The media industry is quietly shifting. Not because of a new content format or distribution model, but because AI has developed an insatiable appetite for video training data. Buried in hard drives, LTO tapes, and aging CMS platforms is something incredibly valuable to this new economy: diverse, real-world video footage.
The opportunity for media companies is clear. Archives can be licensed not just as content, but as training data for machine learning. But doing so requires solving three major bottlenecks in the pipeline: search, ingestion, and transport.
We spoke with veteran producer John Wesley Chisholm, founder of Arcadia Entertainment, on The Search Party Podcast about how his team became one of the first to license a full media archive for video training data for AI. The conversation offers a behind-the-scenes look at what it really takes to turn decades of footage into data that machines can learn from.
“It’s not just about what’s in the footage,” John says. “It’s about whether you can find it, extract it, and deliver it in a way that makes sense to the buyer.”
What AI Teams Want and Why Media Has It
AI developers are not just training on random clips from YouTube. They are looking for footage that includes specific human behaviors, camera angles, environmental diversity, and authentic storytelling. These data points improve the model’s ability to generalize to real-world scenarios.
A recent analysis from Reuters confirmed that tech companies are quietly racing to secure licensing deals for video, image, and audio datasets from private sources because public data is either too limited or legally risky to use at scale. (Reuters, April 2024)
Media archives already contain this kind of footage. But if the content is stored across disconnected systems, is poorly labeled, or lacks metadata, it remains out of reach for AI teams.
The Challenge with Search
Most archives were built for post-production, not machine search. File names are vague. Metadata is minimal. Developers cannot use a file called “INT_FINAL_V2.mov” when what they need is a clip showing “two people arguing in a moving car at night.”
Solution: Archives need to be enriched with scene-level metadata using tools that can recognize both audio and visuals. Computer vision and natural language models can automatically tag footage based on what is seen and heard.
The Challenge with Ingestion
Even when the right footage is found, preparing it for model training is another hurdle. Raw video may have inconsistent formats, missing transcripts, or lack frame-level synchronization.
Solution: A preprocessing pipeline is required. This includes transcoding, tagging, scene segmentation, and generating accurate transcripts. According to IBM, data preparation accounts for over 80 percent of the time and effort in building AI systems. (IBM Blog, 2021) Precision is more important than resolution.
The Challenge with Transport
Legacy media is often stored in offline or on-premises formats like LTO tapes, or isolated DAM systems. Transferring terabytes or petabytes of this data to cloud environments can be expensive and technically complex.
Solution: Cloud egress and storage expenses can become a major issue when handling large video archives. One effective strategy is to deploy containerized indexing and search tools directly within the archive holder’s own cloud environment—such as AWS, Azure, or Google Cloud. This hybrid approach reduces the need to move full video files, leading to an average cost reduction of around 25%, based on internal benchmarks.
Who’s Buying This Video Training Data?
As artificial intelligence rapidly evolves—particularly in computer vision, robotics, and generative models – the demand for high-quality, diverse video datasets is surging. From autonomous driving to text-to-video generation, AI companies are seeking real-world footage to improve their systems.
We outlined the landscape in a recent post, The Top 10 Places Buying Video for AI Training Data. Demand is rapidly rising as generative video tools require high-quality reference footage for fine-tuning and prompt conditioning.
What Comes Next
The AI economy will need more than synthetic images and text. It will require grounded, richly annotated, context-specific video data. This is an inflection point for the media industry. Companies must choose whether to remain content factories or to become data suppliers.
The demand for high-quality, human-context-rich video training data will only grow. The first step is making archives searchable.
