Wikipedia at 25: Why It’s Still the Internet’s Knowledge Backbone

Wikipedia, the world's largest encyclopedia, is celebrating its 25th anniversary as a foundational source of reliable information on the internet. The platform now hosts over 66 million articles across multiple languages and serves as the most cited domain for AI systems like ChatGPT, with nearly 50% of its citations originating from Wikipedia. As social media faces challenges with misinformation and polarization, Wikipedia has become what some experts call "the closest thing the internet has to a public utility," quietly powering AI search results and Google's AI Overviews.

If the internet were a city, then Wikipedia would be the electricity. It’s the thing that is silently running in the background, effortlessly making things just appear to work. It keeps the lights on. Flip a switch, and there it is. So steadfast and convenient that it’s easy to forget it’s even there. Or that it’s free. Or that we need it. Wikipedia is the world’s largest encyclopedia, and this year, it is celebrating its 25th birthday. After more than two decades of accumulating human knowledge, “it’s the closest thing the internet has to a public utility,” Lulu Garcia-Navarro https://en.wikipedia.org/wiki/Lulu Garcia-Navarro , journalist for The New York Times , wrote. While social media algorithms are churning out AI slop and fueling extreme polarization, Wikipedia appears to be standing as one of the few bastions of reliable information on the web. My high-school teachers would be shocked to hear this. In this article, I’m going to explore how Wikipedia got to where it is today, why AI systems use it as a trusted and foundational source of real-world knowledge, the downsides of Wikipedia’s editorial model, and what it all means for your brand. I’ll wrap things up with a practical framework for what brands can and should do with this information. “The Closest Thing The Internet Has To A Public Utility” No doubt, the hallmark of any functioning utility is that people use it without even thinking about it. And most people use Wikipedia every day, or really, several times a day. You may read it so often that you don’t even realize that what you’re reading is from Wikipedia. The reason is that when you search for something on Google, like “When is F1 coming to Las Vegas?”, you get an answer that appears to come from Google the AI Overviews https://ipullrank.com/everything-we-know-about-ai-overviews . However, that information is actually usually coming from a Wikipedia page and not from Google itself. And for LLMs, Wikipedia is also often the most cited domain. In fact, a study by Profound https://www.tryprofound.com/blog/ai-platform-citation-patterns found that Wikipedia was the leading source for ChatGPT, with almost 50% of citations. Wikipedia is quietly powering AI search, and honestly, as a lover of the internet aren’t we all? I personally feel they aren’t getting enough credit for it. For brands and marketers, however, the connection between Wikipedia and AI search systems is more than a curiosity. It holds the key to visibility in the AI era. 25 Years of Building the World’s Knowledge On January 15, 2001, co-founder of Wikipedia Jimmy Wales https://en.wikipedia.org/wiki/Jimmy Wales typed “Hello, world” into Wikipedia’s first entry. Within just a few months, Wikipedia had hundreds of active volunteers, writing dozens of articles published online every day. “Many years ago, when the world was still learning about this strange new thing called Wikipedia, most people were sure it was a terrible idea,” Wales said in his book The Seven Rules of Trust . “The public would never trust Wikipedia. And without trust, Wikipedia would be nothing.” Eight months after the launch, however, Wikipedia already had around 8,000 articles. At the time, the Encyclopædia Britannica covered approximately 75,000 articles, held within a shelf-breaking 32-volume set. Wales realized then that if Wikipedia kept growing at the same rate, it would surpass Britannica in six years. Turns out Wikipedia had more articles than Britannica just two years after its launch. In 2007, six years after the launch, Wikipedia had two million articles; more than twenty-six times bigger than Britannica. And that was twenty years ago. Today, there are over 66 million articles across Wikipedia. Around 7 million of those articles are in English. With a word count of over 5 billion words, it would take one English-speaking person about 38 years to read all of them. And while English is the largest language on the site, there are articles in more than 340 languages. In the past decade alone, Wikipedia’s articles have been viewed a total of 1.9 trillion times, or about 508 million views per day on average. Along with the other Wikimedia sister projects https://en.wikipedia.org/wiki/Wikipedia:Wikimedia sister projects Wikidata, Wikimedia Commons, Wikiquote, etc. , the total monthly page views are 26 billion. Over a year, it is 300 billion. Remember that the Earth only has 8.3 billion people on it. And, unlike most other high-traffic websites, Wikipedia doesn’t use ads. It’s also completely free to access, primarily relying on donations from the public and contributions from a community of volunteer editors. The website itself is hosted and managed by the nonprofit Wikimedia Foundation https://wikimediafoundation.org/ . “The hallmark of an excellent utility – electricity, drinking water, plumbing, and sewage – is that people use it all the time but don’t think about it,” Wales said in his book. So how did a volunteer-run nonprofit become the backbone of a projected 7 trillion-dollar https://www.reuters.com/commentary/breakingviews/ai-dreams-crash-into-stark-7-trln-reality-2026-04-07/ AI industry? Why AI Runs on Wikipedia If you happen to be a writer like I am, then you know AI is both helpful and useless for fact-checking. LLMs are probabilistic engines, designed to string together plausible and statistically likely words into cohesive sentences. In a way, LLMs are simulating language, sometimes very well, but sometimes they just make stuff up. We call these factual inaccuracies “hallucinations.” That’s where Wikipedia comes in. Wikipedia is a repository of generally agreed-upon facts, verified by hundreds of thousands of editors, says Bryan Marvin https://www.linkedin.com/in/bryan-marvin/ , Relevance Engineer at iPullRank. And edits on Wikipedia can show up almost instantly in AI search results. “When it comes to the retrieval side and showing up in actual AI search like AI Overviews and AI mode…they’re trying to find the freshest content possible to serve as grounding information for their answers as well as using it for fact-checking,” Marvin said. “Grounding” is what the industry calls the process of connecting AI models to accurate, real-world data sources to reduce hallucinations. In October 2025, Wikipedia’s human viewership was down by 8% https://diff.wikimedia.org/2025/10/17/new-user-trends-on-wikipedia/ — a decline the Wikimedia Foundation attributes to the rise in generative AI and AI search. On the other hand, views from bots, web crawlers, and other nonhuman agents are up, reaching more than 88 billion in 2025 alone. In fact, almost all LLMs are trained on Wikipedia datasets, making this vast source of human-created knowledge indispensable to the outputs of AI search systems. Part of the reason for this is that generative AI cannot exist in isolation from human-created knowledge. If AI systems were to train on other AI-generated content, the quality would degrade over time, leading eventually to a phenomenon known as “model collapse.” Wikipedia provides human-created knowledge from the real world. And its volunteer editors do things AI systems cannot: debate facts, establish consensus, scour through archives, photograph real places, and write in over 300 languages as native speakers. Wikipedia has also banned the use of AI to generate its articles. So, AI companies want access to Wikipedia’s data sets because they are neutral, comprehensive, and reliable. But what does Wikipedia want from AI companies? The answer is simple: attribution and financial support. If AI cites https://ipullrank.com/ai-search-measurement Wikipedia, then more users are likely to visit the site, which will help to sustain the volunteer and donor community. Financial support, on the other hand, must come from the AI companies themselves. And that means obtaining content through Wikimedia Enterprise https://enterprise.wikimedia.com/ , the commercial arm of Wikimedia, instead of using web crawlers to scrape it for free. AI Companies Now Pay for Structured, High-Speed Access To Wikipedia’s Database When Google places AIO summaries above Wikipedia results, it makes the site less visible, which some experts have called an “existential threat” to the platform. Combined with tech behemoths and AI startups alike scraping the site for free, Wikimedia needed to find a way to work with these companies instead of against them. Their commercial APIs do just that. According to Wikimedia, their commercial API users Amazon, Meta, Microsoft, Perplexity, Mistral, and the list keeps growing have several basic needs: Freshness : Commercial users want content “hot off the press” so they have the most current worldview. System Reliability : Commercial users want reliable uptime on APIs. Content Integrity : Commercial users face the same problems that Wikipedia does in terms of vandalism and misinformation. They want to understand the revision history of an article to make a judgment call on what to publish, and that requires additional layers of metadata and other contextual data “signals.” Machine Readability : Commercial users want clean and consistent schema for working with data. With all these needs in mind, Wikimedia Enterprise now offers three commercial APIs for AI companies to pay for structured, instant access to its datasets: Realtime API: Streams live edits as they happen On-demand API: Pulls single articles at any time Snapshot API: Retrieves entire Wikimedia projects as a database dump file Together, these API’s create a massive funnel that propels the entirety of human knowledge into the hands of companies that churn out mostly accurate summaries. A recent analysis https://www.nytimes.com/2026/04/07/technology/google-ai-overviews-accuracy.html?unlocked article code=1.ZFA.2 F5.ZJ0ZGqdBqvTM&smid=url-share by The Times found AIOs are accurate 9 out of 10 times. Wikipedia’s Equity Problem Since its founding in 2001, Wikipedia has always functioned as a nonprofit with a decentralized network of editing by a few million, mostly anonymous, unpaid contributors. Of the millions of recorded editors, some tens of thousands account for the majority of the content. Part of the appeal of a diverse base of editors is that when editors with opposing viewpoints collaborate on an article, their debates tend to be longer and more substantive, and that resistance makes the final articles more diverse and of higher quality than those whose authors share the same viewpoints. This was confirmed by a study https://www.nature.com/articles/s41562-019-0541-6 published in Nature . But there is a fundamental flaw with this model, or rather, a couple of flaws. First, real-world entities https://ipullrank.com/ai-search-entity-recognition are not represented equitably on the site: - On English Wikipedia, only 19% of biographies are about women. - Biographies about women who do meet Wikipedia’s criteria are more often flagged as non-notable and nominated for deletion compared to men’s biographies. - Despite Africa having twice Europe’s population, the continent as a whole has only 15% of the number of articles. - Apparently, there are more articles on Wikipedia about Antarctica than about most countries in Africa, Latin America, or Asia. Second, contributors are not contributing equally: - Wikimedia contributors are 87% male. - Almost 50% live in Europe and 20% in North America. Compared to 10% and 5% of the global population, respectively. - Fewer than 1% of Wikipedia’s editor base in the U.S. identify as Black or African American. - Only 1.5% of Wikipedia editors are based in Africa, although people in Africa comprise 17% of the world’s population. The lack of diversity in articles and editors has a downstream impact on every AI system trained on the site’s content. As the Wikimedia Foundation has said, “AI only knows what humans teach it.” The homogenous nature of the site’s contributors leads to content that lacks representation for people of color, women, non-native English speakers, and many other communities who have historically and to this day lack adequate representation. Such content gaps also play out for founders, industries, and topics that aren’t mainstream, and without coverage, these entities have significantly less visibility in AI systems. What This Means For Your Brand Wikipedia is essentially a mechanism for AI authority. If your brand’s “topic pillars” — the topics most closely associated with your brand and industry — aren’t represented in Wikipedia, AI systems will miss important context about your brand. “Let’s drop SEO and just talk about the brand value of having a branded Wikipedia page that you can link to. I mean, honestly, the amount of authority that’s lent to a brand by being present in Wikipedia says a lot.” Marvin said. “Then you talk about the fact that you’re going to be included in every training set that exists moving forward for every AI. So there are marketing advantages that are very real.” If you’re not in the training data, you’re not competing for 60%-70% of the synthetic queries that are being generated in the query fan-out https://ipullrank.com/how-ai-mode-works that include branded terms, Marvin says. While it may take months to over a year for new Wikipedia articles to appear in an AI model’s training set, live retrieval for AIOs and AI Mode https://ipullrank.com/how-ai-mode-works happens almost immediately, as AI systems look for the freshest content possible for grounding. So how do brands get on Wikipedia? First, you need to start building authority. Writing an article or contributing to a Wikipedia page isn’t the hard part; gaining notoriety as a real-world entity is. What Brands Should Do Now Want to take action? Here’s where to start: Step 1: Audit your Wikipedia presence Start by seeing whether your brand has a page. Then check if your founder or founders do. Read what Wikipedia says about your industry. Use Wikidata https://en.wikipedia.org/wiki/Wikidata to check your entity relationships. Wikidata is a structured knowledge base that acts as a central storage for data used by Wikimedia projects. Look into how your brand’s topic categories are represented. By using the “Links to related articles” and “See also” sections, explore other Wikipedia articles that link to the one you’re reading. This is a quick way to find related angles and context you might have missed. Step 2: Earn real-world notability Wikipedia requires independent third-party coverage, which can be difficult to get. Information must be verifiable. If no reliable, independent sources are available to verify information, then the topic likely isn’t eligible for its own article. Independent sources must have editorial independence and no conflicts of interest https://en.wikipedia.org/wiki/Wikipedia:Conflict of interest no potential for personal, financial, or political gain . Note that your ads, your press releases, your promotional activity, your publicity, your autobiography, or your website are not considered independent sources. To earn real-world notability, you’ll need to focus on gaining independent media coverage to establish noteworthy milestones for you or your brand. Step 3: Start with Wikidata Wikidata has a lower barrier to entry than Wikipedia. Use it to build the entity relationships that can help establish authority, building a foundation for an eventual Wikipedia article. To get a Wikidata page, go to Wikidata.org http://wikidata.org , make sure the topic doesn’t already exist, and create a new item. Remember that you’ll still need noteworthy, verifiable coverage from third-party sources. Step 4: Monitor and protect your page If your brand, industry, or topic categories already have Wikipedia pages, monitor them for inaccurate edits. Check the page’s edit history and talk pages regularly. The talk pages are discussion forums associated with every Wikipedia article where editors can discuss improvements, resolve disputes, and build consensus. Understand that even flagged or disputed content has the potential to be pulled into AI-generated summaries, so be sure to figure out the root of all inaccuracies right away. Note that Wikimedia does provide vandalism signals and edit flags to enterprise licensees the AI companies using their data . But whether AI companies act on them before surfacing content is entirely up to those companies. Step 6: Look beyond your own page Look at what topics in your industry are underdeveloped on Wikipedia. Contributing to other Wikipedia articles not just your own page can help build broader topical authority by establishing the context for your industry and where your brand fits within it. Look to the footnotes and references section at the bottom of the Wikipedia articles. They typically point to academic studies, news coverage, and institutional reports that all work together to make an article more credible. Use them as a guide to consider what you can contribute to other articles beyond your own page to improve the larger knowledge ecosystem. Step 7: Invest in an AI Search strategy Remember that your brand’s visibility and influence in AI search are shaped by multiple factors across the web. Wikipedia is one part, albeit a crucial one, of a wider network of invisible forces shaping how your brand is represented in search results. That’s why I’d recommend working with a team to conduct an AI search audit and omnimedia content audit as part of your broader AI Search Strategy https://ipullrank.com/ai-search-strategy-program . iPullRank offers an AI Search Audit https://ipullrank.com/ai-search-audit to help make sure your website’s technical readiness and your brand’s authority signals line up to even qualify you to get cited in AI search. And our Omnimedia Content Audit https://ipullrank.com/omnimedia-content-audits takes a holistic view of all your brand’s owned, earned and shared media content across platforms to help identify weak areas, while the Omnimedia Content Plan is the roadmap to fill those coverage gaps. “The Last Best Place on the Internet” Wikipedia isn’t perfect. There’s a lack of editor diversity. There are significant content gaps. Editors have personal biases. All of this is true. But in a world flooded with AI-generated content, it might actually be the last best place on the internet https://www.wired.com/story/wikipedia-online-encyclopedia-best-place-internet/ — its human-edited foundation both a flaw and a strength. Despite what all of my high-school teachers have said, 25 years after its founding, Wikipedia is now widely considered one of the most trustworthy sources of information on the internet. It’s perceived to be so trustworthy that Google, Microsoft, Meta, and Amazon are all paying for access to it as a foundational data source for their AI systems. Having an entry on Wikipedia is one of the most important ways brands can build authority within the knowledge ecosystem that AI search uses to generate responses. Real humans, publishing articles on a trusted site, reporting on your brand, and supported by verifiable sources. If you want your clients and customers to trust your brand, look to Wikipedia.