Shaun White says AI is leveling pro sports access, from lifts to real-time coaching

Olympic snowboarder Shaun White said he used to compete without the same advantages as many rivals, from expensive mountain access to full-time coaching. At Fortune's Brainstorm Tech conference in Aspen, he described how that gap is shrinking as AI makes it easier for athletes to get performance guidance they previously could not afford.

White framed the change as a straight line from scarcity to information. “It is leveling the playing field in a way,” he said, adding, “This is really going to be accessible for everyone…It'll give a lot of information to athletes that wouldn't have had this type of access before, and that's really the hope.” In other words: the underdog edge may no longer be just grit and hours on the hill, but also who can access high-quality feedback loops. AI is trying to close that access gap.

This is not a futuristic sci-fi claim. AI is already embedded in sports operations, and that matters because once analytics systems prove themselves in live settings, they tend to expand. In baseball, MLB debuted its Automatic Ball-Strike (ABS) system, which allows players to challenge and overturn an umpire's call at home plate. Tennis and soccer have also seen automated line and boundary calls and Semi-Automated Offside Technology (SAOT), respectively. The pattern is familiar: start with rules enforcement or measurement, then build toward broader “understanding” of play.

White's pitch goes one step further, tying AI to coaching and injury risk rather than only officiating. For athletes, AI can pair with wearable biosensors to record movements and suggest improvements to form. The broad promise is that athletes can get guidance between runs, not just from a coach on-site during training sessions. And in an Olympics context, the International Olympics Committee has outlined an AI agenda with the intention of integrating the technology into judging. That is a big deal because it signals that AI is not only for training. It is also for how performance is evaluated.

The story even includes a concrete example from the 2026 Milano Cortina Winter Olympics. The source notes that ski jumpers used high-speed video and motion analysis to dissect takeoff timing, aerodynamics, and in-run speed. It contrasts older approaches, where snowboarders, skiers, and skaters would enlist friends or parents to take videos of runs, with the newer capability: AI can provide nearly real-time feedback with specific metrics to improve performance. The speaker for this part was Granville Valentine, managing director of AI Sales and GTM for Google Cloud in North America, who also spoke at Brainstorm Tech.

Valentine described how Google's Gemini can generate world models down to the hundredth of a millimeter and identify an athlete's skeletal structure and center of gravity. He also said the value is not just collecting data such as velocity, speed, and rotation, but making sense of it: “making sense of it, collating it, and giving it back as really easy human-level coach ups in between runs in real time,” according to the source. That phrase, “coach ups,” is doing a lot of work. It suggests AI is being positioned as an always-available layer that translates sensor data into actionable guidance. For teams, federations, and sponsors, that can change how training budgets are allocated, potentially shifting dollars from purely human coaching time toward systems and platforms that scale feedback across athletes.

White connected the performance analytics opportunity to career longevity. He said he has adopted a similar philosophy in thinking about AI that he saw when snowboarding facilities evolved during his career, moving from halfpipes hand-dug with shovels to larger, cutter-carved pipes. In the same spirit, he argued AI can bolster not just performance but potentially reduce injuries by giving projections about how a trick could strain someone's body. The risk calculus is the point. White compared the old approach to “rock, paper, scissors,” explaining that when he came up, athletes would largely go for a big trick based on inspiration, without much information about how it might affect the body.

In his framing, today's AI can provide projections that turn guesswork into measured risk, hopefully not “career ending” but at least “maybe season-ending.” That is the dark mirror of the leveling claim. When information becomes available, outcomes can become less random, which affects selection, sponsorship decisions, and the competitive pecking order. If athletes can evaluate strain and consequences earlier, training programs can optimize for both progression and preservation, and federations could push standardized measurement rather than relying on subjective “feel.”

But White also drew a boundary around what AI should not do. He stressed that sports still depend on risk, spontaneity, and human skill. He argued AI can democratize access to performance data and can be used by judges to more objectively assess the technical prowess of a competitive run. At the end of the day, though, he insisted, the goal is not to replace the human experience. “We're not trying to replace the human experience,” he said, warning against “analysis paralysis,” where athletes get so bogged down in details that they do not actually get out there and do the sport. For decision-makers, that tension is not just philosophy. It is a design requirement: systems must deliver timely, interpretable guidance without drowning users in dashboards.

For executives watching this space, the strategic stake is clear. AI is moving from separate categories like officiating and analytics into end-to-end performance loops: sensors capture movement, models generate feedback, and judging systems may incorporate AI in competition. If that adoption accelerates, the organizations that win will likely be the ones that turn data into decisions athletes can actually use between runs, not just metrics that look impressive in reports.