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)”.
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)”
The Global A.I. Divide
Article by Adam Satariano and Paul Mozur: “Last month, Sam Altman, the chief executive of the artificial intelligence company OpenAI, donned a helmet, work boots and a luminescent high-visibility vest to visit the construction site of the company’s new data center project in Texas.
Bigger than New York’s Central Park, the estimated $60 billion project, which has its own natural gas plant, will be one of the most powerful computing hubs ever created when completed as soon as next year.
Around the same time as Mr. Altman’s visit to Texas, Nicolás Wolovick, a computer science professor at the National University of Córdoba in Argentina, was running what counts as one of his country’s most advanced A.I. computing hubs. It was in a converted room at the university, where wires snaked between aging A.I. chips and server computers.
“Everything is becoming more split,” Dr. Wolovick said. “We are losing.”
Artificial intelligence has created a new digital divide, fracturing the world between nations with the computing power for building cutting-edge A.I. systems and those without. The split is influencing geopolitics and global economics, creating new dependencies and prompting a desperate rush to not be excluded from a technology race that could reorder economies, drive scientific discovery and change the way that people live and work.
The biggest beneficiaries by far are the United States, China and the European Union. Those regions host more than half of the world’s most powerful data centers, which are used for developing the most complex A.I. systems, according to data compiled by Oxford University researchers. Only 32 countries, or about 16 percent of nations, have these large facilities filled with microchips and computers, giving them what is known in industry parlance as “compute power.”..(More)”.
AI is supercharging war. Could it also help broker peace?
Article by Tina Amirtha: “Can we measure what is in our hearts and minds, and could it help us end wars any sooner? These are the questions that consume entrepreneur Shawn Guttman, a Canadian émigré who recently gave up his yearslong teaching position in Israel to accelerate a path to peace—using an algorithm.
Living some 75 miles north of Tel Aviv, Guttman is no stranger to the uncertainties of conflict. Over the past few months, miscalculated drone strikes and imprecise missile targets—some intended for larger cities—have occasionally landed dangerously close to his town, sending him to bomb shelters more than once.
“When something big happens, we can point to it and say, ‘Right, that happened because five years ago we did A, B, and C, and look at its effect,’” he says over Google Meet from his office, following a recent trip to the shelter. Behind him, souvenirs from the 1979 Egypt-Israel and 1994 Israel-Jordan peace treaties are visible. “I’m tired of that perspective.”
The startup he cofounded, Didi, is taking a different approach. Its aim is to analyze data across news outlets, political discourse, and social media to identify opportune moments to broker peace. Inspired by political scientist I. William Zartman’s “ripeness” theory, the algorithm—called the Ripeness Index—is designed to tell negotiators, organizers, diplomats, and nongovernmental organizations (NGOs) exactly when conditions are “ripe” to initiate peace negotiations, build coalitions, or launch grassroots campaigns.
During ongoing U.S.-led negotiations over the war in Gaza, both Israel and Hamas have entrenched themselves in opposing bargaining positions. Meanwhile, Israel’s traditional allies, including the U.S., have expressed growing frustration over the war and the dire humanitarian conditions in the enclave, where the threat of famine looms.
In Israel, Didi’s data is already informing grassroots organizations as they strategize which media outlets to target and how to time public actions, such as protests, in coordination with coalition partners. Guttman and his collaborators hope that eventually negotiators will use the model’s insights to help broker lasting peace.
Guttman’s project is part of a rising wave of so-called PeaceTech—a movement using technology to make negotiations more inclusive and data-driven. This includes AI from Hala Systems, which uses satellite imagery and data fusion to monitor ceasefires in Yemen and Ukraine. Another AI startup, Remesh, has been active across the Middle East, helping organizations of all sizes canvas key stakeholders. Its algorithm clusters similar opinions, giving policymakers and mediators a clearer view of public sentiment and division.
A range of NGOs and academic researchers have also developed digital tools for peacebuilding. The nonprofit Computational Democracy Project created Pol.is, an open-source platform that enables citizens to crowdsource outcomes to public debates. Meanwhile, the Futures Lab at the Center for Strategic and International Studies built a peace agreement simulator, complete with a chart to track how well each stakeholder’s needs are met.
Guttman knows it’s an uphill battle. In addition to the ethical and privacy concerns of using AI to interpret public sentiment, PeaceTech also faces financial hurdles. These companies must find ways to sustain themselves amid shrinking public funding and a transatlantic surge in defense spending, which has pulled resources away from peacebuilding initiatives.
Still, Guttman and his investors remain undeterred. One way to view the opportunity for PeaceTech is by looking at the economic toll of war. In its Global Peace Index 2024, the Institute for Economics and Peace’s Vision of Humanity platform estimated that economic disruption due to violence and the fear of violence cost the world $19.1 trillion in 2023, or about 13 percent of global GDP. Guttman sees plenty of commercial potential in times of peace as well.
“Can we make billions of dollars,” Guttman asks, “and save the world—and create peace?” ..(More)”….See also Kluz Prize for PeaceTech (Applications Open)
Sentinel Cities for Public Health
Article by Jesse Rothman, Paromita Hore & Andrew McCartor: “In 2017, a New York City health inspector visited the home of a 5-year-old child with an elevated blood lead level. With no sign of lead paint—the usual suspect in such cases—the inspector discovered dangerous levels of lead in a bright yellow container of “Georgian Saffron,” a spice obtained in the family’s home country. It was not the first case associated with the use of lead-containing Georgian spices—the NYC Health Department shared their findings with authorities in Georgia, which catalyzed a survey of children’s blood lead levels in Georgia, and led to increased regulatory enforcement and education. Significant declines in spice lead levels in the country have had ripple effects in NYC also: not only a drop in spice samples from Georgia containing detectable lead but also a significant reduction in blood lead levels among NYC children of Georgian ancestry.
This wasn’t a lucky break—it was the result of a systematic approach to transform local detection into global impact. Findings from local NYC surveillance are, of course, not limited to Georgian spices. Surveillance activities have identified a variety of lead-containing consumer products from around the world, from cosmetics and medicines to ceramics and other goods. Routinely surveying local stores for lead-containing products has resulted in the removal of over 30,000 hazardous consumer products from NYC store shelves since 2010.
How can we replicate and scale up NYC’s model to address the global crisis of lead poisoning?…(More)”.
The path for AI in poor nations does not need to be paved with billions
Editorial in Nature: “Coinciding with US President Donald Trump’s tour of Gulf states last week, Saudi Arabia announced that it is embarking on a large-scale artificial intelligence (AI) initiative. The proposed venture will have state backing and considerable involvement from US technology firms. It is the latest move in a global expansion of AI ambitions beyond the existing heartlands of the United States, China and Europe. However, as Nature India, Nature Africa and Nature Middle East report in a series of articles on AI in low- and middle-income countries (LMICs) published on 21 May (see go.nature.com/45jy3qq), the path to home-grown AI doesn’t need to be paved with billions, or even hundreds of millions, of dollars, or depend exclusively on partners in Western nations or China…, as a News Feature that appears in the series makes plain (see go.nature.com/3yrd3u2), many initiatives in LMICs aren’t focusing on scaling up, but on ‘scaling right’. They are “building models that work for local users, in their languages, and within their social and economic realities”.
More such local initiatives are needed. Some of the most popular AI applications, such as OpenAI’s ChatGPT and Google Gemini, are trained mainly on data in European languages. That would mean that the model is less effective for users who speak Hindi, Arabic, Swahili, Xhosa and countless other languages. Countries are boosting home-grown apps by funding start-up companies, establishing AI education programmes, building AI research and regulatory capacity and through public engagement.
Those LMICs that have started investing in AI began by establishing an AI strategy, including policies for AI research. However, as things stand, most of the 55 member states of the African Union and of the 22 members of the League of Arab States have not produced an AI strategy. That must change…(More)”.
The AI Policy Playbook
Playbook by AI Policymaker Network & Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH: “It moves away from talking about AI ethics in abstract terms but tells of building policies that work right-away in emerging economies and respond to immediate development priorities. The Playbook emphasises that a one-size-fits-all solution doesn’t work. Rather, it illustrates shared challenges—like limited research capacity, fragmented data ecosystems, and compounding AI risks—while spotlighting national innovations and success stories. From drafting AI strategies to engaging communities and safeguarding rights, it lays out a roadmap grounded in local realities….What can you expect to find in the AI Policy Playbook:
- Policymaker Interviews
Real-world insights from policymakers to understand their challenges and best practices. - Policy Process Analysis
Key elements from existing policies to extract effective strategies for AI governance, as well as policy mapping. - Case Studies
Examples of successes and lessons learnt from various countries to provide practical guidance. - Recommendations
Concrete solutions and recommendations from actors in the field to improve the policy development process, including quick tips for implementation and handling challenges.
What distinguishes this initiative is its commitment to peer learning and co-creation. The Africa-Asia AI Policymaker Network comprises over 30 high-level government partners who anchor the Playbook in real-world policy contexts. This ensures that the frameworks are not only theoretically sound but politically and socially implementable…(More)”
Europe’s dream to wean off US tech gets reality check
Article by Pieter Haeck and Mathieu Pollet: “..As the U.S. continues to up the ante in questioning transatlantic ties, calls are growing in Europe to reduce the continent’s reliance on U.S. technology in critical areas such as cloud services, artificial intelligence and microchips, and to opt for European alternatives instead.
But the European Commission is preparing on Thursday to acknowledge publicly what many have said in private: Europe is nowhere near being able to wean itself off U.S. Big Tech.
In a new International Digital Strategy the EU will instead promote collaboration with the U.S., according to a draft seen by POLITICO, as well as with other tech players including China, Japan, India and South Korea. “Decoupling is unrealistic and cooperation will remain significant across the technological value chain,” the draft reads.
It’s a reality check after a year that has seen calls for a technologically sovereign Europe gain significant traction. In December the Commission appointed Finland’s Henna Virkkunen as the first-ever commissioner in charge of tech sovereignty. After few months in office, European Parliament lawmakers embarked on an effort to draft a blueprint for tech sovereignty.
Even more consequential has been the rapid rise of the so-called Eurostack movement, which advocates building out a European tech infrastructure and has brought together effective voices including competition economist Cristina Caffarra and Kai Zenner, an assistant to key European lawmaker Axel Voss.
There’s wide agreement on the problem: U.S. cloud giants capture over two-thirds of the European market, the U.S. outpaces the EU in nurturing companies for artificial intelligence, and Europe’s stake in the global microchips market has crumbled to around 10 percent. Thursday’s strategy will acknowledge the U.S.’s “superior ability to innovate” and “Europe’s failure to capitalise on the digital revolution.”
What’s missing are viable solutions to the complex problem of unwinding deep-rooted dependencies….(More)”
Reliable data facilitates better policy implementation
Article by Ganesh Rao and Parul Agarwal: “Across India, state government departments are at the forefront of improving human capabilities through education, health, and nutrition programmes. Their ability to do so effectively depends on administrative (or admin) data1 collected and maintained by their staff. This data is collected as part of regular operations and informs both day-to-day decision-making and long-term policy. While policymaking can draw on (reasonably reliable) sample surveys alone, effective implementation of schemes and services requires accurate individual-level admin data. However, unreliable admin data can be a severe constraint, forcing bureaucrats to rely on intuition, experience, and informed guesses. Improving the reliability of admin data can greatly enhance state capacity, thereby improving governance and citizen outcomes.
There has been some progress on this front in recent years. For instance, the Jan Dhan-Aadhaar-Mobile (JAM) trinity has significantly improved direct benefit transfer (DBT) mechanisms by ensuring that certain recipient data is reliable. However, challenges remain in accurately capturing the well-being of targeted citizens. Despite significant investments in the digitisation of data collection and management systems, persistent reliability issues undermine the government’s efforts to build a data-driven decision-making culture…
There is growing evidence of serious quality issues in admin data. At CEGIS, we have conducted extensive analyses of admin data across multiple states, uncovering systemic issues in key indicators across sectors and platforms. These quality issues compound over time, undermining both micro-level service delivery and macro-level policy planning. This results in distorted budget allocations, gaps in service provision, and weakened frontline accountability…(More)”.
Project Push creates an archive of news alerts from around the world
Article by Neel Dhanesha: “A little over a year ago, Matt Taylor began to feel like he was getting a few too many push notifications from the BBC News app.
It’s a feeling many of us can probably relate to. Many people, myself included, have turned off news notifications entirely in the past few months. Taylor, however, went in the opposite direction.
Instead of turning off notifications, he decided to see how the BBC — the most popular news app in the U.K., where Taylor lives — compared to other news organizations around the world. So he dug out an old Google Pixel phone, downloaded 61 news apps onto it, and signed up for push notifications on all of them.
As notifications roll in, a custom-built script (made with the help of ChatGPT) uploads their text to a server and a Bluesky page, providing a near real-time view of push notifications from services around the world. Taylor calls it Project Push.
People who work in news “take the front page very seriously,” said Taylor, a product manager at the Financial Times who built Project Push in his spare time. “There are lots of editors who care a lot about that, but actually one of the most important people in the newsroom is the person who decides that they’re going to press a button that sends an immediate notification to millions of people’s phones.”
The Project Push feed is a fascinating portrait of the news today. There are the expected alerts — breaking news, updates to ongoing stories like the wars in Gaza and Ukraine, the latest shenanigans in Washington — but also:
— Updates on infrastructure plans that, without the context, become absolutely baffling (a train will instead be a bus?).
— Naked attempts to increase engagement.
— Culture updates that some may argue aren’t deserving of a push alert from the Associated Press.
— Whatever this is.
Taylor tells me he’s noticed some geographic differences in how news outlets approach push notifications. Publishers based in Asia and the Middle East, for example, send far more notifications than European or American ones; CNN Indonesia alone pushed about 17,000 of the 160,000 or so notifications Project Push has logged over the past year…(More)”.