Every weekend, Alex Palou steps into the cockpit of the No. 10 IndyCar with the aim of being the most prepared among the 25 crews that line the pit lane. In 2025, that preparation gained a new ally: OpenAI. What began as a research-only collaboration in February 2025 has since expanded into full-race sponsorship, a primary logo on the No. 10 chassis and a dedicated AI-driven workflow that touches everything from car setup to pit-stop choreography.
The partnership was sparked at a Women in Motorsports gathering, where OpenAI researcher Joyce Ruffell met the Ganassi engineering team. Both sides saw an opportunity to turn the avalanche of telemetry—more than 200 sensors per car producing nearly one billion data points per hour—into actionable insight. By the 2026 season, the alliance was no longer a lab experiment; it was a visible part of race weekend, with OpenAI’s branding on the car during high-profile events in Long Beach and Washington, D.C.
From raw telemetry to actionable strategy
The core challenge for modern IndyCar lies in extracting usable information from an ocean of numbers before the next green flag. Palou explains that the crew often has “only an hour between sessions to spot the critical bits that can make us faster.” Telemetry analysis traditionally required manual sifting, but OpenAI’s language models can digest the data, flag anomalies, and suggest setup tweaks in real time. This agility proved decisive when the team faced the 1.7-mile National Mall street circuit—an event with zero historical laps and just six months of preparation.
Using OpenAI’s tools, engineers reverse-engineered the new circuit by matching its geometry to known tracks. By feeding the AI the lengths of straights, corner radii and expected surface bumps, the system generated a provisional setup—ride heights, suspension stiffness, aerodynamic balance—that mirrored a hybrid of Detroit and other street venues. The payoff was immediate: Palou captured pole position, beating his nearest rival by more than half a second. Although two problematic pit stops and a spin later relegated him to 20th, the underlying model had already demonstrated that the car was the fastest on the grid.
Learning when to trust the algorithm
While the data-driven model can propose the mathematically optimal strategy, it does not always anticipate unpredictable race dynamics such as sudden cautions. Palou notes that during a caution-heavy race at Long Beach, the AI suggested a fuel-saving plan that conflicted with the crew’s intuition based on past experience. The team ultimately blended the two perspectives, opting for a hybrid approach that balanced the AI’s calculated efficiency with human judgment. This tension—between algorithmic recommendation and seasoned intuition—remains a focal point of the ongoing collaboration.
During the 2026 offseason, Chip Ganassi Racing pledged to deepen its AI integration, aiming to surface only the information that matters directly to the driver. Palou envisions a future where the AI can instantly predict tire degradation on the upcoming 2028 chassis, allowing engineers to lock in the optimal setup before the first practice run. He estimates the team is currently leveraging “about ten percent of the technology’s potential,” a figure likely to grow as the models become more refined and the sport’s data streams expand.
Looking ahead: the 2028 car and beyond
The next wave of opportunity arrives with IndyCar’s 2028 specification car, which will debut with limited on-track testing. Palou believes that OpenAI’s simulation capabilities could compress months of development into weeks, delivering precise forecasts for tire wear, aerodynamic drag, and power-unit behavior. Such foresight would give Chip Ganassi Racing a decisive edge, especially in the high-stakes environment of a championship-deciding weekend.
From turning raw sensor feeds into a pole-winning setup on a brand-new street circuit, to navigating the uneasy balance between algorithmic advice and driver feel, the partnership is still in its early chapters. Yet the results—multiple championships, a historic 18th title for the organization, and a visible AI presence on the car—signal that the fastest laps of the future may be written in code as much as in rubber.
