AI winters as legitimacy crises. From the history of technological promises to governance models of generative AI
TL;DR
This article argues that the classical AI winters were primarily legitimacy crises, in which the system of justifications linking technical promise, funding, commercialization, and social acceptability collapsed, and proposes a hierarchical interaction model of four legitimacy gaps.
Abstract
In the debate on artificial intelligence, the term “AI winter” is often used as shorthand for a technical failure or a temporary decline in market interest. This article proposes a different interpretation. We argue that the classical AI winters were primarily legitimacy crises, in which the system of justifications linking technical promise, funding, commercialization, and social acceptability collapsed. Drawing on the history of AI, legitimacy studies, and the sociology of expectations, we propose a hierarchical interaction model of four legitimacy gaps. In this model, the capability gap functions as the technical substrate of AI promises; the institutional assessment and commercialization gaps mediate whether those promises are credited, funded, and productized; and the governance gap operates as a meta-condition of legal, moral, and political authorization. On this basis, we reinterpret the first and second AI winters. We argue that the episodes later grouped under the first AI winter can be read primarily as a crisis of capability and assessment, whereas the second was a crisis of product, brand, and commercial ecosystem. We then argue that the contemporary boom of generative models does not herald a simple repeat of past winters. A more likely scenario is a regulatory-economic cooling driven by compliance costs, disputes over training data, infrastructure concentration, information manipulation, algorithmic verification, and increasing documentation requirements. In this scenario, generative AI becomes contested not only as an automation technology, but also as epistemic infrastructure involved in producing, verifying, ranking, and stabilizing public truth. In the final section, we formulate four implications for governance: promise restraint, evidence of deployment, documentation obligations, and greater infrastructural pluralization. This perspective shifts the debate from the question of whether AI works to the question of under what conditions its development remains politically and ethically legitimate.