On (artificial) intelligence: from a concept of measurable human ability to automated evaluative order
This article argues that intelligence should not be understood as a concrete and delimited phenomenon or as a natural kind, but as a historically and culturally produced concept through which selected behaviors, capacities, and performances come to be classified as intelligent. It proposes to shift the question from what intelligence “really is” to how certain performances come to count as intelligent, how they are made measurable, and how those measurements acquire social, political, and technical force. The article’s contribution is to connect the historical operationalization of intelligence with the evaluative infrastructures through which AI reproduces and transforms it. Tracing a genealogy from modern rationality, psychometrics, and standardized testing to artificial intelligence, the article shows how intelligence became actionable through classification, comparison, measurement, and evaluation. Once test scores shaped access to education, credentials, work, and social recognition, measurement no longer merely described ability; it became part of the institutional conditions through which ability was recognized, rewarded, and made socially consequential. Testing thus helped produce feedback loops in which measured intelligence partly reflected the opportunities that testing itself had helped allocate. Intelligence was then linked to merit, qualification, and deservingness, allowing historically contingent criteria of achievement and selection to appear not as products of unequal opportunity, but as natural differences in ability. The article then shows how artificial intelligence inherits this operational history. AI became possible once intelligence had already been reformulated as performance that could be formalized, evaluated, and reproduced apart from the living human subject. Cybernetics, information theory, and early AI translated this conception into computational terms, while contemporary machine learning relocates it into data curation, task definition, model architectures, objectives, metrics, and benchmarks. Although deep learning departs from explicit rules and predefined symbolic representations, it does not escape operationalization: curated datasets and task-specific objectives shape the learned latent spaces through which relations become detectable, comparable, rankable, and optimizable. AI therefore crystallizes historically specific conceptions of intelligence by embedding them in technical systems of training, measurement, comparison, and evaluation. Its social consequences emerge when these evaluative infrastructures shape which performances become recognizable, rewarded, and normalized. The question is therefore which conceptions of intelligence are being technically reproduced, made authoritative, and extended through AI systems.