Several weeks ago, I published an article about AI as a new User Interface paradigm. The emergence of AI as the next major UI platform carries significant implications, particularly for content owners. 

Consumer habits will evolve with this evolution, and content owners will be motivated to provide deeper metadata around their libraries to maximize visibility of their content in an AI world. The need to feed AI engines will likely give rise to AI Engine Optimization (AEO), paralleling how the need to feed search engines gave rise to Search Engine Optimization (SEO).

The Advent of Media Index Files

AEO will push content owners in new directions to maximize the discoverability and utility of their content in an AI-driven world. Publishers who provide structured descriptions of their content will expose surface area for AI engines to create correlations to those titles as their LLMs mine the web. Increased correlation equates to increased visibility.

Technologies have emerged to solve this, providing structured descriptions for different content types. There are multiple AI startups that “X-ray” websites and documents, providing rich descriptive information for better AI discovery and interaction. Mistral.ai, which markets itself as the world's best document understanding API, is a decent example. Mistral examines physical documents via OCR and applies AI analysis to extract structured summary information. The output of these “X-rays” is commonly a JSON file that provides well-structured visibility into a piece of content.

Those same dynamics apply to video. I imagine that they will lead to a new file (likely in JSON or markdown format) that captures detailed information about the video content, including segment-level information with descriptive keywords and embeddings. I refer to that file as a Media Resonance Index, or Media Index for short.

Media Industry Leaders Already Building Solutions

Companies like Coactive AI , TwelveLabs and Sensara Technologies have been developing solutions along these lines for video content for the past few years. Of note, NAB recognized Sensara as a 2025 Product of the Year winner in the Artificial Intelligence and Machine Learning category at this year's conference. At NAB, TwelveLabs also announced an AWS partnership to bring its AI Video Analysis to Amazon Bedrock, which will accelerate adoption and evolution.

These companies can easily output the sort of file I reference above. Their technology scans a video, extracts detailed information about the underlying dynamics and can output that info in a JSON or markdown file that captures highly-accurate interval-span metadata and embeddings. This information can include remarkable detail about:

• Actors and their emotions

• Sounds and audio elements

• Shot types and composition

• Demographic information

• Scene context and settings

• Objects and props

• Languages and speech content

• Locations and places

To date, many of the applications for these technologies have been internal to studios, deployed to optimize operations and media workflows. Increasingly, the value of this output may prove to be external, exposed publicly or delivered as a new sidecar file to distributors, in service of feeding information to AI engines for AIEO.

Transformative Applications Across the Media Value Chain

Exposing this metadata not only maximizes discoverability by AI engines, it also creates extensive new possibilities for innovation, evolution, and value creation across multiple dimensions of the content ecosystem:

Streaming Platform Enhancements

• Precision-targeted user recommendations

• Better, personalized FAST channel programming

• Granular scene search capabilities

Monetization Expansion

• Context-relevant advertising placement

• New ad insertion opportunities based on content analysis, including merchandise tie-in opportunities

• Clip-level monetization through advanced search

Distributor Content Operations

• Automated compliance and rating decisioning

• Intelligent segmentation and chapterization

• Advanced content search and mapping capabilities

Licensing Evolution

• Advanced licensing analytics based on content segments

• International audience assessments and data-driven localization investment decisioning

• New content packaging possibilities through component analysis

Analytics for Business Intelligence

• Scene-level user sentiment analysis with granular audience mapping and behavior tracking

• Financial decision support (aka greenlighting) through richer content performance analytics

• Scene resonance data for better production and edit decisioning

This list represents just a starting point. The true value lies in exposing new surface area to maximize content utility. We’re in the first inning of a whole new ball game.

The Practical Implementation

Imagine engaging one of the aforementioned partners to process an entire content library and create Media Index files (with time-coded information about actors, scene content, objects, places, and emotions in JSON format) for every long-form video. Practically speaking, these output files need to target a manageable size, probably constrained to <20MB, with the ability to be easily reduced to smaller, purpose-built derivatives.

Consider providing these Media Index files (not the video content itself) to AI engines like Perplexity, ChatGPT, Grok, Gemini, and Claude. This move stands to dramatically increase the visibility and utility of the associated content, potentially creating new licensing and monetization channels with these platforms.

Such files can also accompany delivered content to major distributors like Prime Video, Apple, YouTube, and Roku. These aggregators are positioned to derive tremendous value from this information, creating better consumer experiences while unlocking new licensing and monetization opportunities.

Looking ahead, if you subscribe to Roy Price's prediction about the looming emergence of SVOD super apps, these Media Index files could become essential to maximizing outcomes for both studios and consumers. We may already be witnessing the birth of such super apps with X (which recently merged with xAI) and ChatGPT (which announced that it is building a social network).

Short story, Media Index files become currency for relevance of a title in an AI world. They determine visibility, utility, and ultimately, commercial value.

Challenges to Overcome

Several obstacles must be addressed as this world evolves:

• Metadata Sprawl and Versioning. One can extract A LOT of information through tools like Coactive, TwelveLabs and Sensara. It’s important to keep an eye on what data creates value to avoid metadata sprawl and unnecessary complexity. As David Klee underscores in his recent Metadata and Ice Cream post, the value one can extract from metadata does have limits.  Striking the right balance will require iteration. With that, a mechanism for versioning file specs will become important.

• Standardization. If everybody chooses a different framework, things will get messy. There is benefit to a common approach, and that can help enable adoption and utility. Ultimately standardization can address sprawl by focusing on the priority info that should be captured and provided. Perhaps that’s an area where Motion Picture Laboratories, Inc. (MovieLabs) might provide leadership.

• Rights and Legal Complexities. Exposing granular content information and enabling new capabilities will likely raise fresh legal questions that need to be tackled. Some studios may move slowly due to concerns over how parties might use this detailed information. Content companies with fewer constraints and a strong desire for new monetization will move faster.

• Licensing Structures. New rights and legal complexities also mean that new licensing approaches and constructs will be needed around things like new monetization and allowances. Worth noting, the evolution of licensing structures likely presents more opportunity than challenge for content creators.

• Timecode Dependencies. Media Index files are highly reliant on precise timecodes. When content packaging causes even minor timecode shifts, the Media Index files require updates. Technical solutions to maintain synchronization across transformations will be essential to avoid operational overhead and complications.

The Path Forward

I'm watching closely to see which content providers publish Media Index files to AI partners and which distributors request them with their deliveries. Given Amazon's dominance in media cloud services via AWS, my bet would be on Prime Video partnering first with a midsize distributor to pioneer on the distribution side.

Regardless, content owners should begin exploring AEO strategies now, before it becomes an industry standard requirement. Those who establish early expertise in optimizing their content for AI discovery will gain competitive advantages in audience reach, engagement, and monetization opportunities.

The race to optimize content for AI engines has already begun.