AI helps turn Formula 1’s live race data into predictions about tyre performance, pit windows and possible rival moves. It can help explain when a team might pit, but public sources do not establish that software autonomously makes a team’s binding pit call. Formula 1’s current public example, its 2026 Strategy Insight, is a broadcast graphic designed to explain strategic choices—not a disclosure of teams’ private decision systems.
What AI does in F1 race strategy
Race strategy is a sequence of choices under uncertainty: how long to run a set of tyres, when to stop, and how to react to other cars and changing conditions. Data models can compare scenarios and estimate likely outcomes. The strategist still has to interpret those estimates in the context of the race.
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Formula 1’s 2018 account of its F1/AWS programme described deep-learning models trained on more than 65 years of historical race data alongside streaming race data. The stated aims included predicting when a pit window might open or close and estimating tyre-change timing. That is a dated description of a public F1/AWS programme; it does not show that every team uses the same system internally. Formula 1’s 2018 announcement.
Formula 1’s 2026 Strategy Insight is a newer public example. It uses live timing, telemetry and historical race data to explain decisions such as extending a stint or responding to an undercut threat. F1 says the analysis accounts for tyre performance, pit windows, Safety Car risk and rival behaviour; AWS historical modelling helps compare current conditions with earlier scenarios. Formula 1’s 2026 Strategy Insight announcement.
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How a model can inform a pit-stop decision
A pit call is not just a prediction of when tyres will wear out. A team has to weigh the performance of the current tyres against the time lost by stopping and the position of rivals. Models can make those trade-offs easier to compare by estimating windows and showing how different choices may play out.
Estimate the tyre trade-off
Tyre compounds involve a balance between pace and durability. AWS’s 2020 explanation says softer compounds offer more grip and handling but degrade faster, while harder compounds last longer but limit cornering speed and traction. That affects how long a stint may remain competitive; the preferred balance depends on the race situation, not a universal rule. AWS’s 2020 explanation of F1 strategy.
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Compare the cost of stopping with the gain from fresh tyres
A stop can improve performance with a fresh set, but it costs time and can let rivals gain position. AWS’s 2020 post estimated an average pit-stop time cost of around 20 seconds. Treat that as an approximate figure from that source and year, not a current, universal cost: the impact depends on the circuit, race and traffic. AWS’s 2020 explanation of F1 strategy.
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A pit window is a strategic opportunity, not a guaranteed instruction. F1’s public insight descriptions identify rival behaviour, car spread, Safety Car risk and undercut threats as relevant factors. AWS also notes that Safety Cars and yellow flags can change a race strategy. A model’s estimate is conditional on the data and scenario it evaluates; a sudden incident or a rival’s move can change the calculation. F1’s 2026 Strategy Insight announcement and AWS’s overview of F1 Insights.
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What fans see—and what that does not prove about team operations
F1 and AWS publish broadcast-facing insights, including Pit Window estimates based on tyre compound, lap times and car spread; Predicted Pit Stop Strategy based on historical data; and Pit Strategy Battle, which illustrates how strategic changes can affect outcomes. These features help viewers understand possible decisions. They are not evidence that a named team uses the same model or lets it make its private pit calls. AWS’s overview of F1 Insights.
F1’s 2025 Real-Time Race Track experience used Amazon Nova to analyse custom track designs and project metrics, two viable strategies, pit timing, tyre recommendations and weather adjustments. It is an interactive fan experience demonstrating strategy concepts, not evidence that the product is used to make decisions during actual team races. Formula 1’s 2025 announcement. The same announcement described 23 data-driven F1 Insights, according to Kristin Shaff, AWS Global Director of Strategic Partnerships; that figure refers to broadcast insights at the time, not to a count of a team’s internal AI tools.
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How to compare possible strategies
When evaluating two possible calls, compare them on the same factors rather than treating a predicted pit window as an automatic answer:
- Tyre pace and degradation: How might the current and replacement compounds affect pace over the planned stint?
- Stop timing and time lost: When would the stop happen, and what is the likely time cost in that race?
- Rival response: Could another car pit first, gain an undercut opportunity or otherwise change the relative positions?
- Traffic and car spread: Would the car rejoin behind traffic, or does the spacing between cars make a stop more or less attractive?
- Safety Car and yellow-flag sensitivity: How would an interruption alter the value of stopping now versus later?
These are comparison criteria, not a recipe for a universally optimal strategy. The answer depends on the race conditions and the estimates available at the time.
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What public information cannot tell us
The public material explains F1/AWS fan-facing prediction products and offers historical context for the data programme. It does not disclose the current internal software, model performance or decision authority of each F1 team. In particular, it does not establish a measured strategic advantage for a named team or show that a model can make a binding pit call. It is therefore more accurate to say that AI can support and explain strategy decisions than to claim that AI decides when every team pits.
Some widely repeated technical figures are also dated. AWS’s 2020 post cited 300 sensors on each race car and 1.1 million data points per second. Those are figures attributed to that post, not newly verified specifications for every current car. Formula 1’s 2018 announcement cited more than 65 years of historical race data for its model-training account; that is not a current race count or a validation measure. AWS’s 2020 post and Formula 1’s 2018 announcement.
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