Framework
Sometimes Just Take the Loss
Experienced 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.
How it works
- 1Pivoting feels productive — it is action, movement, narrative. But it can be the most expensive form of avoidance a founder makes.
- 2First-time founders often accept failure faster because they have no sunk-cost identity in the company. Experience creates attachment.
- 3Taking the loss requires distinguishing between a business that needs iteration and a thesis that is fundamentally wrong. The answer is almost never found by pivoting while exhausted.
- 4Rest is not a retreat. It is a prerequisite for seeing the next opportunity clearly — something a pivot rarely allows.
- 5Greg Shove has seven exits and still considers this one of the hardest lessons: knowing when to stop restructuring and start over.
Hear it on the podcast
Are You Building a Team of AI Passengers Coasting Toward Irrelevance?Listen to the episodeMore from this episode
AI Drivers vs. AI PassengersEvery 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.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.
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