The intelligence loop and value creation in the age of AI agents

Abstract Research Summary Artificial intelligence (AI) is moving from generating outputs to executing work. An agent that executes work autonomously produces something generative AI does not: operational experience that can raise its future intelligence. Prior research on AI emphasizes value creation through demand‐side feedback, notably data network effects, in which user data improve prediction and reinforce adoption. We develop the supply‐side counterpart, the intelligence loop: delegated usage generates operational experience, agentic capability converts it into future intelligence, and the resulting value maintains or expands usage. The loop cannot operate until autonomous execution is viable, a threshold we call minimum viable intelligence (MVI). Crossing MVI does not guarantee rising intelligence. Comparable deployments of AI agents can diverge as their loops decay, sustain, or accelerate. Three implications follow. Competitive advantage may depend more on the loop than on installed base. An above‐MVI agent supplies scarce expert skill as a non‐rival, replicable resource, so productive capacity can grow faster than expert headcount, with the one‐person company as a limiting case. And a venture with adequate demand, capital, and a capable team can still fail because its loop never functions or later deteriorates. Managerial Summary Artificial intelligence (AI) agents can execute work, and the experience generated through execution can raise their future intelligence, a feedback process we call the intelligence loop. An agent crosses a workflow's minimum viable intelligence (MVI) when it executes reliably without step‐by‐step direction, autonomous execution creates at least as much net value as the best alternative after oversight and correction costs, and governance arrangements keep residual risk acceptable and accountability clear. Investment belongs where one of these conditions keeps a valuable workflow below MVI. Deployment can begin with narrow, feedback‐rich workflows near MVI that ideally lead to harder workflows, since base model upgrades can match gains on simple work. Even above MVI, the loop is not automatic: it stalls when feedback is not timely and diagnostic, experience yields little intelligence gain, gains add little user value, or value does not maintain or expand usage. Human involvement shifts from executing work to specifying goals, evaluating outputs, governing execution, and supplying tacit knowledge. When the loop sustains or accelerates, productive capacity can grow faster than expert headcount. With capable base models widely available, advantage depends less on access to AI than on building and governing the loop, controlling proprietary operational experience, and converting it into intelligence more effectively than rivals. A venture with adequate demand, capital, and a capable team can fail because its loop never functions or later deteriorates. Cutting evaluation or oversight to scale faster may increase output in the short run but weaken the loop even while revenue remains healthy.