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Hello readers,
Welcome to the AI For All newsletter! Today, we’re talking about how AI is (maybe) winding back the cruel hands of time, why AI chips are becoming communications platforms, and more!
AI in Action: AI is learning to erase what time already wrote
Six independent aging tests looked at the same blood samples and reached the same verdict: patients on an AI-designed drug called rentosertib got biologically younger. The effect peaked around week four, with biological age dropping three to four years on most of those proteomic clocks and up to six years on one, according to results published in Nature Biotechnology and reported by Medical News Today. The catch is what the drug was actually built for: treating idiopathic pulmonary fibrosis, a scarring lung disease, not slowing anyone's clock. Every participant had IPF; none was a healthy volunteer chasing longevity.
Rentosertib exists because of two separate AI systems. Insilico Medicine's PandaOmics platform flagged TNIK, an enzyme tied to fibrosis and cell growth, as a target worth pursuing; its Chemistry42 platform then generated and refined the molecule built to block it. Neurologist Peter Gliebus, who wasn't involved in the study, noted the program moved from target discovery to a preclinical candidate in about 18 months — a timeline that would once have taken years of trial-and-error chemistry. The company has since built a business around the approach, licensing candidates to Eli Lilly, Servier, and Takeda, among others, on the strength of AI-originated pipelines rather than repurposed generic drugs.
The caveats are as important as the numbers. Geriatrician Zeeshan Khan pointed out that a younger score on an aging clock isn't the same as a longer, healthier life, and internist Dung Trinh cautioned that a 12-week study in sick patients could be picking up disease improvement rather than aging reversal. What the trial does offer is a template: a proof-of-concept for folding aging biomarkers directly into ordinary disease trials, rather than running separate, harder-to-fund longevity studies. If that framework holds up in bigger, longer trials, drugs built for one disease could start getting evaluated for a second effect almost by default.
🔥 Rapid Fire
Commentary: AI Is Already In Dangerous Hands
Video: The AI Safety Grift
The media wins yet another Fell For It Again Award
Related: Dispelling AI Doomerism
Analysis: The Hater’s Guide to Broadcom
Inside ‘Project Lily’ — the humans reading your ChatGPT chats
AI debt is part of why borrowing costs are rising per Fed Chair
NASA and IBM launch AI foundation model for lunar science
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📖 What We’re Reading
For decades, semiconductor innovation followed a familiar path. Each new generation of chips became faster, smaller, and more power-efficient. Connectivity steadily improved as Wi-Fi, Bluetooth, Zigbee, Thread, and Matter became standard features across embedded platforms. More recently, AI accelerators and neural processing units (NPUs) shifted the industry’s attention toward running machine learning models directly on edge devices.
