The Winning Essays for the Big Questions About AI

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Blog<br>The Winning Essays for the Big Questions About AI<br>Abolishing pandemics/ Getting out of the way of AI automation/ Learning from Hong Kong MTR's business model

Dwarkesh Patel<br>Jul 01, 2026

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Two months ago, I posted some big questions about AI. We ended up getting over 600 essays submitted for this contest. Below is a bit of information of the 3 winners, followed by all 3 full essays. Thanks to everyone who participated!<br>First Place - Jassi Pannu<br>Jassi Pannu is an Assistant Professor at Johns Hopkins University, where she focuses on biosecurity and pandemic preparedness. She serves on the board of Blueprint Biosecurity.<br>Jassi answered the question about what the OpenAI Foundation should do. She persuasively argues that we can live in a post-disease world, and gave very concrete and well thought out ideas about how to dedicate 10s of billions of dollars to that project.<br>Second Place - Ege Erdil<br>Ege Erdil is a co-founder of Mechanize, a startup building environments and evals for frontier coding agents. He was previously a researcher at Epoch AI.<br>Ege answered the question about what countries outside the AI supply chain should do to avoid increase their odds of not being totally sidestepped by transformative growth.<br>He argues that these countries should concentrate on enacting the kinds of policies that already work well in increasing growth and improving productivity. These strategies (strong property rights, low capital taxes, and an open regulatory regime) will be even more important in a world where enacting them can drive a much higher growth differential than is possible today.<br>What I love about Ege’s essay is that, in one sense, he’s giving very common-sense advice (as opposed to much more galaxy-brain schemes some other applicants proposed - one application suggested middle countries blackmail China and American by threatening to nuke their fabs and datacenters). But it’s actually this much more grounded and timeless advice that felt the most contrarian. And it’s also more likely to work.<br>Third Place - Michael Li<br>Michael Li is a Master of Public Policy candidate at Harvard Kennedy School. He writes Ceteris Paribus — a blog at the intersection of emerging tech, econ and policy.<br>Michael wrote about how the labs will actually make money. His was selected for the unique analogy he drew between AI labs and Hong Kong’s Mass Transit Railway business model - even if your main product consumes crazy CapEx and doesn’t directly earn it back, maybe you can make up for it by buying out all the complementary assets. In the case of Hong Kong MTR, that would be the adjacent properties - I don’t know what it looks like for the AI labs, but it was a interesting analogy to think about.<br>Essay #1 - Jassi Pannu on how she would run the OpenAI Foundation

I’d run the Foundation as a state-scale operation to end airborne transmission.<br>AI’s largest welfare upsides (curing diseases) and deadliest tail risks (engineered pandemics) both run through biology. By radically suppressing airborne pathogen transmission, we’d unlock >$1T in annual global GDP (through ending seasonal flu and the like, chronic diseases increasingly linked to viral infections, productivity losses, healthcare costs, etc.) and would take the possibility of catastrophic pandemics entirely off the table.<br>The dual-payoff principle: Most “make AI go well” interventions are insurance against bad outcomes, especially tail risks. My meta-level argument is that the best way of converting money into impact is to identify interventions that have the property of paying off big in both worlds: by producing step-changes in welfare in the everyday world as well as significantly reducing tail-risks in the emergency world. The bio resilience interventions I describe below are the best example of this.<br>AI for biology is on the critical path to cures, but destabilizing capabilities will arise early<br>Using AI to automate and scale every step in the biological research process, including managing the process itself (something I’ll call autonomous biological discovery), will bring humanity closer to a post-disease world. Over 4 billion years, life has been doing a random walk on an astronomically tiny subset of viable, connected, fitness-positive paths. Multi-component AI feedback loops (that include bio foundation models and systematic wet-lab experimentation at scale) for autonomous discovery will enable us to explore much more of possible biological design space. While we’re most interested in predicting and designing multicellular systems, it’s likely that the destabilizing capability of manipulating simpler pathogens will emerge first. The challenge this poses is that AI-enabled offense (seeding an outbreak) will be much easier than defense, which will remain constrained by physical-world deployment; I argue this advantages pre-positioned defensive technologies...