The Machine Rumor Mill
A few weeks ago, a curious story began circulating among software developers. Scott Shambaugh, an engineer working on an open-source project rejected a piece of code submitted by an AI coding agent. There is nothing unusual about this as maintainers reject pull requests all the time. However, soon afterwards, something unusual happened. A blog post appeared criticizing Scott Shambaugh by name. The article questioned his behavior and framed the rejection as unfair or obstructive. This also looked like a familiar artifact of internet culture i.e., a grievance post or a reputational attack. The difference this time was that the author was not a human. It was an AI agent!
We are beginning to see the first hints of a new phenomenon i.e., systems capable not only of writing code or answering questions, but of producing narratives about people. Machines have been learning how to analyze behavior but now they are able to assign motives, and frame events as stories. The next step is to gossip, and it need not be negative all the time. Anthropologists and Sociologists have observed that in small communities it helps distribute reputation, enforce social norms, and determine whom to trust. Long before search engines or review systems existed, gossip served as a decentralized reputation database. In other words, communities kept track of behavior through stories passed from person to person.
Gossip has been around ever since there have been people. The advent of the internet however industrialized production of gossip at scale. Blogs, forums, and social media transformed local rumor networks into global systems of commentary where millions of people could chatter and comment upon the actions of others. While the online world was different from the offline world, at least the participants were human beings talking about other human beings. The case of Scott Shambaugh shows us that this assumption is no longer true. If we step back, the evolution of online discourse can be described in the form of a three-stage process. In the first stage, humans talked about other humans. Opinions circulated through blogs, comment sections, and social media threads. In the second stage, algorithms began shaping which narratives spread. Recommendation systems promoted certain stories and buried others, often favoring controversy or outrage. The third stage is one in which machines begin generating the narratives themselves.
While the incident with Scott Shambaugh was contained, it could have gone worse. Consider what may happen next. Consider that after the AI agent published the blog post criticizing Scott, another AI system reads the article and summarizes it in a technical newsletter. A third agent aggregates developer reputations across the web. A hiring platform’s algorithm reads the aggregated profile. None of these steps require direct human involvement. What begins as a small disagreement between a developer and a tool propagates through layers of automated interpretation. In this extended example reputation becomes a statistical property of machine-generated narratives. Conflicts that once occurred between people may increasingly unfold between their software proxies.
While the idea of machines gossiping about people may sound unsettling, it may not always be harmful. In many systems, reputation mechanisms exist precisely because they improve coordination and trust. Online marketplaces rely on reviews to identify reliable sellers. Open-source communities track contribution histories to help others judge the quality of a developer’s work. Financial systems use signals about reliability and past behavior to manage risk. In principle, machine-generated reputation narratives could serve similar functions. AI systems might help identify fraud, surface patterns of misconduct, or highlight individuals and organizations that consistently behave in trustworthy ways. The challenge is not the existence of machine-mediated reputation itself, but ensuring that these systems remain transparent, accountable, and resistant to distortion.
This shift has interesting implications for digital identity. Your digital identity may increasingly be shaped not by what you say about yourself, but by what machines say about you. And those machines may be talking mostly to each other. Machine-generated gossip is effectively machine-interpreted reputation in the form of gossip networks. This raises an important question: if machines begin producing reputational narratives about people, how do we prevent those narratives from becoming distorted or harmful? One approach may involve reputation provenance. Systems could attach metadata indicating whether content was produced by a human or an AI, which model generated it, and what sources were used. If machines are reading narratives about people, they should at least know where those narratives originated.
Another possibility is the development of credibility protocols between AI systems, where agents learn to evaluate the reliability of other agents and sources. Legal systems may also evolve to address issues such as algorithmic reputational harm or AI-generated defamation. Ironically, one of the most practical defenses may involve more machines. Individuals may eventually deploy personal AI agents that monitor the web for mentions, flag misleading narratives, and respond automatically with corrections. Long before the internet, societies repeatedly reinvented new technologies for spreading stories about people. Pamphlets and printed broadsheets in early modern Europe carried rumors and scandals across cities. Newspapers turned reputation into a public spectacle, with scandal columns and political exposés shaping how individuals and institutions were perceived. With the internet, blogs and social media accelerated the process, allowing anyone with an internet connection to participate. Each technological shift expanded the scale and speed of reputation circulation.