resilience

Making systems resilient is fundamentally at odds with optimization, because optimizing a system means taking out any slack. A truly optimized, and thus efficient, system is only possible with near-perfect knowledge about the system, together with the ability to observe and implement a response. For a system to be reliable, on the other hand, there

The problem (or at least one of the problems) is that the twin edicts to simultaneously optimize your team and life and to be flexible in light of an uncertain future are in opposition to each other. Optimization presumes a kind of certainty about the circumstances one is optimizing for, but that certainty is, more often than not, illusory.

Resilience requires a kind of elasticity, an ability to stretch and reach but then to return, to spring back into a former shape—or perhaps to shapeshift into something new if the circumstances require it. Resilience is stretchy where optimization is brittle; resilience invites change where optimization demands continuity. But whether we’re talking