Is technical depth always the right starting point when assessing DeepTech talent?

In hiring conversations in Japan, a different consideration often becomes more relevant when commercially experienced professionals step into DeepTech environments. The focus gradually shifts from what individuals know to how they operate when the technology itself cannot yet offer certainty.

One practical way to understand emerging capability is to explore how important decisions were made without clear technical validation. Conversations about navigating trade offs and creating direction while outcomes remained unclear often reveal more than a review of achievements alone.

A pattern frequently observed is that some individuals try to restore clarity too quickly. Others sit with the uncertainty for a while, test assumptions carefully, and build direction step by step. This difference can signal how naturally someone may adapt to DeepTech contexts.

Assessing talent may therefore be less about existing domain knowledge and more about recognising decision making patterns in unfamiliar territory.

In your experience, what helps you recognise someone who can create progress in DeepTech before certainty fully exists?