Can AI help us reach net zero?

Picture a coastal region where engineers race against rising emissions, deploying fleets of carbon removal machines that must be tuned daily to shifting weather patterns and energy supplies, yet manual adjustments leave the systems running far below their potential and delay meaningful impact on global targets.

The Speaker's Central Claim

Priya Donti centers her talk on the question of whether AI can help reach net zero. She argues that AI systems can optimize carbon removal technologies and accelerate their deployment to achieve climate goals at global scale. Through this lens, the talk frames machine learning as a practical tool within broader decarbonization and policy efforts, showing how optimized modeling, prediction, and deployment turn promising technologies into solutions that operate effectively at the magnitude required for net-zero outcomes.

Connecting AI to the Opening Challenge

Returning to the coastal facilities, Donti's perspective clarifies why the daily tuning problems persist and how they might be addressed. By applying AI to model complex variables in real time, operators could predict the best configurations for each machine, forecast energy availability, and prioritize deployment locations that deliver the largest removal gains. This approach shifts the work from reactive fixes to proactive scaling, directly supporting the acceleration needed to move from isolated projects to coordinated global action.

Policy and Deployment at Scale

Donti ties these technical capabilities to policy considerations, noting that optimized deployment must align with regulatory frameworks and decarbonization roadmaps. Machine learning models can help planners test scenarios that balance technological performance with societal constraints, ensuring that carbon removal expands responsibly rather than haphazardly. In this way, the talk presents AI not as a standalone fix but as an accelerator that works alongside human decision-making and institutional structures.

A Question to Carry Forward

If AI truly optimizes modeling, prediction, and deployment for carbon removal, the remaining challenge becomes one of integration: how will societies choose to direct these efficiencies so that net-zero progress keeps pace with the climate timeline we face?