The challenge of aligning advanced AI systems with human values presents one of the most critical yet under-resourced problems in technology today. While theoretical research continues, there’s currently no effective mechanism to attract broad expertise, incentivize concrete solutions, and direct global attention toward verifiable breakthroughs in AI safety.

A Prize for Alignment Breakthroughs

One approach could involve creating a large-scale incentive competition with a prize pool of $1 billion for demonstrable progress in AI alignment. This could take two forms:

1. A theoretical breakthrough providing fundamental guarantees about aligned AI behavior under specific conditions
2. A practical system demonstrating robust alignment properties at scale

The competition might feature independent verification, intermediate milestone prizes, and open participation to engage researchers beyond academia - from independent thinkers to AI labs. Clear technical criteria would define winning submissions while maintaining flexibility for unexpected discoveries.

Stakeholder Value and Execution

The prize could create multiple layers of benefit:

• For humanity by addressing an existential risk
• For researchers through recognition and funding
• For AI developers through access to safety solutions

An execution plan might begin with a smaller $10M pilot prize focused on a specific alignment subproblem. If successful, this could scale through phased development:

1. Establishing technical criteria and governance (6 months)
2. Launching with initial funding and outreach (3 months)
3. Running the competition with adaptive criteria as the field evolves

Differentiation from Existing Efforts

Unlike grant-based approaches that fund research processes, this would incentivize concrete outcomes through:

• Clear outcome-based rewards rather than incremental funding
• Broader participation beyond academic circles
• Verification mechanisms ensuring real-world applicability

The competitive advantage lies in creating the largest alignment incentive structure while avoiding the limitations of either fully open-ended research or narrowly defined engineering challenges.

Key challenges like defining measurable alignment criteria and preventing gaming of incentives would require ongoing advisory oversight, but the potential impact makes this approach worth considering.