Stefaan Verhulst

Blog by Tajh L. Taylor: “The public discourse about new data center construction has reached a fever pitch. Local and state governments are enacting moratoria on new data center construction, because the political reality is that citizens have decided they don’t want to endure escalating energy prices and water consumption for the benefit of a few massive corporations datacenter bans. AI companies themselves are going into so much debt to build out computing infrastructure that it is possibly affecting 30 year Treasury yields, which are at their highest point since 2001. As is always the case, when public discourse gets this polarized, the sensible path forward becomes harder to see.

I feel the need to keep repeating this: not all AI needs massive amounts of water or power, nor does it even necessarily need to be hosted in massive data centers powered by highly polluting generators futurism. Lots of good and useful AI applications are being deployed that run on more ordinary-scaled hardware, or even small devices “at the edge” – meaning in your office, on the factory floor, in your home, on your phone. The application areas are varied and include medical diagnosis, voice assistive technology, sorting produce, fraud detection, et cetera. Many of these application areas predate the current generative AI boom, but have benefited from the substantial investments into AI research and product development.

Moreover, not all AI labs working on gen AI are working on hyperscaled intelligence. Some of the labs working on the frontier of small and efficent AI:

• Rootcomputer builds and studies models with 7B or fewer parameters. That’s small enough to run on many consumer devices such as phones and laptops.
• Sakana.ai is doing research in a few different directions, including on efficiency of key-value memory in training while keeping compute efficiency low.
• Liquid.ai works on device-native foundation models aimed at deployment on phones, laptops, cars, and other embedded hardware.

Some of the bigger and more well-known companies also work on this:

• Qwen is Alibaba’s AI lab, and they have produced a series of increasingly powerful small models that have taken almost all the recent attention in the AI self hosting world.
• Apple ML Research works on intelligence specifically constrained to (Apple) phone/laptop hardware

It’s not a coincidence that you don’t hear or see much about this work. The loudest voices in this conversation aren’t interested in these paths, and they aren’t doing themselves any favors in persuading anyone to take their positions who isn’t already aligned. Like so much discourse these days, it is more of a sorting exercise than a discussion to develop thoughts and change minds.

Here’s what will break the logjam: as the frontier of the most powerful AI continues to advance, fewer use cases need frontier capability. And as companies deploying AI have learned in the past few months, it isn’t a smart use of budgets to max out token spend on the most expensive models for every task. As long as frontier model advancement continues to advance inference costs (which seems to be the case as parameter sizes grow at the largest scales), there will be incentive to carefully decide which tasks can be done by which models. At some point, the irrational exuberance around AI investment and spending will give way to rationality – but to distort the Keynes saying, it can remain irrational much longer than we can bear the cost…(More)”.

Book by Alessandro Crimi: “This book explores the synergy between artificial intelligence and medtech, offering a roadmap toward achieving the United Nations’ Sustainable Development Goals. These technologies are part of a wave of innovation transforming how we learn, work, communicate, and live, from self-driving cars to AI-designed drugs and quantum computing. Yet most advances remain concentrated in high-income countries, leaving many low-income nations struggling to keep up.

I examine the social, political, and economic factors limiting development, highlight successful strategies, and explore AI and biotechnologies. Through case studies, the book shows how these tools can improve healthcare access, promote sustainable agriculture, and tackle other global challenges while considering ethical implications. By focusing on solutions accessible to low-income countries, it offers insights for innovators in middle- and high-income nations launching ventures with limited resources.

Future trends rely on interdisciplinary approaches integrating economy, sociology, and technology, recognizing that climate change, new health challenges, demographic shifts, and technologies are reshaping societies worldwide. Developing and advanced economies are deeply interconnected, making this broader perspective essential…(More)”.

Article by Denice Ross & Chris Dick: “Federal data benefit American lives and livelihoods in ways most people never see, touching every corner of our lives. This includes a farmer pricing a crop, a county planning a hospital, a business siting a warehouse — all of these decisions use federal data. Other data save patients money by identifying generic drugs that can replace more expensive brand names, help airplanes avoid deadly bird strikes, and warn consumers about recalls of dangerous products. 

Because these data are mostly invisible, their disappearance is invisible too — that is, until we need the data and they aren’t there anymore.

As federal data policy nerds, the question we get asked all the time is How much data has the current administration terminated?

The answer is that it depends on what we count as “data” AND what counts as a termination. That’s not a dodge – working through those two critical nuances is the substance of this piece.

The answer also depends on what the information will be used for. Ours is a data policy question, so we looked for structured, numerical datasets that have been terminated – meaning there will be no collections of those data in the future. Defining terminations that way lets us ask how agencies consulted with the public about a dataset’s value before ending it, and what its loss means for the federal government’s ability to serve the American people.

The purpose of the Federal Data Terminations Tracker is to be the most policy-relevant, verified accounting of federal data terminations available.

To create this Tracker, the dataindex.us team identified dozens of federal datasets and hundreds of data elements that have been terminated – significantly fewer than other reports, but more tailored to informing future data policies needed to run a modern society. These are data that have long underpinned policymaking, journalism, advocacy and research that improve American lives and livelihoods. These figures will change as terminations continue, collections are merged, and as court orders restore data. 

Want more details on why this question about federal data losses is so tricky? Read on. Want to see what data have been terminated? Visit the Federal Data Terminations Tracker at dataindex.us/terminations-tracker…(More)”.

Article by Rachel Santarsiero: “…The fossil fuel industry is trying to weaken methane reporting requirements while simultaneously fighting to preserve the related federal emissions database it relies on for credibility, according to U.S. Environmental Protection Agency (EPA) records recently released in response to a freedom of information request.

When the Trump administration announced plans last year to repeal the federal government’s greenhouse gas reporting system, rather than celebrate, many oil and gas companies publicly urged the EPA to preserve it. But for more than a year, behind closed doors, the fossil fuel industry has been pushing to weaken one of the reporting program’s most consequential elements.

Oil and gas companies fear this Biden-era revision to the methane reporting rule known as Subpart W, would force them to disclose much higher — and previously hidden — pollution levels. Those concerns, brewing for years, grew into a wave that ultimately would break onto the friendly shores of the Trump EPA in response to the agency’s expected overhaul of both the methane reporting rule and the Greenhouse Gas Reporting Program (GHGRP), the most comprehensive system for tracking the nation’s greenhouse gases, industry comments and public records show.

At a private trade group meeting in 2023, one energy analyst said the revised methane rule would be a “major PR headache” for oil and gas companies. “How do we even go talk to our investors and explain that this is what’s happening?” he said. “Things that historically potentially had gone unreported will now have to be reported.”

In 2025, an industry consultant predicted that some companies could see their reported methane emissions increase by four to ten times under the rule’s new methodology. As recently as this spring, another consultant speaking to a gas group conference warned that updated disclosure requirements would increase reported methane emissions by roughly 16 percent. Last month, major gas producer EQT cited this revised rule as one reason its reported methane emissions rose in 2025…(More)”.

Paper by Atoosa Kasirzadeh & Iason Gabriel: “The creation of effective governance mechanisms for artificial intelligence (AI) agents requires a deeper understanding of their core properties and the implications they have for deployment. This paper provides a characterization of AI agents that focuses on four dimensions: autonomy, efficacy, goal complexity and generality. We propose different gradations for each dimension and argue that each dimension raises unique questions about the design, operation and governance of these systems. Moreover, we draw on this framework to construct ‘agentic profiles’ for different kinds of AI agent. These profiles help to illuminate cross-cutting technical and non-technical governance challenges posed by different classes of AI agents, ranging from narrow task-specific assistants to highly autonomous general-purpose systems. By mapping out key axes of variation and continuity across four dimensions, agentic profiles provide developers, policymakers and members of the public with guidance for effective AI governance…(More)”.

Book by Grace Huckins: “Science is in the midst of an under-recognized revolution. For centuries, prediction in science went hand in hand with understanding: knowledge of what advanced in tandem with knowledge of how and why. But in recent years, AI tools have enabled scientists to make predictions that previously would have been impossible, even if they don’t understand why those predictions hold true. Already, scientists have used these AI ‘oracles’ to design new drug candidates and help paralyzed people regain the ability to speak. These are consequential achievements. But they also raise a difficult question: If science can improve lives with prediction alone, should we still seek to understand the universe? In The Prediction Revolution, Grace Huckins, a trained neuroscientist and award-winning journalist, explores how AI is reshaping the relationship between prediction and understanding-and challenges us to consider what science is really for…(More)”.

Report by the Centre for Collective Intelligence (CCI): “How do people feel about devices that can read data from our brains, and about who gets to use the data they collect? Nesta’s Centre for Collective Intelligence, commissioned by the Information Commissioner’s Office (ICO), brought together a citizens’ jury to find out.

Neurotechnologies are devices that record signals from, or send signals to, the brain and nervous system. For years, neurotechnology has mostly been limited to medical uses such as cochlear implants or EEGs. Neurotechnology devices are increasingly moving beyond healthcare into consumer products for wellbeing, the workplace and education.

As this happens, it raises difficult questions about how the data derived from our brains should be protected. Developers currently face an uncertain regulatory landscape, and without clear guidance there is a risk of controversial practices or public backlash that could undermine trust in the technology.

What we did

Over two weeks in March 2026, 20 members of the UK public met for three online sessions. Guided by expert facilitators, they watched short informational videos, heard from experts, and, most importantly, deliberated together. The sessions moved from jurors’ first impressions to what they expect of regulators and developers, guided throughout by a single question: how do we make the most of the benefits of neurotechnology while managing the risks?

A citizens’ jury is well suited to a topic that is both technically complex and ethically contested. By giving a diverse group the time, information and space to deliberate, it produces considered judgements…(More)”.

Article by Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen: “We are releasing a revised version of “Canaries in the Coal Mine? Six Facts About the Recent Employment Effects of Artificial Intelligence.”  Using payroll data from ADP, we document six facts about how employment has evolved since the release of ChatGPT, with particular attention to differences by age and AI exposure.

The updated data strengthen several patterns we first documented in August 2025. Most notably, the employment gap for young workers in highly AI-exposed occupations has continued to widen through mid-2026.

Our six main facts

1. We do not see widespread, economy-wide job displacement associated with AI.
2. However, young workers in AI-exposed occupations are increasingly falling behind their less-exposed peers. Employment among workers ages 22–25 in highly AI-exposed occupations now stands about 19% below where it would be if it had kept pace with employment among similarly aged workers in less-exposed occupations. Experienced workers show no comparable gap.
3. This divergence has widened steadily since we first documented it in August 2025: by this same measure, the shortfall was 15% at the July 2025 data vintage and is 19% as of June 2026.
4. The adjustment appears to operate primarily through reduced hiring of young workers rather than increased separations.
5. The declines are concentrated in occupations where AI usage tends to automate human tasks. In occupations where AI is used more to complement workers, employment is flat or rising, particularly among more experienced workers.
6. So far, adjustment is showing up primarily in employment rather than base pay…(More)”.

Paper by Jasper Roe and Mike Perkins: “Conversations regarding the societal implications of artificial intelligence (AI) and its integration into educational processes are ongoing. An area of great significance in this regard is agency, including how the use of AI technologies impacts an individual’s ability to exercise autonomy. In educational contexts, this remains a relatively unexplored yet vital subject. To shed light on this subject, this paper provides an exploratory thematic review of works which investigate the relationship between generative artificial intelligence (GenAI) and agency in education, mapping and interpreting the available literature through the lens of critical digital pedagogy (CDP). The findings suggest that while GenAI may enhance learner agency through personalisation and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review also contributes to new sociotechnical perspectives on AI, highlighting how GenAI technologies may shape and be shaped by power dynamics and pedagogical ideologies. This review contributes to the existing debate on how AI technologies can be deployed in education and society responsibly by exposing how the use of these technologies may lead to unintended impacts, including the entrenchment of inequality and the reshaping of concepts of individual agency, authorship, and autonomy…(More)”.

Collected Essays by Moshe Maor: “…explains the phenomenon of sustained policy overinvestment or overreaction over extended periods. These long-term patterns of policy overproduction, known as policy bubbles, arise from psychological dynamics, institutional routines, and deliberate strategic action that generate positive feedback loops and lock policymakers into persistent overinvestment.

Leading expert Moshe Maor underscores the need to examine policy bubbles across their full life cycle: emergence, maturation, and termination or incorporation. Drawing on diverse case studies — including the NATO expansion foreign policy bubble, the corresponding political bubble in the U.S. Senate, and the U.S. crime policy bubble — the book traces the full life cycle of real-world policy bubbles and outlines how governments can better detect, understand, and respond to them. Through this analysis, Maor provides a comprehensive account of how policy bubbles persist and why uncovering their underlying drivers is essential for designing proportionate, evidence-aligned, and resilient policies.

Investigating a highly relevant policy anomaly, Policy Bubbles is an essential resource for scholars and students of public policy, public administration, and public management. Economists studying financial and asset bubbles, as well as government analysts, will likewise benefit from its groundbreaking insights…(More)”