AI systems are not information systems. Should we care?

In response to the rapid proliferation of artificial intelligence (AI), in particular generative AI, research on its application has mushroomed. However, problems arise when AI is taken to be just another IT artifact without fully appreciating what is new and different about it. We take two surprises about AI’s peculiar behavior, found in the public domain, to problematize commonly held understandings of computing. We illustrate how the fact that AI systems exhibit inherent inaccuracies, and AI developers are unable to fully understand their own creations, challenges common expectations. Based on this insight, we put forward the provocative thesis that AI systems are not, in fact, information systems. We derive an ideal type understanding of information systems, as a rhetorical device, and analyze in detail the differences with AI systems. For doing so we focus on AI systems like ChatGPT that derive their core functionality from generative AI models. We show that they differ in principle in how they encode information parametrically rather than explicitly, function probabilistically rather than deterministically, remain static after training rather than maintain currency, are created in a trial-and-error process rather than engineered top down, and present practically as “black boxes” rather than auditable systems. Our analysis contributes a conceptual foundation for understanding AI systems on their own terms, avoiding category errors. This allows positing productive new research questions about AI system use, application, and design that will help the IS discipline maintain relevance, as AI reshapes computing at its core.