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	<title>Plausibility - Trotzendorff</title>
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	<description>Running over sticks and stones</description>
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		<title>We Are Entering the Age of Plausibility Overload</title>
		<link>https://trotzendorff.de/psychology/we-are-entering-the-age-of-plausibility-overload/</link>
					<comments>https://trotzendorff.de/psychology/we-are-entering-the-age-of-plausibility-overload/#respond</comments>
		
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		<pubDate>Thu, 28 May 2026 10:58:58 +0000</pubDate>
				<category><![CDATA[Psychology]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Plausibility]]></category>
		<category><![CDATA[Technology]]></category>
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					<description><![CDATA[We all knew AI would eventually generate fake citations. That was almost the boring part. The more interesting question is why so many of them passed through systems designed to evaluate knowledge in the first place. A recent study audited 111 million references across 2.5 million papers and preprints. Its estimate: nearly 147,000 hallucinated citations entered scientific literature in 2025 alone, many surviving peer review and later appearing in published journal articles. The numbers are striking. But that was not the part that stayed with me. The Weak Point What stayed with me was how little friction a plausible-looking citation can encounter once a system is already operating near capacity. Science has always depended partly on trust. Organizations do, too. Peer reviewers are overloaded, researchers publish under pressure, managers skim presentations between meetings. Very few people can independently verify the assumptions behind a market forecast, an AI roadmap or a strategy paper. So credibility often gets assessed indirectly: institutional reputation, internal alignment, familiar language, confidence. Large language models fit remarkably well into environments like these. &#8230;]]></description>
		
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