This year’s edition of the Singapore Grand Prix was hardly a thrilling affair. The Marina Bay circuit did little to shed its reputation as a track with limited overtaking opportunities, with Mercedes-AMG Petronas F1 Team driver George Russell taking the chequered flag on Oct 5 in convincing fashion after surging ahead of the grid from his pole position secured during Oct 4’s qualifying rounds.
The victory was especially sweet for Russell, who had crashed into a barrier within the final laps of the race in 2023 during a fierce battle for a top-three position. In 2024, he suffered a panic attack post-race, with his internal temperature climbing dangerously high following a gruelling fight for fourth place.
“The hotter it gets, the more it influences our bodies,” says Atlassian Williams Racing driver Carlos Sainz. “This year, we’re going to be wearing cooling vests, so we will see how that works. So a lot of it is about cooling down: cooling down the driver, cooling the car, cooling the garage, cooling the brakes,” adds the 2023 Singapore Grand Prix winner.
Conditions on track were exactly as Sainz described, with the thermostat recording 33 degrees Celsius at 81% humidity on Sunday night. Still, the sticky weather did little to spoil the excitement, with Formula One’s (F1) organisers reporting an attendance of some 300,641 attendees over the three days, the second-highest ever turnout since the night race began in 2008.
Sainz climbed an impressive eight places to secure 10th place at this year’s race through steady tyre management and a mistake-free drive. He says post-race: “We were able to extend our medium tyre longer than anybody else into the race and once I put the softs on, I was able to do a very fast stint and some strong overtakes on track.”
Where horsepower meets cloud power
See also: Singapore’s digital economy hits $128 bil as AI use among SMEs triples
For all its physical and tactical factors, the Singapore Grand Prix also marks a pause before F1 enters a new era. The 2026 F1 season will bring sweeping changes to the sport’s technical regulations. New rules will mandate higher electrical output from hybrid systems, overhaul aerodynamics and introduce sustainable fuels. Since these changes make today’s power units redundant, teams without in-house engine programmes risk becoming wholly dependent on suppliers.
For Oracle Red Bull Racing, the solution was to establish the Red Bull Ford Powertrains team to build its own next-generation hybrid engine. Between 2019 and the current 2025 season, the Milton Keynes-based outfit has been using Honda-developed engines, a stint that has yielded the team two Constructors’ titles in 2022 and 2023. Red Bull’s “The Flying Dutchman”, Max Verstappen, himself clinched four consecutive Drivers’ titles from 2021 to 2024, calling the partnership “a ride that [he] enjoyed a lot” earlier this year in April.
Red Bull’s team principal, Laurent Mekies, has acknowledged that developing the new engine for 2026 will be “a mountain to climb”, with “a lot of sleepless nights” expected. The climb, however, isn’t only mechanical. Behind the scenes, Red Bull is also re-engineering how it designs and tests engines — shifting the bulk of its computational heavy lifting into the cloud. Launching an engine business from scratch would typically require vast data centres, high-performance computing clusters and a significant boost to the electricity supply. Instead, the team is developing the new sustainable-fuel power unit almost entirely in Oracle Cloud Infrastructure (OCI).
See also: Social trading: Breaking barriers for all traders
“We looked at all options, on-premise and cloud. It turns out we would have had to build a large data centre and increase the power supply to our campus in order to run it on-premise,” says Matt Cadieux, Oracle Red Bull Racing’s chief information officer.
That decision has major cost implications: a single modern F1 power unit can cost on the order of US$10 million ($12.9 million) to US$11 million, and under the new 2026 regulations, the engine development programme faces a proposed US$140 million cost cap. By contrast, Red Bull’s move to OCI has enabled them to execute over 150 billion simulations in a season and scale compute resources dynamically, often at a lower marginal cost than building and powering in-house server farms.
Using OCI, Red Bull engineers can spin up computing resources within hours to run computational fluid dynamics (CFD) simulations, which are virtual wind tunnels that test fuel injection patterns, combustion chambers and airflow without building costly prototypes. Cadieux shares that the powertrain division now runs more CFD simulations than the chassis team, a workload that would overwhelm most on-premise systems.
The move is as much about economics as speed. Cloud is often criticised as costly in the long run, yet Red Bull concluded the model was competitive under F1’s strict cost caps. Once capital expenditure, electricity and freight were factored in, OCI proved viable. Crucially, it eliminated the need to “sweat” hardware for years before replacing it, an unattractive prospect in a sport bound by financial limits, says Cadieux.
AI on the pit wall
Generative AI is the next layer of Red Bull’s digital playbook, particularly at the pit wall. After a race, teams have just 30 minutes to decide whether to protest a penalty, a frantic window that requires combing through thousands of pages of regulatory rulings to build a case.
To help with that, the team is piloting a generative AI solution from Oracle that combines retrieval-augmented generation with large language models, allowing engineers to query historical regulations and surface relevant precedents in real time. The tool promises to turn hours of manual research into seconds, sharpening the team’s ability to adapt to sporting rules over a race weekend.
To stay ahead of the latest tech trends, click here for DigitalEdge Section
Even so, Cadieux stresses that AI will not replace human judgment. “AI will do the legwork [such as processing data and framing options] but humans will always be in the loop, especially for high-risk, high-consequence decisions, [where a wrong decision could result in lost points or championships].”
Training the human edge
The Mercedes team underscores the same point, but in training for the drivers. Its driver-in-loop simulator — part hardware rig, part digital twin — replicates suspension behaviour, tyre grip, aerodynamics and powertrain response in a controlled environment. For drivers, it offers early familiarity with circuits. For engineers, it serves as a laboratory to fine-tune car set-ups before the first practice.
The simulator produces torrents of data and demands collaboration from specialists in real time. By using TeamViewer’s Tensor platform, the Mercedes team’s engineers can securely log in from anywhere to monitor systems and make changes to simulator devices, even when they are far from the team’s base in Brackley, England.
“Not only are the simulators a sensitive area, they’re also very secretive. We need to be able to trust that data security is in place. With TeamViewer, we can have that peace of mind,” says Christian Damm, a simulator development engineer at Mercedes-AMG Petronas F1 Team.
He adds that Tensor’s scalability and centralised management allow the team to add or remove users and machines quickly, cutting the time it takes to provision resources and secure them within groups. The platform also delivers higher resolution and smoother performance than previous software, eliminating the stuttering and freezing that once disrupted sessions. Its ability to run at higher bit rates and adjust visual fidelity ensures a consistent, high-quality experience.
Steven Riley, Mercedes-AMG Petronas F1 Team’s head of IT Operations and Service Management, adds: “Due to the cost cap, all our technology deployments have to deliver value. [By using TeamViewer across our digital workplaces, we can] ensure our drivers and team members can access all the data they need in real time to make the split-second decisions [during a race] to get us over the finish line .”
Whether in Red Bull’s cloud-based engine development or Mercedes’ remote-access driver simulator, the underlying logic is the same: extract maximum performance from finite budgets, strip friction from critical workflows and keep humans firmly in the loop. Fans may only see cars on track, but the race is increasingly shaped by invisible infrastructure, such as cloud systems, AI tools and simulators that prepare drivers and machines before the lights go out.
