PERSON
Albert Bandura
The Stanford psychologist who proved that belief in one’s own capability is not a personality trait but a buildable, domain-specific judgment—and whose framework for
self-efficacy explains, with uncomfortable precision, why AI destabilizes experts more deeply than any previous technology.
Self-efficacy, Albert Bandura insisted, is not confidence. It is the specific, evidence-based judgment that one can execute the actions required to produce a given outcome in a given domain—and its specificity is everything. The surgeon who commands a familiar theater may falter in an unfamiliar one; the programmer who debugs with mastery may feel lost before the prompt field of an AI tool. Bandura spent five decades at Stanford documenting how these domain-local beliefs are built through four sources—
mastery experiences,
vicarious learning,
social persuasion, and
physiological states—and how they govern behavior, persistence, and recovery from setback with the regularity of physical laws. His framework meets the AI moment at its most painful nerve: when
large language models automate the very tasks through which professionals built their mastery experiences, they do not merely remove work; they invalidate the evidentiary base on which professional identity rests. The expert has not failed—the framework that made