Recent election results are presented as a clear warning about AI’s public standing. Nearly 50 ballot measures concerning data-center construction appeared nationwide, and each was on course to restrict expansion; opposition also featured in the successful campaign of Texas governor-elect Gina Hinojosa. Earlier polling found voters would rather live near a coal plant than a data center. The argument is that these facilities are not merely unpopular because they are noisy, energy-intensive eyesores: they make visible a wider backlash against the AI industry. Adoption and investment do not prove broad public benefit when gains are concentrated among a few companies and investors.
The industry has helped fuel that backlash by emphasizing job losses, publicizing catastrophic-risk claims, facing cybercrime allegations, and visibly aligning its leaders with the Trump administration. Despite AI’s remarkable technical achievements, its effects are described as making users and even its enthusiastic builders miserable, with productivity tools generating exhausting cycles of oversight. The proposed response is not another open letter but a fundamental reset: workers should challenge their employers to pursue a more beneficial direction, and the sector must make a convincing, popular case for its value. The author is pessimistic, recalling that tech leaders accommodated Trump both reluctantly in 2016 and enthusiastically in 2024, but asks what collective action could restore public trust.
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Investors erased about $2.5 trillion from Nasdaq’s market value as Democratic election victories raised concerns about AI expansion. AI data-center stocks fell amid uncertainty over plans to build more facilities. (reuters.com)
Thinking Machines introduced Inkling 2 Small, a 594-billion-parameter mixture-of-experts model with image input and stronger coding, vision, reasoning, and autonomous-work capabilities. It is available through Tinker with closed weights, with Apache 2.0 weights planned after safety testing. (thinkingmachines.ai)
China plans comprehensive AI rules covering model development, data, computing, and deployment, with safety standards, ethical guidance, and regular reviews. It will ease compliance through transition periods and regulatory sandboxes while requiring companies to disclose risks and incidents and tracking AI’s effects on jobs. (bloomberg.com)
AI can boost productivity as people progress from autocomplete to coding assistants and agents, but overreliance can send them past a peak into low-quality “slop.” The author explains who is most at risk and how to avoid or recover from the habit. (thomasjiralerspong.substack.com)
A North Carolina woman allegedly used ChatGPT to develop a plan to poison her husband, including questions about lethal doses and avoiding detection. Her husband’s lawsuit accuses OpenAI and Sam Altman of failing to stop the chats, while she faces attempted-murder and food-contamination charges. (arstechnica.com)
Z.ai’s upcoming GLM-5.5 models, in 32B, 122B, and 256B sizes, use an internal training pipeline spanning web pages, research papers, books, code, video, and audio. The pipeline includes 1 trillion web pages, 2 billion scientific PDFs, and 500 million code repositories. (twitter.com)
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