Documentation vs Storytelling (AI Video)

Two categories of video with opposite AI implications—documentation should stay real, storytelling can be generated

Gaurav Misra
Mastering onboarding | Gaurav Misra

Documentation vs Storytelling (AI Video)

"Videos divide into two categories. On one side is what is documentation... And there's actually no benefit to AI-generated video in any of this. Actually, all of this, it's just negative. On the other side, you have what we think of as storytelling... And if we can enable more people to tell stories and entertain other people and get their message out there, that is pure positive." - Gaurav Misra

What It Is

A framework for categorizing video use cases to guide AI product development and avoid harmful applications. Video content falls into two fundamentally different categories—documentation and storytelling—with opposite implications for AI generation.

This distinction helps product teams building AI video tools make principled decisions about which capabilities to enable and which to restrict.

How It Works

Documentation (AI-negative): Personal documentation:

  • Videos with friends at restaurants
  • Recording memories for yourself
  • Capturing real moments

Historical documentation:

  • Reporter documenting events
  • Crime scene evidence
  • Natural disaster footage
  • Historical record

"There's actually no benefit to AI-generated video in any of this. Actually, all of this, it's just negative. If we are generating fake versions of reality to fool people, there's just nothing good about that."

Storytelling (AI-positive):

  • Ads and commercials
  • Social media content
  • TV shows and movies
  • Entertainment content

"Nobody believes if you watch a Geico commercial, you're not thinking that the gecko is real selling insurance somewhere out there. You know that this is fabricated and it's for entertainment."

The design implication:

"We want to design products and build products that specifically make it really hard to use on one side [documentation] and really easy to use on the other side [storytelling]. And that's the real challenge."

How to Apply It

  1. Map your use cases - For any AI video capability, list the primary use cases. Which are documentation? Which are storytelling?

  2. Design for storytelling - Make storytelling use cases frictionless. These are where AI adds pure value

  3. Add friction to documentation - Don't enable realistic recreation of real people in real situations without explicit consent. Make it hard to create content that could deceive

  4. Consider the "would anyone be fooled" test - Geico gecko: clearly fabricated. Video of a politician saying something: dangerous. Build for the former

  5. Focus on enabling the previously impossible - Captions focuses on people who "could not have created video before. They didn't have the tools, the skills, the means to be able to create video and now they can." This is pure value creation

When to Use It

Apply this framework when:

  • Building any AI content generation tool (video, audio, images)
  • Deciding which capabilities to enable vs. restrict
  • Evaluating potential misuse of AI generation features
  • Making product positioning decisions about target use cases

The framework can generalize beyond video:

  • AI voice: entertainment/accessibility = good; impersonation = bad
  • AI writing: creative/marketing = good; fake evidence = bad
  • AI images: art/design = good; fake photographs = bad

Source

  • Guest: Gaurav Misra
  • Episode: "Mastering onboarding | Gaurav Misra"
  • Key Discussion: (01:06:35 - 01:08:53) - Full explanation of the documentation vs. storytelling framework
  • YouTube: Watch on YouTube

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