Since the 1960s, racing cars have relied on aerodynamics to gain an edge. Wings weren't just for aesthetics; they were a game-changer, allowing teams like Chaparral and Lotus to harness airflow in ways no one had imagined before.

The early days of aerodynamic testing involved expensive track time and dangerous crashes. Wind tunnels offered a safer alternative but remained limited due to their cost and availability. Enter CFD (Computational Fluid Dynamics), which revolutionised the process by allowing designers to simulate airflow on virtual models, saving both time and money.

Now, as CFD becomes more complex and resource-intensive, AI steps in as the next frontier. Teams can now harness machine learning algorithms to optimise their designs, making the design/validation cycle even faster and more accurate. This shift heralds a new era where the line between design and data science is blurred.

But with great power comes great complexity. Thousands of hours of compute time are now required for detailed simulations, pushing teams to seek out AI solutions that can navigate this computational bottleneck. As the race for performance intensifies, AI might become as crucial a tool in racing as the wings themselves.