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TRUVACE RECORD VERSION
record: TRV-2026-1240
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-01T14:18:38.758737Z
status: published
lens: p_space
sector: entertainment
headline: A capitalist contest: the AI industry v. the creative industries
dek: This paper examines whether artificial intelligence industry developers of large language models should be permitted to use copyrighted works to train their models without permission and compensation to creative industries rightsholders. This is examined in the UK context by contrasting a dominant social imaginary that prioritises market driven-growth of generative artificial intelligence applications that require text and data mining, and an alternative imaginary emphasising equity and non-market values. Policy…
gain_title: (none)
problem_title: AI developers training large language models on copyrighted works without permission or compensation risks exploiting creative labor and privileging Big Tech profit over creator rights.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: AI developers training large language models on copyrighted works without permission or compensation risks exploiting creative labor and privileging Big Tech profit over creator rights.
problem_evidence: use copyrighted works to train their models without permission and compensation to creative industries rightsholders | privilege the interests of Big Tech in exploiting online data for profit
quick_read: A peer-reviewed paper published 18 September 2025 examines whether developers of large language models should be allowed to train on copyrighted works without permission and compensation to rightsholders, focusing on the UK. It frames the issue as a contest between an imaginary centered on market-driven growth of generative AI and one centered on equity.

The stakes are how copyright policy balances AI industry expansion against sustainability of creative industries. The paper contends debate currently favors Big Tech interests in exploiting online data, and points to licensing and other public-good oriented policies as alternatives, though it does not present new empirical data on licensing outcomes or economic effects.
limitation: Analysis is bounded to the UK policy context and to conceptual imaginaries rather than empirical measurement of economic impacts.
tag: Evidence-backed problem
key_points: The paper frames the conflict as between a market-driven imaginary favoring generative AI growth and an alternative imaginary emphasizing equity and non-market values. | Policy options discussed include licensing arrangements for use of copyrighted works in model training. | The analysis is situated in the UK policy context regarding text and data mining exceptions.
rundown: The paper contrasts two social imaginaries: one that prioritizes market-driven growth of generative AI requiring text and data mining, and another that emphasizes equity and non-market values for creative labour.

It argues current policy debates favor Big Tech exploitation of online data for profit and neglects approaches that would let both technology innovation and creative labour contribute to the public good, with licensing raised as a potential mechanism.
sources:
- peer_reviewed | Journal of the British Academy | https://doi.org/10.5871/jba/013.a38 | 2025-09-18
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