Nyquist Limit:The frequency of changes in tech is invalidating our understanding

The piece argues that generative AI is accelerating software change faster than people and organizations can understand, evaluate, and safely manage it. It applies the Nyquist sampling theorem as an analogy: to reconstruct a changing signal reliably, observations must occur at more than twice the signal’s frequency. In software, the “signal” is the system’s actual behavior and changes, while sampling means human review and the formation of an accurate mental model. If deployments and modifications speed up without a corresponding increase in meaningful observation, teams may mistake noise for useful information and lose the ability to make sound judgments.

The author contrasts this emerging pace with the traditional norm that software is read and examined far more often than it is changed. Reports from high-growth technology companies suggest that rapid deployment is producing daily bugs and security problems, while emergency fixes introduce further defects; teams may feel unable to slow down even when systems become opaque and unpredictable. The central prescription is to set a deliberate target rate of change for important systems and ensure that people can inspect and understand them at least twice as often - ideally more. Better prompts or engineering techniques cannot eliminate the underlying limit, the author contends: sustainable speed requires keeping change within the pace at which human understanding can be renewed.