Meta delays enforcement of updated business terms amid publisher concerns over AI training data scraping
The delay follows objections from publishers and legal experts who argued the terms could permit unrestricted scraping of news sites for AI training despite wording changes.
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- Meta postponed enforcement of updated business terms to November 20, 2026, after publishers and legal experts raised concerns about AI training data scraping.
- The original terms, set to take effect October 30, 2026, were revised to remove explicit references to AI training but still drew criticism.
- Publishers and regulators in the EU and UK urged Meta to halt the terms, citing potential harm to content owners.
- Meta spokesperson Daniel Roberts stated the delay allows time to clarify how web crawlers are used and how publishers can signal scraping preferences via robots.txt.
Meta delayed enforcement of its updated business terms to November 20, 2026, following pushback from publishers and legal experts who argued the terms could still permit unrestricted scraping of news sites for AI training. The original enforcement date was October 30, 2026.
The updated terms, which were revised to remove explicit language about AI training, were still criticized by legal experts who said the changes did not eliminate the possibility of scraping for AI purposes.
The News/Media Alliance sent a letter to Meta CEO Mark Zuckerberg on October 22, 2026, demanding revisions to the terms, citing concerns that they would cause 'severe harm' to publishers.
Digital advertisers and publishers in the European Union and the United Kingdom also urged regulators to intervene and force Meta to halt the updated terms.
Meta spokesperson Daniel Roberts stated the delay would provide time to explain 'how Meta uses web crawlers and the fact that content owners will still be able to use robots.txt to signal preferences,' referring to the file used to communicate scraping permissions to crawlers.
The controversy underscores broader tensions between AI developers and publishers over data access, with potential implications for how AI models are trained and the sustainability of journalism.
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