AI and the net-zero journey: energy demand, emissions, and the potential for transition

Abstract Thanks to the availability of massive amounts of data, computing resources, and advanced algorithms, artificial intelligence (AI) has entered nearly every sector. This has sparked significant investment and interest, particularly in building data centers to develop and operate AI models. In this technical review article, we present energy consumption scenarios of data centers and their impact on GHG emissions, considering both near-term projections (up to 2030) and the long-term outlook (2035 and beyond). Through a scenario synthesis augmented by probabilistic uncertainty analysis, we decouple the analysis of AI’s physical energy footprint from its qualitative potential as a decarbonization enabler. In the near-term, the growing demand for AI will likely strain computing resources, increasing electricity consumption and associated CO 2 emissions. However, the long-term outlook includes significant, albeit highly uncertain, promise. AI has the potential to fundamentally optimize processes across industries, from energy production to logistics, potentially decreasing the broader global carbon footprint. While establishing a definitive “net impact” requires complex macroeconomic tracking of rebound effects outside our current scope, this synthesis highlights the pathways required—including zero-carbon grids and algorithmic efficiency—to ensure AI’s systemic decarbonization benefits can realistically offset its substantial infrastructure footprint.