Paper by Sasha Luccioni, Boris Gamazaychikov, Theo Alves da Costa, and Emma Strubell: “In recent years, Artificial Intelligence (AI) models have grown in size and complexity, driving greater demand for computational power and natural resources. In parallel to this trend, transparency around the costs and impacts of these models has decreased, meaning that the users of these technologies have little to no information about their resource demands and subsequent impacts on the environment. Despite this dearth of adequate data, escalating demand for figures quantifying AI’s environmental impacts has led to numerous instances of misinformation evolving from inaccurate or de-contextualized best-effort estimates of greenhouse gas emissions. In this article, we explore pervasive myths and misconceptions shaping public understanding of AI’s environmental impacts, tracing their origins and their spread in both the media and scientific publications. We discuss the importance of data transparency in clarifying misconceptions and mitigating these harms, and conclude with a set of recommendations for how AI developers and policymakers can leverage this information to mitigate negative impacts in the future…(More)”.
The Devil’s Advocate: What Happens When Dissent Becomes Digital
Article by Anthea Roberts: “But what if the devil’s advocate wasn’t human at all? What if it was an AI agent—faceless, rank-agnostic, apolitically neutral? A devil without a career to lose. Here’s where the inversion occurs: artificial intelligence enabling more genuine human conversation.
At Dragonfly Thinking, we’ve been experimenting with this concept. We call this Devil’s Advocate your Critical Friend. It’s an AI agent designed to do what humans find personally difficult and professionally dangerous: provide systematic criticism without career consequences.
The magic isn’t in the AI’s intelligence. It’s in how removing the human face transforms the social dynamics of dissent.
When critical feedback comes from an AI, no one’s promotion is at risk. The criticism can be thorough without being insubordinate. Teams can engage with substance rather than navigating office politics.
The AI might note: “Previous digital transformations show 73% failure rate when legacy system dependencies exceed 40%. This proposal shows significant dependencies.” It’s the AI saying what the tech lead knows but can’t safely voice, at least not alone.
Does criticism from code carry less weight because there’s no skin in the game? Counterintuitively, we’ve found the opposite. Without perceived motives or political agendas, the criticism becomes clearer, more digestible.
Ritualizing Productive Dissent
Imagine every major initiative automatically triggering AI analysis. Not optional. Built in like a financial review.
The ritual unfolds:
Monday, 2 PM: The transformation strategy is pitched. Energy builds. Heads nod. The vision is compelling.
Tuesday, 9 AM: An email arrives: “Devil’s Advocate Analysis – Digital Transformation Initiative.” Sender: DA-System. Twelve pages of systematic critique. People read alone, over coffee. Some sections sting. Others confirm private doubts.
Wednesday, 10 AM: The team reconvenes. Printouts are marked up. The tech lead says, “Section 3.2 about integration dependencies—we need to address this.” The ops head adds, “The adoption curve analysis on page 8 matches what we saw in Phoenix.”
Thursday: A revised strategy goes forward. Not perfect, but honest about assumptions and clear about risks.
When criticism is ritualized and automated, it stops being personal. It becomes data…(More)”.
ChatGPT Has Already Polluted the Internet So Badly That It’s Hobbling Future AI Development
Article by Frank Landymore: “The rapid rise of ChatGPT — and the cavalcade of competitors’ generative models that followed suit — has polluted the internet with so much useless slop that it’s already kneecapping the development of future AI models.
As the AI-generated data clouds the human creations that these models are so heavily dependent on amalgamating, it becomes inevitable that a greater share of what these so-called intelligences learn from and imitate is itself an ersatz AI creation.
Repeat this process enough, and AI development begins to resemble a maximalist game of telephone in which not only is the quality of the content being produced diminished, resembling less and less what it’s originally supposed to be replacing, but in which the participants actively become stupider. The industry likes to describe this scenario as AI “model collapse.”
As a consequence, the finite amount of data predating ChatGPT’s rise becomes extremely valuable. In a new feature, The Register likens this to the demand for “low-background steel,” or steel that was produced before the detonation of the first nuclear bombs, starting in July 1945 with the US’s Trinity test.
Just as the explosion of AI chatbots has irreversibly polluted the internet, so did the detonation of the atom bomb release radionuclides and other particulates that have seeped into virtually all steel produced thereafter. That makes modern metals unsuitable for use in some highly sensitive scientific and medical equipment. And so, what’s old is new: a major source of low-background steel, even today, is WW1 and WW2 era battleships, including a huge naval fleet that was scuttled by German Admiral Ludwig von Reuter in 1919…(More)”.
How to Make Small Beautiful: The Promise of Democratic Innovations
Paper by Christoph Niessen & Wouter Veenendaal: “Small states are on average more likely to be democracies and it is often assumed that democracy functions better in small polities. ‘Small is beautiful’, proponents say. Yet, empirical scholarship shows that, while smallness comes with socio-political proximity, which facilitates participation and policy implementation, it also incentivizes personalism, clientelism and power concentration. Largeness, instead, comes with greater socio-political distance, but strengthens institutional checks and entails scale advantages. In this article, we depart from this trade-off and, wondering ‘how to make small beautiful’, we examine a potential remedy: democratic innovations. To do so, we first show that representative institutions were adopted in small polities by replication rather than by choice, and that they can aggravate the democratic problems associated with smallness. Subsequently, we draw on four usages of direct and deliberative democratic practices in small polities to explore which promises they offer to correct some of these pitfalls…(More)”.
National engagement on public trust in data use for single patient record and GP health record published
HTN Article: “A large-scale public engagement report commissioned by NHSE on building and maintaining public trust in data use across health and care has been published, focusing on the approach to creating a single patient record and the secondary use of GP data.
It noted “relief” and “enthusiasm” from participants around not having to repeat their health history when interacting with different parts of the health and care system, and highlighted concerns about data accuracy, privacy, and security.
120 participants were recruited for tier one, with 98 remaining by the end, for 15 hours of deliberation over three days in locations including Liverpool, Leicester, Portsmouth, and South London. Inclusive engagement for tier two recruited 76 people from “seldom heard groups” such as those with health needs or socially marginalised groups for interviews and small group sessions. A nationally representative ten-minute online survey with 2,000 people was also carried out in tier three.
“To start with, the concept of a single patient record was met with relief and enthusiasm across Tier 1 and Tier 2 participants,” according to the report….
When it comes to GP data, participants were “largely unaware” of secondary uses, but initially expressed comfort in the idea of it being used for saving lives, improving care, prevention, and efficiency in delivery of services. Concerns were broadly similar to those about the single patient record: concerns about data breaches, incorrect data, misuse, sensitivity of data being shared, bias against individuals, and the potential for re-identification. Some participants felt GP data should be treated differently because “it is likely to contain more intimate information”, offering greater risk to the individual patient if data were to be misused. Others felt it should be included alongside secondary care data to ensure a “comprehensive dataset”.
Participants were “reassured” overall by safeguards in place such as de-identification, staff training in data handling and security, and data regulation such as GDPR and the Data Protection Act. “There was a widespread feeling among Tier 1 and Tier 2 participants that the current model of the GP being the data controller for both direct care and secondary uses placed too much of a burden on GPs when it came to how data is used for secondary purposes,” findings show. “They wanted to see a new model which would allow for greater consistency of approach, transparency, and accountability.” Tier one participants suggested this could be a move to national or regional decision-making on secondary use. Tier three participants who only engaged with the topic online were “more resistant” to moving away from GPs as sole data controllers, with the report stating: “This greater reluctance to change demonstrates the need for careful communication with the public about this topic as changes are made, and continued involvement of the public.”..(More)”.
Government at a Glance 2025
OECD Report: “Governments face a highly complex operating environment marked by major demographic, environmental, and digital shifts, alongside low trust and constrained fiscal space.
Responding effectively means concentrating efforts on three fronts: Enhancing individuals’ sense of dignity in their interactions with government, restoring a sense of security amid rapid societal and economic changes, and improving government efficiency and effectiveness to help boost productivity in the economy, while restoring public finances. These priorities converge in the governance of the green transition.
Government at a Glance 2025 offers evidence-based tools to tackle these long-term challenges…
Governments are not yet making the most of digital tools and data to improve effectiveness and efficiency
Data, digital tools and AI all offer the prospect of efficiency gains. OECD countries score, on average, 0.61 on the Digital Government Index (on a 0-1 scale) but could improve their digital policy frameworks, whole-of-government approaches and use of data as a strategic asset. On average, only 47% of OECD governments’ high-value datasets are openly available, falling to just 37% in education and 42% in health and social welfare…(More)”.
Disappearing people: A global demographic data crisis threatens public policy
Article by Jessica M. Espey, Andrew J. Tatem, and Dana R. Thomson: “Every day, decisions that affect our lives—such as where to locate hospitals and how to allocate resources for schools—depend on knowing how many people live where and who they are; for example, their ages, occupations, living conditions, and needs. Such core demographic data in most countries come from a census, a count of the population usually conducted every 10 years. But something alarming is happening to many of these critical data sources. As widely discussed at the United Nations (UN) Statistical Commission meeting in New York in March, fewer countries have managed to complete a census in recent years. And even when they are conducted, censuses have been shown to undercount members of certain groups in important ways. Redressing this predicament requires investment and technological solutions alongside extensive political outreach, citizen engagement, and new partnerships…(More)”
Why PeaceTech must be the next frontier of innovation and investment
Article by Stefaan Verhulst and Artur Kluz: “…amidst this frenzy, a crucial question is being left unasked: Can technology be used not just to win wars, but to prevent them and save people’s lives?
There is an emerging field that dares to pose this question—PeaceTech. It is the use of technology to save human lives, prevent conflict, de-escalate violence, rebuild fractured communities, and secure fragile peace in post-conflict environments.
From early warning systems that predict outbreaks of violence, to platforms ensuring aid transparency, and mobile tools connecting refugees to services: PeaceTech is real, it works—and it is radically underfunded.
Unlike the vast sums pouring into defense startups, peace building efforts, including PeaceTech organizations and ventures, struggle for scraps. The United Nations Secretary-General released in 2020 its ambitious goal to fundraise $1.5 billion in peacebuilding support over a total of seven years. In contrast, private investment in defense tech crossed $34 billion in 2023 alone.
Why is PeaceTech so neglected?
One reason PeaceTech is so neglected is cultural: in the tech world, “peace” can seem abstract or idealistic—soft power in a world of hard tech. In reality, peace is not soft; it is among the hardest, most complex challenges of our time. Peace requires systemic thinking, early intervention, global coordination, and a massive infrastructure of care, trust, and monitoring. Maintaining peace in a hyper-polarized, technologically complex world is a feat of engineering, diplomacy, and foresight.
And it’s a business opportunity. According to the Institute for Economics and Peace, violence costs the global economy over $17 trillion per year—about 13% of global GDP. Even modest improvements in peace would unlock billions in economic value.
Consider the peace dividend from predictive analytics that can help governments or international organizations intervene or mediate before conflict breaks out, or AI-powered verification tools to enforce ceasefires and disinformation controls. PeaceTech, if scaled, could become a multibillion dollar market—and a critical piece of the security architecture of the future…(More)”. ..See also Kluz Prize for PeaceTech (Applications Open)
DeepSeek Inside: Origins, Technology, and Impact
Article by Michael A. Cusumano: “The release of DeepSeek V3 and R1 in January 2025 caused steep declines in the stock prices of companies that provide generative artificial intelligence (GenAI) infrastructure technology and datacenter services. These two large language models (LLMs) came from a little-known Chinese startup with approximately 200 employees versus at least 3,500 for industry-leader OpenAI. DeepSeek seemed to have developed this powerful technology much more cheaply than previously thought possible. If true, DeepSeek had the potential to disrupt the economics of the entire GenAI ecosystem and the dominance of U.S. companies ranging from OpenAI to Nvidia.
DeepSeek-R1 defines itself as “an artificial intelligence language model developed by OpenAI, specifically based on the generative pre-trained transformer (GPT) architecture.” Here, DeepSeek acknowledges that the transformer researchers (who published their landmark paper while at Google in 2017) and OpenAI developed its basic technology. Nonetheless, V3 and R1 display impressive skills in neural-network system design, engineering, and optimization, and DeepSeek’s publications provide rare insights into how the technology actually works. This column reviews, for the non-expert reader, what we know about DeepSeek’s origins, technology, and impact so far…(More)”.
The war over the peace business
Article by Tekendra Parmar: “At the second annual AI+ Expo in Washington, DC, in early June, war is the word of the day.
As a mix of Beltway bureaucrats, military personnel, and Washington’s consultant class peruse the expansive Walter E. Washington Convention Center, a Palantir booth showcases its latest in data-collection suites for “warfighters.” Lockheed Martin touts the many ways it is implementing AI throughout its weaponry systems. On the soundstage, the defense tech darling Mach Industries is selling its newest uncrewed aerial vehicles. “We’re living in a world with great-power competition,” the presenter says. “We can’t rule out the possibility of war — but the best way to prevent a war is deterrence,” he says, flanked by videos of drones flying through what looked like the rugged mountains and valleys of Kandahar.
Hosted by the Special Competitive Studies Project, a think tank led by former Google CEO Eric Schmidt, the expo says it seeks to bridge the gap between Silicon Valley entrepreneurs and Washington policymakers to “strengthen” America and its allies’ “competitiveness in critical technologies.”
One floor below, a startup called Anadyr Horizon is making a very different sales pitch, for software that seeks to prevent war rather than fight it: “Peace tech,” as the company’s cofounder Arvid Bell calls it. Dressed in white khakis and a black pinstripe suit jacket with a dove and olive branch pinned to his lapel (a gift from his husband), the former Harvard political scientist begins by noting that Russia’s all-out invasion of Ukraine had come as a surprise to many political scientists. But his AI software, he says, could predict it.
Long the domain of fantasy and science fiction, the idea of forecasting conflict has now become a serious pursuit. In Isaac Asimov’s 1950s “Foundation” series, the main character develops an algorithm that allows him to predict the decline of the Galactic Empire, angering its rulers and forcing him into exile. During the coronavirus pandemic, the US State Department experimented with AI fed with Twitter data to predict “COVID cases” and “violent events.” In its AI audit two years ago, the State Department revealed that it started training AI on “open-source political, social, and economic datasets” to predict “mass civilian killings.” The UN is also said to have experimented with AI to model the war in Gaza…(More)”… ..See also Kluz Prize for PeaceTech (Applications Open)