THE POLITICS OF THE MACHINE IMAGE

There’s also a deeper question: who decides what AI aesthetics dominate? The answer isn’t consumers — it’s the datasets. Whatever images, styles, and references are most represented in training sets become the building blocks of future advertising. That creates a feedback loop where already dominant cultural aesthetics are amplified, while marginalized ones risk being erased. AI doesn’t just generate visuals; it encodes bias into the visual field.

RESOLUTION: FROM COPYING TO COMMENTING

This doesn’t mean AI aesthetics should be abandoned. In fact, their best use isn’t replication — it’s commentary. Nike’s experimental campaigns with AI-generated streetwear mockups, for example, didn’t just showcase product; they framed AI as part of the conversation about future fashion. The most interesting brands aren’t just using AI as a design shortcut; they’re using it to hold a mirror to culture.

Imagine an ad campaign that leans into AI’s uncanny fingerprints — warped hands, dreamlike textures — as a metaphor for the fractured reality of digital life. Or one that revives past aesthetics (say, ’90s rave flyers or early web grunge) and refracts them through an AI lens to comment on how culture loops back on itself.

This is the space where forecasting and curation matter. Not every AI-generated visual deserves amplification. The challenge — and the opportunity — is in deciding which aesthetics have resonance, which ones are already collapsing into sameness, and which ones can be recontextualized into something meaningful.