Martin Berry is using an AI personal trainer plus simulator hours to prepare for the 94th 24 Hours of Le Mans, ABC reports. Berry is driving the number 61 car for Iron Lynx in the LMGT3 category and qualified 13th in class and 50th overall, ABC reports. Berry told ABC Sport, "I'm quite into AI these days, so I have built this AI personal trainer that has been guiding me for preparation for Le Mans," and described the race as "iconic," ABC reports. The race begins at midnight AEST Sunday and runs 24 hours, ABC reports. Editorial analysis: The combination of individualized AI coaching and high-fidelity simulators reflects a broader pattern of data-driven preparation in professional motorsport, highlighting opportunities and measurement challenges for practitioners.
What happened
Martin Berry is using an AI personal trainer alongside extended simulator sessions to prepare for the 94th 24 Hours of Le Mans, ABC reports. Berry is one of four Australians competing this year and will drive the number 61 car for Iron Lynx in the LMGT3 category, ABC reports. The number 61 qualified 13th in the LMGT3 class and 50th overall for the race start, ABC reports. Berry told ABC Sport, "I'm quite into AI these days, so I have built this AI personal trainer that has been guiding me for preparation for Le Mans," and said the event is "iconic," ABC reports.
Editorial analysis - technical context
The article does not disclose technical specifics of Berry's AI trainer. Industry-pattern observations: AI personal trainers in high-performance sport typically combine personalized training schedules, physiological monitoring, recovery planning, and sometimes telemetry analysis to align physical conditioning with race demands. High-fidelity driving simulators are frequently used to build muscle memory and optimize race lines without track time, and when paired with data-driven coaching they can compress seat time learning curves.
Context and significance
Editorial analysis: Professional motorsport has trended toward integrating telemetry, biomeetrics, and machine learning for marginal gains. For endurance events like Le Mans, where drivers must sustain performance across long stints and limited recovery, tools that personalize workload and simulate extended runs address specific operational constraints that teams and drivers face.
What to watch
For practitioners: watch for disclosure of measurable outcomes tied to these tools, such as lap consistency, physiological markers across stints, or recovery metrics reported by teams. Also observe whether more drivers or teams publicly adopt AI-driven conditioning and whether event-level performance differentials correlate with documented AI-assisted preparation.
Note on sources
All factual claims above are drawn from the ABC story covering Berry's comments and the car's qualifying positions, as reported by ABC Sport and ABC News Australia.
Scoring Rationale #
This is a practitioner-relevant example of AI applied in high-performance sports but is a single-person, descriptive account without technical detail or broad empirical results, making its immediate impact modest.
Practice interview problems based on real data
1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.