Type: article
Abstract: Large Reasoning Models are marketed as systems that ‘think’ before they answer, displaying chains of thought that purport to show their reasoning. Echoing Dreyfus’ Critique of Artificial Reason, this paper critiques this attribution in three steps. First, we trace a genealogy from Leibniz through Turing showing that ‘reasoning,’ understood as formal symbol manipulation, is the founding concept of artificial intelligence – older than the term ‘intelligence’ itself – and supplies the most charitable standard against which such systems can be measured. Second, we argue that current systems fail even this standard: their training procedures provide no mechanism for valid inference, their performance collapses on precisely the problems that stepwise reasoning should master, and their displayed traces are presented as explanations while lacking any fidelity guarantee to the underlying computation. Unlike traditional Explainable AI methods, they are optimized for plausibility, not faithfulness – which, as empirical research suggests, makes them structurally deceptive. Third, we ask why the vocabulary persists despite these findings, and examine the ideological work it performs: it underwrites investment and geopolitical positioning, and sustains a narrative in which each new system appears as a step on a necessary path toward the future.
Institution: Munich School of Philosophy
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7215134121