AI-Generated Content Should Be Evaluated by Its Substance, Not Its Source

AI-generated content has already become a common component of digital communication, and its detection is often considered a central tool for ensuring accountability, based on the assumption that AI authorship is inherently problematic and should therefore be identified and filtered. This position paper argues that labeling a text as ``AI-generated'' offers little meaningful information about its utility or truthfulness, and that the research community should instead target the underlying properties that motivate concern: factual reliability, provenance, and stylistic quality. Our position is supported by a growing body of evidence showing that universal detectors are fundamentally fragile; we illustrate this through a small internal replication and literature review, noting that performance frequently collapses from near-perfect (0.999 AUROC) to near-random (0.56) under simple stylistic shifts or adversarial attacks. Moreover, even a perfectly robust detector would answer the wrong question, since AI-generated text can be accurate and well-sourced while human-written text can be misleading. We outline a research agenda that reframes AI-text analysis around claim-level verification rather than origin classification, allowing the community to adapt constructively to the growing prevalence of AI-assisted writing in education, publishing, and professional settings.