Has F1 become too software defined?

AI is dampening the on-track spectacle and limiting the role of driver skill. But in the paddock, it’s granting engineers more autonomy and the opportunity to differentiate themselves from the rest of the grid.

“It’s like Mario Kart” was Max Verstappen’s damning assessment of 2026’s F1 racing. And it’s not just the drivers. Fan forums and message boards share a similar sentiment. Despite more overtakes, the spectacle is diminished by reducing the role of driver. Instead of talking about late braking, aggressive overtakes or defensive masterclasses, the discourse is dominated by superclipping, energy management and overtake mode.

The visceral sight of Ollie Bearman crashing last month as he swerved to avoid Franco Colapinto provided a visual representation of one of the core issues: vast unexpected speed differential as cars deploy or harvest energy. 

No longer just a ratings issue, but one of safety, F1 has been forced to reconsider its regulations.

Software over skill

The issue is compounded in scenarios where the car automatically deploys. Drivers across the grid have complained about the car “optimising” battery in a way that doesn’t make sense. 

Most energy harvesting is now handled automatically by the car's Electronic Control Unit (ECU) causing unexpected behaviours. Super clipping in particular has been a source of ire, with the car's software sending engine power into the battery instead of the wheels. When the driver presses the throttle to the floor, the wheels get less power than the driver asked for, and the car slows down. 

The software has overridden the driver.

Using AI and automation to drive competitive advantage 

Software sophistication is clearly creating on-track issues, but for engineers, AI and automation provides a platform for competitive differentiation. 

Systems engineering relies on a constant cycle of simulation, testing, and data analysis.  The test configuration and data wrangling in particular is tedious, complex work... not exactly an engineer's favourite part of the job. AI agents are now taking over the work of retrieving and processing data on behalf of engineers. 

The interesting decisions (what to test next, what the data means, what the design should be) stay with the engineer, but AI handles the low-level data exploration. Yet, they are only able to do this because F1 teams have some of the most sophisticated data infrastructures in industrial R&D.

F1 teams worked out how to stream, store and query telemetry at a scale most R&D teams are only starting to think about. Many data architecture problems were solved at McLaren and Williams years before they showed up in aerospace, automotive testing or energy R&D. It's this data and software sophistication that is indirectly leading to automation of the cars (and the new F1 regulations).

Engineers making use of agentic AI and automation are able to make decisions faster, test innovation at pace and prove their worth to a manufacturer in a sport that is dominated by milliseconds and fractional changes.  Ultimately, whilst automation might be a bad thing for F1 drivers and fans, it's having a huge impact on the field of systems engineering. When AI agents take over the boring parts, engineers get to be engineers again. The drivers aren't so lucky.

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