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Gen Z and AI Image Generation

AI image generation has become a native part of how Gen Z creates — for social posts, side hustles, and pure experimentation. Among the leading tools, Midjourney leads on user preference (about 27%), followed by DALL-E and NightCafe, though Stable Diffusion’s open-source ecosystem quietly produces the majority of actual images. Gen Z is the most experimental cohort: roughly two-thirds report trying AI image generators, and younger-skewing platforms like Leonardo.ai draw most of their users from under-25s. They use these tools mainly for social content, micro-business assets, and creative play. But the same technology that powers a quick meme also fuels real authenticity concerns — AI images are now so realistic that people can no longer reliably tell them apart from photos. This page maps the tool landscape, Gen Z’s experimentation, what they actually make, and the casual-creator split underneath it.

4 visualizations 4 sources Last updated June 2026 Free to embed
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Source: Gitnux / WifiTalents creator surveys 2026. Gen Z experimentation figure is a directional estimate.
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Chart 1 · Tool landscape

Leading AI image generators by preference

AI image generators by user preference (2026)

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Source: SQ Magazine / industry preference surveys 2026: by user-preference share, Midjourney leads at 26.8%, DALL-E 24.4%, NightCafe 23.2%, Stable Diffusion 15.1%. Note the asymmetry — Stable Diffusion’s open-source ecosystem produces ~80% of actual image volume despite lower brand-preference share. Brand preference ≠ output volume.
Chart 2 · Gen Z creators

Gen Z experimentation with AI image tools

Gen Z is the most experimental cohort (directional)

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Source: Gitnux / WifiTalents 2026: ~62–68% of Gen Z report experimenting with AI image generators, and ~70% of users on younger-skewing platforms like Leonardo.ai are under 25. Gen Z is the most experimental cohort for AI imagery. Figures are creator-survey estimates — directional.
Chart 3 · What they make

How Gen Z uses AI image generation

What Gen Z makes with AI image tools (illustrative)

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Source: AIBI synthesis of creator-use data 2026: Gen Z’s top uses are social content (TikTok/Reels/memes), micro-business assets (logos, product shots for Etsy/Shopify), and personal creative projects. Text-to-image is the dominant mode (~68% of sessions). Illustrative distribution of Gen Z use cases.
Chart 4 · Casual vs. creator

A split audience — and a trust caveat

Mainstream scale — and a detection problem

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Source: Imagera / Science 2026: AI imagery has gone mainstream — ~150M monthly users generate ~80M images/day — but human ability to spot AI images has fallen to ~38% (below chance). For Gen Z, that fuels both creative enthusiasm and authenticity concerns. The same tools power casual memes and deepfake worries.

About this data

This page compiles published data on AI image generation, drawing on 2026 industry and creator surveys (SQ Magazine, Gitnux, WifiTalents, Imagera) with Gen-Z-specific figures called out where available.

The AI Behavior Index is the research arm of OneChat AI, an integrated multi-model AI platform. We compile and analyze data from primary research sources to make AI adoption and market trends more accessible to journalists, researchers, and decision-makers.

A note on Gen-Z-specific data: most rigorous AI-image data measures all creators/users, not Gen Z specifically. We surface the Gen-Z cuts that exist (experimentation rates, the under-25 skew on platforms like Leonardo.ai) and clearly label them as directional creator-survey estimates rather than probability-sample measurements. The tool-preference ranking is industry survey data and is overall (not Gen-Z-only).

Methodology notes: tool-share figures are user-preference survey results and differ from actual output volume (Stable Diffusion’s open-source ecosystem produces ~80% of images despite a lower preference share — a gap we flag explicitly). Gen-Z experimentation rates are creator-survey estimates and vary by source (62–68%). The “what they make” chart is an illustrative synthesis of reported use cases, not a measured breakdown. The AI-image-detection figure (~38% human accuracy) is from a peer-reviewed study and applies to all viewers, not just Gen Z.

Sources used on this page:

Corrections or suggestions: research@aibehaviorindex.org

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Every statistic shown is sourced from a publicly available study, survey, or report. We aggregate, organize, and contextualize this data — but the underlying research is conducted by the cited sources. Click any source link to access the original methodology. If you run into any issues or have a study to suggest, contact us at research@aibehaviorindex.org.