Trust as Complexity-Reduction Mechanism

Trust, in Luhmann's framework, is not an emotion but a mechanism for managing complexity. Every social situation presents more possibilities than can be evaluated—the colleague may deliver or default, the institution may honor or betray commitments, the AI output may be accurate or hallucinatory. To verify everything would paralyze action. Trust eliminates paralysis by allowing actors to proceed as if the uncertain future were certain enough to act. This is the temporalization of complexity: converting simultaneous overwhelming possibilities into sequential manageable decisions. Trust is always conditional—it can be withdrawn if violated—and this conditionality makes it adaptive. AI collaboration introduces second-order trust: trusting not just the colleague's competence but the colleague's evaluation of AI output. When teams use AI simultaneously, trust becomes a web of dependencies in which one person's failure to catch an AI error propagates through interconnected work before anyone registers the breach. The trust burden expands faster than institutional verification mechanisms can adapt.