Framework
AI Drivers vs. AI Passengers
Every person on your team is either an AI Driver or an AI Passenger. Drivers own the task, the decision, and the conviction — they interrogate AI output and sign their name to the result. Passengers present AI output as their conclusion without exercising judgment. One is an amplifier; the other is a liability. The distinction is the most important management question in AI adoption.
How it works
- 1A Driver says: 'This is what I think, informed by AI.' A Passenger says: 'This is what AI thinks.' The difference is ownership.
- 2Passengers are not lazy — they are uncalibrated. The role of leadership is to set the expectation clearly and enforce it consistently.
- 3Greg Shove's policy at Section: one strike. Not because he is harsh, but because normalizing deferred thinking compounds invisibly until it collapses visibly.
- 4The Driver/Passenger distinction applies to every knowledge work function — not just AI-native roles. It is a frame for measuring judgment under ambiguity.
- 5Building a team of Drivers requires modeling the behavior at the top: leaders who use AI as a thought partner rather than a ghostwriter set the standard.
Hear it on the podcast
Are You Building a Team of AI Passengers Coasting Toward Irrelevance?Listen to the episodeMore from this episode
The 10% Adoption CeilingEvery organization can reach approximately 10% AI adoption through self-motivated employees alone. This is also the ceiling without active intervention. Getting from 10% to 30–50% requires deliberate change management: leadership modeling behavior, structured learning programs, accountability mechanisms, and a cultural expectation of AI fluency. Most enterprise AI success stories are 10% stories told as if they represent the whole.Sometimes Just Take the LossExperienced founders are often more failure-averse than first-time founders — and that manifests as an over-willingness to pivot. When a company is not working, the seasoned instinct is to restructure, reposition, find an adjacent angle. But sometimes the right move is to accept the loss, rest, and start completely clean. Pivoting into a marginally better version of a broken thesis is avoidance, not resilience.
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