ARTICLE

Video AI Models

Updated 2 May 2025
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Video AI Models

Text-to-video went from “not possible” to “genuinely impressive” in about 18 months. We’re watching a new medium being born.

These models take a text prompt (or an image, or a short clip) and generate video — complete with motion, lighting, physics, and increasingly coherent narratives. The output is getting good enough to use commercially, and the implications are enormous.

The Current Landscape

Sora — OpenAI

The headline-grabber. Sora generates up to 60 seconds of video that demonstrates genuine understanding of physics, light, and motion. People walk naturally. Water flows. Cameras pan. It’s not perfect — look closely and you’ll see artefacts — but the direction is clear.

Strengths: Cinematic quality, physical coherence, prompt adherence Limitations: Slow generation, expensive, occasional physics breaks Access: Available in ChatGPT Plus/Pro

Veo 2 — Google DeepMind

Google’s answer. Strong on motion understanding and video quality. The integration with YouTube and Google’s creative tools makes it interesting for content creators at scale.

Strengths: Motion quality, Google ecosystem integration Limitations: Less publicly accessible than competitors

Runway Gen-3 Alpha — Runway

Runway pioneered this space. Gen-3 Alpha focuses on creative control — camera movements, style consistency, specific motion descriptions. Used heavily by professional creatives.

Strengths: Creative control, professional workflow integration, motion brush tool Limitations: Shorter clips, less physics coherence than Sora

Kling 1.6 — Kuaishou

The dark horse. Kling emerged from Chinese social media company Kuaishou and surprised everyone with its quality. Strong motion, good faces, competitive with Western models.

Strengths: Quality-to-cost ratio, face consistency, motion Limitations: Less documentation in English, content policies differ

Pika 2.0 — Pika Labs

Focused on quick, iterative creation. Good for social media content, short clips, creative experiments. Lower barrier to entry than the cinematic tools.

Strengths: Speed, ease of use, iteration-friendly Limitations: Lower quality ceiling than Sora/Veo

How They Work (Simply)

Video models are built on the same foundation as image models — transformer architectures and diffusion processes — but extended into the time dimension.

  1. Text understanding — The prompt is encoded (just like in image models)
  2. Temporal generation — The model generates frames that are temporally coherent (things move naturally between frames)
  3. Physics learning — Trained on millions of real videos, these models learn that objects fall, water flows, light casts shadows
  4. Refinement — Multiple passes clean up artefacts and improve consistency

The hard part isn’t generating pretty frames — it’s maintaining consistency across time. A character should look the same in frame 1 and frame 300. Physics should be consistent. The camera should move smoothly.

Why This Matters

For Creators

Video production used to require cameras, crews, locations, budgets. Now it requires a prompt. This doesn’t replace professional filmmaking, but it demolishes the barrier to entry for everyone else.

For Businesses

Product demos, marketing content, training videos, prototyping — all become faster and cheaper to produce.

For Society

This is where it gets serious. The same technology that lets an artist create a music video lets a bad actor create a convincing deepfake. The ability to generate realistic video of anyone doing or saying anything is a security challenge we’re not ready for.

The EU AI Act requires disclosure of AI-generated content. The question is enforcement.

What to Watch

  • Consistency — Can you make a 10-minute video where the character never changes appearance?
  • Control — Can you direct precisely, or just hope the AI understands?
  • Audio integration — Video + voice + music from one system
  • Real-time — Live video generation for games, VR, streaming
  • Legal clarity — Who owns what? See Court Rulings

Go Deeper

Sources

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