The future of the copywriter: Working to a deeper understandings
The story so far
There’s a version of the AI story in which the copywriter disappears. It goes like this: the machine creates competent marketing copy in seconds, and the human who used to write it becomes redundant.
As a story, it makes a kind of sense. But, crucially, it’s wrong. What’s actually happening is more interesting, more nuanced – and, for the copywriters paying attention, it’s much more promising.
For most of its history, copywriting has involved two very different kinds of work. The first is strategic. It’s based around understanding a brand’s voice, its customers, and what a piece of language is trying to achieve. The second is mechanical – it’s to do with actually producing the words, editing them, formatting them, and repeating that process across however many variants a campaign requires. These two kinds of work have always been part of the same job description, but they’re not the same thing. AI is now capable of handling the second. The first remains human.
What’s copywriting for, anyway?
The distinction matters because it reframes what a copywriter is actually for. No copywriter’s value ever really resided in their ability to type quickly or to produce grammatically correct sentences at volume. A good copywriter isn’t assessed by their words-per-minute on a keyboard. Instead, the value resides in their understanding of what those sentences are supposed to do. And, with AI handling generation at scale, that judgement – as to voice, or intention – only becomes more valuable.
The role, in practice, shifts from producing content to directing the system that produces it. The copywriter sets the brand guardrails, evaluates the outputs, identifies what’s working and what isn’t, and ensures that a voice built over years of careful work remains intact across thousands of personalised messages. That is a harder and more sophisticated job than producing those messages manually would have been. It requires expertise, built up over years of experience, and bolstered by a deep understanding of cultural and linguistic nuances.
This shift has a practical consequence that teams are only beginning to reckon with. The content demands placed on modern marketing operations have grown well beyond what any human writing team could manually meet. Human copywriters, however talented and numerous, cannot scale to meet this volume.
The problem at hand
The temptation is to throw a generic AI tool at the problem. But this introduces a different failure mode: if everyone is generating content from the same foundational model, trained on the same public data, the resulting copy exhibits the same patterns and the same cadences. Consumers have grown adept at recognising it. The volume problem gets solved while a differentiation problem is created in its place.
What the best-positioned teams are doing now is something more considered. They are using AI that has been trained specifically on their brand’s own linguistic patterns and historical performance data – systems that can generate thousands of variants whilst maintaining the voice that makes a brand distinct. The copywriter, in this model, is the person who built and continues to shape those guardrails. Their expertise is embedded in the system, rather than expressed through individual pieces of output.
Where next?
The copywriters who will struggle in the next few years are those who define their role by the mechanical half of it. Those who understand their value as strategic and editorial — the people who know what good looks like, and can build the infrastructure to produce it at scale — are in a stronger position than they might currently believe.
The machine needs directing. And that remains – and will remain – a human job.
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