground to advance both research and the practice of designing for serendipity, leading to more ethical and effective design practices…(More)”.

What Counts as Discovery?


Essay by Nisheeth Vishnoi: “Long before there were “scientists,” there was science. Across every continent, humans developed knowledge systems grounded in experience, abstraction, and prediction—driven not merely by curiosity, but by a desire to transform patterns into principles, and observation into discovery. Farmers tracked solstices, sailors read stars, artisans perfected metallurgy, and physicians documented plant remedies. They built calendars, mapped cycles, and tested interventions—turning empirical insight into reliable knowledge.

From the oral sciences of Africa, which encoded botanical, medical, and ecological knowledge across generations, to the astronomical observatories of Mesoamerica, where priests tracked solstices, eclipses, and planetary motion with remarkable accuracy, early human civilizations sought more than survival. In Babylon, scribes logged celestial movements and built predictive models; in India, the architects of Vedic altars designed ritual structures whose proportions mirrored cosmic rhythms, embedding arithmetic and geometry into sacred form. Across these diverse cultures, discovery was not a separate enterprise—it was entwined with ritual, survival, and meaning. Yet the tools were recognizably scientific: systematic observation, abstraction, and the search for hidden order.

This was science before the name. And it reminds us that discovery has never belonged to any one civilization or era. Discovery is not intelligence itself, but one of its sharpest expressions—an act that turns perception into principle through a conceptual leap. While intelligence is broader and encompasses adaptation, inference, and learning in various forms (biological, cultural, and even mechanical), discovery marks those moments when something new is framed, not just found. 

Life forms learn, adapt, and even innovate. But it is humans who turned observation into explanation, explanation into abstraction, and abstraction into method. The rise of formal science brought mathematical structure and experiment, but it did not invent the impulse to understand—it gave it form, language, and reach.

And today, we stand at the edge of something unfamiliar: the possibility of lifeless discoveries. Artificial Intelligence machines, built without awareness or curiosity, are beginning to surface patterns and propose explanations, sometimes without our full understanding. If science has long been a dialogue between the world and living minds, we are now entering a strange new phase: abstraction without awareness, discovery without a discoverer.

AI systems now assist in everything from understanding black holes to predicting protein folds and even symbolic equation discovery. They parse vast datasets, detect regularities, and generate increasingly sophisticated outputs. Some claim they’re not just accelerating research, but beginning to reshape science itself—perhaps even to discover.

But what truly counts as a scientific discovery? This essay examines that question…(More)”

A.I. Is Starting to Wear Down Democracy


Article by Steven Lee Myers and Stuart A. Thompson: “Since the explosion of generative artificial intelligence over the last two years, the technology has demeaned or defamed opponents and, for the first time, officials and experts said, begun to have an impact on election results.

Free and easy to use, A.I. tools have generated a flood of fake photos and videos of candidates or supporters saying things they did not or appearing in places they were not — all spread with the relative impunity of anonymity online.

The technology has amplified social and partisan divisions and bolstered antigovernment sentiment, especially on the far right, which has surged in recent elections in Germany, Poland and Portugal.

In Romania, a Russian influence operation using A.I. tainted the first round of last year’s presidential election, according to government officials. A court there nullified that result, forcing a new vote last month and bringing a new wave of fabrications. It was the first major election in which A.I. played a decisive role in the outcome. It is unlikely to be the last.

As the technology improves, officials and experts warn, it is undermining faith in electoral integrity and eroding the political consensus necessary for democratic societies to function.

Madalina Botan, a professor at the National University of Political Studies and Public Administration in Romania’s capital, Bucharest, said there was no question that the technology was already “being used for obviously malevolent purposes” to manipulate voters.

“These mechanics are so sophisticated that they truly managed to get a piece of content to go very viral in a very limited amount of time,” she said. “What can compete with this?”

In the unusually concentrated wave of elections that took place in 2024, A.I. was used in more than 80 percent, according to the International Panel on the Information Environment, an independent organization of scientists based in Switzerland.

It documented 215 instances of A.I. in elections that year, based on government statements, research and news reports. Already this year, A.I. has played a role in at least nine more major elections, from Canada to Australia…(More)”.

Further Reflections on the Journey Towards an International Framework for Data Governance


Paper by Steve MacFeely, Angela Me, Rachael Beaven, Joseph Costanzo, David Passarelli, Carolina Rossini, Friederike Schueuer, Malarvizhi Veerappan, and Stefaan Verhulst: “The use of data is paramount both to inform individual decisions and to address major global challenges. Data are the lifeblood of the digital economy, feeding algorithms, currencies, artificial intelligence, and driving international services trade, improving the way we respond to crises, informing logistics, shaping markets, communications and politics. But data are not just an economic commodity, to be traded and harvested, they are a personal and social artifact. They contain our most personal and sensitive information – our financial and health records, our networks, our memories, and our most intimate secrets and aspirations. With the advent of digitalization and the internet, our data are ubiquitous – we are the sum of our data. Consequently, this powerful treasure trove needs to be protected carefully. This paper presents arguments for an international data governance framework, the barriers to achieving such a framework and some of the costs of failure. It also articulates why the United Nations is uniquely positioned to host such a framework, and learning from history, the opportunity available to solve a global problem…(More)”.

AI and Social Media: A Political Economy Perspective


Paper by Daron Acemoglu, Asuman Ozdaglar & James Siderius: “We consider the political consequences of the use of artificial intelligence (AI) by online platforms engaged in social media content dissemination, entertainment, or electronic commerce. We identify two distinct but complementary mechanisms, the social media channel and the digital ads channel, which together and separately contribute to the polarization of voters and consequently the polarization of parties. First, AI-driven recommendations aimed at maximizing user engagement on platforms create echo chambers (or “filter bubbles”) that increase the likelihood that individuals are not confronted with counter-attitudinal content. Consequently, social media engagement makes voters more polarized, and then parties respond by becoming more polarized themselves. Second, we show that party competition can encourage platforms to rely more on targeted digital ads for monetization (as opposed to a subscription-based business model), and such ads in turn make the electorate more polarized, further contributing to the polarization of parties. These effects do not arise when one party is dominant, in which case the profit-maximizing business model of the platform is subscription-based. We discuss the impact regulations can have on the polarizing effects of AI-powered online platforms…(More)”.

The Data-Informed City: A Conceptual Framework for Advancing Research and Practice


Paper by Jorrit de Jong, Fernando Fernandez-Monge et al: “Over the last decades, scholars and practitioners have focused their attention on the use of data for improving public action, with a renewed interest in the emergence of big data and artificial intelligence. The potential of data is particularly salient in cities, where vast amounts of data are being generated from traditional and novel sources. Despite this growing interest, there is a need for a conceptual and operational understanding of the beneficial uses of data. This article presents a comprehensive and precise account of how cities can use data to address problems more effectively, efficiently, equitably, and in a more accountable manner. It does so by synthesizing and augmenting current research with empirical evidence derived from original research and learnings from a program designed to strengthen city governments’ data capacity. The framework can be used to support longitudinal and comparative analyses as well as explore questions such as how different uses of data employed at various levels of maturity can yield disparate outcomes. Practitioners can use the framework to identify and prioritize areas in which building data capacity might further the goals of their teams and organizations…(More)

Global Youth Participation Index – GYPI


About: “The GYPI Report offers a powerful, data-driven overview of youth political participation in over 141 countries. From voting rights to civic activism, the report explores how young people engage in politics and where gaps persist. Inside, you’ll find:

  • Global rankings and country-level scores across four key dimensions of youth participation: Socio-Economic, Civic Space, Political Affairs and Elections,
  • Regional insights and thematic trends,
  • Actionable recommendations for policymakers, civil society, and international organisations.

Whether you’re a decision-maker, activist, researcher, or advocate, the report gives you the tools to better understand and strengthen youth participation in public life…(More)”.