As of early 2026, OpenAI has announced that ChatGPT has 700 million weekly active users.
People usually treat AI as a tool, as if it merely accelerates the process of accessing information. But the more frequently I use it, the more I realize it is reshaping the entire information ecosystem. It is redefining what counts as a question, an answer, knowledge, and authorship.
In traditional information structures, answers are tied to identifiable sources. A book has an author, a paper has an institution, a news article has a media outlet, and an opinion carries a stance. What large language models break is this binding between content and origin. The text they generate has no single author. It is an integration of data, an average of countless existing texts. Readers are increasingly exposed to information that appears rational, neutral, and reliable, yet cannot be traced to a clear source. This shifts how authority is perceived. Traditional authority came from identifiable authorship. Generative AI produces what might be called structural authority. People trust it not because they know who is speaking, but because the system appears stable, vast, and technically sophisticated. At the same time, others react in the opposite direction. The lack of traceability leads them to distrust AI entirely.
Because generative AI is fundamentally statistical aggregation, it tends to produce the most common and socially accepted forms of expression. What it reinforces is average knowledge rather than frontier knowledge. Innovation, deviation, extreme positions, and unverified ideas are structurally disadvantaged in such systems. This risks obscuring the fact that knowledge production has always depended on conflict, disagreement, and experimentation.
AI also alters the human relationship with memory. When external systems can instantly generate explanations, the need to memorize information declines. This resembles early science fiction fantasies of memory implants. People only need to know how to retrieve information, not store every answer. The search engine era already moved in this direction, but generative AI goes further because it not only retrieves but also organizes and narrates. This may produce a new cognitive division of labor in which humans focus on navigation while machines fill in content.
Large language models do not just change answers. They reshape questions themselves. Because they are always responsive and expandable, people learn to phrase questions in ways machines can process. We increasingly break problems into parts, standardize language, and reduce ambiguity. This trains a new thinking pattern. Questions become instructions. Inquiry shifts from open exploration to output optimization. Over time, we may start judging a question by whether AI can answer it. Problems that resist systematization risk being sidelined.
At the same time, AI transforms the role of writing. In many contexts, writing now involves selecting, editing, and prompting rather than constructing text from scratch. Writing once meant expressing an individual mind. It increasingly resembles editing a system-generated draft. The labor of writing is redistributed into collaboration between humans and the model.
Within AI writing environments, the author is no longer the sole producer, and the reader is no longer a passive receiver. Each prompt becomes a joint act of creation. Ownership of the text becomes ambiguous. It grows harder to distinguish what is written by a person and what is written by a model, and even harder to assign responsibility and creativity. Traditional concepts in literature, art, and academia are challenged, and intellectual property itself may need to be reconsidered.
Large language models also introduce a new form of linguistic power. Whoever controls language models participates in shaping the boundaries of discourse. AI appears neutral, yet training data, filtering rules, and output strategies carry cultural and political choices. Some expressions are encouraged while others are constrained. This is not overt censorship but structural language design. The power is less visible than traditional media authority, yet potentially broader in reach.
Taken together, large language models reshape every layer of information practice. They affect who speaks, how speech is structured, why it is produced, and how we understand speech itself. In this sense, we are entering a fundamentally new information ecology.
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