Meta projects—initiatives designed to improve other projects or systems—are common in fields like research, philanthropy, and organizational development. However, these projects often operate without clear metrics to measure their own effectiveness. This creates a gap: while they aim to enhance other systems, their success is rarely quantified, making it hard to compare, refine, or allocate resources to them efficiently.

A Systematic Approach to Evaluating Meta Projects

One approach could involve developing standardized frameworks to assess the impact, cost-effectiveness, and scalability of meta projects. This might include:

• Creating adaptable evaluation tools (e.g., software, dashboards) to track performance.
• Offering consulting services to help project teams integrate measurement early in their design process.

The goal would be to make evaluation iterative, refining frameworks based on real-world feedback. For example, a lightweight version could first be tested with AI safety meta projects before expanding to other domains.

Stakeholders and Incentives

Those who could benefit include:

1. Meta project designers, who would gain insights to improve their work or secure funding.
2. Funders, who could make better-informed decisions about where to invest.
3. End beneficiaries, who would indirectly benefit from more effective systems.

Challenges like resistance to evaluation or long feedback loops might be addressed by positioning measurement as a collaborative tool rather than an audit and by using short-term proxies for long-term impact.

Execution and Adaptation

An MVP might begin with a simple framework piloted in a niche area like AI safety. Open-source tools could lower adoption barriers, while consulting services could demonstrate value to early adopters. Over time, the approach could expand to other fields, with adjustments to fit varying contexts—for instance, tailoring metrics for policy-focused meta projects versus research-funding ones.

Existing evaluation methods, like GiveWell’s charity assessments or organizational KPIs, focus on direct interventions rather than meta-level systems. Adapting similar rigor to meta projects could fill a critical gap in accountability and learning.