AI-native companies can close this loop: the comparator is…

April 2, 2026

Continues But in practice the loop is open: the comparator is a manager's conscious…

AI-native companies can close this loop: the comparator is an AI agent, the cycle approaches zero, and goals are revised every second — reacting to changes as fast as a thermostat reacts to temperature.

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Feedback Loops

Goal-Setting Theory — specific difficult goals outperform vague goals; four mechanisms, three moderators (bib)

Self-Efficacy and Goals — Bandura's self-efficacy as the central mediating variable

Discrepancy Production vs Reduction — feed-forward (setting harder goals) vs feedback (closing the gap)

Learning vs Performance Goals — on complex tasks, learning goals outperform performance goals

Locke & Latham (2002) — source summary: 35-year retrospective on goal-setting theory (bib)

2039578081070137359 — Locke & Latham: goal-setting as feedback control (but see discrepancy production for the nuance)

2039578424977850584 — Locke & Latham answered the management question in 1990

2039578726334394790 — they explicitly cite cybernetic engineering as their model

2039579041183998043 — but in practice the management loop is open: quarterly checks, manual action

2039579188970365285 — AI-native companies can close this loop to seconds

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Scrum Scrum sits in the middle — faster than OKR, but still batched into fixed-length sprints. The user's xettel argues that even sprint-length loops are too slow for AI-native companies. →