F1's artificial intelligence is not a way to reduce jobs, but a way to create all the dollars in the cost cap for even more
[SINGAPORE] Artificial Intelligence (AI) surged into public awareness along with ChatGPT in 2023, but businesses and businesses use it under longer and more technical monikers such as “machine learning.”
Ten Formula (F1) teams that appeared in Singapore over the race weekend, which culminated in George Russell's victory and McLaren's second title, have been using it for a long time in a critical use case for data processing. According to AWS, F1 CAR has around 300 sensors, generating 1.1 million telemetry data points per second.
In 2022, the first 1 terabyte of data per Mercedes-Benz F1 team's car on the weekend of the race, grew to approximately 11 terabytes of data sent to and from the UK factories.
Much of this data is not in the same format, and it is not easy to generate insights that are converted to large millisecond paces on a track. The longer it takes to analyze the data, the less time it takes to focus on other parts of the car development.
Therefore, the use of AI in the F1 world has become particularly important, far beyond public deployments such as Google's large-scale language model Gemini and Adobe Photoshop's generation AI.
The Alpine and Sauber drivers were to occupy four of the four bottom five positions at the Singapore Grand Prix on Sunday (October 5), but were extremely competitive in terms of the amount of data generated over the three days of the running.
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AI was called a “game changer” by the head of Alpine's technical and integrated partnership, Ian Goddard, during Thursday's media tour.
Not only can AI help engineers solve technical equations, it also helps them identify sound-mediated defects, build correlations between racetrack data and simulator data, and free up resources for use elsewhere.
“In Formula One, we're continuing to talk about performance, but if it could be operational efficiency… the overall factory becomes more efficient, the faster the car (upgrades),” Goddard said.
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In the qualifying round, Aston Martin's 10th place Fernando Alonso's Mercedes-Benz Singapore Grand Prix Posetter George Russell was under two seconds.
Such a small difference could mean teams like second-placed Mercedes win around US$124 million at the end of the season, compared to Aston Martin's US$83 million. 7th place.
An increased pace of development will lead to cost savings for Formula 1 teams, further increasing profitability.
The use of AI to achieve this is still “early” and Goddard, who has seen countless changes in automotive development over nearly 30 years in Formula 1, has little predictable how it will be used in just a few years.
On Sunday, head of the Audi F1 Project, Mattia Binott (who will officially take over Sauber next year), reflected his emotions and described machine learning as a “really big” confusion in the F1 development process.
Like the normal world, regulations still follow the pace of AI and machine learning development. Goddard said he hopes the rules will ultimately cover AI use, but he cannot predict when or how that will happen.
At the media roundtable on Sunday, Global Motorsport Governing Body Fia said it would accept the use of new technologies such as AI. He also warned that he didn't want F1 to be a “war to buy the best supercomputers,” but noted that the official rules regarding AI hardware for F1 teams are still around three to four years away.
“It's a huge opportunity for every team,” Binott said. “Everyone who is the sharpest may have the most benefits.”
Goddard said the use of AI in Formula 1 is at a point where the team is unfolding about the issue “we didn't think about it six months ago, not to mention how to solve it.”
However, Binotto warned that even though all Formula 1 teams invest in charging automotive developments in the use of AI, the team still lacks a “good understanding” about the true potential of technology.
Goddard is somewhat excited about the technology potential, saying that almost every role in the Formula 1 team is being enhanced or enhanced by AI, whether it is engineering or business processes. The current challenge is to trust the output and correlate it with existing results, he said.
Starting in 2026, Audi, like other teams, plans to use AI to improve its technology development and data collection process, but Binotto said it believes it is about deploying AI in the right sector rather than using the most cutting edge features.
“(AI) is quick… if you're not focusing on the right things, it can be a waste of energy,” he said.
Cost cap limits around F1 AI
The F1 cost cap is limited to the amount of teams that can be spent in a season, but it also appears when deciding how to allocate budgets to traditional development passes such as wind tunnel testing and AI use.
From 2023 to 2025, the limit was USD 135 million (adjusted for inflation). It will be US$215 million to reflect the new regulations and various accounting processes.
“Every dollar we spend within the cost cap, you are very justifying your spending. “Is there the best value we can get for that dollar?” Goddard explained. “The dollars per milliseconds of performance on the track.”
The FIA might want to discourage supercomputer arms race between teams, but the team itself is aware that they spend USD 10 million on something like “the world's biggest supercomputer” without knowing whether it's the best use of money or not.
Despite the limitations, there may be ways in which teams can use AI to improve efficiency outside of car development that does not fall within the cost cap range.
“You can use AI outside the cap for unrelated activities,” Binott said. “There are many things you can learn using AI outside the cap.”
Such applications could be marketing and fan engagement used by IBM for Scuderia ferrari. With staff and engineers not focusing on the details, spending a dollar more than before using AI can go further to slow them down.
Unlike many industries where AI is driving unemployment adoption, the future in Formula 1 is not only about creating every millisecond of performance counts, but also making every dollar more efficient.
This will result in a more sustainable and efficient process throughout the F1 paddock, Binott said.
