AI watermarks are designed to make generated content detectable, but the same hidden mechanisms could potentially carry much more than provenance. This article explores how watermarking could become a covert channel for identifiers, hidden state, or model-to-model communication; how subtle bugs or backdoors could be introduced into generated code; whether related AI models might share the same blind spots during review; and what current research says about using open-weight models to remove published watermark schemes.