One way to address the tension between mitigating existential risks and shaping humanity's long-term future is to develop a structured approach for balancing these priorities. The challenge lies in avoiding premature lock-ins—irreversible decisions that could constrain future options—while ensuring that immediate existential threats are addressed. Without a clear framework, resources might be misallocated, leading to either excessive short-term focus or neglecting urgent risks in favor of abstract long-term planning.

Balancing Immediate Risks and Long-Term Reflection

The idea suggests a dynamic, iterative process to determine when and how much effort should go into long-term reflection versus existential risk reduction. This could involve:

• Research: Identifying critical lock-in points, such as AI governance frameworks or space colonization policies, and estimating their timelines.
• Coordination: Creating interdisciplinary forums where experts in existential risks and long-term futures can align efforts.
• Advocacy: Encouraging flexible policy designs—like sunset clauses or modular regulations—to avoid premature rigidity.

A tiered approach could start with small-scale reflection efforts, scaling up as risks are mitigated. For example, early-stage workshops could explore AI governance without diverting significant resources from AI safety research.

Integration with Existing Efforts

While organizations like the Future of Humanity Institute and the Centre for the Study of Existential Risk focus on risk analysis, and the Long Now Foundation promotes cultural long-term thinking, this approach bridges the gap. It explicitly connects risk mitigation with future-shaping by:

• Highlighting where lock-ins might occur (e.g., AI policy decisions).
• Proposing adaptable governance structures to keep future options open.

An MVP could be a collaborative research paper or workshop series mapping key lock-in risks, followed by pilot advocacy for reversible policies in high-stakes areas like AI.

Execution and Feasibility

Initial steps might include:

1. Analyzing historical lock-ins (e.g., climate policy) to assess predictability.
2. Modeling resource trade-offs in specific domains (e.g., AI safety budgets vs. governance research).

Funding could come from interdisciplinary research grants or philanthropic donations, as the focus is coordination rather than revenue generation. The main advantage is integrating two often-siloed priorities—existential risk reduction and long-term reflection—into a cohesive strategy.

By framing this as an adaptive process, the approach could help decision-makers avoid irreversible mistakes while still addressing urgent threats.