Stefaan Verhulst

Report by the National Academies: “Foundation models – artificial intelligence systems trained on massive data sets to perform a wide range of tasks – have the potential to transform scientific discovery and innovation. At the request of the U.S. Department of Energy (DOE), the National Academies conducted a study to consider the capabilities of current foundation models as well as future possibilities and challenges. Foundation Models for Scientific Discovery and Innovation explores how foundation models can complement traditional computational methods to advance scientific discovery, highlights successful use cases, and recommends strategic approaches and investments to support DOE’s mission…(More)”.

Blog by Daniel Schuman: “The Government Publishing Office grabbed the spotlight at the final Congressional Data Task Force meeting of 2025 last Wednesday by announcing that it is launching a Model Context Protocol server for artificial intelligence tools to access official GPO publication information. The MCP server lets AI tools like ChatGPT and Gemini pull in official GPO documents, allowing them to rely on current, authoritative information when answering questions.

Here’s why this matters. Large Language Models are trained on large collections of text, but that training is fixed at a point in time and can become outdated. As a result, an AI may not know about recent events or changes and may even give confident but incorrect answers.

Technologies like an MCP server address this problem by allowing an AI system to consult trusted, up-to-date sources when it needs them. When a question requires current or authoritative information, the AI can request that information from the MCP server, which returns official data—such as publications from the Government Publishing Office—that the AI can then use in its response. Most importantly, the design of an MCP server allows for machine-to-machine access, helping ensure responses are grounded in authoritative sources rather than generated guesses.

Adding MCP creates another mechanism for the public to access GPO publications, alongside search, APIs, and bulk data access. It is a good example of the legislative branch racing ahead to meet the public need for authoritative, machine-readable information.

GPO’s Mark Caudill said his office implemented the MCP both to respond to growing demand for AI-accessible data and to avoid having to choose the “best” AI agent. This is in line with GPO’s mission of being a trusted repository of the official record of the federal government. With a wide range of AI tools in use, from general use ones like ChatGPT and Gemini to more specific ones geared toward legal research, GPO’s adoption of MCP allows it to be agnostic across that ecosystem.

A user would configure the LLM of their choice to connect to GovInfo’s MCP, allowing it to draw data from GPO publications rather than being limited to its training data. How well the model interprets those publications and returns quality answers to users is beyond GPO’s control.

GPO also has expanded access to data in ways that don’t involve AI, including expansion of its customizable RSS feeds for users interested in specific types of documents or the latest data from specific federal offices or courts.. The video and slides for the event are available on the Legislative Branch Innovation Hub…(More)”.

JRC policy brief: “…emphasizes the importance of including an inclusive approach in regulatory sandboxing, to ensure digital innovations deliver equitable and impactful outcomes. The document outlines the risks associated with improper design of sandboxing, such as biases and exclusion, and provides a practical guide to ensure inclusion in regulatory sandboxes. The main recommendations are grouped among six themes: scoping, regulations and safeguards, stakeholder engagement, data and technology, governance, and reach and impact. Ultimately, it underscores that while inclusive sandboxing may require more resources, it delivers greater value by fostering equitable innovation that benefits all stakeholders…(More)”.

Article by Tanjimul Islam: “Starlink, the satellite internet service run by Elon Musk’s SpaceX, launched in 2019. Since then, it has become available in more than 150 markets, with 8 million users. 

Starlink’s expansion has at times struggled against regulatory red tape. But during the period of time Elon Musk served in U.S. President Donald Trump’s government, Musk’s Starlink was activated or approved in at least 13 countries, including India, Vietnam, Pakistan, and Bangladesh. In some of these places, Starlink’s applications had stalled for years until they were suddenly greenlit. Rest of World’s recent investigative feature explores the way Musk’s business benefited from his close ties to Trump: “There were [American] government officials, whether authorized or not, who were basically saying, if you want favorable treatment from Trump, you better be good to Musk,” said Blair Levin, who led the Obama administration’s National Broadband Plan and was formerly chief of staff with the Federal Communications Commission. 

Part of space-based internet’s appeal is that it reaches areas where traditional internet infrastructure is sparse or nonexistent. Starlink — and the companies trying to compete with it — is now fueling a satellite boom. It’s estimated that in 2025, a SpaceX rocket has, on average, brought Starlink satellites into space every three days. Despite the billions of dollars of investment, and the thousands of satellites zipping around our planet, it’s still hard to really picture the satellite internet industry, which is why Rest of World put together this visualization…(More)”.

Article by Stefaan Verhulst: “The European Union’s pursuit to create a single data market has always been a balancing act between fostering public interest goals and safeguarding private enterprise. The Data Act (Regulation (EU) 2023/2854), which became applicable on September 12 2025, codified this tension, particularly in its Business-to-Government (B2G) provisions under Chapter V.

Initially, these provisions required data holders to share data with public sector bodies in cases of “exceptional need”, which was divided into two tracks: urgent Public Emergencies and non-emergency Public Interest Tasks.

However, the European Commission’s Digital Omnibus Package, published last month, has signaled a definitive pivot. The core message: B2G data sharing is now being refined and confined as a measure of last resort. This narrowing protects the private sector but simultaneously creates a critical challenge: without a designated data steward within both the public and private sector this restrictive, haphazard approach will fail to build the trusted, long-term data ecosystems necessary to address emergency and non-emergency, systemic societal challenges…(More)”.

Report by the American Statistical Association (ASA):”… The report documents significant challenges facing the 13 federal statistical agencies and outlines nine new recommendations to strengthen the nation’s statistical infrastructure.

Federal statistics—produced by agencies including the Bureau of Labor Statistics, the Census Bureau and the National Center for Health Statistics—serve as essential infrastructure for economic policy, public health decisions and democratic governance. These data inform everything from interest rate decisions to public health responses and business planning.

“Federal statistics are fundamental infrastructure, similar to roads, bridges and power grids,” said ASA Executive Director Ron Wasserstein. “This report shows that immediate investment and coordination are needed to ensure these agencies can meet current and future information needs.”

Key Findings

The report documents the following concerning trends:

• Staffing losses: Most agencies have lost 20-30% of their staff, affecting their ability to innovate and meet expanding demands for more timely and granular data.
• Budget constraints: Eight of 13 agencies have lost at least 16% of purchasing power since FY2009, even as congressional mandates have increased.
• Declining public trust: The percentage of US.adults who trust federal statistics declined from 57% in June 2025 to 52% in September 2025, according to surveys conducted by NORC at the University of Chicago.
• System coordination challenges: The decentralized structure of the federal statistical system, while promoting subject-matter expertise, lacks dedicated funding for system-wide initiatives such as joint IT upgrades and coordinated data-sharing…(More)”.

Essay by Daniel Benaim: “When U.S. President Donald Trump visited the Arab Gulf in May, his focus was not on Gaza, Iran, or even normalization between Israel and Saudi Arabia. Instead, it was on business deals and, above all, artificial intelligence. During the trip, Trump agreed to sell advanced U.S. chips to Saudi Arabia and the United Arab Emirates and to invest in AI mega-campuses in the Gulf that will host U.S. firms. One such site, in Abu Dhabi, could become the worldʼs largest concentrated cluster of the computing power fueling artificial intelligence. The Gulf countries, in turn, promised to invest tens of billions of dollars in AI on U.S. soil. And last month, during his trip to Washington, Saudi Crown Prince Mohammed bin Salman (also known as MBS) won final approval to import tens of thousands of advanced U.S. semiconductors, which had been promised to Saudi Arabia earlier in the year.

Armed with chips, sovereign wealth, and abundant energy, Gulf states could surpass Europe and India in terms of AI infrastructure—eventually becoming the world’s third biggest hub for AI computing power, behind the United States and China. Computing power has now taken its place alongside crude oil as a pillar of the U.S.-Gulf relationship, and the Gulf states have become a partner of first resort for the Trump administration.

The upside of this AI cooperation is significant. If done right, the deals will channel the vast wealth of Gulf states into American AI companies and allow these firms to expand to areas with few power and permitting bottlenecks. With the Gulf’s connectivity to surrounding regions, the reach of the United States’ AI stack—that is, the layers of hardware and software that AI is built on—could extend to billions of users across Africa, Central Asia, and the Middle East. The deals could also enable the United States to dislodge China as the Gulf’s top technology partner, which would be a big win for Washington over Beijing…(More)”.

Paper by Liza Dahiya & Rachit Bagga: “Social media platforms, particularly Reddit’s r/Epilepsy community, offer a unique perspective into the experiences of people with epilepsy (PWE) & their caregivers. This study analyzes 57k posts & 533k comments to uncover linguistic, emotional, and thematic patterns across demographics such as age, gender, and relationship to the PWE. We identify significant variation in language and discussion topics across these groups, as validated by statistical tests and topic modeling. Group-specific concerns emerged: teenagers frequently discuss marijuana and gaming-related triggers; women emphasize pregnancy and hormonal impacts; and caregivers, particularly parents and romantic partners, express a heightened emotional burden. We further examine the prevalence of depression signals across demographics, revealing that 39.75% of posts exhibit signs of severe depression, frequently co-occurring with anxiety and emotional distress. To quantify engagement within the community, we introduce a novel metric, F(P), which integrates post length, sentiment polarity, and readability. Posts expressing emotional or mental health concerns show significantly higher F(P) scores, underscoring the platform’s potential as a real-time support and outreach mechanism…(More)”.

Paper by Sebastian Singler, Ali A. Guenduez & Mehmet A. Demircioglu: “Public sector innovations often fail if they do not meet citizens’ expectations. However, little is known about how well public servants understand these expectations. This study identifies a perception gap between citizens and public servants regarding innovation characteristics, which are specific attributes of public sector innovations that shape citizen support and legitimacy. Using Q-methodology with Swiss citizens and public servants, we identify four distinct citizen groups: result-centric, trust-centric, certainty-centric, and cost- and rule-of-law-centric. Each group emphasizes different characteristics, such as ease of use, efficiency, trialability, and trust. By contrast, public servants perceive only three homogenized citizen groups – customer-centric, trust-centric, and result-centric – overlooking expectations related to democratic participation and co-creation. This mismatch risks undermining the legitimacy and adoption of innovations. The study advances a citizen-centred view of innovation characteristics, highlights the importance of citizen heterogeneity, and provides practical guidance on designing innovations that align with diverse citizen expectations…(More)”.

Article by Emanuel Maiberg: “Online survey research, a fundamental method for data collection in many scientific studies, is facing an existential threat because of large language models, according to new research published in the Proceedings of the National Academy of Sciences (PNAS). The author of the paper, associate professor of government at Dartmouth and director of the Polarization Research Lab Sean Westwood, created an AI tool he calls “an autonomous synthetic respondent,” which can answer survey questions and “demonstrated a near-flawless ability to bypass the full range” of “state-of-the-art” methods for detecting bots. 

According to the paper, the AI agent evaded detection 99.8 percent of the time.

“We can no longer trust that survey responses are coming from real people,” Westwood said in a press release. “With survey data tainted by bots, AI can poison the entire knowledge ecosystem.”

Survey research relies on attention check questions (ACQs), behavioral flags, and response pattern analysis to detect inattentive humans or automated bots. Westwood said these methods are now obsolete after his AI agent bypassed the full range of standard ACQs and other detection methods outlined in prominent papers, including one paper designed to detect AI responses. The AI agent also successfully avoided “reverse shibboleth” questions designed to detect nonhuman actors by presenting tasks that an LLM could complete easily, but are nearly impossible for a human…(More)”.