Why AI copyright became personal

I did not start thinking about AI and copyright because of a legal paper. I started thinking about it because of artists.

When I lived in the North East of England, I discovered a strong artist community. I collected work across photography, oil, watercolour and other styles. Those pieces mattered because each represented more than an image. They represented the choices, effort and interpretation of the person who made it.

I also modelled for a group of artists. Several artists looked at the same person and created different versions of me. That moment stayed with me because it showed that creative work does not come only from the subject. It comes from the artist's eye, practice, taste and judgement.

That experience gives me a useful way to think about AI.

Recently, I generated a comic-style image of myself to accompany a Press and Journal feature. The idea made sense on the surface. The Press and Journal sits within DC Thomson, a publisher with a well-known comic heritage. A comic-inspired LinkedIn post felt fun and relevant.

The first result looked good at a glance. Then the issue became obvious. The generated image included characters and visual cues that sat too close to recognisable comic properties. I had not copied and pasted an existing picture. I had not typed a request for a specific protected character. Yet the output still created a real risk.

That is the practical problem with AI-generated content. It can look original enough to tempt you, but familiar enough to create legal, ethical or brand concerns.

Human learning and AI learning do not create the same commercial effect

People often say that AI learns from existing work in the same way that humans do. That argument contains some truth. Artists learn by studying other artists. Writers learn by reading. Designers learn by looking at layout, colour, proportion and style.

But the comparison has limits. A person learns slowly. They practise. They make mistakes. They bring their own experience and limitations to the work. Most importantly, they develop an interpretation rather than producing thousands of commercial alternatives in seconds.

AI changes the scale. It can learn from huge volumes of material, then produce outputs quickly and cheaply. That creates value, but it also raises questions about whose value it builds on.

If an artist sits in a room and creates their own interpretation, that feels very different from a system that can absorb huge amounts of creative work and generate competing outputs at industrial speed.

Why this matters to businesses

This is not only an artist issue. Organisations now use AI for marketing images, blogs, policies, proposals, presentations, social media posts, coding, meeting notes and document summaries.

That creates a simple risk: the business may publish or rely on content that nobody has properly reviewed.

An AI image might resemble a protected character. An AI article might repeat someone else's phrasing. A generated campaign might sit too close to a known brand. A proposal might include material that entered the tool from another client document. A summary might leak confidential details into an unmanaged service.

AI does not remove responsibility. It changes where responsibility appears.

A practical review test

Organisations do not need to avoid AI completely. They need judgement, policy and review. Before publication, ask:

·        Does the output resemble a known artist, character, brand or campaign?

·        Could a third party believe the content has endorsement or official association?

·        Did the prompt use client data, confidential information or protected material?

·        Does the result need human review before use?

·        Would we feel comfortable explaining how we created it?

That final question matters most. AI can support early ideas, rough drafts and exploration. It should not turn other people's work into invisible raw material, and it should not give organisations a shortcut around copyright, consent or professional judgement.

This article starts a wider series on AI, ownership and responsibility. The next articles will look at output ownership, artists, grey AI use inside organisations, image-based sexual abuse and practical AI governance.