Reigning Olympic road race champion Kristen Faulkner is training with AI, records highest ever power

Anyone looking for the reigning Olympic road race champion in the women's peloton in 2026 has had to look hard. Kristen Faulkner, who won gold in Paris in 2024, has made just one European appearance this season — but says she is breaking her power records. She has shared an update on LinkedIn.

Faulkner's only race so far has been the Tour of Flanders, which ended with a DNF. Last year she was forced to end her season early after failing to finish stage five of the Tour de France Femmes. We have seen relatively little of her since her triumphant day in Paris. Which makes her latest update all the more interesting.

"For the last two months, I've been coding whenever I’m not training. 10+ hours a day. My training data open in front of me. I have a lot of questions I want to answer and a lot of data to synthesize," the Alaska-born rider begins.

"Sometimes I'd get back from my ride and jump onto my laptop in kit before putting my bike away. I'd start a coding session and let it run while I showered. I studied computer science in college, and I have always loved building. It gives me so much energy. When I spent my winter in SF and watched the AI boom up close, I wanted to build again."

"So little performance research is done on women, particularly regarding the needs of elite female athletes," Faulkner explains. "So I took matters into my own hands, and I started writing the research myself. I did not want to keep waiting for someone else to study the questions that matter to my body."

All of her own data feeds her personal research. "For nine years, I collected biometric data that I struggled to synthesize. Heart rate. HRV. Sleep. Weight. Power. Temperature. Training load. Menstrual cycle phases. Bloodwork. DEXA scans."

"Every app gave me one piece of the story, but the answer was never in one app. It was in how it all interacted. So I built a system that pulls in the data sources I actually use as an athlete and runs them against 4,400 hours of my own training history. It does not just show me dashboards. It builds personal models of my physiology."

That does not translate directly to other athletes.

Every model is trained on my body. "Every finding is specific to my history. And every output is actionable, not just interesting. I used this to help me prepare for the Pan Am Championships, where I won 3 gold medals this year. Today [Wednesday], I produced my best 20-minute power ever with training help from this app.'

The 33-year-old intends to take her new obsession further. "AI is going to change women’s performance research from the bottom up, and I want to be a part of it," she writes. "I studied computer science at Harvard. I worked in venture capital. I actively invest in AI companies. I race on the Women’s WorldTour. I am training to defend Olympic gold on home soil in LA 2028. I’ve applied all of that knowledge to building this."

She closes with a glimpse into her own story. "I came into cycling late. I did not win because I had the deepest race history or the most experience. I won because I used my brain as much as I could. Before my first European race, I made flashcards of the riders, I studied every corner of every course, and I analyzed my data rigorously. I am doing the same thing now, with AI."