Jassi Pannu, an assistant professor at Johns Hopkins and board member of Blueprint Biosecurity, won first place with a plan to eliminate airborne disease transmission. She argues that AI’s biggest benefits and worst risks both involve biology: curing diseases versus creating engineered pandemics. Pannu proposes spending $40–60 billion over ten years on passive, pathogen-agnostic infrastructure—think far-UVC lamps in schools and transit hubs—to cut seasonal flu deaths by 60 percent and reduce pandemic odds tenfold. Her four-step roadmap starts with a $5 billion, three-year DARPA-style push to nail down safety, effectiveness, and deployment models, then scales manufacturing, subsidizes installations, and funds ongoing monitoring. Second place went to Ege Erdil, co-founder of Mechanize and former Epoch AI researcher. He tackled how nations outside the AI hardware and software supply chain can stay relevant. His advice sticks to proven economic policies: strong property rights, low capital taxes, open regulations. In a world where policy improvements can generate faster growth than ever, Erdil says these steps are both low-drama and high-impact. He contrasts this grounded approach with outlandish proposals—like threatening to nuke rival tech hubs—that some entrants suggested. Erdil’s argument: common-sense rules are actually the contrarian play. Michael Li, a Harvard Kennedy School MPP candidate, claimed third place by comparing AI labs to Hong Kong’s Mass Transit Railway. The MTR makes up for heavy capital costs on trains and tracks by owning the real estate around stations. Li suggests AI companies could follow a similar playbook: invest in or acquire “complementary assets” that capture value beyond selling algorithms or compute cycles. He doesn’t spell out which assets those might be—real estate adjacent to data centers, perhaps—but the analogy reframes how labs could turn massive upfront R&D spending into sustainable profits. The three essays together span biosecurity, economic policy, and business strategy, all centered on how to steer AI’s next wave.