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We are an input, now. Generative AI and the death of the journalist as human memory-maker

Published online by Cambridge University Press:  21 July 2026

Laura Guien
Affiliation:
Independent Journalist, France
Andrew Hoskins*
Affiliation:
University of Edinburgh , UK
*
Corresponding author: Andrew Hoskins; Email: andrew.hoskins@ed.ac.uk

Abstract

Journalists are mnemonically dispossessed in the age of large language models. To accept News Corp CEO Robert Thomson’s 2026 redefinition of journalism as an ‘input’ alongside semiconductors and datacentres is to deny the human provenance of what it means to make and to consume news. Licensing deals now being signed between major press groups and AI companies enact a transfer of mnemonic sovereignty from journalists as human ‘agents of memory’ (Zelizer, 2008) to the algorithm, to the machine. Generative AI produces an intention economy, restructuring how information flows through society, with each interaction with AI deepening its understanding not just of what you know, but of what you do not yet know to ask (Fang, 2025). This gives AI systems functional agency, anticipating the curiosities it wants us to formulate, generating a ‘past that never existed’. We are at a tipping point in the battle over journalism’s soul in AI’s seizure of human agency in the making of memory, and in how the production of news becomes infrastructure food. Journalists are caught up in the production of content used to feed AI models and systems, which will shape what people are trying to know, rather than their value being derived from the work of journalists as human agents of memory, of having created a past that they have a stake in. We set out how the battle for journalism’s soul is not yet lost in the emergence of a new memorial front of ‘archival journalism’, institutional glitches, and infrastructural exits.

Information

Type
Commentary
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press