AI is turning every story into raw material

There’s an idea in AI called “liquid content.” It typically refers to the idea of morphing the facts, ideas, and expressions from one medium to another. The most well-known example is a feature within Google’s NotebookLM: Once you’ve filled a folder with various kinds of data, it can whip up a podcast about that data, enlisting a couple of cheery AI-generated voices to give you an overview, analysis, or debate.

Taken to its logical extreme, liquid content suggests a future for media companies where what you create is repurposed across any and all formats. Making a podcast? With the right tools and prompting, in mere minutes, it can be reimagined as a series of clips, a feature article, or even an interactive presentation. And if you’re a traditional news publisher, all that content can serve as raw material for videos, which you may have dismissed in previous eras as too expensive to produce.

This isn’t theoretical anymore. I recently attended a couple of industry conferences—the NAB Show and Adobe Summit—and systems that intelligently derive one type of content from another are becoming more common. Just two examples: Amagi showed off an AI system that can scan a live newscast, understand the different stories covered, and create short-form videos for each one on the fly, populating a TikTok or Instagram feed almost as soon as the news is out. And Stringr’s Genna system can intelligently turn any news article into a video, mining photos and licensed video repositories (e.g., Getty) for footage.

Repurposing content isn’t new, of course, but now that artificial intelligence can do most of the heavy lifting—interpreting the content, determining how it’s best expressed in a new form, and then pulling all the levers to do the actual work—it can be done much faster and cheaper than ever before. 

Production is the easy part

If you’re detecting a “not so fast” turn, you’re right to be skeptical. AI can be a great catalyst in reimagining content, but it doesn’t solve every problem associated with pushing into new formats, and can even create new ones. The opportunity is real, but publishers should treat liquid content less like a magic growth engine and more like a new production layer that requires nurturing. As media companies turn to AI to expand their content footprint, there are important reality checks to keep in mind.

1. Using generative content will likely produce diminishing returns. A quick but important distinction: There’s a difference between using AI to assemble content and using it to create content. It’s particularly relevant in visual media, where accuracy in the imagery matters greatly.

Besides the obvious ethical issues in using generative video in news media, there’s another problem: Audiences don’t respond to it in the same way. Inception Media is a podcast company based on AI-generated scripts and synthetic voices. It does respectable numbers, but they’re far below what it might get from human-driven shows.

AI may be a great accelerator, but audiences still value authenticity. Publications looking to take their first steps into podcasting or short-form video with AI may find the audience numbers lacking. The safer route is to stick with non-generative content and simply use AI to assemble existing footage and imagery. But that still requires you to either produce or acquire that material, blunting any cost savings.

2. Good AI needs good data. For AI to understand and interpret content reliably, it needs the data surrounding that content to be as accurate and comprehensive as possible. That means things like tags, categorization, metadata, dates, and notes (e.g., exactly who appears in a video) should all be present and correct.

Even if your existing operations do this well, there’s a good chance it wasn’t always the case, and data is often garbled, isolated, or lost during system migrations. It’s an unfortunate truth in media that messy data operations are more common than well-nurtured ones, and that will hamper many outlets from fully taking advantage of their archives.

3. This all still needs management. AI is a tool that gets better and more versatile every day, but it’s still far from perfect. It can hallucinate and misinterpret, and because it lacks experience with the real world, it sometimes makes mistakes humans never would (pointing out that volleyball is hard to play without a ball, for instance). Audiences have low tolerance for slop or poor quality.

In short, AI can do a lot, but humans are still needed. And not just to review the work of the AI: Venturing into new platforms requires more strategic thinking than simply putting the content out there. To zero in on just one use case: AI can do competent translation, but that doesn’t mean you can skip the hard work of managing and nurturing a new market.

The archives get interesting

All that said, using AI as the ultimate content-repurposing engine still has great potential for those who figure out how to do it right.

1. Archives are a gold mine. Most outlets will reshare evergreen “hits” on social media, which can drive a decent amount of views. AI can turbocharge this idea—not just resharing an article once, but extracting the best parts and turning each “nugget” into its own video, gallery, or social post. AI can likewise expand on the “this day in history” idea, looking at patterns in current news and trends and finding the perfect stories to resurrect and remix.

2. Access to newer, younger audiences. Many small and midsize outlets simply haven’t had enough content to really monetize on a platform like YouTube or Instagram Reels. Success is often a numbers game, demanding regular posting to even have a hope of showing up in someone’s feed. AI-assembled video won’t attract the same audience as MrBeast, but it can open your brand up to younger audiences, 63% of whom primarily get their news from these platforms.

3. It takes a fraction of the staff. Venturing into a new platform used to require weeks of study, hiring dedicated staff, and building out a strategy. Now AI can accelerate all of that—not just the nuts and bolts of remixing the content itself. As already mentioned, humans still need to manage the process and have the final judgment over whatever’s produced, but building a content-remixing department won’t be nearly as expensive as a pivot to video.

That doesn’t necessarily answer the big question, however: Will the ROI be worth it? As more media adopts remixing strategies and agentic systems, the inevitable result will be a large increase in supply of repurposed content—especially video. That suggests a commensurate drop in demand, diluting audiences even further. As a result, the revenue benefits of a remixing strategy could be incremental at best.

However, there’s an X factor. For niche publications with few competitors, there’s less of a danger of saturating their market, and making a move to a multimedia strategy—cheaply—might improve audience growth and retention with readers who prefer formats like video and podcasts. That counts for local and regional publications, too.

The dream of a general-purpose content engine that can reliably spin out engaging stories in any format is getting less fictional by the day. But it’s still just a machine. Building a successful strategy around it requires intention, careful curation, and a strong understanding of both the audience and the platform they’re on. Liquid content may be a powerful idea, but there’s still art in the pour.