F1 Drivers Grapple with Unpredictable 'Self-Learning' Power Unit Algorithms

Formula 1 drivers and teams are increasingly grappling with the complex, often unpredictable behavior of sophisticated "self-learning" algorithms embedded within current hybrid power units, a challenge that offers a stark preview of the heightened complexities anticipated for the heavily revised 2026 power unit regulations. Recent events, particularly during the Belgian Grand Prix weekend, brought this underlying technological struggle to the forefront, revealing that even top-tier engineers are at times perplexed by the power units’ performance deviations.

The issue was highlighted most acutely when McLaren Team Principal Andrea Stella addressed the media at Spa-Francorchamps. His candid admission offered a rare glimpse into the technical labyrinth facing F1 operations: "When we try to analyse this, it gets immediately quite complicated. In fact, when I left the debrief to come here for this media session, there were still discussions ongoing as to why we see differences when these differences would not be scheduled in terms of the underlying parameters." This statement underscores a significant challenge: teams themselves do not always fully comprehend the real-time intricacies governing their power units.

This confusion manifested clearly in driver performance. McLaren’s Oscar Piastri, for instance, reported a substantial time deficit to teammate Lando Norris on the straights during qualifying, a discrepancy that defied conventional analysis. Similarly, Mercedes driver George Russell openly admitted his bewilderment regarding an unexplained straight-line speed deficit, suggesting a systemic, rather than isolated, problem. These incidents illustrate a growing sentiment among drivers of a lack of control, where performance margins can be dictated by unseen, computational forces.

Unpacking the "Self-Learning" Phenomenon

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The core of the issue, according to Stella, lies in the power unit’s "self-learning elements." It is crucial to clarify that this does not refer to artificial intelligence in the generalized public understanding, but rather to highly advanced algorithms and sophisticated self-learning systems. These systems are designed to process a multitude of parameters—ranging from track conditions and tire degradation to driver input and energy levels—to predict and deploy an optimal energy management strategy. The algorithm continuously learns from previous laps, dynamically adapting its deployment strategy based on the input it receives from the driver and the car’s real-time state. Piastri noted this adaptive nature even extends to the out-lap, impacting subsequent performance. "The engines are so complicated, they are sensitive to everything. Even things you do on an out-lap can dictate what happens [next]. So, it’s a very tricky way of going racing, but it’s what we’ve got."

This anticipatory mechanism operates not only between laps but also fluidly within a single lap. Should there be any slight deviation from the pre-programmed settings and simulations—be it a different braking point, a minor throttle lift, or a change in grip—the algorithm swiftly recalculates and projects the optimal energy deployment for the remainder of that lap. While designed for efficiency, this continuous adaptation can catch drivers off guard, creating an unpredictable driving experience.

A prominent example of this unpredictability unfolded during Q3 at the Belgian Grand Prix involving Isack Hadjar. The Frenchman attempted to provide a tow for his Red Bull teammate Max Verstappen, which required him to momentarily slow down on the exit of Turn 14. This deliberate, albeit unconventional, deviation from the pre-determined deployment strategy confused his power unit. Upon re-engaging the throttle, Hadjar experienced an unexpected and inconsistent power delivery.

"What is difficult is to guess what the engine is going to give you, because once you stop out of Turn 14 and deliver the power again, the engine is a bit confused because you’ve stopped for no reason. The software is confused," Hadjar explained. He further elaborated on the consequence: "On that first attempt in Q3 I had way too much power, so I pulled away from him. And the second attempt, I didn’t have enough. So if anything, he was catching me and I couldn’t tow him the whole way. That was very difficult to judge."

While Hadjar’s situation was an extreme case, the power unit’s algorithms respond similarly to far more subtle differences. This sensitivity leads to the feeling of powerlessness expressed by many drivers, particularly on circuits that are "energy-poor," meaning they demand a high degree of energy management due to long straights and limited regeneration opportunities. Piastri articulated this frustration succinctly: "When you’ve got qualifying grids decided by computers behaving or misbehaving, it’s a pretty crap way of going racing. Here it’s obviously exaggerated a lot and made worse, but yeah, when you come in from a qualifying session, you look at all the corners and go, ‘I’m on par with my team-mate and yet I’m two tenths behind at the end of it’. It’s not a very nice feeling."

Driver Influence and External Variables

The growing reliance on these complex systems inevitably raises questions about the extent of driver influence over these deployment variations. Lando Norris, the reigning world champion, offered his perspective on this at Spa: "It’s got nothing to do with you as a driver, really. Sometimes it does and we’re talking about being a few metres early on the button or whatever. It just makes a big difference at times, but there are certain things within your control and a lot of things, way too many things, that are out of your control." He added, "It’s a shame that there’s so many things that can dictate your own pace in qualifying. It’s not down to the driver. It’s just down to hoping you get lucky that the power unit does what it should do and doesn’t do something silly."

Norris’s comments highlight a nuanced reality. While drivers retain some agency, it often comes down to minute input variations. For example, a driver lifting the throttle slightly too late or applying more than 60% throttle too early on a corner exit can disrupt the pre-determined energy management strategy. This deviation prompts the algorithm to adapt, anticipating a new optimal approach for the remaining lap. This explains Norris’s earlier remark in China that drivers now differentiate themselves not by having "the biggest balls," but by driving the power unit "correctly," even if it feels unnatural. This also sheds light on George Russell’s situation; Mercedes initially attributed his straight-line deficit after Silverstone to his driving style, but despite adjustments, the issue persisted at Spa, suggesting factors beyond his direct control.

Beyond driver input, external parameters significantly influence these algorithms. Andrea Stella explained, "The algorithm is sensitive to external parameters." He cited changing grip levels and wind as two primary examples. A stronger headwind, for instance, might cause the algorithm to perceive a straight as longer than it actually is. Consequently, the predicted deployment strategy no longer aligns with reality, and the system attempts to recalibrate for the rest of the lap under these new, unanticipated circumstances. "Different would be if you have much more headwind, then the straight would last longer, and this would be a parameter that you can not necessarily include in a very robust way in your modelling, because this one has a certain random character," Stella elaborated. "That’s why we talk about wind, grip level, driver’s input, these things may be more difficult to be captured in a modelling, and therefore in a simulation."

Implications for Competition and Driver Adaptation

These algorithmic sensitivities affect drivers in two critical ways. Firstly, they can lead to reduced energy deployment at crucial points on the track, resulting in measurable time loss compared to a teammate or rival, as experienced by Piastri and Russell. Secondly, and perhaps more fundamentally, they influence the very act of driving. Stella explained that changes in energy harvesting, such as an "additional amount of harvesting with a super clip before braking," can alter braking points. "Then you are approaching the braking [zone] 10 km/h slower, and your braking point changes," he said. "This is quite difficult to master, I would say, for the drivers, and it’s one of the reasons why I’m sure the drivers talk about the difficulties to exploit the power unit from a driving point of view. It’s not only to get the most out of the power unit, but also because the variations of the power unit affect the references as you approach a corner." This unpredictability, coupled with the fact that these variations can occur without warning, makes continuous adaptation exceedingly challenging and, for many drivers, profoundly unnatural.

Looking Ahead: The 2026 Power Unit Landscape

While the immediate impact of these issues might lessen on less energy-hungry circuits like Budapest and Zandvoort, the long-term outlook suggests these challenges are deeply embedded within the DNA of current and future F1 regulations. The first reason is that teams continue to be surprised by what Stella termed "random factors." "The investigation that has happened, I think, has only partly clarified or convinced ourselves that we understand the reason why there were deviations from the anticipated pattern of releasing the electrical energy," Stella admitted. "There’s still some random factors, which I think they are only apparently random. In engineering, things happen for a reason rather than out of chance. It’s that sometimes these reasons are so sensitive to little parameters, that it looks like you can’t figure out, at least in the short term, the reason. So I think we are still a little away, I would say a few races away from having a full understanding of the power unit behaviour and exploitation."

Teams are actively working to refine their simulation tools and models to gain a more robust understanding of these complex systems. McLaren, for instance, recently received several long-requested Mercedes HPP simulation tools, indicating a concerted effort across the paddock to mitigate these "unpleasant surprises."

However, Oscar Piastri’s assessment offers a sobering perspective on the future. He believes these characteristics are "ingrained into these engines." The upcoming 2026 regulations will feature a drastically increased electrical component, accounting for approximately 50% of the total power output (up from around 20% currently), alongside a move to 100% sustainable fuels. This shift will place an even greater emphasis on energy management and recovery, making the sophisticated algorithms and their "self-learning" capabilities more critical than ever. While a subsequent two-step move towards a 60-40 split in 2028 might marginally reduce the electrical power emphasis, Piastri remains unconvinced it will eliminate these inherent shortcomings. "The change in fuel flow and the less deployment are not going to fix those specific issues. It has to do with how the engine is calibrated, how the engine learns, it’s kind of ingrained into these engines," he concluded.

The current struggles with unpredictable power unit behavior, driven by advanced algorithms sensitive to both driver input and environmental factors, serve as a potent precursor to the engineering and driving challenges that will define the 2026 Formula 1 season. As the sport moves towards an even more electrified future, the intricate dance between human and machine is set to become even more complex, potentially reshaping the very essence of driver skill and competitive balance.

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Jonas Leo: Jonas Leo is a passionate motorsport journalist and lifelong Formula 1 enthusiast. With a sharp eye for race strategy and driver performance, he brings readers closer to the world of Grand Prix racing through in-depth analysis, breaking news, and exclusive paddock insights. Jonas has covered everything from preseason testing to dramatic title deciders, capturing the emotion and precision that define modern F1. When he’s not tracking lap times or pit stop tactics, he enjoys exploring classic racing archives and writing about the evolution of F1 technology.