Evaluate three theories of victory for AI futures on equal grounds

National leaders have options for how to navigate the world through the AI transition:

• National project: Race for becoming a hegemon that uses aligned ASI to suppress and maim “unsafe” projects, or just race ahead of them
• International collaboration and slowdown if needed
• Muddle through: little government involvement

Each of the three theories of victory relies on premises and assumptions about AI timelines, risks, strategic advantage, incentives and government capacity.

Additionally, each of the three "plans" has its own failure modes and plans for how to avoid them. Examples: International collaboration collapsing, a national power-grab project triggering war, muddle-through ending in extreme power concentration or loss-of-control.

Different "camps" of AI policy have their own explanations for why their strategy is the most sound (or the lesser evil). People from each camp have their own underlying assumptions for what "ground truth" is, and answers for how to avoid their plan's failure modes.

Your task is to:

1. Surface what assumptions about the world each plan rests on
2. Surface potential failure modes of each plan
3. Find out what answers the advocates of the different plans have for each
4. Rate the assumptions by how contested/unprecedented/[your own criteria] they are
5. Do so with as much of an unbiased perspective as you can. Ideally, you read many different opinions and get interviews with people representing different worldviews. You may use an ideological Turing test criterion: a member of each camp reads the section on their own plan and confirms it's a fair statement of their assumptions and their answers to the failure modes.
6. Distill your results in a well-written essay that meets all relevant audiences where they are.

Finally, you can identify policy measures and research directions that are robustly beneficial across scenarios.

When plans differ a lot between each other, and have contradicting factual assumptions, someone is wrong. It may even be the case that advocates of one plan or the other have unspoken assumptions that are load-bearing, but were not critically questioned. (There are some subjective factors as well, like how to weigh different risks by how "bad" they are. Clean argumentation separates factual and evaluative cruxes)

An unfortunate thing many people do is to commit to a worldview first, then rationalize how its gaps can be filled. This creates division within the AI safety field, and hurts our ability to plan and work together.

A well-argued essay need not fall into both-sideism. If the process clearly reveals that one plan is more flawed than the other, it should transparently explain how it got there. If done with clear and fair methodology from the ground up, treating all sides with respect, it may even convince people to switch sides.

Three paths to impact. First, decision relevance under time pressure: governments will choose among these strategies within years, likely during a crisis, and a pre-existing crux map lets decision makers see which plan's preconditions actually hold at that moment Second, research allocation: separating cruxes that near-term evidence can resolve from permanent worldview differences tells the field where empirical work (capability forecasting, verification prototypes, breakout-time analysis) actually changes minds, and where it is wasted. Third, pre-committed convergence: because each camp has signed off on what their plan assumes, later evidence that breaks an assumption is much harder to explain away, shortening the time from warning sign to coordinated response.

Lead authors, lead researchers. Most of the initiative is with the mentees.

Strong writing. I decide who to accept based on your prior publications/essays. Strong first-principles reasoning.

Being a fast learner.

Willingness to over-communicate and work in a team.

High initiative.