The Boundaries of AI Creation: When Not to Use It

The stronger a technology becomes, the more important it is to know where its boundaries are.
AI generation can already produce highly believable images and videos. That "believability" brings a risk: it may be used where it should not be used—in scenarios that require authenticity. Once that line is crossed, the cost is often much greater than simply "not being able to make it." This article discusses how to draw that boundary.

1. Four types of scenarios where it should not be used

1. Scenarios that require a true record

If the function of the content is to "record something that truly happened," generation is not suitable. News footage, documentary images, evidentiary materials, on-site records of events—the entire value of these types of content rests on "this is real." Replacing or supplementing them with generated content changes the nature of the content, even if it looks exactly the same. The standard for judgment is simple: if the audience knew it was generated, would they still accept it? If the answer is "no," then it should not be used.

There is an easily confused point here: using AI to fill in "images that cannot be filmed" and "fabricating images" are two different things.

For example, a documentary may need to show "a street from thirty years ago," but it is impossible to actually go back in time to film it. Presenting it through generation (while making clear that it is illustrative) is entirely different in nature from "fabricating a piece of news footage from thirty years ago and claiming it is real archival material." The difference lies in whether its nature has been explained to the audience.

2. Content involving factual statements

The second type is corporate and commercial content. Corporate promotional videos, product explanations, videos related to financial reports—the numbers, qualifications, and promises in these pieces of content will be received by the audience as facts. So: do not generate scenarios the company does not actually have (factories that do not exist, teams that do not exist), do not let the visuals imply capabilities the product does not have, and do not use generation to present unverified numbers.

What AI is suited to handling is the form of expression—how to present existing information more clearly and with more quality. It is not suited to handling the facts themselves. Once this line is clearly drawn, AI actually has a great deal of room in corporate content.

3. Materials subject to rights restrictions

The third type concerns the source of materials. The generation process may involve other people’s likenesses, protected works, or trademarked images. Even if these types of content can be generated technically, that does not mean they can be used. A few practical approaches:

1. Sources should be traceable. Materials used in a project should be traceable back to their origins. This is especially important in team collaboration—after materials pass through multiple people, their sources are easily forgotten. This also shows that material management is not only an efficiency issue: NadouPro’s image, video, and audio asset management is used to centrally manage media assets in personal or team spaces and output searchable assets, so the sources and uses of materials can be checked.
2. Distinguish between "reference" and "use." Referencing a certain style or a certain compositional approach is different from directly using a protected work. The former is learning; the latter is use.
3. Be cautious when processing existing materials. In the editing toolset, erase can remove specified objects or areas from an image and fill in the background, while redraw uses masks and prompts to regenerate local areas—these capabilities are reasonable when used to fix flaws in your own materials, but their nature is different when used to remove watermarks or signatures from other people’s works.

4. Skipping judgment for the sake of convenience

The fourth type is not a content issue, but a process issue. Some work originally requires judgment, but for the sake of efficiency it is handed over to a tool, and the judgment step that should have existed is skipped. Typical examples include:

• Using a proposal in place of a decision. Taking a shot plan and executing it directly, without considering whether it fits the intent of the film.
• Using "it looks good" in place of standards. Having no clear quality standard, and deciding whether to use a generated result based on whether it looks good.
• Using "AI says this is better" as the reason. Replacing your own judgment with the tool’s default output.

These kinds of problems will not be exposed immediately, but they will accumulate—the final work will end up feeling like "everything is right, but it does not resemble a complete expression."

2. How to draw the boundary

Putting the four types above together, one principle can be summarized: AI handles the "mode of presentation," while humans are responsible for "facts and judgment." The advantage of this division is that it is actionable:

• Whether the facts in the content are accurate → humans verify
• Whether the presentation of the content is effective → AI can participate
• Whether this decision aligns with the intent → humans judge
• Whether the efficiency of this execution can be improved → AI can take it on

It should be noted that this boundary is not a restriction on "whether AI can be used," but a distinction about which layer it is used in. In the same project, humans can oversee the factual layer while AI is used extensively in the presentation layer—the two can run in parallel without contradiction.

3. Within the boundary, the room is actually large

This point needs to be emphasized: drawing a clear boundary does not mean restricting use. In fact, the vast majority of creative work falls on the "presentation" layer:

• Scenes that cannot be filmed — generation is the only means.
• Atmosphere and concepts — generation is more flexible than live shooting.
• Visualizing abstract concepts — generation can provide multiple possibilities.
• Repairing and unifying existing materials — generation can handle high-cost work.
• Deriving multiple versions and multiple formats — generation can complete this in batches.

These are all reasonable applications with clear value. So the places where the boundary truly needs to be guarded are only a small number of key points involving authenticity.

4. A few practical approaches

1. Clarify the nature of the content. At the start of the project, confirm: what is the function of this piece of content? Is it to record, to reason, or to express? Its nature determines where the boundary lies.
2. Set up a verification step for factual information. Numbers, qualifications, and promises should be confirmed by humans, and checked again before the final cut.
3. Record material sources. Especially in team collaboration projects, traceable sources can avoid later risks.
4. Maintain an "annotation awareness" for generated visuals. In situations where the nature of the content needs to be explained (such as documentary content), make clear which parts are records and which parts are illustrative.
5. Do not trade judgment for efficiency. For any question involving "whether this is right," do not hand it over to a tool to answer.

5. A few common misconceptions

• Misconception 1: If the technology can do it, it can be used. Technical feasibility and appropriateness of use are two different things.
• Misconception 2: Treating the entire project as "presentation." This ignores the parts that involve facts and rights.
• Misconception 3: Thinking that citing the source is enough. Rights restrictions are not only a matter of attribution.
• Misconception 4: Treating boundaries as constraints. Boundaries define "which positions require oversight," not "what cannot be used."
• Misconception 5: Not distinguishing between "illustration" and "record." This is where documentary and commercial content most easily run into problems.

6. A self-checklist

1. What is the function of this piece of content? To record, to reason, or to express.
2. Are the factual statements accurate? Have they been checked?
3. Do the visuals imply capabilities that do not exist? Especially in product and corporate content.
4. Are the material sources clear? Are they traceable, and do they involve other people’s rights?
5. Did I make the key judgments? Did I hand over decisions I should make myself to a tool?
6. Have the places where the nature of the content needs to be explained been annotated? Are illustration and record clearly distinguished?

Finally

The purpose of writing this article is not to set limits on the use of AI—quite the opposite: clear boundaries make use more reassuring. Because risk often does not arise from the fact that "AI was used," but from "using it without realizing where it was used." When you know which positions require your own oversight, the remaining parts can make fuller use of tools. The capabilities of technology will continue to grow, and generated content will become increasingly difficult to distinguish. Under this trend, the ability to judge "when not to use it" will be more important than "how to use it." This is probably the same issue all tools encounter after they mature: what determines the value of a work has never been capability itself, but the judgment used to apply it.

Contains AI-generated information that may be incorrect. Please verify independently.