Conceptual adaptation
4 writings
A Polyphonic Conception of AI Understanding
With Pierre Beckmann.
Argues that whether large language models understand what they talk about is a question both unavoidable and ill-framed: unavoidable because users must tell outputs that merit epistemic trust from those that do not, and no purely mathematical or statistical vocabulary can draw that line without reintroducing the concept of understanding in all but name; ill-framed because the inherited concept presupposes a single locus of understanding. Mechanistic interpretability instead reveals LLMs to be deeply polyphonic systems whose outputs emerge from parallel mechanisms of uneven reliability. Drawing on evidence of medical, mathematical, logical, and spatial understanding in LLMs, the paper engineers a conception fit for such systems: a model understands a domain insofar as it contains sound circuitry that tracks relevant structure and reliably recruits that circuitry to control its outputs—which turns seemingly idle metaphysical disputes into tractable empirical questions that can guide the allocation of trust.
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Evolving Minds, Evolving Language: Metaphor as a Process of Conceptual Adaptation to Artificial Intelligence
Manuscript (+Slideshow)
Argues that cognitively loaded terms such as “understanding” and “reasoning,” when applied to large language models, are best understood neither literally nor as merely loose talk, but as metaphors that adapt our conceptual repertoire to new circumstances under pressure of conceptual needs. Drawing on the Strawson-Kant tradition on imagination and concept-application, inferentialist semantics, Bermudez’s theory of rational framing, and Gentner’s structure-mapping account of analogy, it develops a four-part framework: inferential transfer rather than referential transfer, the aptic normativity of conceptual needs, the empirical evaluation of transferred inferences through mechanistic interpretability, and the career of AI metaphors from novelty toward possible conventionalization. The result is a shift away from all-or-nothing disputes about the literal applicability of cognitive terms to AI toward graded and empirically tractable questions about which inferential transfers are apt, revealing why our cognitive vocabulary for AI is evaluatively and practically consequential.
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The Invented Inventor: Adapting Intellectual Property to Generative AI
Under review
As AI increasingly drives discovery, the concept of inventor faces severe strain. Recent judicial decisions, such as the Swiss Federal Administrative Court’s 2025 DABUS ruling, expose a deepening tension: courts demand intellectual creation by a natural person even as human contributions to AI-assisted discovery become increasingly nominal. This paper approaches the resulting tension from the standpoint of political philosophy rather than jurisprudence: the strain AI places on the concept of inventorship is too fundamental to be resolved by interpretative methods taking existing conceptual architectures for granted. Inspired by Hume’s genealogy of property, the paper reconstructs the historical “need matrices” that forged the concept of inventorship, tracing its evolution from Venetian guild economics through Romantic genius ideology to corporate R&D. This reveals the concept to be an overburdened bundle serving four social functions: incentivising innovation, disseminating knowledge, legitimating monopolies, and resolving priority disputes. It also clarifies the mismatch between the concept and the emerging realities of AI-driven discovery. To resolve this mismatch, we must disaggregate the concept of inventorship and develop specialised conceptual resources for each of these functions. If we invented the notion of inventor to perform certain functions, we can reinvent it to perform them better.
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Needs of the Mind: How Aptic Normativity Can Guide Conceptual Adaptation
Philosophical Studies. 2026. doi:10.1007/s11098-026-02511-3
The article offers an account of “needs of the mind” in terms of a distinctively aptic normativity–a normativity of fittingness. After reconstructing the history of different conceptions of needs and their gradual subjectivization, the article focuses on conceptual needs and argues that they register a cognitive privation that goes beyond a shortage of words, marking a mismatch between our conceptual repertoire and our situation that reorients conceptual engineering from detached amelioration to situated adaptation. This makes a needs-first approach uniquely suited to guiding conceptual adaptation in times of technological disruption.
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