The F1 Engine Problem

As we wait for the new season of Drive to Survive, I wanted to share an analogy I've been using to explain the current state of AI.

Imagine someone handed you a Formula 1 engine.
A thousand horsepower of precision engineering.
The same power plant that pushes cars past 230 mph.

Now imagine you strapped it to your bicycle.

That's what most companies are doing with AI.

The engine isn't the problem. Your bicycle is.

Everyone Got the Same Engine

GPT. Claude. Gemini. These are F1 engines. And for the first time in history, everyone has access to them.

Here's what most people miss about Formula 1: multiple teams often run effectively the same engine.

Mercedes supplies McLaren, Williams, and Aston Martin. Ferrari supplies Haas and Sauber. Same horsepower. Same potential.

Yet the results are wildly different.

McLaren wins races. Williams fights to escape the back. Same engine.

The engine is commoditized. The car is the differentiator.

This is exactly what's happening with AI. Everyone has access to the same engines. The differentiation isn't the model you picked. It's everything you built around it.

The Wrapper Ceiling

Give everyone an F1 engine and three patterns emerge.

The Wrapper.
A thin UI, a clever prompt, a logo. It makes noise. It demos well. It doesn't change outcomes.

The Bolt-On.
An AI chatbot bolted into legacy software. Technically attached. Functionally isolated. The system was never designed for AI to actually run the work.

The Fake Native.
"AI-native" products that still mirror legacy workflows. New paint job. Same old frame.

All of them hit the same ceiling.

When the next model ships, the engine improves—but the system can't absorb it.

You can't compound on a broken foundation.

What an F1 Car Actually Looks Like

In Formula 1, championships are rarely won by engine alone.

They're won by teams that design everything for the engine.

Infrastructure built for power.
Aerodynamics designed around output. In AI terms: systems that feed context correctly, move data cleanly, and let models operate continuously—not intermittently.

A chassis that can handle force.
F1 cars channel massive power without tearing apart. In AI: orchestration, validation, and control layers that turn raw output into reliable work.

A pit crew that adapts in real time.
Strategy changes mid-race. In AI: teams that iterate constantly, not "set it and forget it" deployments.

A driver who knows the track.
The best car fails with the wrong driver. In AI: deep domain expertise. People who actually understand the work being done at 2am when things break.

This is the formula:

F1 Engine + Purpose-Built Infrastructure + Technical Excellence + Domain Expertise

Most companies have the engine.
Some are building infrastructure.
Very few combine it with real domain knowledge.

A Concrete Example

In legal, this difference is obvious.

One team adds an AI chatbot that summarizes documents.
Another rebuilds diligence so documents flow through extraction, normalization, cross-checks, and memo generation automatically.

Same model. Same engine.

One saves minutes.
The other changes how deals get done.

The Compounding Advantage

Here's the part the bicycle builders miss.

When the engine improves, the F1 car gets faster.
Every aerodynamic tweak, every workflow refinement, every systems improvement compounds with each model upgrade.

When the engine improves, the bicycle stays a bicycle.

It doesn't matter how powerful GPT-5 or GPT-6 is if your system was built for GPT-4-level output.

The teams building real AI infrastructure today aren't just winning now. They're building systems that will absorb every future engine upgrade.

The gap widens with every release.

The Quiet Builders

While hype cycles peak and crash, a few teams are doing something different.

They aren't announcing features.
They aren't shipping chatbots.
They aren't bolting engines onto bicycles.

They're building cars.

In legal. In healthcare. In finance. In places where failure has real consequences. Small teams pairing deep domain expertise with serious technical execution.

They're building the chassis.
Refining the aerodynamics.
Training the pit crew.

They're not loud.

They're ready.

The Race Has Started

Everyone got an F1 engine.
Same power. Same potential.

Some strapped it to bicycles.
Some bolted it onto Corollas.

And somewhere, quietly, the real cars are pulling away.

The disappointment isn't AI.
It's that most people are still thinking in bicycles.