AI Copywriting

AI copywriting is the use of a language model to draft short-form persuasive text — advertisements, landing page headlines, product descriptions, subject lines, sales emails — as distinct from long-form article writing. The distinction matters because the two jobs have different economics. Short-form copy is produced in volume, is heavily templated, and is judged by whether it converts rather than whether it reads well, which makes it the format where generation genuinely replaces drafting rather than merely accelerating it. A single product launch may need forty subject-line variants, and a human writing forty variants gets worse after the tenth while a model does not get tired. How the tools work: a copywriting platform is a prompt template library plus a generation loop. Each template encodes a proven structure — problem/agitate/solve, features-to-benefits, before-after-bridge — with slots for the product, audience and tone. The user fills the slots, the platform expands them into a full prompt behind the scenes, and returns several variants at a high temperature so the outputs differ from each other. The better platforms then let you mark a variant as good and regenerate around it, which is few-shot prompting exposed as a button. Copy.ai and Jasper are the two archetypes in this category and they split on exactly this axis: Copy.ai leads with template breadth and a workflow builder for repeatable jobs, Jasper leads with brand voice governance for teams. A worked example — writing product descriptions for a 200-item catalogue: define one template with slots for name, three attributes and a tone; run it over the catalogue export as a batch; generate three variants per item; have a human accept, edit or reject each. The realistic time saving is not the writing, it is the not-starting: a human editing 200 mediocre drafts is meaningfully faster than a human producing 200 from a blank page, and roughly a third will need substantive rewriting anyway. Limits that buyers consistently underestimate. Generated copy is fluent by construction and persuasive only by accident, because the model optimises for plausible next words rather than for conversion; the variant that reads best is not reliably the variant that performs best, which is what A/B testing is for. Claims are the other hazard — a model will happily invent a percentage, a guarantee or a compliance statement, and in regulated categories that is a legal exposure rather than an editing chore. And output converges: every team prompting the same platform with the same templates about the same product category produces recognisably similar copy, which is the mechanism behind the complaint that everything now sounds the same.