Raphael Ronge

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.

Whether Generative AI (GenAI) is creative is a question that has generated considerable philosophical debate yet remains unresolved. We argue that this impasse is not accidental but symptomatic of a wrong concep-tualization: The dominant question – is GenAI a creative agent? – presupposes that creativity is a property residing in a system. Instead, we propose to ask the question: what role does GenAI play as a medium of creative practice? This means that GenAI is neither a passive tool, nor an autonomous system, but a tech-nology that actively structures the possibility space of creative expression. We propose five “dimensions of creativity” – modal, expressive, disruptive, reflexive, and integrated – understood as a process in which each dimension generates a tension that the next takes up and transforms. Applying this framework to GenAI, we show that the creative process itself changes under the new technical conditions GenAI introduces, and that the central risk is a drift toward pure reproduction – the collapse of creative practice into the recombination of what the medium has already mapped. We argue that resisting this drift requires bringing the medium to its own limits: making the technological conditions of creative practice visible and transparent, thereby transforming reproductivity back into creativity.

Each technology advances at its own pace, often indifferent to theoretical and philosophical-scientific conceptualizations. In the case of technologies like artificial intelligence (AI), and especially generative AI (GenAI), developments are so rapid that conceptual and epistemological reflections struggle to keep up, even at the level of basic definitions. Yet these definitions carry significant non-theoretical implications, including social, legal, and policy-related consequences. In this paper, I offer some reflections on the definition of GenAI proposed by Ronge et al. (2025), using it as an opportunity to highlight the most relevant aspects of the ongoing debate sparked by these new AI systems. In doing so, we will seek to explore the implications of generative AI systems in the context of human interaction, particularly in light of their role as active supports rather than passive tools. This shift, coupled with the increasingly anthropomorphic perception of their capabilities, sets GenAI systems apart from any previous technologies, not only in terms of technical features but also in how they are perceived by everyday users.

Erstmals 1969 veröffentlicht, wurde das Buch Perceptrons von Marvin Minsky und Seymour Papert auf die eine oder andere Weise über die Jahrzehnte immer wieder aufgegriffen, zitiert und zusammen mit dessen konnektionistischer Gegenposition als eine der großen Kontroversen der KI-Geschichte stilisiert. Es ist allerdings längst nicht eindeutig, inwieweit Inhalt und Auswirkung des Buches übereinstimmen. Dies lässt sich mithilfe der Carnap’schen Aussagenanalyse genauer beleuchten. Carnap beschreibt nach welchen Mechanismen Aussagen Inhalt und subjektive Vorstellungen vermischen und was es bedeutet, wenn eine Aussage nur scheinbar ein Problem behandelt. Wendet man diese Analyse auf Perceptrons an, lassen sich nicht nur die vergangene Kontroverse besser verstehen, sondern auch Rückschlüsse auf heutige KI-Debatten ziehen. Unser Beitrag eröffnet damit die Möglichkeit, gegenwärtige Grenzdiskussionen Künstlicher Intelligenz sachlich zu betrachten.

The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model BERT and the differences between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis.