The net-zero racing series puts AI at the wheel of race strategy.
Formula E and Google Cloud formalised their partnership in January 2025, with Google Cloud named Official Cloud Technology Services Partner and Official Cloud Security Partner of the ABB FIA Formula E World Championship. The collaboration built on earlier joint pilots across the 2024 season. The Driver Agent, built on Google Cloud Vertex AI and Gemini, ingests high-volume telemetry — lap times, speed, braking, acceleration, G-forces — and delivers real-time coaching insights via text or audio, compressing complex car and track data into actionable guidance during and after sessions. The Mountain Recharge project tied to the 2024 Monaco E-Prix was the operational proof of concept: Google AI Studio and Gemini models mapped the optimal descent route for the GENBETA car starting with 1% battery, identifying braking zones to regenerate enough energy through regenerative braking to complete a full lap. A Strategy Agent was also integrated into live broadcasts, delivering tailored race insights and predictions as events unfold. In January 2026, Google Cloud was elevated to Principal Partner and Principal Artificial Intelligence Partner — confirming the progression from limited pilot to full operational dependency. Formula E is the only FIA World Championship to have achieved net-zero carbon status since inception. Status: production from 2024-25 season; elevated to Principal AI Partner January 2026.
Why it matters
Formula E is the most technically ambitious use case for AI in motorsport outside of F1: it is the first championship to use GenAI for real-time competitive coaching (Driver Agent), operational carbon reduction (digital twins), and broadcast intelligence (Strategy Agent) simultaneously. The Mountain Recharge proof of concept is worth dwelling on — using Gemini to optimise a descent with 1% battery is not a marketing demo; it demonstrates that AI-assisted race engineering can identify solutions human engineers might not find under time pressure. The structural question is competitive equity: if AI coaching tools are available to all teams, the advantage shifts from access to quality of model and quality of data. Teams with richer telemetry histories will train better models. This creates a path dependency that entrenches existing competitive hierarchies rather than levelling the field.