Running synthetic "AI influencer" accounts (Feb-Jun 2026 creator practice): consistency methods, a local dashboard, and the disclosure obligations creators tend to skip
Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated. Dan Kieft says he runs about five AI-influencer pages, some with millions of impressions a month, and built a free local dashboard that stores nine personas (each in a niche, e.g. a yoga instructor) and one-click generates character sheets, close-ups and "feature" sheets, then scenes for posting (R3). Consistency methods (R2): detailed prompts for skin texture and imperfections to avoid a plastic look; generate a background grid with the character as reference and upscale a chosen panel; edit with "keep the exact same person, face, skin texture, expression, pose, background, proportions and camera framing" and change only clothes or pose; pose transfer by giving two ref...
Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated.
Dan Kieft says he runs about five AI-influencer pages, some with millions of impressions a month, and built a free local dashboard that stores nine personas (each in a niche, e.g. a yoga instructor) and one-click generates character sheets, close-ups and "feature" sheets, then scenes for posting (R3). Consistency methods (R2): detailed prompts for skin texture and imperfections to avoid a plastic look; generate a background grid with the character as reference and upscale a chosen panel; edit with "keep the exact same person, face, skin texture, expression, pose, background, proportions and camera framing" and change only clothes or pose; pose transfer by giving two references (identity image + pose image) and stating which attributes come from each; flat-background references give weaker scene understanding. Missing from the videos: disclosure. Platform rules require labelling realistic AI content (TikTok creator/auto labels, C3-B398-01; YouTube altered/synthetic disclosure from earlier batches), auto-labels are unreliable (C3-B425-01), and where content could mislead people into thinking a real person exists, EU AI Act Art. 50 deep-fake disclosure may apply to professional deployers (C3-B426-01; whether a wholly fictional persona counts as a deep fake is a case-by-case question). Monetising undisclosed synthetic personas also risks platform authenticity policies.
Curator qualifications: Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated. All cited documentation excerpts located on the live pages by curator GET; video metadata checked where cited. Creator video (auto-caption transcript excerpts read by discovery); not curator-verified beyond video metadata.
Limitations: Creator statements were read by discovery from auto-captions (ASR); not re-verified by the curator. Condensed read; impression claims unverified; disclosure analysis is ours, not the creator's.
Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.
[sourced_fact] Dan Kieft says he runs about five AI-influencer pages, some with millions of impressions a month, and built a free local dashboard that stores nine personas (each in a niche, e.g. a yoga instructor) and one-click generates character sheets, close-ups and "feature" sheets, then scenes for posting (R3). Consistency methods (R2): detailed prompts for skin texture and imperfections to avoid a plastic look; generate a background grid with the character as reference and upscale a chosen panel; edit with "keep the exact same person, face, skin texture, expression, pose, background, proportions and camera framing" and change only clothes or pose; pose transfer by giving two references (identity image + pose image) and stating which attributes come from each; flat-background references give weaker scene understanding. Missing from the videos: disclosure. Platform rules require labelling realistic AI content (TikTok creator/auto labels, C3-B398-01; YouTube altered/synthetic disclosure from earlier batches), auto-labels are unreliable (C3-B425-01), and where content could mislead people into thinking a real person exists, EU AI Act Art. 50 deep-fake disclosure may apply to professional deployers (C3-B426-01; whether a wholly fictional persona counts as a deep fake is a case-by-case question). Monetising undisclosed synthetic personas also risks platform authenticity policies. · locator: opening; midsource
Rights
component
license
hosting
redistribution
modification
training
Evidence and notes
documentation
unknown
permitted
permitted
unknown
unknown
Applies to this original summary only, not to the cited sources.