Overcoming Data Bottleneck in Large Video Models

In AI, we've long celebrated models. Bigger, better, faster. LLMs have dominated the last 18 months, with GPT-4, Claude, and Gemini setting the tone. But a subtle shift is underway: the next frontier isn't just text, it's video. And video is a different beast.

The Rise of Large Video Models (LVMs)

We're entering the era of Large Video Models (LVMs) - multimodal AI systems trained to understand, predict, and even generate moving images with semantic depth. Think of them as the video equivalents of LLMs. They ingest not just pixels, but motion, context, dialogue, behavior. OpenAI's Sora stunned the industry with its cinematic coherence. Meta's Make-A-Video, Google's Veo, and Runway's Gen-2 all followed suit. But there's a quiet truth underneath these glossy demos: models are no longer the constraint, data is.

The Model-Data Inversion

Every LVM is only as good as the dataset it trains on. But unlike text, video is messy, unstructured, and expensive to annotate. Most AI labs cobble together datasets from YouTube dumps, surveillance footage, or synthetic environments. It works—until it doesn't.

As LVMs move into verticals like fashion, beauty, robotics, and retail, the gap between available and usable video data becomes a serious bottleneck. Consider these examples:

• A retail LVM needs multi-angle, high-res footage of real products being handled in context, not static catalog shots.

• A fashion LVM needs to understand fabric drape, motion, body diversity, not just static photos of models.

• A beauty LVM needs makeup application processes, not product shots.

• A robotics LVM needs human-object interaction in varied lighting and environments, not sanitized lab conditions.

  
  

In other words: the web doesn't have the datasets you need, and even if it did, they're not structured for model training. LVMs require temporal continuity, object tracking, motion cues, synchronized audio, and contextual metadata. That's not something you can scrape from TikTok.

From DIY to DaaS

Until now, most AI teams have built their own pipelines: sourcing raw video, tagging it, segmenting it, normalizing formats, building retrieval layers. It's laborious, slow, and costly. That made sense when only a few firms were training multimodal models. Now, with hundreds of startups and labs building domain-specific LVMs, the need for off-the-shelf, high-quality video datasets is becoming critical.

Think of it as the ImageNet moment for video—but sliced by category and use case. We're already seeing early demand from AI teams building vertical models in beauty, apparel try-ons, retail shelf recognition, and gesture-based control systems. What they want is not more video: they want structured, AI-ready video.

The Future: Data-as-a-Service

Just as the cloud made compute scalable and LLM APIs made language modular, video data is about to become productized. Companies will increasingly look for Data-as-a-Service (DaaS) solutions: not just raw footage, but metadata-rich, annotated, permissioned datasets ready to plug into training loops.

This changes the game for both AI teams and content owners. For AI builders, it means skipping the grunt work and focusing on modeling. For video creators, it unlocks new monetization paths, where a clip becomes not just content, but data capital.

The Structured Video Layer

We're at the beginning of a new layer in the AI stack: the structured video layer. And the teams that figure out how to supply it at scale, across verticals, will shape what AI understands and how it sees the world.

As with most platform shifts, the gold rush isn't always in the tools, it's often in the picks, shovels, and structured datasets.