Where Are Video-Generation AIs Today?
From 2024 to 2026, video-generation AIs have quickly stepped onto the practical entry point. For short clips of a few seconds, they increasingly deliver photorealistic textures, consistent lighting, and reasonably natural camera movement in more scenarios. Notably, OpenAI's Sora, creator-friendly Runway, the rising presence of Chinese entrants Kling, and Google's Veo have been the talk of the town.
However, with expectations high, it's important to ground them. The current battleground is production tasks that yield value in short-length formats such as advertising, social media, concept validation, storyboards, and B-roll, rather than making a movie from start to finish. This article organizes what you can do now, where the pitfalls are, and how to use them to make work easier, focusing on four representative models.
A Rough Positioning of the Four Representative Models
- Sora: A direction strong in longer timelines and complex scene understanding. “a sense of world-simulation” is a talking point.
- Runway: Strong for production workflows. Not only text generation but video editing · replacements · style transfer are rich in on-site features.
- Kling: Notable for high-quality outputs. Strong when its depiction of people and visual style align with preferences.
- Veo: Google's video-generation offering. High resolution, long-form, and prompt adherence hold strong expectations.
Rather than asking which is the strongest, it's more practical to view them as excelling in different stages of the workflow. Next, we'll examine important evaluation axes from a practical, on-site perspective.
Evaluation Axes: What to Look For to Tell If It’s Useful
1) Temporal Consistency
Video is not a single frame; it's essential that subjects don't drift across frames. If patterns on clothing, facial features, or background signs vary from frame to frame, that quickly reveals AI glitches. Currently, shorter clips are more stable; longer takes tend to break down more often.
2) Prompt Adherence
How well can it follow shooting instructions like “in the evening backlight, 35mm lens framing, slow dolly-in”? Differences among models are common, and prompt-design skill matters too.
3) Camera Work and Physical Plausibility
Understanding of camera moves, depth of field, motion blur, and other film grammar helps make the result look plausible. Conversely, when objects multiply or fingers melt, it's still a common failure.
4) Production Workflow Integration
Even with high generation quality, if export, versioning, lip-sync, editing, and replacements are weak, it won't get used. Here, the “completeness” as a production tool—like Runway—becomes decisive.
Sora: The Symbol of Scene Generation Where World Understanding Has Advanced
Sora is often talked about for its ability to generate longer videos that feel plausible, but the essence is that it can unfold while preserving the relationships between elements inside a scene as time progresses. For example, with a connected layout of characters, backgrounds, props, and light sources as time moves forward.
- Suitable for: Concept videos in the planning stage, a moving version of storyboard for commercials, and world-building prototyping
- Common pitfalls: Precise logos or text, strict identity of people (keeping the same actor across shots), and narrative continuity
In practice, value comes from alignment on direction rather than final footage. It speeds up internal explanations and client verifications, reducing wasted production.
Runway: A Field-Friendly Tool Where Generative AI Is Also Helpful for Production
Runway's strength lies not in the model's standalone performance but in how short the distance is to editing work. It goes beyond text-to-video to support editing, replacement, and background processing, making it well-suited for so-called post-production tasks.
- Suitable for: Mass production of short-form content for social networks, generating variation of materials, editing existing footage (object removal, replacement, etc.), B-roll generation
- Tips for operation: Start with existing footage and process it with AI to safely generate value; when comfortable, expand to full generation
Rather than aiming for a one-shot “god video,” focusing on shortening the editing process yields more favorable ROI.
Kling: The Strength When the Quality Hits
Kling has gained attention for its visual quality and mood creation, and in certain styles, many feel “this is usable.” People tend to expect strong results in character depiction and texture.
- Suitable for: Short-form with a commercial tone, MV-like atmospheric visuals, still shots that strike with composition
- Important caveats: Commercial rights, training data, regional restrictions, and other conditions must be checked—legal guidance before projects is safer
Because tastes vary, it's powerful to maintain a team-wide collection of “winning patterns” where this model excels in certain aesthetics.
Veo: High-Quality, Long-Form, and Motion-grammar Expectations
Veo, Google's video-generation project, has drawn attention for resolution, prompt adherence, and long-form capability. In practice, the difference is whether an instruction about shooting such as lens choice, camera movement, lighting, and visual styling can be followed; when that works, production control improves significantly.
- Suitable for: Advertising/brand expression that emphasizes shooting design, live-action-like B-roll, and concept-validation cuts
- Gap between expectation and reality: The longer the content, the more difficult it is to maintain identity of people and consistency of props—this is a common challenge
Current Reality: What It Can Do and What Is Still Hard
What It Can Do (Where results are more likely)
- 5–10 seconds short clips to establish “mood,” “world,” and “tempo”
- For advertising and social media, generate large variations to use in A/B tests
- Before shooting, create a richer moving previs (previsualization) than a storyboard
- Modify existing footage (background replacements, etc.) to reduce reshoots
Things That Are Still Hard (Common Pitfalls)
- Maintaining the same person across multiple cuts to support dialogue-based scenes
- Stable generation of elements like logos, text, UI where exactness is required
- Long-form storytelling continuity (props positions, outfits, time-of-day)
- Developing a commercially usable model that clearly addresses rights, likeness, and training data concerns
Practical Guide to Usage: Work Back from Your Goal
Case A: Winning by Volume in Advertising & Social
The winning approach is to mass-produce → select → edit. Focus on tools with strong editing workflows like Runway, and use multiple generation models to pull out “hits.”
- Submit the same prompt to multiple models and generate 10–30 clips
- Pick the good cuts, adjust color, timing, and captions by humans
- Record winning patterns via A/B testing and reuse
Case B: Planning/Preproduction to Accelerate Alignment
Use scene-generation-oriented tools like Sora or Veo to reduce concept-explanation cost. The goal here is to convey intent rather than achieve perfection.
Tip: Just adding motion to a storyboard reduces misalignment among stakeholders. As a result, shooting and production iterations drop and total cost decreases.
Case C: Safe and Efficient Use with Existing Materials
Even if full generation is hard, editing existing footage is easier to put into production. Background replacements, removing unwanted items, and style adjustments help avoid reshoots.
Prompt Tips: Write Visual Prompts as “Shooting Instructions”
Text is more stable when aligned with on-site language rather than being poetic. It's recommended to list briefly: subject, location, time of day, lens feel, camera movement, lighting, tone of the image, and prohibitions.
- Example (Short-form Commercial Style): “Night city after rain. Neon reflections. 35mm, shallow depth of field. Slow dolly-in. Cinematic, natural skin. Do not generate text or logos.”
Additionally, elements prone to breaking (text, fingers, dense crowds, high-motion scenes) should be avoided first to establish a stable baseline, then added in later steps.
Risks and Rules to Keep in Mind When Introducing
Video-generation AIs are expressive, but this increases risks around rights, misinformation, and brand damage. At minimum, make the following a team-wide rule set for safety.
- Rights checks: Review terms of use (commercial rights, secondary use, ownership of outputs) per project
- Portraits/Celebrities: Be especially careful with likenesses; ads have high backlash risk
- Watermarks / Detection: Prepare for policy changes on platforms; maintain asset management and source records
- Final responsibility: Even if AI creates, the public-facing responsibility lies with humans. Avoid rushing the review process
Future Outlook: Growth Will Be in Production Standardization, Not Just Longer Content
Long-form content will still advance, but the most effective gains in practice come from 1) integrating into the production workflow and 2) ensuring consistency across characters, products, and worlds. In other words, it's not just the model's evolution that matters; the operational model—including editing, management, approvals, and legal—will determine success.
Valuing video-generation AI now isn’t just a study for the future; there are already areas where it pays off today—short marketing materials, previs, B-roll, and revision work. Start small and build more team-winning patterns to accelerate your workflow.
