One Instagram reel claimed 10 million views. A 22-year-old medical student in India told WIRED he built an AI-made 'MAGA girl' persona to attract U.S. conservative audiences and funnel followers into paid subscriptions.

How the persona was built

Sam, a 22-year-old aspiring orthopedic surgeon who uses a pseudonym, says he turned to image-generation tools after other side hustles barely paid. He told WIRED he used Google Gemini’s Nano Banana Pro to design a photorealistic young woman, then gave her a backstory: Emily Hart, a registered nurse and a Jennifer Lawrence look-alike.

He created an Instagram account for the persona — @emily_hart.nurse — and posted staged photos of ice fishing, shooting at a range and drinking beer. Captions were deliberately provocative. One read, “If you want a reason to unfollow: Christ is king, abortion is murder, and all illegals must be deported.” Another read, “POV: You were assigned intelligent at birth, but you identify as liberal.”

Sam says he studied the themes and phrases that resonate with a certain American conservative audience. He didn't live in the United States, but he claims he learned the rhetoric well enough to mimic it convincingly in captions and short videos. He says the image generator helped him fine-tune a distinct look and that the AI suggested leaning into a particular political niche rather than making a generic model — a tactic he said translated into attention.

Viral reach and quick monetization

Sam told WIRED that his reels started drawing massive view counts almost immediately, with individual posts claiming 3 million, 5 million and even 10 million views. The account gathered more than 10,000 followers in a matter of weeks, he said.

He used that following to funnel fans to subscription platforms and merchandise. Emily Hart’s followers were invited to subscribe to softcore content on Fanvue, an OnlyFans competitor, and to buy MAGA-themed T-shirts. Sam estimates he was making a few thousand dollars a month once the operation scaled, with earnings coming from subscription revenue and direct merchandise sales. He said his biggest costs were the time spent crafting content and running the account.

Why the tactic worked

Sam credits two things for the account's rapid growth: polarized content and platform algorithms that amplify engagement. He says the conservative-leaning captions generated strong reactions, which stirred the platform’s recommendation systems.

He framed it bluntly: the persona was designed to be both attractive and confrontational, a combination that, he claims, drew views and clicks. That combination, he says, let him stand out among millions of generic influencer accounts. What Sam calls a "cheat code" was partly technical — using image-generation tools to create a consistent visual brand — and partly social: leaning into cultural and political hot buttons that reliably trigger engagement among a loyal audience.

Platforms, AI and the gray market

The Emily Hart case shows how modern image generators and subscription platforms can be combined into a low-friction revenue stream and also exposes gaps in how social networks and creator platforms identify synthetic personas and the people behind them. Fanvue and Instagram host massive amounts of user content and rely on a mix of automated systems and human moderation to police violations. When a creator posts AI-generated images that don’t obviously break a platform’s rules, enforcement becomes difficult.

The episode highlights practical and ethical challenges for platforms: automated systems can be gamed, moderation scales unevenly, and verification of a creator’s identity remains a persistent gap.

This episode underscores how easily AI-generated personas can be turned into cash and where enforcement breaks down: creators can post images that don't clearly violate platform rules, moderation leans on imperfect automation, and identity verification remains weak — all of which make low-friction monetization possible.

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"The algorithm loved it," Sam said.