TL;DR: We wrote a post on possible success stories of a transition to TAI to better understand which factors causally reduce the risk of AI risk. Furthermore, we separately explain these catalysts for success in more detail and this post can thus be thought of as a high-level overview of different AI governance strategies.  

Summary

Thinking through scenarios where TAI goes well informs our goals regarding AI safety and leads to concrete action plans. Thus, in this post,

  • We sketch stories where the development and deployment of transformative AI go well. We broadly cluster them like
    1. Alignment won’t be a problem, …
      • Because alignment is easy: Scenario 1
      • We get lucky with the first AI: Scenario 4
    2. Alignment is hard, but …
      • We can solve it together, because …
        • We can effectively deploy governance and technical strategies in combination together: Scenario 2
        • Humanity will wake up due to an accident: Scenario 3
        • The US and China will realize their shared interests: Scenario 5
      • One player can win the race, by …
        • Launching an Apollo Project for AI: Scenario 6
  • We categorize central points of influence that seem relevant for causing the success of our sketches. The categories with some examples are:
    1. Governance: domestic laws, international treaties, safety regulations, whistleblower protection, auditing firms, compute governance and contingency plans
    2. Technical: Red teaming, benchmarks, fire alarms, forecasting and information security
    3. Societal: Norms in AI, publicity and field-building
  • We lay out some central causal variables for our stories in the third chapter. They include the level of cooperation, AI timelines, take-off speeds, size of the alignment tax, type of actors and number of actors

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