Sources & Methodology
AI Pulse is built on open, trusted data from three complementary sources, processed with the TokenFlop methodology.
Built on open, trusted data
Fruggr AI Law Database
133 laws & regulations tracked across 46 jurisdictions. Maintained by our governance team.
MIT AI Risk Repository
1,700+ AI risks catalogued across 7 domains with causal taxonomy.
OECD AI Policy Observatory
National AI adoption indices and policy indicators across 80+ jurisdictions.
TokenFlop Methodology
TokenFlop is an open, physics-based computational model developed by Digital4Better that estimates the environmental footprint of AI systems. It converts FLOPs into GPU hours, then into energy consumption, and finally into CO₂ equivalent — factoring in hardware type, datacenter PUE, energy mix by region, and manufacturing footprint.
The methodology follows ISO 14040 / ITU L.1410 standards and covers training, fine-tuning and inference across text, image, audio and video modalities.
Read the full methodologyCredits & Acknowledgements
AI Pulse is maintained by the Digital4Better / fruggr team. The AI law database is curated monthly by our governance experts. Risk data comes from the MIT AI Risk Repository (Slattery et al., 2024, CC BY 4.0). Incident data is sourced from the OECD AI Incidents Monitor. Adoption indices from the OECD AI Policy Observatory.