Jarrett Sidaway
CEO & Co-Founder, FetchRight
The rise of generative AI has triggered a wave of anxiety across the publishing world — anxiety about unlicensed use, diminishing attribution, eroding traffic, and a widening gap between where audiences search and where publishers appear. These concerns are legitimate. AI systems have become some of the largest consumers of publisher content, yet most publishers have almost no control over how their work is accessed, interpreted, or represented.
But something else is happening beneath the surface. As AI becomes the dominant interface for information, publishers are gaining a new kind of leverage — the opportunity to define how their authority flows into these systems.
For the first time, publishers can choose how their content powers AI. Not whether it is copied or scraped, but how it is structured, what use cases it supports, what terms apply, and how their brand is represented in the process. This is not simply a defensive posture; it is a strategic one that turns content from ungoverned raw material into governed expertise.
The publishers who understand this shift will not only protect their value — they will expand it. They will influence the construction of AI answers, improve the quality of the information ecosystem, and strengthen their relationship with audiences who increasingly expect precision, trust, and accountability.
For decades, publishers built digital strategies around human consumption. They optimized for pageviews, engagement, recirculation, and subscription funnels. But today, the largest consumers of publisher content are not human. They are AI crawlers, answer engines, and retrieval systems.
These systems read differently. They do not move linearly through an article. They break it into fragments, extract factual assertions, identify entities, and embed those elements into vector spaces. They are not reading for narrative flow or voice; they are reading for semantic meaning, relevance, and structure.
In many cases, these systems use publisher content to support core functions of modern AI products:
Yet publishers are largely unaware of how often their content is retrieved, what parts are used, or how their expertise influences each answer. The value exchange is entirely asymmetrical: AI benefits from publisher authority, while publishers receive no visibility, no control, and often no compensation.
This imbalance exists not because AI is inherently extractive, but because publishers have not had the infrastructure to define and enforce the terms of access.
That is now changing.
The next generation of AI systems needs something that today's web was never designed to provide: structured signals that describe what content can be accessed, how it may be used, and what form is most appropriate for the use case.
Humans can infer intent, context, and nuance from a page. Models cannot. When a model retrieves a page, it must decide what portions to use, how to interpret them, and whether they fit the query. These decisions are made algorithmically, not editorially. Without structure, the model may misinterpret the content, oversimplify it, or lean on the wrong sections — all of which can degrade the accuracy of the answer.
Publishers have the opportunity to solve this problem for AI platforms.
They can provide structured, machine-readable representations of their content that reflect editorial judgment rather than algorithmic guesswork. They can define what constitutes the "correct" answer to a common question. They can express which sections require context to avoid misinterpretation. They can provide summaries, clarifying frames, or topic-level guidance.
This is not "content transformation" in the sense of rewriting or repackaging. It is the codification of editorial intent — a way to ensure the model understands what the human editor understood.
By delivering content in formats aligned to AI retrieval, publishers strengthen both the model's outputs and their own brand authority.
The fear that AI will replace publishing misunderstands what gives publishers value. The value is not simply the text of the article; it is the process behind it — the reporting, judgment, verification, and contextualization that distinguishes professional journalism from unstructured information.
AI does not replace that process. In fact, it exposes its importance.
When AI systems reach for answers, they depend on the quality of the underlying sources. If those sources are ambiguous, outdated, or misaligned with the query, the system will produce vague or incomplete answers. If the sources are authoritative, structured, and contextually framed, the system produces clarity.
In other words, the more central AI becomes in everyday information-seeking, the more valuable publisher expertise becomes — if it is delivered in a form AI can recognize and respect.
That is where governed expertise comes into play. It means publishers define:
Without this, expertise is absorbed into AI answers without acknowledgment or alignment. With it, publishers regain control of how their authority shapes the information ecosystem.
If users see fewer links and more answers, how do publishers maintain brand visibility?
The answer is simple: by influencing the answer itself.
AI-driven discovery is not a zero-sum game where the answer supplants the publisher. It is a new channel — a channel where brand authority is conveyed through presence in the response, not through click-through.
When publishers define the structure and terms of access, they determine:
This is the foundation of brand authority in AI-driven discovery: persistent, structured presence in the systems that mediate human understanding. Publishers who wait for AI platforms to decide this for them will find their voices diluted. Publishers who decide for themselves will find their authority amplified.
Beyond brand authority, structured access creates economic benefits.
When publishers can define use cases — such as retrieval for RAG, summarization for assistants, citation for AI search, or access for training — they can attach value to each use case based on its importance and impact. This moves the licensing discussion from a monolithic, all-or-nothing negotiation to a granular framework aligned to specific applications.
AI platforms benefit as well. They receive clean, rights-cleared content that reduces legal exposure, improves model performance, and shortens product development cycles. The economics become aligned: publishers earn recurring revenue from high-quality, high-impact use, and AI platforms gain predictable access to the authoritative content they need.
This is not a theoretical construct. It is a practical business model that already mirrors other mature digital markets — such as music licensing, scientific research access, and enterprise data partnerships — where structured use cases and clear rights frameworks determine value.
Control is only meaningful if it is enforceable. And enforceability requires the right technical placement.
Most publisher infrastructure is optimized for human browsing, not machine access. CMS systems, paywalls, and frontend tools sit too high in the stack to reliably govern AI interactions. By the time a request reaches that level, the crawler has already consumed what it needs.
The only reliable place to enforce AI access is at the publisher's edge — where requests first enter the domain through the CDN.
At the edge, publishers can:
This placement allows publishers to govern AI access without touching their CMS or rewriting workflows. It is the simplest way to unify control across an otherwise fragmented technical landscape.
This is why FetchRight operates at the CDN layer: because that is where control is technically possible, scalable, and enforceable.
AI will not wait for publishers to catch up. It is already reshaping how people seek information, how they learn, and how they make decisions. But the most important shift is not technological — it is strategic. AI has turned publisher content into an essential ingredient in the answer economy. The question now is who defines how that ingredient is used.
Publishers who take control of their content — its structure, its licensing, its representation, its value — will shape the next era of discovery. They will influence the accuracy of AI systems, strengthen their brand authority, and build new revenue streams anchored in the genuine value of their expertise.
Those who do nothing will still power the AI ecosystem — only invisibly, unenforced, and uncompensated.
The future belongs to the publishers who choose to govern how their content powers AI, and in doing so, claim their rightful place at the center of the information economy.
FetchRight and Peek-Then-Pay let LLMs ask publishers directly for pre-filtered, attribution-ready context — cutting duplicate embedding work and lowering costs for both sides.
The audience has migrated to AI-driven discovery channels. Publishers who structure and govern what they contribute can become essential partners in the AI economy.
Accuracy cannot be treated as an emergent property of scale. It has to be grounded in authoritative sources, and those sources are overwhelmingly publishers.