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The AI data center e-waste problem is huge — and getting bigger
A Basel Action Network report projects AI data center e-waste could reach 395-617 million metric tons by 2050, far exceeding prior estimates.
The nonprofit Basel Action Network (BAN) published a report arguing AI e-waste has been vastly underestimated because it counts all data center infrastructure, not just servers and GPUs. BAN projects 8.6-13.1 million metric tons of AI-related equipment retired annually, totaling 395-617 million metric tons between 2025 and 2050, based on roughly 70,000 tons per gigawatt and a projected 219GW of capacity by 2030. Less than a quarter of the 68.3 million tons of e-waste generated yearly worldwide is formally collected and recycled, with informal disposal exposing workers and children to toxins like lead and chromium.
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Underwriting Superintelligence: Backing Agents you can Sue — Rune Kvist, AIUC
AIUC raised a $40 million Series A to build AIUC-1, an agent security standard backed by insurance, serving Cursor, Harvey, Lovable, and ElevenLabs.
AIUC, cofounded by former Anthropic product hire Rune Kvist, announced a $40 million Series A led by Ribbit Capital and First Harmonic. The startup builds AIUC-1, an emerging standard for agent security, safety, and reliability, stress-testing agents for jailbreaks, hallucinations, and data leaks. It pairs standards with insurance underwriting through Lloyd's of London and counts Cursor, Harvey, Lovable, and ElevenLabs among its customers. Kvist argues trust and liability, not capability, are becoming the binding constraint on AI adoption.
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Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection
Det-LIME extends LIME to multi-instance object detection explanations, improving attribution for harbor seal aerial surveys.
Det-LIME adapts LIME to object detection by combining per-detection weighting, a proximity kernel emphasizing box-adjacent regions, and IoU-based matching to track instances across perturbations. It was evaluated on aerial drone imagery for harbor seal detection plus a seabird case study, and compared against vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient-based attribution. Using Attribution Ratio and Max Saliency Hit Rate metrics, it consistently improved multi-instance attribution and produced box-aligned explanations useful for debugging and data augmentation.
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