Video is one of the richest and most complex marketing formats. For people, it conveys emotion, nuance, and context far better than text alone. For AI systems, it offers a dense stream of multimodal data that supports more precise indexing and synthesis. Not long ago, video was difficult for search crawlers to interpret. Today, AI can effectively “watch” it. Models break video down into parallel visual, audio, and text-based channels. Here’s how to optimize video for AI.
Why video matters for AI: optimizing contextual density
Historically, search engines relied on surrounding text to interpret video. Elements like the title, description, tags, and transcript were the primary levers for optimization. In an AI-driven web, the video file itself becomes core training data. When an AI model such as Gemini 1.5 Pro “watches” a video, it uses a method called discrete tokenization to convert the entire asset into a machine-readable language.
During this process, the AI does three things simultaneously:
Seeing: It captures frames at set intervals to understand what appears on screen.
Hearing: It analyzes the audio track beyond just the spoken words, detecting tone, emotion, and ambient sounds.
Connecting: It aligns what it hears with what it sees – for example, if someone holds up a wrench while saying “wrench,” the model links that visual object to that spoken term.
Videos that deliver clear, specific, high-quality information – often referred to as strong content granularity – tend to be more powerful than simply making videos longer. Modern AI can also infer meaning from “silent”…