Oracle Red Bull Racing data, cloud and AI behind Verstappen’s Mexico City Grand Prix victory, Digi Mart USA News (Mexico City, Mexico): Max Verstappen took his sixth victory at the Mexico City Grand Prix, but according to DPL News he does not race alone. Behind him, Oracle Red Bull Racing deploys between 80 and 90 people, terabytes of data, millions of cloud-based simulations and artificial intelligence to define race strategy.
Fraser Smith, senior Partnership Executive of Oracle Red Bull Racing, told DPL News from the team garage during the first day of free practice at the Autódromo Hermanos Rodríguez. “When you watch the broadcast, you only see what happens on track, but there is an enormous amount of work behind it,” he said.
F1 regulations limit each team to roughly 60 people at the circuit, so Red Bull also operates an operations center at its base in the United Kingdom. Described as “mission control style” like NASA, between 20 and 30 engineers review data in real time and feed information to the pit wall.
Friday is the key day. Free practice sessions 1 and 2 are used to gather information and adjust the car before qualifying.
This year the team cannot rely on history: the RB22 was presented in January, developed for the first time with Ford to meet new F1 regulations that give greater weight to the electrical system. Smith said, “You could previously trust the historical data of your car.
But this new car and engine behave very differently from what we saw in 2025, 2024 and 2023.” The circuit has also been resurfaced in some sections.
The Mexican track presents another challenge: it sits 2,240 meters above sea level, reducing atmospheric pressure by roughly 20%. With less air, cars struggle to stay planted and brakes dissipate heat less effectively.
These factors must be measured and integrated into strategy and can make the difference in maintaining control and setting a record time.
The RB22, which has reached speeds of 344 km/h, also functions as a data-collection machine. While the team works to meet the minimum weight required by regulations and remove any extra grams that could slow the car, Smith was clear that sensors are not touched.
The car carries hundreds of sensors measuring tire and brake temperatures, vehicle dynamics and aerodynamics, including air intakes that verify airflow interaction with the bodywork. “You don’t want to save weight on your sensor capacity, because you need that information to perform the rest of the weekend,” he said.
Data, however, is not everything. “At the end of the day, a person drives that machine,” Smith stressed.
Telemetry may show a clear picture, but the driver can get out of the car and say he feels something different, so the team must find where both readings intersect. Smith said this is one of Verstappen’s strengths: his sensitivity lets him report, for example, understeer in corners two and three and explain why, so engineers can adjust the car.
He also understands telemetry when it is shown to him. “You can be a good driver, but you need the complete package to be a great driver,” he said.
For strategy, the team runs millions of Monte Carlo simulations that consider factors such as weather and tire wear, with the goal of getting the car to the finish in the shortest time possible. The best-performing scenario among all options is selected.
This analysis must be carried out continuously during the race to respond to unexpected events such as an accident, which is why the scalability of Oracle Cloud Infrastructure (OCI) is key. “With that greater capacity we can do more, and with more information you can make clearer decisions,” Smith said.
After each session, engineers, strategists and drivers hold debrief meetings, where everything is discussed quickly and conversations must be clear and concise until a joint decision is reached.
Technology must also fit the budget. Since 2021, F1 has imposed a cost cap on teams.
For 2026 it rose to $215 million, partly because it now includes items that were previously excluded. Smith recalled that before the cap, some teams could spend $300 or $400 million on the car, making competition unsustainable.
Now everything spent on development, testing and technology falls under that limit. “You can’t have your finger on the trigger and test everything,” he warned, so each solution is selected through a cost-benefit analysis.
The team uses OCI for simulations on a pay-as-you-go basis, which Smith said “may sound trivial, but it is very important.” Money not spent outside a race weekend is redirected to other areas of the team.
Artificial intelligence is already on track. This year the team integrated the Oracle AI Pitwall Agent, a tool that reviews thousands of documents and regulations in seconds, useful for example in contesting a penalty.
It also provides information to engineers in race situations not seen in practice. “Fundamentally, it is still the engineers and our people who make the decisions,” Smith clarified, but a tool that confirms the analysis gives more certainty when making the call.
He acknowledged that AI’s potential is still unknown, so the team is exploring its possibilities and uses, including learning from other industries.
Asked how the Oracle relationship gives the team an edge on track, Smith highlighted the breadth of Oracle’s solutions and its willingness to innovate together. When the pit wall agent was developed, Oracle arrived with several options and paths, and both sides built the tool the team uses today.
“That is what makes it an alliance and not a client relationship,” he said.
