This study estimates that AI applications could reduce global emissions by 3.2-5.4 GtCO2e annually by 2035 across the power, food, and mobility sectors. However, this estimate is based on limited assumptions and does not consider the full range of economic interconnections and potential rebound effects. These reductions would primarily come from transforming complex systems, accelerating technology discovery, and nudging behavioral change.

The authors acknowledge support from organizations with interests in climate change mitigation, such as the Economic and Social Research Council, the Quadrature Climate Foundation, and the Grantham Foundation for the Protection of the Environment. While these funders may have a general interest in promoting positive findings related to climate action, there is no specific evidence suggesting undue influence on the research or its conclusions.

This study offers a comprehensive bottom-up analysis of AI's potential to accelerate the climate transition across three key sectors, presenting a novel approach and valuable insights. The methodology is generally sound, but the limitations regarding the scope of analysis, the exclusion of rebound effects, and certain assumptions about emissions factors and data center energy use need to be considered. Overall, the research provides a valuable contribution to the field, but the conclusions should be interpreted cautiously given these limitations. The study exhibits a strong theoretical basis, detailed methodology and robust analysis, warranting a rating of 4 despite the highlighted limitations. The lack of inclusion of rebound effects and inter-sectoral dynamics, while acknowledged, prevents it from receiving the highest rating. Transparency regarding funding and potential COIs further increases confidence in the overall integrity of the work.