The intense demand and limited supply of compute and power are creating strange bedfellows in the AI industry. This dynamic forces companies with strong models but weak infrastructure (Anthropic) into partnerships with rivals who have excess compute capacity (Musk's SpaceX), fundamentally reshaping market alliances based on comparative advantage.

While F1 heavily regulates physical and computational fluid dynamics (CFD) testing, there are currently no rules governing the use of AI. This regulatory gap creates a new frontier for teams to gain a competitive advantage, pushing them to explore AI for strategy and design in ways they can't with traditional methods.

Automotive OEM Jaguar Land Rover implemented AI to accelerate its external aerodynamic design workflows. This change increased their capacity from evaluating 50 designs per day with traditional solvers to 1,500 designs per day, a 30-fold improvement in iteration velocity.

The engineering process evolved from physical prototypes to digital simulations. AI models now represent a third leap, accelerating design iterations from days to minutes. This allows for exploring thousands of options instead of dozens, drastically shortening development cycles.

Unlike compute-rich giants, AppLovin's bootstrapped culture enforces extreme efficiency in its AI infrastructure. Engineers don't have unlimited GPUs, forcing them to optimize code and models for cost and performance. This constraint-driven approach leads to significant cost savings and a lean operational model.

Counter-intuitively, as AI models become more efficient, the total consumption of compute resources will rise. This economic principle, Jevons Paradox, states that increased efficiency lowers costs, which in turn unlocks more applications and drives greater overall demand.

Breakthroughs like neural network "pruning" can reduce model size by 90% without losing accuracy, offering a 10x reduction in inference costs. This highlights that algorithmic innovation, not just acquiring more hardware, will be a key competitive vector in the AI race, enabling more output with less energy.

As AI demand outstrips Earth's power supply, the industry is pursuing two strategies. Elon Musk is escaping the constraint by moving data centers to space. Everyone else must innovate on compute efficiency through new chip designs and model architectures to achieve 70-100x gains per token.

Due to their extreme agility and constant week-over-week design changes, Formula One teams are the ultimate stress test for new engineering AI workflows. If a platform can meet their demands for speed and accuracy, it proves its viability for more traditional, slower-paced enterprise OEMs.