The Winning Essays for the Big Questions About AI

The Winning Essays for the Big Questions About AI

Abolishing pandemics/ Getting out of the way of AI automation/ Learning from Hong Kong MTR's business model

Dwarkesh Patel

Jul 01, 2026

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!

First Place - Jassi Pannu

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.

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.

Second Place - Ege Erdil

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.

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.

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.

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.

Third Place - Michael Li

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.

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.

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.

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.

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.

AI for biology is on the critical path to cures, but destabilizing capabilities will arise early

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 already embedded in our infrastructure.

There’s a clear path to ending airborne transmission, using physical infrastructure.

Regardless of what you think about the above, though, ending airborne transmission can be more than justified based on everyday benefits. Respiratory infections cause acute illness and productivity losses, but are increasingly linked to dementia, cardiovascular disease, and more; even “normal” childhood respiratory infections are being linked to long-term neurodevelopmental outcomes.

After evaluating many approaches, I’d argue ending airborne transmission is more achievable than most realize, through a specific, under-appreciated approach. I’m currently sitting in a building that provides me with pathogen-free water, keeps my food cold and pathogen-free, helps me heat my food to eliminate pathogens, and pipes away sewage. We have already embedded technologies all around us that enable a post-cholera, post-typhoid, post-dysentery world.

There is passive, pathogen-agnostic (works against any pathogen), physical infrastructure tech capable of making our buildings entirely free of respiratory pathogens, such as lamps that emit wavelengths safe for humans but are deadly for bugs (called far-UVC). Researchers have suspected these would work at scale for decades; the reasons we haven’t deployed them are primarily non-technological. Consider this analogical case.

We now live in a post-smallpox world. This is one of humanity’s greatest accomplishments. How long did it take for us to do this? Jenner demonstrated vaccination could prevent smallpox in 1796. 171 years later, in 1967, D.A. Henderson launched the campaign that would successfully eradicate smallpox. In that period of time, humanity discovered electromagnetism, thermodynamics, general relativity, and we were 2 years from landing on the moon. Eradication was accomplished within a mere 10 years (with limited tech advances). Delays in smallpox eradication, clean water, and pasteurized milk were not due to lack of tech advancement; they were primarily market and coordination failures exacerbated by lack of political will. This is why this problem is so philanthropy-shaped.

4 steps to ending airborne transmission

Total: ~$40-$60B over 10 years for physical infrastructure to end airborne transmission; the rest of OAIF’s stake remaining for other interventions meeting the dual-payoff principle. By year 10, every primary school and major transport hub in OECD countries operates with passive pathogen-reduction infrastructure as default. Seasonal flu mortality is reduced by 60%. The probability of a respiratory pathogen achieving pandemic-scale spread is reduced by an order of magnitude.

Push-funding to resolve the target product profile ($5B, Years 1-3)→ Hire Jacob Swett, director of Blueprint Biosecurity, to lead a DARPA-style program office focused on: a) pathogen inactivation data from human aerosols, b) computational modeling for deployment, c) safety studies beyond conventional UV effects, d) gold standard cluster-randomized trials powered to detect plausible effect sizes. By the end of year 3, deliver a validated TPP for far-UVC lamps and real-world efficacy data demonstrating >30% transmission reduction.

AMCs to guarantee demand and pull private capital ($15B, Years 1-5)→ Create laddered purchase commitments for (a) 100K far-UVC fixtures that meet an interim TPP, (b) 1M for fixtures meeting the full TPP including safety and efficacy validation, (c) 10M commitment to retrofit specific buildings (see step 3). Modeled on Kremer’s pneumococcal vaccine AMC and Ransohoff/Frontier’s carbon removal commitments. By year 5, expectation is $30-50B in private capital mobilized, and supply chain capacity built to retrofit ~10% of global building stock.

Large-scale deployments to generate evidence ($15-25B, Years 2-7)→ Years 2-4: Deploy in all hospitals and long-term care facilities in 50 largest metro areas globally. Years 3-6: Primary and secondary schools in the same metro areas. Years 2-7: Major airports and high-density workplaces. By year 7, substantial real-world evidence base.

Political infrastructure and state handoff ($3-5B, Years 1-10)→ Smallpox eradication was a genuinely contingent historical event predicated on political will. Fund a memetic campaign à la the Rockefeller Foundation’s IHD yellow fever playbook which would transform respiratory transmission from “normal” to undesirable/unnecessary, and the training of a cadre of thousands that move into governments to build the political constituency and institutional infrastructure needed for global deployment and standards-setting. When pilot deployments demonstrate ≥40% reduction across 3 OECD countries, the Foundation transitions to catalyst rather than principal funder role, activating state procurement at orders-of-magnitude scale.

Essay #2 - Ege Erdil on what countries outside the AI production chain should do

I think a good baseline which very few countries, in the AI supply chain or not, will beat is “do nothing and ignore populist pressures to take radical actions”.

This is because people are naturally technologically conservative, and they hate economic disruption that causes job losses. Full automation of human labor by AI would bring about rapid technological and economic progress that would result in all humans losing their jobs. So the default expectation should be that policymaking in an era of AI automation will be profoundly irrational and counterproductive.

Resisting this political pressure will be hard enough for even the most functional governments. Expecting more from governments with poor track records such as India and Nigeria is unreasonable.

What does good policy in the AI era look like?

Having given this basic but uninspiring answer, I’ll now flesh out what I actually think will be important for good policymaking in the era of AI automation, though in practice it’s unlikely to have much relevance for how policy decisions will be taken.

Today, the economic output of a country depends on its endowment of natural resources, on how much physical and human capital it has, and how efficiently it’s able to make use of these resources. The major shift with AI will be that human capital will drop out of this equation. If a country wants to do well in a world after full automation of labor; they need more natural resources, more capital, or more ability to make use of these inputs effectively, i.e. more total factor productivity.

While the capital elasticity of output will dominate any other factor of production, how much capital a country ends up with is itself endogenous. Capital moves across borders more easily than labor, and it will likely flow to the places where factors complementary to it – total factor productivity and natural resources – are abundant.

So I think the pillars of good policy in the AI era involve going in the following directions, to whatever extent possible:

Get out of the way of AI automation. Repeal or revise occupational licensing laws, liability law

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