Leading with Complexity and Wisdom for AI Safety Leaders

AI Safety organisations are growing fast. Many of their leaders are coming from technical or policy backgrounds with no management or soft skill training. 79% of AI Safety organisations have no dedicated people leadership (RAISEimpact). A survey of 25 EA leaders (assuming there is a strong overlap between AIS and EA in terms of mindset; without assuming that both groups are exactly the same) found that many of the top sought-after skills are management, not research (Meta-Coordination Forum 2024). Yet only 3 of 20+ AI Safety training programs are for non-researchers (Chris Clay, EA Forum, Aug 2025).

The result is that many brilliant people run teams with a leadership style derived from how they would do science. They treat most problems as being reducible to its components, i.e. complicated (solvable with enough analysis). They become the single point of coordination because they can't delegate what they don't understand as a skill (Mahajan, EA Forum, June 2026), avoid difficult people decisions because intellectual cultures frame directness as unkind (RAISEimpact; EA Forum coaching trial, 2022), and confuse intellectual rigour with management competence (Kat Woods, EA Forum, Aug 2025).

Existing programs address important parts of this, such as RAISEimpact (raiseimpact.org) building operational systems inside orgs, and WorkStream (workstreamnonprofit.org) providing operations consulting. But none of them address the modality and quality of judgment itself, i.e. the capacity to reason wisely under radical uncertainty, distinguish which parts of a problem need analysis and which parts need complexity approaches, as well as when to consult the team ("crowd wisdom") or AI on high-stakes decisions and trade off multiple perspective against ones own biased judgment.

This project fills that gap by combining two scientific frameworks for organisational and leadership settings:

• Complexity-informed domain assessment (drawing on Dave Snowden's work on sense-making in organisations): a framework that distinguishes Complicated domains (where expert analysis works) from Complex domains (where cause and effect are only visible in retrospect). Most challenges in scaling AI Safety orgs (team dynamics, strategic direction, culture, hiring judgment, etc.) are in fact Complex. Leading them as if they are Complicated is a category error that leads to suboptimal outcomes.
• Wise reasoning (Igor Grossmann, University of Waterloo): provides empirically validated exercises that measurably improve judgment. His RCT with 555 participants showed that the technique of "distanced self-reflection" produces significant gains in intellectual humility, perspective-taking, and recognition of uncertainty (published in Psychological Science, 2021).

The program has two phases:

• Phase 1: A one-day intensive online workshop where 8–10 AI Safety leaders, preferably with a technical or scientific background, work on a real decision they're currently stuck on. Participants experience what it feels like to reason differently using the above frameworks.
• Phase 2: Four 1-on1 coaching sessions per participant over three months, focused on specific management challenges. The workshop insights should become a sustained practice.

Planned Outputs:

1. a tested workshop design that could be replicated across the field
2. pre/post assessment data (incl. using Grossmann's Situated Wise Reasoning Scale (SWIS) adapted for management contexts)
3. a public write-up of what worked and what didn't, shared on Substack, LessWrong, or other suggested outputs; so others can build on it