Our U.S. Media & Entertainment Analyst Benjamin Swinburne discusses how GenAI is transforming content creation, distribution and also raising some serious ethical questions.
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Welcome to Thoughts on the Market. I’m Ben Swinburne, Morgan Stanley’s U.S. Media and Entertainment Analyst. Today – GenAI is poised to shake up the entertainment business.
It’s Wednesday, July 23, at 10am in New York.
It's never been easier to create art for anyone – with a little help from GenerativeAI. You can transform photos of yourself or loved ones in the style of a popular Japanese movie studio or any era of visual art to your liking. You can create a short movie by simply typing in a few prompts. Even I can speak to youin several different languages. I can ask about the weather:
Hvordan er været i dag?
Wie ist das wetter heute?
आज मौसम कैसा है?
In the media and entertainment industry, GenAI is expected to bring about a seismic shift in how content is made and consumed. A recent production used AI to de-age actors and recreate the likeness of a deceased performer—cutting what used to take hundreds of VFX artists a year to just a few months with a small team. There are many other examples of how GenAI is revolutionizing how stories are told, from scriptwriting and editing to visual effects and dubbing.
In music, GenAI is helping music labels identify emerging talent and generate new compositions. GenAI can even create songs using the voices of long-gone artists – potentially extending revenue far beyond an artist’s lifetime.
GenAI-driven tools have the potential to reduce TV and film production costs by 10–30 percent, with animation and post-production among the biggest savings opportunities. GenAI could also transform how content reaches audiences. Recommendation engines can become even more predictive, using behavioral data to serve up exactly what listeners want—sometimes before we know what we want.
And there’s more studios can achieve in post production. GenAI can already dub content in multiple languages, even syncing mouth movements to match the new dialogue. This makes global distribution faster, cheaper, and more culturally relevant.
With better engagement comes better monetization. Platforms will use GenAI to introduce new pricing tiers, targeted advertising, and personalized superfan content that taps into niche audiences willing to pay more.
But all this innovation brings up profound ethical concerns.
First, there’s the issue of consent and copyright. Can GenAI tools legally use an actor’s name, likeness or voice? Then there’s the question of authorship. If an AI writes a script or composes a song, who owns the rights? The creator or the GenAI model?
Labor unions are understandably worried. In 2023, AI was a major sticking point in negotiations between Hollywood studios and writers’ and actors’ guilds. The fear? That AI could replace human jobs or devalue creative work.
There are also legal battles. Multiple lawsuits are underway over whether AI models trained on copyrighted material without permission violate intellectual property laws. The outcomes of these cases could reshape the entire industry.
But here’s a big question no one can ignore: Will audiences care if content is AI-generated? Some consumers are fascinated by AI-created music or visuals, while others crave the emotional depth and authenticity that comes from human storytelling. Made-by-humans could become a premium label in itself.
Now, despite GenAI’s rapid rise, not every corner of entertainment is vulnerable. Live sports, concerts, and theater remain largely insulated from AI disruption. These experiences thrive on real-time emotion, unpredictability, and human connection—things AI can’t replicate. In an AI-saturated world, the value of live events and sports rights will rise, favoring owners of sports rights and live platforms.
So where do we go from here?
By and large, we’re entering an era where storytelling is no longer limited by budget or geography. GenAI is lowering the barriers to entry, expanding the creative class, and reshaping the economics of media. The winners in this new landscape will likely be companies that can scale—platforms with massive user bases, deep data pools, and the engineering talent to integrate GenAI seamlessly. But there’s also room for agile newcomers who can innovate faster than the incumbents and disrupt the disrupters.
No doubt, as the tools get better, the questions get harder. And that’s where the real story begins.
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