It has been 25 years since Wikipedia appeared on the internet. What began as an experiment became the world’s largest online information source. Now, it is among the most important sources of training data for AI.
This month, the Wikimedia Foundation, the non-profit that operates Wikipedia, announced commercial partnerships with Microsoft, Meta, Perplexity, Mistral, and Amazon. The deals formalise what the AI industry has relied on quietly for years.
For two decades, Wikipedia’s content has been free to access. That made it a default source of structured text for search engines and later for large language models, even as concerns arose over Wikipedia’s factual accuracy.
According to a Pew Research report, Wikipedia hosts more than 66 million articles across 342 languages, including all 22 official languages of India. The English edition alone has over 7 million articles and more than 5 billion words.
In total, Wikipedia’s text, images, videos and media files occupy roughly 775 terabytes of storage. That scale makes it uniquely attractive for training AI systems that need vast amounts of high-quality language data.
The platform also offers something rare: articles written in a relatively neutral tone, heavily cited, constantly revised and maintained by around 2,50,000 volunteer editors worldwide.
AI Researchers’ Goldmine
For AI researchers, that combination is gold. Even though companies have used Reddit APIs and other sources, the quality of Wikipedia’s text remains hard to match.
This is why Wikipedia keeps appearing inside AI pipelines. Models like GPT and Llama include Wikipedia as a core component of their training data. Even when models are not trained on raw Wikipedia dumps, curated datasets and retrieval systems pull answers from Wikipedia in real-time.
The importance of this data has only increased as AI moves from research labs into consumer products. Chatbots, AI search summaries, and digital assistants frequently surface information from Wikipedia. In 2025 alone, non-human agents generated more than 88 billion page views on Wikipedia, according to the report.
“It has always been used in NLU (natural language understanding) tasks, including LLMs. It’s a go-to dataset and easiest to scrape without legal problems,” Chintan Parikh, AI researcher at Indic language solutions company Reverie Language Technologies, tells AIM.
“Since every search engine or ‘AI search or ‘deep research tools’ reads Wikipedia pages for user queries, over the past years, it has increased load on Wikipedia servers,” Parikh adds, explaining that Microsoft, Meta, and others are just “donating” money to Wikipedia so they can sustain this increased load put by the end users. “Nothing changes for AI startups.”
At the same time, human page views have declined. In October 2025, Wikimedia reported that human traffic was down about 8% compared with the same period a year earlier, a drop linked to the growing use of AI-generated summaries in search and chat interfaces.
This has created a financial problem. Wikipedia does not run advertisements. It relies largely on small donations from readers to cover costs like servers, bandwidth, and moderation tools. Heavy automated scraping drives up infrastructure expenses without contributing revenue.
For years, Wikimedia absorbed those costs in the spirit of openness. With AI usage exploding, that approach has become unsustainable.
The new partnerships are meant to close that gap. Through its Wikimedia Enterprise product, the foundation offers paying companies structured, reliable access to Wikipedia content tailored for large-scale use. Instead of scraping the public site, AI companies receive data feeds optimised for training and product integration.
Trust Factor
AI systems amplify whatever data they learn from. Errors, bias and gaps in training data tend to show up in outputs at scale. Wikipedia’s editorial process does not eliminate those issues, but it reduces them compared to much of the open web.
Osama Manzar, founder and director of Delhi-based non-profit Digital Empowerment Foundation, sees Wikipedia’s role in a wider historical frame. He credits it with bringing knowledge online at scale and making information far more accessible. At the same time, he argues that it still reflects an elite layer of information seekers.
In his view, Wikipedia mostly digitised knowledge that already existed in books or print. It did not create new knowledge so much as make old knowledge usable online.
Manzar also points to a deeper limitation in India. India’s knowledge production and consumption, he argues, is still heavily oral. Wikipedia operates largely through written scripts. It covers all 22 official Indian languages, but that still excludes hundreds of oral languages spoken across the country. Languages like Gondi, Mundari, or Orang remain largely outside the platform’s core, and the platform remains inaccessible to rural and marginal communities.
At the same time, Manzar believes Wikipedia has achieved something rare on today’s web: trust.
He recalls that two decades ago, encyclopedias like Britannica defined authoritative knowledge. Today, Wikipedia is often the first result people click when they search for a person or topic. In a web flooded with misinformation, fake news and unchecked social media content, Wikipedia has become one of the few widely trusted destinations.
He attributes that to its insistence on references, the practice of publishing multiple viewpoints, and a peer-reviewed editing culture.
Looking ahead, Manzar sees both danger and opportunity in AI. He warns that AI will flood the internet with automatically generated content at the cost of reliability. That makes Wikipedia’s editorial discipline more valuable, not less.
He also sees a chance for Wikipedia to rethink its format. Voice-enabled content creation could help bring in oral languages and oral histories. But he cautions that oral formats make misinformation easier to spread and harder to verify.
He is also sceptical of corporate funding, warning that large companies could shape priorities based on commercial incentives rather than public value.
Right now, Wikipedia stands at a familiar crossroads. In the age of search, it became the backbone of online knowledge. In the age of AI, its data is being baked into machine intelligence itself. It’s now up to the online encyclopedia to decide how it wants these new AI partnerships to strengthen its mission of keeping human knowledge alive and accessible.
The post For 25 Years, Wikipedia Trained AI. Now it Wants Payback appeared first on Analytics India Magazine.
