Negative Claims, Topical Drift, and the Governance of Generative Visibility
Editor’s Note: In part-two of this series, the focus shifts from attribution and structure to risk, examining how negative claims, topical drift, and generative reuse can quietly reshape visibility, credibility, and accountability. As AI systems increasingly determine what information is surfaced, summarized, and remembered, communicators must think beyond publication and toward governance, ensuring their messages remain accurate, contextual, and defensible long after they leave the newsroom.
I spent two decades building newsrooms and SaaS platforms for institutional communications, first as a journalist, then as founder of iPressroom, later as an advisor focused on how information moves through modern systems.
In the first part of this series, I argued that press releases now serve two audiences simultaneously: the humans who read them and the machines that summarize them. This column focuses on a specific, technical problem. Large language models tend to overgeneralize and collapse nuance. One effective countermeasure is explicit boundary setting combined and the use of disciplined metaphors.
In AI-mediated environments, boundaries function like negative keywords in Boolean search. By stating what something is not, and by anchoring analogies carefully, you reduce the risk of misclassification during summarization.
Consider a boundary statement in a financial services technology release.
"The platform does not replace human compliance officers and does not automate final determinations related to suspicious activity reporting or claims adjudication without a human in the loop, the company said."
This sentence defines scope and narrows interpretive range.
An anchored comparison may follow.
"In practical terms, the system functions more like a spell checker for compliance workflows than a decision maker: it can surface patterns, flag anomalies, and suggest areas that warrant review, but it does not decide what is true, compliant, or actionable."
Literal explanation. Analogy. Return to literal framing. The analogy is sandwiched between literal references.
"Final determinations, regulatory judgments, and reporting decisions remain the responsibility of trained human professionals."
This sequencing reduces topical drift. The metaphor clarifies function but is immediately grounded in literal constraints.
How LLMs Process Wire Releases
To understand why this matters, it helps to understand what happens to a press release after it crosses the wire.
Large language models do not read press releases the way we do. LLMs do not evaluate credibility, weigh sourcing, or flag unsupported claims. They process text as statistical patterns. A sentence that appears frequently across multiple sources, or that follows the structural conventions of authoritative writing, carries more weight in the model's representation of a topic, regardless of whether the underlying claim is accurate.
Wire distribution amplifies this effect. When a release is picked up by dozens of outlets, syndicated across news aggregators, and indexed by search engines, it creates the appearance of consensus. Each republication reinforces the same language. The model encounters the same phrasing in multiple contexts and treats that repetition as a signal of reliability.
This is how generative systems build what researchers call a "knowledge representation" of an entity. If you don;t know what an entity is in natural language processing, you need to. Read this.
LLMs do not store press releases. They ingest the release into a statistical composite, which is basically a weighted blend of everything it has encountered about a company, a product, or an event, which are all examples of possible entities. Sentences that are clear and structurally consistent have more influence on that composite than sentences that are vague, hedged, emotional, or imprecise.
Attribution matters in this process for a specific reason. When a claim is attached to a named source like a CEO, a regulatory filing, a published study, the model can associate the claim with the entity that made it. When a claim floats without attribution (as I covered in last week’s column), the model may assign it to the wrong entity, merge it with adjacent content, or discard it as noise. As we learned, in generative search results, the difference between "the company reported" and a freestanding assertion can determine whether the claim survives summarization at all.
Negative claims carry particular weight because they establish boundaries that resist compression. A model summarizing a fintech company's capabilities is less likely to hallucinate regulatory functions if the source material explicitly states what the platform does not do. Affirmative claims alone leave gaps that the model may fill with inferences drawn from similar companies, adjacent industries, or outdated training data. That’s how your message can get watered down, or lost entirely.
This is not a design flaw. It is a structural feature of how statistical language models generalize. They are built to predict likely continuations of text, and they do so by drawing on distributional patterns across their training corpus. A press release that relies on implication rather than explicit statement is, in effect, leaving the model to guess. And the model will guess based on whatever patterns dominate its training data for that sector. So truth is reinforced by volume and message amplification.
Press Releases as Generative Inputs
As AI systems increasingly mediate how information about financial institutions is summarized, recalled, and presented as authoritative, press releases function as structured inputs within generative ecosystems. They are a way to introduce your message into the training data LLMs ingest.
Wire services occupy a distinctive position in that system. Their content is widely distributed, frequently referenced, and is one of the data pipelines that large language models train on. A press release distributed through a major wire service is not just for people. It enters an information supply chain that includes search indexes, retrieval-augmented generation pipelines, and the training corpora of future model versions.
The practical consequence is that a press release now has two audiences: the humans who read it today and the systems that will summarize it in perpetuity. Writing for one without considering the other is increasingly costly.
As more people turn to AI systems to understand companies, products, and events, the importance of feeding accurate, attributable information into those systems grows.
This is not a theory.
Research by the Atlantic Council's Digital Forensic Research Lab has shown how coordinated networks such as Doppelgänger shaped public understanding by flooding information ecosystems with content engineered to appear credible, attributable, and widely referenced. The operation manipulated inputs that downstream systems treated as authoritative.
Press releases operate within the same informational architecture. The difference is intent. A well-constructed release uses the same structural levers (attribution, repetition, distributional reach) in the service of accuracy rather than manipulation. A careless one leaves those levers unattended and available for misinterpretation.
When written with clarity, attribution, and disciplined scope, press releases constrain how AI systems summarize institutional facts. When written carelessly, they can amplify distortion
In generative search environments, visibility is not simply about reach. It is about interpretive control.
Optimizing press releases for large language models is about engineering clarity in environments where AI systems increasingly shape what the public encounters first — and often accepts as true.
