Three essays won a contest on major AI challenges. First, Jassi Pannu outlines a $40–60 billion plan to end airborne disease transmission using far-UVC lamps and infrastructure. Second, Ege Erdil advises middle-power countries to boost growth through proven policies like strong property rights and low taxes. Third, Michael Li likens AI labs’ business model to Hong Kong’s MTR, suggesting labs buy up complementary assets to offset high CapEx.

• $40–60 billion over ten years on far-UVC lamps and passive infrastructure could cut seasonal flu deaths by 60% and reduce pandemic odds tenfold
• Middle-power countries without AI hardware/software can still win by sticking to boring proven policy: strong property rights, low capital taxes, open regulations, rather than drastic moves
• AI labs could offset massive compute/R&D costs by owning "complementary assets" around their core business, similar to how Hong Kong's MTR subsidizes rail with adjacent real estate

This post announces the top three winners from a 600+ submission essay contest on major AI challenges. Jassi Pannu outlines a state-scale plan to end airborne pathogens, Ege Erdil advises growth-focused policies for countries outside the AI supply chain, and Michael Li compares AI labs’ economics to Hong Kong’s MTR model. Each full essay follows the brief winner descriptions.

• Far-UVC lighting infrastructure could cut seasonal flu mortality 60% and add $1T+ to global GDP for a $40-60B/10-year investment, while also blunting engineered pandemic risk.
• Erdil argues countries outside the AI supply chain should pursue standard growth policies (property rights, low capital taxes, light regulation) rather than geopolitical maneuvering to stay economically relevant.
• Li compares AI labs to Hong Kong's MTR, suggesting labs could offset massive compute costs by owning complementary "adjacent" assets rather than relying solely on core product revenue.