Is imitation theft or apprenticeship?

"In 18th-century Europe, aspiring painters learned to paint by copying other artists. They spent years in studios reproducing sketches and practicing technique. The point was to learn through repetition, and copying helped these aspiring artists understand what good looked like. Imitation was just part of the process. Only after years of apprenticeship did painters begin to branch off."

"Today, we're talking about voice and style - something we used to develop through practice and feedback. Now that we have tools that help us produce faster than ever, we've failed to teach what good work looks like along the way. When something feels like it was produced with AI, we discount its quality. We often blame the tool, or worse, we blame the person using it. To bring some clarity, we're bringing back two forgotten thinkers: Anna Laetitia Barbauld and Richard Bentley. Together, they offer a path forward."

"In the late 1700s, Anna Laetitia Barbauld was writing poems, essays, and teaching at a time when few women had access to formal intellectual training. She taught at Warrington Academy, a progressive school in England that served students excluded from elite institutions. Many of her pupils had never been taught how to write with structure or style. Her method was simple: start by copying existing essays and poems. Over time, that helped students develop a sense of their own style by first understanding others'."

Aspiring craftsmen historically learned voice and technique through prolonged imitation and apprenticeship, copying masterworks to internalize standards. Anna Laetitia Barbauld taught students by having them copy essays and poems so they could develop their own style from understanding others'. Modern AI tools accelerate production but often bypass practice and feedback that teach what good work looks like. Work that appears AI-produced is frequently discounted and the tool or user is blamed. Reviving methods that emphasize imitation, practice, and guided feedback offers a way to preserve voice, intent, and standards in AI-assisted work.

Read at Medium