AI の大問に関する最優秀論文発表（23 分読み）

Two months ago, I posted some big questions about AI. We had 600 essays submitted for this contest. Below is a bit of information of the 3 winners, followed by all 3 full essay. 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.

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.

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 laws, data protection laws, and intellectual property. Abolish price and wage controls. Dismantle cartels and unions of human workers who will try to protect themselves from AI competition. Apply safety and security standards consistently between humans and AIs, instead of discriminating in favor of humans. Make it much easier to start new businesses, as AIs will need organizational structures just as much as humans do in order to bring out their productive potential.
• Provide political and legal stability. Investment will be anemic under instability, and if a country can stand out as an island of stability in what will likely be tumultuous decades for the world, it will attract enormous amounts of investment. The lowest bar is avoiding civil wars, revolutions or coups (not trivial for Nigeria to clear, since there was a serious military conspiracy to overthrow Tinubu as recently as September 2025); but this is not enough by itself. People must be confident that their investments will not be expropriated, and that the core operation of their businesses won’t suddenly be declared illegal.
• Increase capital formation in your country. Reduce or eliminate taxes on capital gains and corporate income, don’t let important projects be held up by permitting, don’t hamper construction by requirements of “having to pay prevailing wages”. Remove exchange, interest rate, and capital controls.
• If industries necessary for AI deployment are broken, urgently fix them. For example, Nigeria’s grid sucks and needs to be fixed before anything else can happen, and this is downstream of the entire economy’s price system being messed up by government restrictions. If this isn’t fixed, the country will never be able to accumulate the kind of capital it needs to be productive in an era of AI automation.

These recommendations feel extreme because humans instinctively like policies that favor labor over capital. This already causes problems in our world where capital’s output share is only around 30%, but it will likely become a critical obstacle in a world where labor has been made obsolete and capital’s output share is much higher.

If a country that’s currently in bad shape succeeds in doing these, they would attract a ridiculous amount of outside investment and be transformed in the decades in which AI automates the world economy. The fact that they had no previous AI labs or fab companies with trillion dollar valuations would be irrelevant, because those very companies would at that moment be in the process of automating their moat – their human capital and organizational knowledge – away.

Will this happen?

Probably not. As I’ve said, the relevant countries are far too dysfunctional for these radical reforms to be adopted, and the countries currently in the AI supply chain (e.g. the US and China) already perform well relative to other countries on the metrics I’ve listed.

However, there’s still some hope for countries outside the AI supply chain: the incumbents can screw up. This is not that implausible, because policy quality in the industrial era will not be that strongly correlated with policy quality in the AI era, just like how the best foraging economies in the world weren’t the best agrarian ones and the best agrarian economies weren’t the best industrial ones.

Concretely, the incumbents can outlaw AI automation in key industries, slow down datacenter buildouts, expropriate capital holders, impose strict safety standards for rolling out AI agents in critical industries like medicine and law, et cetera. There’s virtually no limit to the mistakes that they can make in the coming period of acute irrationality. If a mediocre country simply holds on and doesn’t make any big blunders, they will probably come out the other side of this in a much better relative position than they had been in at the start.

A subway company solved AI’s business model

There is an industry with the following economics: billions in upfront capital before earning a dollar. Core service priced near marginal cost. Enormous value created for users, almost none captured by the builder. Relentless pressure to keep investing in the next generation of infrastructure. I’m not talking about AI labs. I’m talking about Hong Kong’s Mass Transit Railway.

Many have reached for the railroads analogy when discussing the business of AI. Most conclude that the lesson is commercial viability requires state subsidy for a general purpose technology with public good properties.

I want to challenge that, because Hong Kong’s MTR actually solved the problem. It’s one of the only mass transit systems in the world that is commercially self-sustaining, publicly listed, paying dividends with no government operating subsidy.

The financials are structurally identical

MTR’s core rail service has never funded its own expansion. In 2018, its best pre-covid year, transport operations earned HK$2.0 billion in EBIT. Estimated capital expenditure for 2024–2026 is HK$87.9 billion, nearly all rail-related. Three years of peak rail earnings would cover 8% of that. The rail has never paid for itself through fares. It was never designed to.

MTR fares are kept affordable through a government fare adjustment mechanism. You can’t price transit to recover full construction costs because it would be unaffordable and defeat the purpose. Each rail line can maybe cover its operating costs, but fare revenue never stretches to fund the next line. AI API pricing faces the same constraint from the other direction. Distillation and open source alternatives deflate API prices roughly 10x per year, and any lab that prices above marginal cost loses volume to rivals. Each model can be operationally profitable on inference, but the margin never stretches to fund the next training run.

The standard global solution is subsidy. London Underground requires billions in TfL grants. China’s national HSR carries a trillion dollars in debt with 94% of routes unprofitable. AI is on the same trajectory: CHIPS Act, Stargate, sovereign wealth fund investments, Pentagon contracts. The default endpoint is subsidy-dependent quasi-public infrastructure.

MTR found another way.

Rail plus property

When MTR was built in 1979, its designers understood that fares alone would never recover construction costs. So they structured the corporation around a different premise: the rail line would make surrounding land valuable. So own the land.

MTR develops residential towers, offices and shopping malls above and adjacent to its stations, capturing the value appreciation that its own infrastructure creates. Property profits cross-subsidize rail operations and fund the next line. Today MTR owns 13 shopping malls, manages 47 developments above its stations, and property generates the majority of actual profit. Don’t try to capture value through the rail service itself. Own the assets that appreciate because of the rail service.

The AI parallel

“When do labs make money?” has the same structure as “when does rail pay for itself through fares?” It doesn’t, and that’s the wrong question.

A biotech startup uses a frontier model to screen drug compounds, shaving two years off a clinical trial. A logistics firm uses it to optimize routing, saving $40 million in fuel costs. A solo developer ships in a weekend what used to take a five person team three months. In each case the model provider captures a fraction of a percent through API fees. The provider can’t charge more, because four other labs and a dozen open source alternatives offer comparable capability. The surplus flows to the users and the broader economy. This is what general purpose technologies do. The steam engine, electricity and TCP/IP generated zero revenue for their creators.

MTR’s lesson: stop trying to make fares cover construction. Find the property.

Four candidates, ranked by defensibility

Government-granted deployment rights come first. A government grants a lab exclusive deployment access to national health records, tax systems or defense logistics. The lab accumulates domain data, integration depth and regulatory clearance that takes years to replicate. This is MTR’s own mechanism: development rights granted by the state, justified by natural monopoly properties.

Accumulated RL reward data is second. Billions of interaction signals that train the next model generation. Unlike weights (which depreciate via distillation), RL data is practically non-replicable and compounds across generations. It doesn’t convert to revenue directly, but it’s a land bank. Appreciating, undeveloped.

Forward-deployed integration is third. Instead of selling model access to a consulting firm that captures the productivity surplus, own the service delivery end to end. The way Palantir embeds engineers inside government agencies rather than licensing software. The lab doesn’t charge the law firm an API fee. The lab becomes the legal research service, priced against the outcome it delivers rather than the tokens it serves. Switching costs compound with accumulated domain data and institutional knowledge. This is MTR’s shopping mall: capture the foot traffic the rail creates rather than charging passengers more for the ride.

Data trusteeship over national datasets is fourth. Governments sit on enormous under-leveraged datasets (patient records, tax filings). A frontier lab designated as trustee gets exclusive access to train on and build products against this data. The scarcity is genuine, justified by the same logic as MTR’s land. You don’t want five competing labs accessing fifty million patient records any more than you want five developers building on the same plot. But this creates a public-private data monopoly and would require careful governance: clear boundaries on usage, benefits flowing back to the public, independent monitoring and real sanctions for misuse.

The reframe

The labs that survive won’t be the ones that make the API profitable. They’ll be the ones that identify their property above the station and build toward it now. The API is the rail. It will never be profitable enough. The money is in what appreciates around it.

The policy question follows: instead of subsidizing training runs, governments should design institutional mechanisms (deployment rights frameworks, data trusteeship structures, productivity measurement standards) that let labs capture the surplus their infrastructure creates.

There’s a final irony. The AI policy conversation is dominated by the US-China frame. American free market labs versus Chinese state-funded champions. The most relevant institutional model may be neither. It may be Hong Kong’s: a 45 year old public-private hybrid, commercially operated, self-financing through institutional design rather than ideology. The model that makes frontier AI sustainable might already exist. It just runs trains.