Identifying AI Content Is a Fool's Errand (vale.rocks)

The piece argues a hard reality: there is no reliable way to determine whether a piece of text, image or video was generated or altered by AI, and there likely never will be. Modern generative models are trained on the full corpus of human output and are explicitly optimized to erase detectable patterns, while humans and models feed each other stylistically, blurring any “AI vibe.” Commercial detectors (e.g., GPTZero) rely on statistical cues and longer inputs but admit they aren’t 100% accurate; as models proliferate and can be fine‑tuned or jailbreaked, pattern‑based detection becomes an endless, losing arms race. Technically, common defenses—watermarks, metadata (C2PA), stylometric markers (SynthID), or probability‑based text watermarks—are fragile: metadata can be stripped or screenshots taken, visual marks cropped or edited, stylometry circumvented by paraphrasing/back‑translation, and probability nudges defeated by transformations. Images and video once had visual “tells” (odd fingers, frame continuity issues) but those artifacts are shrinking as models improve. The implication for AI/ML is profound: provenance and trust must shift from binary detection to policies, provenance infrastructures and human-centered verification; automated detectors should be treated as weak heuristics, not definitive evidence, especially in high‑stakes contexts like education, media authenticity and legal disputes.