Left unchecked, the visual culture of entertainment is headed into a phase of post-authenticity, a period where artificial media will have an even more damaging impact on how culture is made, represented, and sold. Like the video of Minaj and Holland, these skewed and skewering visuals, as they grow in intensity through advertising campaigns and marketing efficiency, are a reminder that the present is the future: a constant, ferocious collapse of the real into the unreal, an ungovernable reality where the remixing of stereotypes is not only accepted but big business.

To make a product viable in the marketplace, one must first test it, and that is where the world currently sits: The borderless commercialization of AI is in full swing. The thing is, as generative AI tools continue to adapt and scale, the commercialization of them will find root in a culture already poisoned by racial division and gender imbalance. “If everything is mediated on screens anyway, who can tell what is actual truth?” Taylor says. “The technology that we create will never be neutral.” 

Still, Lori McCreary tells me she is cautiously hopeful about what’s unfolding in the AI space. A former computer scientist, McCreary founded Revelations Entertainment with the mission of fusing “artistic integrity with technological innovation.” Since 1996, and alongside her cofounder Morgan Freeman, she has produced a slate of movies and TV projects that includes everything from Invictus to The Story of God and the Emmy-nominated miniseries Through the Wormhole. She views generative AI as just another tool, but one with drawbacks.

“The main strength of generative AI is ironically also its biggest weakness,” McCreary says, “namely, that it is heavily based on pre-learning an existing data set, and most data sets—including the entertainment industry’s history of films and content—are inherently biased.” In her formulation, “bias has ‘inertia,’ and through [AI’s] tendency to learn and emulate previous examples, its systems tend to propagate that bias forward into the future, despite our best efforts to avoid this built-in phenomenon.”

She shares one example: “If you ask a generative AI system to give you some Academy Award-worthy plotlines, it will go through millions of pieces of data and find trends—from Hollywood’s movie history—of mostly white leading actors in mostly white-centric stories. So the AI will then amalgamate what it observes has been ‘award-winning’ content in the past.” This, she says, “can easily propagate past biases well into the future, creating yet further inertia in that direction.”

What this momentum engenders is a dangerous disparity in how and whose stories get green-lit. That’s not to say that imbalance doesn’t already exist—Hollywood’s earliest pictures were riddled with prejudice, and the industry still suffers from racial conservatism—but what the commercialization of generative AI portends is something deeply uncontrollable. Already we are witnessing the poisonous churn of racial and gendered masking across TikTok and Twitter, where bigotry is rewarded with virality. On YouTube, celebrities are rendered in a brutish hue of exaggeration for shits and giggles. All around, cultural distortions amplify in whispers and roars.