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AI is likely to bypass some established routes to knowledge, but it is unlikely to eliminate mediation. When a system answers directly instead of sending a reader to a publisher, encyclopedia, expert, or other source, it disintermediates that route. Yet the AI system becomes a new intermediary: it selects, combines, and frames information. The central change is therefore who mediates knowledge, how visible that mediation is, and who benefits from it.
What does disintermediation of knowledge mean?
Disintermediation means bypassing an intermediary that once connected people to information or expertise. A person might previously have searched a publisher’s site, consulted an encyclopedia, or followed a professional’s guidance. A generative AI system can instead provide a synthesized answer in the same interface where the question is asked.
That can shorten the path to a useful explanation, but it does not make the answer unmediated. The system’s training data, retrieval process, source selection, and wording shape what the user sees. Some of those choices may be difficult for the user to inspect. AI can disintermediate a familiar gateway while becoming a new gateway in its place.
What changes for a reader who gets an AI answer?
Convenience and synthesis
A generated response can gather material into one answer rather than requiring a reader to open and compare several sources. This may make information easier to reach, particularly when a reader needs an initial explanation or a synthesis across topics.
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Source visibility and verification
The convenience comes with a trade-off if the answer does not clearly identify its sources or show how claims relate to them. A source list alone may not reveal which source supports a particular statement, what context was omitted, or whether the source is authoritative for the question. Without a visible evidentiary trail, readers have less ability to check accuracy and context for themselves.
Context, quality, and authority
Summarization can compress nuance; a generated response may also contain errors or reflect bias in the material or choices behind it. These are concerns identified in interview research about LLMs and Wikipedia, not a demonstrated rate of failure for every AI system. In health communication, a 2025 conceptual article describes generative AI as a mediator that aggregates and synthesizes health content, potentially changing how people relate to professionals, peer guides, and other sources of authority. That framework does not establish clinical safety or patient outcomes.
Why publishers worry about more than page views
The Tow Center for Digital Journalism’s 2025 report Journalism Zero: How Platforms and Publishers are Navigating AI describes publisher concerns about third-party summaries and generative search. When a summary separates reporting from its creators, a publisher may lose opportunities to bring readers to its own site, build a direct audience relationship, and make its brand visible. The report also describes uncertainty about traffic, compensation, and the value exchanged between publishers and AI platforms.
Licensing is one way some relationships between AI companies and news organizations are being formalized, but the report does not establish that licensing resolves the broader value-exchange problem. It also discusses cultivating direct audience relationships as a publisher strategy. That may help publishers retain a connection with readers, but it cannot guarantee protection from changes in how platforms distribute information.
Could AI weaken Wikipedia’s knowledge ecosystem?
Wikipedia illustrates why knowledge sustainability involves more than whether readers can get an answer. Its volunteer contributors create and maintain material that readers and other services rely on. A 2025 AI & SOCIETY article, “An endangered species: how LLMs threaten Wikipedia’s sustainability,” reports interviewees’ concerns that LLMs can place an intermediary between users and original sources, with potential consequences for source visibility, information quality, and bias.
A separate 2025 article in the Journal of the Association for Information Science and Technology, “Death by AI: Will large language models diminish Wikipedia?”, proposes a possible feedback cycle: if more people receive answers without visiting Wikipedia, contributor participation might also decline; less participation could make content less current and, in turn, less valuable. This is a plausible scenario discussed by the article, not proof that the cycle is already happening or that it is inevitable.
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Are intermediaries always a problem?
No. Intermediaries can provide infrastructure, organization, editorial processes, and ways to distribute knowledge. A 2024 article in Business & Information Systems Engineering, “Open Science,” describes publishers and internet platforms in this role. It also notes that intermediaries may have vested interests that affect openness and access.
This balance matters: removing one gateway does not automatically make knowledge more open or reliable. A new intermediary may make information easier to reach while gaining influence over which sources are surfaced, how they are presented, and who can benefit economically. The relevant questions are both what a system enables and whose interests shape its operation.
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How to judge whether a new route improves access to knowledge
Rather than asking only whether AI replaces an existing intermediary, consider what happens to the functions that intermediary performed:
- Access: Does the system make useful information easier to find and synthesize?
- Provenance: Can readers identify and inspect the original sources behind important claims?
- Quality and context: Does the answer retain relevant nuance, or obscure uncertainty and competing evidence?
- Sustainability: Does the arrangement support the people, publishers, and communities that create and maintain knowledge?
- Authority and accountability: Who selects and frames the information, and who can be held responsible for that presentation?
These questions apply differently across fields. The risks for a news publisher’s audience relationship are not identical to the possible contributor effects on Wikipedia or the authority questions raised by health information. The available studies examine particular sectors and mechanisms; they do not settle whether AI will improve or damage knowledge production overall.
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