Generative AI and the Homogenization of Brand Identity: Why AI-Generated Brands Struggle to Feel Original, and Why It Matters for the Future of Design

Abstract Book of the 4th International Academic Conference on Social Sciences and Humanities

Year: 2026

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Generative AI and the Homogenization of Brand Identity: Why AI-Generated Brands Struggle to Feel Original, and Why It Matters for the Future of Design

Khushi Choudhary

ABSTRACT:

What happens when Artificial Intelligence models are asked to design a brand identity, a logo, a palette, a tagline, and the visual language for a company? When you look at them, they look polished and commercially ready. However, when you analyze them deeply, they show that they lack the historical, cultural, and real-world context that makes a brand feel original and authentic. This paper examines that gap by testing two hypotheses about the limitations of AI-generated brands.
The first, the Convergence Hypothesis, argues that AI-generated brands often look alike. Across unrelated products such as cereal, soda, chocolate, jeans, or sneakers, AI returns similar outputs like warm cream backgrounds, earth-tone palettes, sans-serif fonts, and short taglines. AI produces the average visuals of the brands it has already seen. The second, the Surface Authenticity Hypothesis, argues that AI-generated brands mimic authenticity without the underlying context. They imitate
heritage, logos, warmth, founder language, and origin cues, but they are not able to create the real, authentic substance like history, culture, rituals, or stories that have been built over time. The study uses a quantitative analysis with the UEQ tool to compare real and AI-generated brands across attractiveness, clarity, efficiency, dependability, stimulation, and novelty.

Keywords: Brand homogenization; Brand identity; Large language models; Synthetic brands; User Experience