How AI is transforming content repurposing for media companies

AI and the rise of 'liquid content'

AI is introducing a concept called "liquid content", which refers to the ability to transform facts, ideas, and expressions from one medium into another. A prominent example is Google’s NotebookLM, a tool that can generate a podcast from a folder of diverse data, complete with AI-generated voices for analysis or debate.

This technology suggests a future where media companies can repurpose a single piece of content across multiple formats—such as turning a podcast into clips, articles, or interactive presentations—in a matter of minutes. For traditional news publishers, this means previously costly formats like video can now be produced more efficiently.

Real-world applications of AI-driven content repurposing

AI-powered systems that convert one type of content into another are no longer theoretical. Recent industry conferences, including the NAB Show and Adobe Summit, showcased tools that are making this a reality. Two standout examples include:

• Amagi’s AI system: Scans live newscasts, identifies individual stories, and automatically generates short-form videos for platforms like TikTok or Instagram in real time.
• Stringr’s Genna system: Converts news articles into videos by sourcing relevant photos and licensed footage from repositories like Getty Images.

While content repurposing is not a new concept, AI is accelerating the process by handling interpretation, adaptation, and production, making it faster and more cost-effective than ever before.

Challenges and limitations of AI-generated content

Despite its potential, AI is not a silver bullet for content expansion. Publishers must approach liquid content as a new production layer rather than a magic growth engine. Key considerations include:

1. Diminishing returns from generative content

There’s a critical distinction between using AI to assemble content and using it to create content. This is especially relevant in visual media, where accuracy and authenticity are paramount.

For example, Inception Media, a podcast company relying on AI-generated scripts and synthetic voices, achieves respectable but significantly lower engagement compared to human-driven shows. While AI can accelerate production, audiences still prioritize authenticity.

2. Ethical and audience trust concerns

Using generative video in news media raises ethical issues, particularly around misinformation and the potential for audiences to reject synthetic content. Publishers must balance efficiency with credibility to maintain trust.

Key takeaways for media companies

AI is revolutionizing content repurposing, but success depends on:

• Treating AI as a production layer, not a standalone solution.
• Prioritizing authenticity and accuracy, especially in visual and news media.
• Avoiding over-reliance on generative content to prevent diminishing audience engagement.