No. 2
02/11/2026 · 8 pieces
8 picks from 305 pieces reviewed in 20 publications across 4 regions.
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01 ★★★★★
Following AI chips through customs: where the compute really goes
Where do the world's AI chips actually end up, when chipmakers and cloud companies rarely say? Isabel Juniewicz and Lucio Melito of Epoch AI build an answer from customs records in 16 major destinations, netting out transit hubs and adding chips made and used in China. Read it for the method as much as the results: it shows how far public trade data can go, and where it goes blind. We picked it because it is original data with its limits stated plainly. The piece launches Epoch's own data explorer, the only stake we found.
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02 ★★★★☆
Reading Beijing's signals before the next US-China AI dialogue
What does China actually want from the AI dialogue it agreed with Washington during Xi Jinping's state visit? Zichen Wang translates and annotates an article from Yuyuantantian, an outlet of China's state broadcaster that cites people on the Chinese negotiating side. Read it to see how Beijing frames AI risk differently from American safety circles, which issues it treats as ready for agreement, and what the article leaves unsaid. We picked it because it puts a Chinese-language policy signal in front of English readers with careful commentary. Wang writes the Pekingnology newsletter; we found no stake.
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03 ★★★★☆
Open-weight AI from China: how Beijing weighs the risks
Chinese labs now release open models close to the American frontier. Does anyone in China worry about what criminals could do with them? Gabriel Wagner sets out the case against open weights, asks what evidence there is of real misuse, and quotes what Chinese standard-setters and regulators have actually written about the risk. Read it for a view of China's AI safety debate drawn from Chinese documents, which English-language coverage rarely uses. We picked it because it weighs both sides before reaching a view. Wagner works at Concordia AI, a Beijing-based AI safety advisory whose newsletter argues China takes safety more seriously than many think: a stake worth knowing.
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04 ★★★★☆
Is China really spending half its AI on slop?
A viral claim says more than half of China's AI computing goes on cheap AI-made dramas and videos. Where did the figure come from, does it add up, and why did it sting so much? Zilan Qian traces the number back through investor calls and state media, checks it with rough arithmetic, then asks why a society so devoted to usefulness feels ashamed of its favourite entertainment. Read it for a debate that rarely reaches English, told from Chinese sources. We picked it for combining a careful fact-check with a real argument. Qian is a research scholar at GovAI who studies AI in China; we found no stake.
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05 ★★★★☆
An early-warning system for how people use chatbots
How would anyone know, early, that people were changing how they use chatbots in ways that might cause harm? Clara Collier, editor in chief of Asterisk, interviews Jessica Dai, a computer science PhD candidate at UC Berkeley whose team analysed three years of posts on the r/ChatGPT forum. Read it for a concrete picture of what an adverse event reporting system for AI could look like, and the practical obstacles in the way. We picked it for pairing a real dataset with a policy proposal. Dai is discussing the team's own paper; beyond that we found no stake.
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06 ★★★★☆
AI that works with people: a research agenda beyond full automation
Is building AI mainly a project of replacing human labour, or could research aim somewhere else? Gabriel Unger, an economist at Stanford's Digital Economy Lab, and Arvind Narayanan argue that computer science has drifted towards automation by habit as well as incentive, and sketch the technical problems that collaboration between people and AI would need solved. Read it for a positive programme, not another critique. We picked it because it engages the economics and the history of the field, not just the ethics. The authors argue from a stated position; their interest is academic, and we found no commercial stake.
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07 ★★★☆☆
A Chinese scholar's view of America's grip on data centres
Washington often worries that China is winning the AI race by building infrastructure abroad. How does the race look from Beijing? Jeffrey Ding translates and summarises an essay by a Peking University researcher who argues the opposite: American firms dominate the world's data centres through a hub-and-satellite system that leaves other countries carrying the costs. Read it for an argument Western readers rarely meet in its own words, with a link to the full translation. We picked it because it unsettles a common assumption about the AI race. Ding is an assistant professor of political science at George Washington University who translates Chinese writing on AI; we found no stake.
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08 ★★★☆☆
What we lose when machines do our sensing for us
As phones and now AI take over navigating, remembering and choosing, what happens to the bodily skills those tasks used to build? Melanie Challenger argues that some technologies strengthen our capacities while others quietly replace the actions that form them, and draws on evolution and cognitive science to tell the two apart. Read it for a distinction worth applying to your own habits. We picked it as this issue's wildcard: a piece about AI that starts from the body rather than the model. Challenger writes from personal experience as well as research; we found no stake.
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