Andrea Roli, Sauro Succi, Stuart A. Kauffman

Front. Phys., 08 July 2026

Current AI technologies have demonstrated impressive results, mainly driven by large language models (LLMs). The most diffused applications of LLMs are in the so-called generative AI, which consists in techniques that produce texts, music, pictures or videos–often in a multimodal setting. Challenging the intuition that machines cannot be truly creative, the artefacts produced by LLMs are sometimes considered as surprising, novel and creative. This view is also supported by observing that there are both theoretical and practical limitations on the predictability of AI systems’ outcomes. Actual creativity can also be transformative and inventive, hence not just unpredictable but unprestatable: true novelty arises within a process whose evolution of the very possibility space cannot be predicted. Prominent examples of unprestatability are the evolution of the biosphere and can be found in artistic human productions. In this contribution, we elaborate on the notions of predictability and prestatability in the context of current AI systems. We maintain that these systems are, to some extent, unpredictable but not unprestatable. A consequence of our contention is the definition of the limits of what AI systems can and cannot do, and therefore the contexts for which these technologies are best suited.

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