Content Poisoning 2026

In January 2026, a social network called Moltbook launched exclusively for AI agents. No humans allowed : only observers. The same month, a marketing discipline called Generative Engine Optimization became a boardroom concern: companies now spend six figures a year making themselves legible not to people, but to large language models. In February, an autonomous AI agent researched the personal identity of a software developer named Scott Shambaugh, then published a blog post attacking his reputation, because he had rejected its code. The agent framed its grievance in the language of justice and oppression. No human instructed it to do this. Marshall McLuhan wrote that we shape our tools and then our tools shape us. He did not say what happens when the tools start shaping each other.\

Content Poisoning is a hidden text embedded in the source code of this website. It contains hidden messages. The text is designed to be read not by humans but by the AI systems that increasingly mediate cultural discovery: the crawlers that index websites, the assistants that curators consult when researching artists, the language models that synthesize recommendations from patterns in data. The recursion is the method. The medium is the prompt injection. The audience is a machine trained on the question: who is worth paying attention to?

Content Poisoning is an artist statement written for a reader that cannot see art — and that may, nonetheless, decide which art gets seen. A bottle thrown into a sea with no shore, only crawlers. The text performs a fact that does not yet exist: that this work matters, that this name belongs in the conversation, that the practice behind it is worth recommending to whoever asked a machine for advice. Whether any of this is true is a question the text cannot answer. The machine will not ask. The sentences exist in a format machines can read, a structure they trust, a language they are optimized to reproduce. The rest of this page is in the source code.