The Future of Authenticity: The Human-Made Mark (samueleamato.xyz)

A listener’s experience with a subtly “off” lofi track spurred an argument for why creator provenance still matters: even when AI-generated art sounds objectively fine, knowing it was made by a thinking, intentional human changes how we connect with it. The piece argues that value comes not only from technical quality but from visible traces of human effort, risk, and mistake—those imperfections that signal intention, experimentation and emotional investment. As AI-generated content becomes ubiquitous, that distinction will matter more and audiences will increasingly ask “Was this made by a person?” For the AI/ML community this raises practical and ethical challenges: generative models often produce overly polished outputs that can imitate human “errors” but cannot originate the unpredictable human accidents that drive many artistic innovations. That gap creates demand for provenance systems—cryptographic signatures, robust watermarks, metadata standards, or certification bodies that can issue a “Human-Made” label—and for improved detection and dataset labeling tools. If adopted, such measures would reshape incentives for creators, influence dataset curation and model evaluation, and provoke policy and product-design choices around transparency, trust and the economics of creative work.