“The future of entertainment will not merely be *influenced* by artificial intelligence; it will be *redefined* by it, from content genesis to consumption.” This isn't a speculative prophecy from a Silicon Valley futurist, but the inescapable reality dawning on even the most established media titans. While the immediate headlines focus on corporate restructuring and the financial mechanics of asset separation, the underlying currents shaping these moves are inextricably linked to the burgeoning power of AI. Traditional media, long accustomed to its linear fortresses, is being forced to unbundle, streamline, and adapt, not just for market efficiency, but to lay the groundwork for an era where intelligence amplifies every facet of creation and distribution.
This strategic recalibration was a central theme when Michael Burns, Vice Chairman of Lionsgate, recently spoke with the panel on CNBC’s *Fast Money*. Burns offered candid insights into Lionsgate’s successful spin-off of Starz and the broader industry trend of de-consolidation, the enduring value of content franchises, and the stark choices facing linear television networks. His perspective, steeped in decades of media finance, provides a crucial lens through which to examine how AI is poised to revolutionize the very structures he describes.
Lionsgate's decision to spin off Starz, a move Burns notes took "a long time to make happen," was rooted in a clear strategic vision: to become a "pure-play content business" on one side and a separate public company for Starz. This strategy, according to Burns, aligns with what "the Street tends to favor simplicity, pure-play assets." For a technology journalist observing the startup ecosystem, this unbundling isn't just about financial engineering; it’s a direct response to a market increasingly demanding specialization and efficiency, trends AI is set to turbocharge. In an AI-driven landscape, pure-play content studios can leverage generative AI for faster script development, AI-powered pre-visualization, and automated post-production workflows. A streamlined, focused entity can more readily integrate these advanced tools, shedding the legacy baggage that bogs down diversified conglomerates. This specialization also enables more precise data collection and feedback loops, essential for training and refining proprietary AI models that understand audience preferences and content trends with unprecedented granularity.
Burns's commentary on the precarious position of linear television networks further underscores the imperative for digital transformation, a transition heavily reliant on AI. He states unequivocally, "If they don't make a move digitally, I think that linear has got a big problem." While he acknowledges that linear players "can convert to linear to apps and all that," he adds pointedly, "they have to make that move, and frankly, I think they've taken a long time to make that move." This observation speaks volumes about the slow pace of digital adoption within traditional media, a gap that AI-native startups are eagerly filling. AI can provide the critical infrastructure for this digital pivot: optimizing streaming infrastructure, personalizing content recommendations to retain subscribers, and dynamically inserting targeted ads to maximize revenue in an increasingly fragmented viewing landscape. The very survival of these legacy players hinges on their ability to shed their analog skin and embrace the intelligent, data-driven operational models that AI enables.
The enduring power of established franchises, exemplified by Lionsgate's *John Wick* series, highlights another crucial aspect of the media business that AI can amplify. Burns proudly notes the *John Wick* franchise's "1.1 billion cumulative gross," and how the spin-off, *Ballerina*, despite a $25 million opening weekend (he "wish it did $35 million"), garnered a "93% positive" audience reaction and "great Rotten Tomatoes scores," indicating it "will have legs." More strikingly, he observes that "every single one of the John Wick titles uptick across every single platform" when *Ballerina* was coming out. This symbiotic relationship between franchise titles is a goldmine for AI. Generative AI can be employed to explore new narrative arcs, create spin-offs, or even produce entire animated series based on existing IP, expanding the universe without requiring massive human creative teams from scratch. AI-powered analytics can identify optimal release windows, marketing strategies, and even specific character interactions that resonate most with fans, ensuring that each new installment or derivative product maximizes its audience appeal and extends the franchise's lifespan. The ability to predict and capitalize on audience engagement across platforms, as seen with *John Wick*, becomes significantly more powerful with intelligent systems analyzing vast datasets of consumption patterns and sentiment.
Beyond current hits, Burns emphasizes the long-term value of content libraries, proudly stating, "No one gives us the credit for the library that we've built. $20 billion of content spent over the last 20 years." He highlights Steve Mnuchin's increased stake, asserting, "If you looked up smart money, it'd probably be a picture of Steve Mnuchin... He understands our business... He likes the model of what we have." This "smart money" understands that in an AI-driven world, content libraries are not just passive assets; they are invaluable datasets. Every frame, every line of dialogue, every musical note, and every audience reaction within that $20 billion library can be used to train sophisticated AI models. These models can then be deployed for everything from highly efficient content indexing and search, to automated localization (dubbing, subtitling), to generating hyper-personalized content experiences for individual viewers. The deeper and richer the library, the more robust and versatile the AI models that can be built upon it, creating a powerful competitive moat. For startups, this means the battle for content acquisition will intensify, as these libraries represent proprietary training data for the next generation of media AI.
Looking ahead, Burns doesn't foresee a re-bundling of the splintered media giants, stating, "I don't see them coming back together. I think once you separate pure content... they're not going to come back together." He predicts "consolidation in the linear networks" and "combination of linear networks with some of the non-commercial channels," all driven by the relentless pursuit of "saving money, economics... economic benefit." This future of media, characterized by further consolidation in distribution (linear) and continued specialization in content, sets the stage for AI to become the ultimate arbiter of value. The ability to extract maximum economic benefit from both content creation and distribution will increasingly depend on sophisticated AI-driven efficiencies. From predictive analytics for greenlighting projects, to optimizing production budgets, to automating rights management and distribution, AI will be the invisible hand guiding strategic decisions. The "five great libraries in the world," as Burns describes them, including Lionsgate’s, will become the foundational data sets for a new era of intelligent entertainment, where human creativity is amplified and monetized by the relentless march of algorithmic innovation. The media industry is not just unbundling; it’s intelligently reconfiguring itself for an AI-first future, and those who fail to adapt will find their narratives quickly fading to black.
