In Formula 1, AI is moving faster than the sport’s rulebook—changing how teams sift data between sessions, how engineers answer technical questions, and even how drivers prepare. But the FIA is drafting new limits to prevent AI from turning into an arms race i
When Liam Lawson climbs into the cockpit, the work doesn’t stop at steering. He is listening to his engineer, watching tire temperatures, managing energy use lap by lap—and processing decisions that can steal or save precious time.
This season’s new regulations have made it even more exhausting. “This year with our new regulations. the energy management that we have to look after is what makes us quite physically [and] mentally tired. because there’s a lot of things we have to think about. ” Lawson says. “There’s a lot of things that I have to manage while I’m driving and make sure that I do consistently.”.
The job is now inseparable from information flow. Lawson points to constant communication as part of the strain: his engineer talks to him every lap. and the team’s analysis becomes something he has to hold in his head while driving. “There’s a lot of communication with the team as well,” he says. “My engineer talks to me every single lap. and that’s a lot of stuff that we’re communicating and processing as well. For us, it’s all in the car.”.
That data-heavy reality is exactly why Formula 1 is looking at AI—not as a distant technology. but as a practical lever for speed and competitiveness. Teams already use AI tools to organize information, analyze performance, support engineering work, and prepare drivers. The changes are showing up between races and across race weekends, where time is always tight.
The FIA. the sport’s international governing body. is now trying to define how AI can be used before the competitive gap between teams becomes harder to police. The regulator is developing rules for AI use, with updates expected to phase in across 2027 and 2028. The aim is to keep car development led mainly by human engineers.
A major concern is computational power. The FIA has long regulated how much computing teams can use for aerodynamics modeling—an area that helps determine car shape. Those rules were meant to stop Formula 1 from becoming a “spending race” where the richest teams buy the fastest simulations and turn that into a predictable advantage. AI and machine learning raise similar risks.
Dominic Harlow, FIA Single Seater Deputy Technical Director, puts it plainly. “The same is true with AI and ML,” he says. “We think that there’s a legitimate risk if we just left it completely alone. it would become a huge area of development. potential spending. and maybe [a] performance differentiation that detracts ultimately from the racing.”.
Harlow says the FIA’s goal is not to slow teams down for the sake of it. It wants useful technical development, but without letting the sport lose the human work that keeps it distinct. “We also want to preserve some of the intellectual challenge for the teams. for the engineers. as well as for the drivers. ” Harlow says. “F1’s a team sport. There’s driver skill. but there’s engineer[ing] skill. there’s operational skill. there’s even the people putting together the wind tunnel models in the aerodynamics department. the people designing new shapes to evaluate. They’re all putting their creativity and blood, sweat, and tears into doing it.”.
Even as the FIA writes its boundaries, teams are already operating inside them—using AI as a tool to move faster rather than to replace judgment.
At Atlassian Williams, Russell Paddon works as Driver Performance Engineer, linking the team’s drivers with its engineering operation. His job is translating driver feedback into engineering requirements that can improve performance on track. Paddon says he uses AI tools across Atlassian and the team’s latest AI partner. Anthropic. to make that work faster.
“The benefits we’ve had from some of the Atlassian toolsets [Rovo] has been more on organization and structure and how we share information internally,” Paddon says.
In Formula 1, saving time can be as valuable as adding technical capacity. The calendar leaves narrow windows between races and within race weekends, and AI helps reduce the time spent sorting notes, searching documents, and preparing information for engineers and drivers.
“Often, time is our biggest enemy in Formula 1 because the season moves on so quickly,” Paddon says. “We’re always struggling to stay ahead of the curve and in between races trying to learn [from] the last weekend and then take it forward to the following weekend is really quite important. That’s where AI has kind of shortened that timeframe.”.
For Paddon, one of the biggest shifts is historical analysis. Instead of manually digging through old reports and studies, he can query the team’s knowledge base for relevant patterns.
“Within the Atlassian suite of tools, [I can] query [a] database,” Paddon says. “For example, I might want to look at a trend in driver performance, which was two years ago. It can dig through all the information we have and pull out any relevant projects. reports. studies. any analysis that’s been done. and [it] just brings it all into one convenient place. So, that’s been a big game changer.”.
AI has also changed how he answers technical questions. In the past, answering a new performance question often meant writing code or building a tool tailored to a specific use case. Paddon says that work now takes less time.
“A lot of my time was previously spent either having to code and write tools to be able to answer some questions. and I’d spend probably 60% of the time actually trying to do that and get the data that I was interested in and not really adding value to the team because I’m just having to keep writing new scripts every weekend for a different use case. ” Paddon admits. “[I was] not really adding value to the team because I’m just having to keep writing new scripts every weekend for a different use case. That’s completely [almost] disappeared in my experience. where I can very quickly state what I need from a dataset. and I can query it. I can [then] get Claude to either write me a tool or I can very quickly just ask it to return the results.”.
He describes the practical payoff as more room to focus on meaning—what the data says for the driver, the engineers, and the car. He says the tools have improved his relationship with drivers and helped collaboration more closely with other engineers, including on car development.
The influence isn’t confined to the garage. Drivers are using AI in their routines too.
Lewis Hamilton recently appeared in a campaign for Perplexity Computer showing him using the tool before a race to create an interactive box-breathing guide and curate a music playlist. “Perplexity is a product that helps him find those extra percentage point improvements to help him be the best in every part of his life. ” says Ryan Foutty. Perplexity’s VP of Business. “He’s also separately talked about publicly how he’s using it around his training and running: what should he eat before and after [and] his recovery plan. What you see in the content, it ultimately comes from real use cases that Lewis has had.”.
Lawson, also a music fan, says he uses AI tools to refresh his playlists and learn how to play music. “It’s just something that’s a lot easier now than it was probably 10 years ago,” he says. “For me, music’s a way to switch off, reset, and unwind more than anything.”
Across the sport. the uses vary: some help teams organize internal information. others support technical analysis. and others help drivers prepare or unwind. It is precisely that range that makes the FIA’s task more urgent—and more difficult. As teams adopt more AI tools. Formula 1 will need to decide which uses fit the sport’s competitive structure and which could distort it.
Teams will keep hunting small gains, whether on the data dashboard or inside the pressure of a race weekend. The FIA, for its part, is trying to preserve a central idea: the sport still needs human skill, engineering judgment, and operational decision-making.
“It’s just the philosophy of maintaining a sporting element rather than machines racing machines,” Harlow says.
Formula 1 AI FIA Dominic Harlow Liam Lawson Salesforce Atlassian Williams Russell Paddon Anthropic Rovo Perplexity Computer Lewis Hamilton energy management tire temperatures aerodynamics modeling