The F1 Principle

Ford lists its current 5.0-liter Mustang V8 at 480 horsepower at 7,150 rpm.

The other recovered energy under braking and put up to 120 kW back into the drivetrain, 4 megajoules per lap.

Formula 1’s own explainer says the new unit “retains the 1.6-litre turbocharged V6 engine (with a few tweaks) but deletes the MGU-H,” while the electric motor moves to “350kW to the rear wheels, up from 120kW,” and the electrical share of…

The combustion side actually gets smaller: the FIA’s technical explainer has the internal combustion engine dropping “from between 550-560kW to 400kW,” with recovery rising to around 8.5 megajoules per lap.

Formula 1 says the 2026 units “will still provide over 1,000 horsepower” (Formula 1). Read that again.

Mercedes AMG High Performance Powertrains reports its F1 unit “reaching more than 50% thermal efficiency”.

When Mercedes first broke that barrier on the dyno in 2017, the 2014 unit had been at 44 percent.

A typical road-car engine turns roughly 35 percent of its fuel into motion.

On the GAIA agent benchmark, the same model, Claude Sonnet 4.5, scores 30.91% in one scaffold and 74.55% in another.

A 2026 paper that held the model fixed and changed only the scaffold found the same pattern across benchmarks and concluded that “harness-induced variance can substantially exceed model-induced variance”.

Meanwhile the engines themselves are converging: Stanford’s AI Index measured the gap between the best closed model and the best open-weight model at 8.04% in January 2024 and 1.70% by February 2025.

Mercedes says its car carries “over 250 sensors” and produces about “30 megabytes per lap,” more than a terabyte per car per weekend, over 17 separate data buses.

AWS, which runs F1’s data platform, puts it at 300 sensors per car “generating more than 1.1 million data points per second” (AWS). The strategy is not improvised either.

F1’s own insiders’ guide has a strategist explaining that “Strategy is 98 per cent preparation,” that the simulations run on Saturday night, and that “Everything is based on statistics”.

We have Monte Carlo, we have gen AI applications” (AWS Executive Insights). And the pit crew rehearses until the stop is muscle memory.

The record is 1.80 seconds, set by McLaren at the Qatar Grand Prix on October 8, 2023.

LangChain’s State of Agent Engineering found 94% of teams have observability tooling and only 77.2% actually evaluate their agents’ output.

McKinsey reports that more than two-thirds of high-performing companies name data, not the model, as the primary obstacle to scaling AI.

Instead, they were building with simple, composable patterns”.

Simon Willison’s definition of a coding agent is even shorter: “LLM + system prompt + tools in a loop”.