"In January 2026, Wikipedia will turn 25 years old, a reminder that one of the internet's most‑visited sites began as an experiment many experts assumed would fail. It grew from a simple idea to more than 65 million articles in over 300 languages not by ignoring criticism, but by listening: adding quality controls, building policies, and keeping humans at the center of oversight and content creation."
"By 2026, a substantial share of search will be handled by AI assistants and large language models that summarize and curate answers directly, rather than pointing users to a page of links. Research already shows that AI overviews and answer boxes capture user attention and often satisfy information needs with zero clicks, depressing traffic for publishers across news, reference, and niche information sites."
"This is not just an SEO story; it is a power shift. When discovery happens inside opaque "answer engines," the visible marketplace of links becomes a single, pre‑digested verdict where ranking, sourcing, and even dissenting views are hidden. For media organizations, that means less direct audience contact, weaker brand recognition, and fewer chances to explain context, nuance, and methodology in their own voice. In this environment, the real leverage lies in shaping the knowledge that AI systems rely on behind the scenes."
Wikipedia reached global scale by adding quality controls, building governing policies, and keeping humans central to oversight and content creation, now exceeding 65 million articles in over 300 languages. AI assistants and large language models will handle a substantial share of search by summarizing and curating answers directly, often satisfying queries with zero clicks and reducing traffic to publishers across news, reference, and niche sites. Opaque answer engines can hide ranking, sourcing, and dissenting views, weakening direct audience contact and brand recognition. The most effective response is to shape and supply well-governed, structured, high-reliability datasets to reduce hallucinations, curb misinformation, and improve AI output quality.
Read at Nieman Lab
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